Reviewerly/exHarmony huggingface dataset:
A speech-enabled virtual assistant for efficient human–robot interaction in industrial environments

This paper presents a natural language-enabled virtual assistant (VA), named Max, developed to support flexible and scalable human–robot interactions (HRI) with industrial robots. Regardless of the numerous natural language interfaces already proposed for intuitive HRI on the industrial shop floor, most of those interfaces remain tightly bound with a specific robotic system. Besides, the lack of a natural and efficient human–robot communication protocol hinders the user experience. Therefore, three key elements characterize the proposed framework. First, a Client-Server style architecture is introduced so Max can provide a centralized solution for managing and controlling various types of robots deployed on the shop floor. Second, inspired by human-human communication, two conversation strategies, lexical-semantic and general diversion strategies, are used to guide Max's response generation. These conversation strategies were embedded to improve the operator's engagement with the manufacturing tasks. Third, we fine-tuned the state-of-the-art (SOTA) pre-trained model, Bidirectional Encoder Representations from Transformers (BERT), to support a highly accurate prediction of requested intents from the operator and robot services. Multiple experiments were conducted using the latest iteration of our autonomous industrial mobile manipulator, "Little Helper (LH)", to validate Max's performance in a real manufacturing environment.
Classical and quantum correlations between two qubits interacting with a field in thermal spin states

Abstract In this manuscript, we introduce a quantum physical model consisting of two-atom system that interact with a quantized field initially prepared in the thermal spin states (TSSs). We study the effect of the main parameters of the model on the dynamical behavior atom–atom coherence, atoms-field entanglement, atom–atom entanglement and classical correlation. We show how the quantumness measures can be influenced by the spin number and thermal noise in the absence and presence of time-dependent coupling effect. We obtain that, despite the destructive influence of thermal noise, a considerable amount of coherence, entanglement and classical correlation still remain during the time evolution according the values of spin number. The results also show that the TSSs can offer the advantage of generating the maximal amount of coherence, entanglement and classical correlation during the dynamics.
Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks.
A systematic literature review on co-design education and preparing future designers for their role in co-design

ABSTRACTAs emphasised by various researchers, the role of designers is now less about 'designing for' and more about 'designing with' users and other stakeholders. The main reason behind this shift relates to the importance of the active involvement of 'people with lived experience' in co-design. Now, designers are also expected to facilitate the active participation of non-designers in co-design processes. However, it is noted that the importance of co-design and the role of designers in co-design have not been widely integrated into design education. Thus, a systematic literature review is conducted to find out the necessity and prevalence of co-design education in higher education programmes and its instructional methods. This paper presents the outcomes of the review of seventeen (n = 17) studies, published between 2007 and 2021, analysed through thematic analysis via NVivo. The findings of this study shed light on the necessity, structure, and challenges of co-design education, as well as evaluation, gaps, and opportunities in this area. Specifically, the significance of motivating students to acquire the necessary mindset for co-designing, which can potentially initiate a meaningful co-design process through game-based approaches and self-learning, is underlined.KEYWORDS: Co-design educationdesigners' role in co-designdesign educationco-designing mindset AcknowledgementsThe authors would like to extend their appreciation to the European Commission for providing support and funding to the PhD research of the corresponding author through the Erasmus+ KA2 project 'T-CREPE' (Textile Engineering for Co-Creation Paradigms in Education). Grant Agreement Number: 612641.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/15710882.2023.2242840.Additional informationFundingThis work was supported by the European Commission [612641].
Precise region semantics‐assisted GAN for pose‐guided person image generation

Abstract Generating a realistic person's image from one source pose conditioned on another different target pose is a promising computer vision task. The previous mainstream methods mainly focus on exploring the transformation relationship between the keypoint‐based source pose and the target pose, but rarely investigate the region‐based human semantic information. Some current methods that adopt the parsing map neither consider the precise local pose‐semantic matching issues nor the correspondence between two different poses. In this study, a Region Semantics‐Assisted Generative Adversarial Network (RSA‐GAN) is proposed for the pose‐guided person image generation task. In particular, a regional pose‐guided semantic fusion module is first developed to solve the imprecise match issue between the semantic parsing map from a certain source image and the corresponding keypoints in the source pose. To well align the style of the human in the source image with the target pose, a pose correspondence guided style injection module is designed to learn the correspondence between the source pose and the target pose. In addition, one gated depth‐wise convolutional cross‐attention based style integration module is proposed to distribute the well‐aligned coarse style information together with the precisely matched pose‐guided semantic information towards the target pose. The experimental results indicate that the proposed RSA‐GAN achieves a 23% reduction in LPIPS compared to the method without using the semantic maps and a 6.9% reduction in FID for the method with semantic maps, respectively, and also shows higher realistic qualitative results.
A database of detrital zircon U–Pb ages and Hf isotopic compositions from the Tarim, West Kunlun, Pamir, Tajik and Tianshuihai terranes

Abstract With the development of detrital zircon U–Pb ages and Hf isotope analysis as conventional sedimentological research methods, a large number of studies have been conducted in the Tarim, West Kunlun, Tajik, Pamir and Tianshuihai regions and abundant data have been accumulated. Summarizing these data to characterize sedimentary units and source regions is more important than local studies of specific geological epochs; therefore, we compiled a database of detrital zircon U–Pb ages and Hf isotope analyses from the Tarim, West Kunlun, Pamir, Tajik and Tianshuihai terranes. The database contains data from 90 papers, including 35,281 individual U–Pb ages and 4,181 Hf isotope analyses. The database records the literature source, petrologic sample information, regional geological information, geographic coordinates and analytical parameters for each data point in as much detail as possible to allow readers to review, process, analyse and use the data. The database relies on the Deep‐time Digital Earth (DDE) platform and uses an open database update system to enable sharing and collaborative building. We encourage other researchers to contribute relevant published data to facilitate wider use.
Human resource attendance mechanism based on the internet of things: A method based on data fusion

Abstract The development of Internet of Things technology makes people need to deal with an increasing amount of data, which brings the poor data quality, fast data generation speed and other problems. These problems have become one of the main obstacles restricting the development of human resource attendance. At present, data fusion methods as important methods in data fusion technology. This article fully utilizes human resource attendance data and proposes the LSTM‐Attention data fusion method. This method uses LSTM to model the data, extract potential features of the data, and filter out meaningful data in the human resource attendance mechanism through the Attention mechanism to achieve data fusion. Finally, experimental results in this article showed that the LSTM‐Attention data fusion method designed in this article has higher prediction accuracy. It fully mines the correlation between data, and further improves the accuracy of analysis and prediction in the human resource attendance mechanism based on the Internet of Things.
Learning by competing: Competitive multi-generator based adversarial learning

Generative adversarial networks (GANs) have been extensively used for dozens of image enhancement and image translation applications, where several traditional and novel architectures have been and are still being introduced. However, the classical training protocols do not fully explore the significant potential of such architectures. In this paper, we propose a novel conditional GAN (cGAN) framework called competitive multi-generator based cGAN (CMcGAN) for more effective training progress and enhanced image generation. Different from classical adversarial learning protocols, the generators of a CMcGAN are not only challenged by the discriminator for improved performance, but also by other generators. This is achieved by a sharing mechanism, where the current performances of the competing generators are shared among each other, motivating each generator to perform better than the rest. Extensive experiments in several conditional image generation applications show the significant improvements achieved by several traditional architectures when trained following our proposed learning strategy. Moreover, these architectures, trained by our learning strategy, outperformed the state-of-the-art approaches proposed in each tested application.
Incorporating global–local neighbors with Gaussian mixture embedding for few-shot knowledge graph completion

Few-shot knowledge graph completion (FKGC) aims to predict the missing parts of the query triplet based on a small number of known samples. To solve the above task, many existing approaches enhance entity embedding by encoding local neighbor information and obtain few-shot relational representations by encoding support triples. Although these previous studies have achieved promising results, they still suffer from the following two challenges: (1) Remote neighbor contains rich semantic information, how to effectively encode remote neighbor information is the first challenge? (2) Low-frequency relations and complex relations in the knowledge graph lead to uncertainty in the semantics of the relation, how to effectively model the uncertainty of the few-shot relation is the second challenge? For the former issue, we propose a global–local neighbor encoding module, where global encoder captures remote neighbor features based on relation paths and local encoder uses the task-aware attention mechanism to capture local neighbor features. For the latter issue, we employee the adaptive gaussian mixture model to model few-shot relation, which can adapt to different queries by dynamically adjusting component weights. Link prediction experiments are conducted on two benchmark datasets NELL-One and Wiki-One, and the proposed model achieved 14.0% and 7.8% improvement in the evaluation metric Hits@1 respectively, compared to the strong baseline model FAAN.
Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments

The Federated Cloud Computing (FCC) paradigm provides scalability advantages to Cloud Service Providers (CSP) in preserving their Service Level Agreement (SLA) as opposed to single Data Centers (DC). However, existing research has primarily focused on Virtual Machine (VM) placement, with less emphasis on energy efficiency and SLA adherence. In this paper, we propose a novel solution, Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP). Our solution addresses the complex VM placement problem, energy efficiency, and SLA preservation, making it comprehensive and beneficial for CSPs. By leveraging the capabilities of deep learning, our FEDQWP model extracts underlying patterns and optimizes resource allocation. Real-world workloads are extensively evaluated to demonstrate the efficacy of our approach compared to existing solutions. The results show that our DQL model outperforms other algorithms in terms of CPU utilization, migration time, finished tasks, energy consumption, and SLA violations. Specifically, our QLearning model achieves efficient CPU utilization with a median value of 29.02, completes migrations in an average of 0.31 units, finishes an average of 699 tasks, consumes the least energy with an average of 1.85 kWh, and exhibits the lowest number of SLA violations with an average of 0.03 violations proportionally. These quantitative results highlight the superiority of our proposed method in optimizing performance in FCC environments.
Ensemble Stacking Model for Sentiment Analysis of Emirati and Arabic Dialects

Sentiment analysis is the process of examining people's opinions and emotions towards goods, services, organizations, individuals, and other things, through the use of textual data. It involves categorizing text as positive, negative, or neutral to quantify people's beliefs. Social media platforms have become an important source of sentiment analysis data due to their widespread use for sharing opinions and information. As the number of social media users continues to grow, the amount of data generated for sentiment analysis also increases. Previous research on sentiment analysis for the Arabic language has mostly focused on Modern Standard Arabic and various dialects such as Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine. However, there has been no research on the utilization of deep-learning approaches for sentiment analysis of the Emirati dialect, which is an informal form of the Arabic language spoken in the United Arab Emirates. It's important to note that each country in the Arab world has its dialect, and some dialects may even have several sub-dialects. The primary aim of this research is to create a highly advanced deep-learning model that can effectively perform sentiment analysis on the Emirati dialect. To achieve this objective, the authors have proposed and utilized seven different deep-learning models for sentiment analysis of the Emirati dialect. Then, an ensemble stacking model was introduced to combine the best-performing deep learning models used in this study. The ensemble stacking deep learning model consisted of deep learning models with a meta-learner layer of classifiers. The first model combined the two best-performing deep learning models, the second combined the four best-performing models, and the final model combined all seven trained deep learning models in this research. The proposed ensemble stacking deep learning model was assessed on four datasets, three versions of the ESAAD Emirati Sentiment Analysis Annotated Dataset, two versions of Twitter-based Benchmark Arabic Sentiment Analysis Dataset (ASAD), an Arabic Company Reviews dataset, and an English dataset known as a Preprocessed Sentiment Analysis Dataset PSAD. The results of the experiments demonstrated that the proposed ensemble stacking model presented an outstanding performance in terms of accuracy and achieved an accuracy of 95.54% for the ESAAD dataset, 96.71% for the ASAD benchmark dataset, 96.65% for the Arabic Company Reviews Dataset, and 98.53% for the Preprocessed Sentiment Analysis Dataset PSAD.
Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proactive care. Recently, although numerous disease progression models based on machine learning have emerged, they often focus solely on enhancing predictive accuracy and ignore the measurement of result reliability. Consequently, this oversight adversely affects the recognition and acceptance of these models in clinical applications. To address these problems, we propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression. Specifically, we incorporate evidential deep learning into the multi-task learning framework based on recurrent neural networks. We simultaneously perform AD clinical diagnosis and cognitive score predictions while quantifying the uncertainty of each prediction without incurring additional computational costs by leveraging the Dirichlet and Normal-Inverse-Gamma distributions. Moreover, an adaptive weighting scheme is introduced to automatically balance between tasks for more effective training. Finally, experimental results on the TADPOLE dataset validate that our model not only has a comparable predictive performance to similar models but also offers reliable quantification of prediction uncertainties, providing a crucial supplementary factor for risk-sensitive AD progression prediction applications.
Joining Federated Learning to Blockchain for Digital Forensics in IoT

In present times, the Internet of Things (IoT) is becoming the new era in technology by including smart devices in every aspect of our lives. Smart devices in IoT environments are increasing and storing large amounts of sensitive data, which attracts a lot of cybersecurity threats. With these attacks, digital forensics is needed to conduct investigations to identify when and where the attacks happened and acquire information to identify the persons responsible for the attacks. However, digital forensics in an IoT environment is a challenging area of research due to the multiple locations that contain data, traceability of the collected evidence, ensuring integrity, difficulty accessing data from multiple sources, and transparency in the process of collecting evidence. For this reason, we proposed combining two promising technologies to provide a sufficient solution. We used federated learning to train models locally based on data stored on the IoT devices using a dataset designed to represent attacks on the IoT environment. Afterward, we performed aggregation via blockchain by collecting the parameters from the IoT gateway to make the blockchain lightweight. The results of our framework are promising in terms of consumed gas in the blockchain and an accuracy of over 98% using MLP in the federated learning phase.
The Eclectic Approach in English Language Teaching Applications: A Qualitative Synthesis of the Literature

This paper proposes a study of the methodologies used to teach English as a second language through computer applications. It reviews the different methodologies used in language teaching in order to contrast them with different fundamental approaches. It examines the applications used for teaching English online, especially those in line with personalised learning environments and learner-centred autonomy. The use of a mixed methodology is reported, allowing the selection of papers from both a traditional systematic review and the search engine elicit.org. Finally, it is concluded that Blended Learning is the most appropriate method, highlighting that current applications are mainly based on the Audio-Lingual method, which limits their practical use in wider contexts. Further research is needed on software applications that prioritise personalised learning environments with learner-centred autonomy, incorporating technologies based on Blended Learning and artificial intelligence (AI). To this end, it is proposed in the future to develop an application using Automatic Speech Recognition (ASR), Text To Speech (TTS), Natural Language Understanding (NLU) and Dialog Management (DM) algorithms, focusing on the above and simulating the capabilities of a teacher. This may lead to innovations in the development of software applications capable of integrating different methods, teaching modalities and learning methods.
CAM-TMIL: A Weakly-Supervised Segmentation Framework for Histopathology based on CAMs and MIL

Abstract Semantic segmentation plays a significant role in histopathology by assisting pathologists in diagnosis. Although fully-supervised learning achieves excellent success on segmentation for histopathological images, it costs pathologists and experts great efforts on pixel-level annotation in the meantime. Thus, to reduce the annotation workload, we proposed a weakly-supervised learning framework called CAM-TMIL, which assembles methods based on class activation maps (CAMs) and multiple instance learning (MIL) to perform segmentation with image-level labels. By leveraging the MIL method, we effectively alleviate the influence caused by that CAMs only focus on discriminative regions. As a result, we achieved comparable performance with fully-supervised learning on Camelyon 16 only with image-level labels.
Intelligent Path Planning of Mobile Robot Based on Genetic Algorithm

Abstract In this study, in order to deal with the path planning problems with genetic algorithm (GA) and improve its time efficiency, a new method combined with K-Nearest Neighbor (KNN) is proposed. GA is a well-known approach to figure out a path between two designated points while avoiding obstacles in the way. In traditional GA process, a set of solutions from the starting node to the destination are randomly generated in the beginning, which are called initial population. Although not every solution is feasible and the feasible ones may not be ideal, optimal path is likely to be obtained during the iterations. A new method is designed to reduce the number of iterations by constructing a dataset using traditional GA and selecting elite solutions as initial population when facing a new map. The selection criteria is the similarity between the distribution of obstacles and previous ones. Relevant experiments are organized to test the performance of the proposed method by comparing it with the traditional GA. According to the simulation results, the proposed method with elite initial population tends to converge more quickly.