Reviewerly/exHarmony huggingface dataset:
Measurement of key performance indicators of user experience based on software requirements

Software User Experience (UX) reveals product impressions when using or planning to utilize a software product. However, software products are notorious for their uncertainty, adaptability, and continuous operation. Users often evaluate the software UX based on a particular system release or a prototype. Although there is much machine research, little research has been conducted on UX prediction based on requirements items. Moreover, developing a UX-oriented prototype at early software requirements elicitation is time-consuming and laborious. Given a list of initial textual requirements, predicting a system UX Key Performance Indicator (KPI) is essential for success and saving project efforts. This study assesses an application's UX KPI in the first phases of software requirements engineering. Our suggested machine learning algorithms extract UX characteristics from textual requirements to categorize them as UX scales. The anticipated UX scales are then used to forecast the total UX KPI of any software application. The UX predictions should be reliable compared to a real case study of different application prototypes. We trained several machine learning models on a benchmark dataset of software requirements. The models were tested, showing a high performance of the f1-measure, which was 0.91 for the Random Forest Algorithm. Besides, the UX KPIs calculated based on outputs from machine learning models were highly interrelated with those calculated based on our developed software prototypes. Therefore, the proposed approach is automatic, easy to implement, and reliable in estimating the overall application UX before development. We conclude that the new model can evaluate UX instantly without interventions from end-users or UX designers.
New Trends in Digital Technology-Based Psychological and Educational Assessment.

The emergence of digital technology in the field of psychological and educational measurement and assessment broadens the traditional concept of pencil and paper tests. New assessment models built on the proliferation of smartphones, social networks and software developments are opening up new horizons in the field.This study is divided into four sections, each discussing the benefits and limitations of a specific type of technology-based assessment: ambulatory assessment, social networks, gamification and forced-choice testing.The latest developments are clearly relevant in the field of psychological and educational measurement and assessment. Among other benefits, they bring greater ecological validity to the assessment process and eliminate the bias associated with retrospective assessment.Some of these new approaches point to a multidisciplinary scenario with a tradition which has yet to be created. Psychometrics must secure a place in this new world by contributing sound expertise in the measurement of psychological variables. The challenges and debates facing the field of psychology as it incorporates these new approaches are also discussed.
The Method for Identifying the Scope of Cyberattack Stages in Relation to Their Impact on Cyber-Sustainability Control over a System

Industry X.0 is the new age of digitization, when information and communication systems are strongly linked to other systems and processes and are accessed remotely from anywhere at any time. The existing information systems’ security methods are ineffective because they should focus on and assess a broader range of factors in physical and digital spaces, especially because tactics of cybercrimes are always evolving and attackers are getting more inventive in searching for holes that might be exploited. To fight it, it is a need to be one step ahead of the attacker, including understanding the nature, stages and scope of the upcoming cyberattack. The objective of our research is to identify the impact of the scope of a cyberattack’s stages on the cyber resilience of an information and communication system, assessing the level of cybersecurity based on existing technical and operational measures. The research methodology includes a numerical simulation, an analytical comparison and experimental validation. The achieved results allow for the identification of up to 18 attack stages based on the aggregation of technical and organizational security metrics and detection sources. The analytical comparison proved the proposed method to be 13% more effective in identifying the stage of a cyberattack and its scope. Based on this research, the extensive scoping flexibility of the proposed method will enable additional control measures and methods that would reduce the impact of an attack on the robustness while increasing the cyber-sustainability of a system.
Imaging Simulation Method for Novel Rotating Synthetic Aperture System Based on Conditional Convolutional Neural Network

The novel rotating synthetic aperture (RSA) is a new optical imaging system that uses the method of rotating the rectangular primary mirror for dynamic imaging. It has the advantage of being lightweight, with no need for splicing and real-time surface shape maintenance on orbit. The novel imaging method leads to complex image quality degradation characteristics. Therefore, it is vital to use the image quality improvement method to restore and improve the image quality to meet the application requirements. For the RSA system, a new system that has not been applied in orbit, it is difficult to construct suitable large datasets. Therefore, it is necessary to study and establish the dynamic imaging characteristic model of the RSA system, and on this basis provide data support for the corresponding image super resolution and restoration method through simulation. In this paper, we first analyze the imaging characteristics and mathematically model the rectangular rotary pupil of the RSA system. On this basis, combined with the analysis of the physical interpretation of the blur kernel, we find that the optimal blur kernel is not the point spread function (PSF) of the imaging system. Therefore, the simulation method of convolving the input image directly with the PSF is flawed. Furthermore, the weights of a convolutional neural network (CNN) are the same for each input. This means that the normal convolutional layer is not only difficult to accurately estimate the time-varying blur kernel, but also difficult to adapt to the change in the length–width ratio of the primary mirror. To that end, we propose a blur kernel estimation conditional convolutional neural network (CCNN) that is equivalent to multiple normal CNNs. We extend the CNN to a conditional model by taking an encoding as an additional input and using conditionally parameterized convolutions instead of normal convolutions. The CCNN can simulate the imaging characteristics of the rectangular pupil with different length–width ratios and different rotation angles in a controllable manner. The results of semi-physical experiments show that the proposed simulation method achieves a satisfactory performance, which can provide data and theoretical support for the image restoration and super-resolution method of the RSA system.
PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains.
Exploring the phase structure of the multi-flavor Schwinger model with quantum computing

We propose a variational quantum eigensolver suitable for exploring the phase structure of the multi-flavor Schwinger model in the presence of a chemical potential. The parametric ansatz circuit we design is capable of incorporating the symmetries of the model, present in certain parameter regimes, which allows for reducing the number of variational parameters substantially. Moreover, the ansatz circuit can be implementated on both measurement-based and circuit-based quantum hardware. We numerically demonstrate that our ansatz circuit is able to capture the phase structure of the model and allows for faithfully approximating the ground state. Our results show that our approach is suitable for current intermediate-scale quantum hardware and can be readily implemented on existing quantum devices.
DistVAE: Distributed Variational Autoencoder for sequential recommendation

Recommender systems (RS) play a vital role in daily life due to their practical significance. As a branch of RS, the sequential recommendation has attracted much attention because of its effectiveness in helping people find items of interest. Recently, the generative methods based on Variational Autoencoder (VAE) have shown promising performance in modeling temporal dependencies among items in user sequences. These methods require collecting all users' raw data, which incurs high costs and privacy issues. Therefore, this paper makes efforts to implement a distributed sequential VAE method. However, the distributed version of VAE-based sequential recommendation faces the following challenges. Firstly, in a distributed setting, there is a small amount of data on a single device, increasing the randomness of the gradients used to update the global model. Meanwhile, users in recommender systems usually have significantly different preference patterns. It may magnify the above randomness and make the global model update unstable. Secondly, existing generative methods usually adopt GRU to model latent variable dependencies, but GRU performs poorly in modeling the association in long sequences. This limitation makes these methods fail to model the long-term dependence in the inference model. To solve the problems, this paper proposes Distributed Variational AutoEncoder for the sequential recommendation (DistVAE), where a trusted server coordinates thousands of clients to train an insightful generative model without pulling their raw data together. Specifically, the clustering algorithm is integrated into the distributed sequential recommendation framework to dynamically find peers with similar preferences, and a sequential VAE is adopted to wrap a masked attention layer to model the long-term dependence. Experiments on four real-world datasets show that DistVAE outperforms other state-of-the-art centralized methods under the distributed training framework.
Deep learning features in facial identification and the likelihood ratio bound

In recent years, the score-based likelihood ratio (SLR) method for facial comparison has attracted considerable research attention. This method relies on the match scores that are calculated from the features obtained from facial recognition systems, deep learning based in particular. However, this concept has not been completely understood. Therefore, this study is aimed at investigating deep learning facial features, and the SLR levels of their match scores. We propose a new interpretation that the deep learning feature is a class characteristic. Based on a large-scale data set experiment, we present evidence that the log SLR value of deep learning features can reach 8 in some data sets. The study results imply that the SLR of deep learning features is a useful method for facial identification, especially when the suspected image is obtained via a CCTV camera.
Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings

Abstract Artificial intelligence (AI) has become frequently used in data and knowledge production in diverse domain studies. Scholars began to reflect on the plausibility of AI models that learn unexplained tacit knowledge, spawning the emerging research field, eXplainable AI (XAI). However, superior XAI approaches have yet to emerge that can explain the tacit knowledge acquired by AI models into human-understandable explicit knowledge. This paper proposes a novel eXplainable Dimensionality Reduction (XDR) framework, which aims to effectively translate the high-dimensional tacit knowledge learned by AI into explicit knowledge that is understandable to domain experts. We present a case study of recognizing the ethnic styles of village dwellings in Guangdong, China, via an AI model that can recognize the building footprints from satellite imagery. We find that the patio, size, length, direction and asymmetric shape of the village dwellings are the key to distinguish Canton, Hakka, Teochew or their mixed styles. The data-derived results, including key features, proximity relationships and geographical distribution of the styles are consistent with the findings of existing field studies. Moreover, an evidence of Hakka migration was also found in our results, complementing existing knowledge in architectural and historical geography. This proposed XDR framework can assist experts in diverse fields to further expand their domain knowledge.
Distributed Heterogeneous Transfer Learning for Link Prediction in the Positive Unlabeled Setting

Transfer learning focuses on enhancing predictive models for a target domain, by exploiting the knowledge coming from a related source domain. However, most existing transfer learning methods assume that source and target domains are described with the same feature spaces. Heterogeneous transfer learning approaches aim to overcome this limitation, but they usually introduce strong assumptions (e.g., on the number of features), cannot distribute the workload to handle large volumes of data, or cannot work in challenging settings like the Positive-Unlabeled (PU) setting, where only positive and unlabelled examples are available. In this paper, we present a novel heterogeneous distributed transfer learning method that can work also in PU learning setting and overcomes all such limitations.The experimental evaluation was conducted in the context of a link prediction task in the biological domain. The results showed the effectiveness of the proposed method, that outperformed three state-of-the-art heterogeneous transfer learning approaches.
Analysing Longitudinal Social Science Questionnaires: Topic modelling with BERT-based Embeddings

Unsupervised topic modelling is a useful unbiased mechanism for topic labelling of complex longitudinal questionnaires covering multiple domains such as social science and medicine. Manual tagging of such complex datasets increases the propensity of incorrect or inconsistent labels and is a barrier to scaling the processing of longitudinal questionnaires for provision of question banks for data collection agencies. Towards this effort, we propose a tailored BERTopic framework that takes advantage of its novel sentence embedding for creating interpretable topics, and extend it with an enhanced visualisation for comparing the topic model labels with the tags manually assigned to the question literals. The resulting topic clusters uncover instances of mislabelled question tags, while also enabling showcasing the semantic shifts and evolution of the topics across the time span of the longitudinal questionnaires. The tailored BERTopic framework outperforms existing topic modelling baselines for the quantitative evaluation metrics of topic coherence and diversity, while also being 18 times faster than the next best-performing baseline.
Boost CTR Prediction for New Advertisements via Modeling Visual Content

Existing advertisements click-through rate (CTR) prediction models are mainly dependent on behavior ID features, which are learned based on the historical user-ad interactions. Nevertheless, behavior ID features relying on historical user behaviors are not feasible to describe new ads without previous interactions with users. To overcome the limitations of behavior ID features in modeling new ads, we exploit the visual content in ads to boost the performance of CTR prediction models. Specifically, we map each ad into a set of visual IDs based on its visual content. These visual IDs are further used for generating the visual embedding for enhancing CTR prediction models. We formulate the learning of visual IDs into a supervised quantization problem. Due to a lack of class labels for commercial images in advertisements, we exploit image textual descriptions as the supervision to optimize the image extractor for generating effective visual IDs. Meanwhile, since the hard quantization is non-differentiable, we soften the quantization operation to make it support the end-to-end network training. After mapping each image into visual IDs, we learn the embedding for each visual ID based on the historical user-ad interactions accumulated in the past. Since the visual ID embedding depends only on the visual content, it generalizes well to new ads. Meanwhile, the visual ID embedding complements the ad behavior ID embedding. Thus, it can considerably boost the performance of the CTR prediction models previously relying on behavior ID features for both new ads and ads that have accumulated rich user behaviors. After incorporating the visual ID embedding in the CTR prediction model of Baidu online advertising, the average CTR of ads improves by 1.46%, and the total charge increases by 1.10%.
splitDyn: Federated Split Neural Network for Distributed Edge AI Applications

Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications.
Connectivity-Aware Contract for Incentivizing IoT Devices in Complex Wireless Blockchain

Blockchain is considered the critical backbone technology for secure and trusted Internet of Things (IoT) in the future 6G network. However, deploying a blockchain system in a complex wireless IoT network is challenging due to the limited resources, complex wireless environment, and the property of self-interested IoT devices. The existing incentive mechanism of blockchain is not compatible with the wireless IoT network. In this article, to incentivize IoT devices to join the construction of the wireless blockchain network, we propose a multidimensional contract to optimize the blockchain utility while addressing the issues of adverse selection and moral hazard. Specifically, the proposed contract considers the IoT device’s hash power and communication cost and especially explores the connectivity of devices from the perspective of complex network theory. We investigate the energy consumption and the block confirmation probability of the wireless blockchain network via simulations under varied network sizes and average link probability. Numerical results demonstrate that our proposed contract mechanism is feasible, achieves 35% more utility than existing approaches, and increases utility by four times compared with the original PoW-based incentive mechanism.
Verification of Camera-Based Autonomous Systems

We consider the problem of verifying the safety of the trajectories of a camera-based autonomous vehicle in a given 3-D-scene. We give a procedure to verify that all trajectories starting from a given initial region reach a specified target region safely without colliding with obstacles on the way. We also give a prioritization-based falsification procedure that collects unsafe trajectories. Both our procedures are based on the key notion of image-invariant regions, which are regions within which the captured images are identical. We evaluate our methods on a model of an autonomous road-following drone in a variety of 3-D-scenes; our experimental results demonstrate the feasibility and benefits of our approach for both safety analysis and falsification.
MinEnt: Minimum entropy for self-supervised representation learning

Self-supervised representation learning is becoming more and more popular due to its superior performance. According to the information entropy theory, the smaller the information entropy of a feature, the more certain it is and the less redundant it is. Based on this, we propose a simple yet effective self-supervised representation learning method via Minimum Entropy (MinEnt). From the perspective of reducing information entropy, our MinEnt takes the output of the projector towards its nearest minimum entropy as the optimization target. The core of our MinEnt consists of three important steps: 1) normalize along the batch dimension to avoid model collapse, 2) compute the nearest minimum entropy to get the target, 3) compute the loss and backpropagate to optimize the network. Our MinEnt can learn efficient representations, even without the need for techniques such as negative sample pairs, predictors, momentum encoders, cross-correlation matrices, etc. Experimental results on four widely used datasets show that our method achieves competitive results in a simple manner.