Reviewerly/exHarmony huggingface dataset:
Delay Encryption

We introduce a new primitive named Delay Encryption, and give an efficient instantiation based on isogenies of supersingular curves and pairings. Delay Encryption is related to Time-lock Puzzles and Verifiable Delay Functions, and can be roughly described as “time-lock identity based encryption”. It has several applications in distributed protocols, such as sealed bid Vickrey auctions and electronic voting. We give an instantiation of Delay Encryption by modifying Boneh and Frankiln’s IBE scheme, where we replace the master secret key by a long chain of isogenies, as in the isogeny VDF of De Feo, Masson, Petit and Sanso. Similarly to the isogeny-based VDF, our Delay Encryption requires a trusted setup before parameters can be safely used; our trusted setup is identical to that of the VDF, thus the same parameters can be generated once and shared for many executions of both protocols, with possibly different delay parameters. We also discuss several topics around delay protocols based on isogenies that were left untreated by De Feo et al., namely: distributed trusted setup, watermarking, and implementation issues.
Knowledge Commoning: Scaffolding and Technoficing to Overcome Challenges of Knowledge Curation

Extant approaches to information provisioning to farmers to improve agricultural productivity, and thereby alleviate poverty have relied on top-down external expert-driven knowledge. Such external knowledge involves decontextualised content and the use of technical language, and is resource-intensive. An alternative view emphasises the need to explore indigenous knowledge exists in rural communities, which, in contrast, requires the use of local resources, is easily understandable, and has greater potential for adoption. This paper explores how information and communication technologies, specifically videos, can be leveraged to curate such indigenous knowledge and convert it to knowledge commons. Adopting a case study approach that involved multiple sources of data collection over a nine-year period, we unearthed a dynamic process model that we labelled as knowledge commoning. It is a process through which latent-action-oriented knowledge from high-yield farmers embedded within its social context is made available as commons. The creation of knowledge commons is an iterative process between knowledge curation and knowledge dissemination, and is guided by the demand and uptake potential within local farming communities. Further, we describe how socio-cultural barriers in knowledge commoning can be overcome through scaffolding, involving the concealment of social transformation objectives within another goal desired by the community. Technological challenges can be overcome through the process of technoficing, which encompasses pursuing social objectives using technology that is appropriate for the purpose. Building on our process model, we offer contributions to theory, practice, and policy.
Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive volumes of data explosion. The generated data is collected and shared using edge-based IoT structures at a considerably high frequency. Thus, the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes. To address the identified issue, we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme. In particular, we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes, where IoT devices and edge nodes are two parties of the game. IoT devices may make malicious requests to achieve their goals of stealing privacy. Accordingly, edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed. They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs. Built upon a developed application framework to illustrate the concrete data sharing architecture, a novel algorithm is proposed that can derive the optimal evolutionary learning strategy. Furthermore, we numerically simulate evolutionarily stable strategies, and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme. Therefore, the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.
Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide.
Gaze-Hand Alignment

Gaze and freehand gestures suit Augmented Reality as users can interact with objects at a distance without need for a separate input device. We propose Gaze-Hand Alignment as a novel multimodal selection principle, defined by concurrent use of both gaze and hand for pointing and alignment of their input on an object as selection trigger. Gaze naturally precedes manual action and is leveraged for pre-selection, and manual crossing of a pre-selected target completes the selection. We demonstrate the principle in two novel techniques, Gaze&amp;Finger for input by direct alignment of hand and finger raised into the line of sight, and Gaze&amp;Hand for input by indirect alignment of a cursor with relative hand movement. In a menu selection experiment, we evaluate the techniques in comparison with Gaze&amp;Pinch and a hands-only baseline. The study showed the gaze-assisted techniques to outperform hands-only input, and gives insight into trade-offs in combining gaze with direct or indirect, and spatial or semantic freehand gestures.
Unsupervised discriminative feature learning via finding a clustering-friendly embedding space

In this paper, we propose an enhanced deep clustering network (EDCN), which is composed of a Feature Extractor, a Conditional Generator, a Discriminator and a Siamese Network. Specifically, we will utilize two kinds of generated data based on adversarial training, as well as the original data, to train the Feature Extractor for learning effective latent representations. In addition, we adopt the Siamese network to find an embedding space, where a better affinity similarity matrix is obtained as the key to success of spectral clustering in providing reliable pseudo-labels. Particularly, the obtained pseudo-labels will be used to generate realistic data by the Generator. Finally, the discriminator is used to model the real joint distribution of data and corresponding latent representations for Feature Extractor enhancement. To evaluate our proposed EDCN, we conduct extensive experiments on multiple data sets including MNIST, USPS, FRGC, CIFAR-10, STL-10, and Fashion-MNIST by comparing our method with a number of state-of-the-art deep clustering methods, and experimental results demonstrate its effectiveness and superiority.
WBC image classification and generative models based on convolutional neural network

Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system.(i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing.(i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work.This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.
Assessment of Soil Contamination Using GIS and Multi-Variate Analysis: A Case Study in El-Minia Governorate, Egypt

The issue of soil contamination is one of the most important subjects that interests decision-makers all over the world. It is also related to soil fertility and food security. The soils adjacent to the drains in Egypt suffer from increasing concentration of heavy metals, which negatively affects soil and crop quality. Precise spatial distribution maps of heavy metals are an essential key to mitigating the negative impacts on the ecosystem. Sixty random soil locations adjacent to the El-Moheet drainage were chosen on the west side of the Nile River, El-Minia governorate, Egypt. Six heavy metals (Cr, Co, Cu, Cd, Pb, and Zn) were selected to generate their spatial pattern maps using ordinary Kriging (OK). Principal component analysis (PCA) and contamination factors (CF) were applied to evaluate soil contamination levels in the study area. The results showed that the Gaussiang model was a high fit for soil pH, and Pb, the Exponential model was fit for EC, Stable model was fit for OC, Co, Cu, and Cd. In addition, the Spherical model was fit for both Cr and Zn. The MSE values were close to zero in all selected metals, while the values of RMSSE were close to one. The results showed that the soil heavy metal concentrations were grouped into two clusters using PCA. Furthermore, three contamination degrees were obtained (moderate, considerable, and very high), with about 70.7% of the study area characterized by considerable heavy metals concentration, where the average heavy metals concentration (mg kg−1) in this degree was 91.23 ± 19.5, 29.44 ± 5.2, 53.83 ± 10.2, 1.12 ± 0.3, 36.04 ± 18.0, and 101.29 ± 35 for Cr, Co, Cu, Cd, Pb, and Zn, respectively. The current results reflect the mismanagement and use of low-quality water for irrigation in the study area, which increased the toxic element concentration in soil surface layers. In the end, the results of spatial distribution maps of pollutants and their degrees could support decision-makers as a basis for developing appropriate mitigation plans for heavy metals.
Impact of Employees' Workplace Environment on Employees' Performance: A Multi-Mediation Model

This study examined the impact of workplace environment on employee task performance under the mediating role of employee commitment and achievement-striving ability. For this purpose, data were collected from the academic staff under a cross-sectional research design, and they were approached through convenience sampling technique. As per recommendations of established sample size criteria, we distributed a sum of 420 questionnaires among the respondents. Among these distributed questionnaires, only 330 were received back. The returned questionnaires were checked for missing and incomplete responses and after discarding the missing responses useable responses were 314 which were used for the data analysis. Data had been analyzed through structural equation modeling (SEM) by using Smart PLS 3. The SEM was done based on measurement models and structural models. The results indicated that a positive work environment had the power to improve employee performance. Similarly, a positive work environment also improved the employee commitment level and achievement-striving ability significantly. Both employee commitment and achievement-striving ability also improved employee performance. While in the case of mediation, it had also been observed that workplace environment triggered employee commitment and employee achievement-striving ability which further improved employee performance.
Thinking Skills Don’t Protect Service Workers from Replacement by Artificial Intelligence

Despite the documented benefits of Artificial Intelligence (AI) to the service industry, the service employees’ fear of being replaced by AI continues to be a major concern as we transition to the Feeling Economy. This paper builds upon the Feeling Economy framework and the social comparison theory to examine how different service-related tasks (thinking vs feeling) distinctively impact the service employees’ feelings and behavior. Five studies reveal that the presence of AI increases negative outcomes for employees engaging in thinking (vs. feeling) tasks due to its adverse effects on their perceived ability (i.e., relative performance). Findings further indicate that these detrimental effects only happen when service employees compare their abilities with those of AI. This research provides important theoretical and managerial implications, helping to mitigate AI’s negative outcomes on employees’ fear of replacement and reduced job performance.
Robust stable iterated unscented Kalman filter based on maximum correntropy criterion

The Unscented Kalman filter (UKF) based on maximum correntropy criterion (MCC) is robust to heavy-tailed non-Gaussian noise. However, the approximate linear measurement equation obtained by statistical linearization technique may be not accurate enough since it only uses prior information. In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods. Different from the existing UKF algorithms, we only need to perform one-time unscented transformation in each filtering cycle, reducing the execution time of algorithm. Then a nonlinear enhancement model is constructed to handle predictions and observations simultaneously, which will be included in the cost function of robust MCC. In the process of iterative solution, thanks to the latest iteration values which are used to update the measurement information, RS-IMCC-UKF has more accurate results compared with traditional filters. In order to avoid non-positive definite characteristic in covariance matrix and the inverse operation in ill-conditioned matrix, the hyperbolic QR decomposition and Moore–Penrose pseudo-inversion are introduced to improve the numerical stability of the algorithm. Finally, a target tracking is modeled to verify the effectiveness of the algorithm.
Knowledge graph and knowledge reasoning: A systematic review

The knowledge graph (KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.
How pedagogical agents communicate with students: A two-phase systematic review

Technological advancements have improved the capabilities of pedagogical agents to communicate with students. However, an increased use of pedagogical agents in learning environments calls for a deeper understanding of student–agent communication to assess the effectiveness of pedagogical agents in learning. This study is a two-phase systematic review of scientific papers on pedagogical agent communication research published between 2010 and 2020, including review papers and original research papers. In the first phase, this study analyses literature reviews and meta-analyses to find the status and research gaps. The findings indicate that pedagogical agents' characteristics and impact on learning have been reviewed, but pedagogical agent communication and its relation to learning have not. In the second phase, the empirical studies of pedagogical agent communication are reviewed and classified into three categories that describe how pedagogical agent communication facilitates students' learning through (1) students' intrapersonal communication processes, (2) interpersonal communication between students and a pedagogical agent, and (3) by facilitating learning in a group. The findings show that pedagogical agent communication can enhance learning through intrapersonal communication of motivation, self-regulation, self-efficacy, and metacognition. At the interpersonal level, pedagogical agents aim to scaffold learning by giving feedback, prompts, and hints from learning processes and learning results. Pedagogical agents also support learning in a group by facilitating discussions and directing students' collaboration. Despite rapid technological advancements, pedagogical agents are not on the level to communicate fluently and human-like, which is likely to reduce their effectiveness and usability in learning. The review concludes that pedagogical agents’ communication needs to be developed toward adaptive, adequate, relational, and logical communication, which requires a multidisciplinary theoretical approach, the use of artificial intelligence, affective computing, and psychometric assessments. Recommendations for future research addressing the gaps identified in this systematic review are discussed.
Learning a deep dual-level network for robust DeepFake detection

Face manipulation techniques, especially DeepFake techniques, are causing severe social concerns and security problems. When faced with skewed data distributions such as those found in the real world, existing DeepFake detection methods exhibit significantly degraded performance, especially the AUC score. In this paper, we focus on DeepFake detection in real-world situations. We propose a dual-level collaborative framework to detect frame-level and video-level forgeries simultaneously with a joint loss function to optimize both the AUC score and error rate at the same time. Our experiments indicate that the AUC loss boosts imbalanced learning performance and outperforms focal loss, a state-of-the-art loss function to address imbalanced data. In addition, our multitask structure enables mutual reinforcement of frame-level and video-level detection and achieves outstanding performance in imbalanced learning. Our proposed method is also more robust to video quality variations and shows better generalization ability in cross-dataset evaluations than existing DeepFake detection methods. Our implementation is available online at https://github.com/PWB97/Deepfake-detection.
Measurement-based logical qubit entanglement purification

Entanglement purification is the distilling of high-quality entanglement from low-quality entanglement and is a key element in the quantum repeater. As a new branch of entanglement purification, the measurement-based entanglement purification protocol (MBEPP) only requires one to perform the Bell state measurement to couple resource states with noisy pairs, and it tolerates more local noise than the conventional purification protocols. Existing MBEPPs usually focus on physical qubit entanglement. In this paper, we propose a measurement-based logical qubit entanglement purification protocol (MBLEPP) with quantum nondemolition detection (QND), where the qubit is encoded in the quantum parity code. The results show that this MBLEPP can also work with photon loss under the conditions that each block of the logical Bell state measurement contains at least one physical qubit, that at least one of the blocks is intact, and that the entanglement exists between all blocks. Moreover, we also consider the MBLEPP with imperfect QND. We show that below a certain QND error threshold, this MBLEPP can still work. In this way, this MBLEPP not only obtains high-fidelity entanglement but also tolerates photon loss and the error from imperfect QND. This MBLEPP combines the benefits of the MBEPP and quantum error correction code and may have potential application in long-distance quantum communication.