Reviewerly/exHarmony huggingface dataset:
Employing Eye Tracking to Study Visual Attention to Live Streaming: A Case Study of Facebook Live

In recent years, the COVID-19 pandemic has led to the development of a new business model, “Live Streaming + Ecommerce”, which is a new method for commercial sales that shares the goal of sustainable economic growth (SDG 8). As information technology finds its way into the digital lives of internet users, the real-time and interactive nature of live streaming has overturned the traditional entertainment experience of audio and video content, moving towards a more nuanced division of labor with multiple applications. This study used a portable eye tracker to collect eye movement information from participants watching Facebook Live, with 31 participants who had experience using the live streaming platform. The four eye movement indicators, namely, latency of first fixation (LFF), duration of first fixation (DFF), total fixation durations (TFD), and the number of fixations (NOF), were used to analyze the distribution of the visual attention in each region of interest (ROI) and explore the study questions based on the ROIs. The findings of this study were as follows: (1) the fixation order of the ROIs in the live ecommerce platform differed between participants of different sexes; (2) the DFF of the ROIs in the live ecommerce platform differed among participants of different sexes; and (3) regarding the ROIs of participants on the live ecommerce platform, participants of different sexes showed the same attention to the live products according to the TFD and NOF eye movement indicators. This study explored the visual search behaviors of existing consumers watching live ecommerce and provides the results as a reference for operators and researchers of live streaming platforms.
Evaluating and Updating a Design Space for Augmented Reality Television

As Augmented Reality Television (ARTV) transitions out of the feasibility phase, it is crucial to understand the impact of design decisions on the viewers’ ARTV experiences. In a previous study, six ARTV design dimensions were identified by relying on insights from existing prototypes. However, the set of possible dimensions is likely to be broader. Building on top of previous work, we create an ARTV design space and present it in a textual cheat sheet. We subsequently evaluate the cheat sheet in a between-subject study (n = 10), with participants with wide-ranging expertise. We identified six new dimensions (genre, broadcast mode, audience demographics, cartoonish vs. photoreal representation, modality, and privacy), and a new aspect (360°) for the display dimension. In light of our observations, we provide an updated ARTV design space and observe that asking participants to write ARTV scenarios can be an effective method for harvesting novel design dimensions.
FedNKD: A Dependable Federated Learning Using Fine-tuned Random Noise and Knowledge Distillation

Multimedia retrieval models need the ability to extract useful information from large-scale data for clients. As an important part of multimedia retrieval, image classification model directly affects the efficiency and effect of multimedia retrieval. We need a lot of data to train a image classification model applied to multimedia retrieval task. However, with the protection of data privacy, the data used to train the model often needs to be kept on the client side. Federated learning is proposed to use data from all clients to train one model while protecting privacy. When federated learning is applied, the distribution of data across different clients varies greatly. Disregarding this problem yields a final model with unstable performance. To enable federated learning to work dependably in the real world with complex data environments, we propose FedNKD, which utilizes knowledge distillation and random noise. The superior knowledge of each client is distilled into a central server to mitigate the instablity caused by Non-IID data. Importantly, a synthetic dataset is created by some random noise through back propagation of neural networks. The synthetic dataset will contain the abstract features of the real data. Then we will use this synthetic dataset to realize the knowledge distillation while protecting users' privacy. In our experimental scenarios, FedNKD outperforms existing representative algorithms by about 1.5% in accuracy.
Research Deserts and Oases: Evidence from 27 Thousand Economics Journal Articles on Africa*

The distribution of economics research across Africa's 54 countries is highly uneven. I use a simple conceptual model and reduced‐form regressions with cross‐sectional, panel, and subnational data to explore the determinants of this distribution. Of the variation in research across countries, 93% can be explained by peacefulness, political institutions, international tourist arrivals, having English as an official language, and population. Within countries, research is concentrated in regions with higher income and large urban areas. Most research on African countries is context‐specific, so the lack of research in many countries and regions means a smaller potential evidence base for local policymakers.
An efficient privacy-preserving user authentication scheme using image processing and blockchain technologies

User authentication is a measurement challenge for handheld devices and online accounts such as bank accounts, social media accounts etc. because illegal access results in money loss and user privacy. Individual devices, online financial services, and intelligent spaces are three significant areas of concern for customer authentication procedures. Three ways have been identified for authentication factors: i) knowledge-factor, ii) Inherence factor, and iii) possession-factor. This study investigates two-way user authentication through image processing. CNN, RCNN, and Deepface are deep learning algorithms used for image recognition. We used imagechain for image storage and Blockchain for personal information storage (mobile number) to secure the database. The database is stored on an Ethereum-based blockchain. After determining whether the image is fake or real, match the webcam image with the imagechain; if both images match, the one-time password is given to the user’s cellphone number for login access. For image processing, Opencv is employed, and the Python library is used to execute machine and deep learning algorithms for user authentication. Test the proposed model on the 10 to 100 users for authentication. Accuracy of this experiment is 75.35, 76.33, 98.18 and cosine similarities of images are much better between images, but in case of fake image identification it achieved 97.35 % accuracy.
Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases.
Optimizations of Ternary Generative Adversarial Networks

Generative adversarial networks (GANs), which can generate and transform data, have been attracting attention. However, the model must be lightweight and fast when applied in the field. In terms of model weight reduction, B-DCGAN, which restricts the value of the weights to {−1, +1}, has already been proposed. We propose a ternary GAN using the ternary representation {−1, 0, +1} and an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -layer which makes the learning between generator and discriminator more competitive. It succeeded in improving the quality of the output image considerably while maintaining the almost same memory usage as that of B-DCGAN.
CostCO: An automatic cost modeling framework for secure multi-party computation

The last decade has seen an explosion in the number of new secure multi-party computation (MPC) protocols that enable collaborative computation on sensitive data. No single MPC protocol is optimal for all types of computation. As a result, researchers have created hybrid-protocol compilers that translate a program into a hybrid protocol that mixes different MPC protocols. Hybrid-protocol compilers crucially rely on accurate cost models, which are handwritten by the compilers' developers, to choose the correct schedule of protocols. In this paper, we propose CostCO, the first automatic MPC cost modeling framework. CostCO develops a novel API to interface with a variety of MPC protocols, and leverages domain-specific properties of MPC in order to enable efficient and automatic cost-model generation for a wide range of MPC protocols. CostCO employs a two-phase experiment design to efficiently synthesize cost models of the MPC protocol's runtime as well as its memory and network usage. We verify CostCO's modeling accuracy for several full circuits, characterize the engineering effort required to port existing MPC protocols, and demonstrate how hybrid-protocol compilers can leverage CostCO's cost models.
SchedInspector

Improving the performance of job executions is an important goal of HPC batch job schedulers, such as minimizing job waiting time, slowdown, or completion time. Such a goal is often accomplished using carefully designed heuristics based on job features, such as job size and job duration. However, these heuristics overlook important runtime factors (e.g., cluster availability and waiting job patterns), which may vary across time and make a previously sound scheduling decision not hold any longer. In this study, we propose a new approach to incorporate runtime factors into batch job scheduling for better job execution performance. The key idea is to add a scheduling inspector on top of the base job scheduler to scrutinize its scheduling decisions. The inspector will take the runtime factors into consideration and accordingly determine the fitness of the scheduled job. It then either accepts the scheduled job or rejects it and asks the base schedulers to try again later. We realize such an inspector, namely SchedInspector, by leveraging the intelligence of reinforcement learning. Through extensive experiments, we show SchedInspector can intelligently integrate the runtime factors into various batch job scheduling policies, including the state-of-the-art one, to gain better job execution performance, such as smaller average bounded job slowdown (up to 69% better) or average job waiting time (up to 52% better), across various real-world workloads. We also show that although rejecting scheduling decisions may leave the resources idle hence affect the system utilization, SchedInspector is able to achieve the job execution performance improvement with marginal impact on the system utilization (typically less than 1%). We consider one key advantage of SchedInspector is it automatically learns to work with and improve existing job scheduling policies without changing them, which makes it promising to serve as a generic enhancer for various batch job scheduling policies.
Quantum Algorithm for Maximum Satisfiability

Satisfiability (SAT) problem in engineering and computer science is to find the set of assignment values of input variables for the given Boolean function that evaluate this function to TRUE or prove that such satisfying values do not exist. For POS SAT Problem, we propose a novel quantum algorithm for the maximum satisfiability (MAX-SAT) which returns the maximum number of OR terms that are satisfied for the SAT-unsatisfiable function, providing us with the information how far the given Boolean function is from the SAT satisfaction. We use Grover's algorithm with a new block called quantum counter in the oracle circuit.
An incremental cross-modal transfer learning method for gesture interaction

Gesture can be used as an important way for human–robot interaction, since it is able to give accurate and intuitive instructions to the robots. Various sensors can be used to capture gestures. We apply three different sensors that can provide different modalities in recognizing human gestures. Such data also owns its own statistical properties for the purpose of transfer learning: they own the same labeled data, but both the source and the validation data-sets have their own statistical distributions. To tackle the transfer learning problem across different sensors with such kind of data-sets, we propose a weighting method to adjust the probability distributions of the data, which results in a more faster convergence result. We further apply this method in a broad learning system, which has proven to be efficient to learn with the incremental learning capability. The results show that although these three sensors measure different parts of the body using different technologies, transfer learning is able to find out the weighting correlation among the data-sets. It also suggests that using the proposed transfer learning is able to adjust the data which has different distributions which may be similar to the physical correlation between different parts of the body in the context of giving gestures.
Source-free Temporal Attentive Domain Adaptation for Video Action Recognition

With the rapidly increasing video data, many video analysis techniques have been developed and achieved success in recent years. To mitigate the distribution bias of video data across domains, unsupervised video domain adaptation (UVDA) has been proposed and become an active research topic. Nevertheless, existing UVDA methods need to access source domain data during training, which may result in problems of privacy policy violation and transfer inefficiency. To address this issue, we propose a novel source-free temporal attentive domain adaptation (SFTADA) method for video action recognition under the more challenging UVDA setting, such that source domain data is not required for learning the target domain. In our method, an innovative Temporal Attentive aGgregation (TAG) module is designed to combine frame-level features with varying importance weights for video-level representation generation. Without source domain data and label information in the target domain and during testing, an MLP-based attention network is trained to approximate the attentive aggregation function based on class centroids. By minimizing frame-level and video-level loss functions, both the temporal and spatial domain shifts in cross-domain video data can be reduced. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our proposed method in solving the challenging source-free UVDA task.
Evaluation of classifications of the monopodial bronchopulmonary vasculature using clustering methods

Mammalian pulmonary arteries divide multiple times before reaching the vast capillary network of the alveoli. Morphological analyses of the arterial branches can be challenging because more proximal branches are likely biologically distinct from more peripheral parts. Thus, it is useful to group the arterial branches into groups of coherent biology. While the generational approach of dichotomous branching is straightforward, the grouping of arterial branches in the asymmetrically branching monopodial lung is less clear. Several established classification methods return highly dissimilar groupings when employed on the same organ. Here, we established a workflow allowing the quantification of grouping results for the monopodial lung and tested various methods to group the branches of the arterial tree into coherent groups. A mouse lung was imaged by synchrotron x-ray microcomputed tomography, and the arteries were digitally segmented. The arterial tree was divided into its individual segments, morphological properties were assessed from corresponding light microscopic scans, and different grouping methods were employed, such as (fractal) generation or (Strahler) order. The results were ranked by the morphological similarity within and dissimilarity between the resulting groups. Additionally, a method from the mathematical field of cluster analysis was employed for creating a reference classification. In conclusion, there were significant differences in method performance. The Strahler order was significantly superior to the generation system commonly used to classify human lung structure. Furthermore, a clustering approach indicated more precise ways to classify the monopodial lung vasculature exist.
Secure olympics games with technology: Intelligent border surveillance for the 2022 Beijing winter olympics

Building a border surveillance system with integrated surveillance technologies is a crucial security strategy for Olympic venues. Although the previous Olympic Games have a long history of using surveillance technologies, there are perennial challenges in terms of false alarms, energy consumption, and labor and capital costs. In this paper, an IoT-based multi-agent intelligent border surveillance method with less cost is proposed. Following a two-level autonomous mechanism, the method assures agent-level self-patrolling by distributed nodes and system-level multi-agent surveillance task coordination by the central server. The core entity of each distributed node is the IoT-based SmartGM, an autonomous surveillance tower integrating multi-modal surveillance devices. Finally, based on intelligent visual reasoning algorithms, the proposed activation strategy updates surveillance devices’ activation status simultaneously according to global states, ensuring long-term operation. This method has been successfully applied in the ski venue of the 2022 Beijing Winter Olympics.
The Impact of Blockchain Application on the Qualification Rate and Circulation Efficiency of Agricultural Products: A Simulation Analysis with Agent-Based Modelling

In the context of COVID-19, the circulation of agricultural products is increasingly important for the nutrition and health of people. With the changing needs of society and the advancement of technology, the agricultural product circulation system needs to undergo corresponding changes to adapt to the modern fast-paced social system. Blockchain technology couples with the circulation of agricultural products, as its technical features, such as immutability and a distributed ledger database, ensures the speed and stability of the key information circulation process of agricultural products. The research goal of this paper was to clarify the influence of blockchain technology on the qualification rate and circulation efficiency for agricultural products. Based on the main characteristics of blockchain technology and a summary of domestic and foreign theoretical research, this paper simulated the impacts of blockchain technology on the agricultural product circulation system. The results revealed that blockchain technology can improve the qualification rate of agricultural products and thereby ensure their quality and safety. The introduction of blockchain increased the qualification rate by nearly 30%. Moreover, blockchain technology significantly enhanced the efficiency of the agricultural product circulation system, thereby greatly promoting economic benefits. The introduction of blockchain increased circulation efficiency by nearly 15%. Finally, the introduction of blockchain technology can effectively promote the governance level and reduce the supervision costs of the agricultural product circulation system. Through simulation analysis, we found that blockchain technology has a positive impact on both the qualification rate and circulation efficiency for agricultural products. These findings enrich research into the application of blockchain technology in the management and circulation of modern agricultural products.
Data-driven adaptive predictive control of hydrocracking process using a covariance matrix adaption evolution strategy

In this paper, a new data-driven adaptive predictive control architecture to the reactor temperature control problem in hydrocracking process is proposed with completely unknown system dynamics. Specifically, the nonlinear relationship between the reactor temperature and conversion is established using echo state networks (ESNs) with an intrinsic plasticity rule, and a covariance matrix adaption evolution strategy (CMA-ES) is used to optimize the reactor temperature iteratively using the input and state information. Besides, the prescribed performance function is introduced to guarantee the transient and steady performance of the system. In addition, the input constraints and external disturbance are simultaneously considered. Numerical simulation results and industrial application prove the efficacy of the proposed method.
<i>New media literacies</i> for transmedia learning. How students are regarding their transliteracy in Italian <i>licei classici</i>

In recent years, the integration of new media literacies and, consequently, strategies such as transmedia learning in the teaching-learning processes has been a topic of interest among various types of national and international institutions and governments. In this sense, the current article deals with the abilities and habits that Italian students of licei classici have in order to face these new formative contexts that are coming. For this purpose, two quantitative instruments (one referring to digital attitudes and skills and the other corresponding to transmedia attitudes) were administered to 400 students. The results obtained show that the majority of young people have access to devices and that they prefer mobile phones. Furthermore, although they are inclined towards transmedia practices, they have certain difficulties in becoming the creative part that collaborates in a digital citizenship.
Privacy-Preserving and Explainable AI in Industrial Applications

The industrial environment has gone through the fourth revolution, also called “Industry 4.0”, where the main aspect is digitalization. Each device employed in an industrial process is connected to a network called the industrial Internet of things (IIOT). With IIOT manufacturers being capable of tracking every device, it has become easier to prevent or quickly solve failures. Specifically, the large amount of available data has allowed the use of artificial intelligence (AI) algorithms to improve industrial applications in many ways (e.g., failure detection, process optimization, and abnormality detection). Although data are abundant, their access has raised problems due to privacy concerns of manufacturers. Censoring sensitive information is not a desired approach because it negatively impacts the AI performance. To increase trust, there is also the need to understand how AI algorithms make choices, i.e., to no longer regard them as black boxes. This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research. It also presents examples related to privacy-preserving and explainable AI solutions, and comments on the interaction between the identified challenges in the conclusions.