Reviewerly/exHarmony huggingface dataset:
Enhancing the Hardware Pipelining Optimization Technique of the SHA-3 via FPGA

Information is transmitted between multiple insecure routing hops in text, image, video, and audio. Thus, this multi-hop digital data transfer makes secure transmission with confidentiality and integrity imperative. This protection of the transmitted data can be achieved via hashing algorithms. Furthermore, data integrity must be ensured, which is feasible using hashing algorithms. The advanced cryptographic Secure Hashing Algorithm 3 (SHA-3) is not sensitive to a cryptanalysis attack and is widely preferred due to its long-term security in various applications. However, due to the ever-increasing size of the data to be transmitted, an effective improvement is required to fulfill real-time computations with multiple types of optimization. The use of FPGAs is the ideal mechanism to improve algorithm performance and other metrics, such as throughput (Gbps), frequency (MHz), efficiency (Mbps/slices), reduction of area (slices), and power consumption. Providing upgraded computer architectures for SHA-3 is an active area of research, with continuous performance improvements. In this article, we have focused on enhancing the hardware performance metrics of throughput and efficiency by reducing the area cost of the SHA-3 for all output size lengths (224, 256, 384, and 512 bits). Our approach introduces a novel architectural design based on pipelining, which is combined with a simplified format for the round constant (RC) generator in the Iota (ι) step only consisting of 7 bits rather than the standard 64 bits. By reducing hardware resource utilization in the area and minimizing the amount of computation required at the Iota (ι) step, our design achieves the highest levels of throughput and efficiency. Through extensive experimentation, we have demonstrated the remarkable performance of our approach. Our results showcase an impressive throughput rate of 22.94 Gbps and an efficiency rate of 19.95 Mbps/slices. Our work contributes to advancing computer architectures tailored for SHA-3, therefore unlocking new possibilities for secure and high-performance data transmission.
6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Advanced Indoor Positioning Systems (IPS) based on Received Signal Strength (RSS) fingerprints have been paramount in 6G network research and commercial exploitation due to their costeffectiveness and simplicity. Despite their popularity, the advent of 6G has prompted a shift towards exploring Deep Learning algorithms to further enhance their performance and precision. Deep Learning research typically demands large datasets, leading to reliance on data augmentation and crowdsourcing techniques for data collection. However, the traditional centralization of data in crowdsourcing poses privacy risks, and here is where Federated Learning (FL) comes into play. In light of this, our study introduces FL to bridge this divide in a decentralized way, eliminating the need for servers to acquire labeled data directly from users. This approach aims to minimize localization error in RSS fingerprints, preserve user privacy, and reduce system latency, all key goals for 6G networks. Moreover, we explore the use of power transmission techniques to further decrease the latency in the FL system. Our simulation outcomes confirm the superiority of FL over traditional Stochastic Gradient Descent (SGD) methods considering critical evaluation metrics like localization error and global loss, paving the way for efficient 6G implementation.
P‐2.22: Research on 3D Target Detection Algorithm in Automatic Driving Scenario

As a research hotspot in computer vision, 3D object detection has wide application prospects. In the field of autonomous driving, the perception system with three-dimensional object detection function enables the vehicle to perceive the surrounding environment, make the vehicle more intelligent, and play a role in assisting driving. Aiming at two commonly used sensors: monocular camera and lidar, combined with deep learning, this paper separately studies the 3D target detection algorithm of PointPillars based on attention mechanism and the 3D target detection method based on monocular vision. The results show that the detection performance based on monocular vision still has advantages and can be used as an effective supplement to Lidar based three-dimensional target detection, improving the robustness of the sensing system.
P‐2.20: Hand Interaction Acquisition Technology Based on Hall Sensor

The design adopts the assistance of Hall sensor and permanent magnet to realize the capture of finger movement, which has the advantages of low cost and accurate recognition. STM32 is used as the main control module and MDK5 development platform in the design. The platform has the advantages of high integration and convenient development, and also has the characteristics of digital signal processing, high performance, real-time function, low voltage and low power consumption. A permanent magnet is designed at the center of the palm of the glove to form a constant magnetic field near the palm. The Hall sensor is a magnetic field sensor made according to the Hall effect, which can convert the magnetic field strength into a voltage signal. Through the AD conversion module built in STM32 chip, the output voltage of Hall sensor can be collected and converted into finger position and posture information through algorithm.
P‐2.25: Research on Virtual Reality Field Based on Multimodal Emotion Recognition

At present, emotion recognition has become a research hotspot in the field of pattern recognition. Considering the problems of incomplete information and strong interference in single‐modal emotion recognition, multimodal emotion recognition has been widely studied. Multimodal data includes, but is not limited to, emoji, text, and voice modality data. There are various ways to express emotion, among which expression, text and voice are the most direct and reliable emotional information carriers. Therefore, it is of great research and practical significance to comprehensively consider the emotion recognition research of expression, text and voice modalities, and to apply its research results to the field of virtual reality (referred to as VR). This paper analyzes the relevant situation of multimodal emotion recognition, extracts features of voice, text and expression, and then fuses them into multimodal for emotional analysis, and applies it to the VR field. The main work content is as follows: the relevant technologies of multimodal emotion recognition research in the field of VR are introduced, including deep learning related technologies, virtual reality technology, and multimodal fusion methods. In terms of deep learning, the focus is on convolutional neural networks and recurrent neural networks and their variants. In terms of virtual reality technology, the characteristics and applications of virtual reality are introduced. In terms of multimodal fusion, three commonly used fusion methods are introduced.
P‐2.31: An Emergency Rescue Action Recognition Method Based on Improved Spatiotemporal Decomposition Network

In order to solve the problem of too large computer overhead for convolutional neural network in the rescue action recognition, this paper proposes a rescue action recognition method combining spatiotemporal decomposition network and channel attention mechanism. After adding the channel attention mechanism, the model can generate a weight value for each feature channel, and then weight the normalized weight to each feature channel to improve the recognition accuracy of the model. The combined network model has better performance in both computer overhead and recognition accuracy, and the recognition accuracy has improved compared with the original model (S3D) in the case of only RGB video as input on rescue action data. In addition, the recognition accuracy on the public UCF-101 and KTH datasets has also improved. The experiment results show the proposed method can effectively improve the accuracy of action recognition in both the rescue action and public datasets.
P‐2.29: Depth Estimation of Light Field Image Based on Compressed Sensing and Multi‐clue Fusion

In the current research field of light field depth estimation, the occlusion of complex scenes and a large amount of computational data are the problems that every researcher must face. For complex occlusion scenes, this paper proposes a depth estimation method based on the fusion of adaptive defocus cues and constrained angular entropy cues, which is more robust to occlusion. At the same time, the compressed sensing theory is used to compress and reconstruct the light field image to solve the problem of a large amount of data in the process of light field image acquisition, transmission, and processing. The experimental results show that the proposed method has a good overall effect in dealing with the depth estimation of the occlusion scene, and the correct depth information can be obtained. The light field image reconstructed by compressed sensing can not only obtain good depth estimation results but also reduce the amount of data effectively.
Elevating Amodal Segmentation Using ASH-Net Architecture for Accurate Object Boundary Estimation

Amodal segmentation is a critical task in the field of computer vision as it involves accurately estimating object boundaries that extend beyond occlusion. This paper introduces a network named after the Amodal Segmentation Head, ASH-Net, a novel architecture specifically designed for amodal segmentation. ASH-Net is comprised of a ResNet-50 backbone, a Feature Pyramid Network middle layer, and an Amodal Segmentation Head. The evaluation encompasses three diverse datasets, namely COCOA-cls, KINS, and D2SA, providing a comprehensive analysis of ASH-Net’s capabilities. The results obtained demonstrate the superiority of ASH-Net in accurately estimating object boundaries beyond occlusion across multiple datasets. Specifically, ASH-Net achieves an Average Precision of 62.15% on the COCOA-cls dataset, 72.58% on the KINS dataset, and an impressive 91.4% on the D2SA dataset. Through extensive evaluation using average precision and average recall metrics, ASH-Net exhibits exceptional performance compared to state-of-the-art models. These findings highlight the remarkable performance of ASH-Net in overcoming occlusion challenges and accurately delineating object boundaries. This research identifies optimal training parameters, such as coefficient dimensions and aspect ratios, that significantly enhance segmentation performance while maintaining computational efficiency. The proposed ASH-Net architecture and its performance pave the way for improved object recognition, enhanced scene understanding, and the development of practical applications in various domains.
P‐2.17: The Research Status And Prospect of Augmented Reality in Medical Field

With the explosion of metaverse, augmented reality technology (AR) has been well developed. Augmented reality is a technology that connects the real and virtual worlds. In recent years, as the global population aging problem intensifies, the elderly are more dependent on advanced and guaranteed medical technology, AR+ medical has been further developed, and has received investment from many technology giants. AR technology has changed the form of traditional medical treatment. Through the study of AR+ medical treatment, the author analyzed the research results of AR in medical education, hand rehabilitation, medical plastic surgery, clinical medicine and other fields. The future research of AR in the medical field is prospected.
A Blockchain-Enabled Authentication and Conserved Data Aggregation Scheme for Secure Smart Grids

The construction of smart grids provides many benefits.Computational cost, however, drastically grows with an increase in scale since a large amount of data is generated, transmitted, collected, and treated.Although data aggregation technology is helpful when adopting smart grid applications, corresponding security and performance issues should be considered while implementing the mechanism.In this paper, a blockchain-enabled authenticated conserved data aggregation scheme is proposed to balance security concerns and the computational cost of the smart grid.Furthermore, several functions are developed in our scheme to highlight the contributions.First, efficient cryptographic algorithms are integrated instead of computationally-expensive ones.Second, the proposed scheme seamlessly incorporates a blockchain system into a smart grid for better decentralization which can avoid the possible threats and high cost of a centralized system.Third, the scheme is scalable, which can be adapted to manage a great number of metering devices.Fourth, the aggregational operations in our design combine power data and signature, which is more practical.Finally, the proposed scheme covers a one-time key pair and signature mechanism, thus upgrading the privacy protection of blockchain applications in the smart grid.
Toward the Achievement of English Language Learning Competencies Through the Use of an Application: A Case Study

Mobile language learning applications for smart mobile devices have greatly evolved in recent years. These applications have great potential to help students learn the target language. The present research aims to evaluate the achievement of the competence to read various types of written texts in English as a foreign language using the Duolingo application. The methodology followed is quantitative, in which an experimental group and a control group, each consisting of 24 students, were selected through convenience random sampling. In the experimental group, 25 learning sessions were conducted using the Duolingo application over a period of 3 months, while the control group received no treatment. A Pre-Test and a Post-Test, extracted from the official “British Council” website, were implemented. Entrance tests, feedback tests, and exit tests were administered. The results demonstrated that the Duolingo application has a positive and direct influence on the consolidation of the competence to read various types of written texts in English as a foreign language. A significant improvement was evident compared to the initial results with the exit test, with 77.3% of the sample achieving or surpassing the expected level of performance. In conclusion, participants expressed interest in various aspects of Duolingo, such as forums, podcasts, and identifiable themes in each lesson. However, two drawbacks have been identified, namely occasional issues with speech recognition and limitations in advanced language skills.
GameDepot: A Visual Analytics System for Mobile Game Performance Testing

In this paper, we present GameDepot, a visual analytics system designed to enable interactive analysis of performance testing logs for mobile games. Due to the emergence of computational-heavy mobile games, the performance testing of mobile games has become a crucial practice to strike a balance between user experience, battery life, and thermal management. However, policymakers in device manufacturers face challenges in understanding the complex performance testing logs generated for different game and device combinations. To address this issue, we conduct a design study with ten domain experts and develop GameDepot, a visual analytics system with six tightly coordinated views that provide an overview of thousands of testing sessions and facilitate the identification of abnormal events, such as excessive throttling. We implement a data modeling pipeline based on Long Short Term Memory (LSTM) to support advanced data manipulation operations that are needed in practice, such as measuring the similarity between devices and or predicting the performance of an unseen combination of games and devices. Our evaluation demonstrates that GameDepot not only supports the seven essential tasks we identified during the design study but also facilitates the identification of abnormal sessions.
A Comparative Study of Sentence Embeddings for Unsupervised Extractive Multi-document Summarization

Obtaining large-scale and high-quality training data for multi-document summarization (MDS) tasks is time-consuming and resource-intensive, hence, supervised models can only be applied to limited domains and languages. In this paper, we introduce unsupervised extractive methods for both generic and query-focused MDS tasks, intending to produce a relevant summary from a collection of documents without using labeled training data or domain knowledge. More specifically, we leverage the potential of transfer learning from recent sentence embedding models to encode the input documents into rich semantic representations. Moreover, we use a coreference resolution system to resolve the broken pronominal coreference expressions in the generated summaries, aiming to improve their cohesion and textual quality. Furthermore, we provide a comparative analysis of several existing sentence embedding models in the context of unsupervised extractive multi-document summarization. Experiments on the standard DUC’2004-2007 datasets demonstrate that the proposed methods are competitive with previous unsupervised methods and are even comparable to recent supervised deep learning-based methods. The empirical results also show that the SimCSE embedding model, based on contrastive learning, achieves substantial improvements over strong sentence embedding models. Finally, the newly involved coreference resolution method is proven to bring a noticeable improvement to the unsupervised extractive MDS task.
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming

Internet based businesses and products (e.g. e-commerce, music streaming) are becoming more and more sophisticated every day with a lot of focus on improving customer satisfaction. A core way they achieve this is by providing customers with an easy access to their products by structuring them in catalogues using navigation bars and providing recommendations. We refer to these catalogues as product concepts, e.g. product categories on e-commerce websites, public playlists on music streaming platforms. These product concepts typically contain products that are linked with each other through some common features (e.g. a playlist of songs by the same artist). How they are defined in the back-end of the system can be different for different products. In this work, we represent product concepts using database queries and tackle two learning problems. First, given sets of products that all belong to the same unknown product concept, we learn a database query that is a representation of this product concept. Second, we learn product concepts and their corresponding queries when the given sets of products are associated with multiple product concepts. To achieve these goals, we propose two approaches that combine the concepts of PU learning with Decision Trees and Clustering. Our experiments demonstrate, via a simulated setup for a music streaming service, that our approach is effective in solving these problems.
Training Support with Augmented Reality for Machine Setup: A Case Study in the Process Industry

The process industry is often characterised by complex, labour-intensive machine set-up sequences. The cost of downtime and defective products can be very high, so an error-free machine set-up is required. The paper proposes a Visual Management (VM) and Augmented Reality (AR) approach to support the machine set-up training process, with the aim to mitigate the risk of errors and reduce the learning curve for operators. Together with the company GlobalWafers, a specific VM-AR-based machine set-up training was developed and pre-tested in a laboratory environment. Layers and colour codes were developed in order to facilitate the understanding of the correct set-up. Furthermore, information on potential strengths and weaknesses was collected by means of a semi-structured interview during the laboratory tests. Future research activities consist in testing and applying the VM-AR-based machine set-up process in practice, also setting up a specific testing area dedicated to the operators training, at the company's facilities.
Economic Inclusion: Transforming the Lives of the Poor and How to Make Economic Inclusion Work in Africa

In recent years, notably in Africa, a significant worldwide movement to improve and increase economic involvement for the poorest people has evolved. To show the forces of change that will influence Africa’s economic inclusion and several methods that can help to improve economic inclusion in Africa, the chapter used document analysis. The chapter found several major patterns as the sources of exclusion, which include extreme poverty, informality, shock sensitivity, demographic dynamics, and the building of human capital. Once more, the chapter outlined initiatives that can aid in enhancing economic inclusion in Africa, such as promoting more and better jobs and livelihoods, enhancing financial literacy and payment options, using economic inclusion to boost women’s empowerment, and bolstering financial inclusion initiatives, and gender-conscious program delivery. The chapter underlined the importance of women’s empowerment as the foundation for all economic inclusion measures in Africa, in addition to the other ideas covered in the preceding paragraphs. Women’s empowerment programs should make greater investments in the welfare and empowerment of women, empowering them to reject gender stereotypes and step outside of their customary positions.
A Theoretical Approach

Digital capabilities have been studied in recent years with growing interest due to the increasing incorporation of technologies into the teaching-learning process. Concerning universities, digital transformation has been a challenge in the managerial and pedagogical areas. This chapter conducts a thematic review of the literature to assess the evolution of the study of digital capabilities in universities. We grouped the articles with an evolutionary approach focused on digital competencies. Our findings show that the evolution of digital skills over 12 years progresses from focusing on incorporating digital tools to a student-based approach, where teachers play a mentoring or supervisory role. Universities provide the necessary tools to facilitate the rebuilding of digital capabilities. Particularly, the findings evidenced the impact of the COVID-19 pandemic on university activities. The implications are associated with the need to establish access and inclusion for all groups due to the importance of this training for establishing digitized societies.
Attacks on Encrypted Response-Hiding Range Search Schemes in Multiple Dimensions

In this work, we present the first database reconstruction attacks against response-hiding private range search schemes on encrypted databases of arbitrary dimensions. Falzon et al. (VLDB 2022) present a number of range-supporting schemes on arbitrary dimensions exhibiting different security and efficiency trade-offs. Additionally, they characterize a form of leakage, structure pattern leakage, also present in many one-dimensional schemes e.g., Demertzis et al. (SIGMOD 2016) and Faber et al. (ESORICS 2015). We present the first systematic study of this leakage and attack a broad collection of schemes, including schemes that allow the responses to contain false-positives (often considered the gold standard in security). We characterize the information theoretic limitations of a passive persistent adversary. Our work shows that for range queries, structure pattern leakage can be as vulnerable to attacks as access pattern leakage. We give a comprehensive evaluation of our attacks with a complexity analysis, a prototype implementation, and an experimental assessment on real-world datasets.