Holography is a three-dimensional (3D) technology that enables natural stereoscopic viewing with deep depth and expected for practical use in the future. Based on the recording process of holography, the electronic data generated through numerical simulation in a computer are called computer-generated holograms (CGHs). Displaying the generated CGH on a spatial light modulator and reconstructing a 3D object by illuminating it with light is called electro-holography. One of the issues in the development of 3DTV using electro-holography is the compression and transmission of a CGH. Because of the data loss caused by compression in a CGH, the quality of the reconstructed image may be affected, unlike normal 2D images. In wireless transmission of a CGH, not only data loss due to compression but also retransmissions and drops of data due to unstable network environments occur. These may degrade the quality of the reconstructed image, cause frame drops, and decrease the frame rate. In this paper, we developed a system for streaming CGH videos for reconstructing 3D objects using electro-holography. CGH videos were generated by merging multiple CGHs into a timeline, and the uncompressed or lossless compressed CGH videos were streamed via a network such as wired and wireless local area networks, a local 5G network, and mobile network. The performance of the network and quality of the CGH videos and reconstructed images were evaluated. Optically reconstructed images were obtained from the uncompressed CGH videos streamed via the networks. It was also confirmed that the required bit rate could be reduced without degrading the quality of the reconstructed image by using lossless compression. In some cases of wireless transmission, even when packet loss or retransmission occurs, there was no degradation in the reconstructed image quality.

In this study, we introduce a lateral electric-field driving system based on continuous potential-difference driving using lateral transparent electrodes to achieve a wide viewing zone angle in electronic holographic displays. We evaluate light modulation to validate the independent driving capability of each pixel at a high resolution (pixel pitch: 1 μm). Additionally, we demonstrate the feasibility of two-dimensional driving by integrating the driving and ground electrodes.

The study of the spread of pandemics, including COVID-19, is an emerging concern to promote self-care management through social distancing, using state-of-the-art tools and technologies. Existing technologies provide many opportunities to acquire and process large volumes of data to monitor user activities from various perspectives. However, determining disease hotspots remains an open challenge considering user activities and interactions; providing related recommendations to susceptible individuals requires attention. In this article, we propose an approach to determine disease hotspots by modeling users’ activities from both cyber- and real-world spaces. Our approach uniquely connects cyber- and physical-world activities to predict hazardous regions. The availability of such an exciting data set is a non-trivial task; therefore, we produce the data set with much hard work and release it to the broader research community to facilitate further research findings. Once the data set is generated, we model it as a directed multi-attributed and weighted graph to apply classical machine learning and graph neural networks for prediction purposes. Our contribution includes mapping user events from cyber- and physical-world aspects, knowledge extraction, dataset generation, and reasoning at various levels. Within our unique graph model, numerous elements of lifestyle parameters are measured and processed to gain deep insight into a person’s status. As a result, the proposed solution enables the authorities of any pandemic, such as COVID-19, to monitor and take measurable actions to prevent the spread of such a disease and keep the public informed of the probability of catching it.

Mainstream data augmentation techniques involving image-level manipulation operations (e.g., CutMix) compromise the integrity of extracted features, which impedes the application of data augmentation for pixel-level image processing tasks. Moreover, the unexplored potential of test-time augmentation within the HDR domain remains to be validated. In this paper, a full stage data augmentation method called HDR-VDA for HDR video reconstruction is proposed, especially for synthetic video based training datasets. In the training stage, the local area-based mixed data augmentation (LMDA) provides samples encompassing diverse exposure and color patterns, thus the trained model gains improved capabilities in effectively processing poorly-exposure regions, with particular emphasis on areas with rich color details. A motion and ill-exposure guided sample rank and adjustment strategy (MISRA) is utilized to augment specific training samples and compensate extra information. In the testing stage, an HDR-targeted test-time augmentation method (HTTA) is designed for reconstructed HDR frames. After restoring the shape of the test-time augmented HDR output to be consistent with the original inference output, an ill-exposure outlier removal based average ensemble method is used to blend all augmented inference outputs to generate reliable and stable reconstruction results. Experiments demonstrate that HDR-VDA achieves a better PSNR-T score of 38.91 dB, compared with conventional works under the same conditions.

Big data processing is a set of techniques or programming models, which can be deployed on both the cloud servers or edge nodes, to access large-scale data and extract useful information for supporting and providing decisions. Meanwhile, several typical domains of human activity in smart society, such as social networks, medical diagnosis, recommendation systems, transportation, and Internet of Things (IoT), often manage a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. As one of the convincing solutions to carry out analytics for big data, graph processing is especially applicable for these application domains. However, either the intra-device or the inter-device data processing in the edge-cloud architecture is truly prone to be attacked by the malicious Trojans covertly embedded in the counterfeit processing systems developed by some third-party vendors in numerous practical scenarios, leading to identity theft, misjudgment, privacy disclosure, and so on. In this paper, for the first time to our knowledge, we specially build a novel attack model for ubiquitous graph processing in detail, which also has easy scalability for other applications in big data processing, and discuss some common existing mitigations accordingly. Multiple activation mechanisms of Trojans designed in our attack model effectively make the attacks imperceptible to users. Evaluations indicate that the proposed Trojans are highly competitive in stealthiness with trivial extra latency.

Graphs are highly flexible data structures that can model various data and relationships. By using graphs, we can abstract and represent various things in the real world. The technology of artificially generating graphs is important in various fields where graphs are applied to various fields in engineering, including communication networks, social networks, and so on. In this paper, we organize and introduce graph generation techniques from early random-based methods to the latest deep graph generators, focusing on the aspects of feature reproduction and specification. Techniques for reproducing and specifying graph features in graph generation may provide new research methods for classical graph theory and optimization problems on graphs. This paper also presents recent achievements that may lead to further exploration in these fields and discusses the future prospects of graph generation.

This study examines the threat of information leakage when digital ICs, which process sensitive information such as cryptographic operations and handling of personal and confidential information, are mounted on printed circuit boards with split ground (GND) planes. We modeled the mechanism of generating such information leakage and proposed a methodology to control it. It is known that the GND plane of a printed circuit board on which digital integrated circuits are mounted should be solid and undivided to ensure signal integrity, power integrity, and electromagnetic compatibility. However, in actual designs, printed circuit boards may have split GND planes to isolate analog and digital circuits, isolate high-voltage and low-voltage circuits, or integrate multi-function electronic control units. Such split GND planes can increase the risk of electromagnetic information leakage. We, therefore, investigated a side-channel attack standard evaluation board, SASEBO-G, which has been reported to leak cryptographic information superimposed on common-mode currents, known as one of the major causes of electromagnetic emanation. Our experimental results showed that the split GND planes were the dominant cause of common-mode (CM) information leakage. Next, we constructed an equivalent circuit model of the dominant leakage mechanism and confirmed that the behavior of side-channel information leakage superimposed in the simulated CM current was consistent with the measured results. We also confirmed that to mitigate side-channel information leakage in CM caused by the potential difference between the split GND planes, the impedance should be reduced in the information leakage band by connecting the GND planes with capacitors, and the like. In addition, the RF band coupling between cables should be weakened if the cables are connected to the split GND planes.

White-box cryptographic implementations often use masking and shuffling as countermeasures against key extraction attacks. To counter these defenses, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed. These methods aim to breach these countermeasures without needing reverse engineering. However, these non-invasive attacks are expensive and can be thwarted by updating the masking and shuffling techniques. This paper introduces a simple binary injection attack, aptly named clear & return, designed to bypass advanced masking and shuffling defenses employed in white-box cryptography. The attack involves injecting a small amount of assembly code, which effectively disables run-time random sources. This loss of randomness exposes the unprotected lookup value within white-box implementations, making them vulnerable to simple statistical analysis. In experiments targeting open-source white-box cryptographic implementations, the attack strategy of hijacking entries in the Global Offset Table (GOT) or function calls shows effectiveness in circumventing run-time countermeasures.

To fully exploit the attribute information in graphs and dynamically fuse the features from different modalities, this letter proposes the Attributed Graph Clustering Network with Adaptive Feature Fusion (AGC-AFF) for graph clustering, where an Attribute Reconstruction Graph Autoencoder (ARGAE) with masking operation learns to reconstruct the node attributes and adjacency matrix simultaneously, and an Adaptive Feature Fusion (AFF) mechanism dynamically fuses the features from different modules based on node attention. Extensive experiments on various benchmark datasets demonstrate the effectiveness of the proposed method.

This paper reports on the details of the International Competition on Graph Counting Algorithms (ICGCA) held in 2023. The graph counting problem is to count the subgraphs satisfying specified constraints on a given graph. The problem belongs to #P-complete, a computationally tough class. Since many essential systems in modern society, e.g., infrastructure networks, are often represented as graphs, graph counting algorithms are a key technology to efficiently scan all the subgraphs representing the feasible states of the system. In the ICGCA, contestants were asked to count the paths on a graph under a length constraint. The benchmark set included 150 challenging instances, emphasizing graphs resembling infrastructure networks. Eleven solvers were submitted and ranked by the number of benchmarks correctly solved within a time limit. The winning solver, TLDC, was designed based on three fundamental approaches: backtracking search, dynamic programming, and model counting or #SAT (a counting version of Boolean satisfiability). Detailed analyses show that each approach has its own strengths, and one approach is unlikely to dominate the others. The codes and papers of the participating solvers are available: https://afsa.jp/icgca/.

A rectangle-of-influence drawing of a plane graph G is a straight-line planar drawing of G such that there is no vertex in the proper inside of the axis-parallel rectangle defined by the two ends of any edge. In this paper, we show that any given 5-connected plane graph G with five or more vertices on the outer face has a rectangle-of-influence drawing in an integer grid such that W + H ≤ n - 2, where n is the number of vertices in G, W is the width and H is the height of the grid.

In this letter, we give a characterization for a generic construction of bent functions. This characterization enables us to obtain another efficient construction of bent functions and to give a positive answer on a problem of bent functions.

Software refactoring is an important process in software development. During software refactoring, code smell is a popular research topic that refers to design or implementation flaws in the software. Large class is one of the most concerning code smells in software refactoring. Detecting and refactoring such problem has a profound impact on software quality. In past years, software metrics and clustering techniques have commonly been used for the large class detection. However, deep-learning-based approaches have also received considerable attention in recent studies. In this study, we apply graph neural networks (GNNs), an important division of deep learning, to address the problem of large class detection. First, to support the extensive data requirements of the deep learning task, we apply a semiautomatic approach to generate a substantial number of data samples. Next, we design a new type of directed heterogeneous graph (DHG) as an input graph using the methods similarity matrix and software metrics. We construct an input graph for each class sample and make the graph classification with GNNs to identify the smelly classes. In our experiments, we apply three typical GNN model architectures for large class detection and compare the results with those of previous studies. The results show that the proposed approach can achieve more accurate and stable detection performance.

Entity alignment (EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs (KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing embedding-based methods generate aligning entity representation by mining the relevance of triple elements, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA - Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above shortcomings from the perspective of ensemble triple representation considering triple specificity and diversity features of entity role. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic and type spaces, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, the role diversity of triple elements is modeled via triple-aware entity enhancement in TTEA for EA-oriented entity representation. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework makes comparative results.

This work focuses on training dataset enhancement of informative relational triplets for Scene Graph Generation (SGG). Due to the lack of effective supervision, the current SGG model predictions perform poorly for informative relational triplets with inadequate training samples. Therefore, we propose two novel training dataset enhancement modules: Feature Space Triplet Augmentation (FSTA) and Soft Transfer. FSTA leverages a feature generator trained to generate representations of an object in relational triplets. The biased prediction based sampling in FSTA efficiently augments artificial triplets focusing on the challenging ones. In addition, we introduce Soft Transfer, which assigns soft predicate labels to general relational triplets to make more supervisions for informative predicate classes effectively. Experimental results show that integrating FSTA and Soft Transfer achieve high levels of both Recall and mean Recall in Visual Genome dataset. The mean of Recall and mean Recall is the highest among all the existing model-agnostic methods.

Chinese Spelling Correction (CSC) is an important natural language processing task. Existing methods for CSC mostly utilize BERT models, which select a character from a candidate list to correct errors in the sentence. World knowledge refers to structured information and relationships spanning a wide range of domains and subjects, while definition knowledge pertains to textual explanations or descriptions of specific words or concepts. Both forms of knowledge have the potential to enhance a model’s ability to comprehend contextual nuances. As BERT lacks sufficient guidance from world knowledge for error correction and existing models overlook the rich definition knowledge in Chinese dictionaries, the performance of spelling correction models is somewhat compromised. To address these issues, within the world knowledge network, this study injects world knowledge from knowledge graphs into the model to assist in correcting spelling errors caused by a lack of world knowledge. Additionally, the definition knowledge network in this model improves the error correction capability by utilizing the definitions from the Chinese dictionary through a comparative learning approach. Experimental results on the SIGHAN benchmark dataset validate the effectiveness of our approach.

Permutation polynomials have been studied for a long time and have important applications in cryptography, coding theory and combinatorial designs. In this paper, by means of the multivariate method and the resultant, we propose four new classes of permutation quadrinomials over 𝔽q3, where q is a prime power. We also show that they are not quasi-multiplicative equivalent to known ones. Moreover, we compare their differential uniformity with that of some known classes of permutation trinomials for some small q.

With the advent of gated quantum computers and the regular structures for qubit layout, methods for placement, routing, noise estimation, and logic to hardware mapping become imminently required. In this paper, we propose a method for quantum circuit layout that is intended to solve such problems when mapping a quantum circuit to a gated quantum computer. The proposed methodology starts by building a Circuit Interaction Graph (CIG) that represents the ideal hardware layout minimizing the distance and path length between the individual qubits. The CIG is also used to introduce a qubit noise model. Once constructed, the CIG is iteratively reduced to a given architecture (qubit coupling model) specifying the neighborhood, qubits, priority, and qubits noise. The introduced constraints allow us to additionally reduce the graph according to preferred weights of desired properties. We propose two different methods of reducing the CIG: iterative reduction or the iterative isomorphism search algorithm. The proposed method is verified and tested on a set of standard benchmarks with results showing improvement on certain functions while in average improving the cost of the implementation over the current state of the art methods.

In many real-world problems, a complex task is typically composed of a set of subtasks that follow a certain execution order. Traditional multi-agent reinforcement learning methods perform poorly in such multi-task cases, as they consider the whole problem as one task. For such multi-agent multi-task problems, heterogeneous relationships i.e., subtask-subtask, agent-agent, and subtask-agent, are important characters which should be explored to facilitate the learning performance. This paper proposes a dynamic heterogeneous graph based agent allocation-action learning framework. Specifically, a dynamic heterogeneous graph model is firstly designed to characterize the variation of heterogeneous relationships with the time going on. Then a multi-subgraph partition method is invented to extract features of heterogeneous graphs. Leveraging the extracted features, a hierarchical framework is designed to learn the dynamic allocation of agents among subtasks, as well as cooperative behaviors. Experimental results demonstrate that our framework outperforms recent representative methods on two challenging tasks, i.e., SAVETHECITY and Google Research Football full game.

Human action recognition (HAR) exhibits limited accuracy in video surveillance due to the 2D information captured with monocular cameras. To address the problem, a depth estimation-based human skeleton action recognition method (SARDE) is proposed in this study, with the aim of transforming 2D human action data into 3D format to dig hidden action clues in the 2D data. SARDE comprises two tasks, i.e., human skeleton action recognition and monocular depth estimation. The two tasks are integrated in a multi-task manner in end-to-end training to comprehensively utilize the correlation between action recognition and depth estimation by sharing parameters to learn the depth features effectively for human action recognition. In this study, graph-structured networks with inception blocks and skip connections are investigated for depth estimation. The experimental results verify the effectiveness and superiority of the proposed method in skeleton action recognition that the method reaches state-of-the-art on the datasets.