Reviewerly/exHarmony huggingface dataset:
Application of Attention Mechanism-Based Dual-Modality SSD in RGB-D Hand Detection

Multimodal gesture recognition is a crucial research area in human-computer interaction. This paper proposes a static gesture multimodal recognition technology based on the Single Shot MultiBox Detector (SSD). Firstly, RGB image data and Depth image data are input into the VGG network to extract features. Then, trained features are concatenated in the fusion process, and the weights of features are adaptively learned with attention mechanisms. Results show that combining the two modalities improves model accuracy compared to using RGB images and Depth images separately. Next, the VGG network is replaced with the MobileNet v1 network as the backbone to make the model faster. The proposed method is tested on the Hand Gesture Dataset. The results indicate that the proposed method is superior to the single-modal gesture recognition SSD network.
Minimal encodings of canonical k-mers for general alphabets and even k-mer sizes

To index or compare sequences efficiently, often k-mers, i.e., substrings of fixed length k, are used. For efficient indexing or storage, k-mers are encoded as integers, e.g., applying some bijective mapping between all possible σ^k k-mers and the interval [0,σ^k-1], where σ is the alphabet size.
The QARMAv2 Family of Tweakable Block Ciphers

We introduce the QARMAv2 family of tweakable block ciphers. It is a redesign of QARMA (from FSE 2017) to improve its security bounds and allow for longer tweaks, while keeping similar latency and area. The wider tweak input caters to both specific use cases and the design of modes of operation with higher security bounds. This is achieved through new key and tweak schedules, revised S-Box and linear layer choices, and a more comprehensive security analysis. QARMAv2 offers competitive latency and area in fully unrolled hardware implementations.Some of our results may be of independent interest. These include: new MILP models of certain classes of diffusion matrices; the comparative analysis of a full reflection cipher against an iterative half-cipher; our boomerang attack framework; and an improved approach to doubling the width of a block cipher.
Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning

Few-shot video classification methods aim to recognize a new class with only a few training examples. Distinct from previous few-shot methods, we explicitly consider the relations in cross-video domains and take full advantage of the cross-video frame matching in a hierarchy learning fashion. In this paper, we propose a Dual-Hierarchy Graph Neural Network to realize comprehensive cross-video frame matching and video relation modeling. In the first hierarchy of the graph neural network, we build a Cross-video Frame Matching Graph to extract robust frame-level features via accumulating information across frames sampled from both query and support videos. Then, frame representations are accumulated to obtain the video-level features. In the second hierarchy of the graph neural network, we construct a Video Relation Graph by taking the video-level features as nodes, which can adaptively learn positive relations between query and support videos. We get the predicted label of the query video through the matching learning of edges connecting video nodes. We evaluate the model on three benchmarks: HMDB51, Kinetics, and UCF101. Extensive experiments on benchmark datasets demonstrate that our model significantly improves few-shot video classification across a wide range of competitive baselines and showcases the strong generalization of our framework. The source code and models will be publicly available at https://github.com/JiaMingZhong2621/DHGNN.
Probabilistic amplitude shaping for continuous-variable quantum key distribution with discrete modulation over a wiretap channel

To achieve the maximum information transfer and face a possible eavesdropper, the samples transmitted in continuous-variable quantum key distribution (CV-QKD) protocols are to be drawn from a continuous Gaussian distribution. As a matter of fact, in practical implementations the transmitter has a finite (power) dynamics and the Gaussian sampling can be only approximated. This requires the quantum protocols to operate at small powers. In this paper, we show that a suitable probabilistic amplitude shaping of a finite set of symbols allows to approximate at will the optimal channel capacity also for increasing average powers. We investigate the feasibility of this approach in the framework of CV-QKD, propose a protocol employing discrete quadrature amplitude modulation assisted with probabilistic amplitude shaping, and we perform the key generation rate analysis assuming a wiretap channel and lossless homodyne detection.
Storing quantum information in a generalised Dicke model via a simple rotation

Abstract A method for storing quantum information is presented for three-level atomic systems interacting dipolarly with a single radiation field. The method involves performing simple local SU(2) rotations on the Hamiltonian. Under equal detuning, these transformations decouple one of the atomic levels from the electromagnetic field for the Λ- and V -configurations, yielding two effective two-level systems (qubits) plus an isolated atomic level; this allows for the exchange of information between the qubits. This rotation preserves the quantum phase diagram of the system. The method could possibly be used as a means to manipulate quantum information, such as storage and retrieval, or communication via a transmission line.
Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model

Face age progression aims to change an individual’s face from a provided face image to forecast how that image will look in the future. Face aging is gaining much attention in today’s environment, which needs better security and a touchless unique identification mechanism. Researchers are focused on creating face processing algorithms to address the difficulty of producing realistic aged faces for smart system applications over the earlier decades. In the literature, the two basic needs of face age progression, aging accuracy, and identity preservation are not thoroughly addressed. According to the extraordinary gains in image synthesis made by deep generative methods and their significant influence on a wide variety of practical applications such as identifying missing persons using entertainment, childhood images, and so on, face age progression/regression has reawakened attention. The majority of present techniques concentrate on face age progression and is beneficial and productive in learning the transition across age groups utilizing paired data, i.e., face images of the similar individual at various ages. Through the motivation of the important success attained by Generative Adversarial Networks (GANs), this paper uses tactics to implement the improved Cycle GAN-based intelligent face age progression model. Initially, the standard datasets for the face progression are gathered, and the face is detected using the Viola-Jones object detection algorithm. Then, the pre-processing of the facial image is performed by median filtering and contrast enhancement techniques. Once the image is pre-processed, the Hyperparameter Tuning-Cycle GAN (HT-Cycle GAN) is adopted for face age progression. As an improvement, the hyperparameters of the Cycle GAN are optimized or tuned by the modified Galactic Swarm Optimization (GSO), known as Best Fitness-based Galactic Swarm Optimization (BF-GSO). From the evaluation of statistical analysis, the similarity score of BF-GSO-HT-CycleGAN is 0.80%, 3.33%, and 2.86% higher than cGAN, CycleGAN, and Dubbed FaceGAN, respectively. Here, the Dubbed FaceGAN is the 2nd greatest network. Furthermore, compared to traditional models utilizing distinct standard datasets, the experimental findings show that the suggested technique attains efficiency, accuracy, and flexibility.
Innovative Potential of Scientific and Pedagogical Workers of the University

The relevance of the problem of the article stems from the need to study the innovative potential of scientific and pedagogical staff of the university in a rapidly changing socio­economic, political, cultural and scientific conditions. The goal of the article is to identify the innovative potential of the faculty and characterize the ways of its research. The article draws attention to the fact that one of the most significant ways of expressing the potential is the presence of its resource. The practical significance of the results of the study lies in the possibility of their use by the university authorities to solve urgent problems related to the higher education system.
A Two-Stage Modality Fusion Approach for Recognizing Human Actions

Human action recognition (HAR) task, as one of the most representative human-centric visual understanding tasks, has received extensive attention from academia and industry. The key issue of this task is to mine the feature representations that can effectively describe the characteristics of human actions. In this article, we make full use of the information from multiple modalities in video, and propose a two-stage modality fusion strategy, which effectively mitigates the modality gaps between different modalities. First, in the first-stage fusion, based on the two-stream network, homogeneous RGB image’s and optical flow’s representations are aligned and integrated through generative adversarial network (GAN) and deep belief networks (DBNs), respectively. Then, in the second-stage fusion, the features obtained in the first stage are fused with the heterogeneous skeleton features through the proposed adaptive bilinear pooling network. Finally, the fused discriminative features are input into the classifier for recognition. Through this two-stage fusion strategy, the consistent and complementary representations between different modalities are fully mined. Experimental results show that our proposed approach outperforms several other methods on NTU-RGB + D (CS 94.5%, CV 98.8%), NTU-RGB + D 120 (CS 93.4%, CSet 94.8%), Northwestern UCLA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\text {1,2}}^{{3}} 97.2$ </tex-math></inline-formula> %), and Penn Action datasets (Top1 99.8%).
High-efficiency TRNG Design Based on Multi-bit Dual-ring Oscillator

Unpredictable true random numbers are required in security technology fields such as information encryption, key generation, mask generation for anti-side-channel analysis, algorithm initialization, and so on. At present, the true random number generator (TRNG) is not enough to provide fast random bits by low-speed bits generation. Therefore, it is necessary to design a faster TRNG. This work presents an ultra-compact TRNG with high throughput based on a novel extendable dual-ring oscillator (DRO). Owing to multiple bits output per cycle in DRO can be used to obtain the original random sequence, the proposed DRO achieves a maximum resource utilization to build a more efficient TRNG, compared with the conventional TRNG system based on ring oscillator (RO), which only has a single output and needs to build multiple groups of ring oscillators. TRNG based on the 2-bit DRO and its 8-bit derivative structure has been verified on Xilinx Artix-7 and Kintex-7 FPGA under the automatic layout and routing and has achieved a throughput of 550 Mbps and 1,100 Mbps, respectively. Moreover, in terms of throughput performance over operating frequency, hardware consumption, and entropy, the proposed scheme has obvious advantages. Finally, the generated sequences show good randomness in the test of NIST SP800-22 and Dieharder test suite and pass the entropy estimation test kit NIST SP800-90B and AIS-31.
Effectiveness of Online Informal Language Learning Applications in English Language Teaching: A Behavioral Perspective

This study aimed to ascertain the learning model adopted by university lecturers in the digital era. Utilising an action research design, a mixed-method approach was employed with 32 students participating. Data were collected through two cycles of learning outcomes using online informal language learning (OILL) integrated with smartphones. These outcomes and observations were documented through photographs, video recordings, and classroom observation forms. Descriptive and content analyses were employed for evaluation and interpretation. The results revealed that a majority of students perceived the collaborative learning model, which integrates OILL with smartphones, as a technology-driven process that facilitated more flexible learning in the classroom. Crucial to this model's success was the level of student engagement, which influenced their behaviour towards OILL and smartphone use. Students in this study exhibited positive attitudes, evidenced by their enhanced self-direction, motivation, and improvements in various linguistic skills, critical thinking, and teamwork. The persistent use of the OILL and smartphone collaborative learning model by lecturers during the pandemic was observed, indicating its perceived superiority over traditional learning models, especially given the technological communication and interaction challenges experienced during the pandemic. The study underscores the importance of considering behavioural factors and the quality of OILL and smartphone applications in influencing student learning behaviour and teaching models. Therefore, the integration of OILL applications into a blended or hybrid teaching environment is suggested as an effective strategy for enhancing the quality of education in today's digital classrooms. It is recommended that future research adopt a quantitative approach with a more extensive sample to further elucidate the dynamics of learning outcomes associated with the use of OILL integrated with smartphones in the digital age.
A Concrete Construction Encryption Mechanism Based Spatio Temporal Analysis for Securing BigData Storage in Cloud

Cloud computing has regenerated how processing infrastructure is abstracted and used as a significant architecture for large-scale computation. In addition to the issues posed by Big Data storage, the rapid growth of cloud computing increases the complexity of data confidentiality, data security, and user access regulations, resulting in a loss of cloud service trustworthiness. The country and society rely heavily on its security. We introduce a new, improved NTRU cryptosystem that raises an alert whenever it detects quantum computing attacks to extract spatial and temporal features. A Spatio-temporal constrained Secure and Verifiable Attribute-Based Access Control Scheme (SSVAABAC) is proposed in this research. This method’s primary purpose is to successfully update and check access policies to improve authorization flexibility, resource usage, and business timeliness. On the one hand, INTRU cryptosystem and Attribute Based Access Control (ABAC) Schemes are combined to improve the efficiency of security strength in cloud servers and provide efficient access policies enforced access decisions without adding any permission. It is easier to modify attributes than to change or define new roles in cloud servers by data owners, resulting in less computational overhead than traditional methods. On the other hand, the developed SSVA-ACS will support temporal and spatial constrained attributes to enable the user’s accessibility of cypher text from CSs associated with the location and valid time interval. The effectiveness of decryption is successful if the Access policies and Spatio-temporal details of users satisfy data owners’ detail. Thus, the proposed scheme fits the user secret key for the users specified in the location and time interval. As a result, a concrete structure implements the suggested technique by implementing an encryption device that associates a user’s time-related privilege with the current access time. The final results show that the SVACS and ABAC are more effective than the SSVAACS.
Improving Gossip Learning via Limited Model Merging

Decentralized machine learning provides a unique opportunity to create data-driven applications without the need for large investments in centralized infrastructure. In our previous works, we introduced gossip learning for this purpose: models perform random walks in the network, and the nodes train the received models on the locally available data. We also proposed various improvements, like model sub-sampling, merging, and token-based flow control. Gossip learning is robust to failures, and does not require synchronization. Efficiency in terms of network bandwidth is also a major concern in the case of decentralized learning algorithms, especially when they are deployed in a network of IoT devices or smartphones. Here, we improve the model merging method to allow gossip learning to benefit more from token-based flow control. We experimentally evaluate our solution over several classification problems in simulations using an availability trace based on real-world smartphone measurements. Our results indicate that the improved variant significantly outperforms previously proposed solutions.
SOLAR LOCATION ESTIMATION USING LOGSIG BASED ACTIVATION FUNCTION USING ARTIFICIAL NEURAL NETWORK APPROACH

Solar panel is one of the renewable energy that can reduce the environmental pollution and have a wide potential of application. The exact solar prediction module will give a big impact on the management of solar power plants and the design of solar energy systems. This paper attempts to find the best Artificial Neural Network (ANN) based logsig transfer function and various training algorithm that can be used to calculate the temperature module (Tm) in Malaysia. This can be done by simulating the collected data of four weather variables which are the ambient temperature (Ta), local wind speed (Vw), solar radiation flux (Gt) and the relative humidity (Rh) into the Neural Network Tool in MATLAB. Three different ANN transfer function and 14 types of training were compared to choose the best method. Finally, an equation for the ANN model will be generated in order to calculate the temperature module based on ambient temperature, local wind speed, solar radiation and relative humidity variables.
Reinforcement Learning based NLP

In the field of Natural Language Processing (NLP), reinforcement learning (RL) has drawn attention as a viable method for training models. An agent is trained to interact with a linguistic environment in order to carry out a given task using RL- based NLP, and the agent learns from feedback in the form of rewards or penalties. This method has been effectively used for a variety of linguistic problems, including text summarization, conversation systems, and machine translation. Sequence-to- sequence Two common methods used in RL-based NLP are reinforcement learning and deep reinforcement learning. Sequence-to-sequence While deep reinforcement learning includes training a neural network to discover the optimum strategy for a language challenge, reinforcement learning (RL) trains a model to create a series of words or characters that most closely matches a goal sequence. In several linguistic challenges, RL-based NLP has demonstrated promising results and attained cutting-edgeperformance. There are still issues to be solved, such as the need for more effective exploration tactics, data scarcity, and sample efficiency. In summary, RL-based NLP represents a potential line of inquiry for NLP research in the future. This method outperforms more established NLP strategies in a variety of language problems and has the added benefit of being able to improve over time with user feedback. To further enhance RL-based NLP's effectiveness and increase its applicability to real-world settings, future research should concentrate on resolving the difficulties associated with thisapproach.
Customised Holiday Experiences through Artificial Intelligence: Case Studies from the Aviation and Hospitality Sectors

This article explores the impact of artificial intelligence (AI) on the aviation and hospitality industries, both of which are rapidly evolving due to technological advancements. It aims to understand the increasing importance of artificial intelligence by examining the various ways in which it is used in these sectors through qualitative research. The research included an analysis of online sources such as airport and hotel websites, booking platforms, and social media accounts of travel-related businesses. This comprehensive data collection provides insight into the various applications of artificial intelligence in tourism. Thematic analysis was then used to categorise the data according to similar uses, providing a detailed understanding of the role of AI in these areas. It compares and examines artificial intelligence applications adopted by aviation and hospitality organisations, evaluating their effectiveness and differences. The study reveals the various ways in which AI is being integrated into these industries and highlights its significant contributions across various dimensions. It also highlights how AI can deliver competitive advantage, improve customer experiences, and introduce innovative paradigms to the aviation and hospitality industries. One important aspect of the research is its ability to provide a deeper understanding of emerging AI trends in these sectors and lay a strong foundation for future research. Ultimately, this study provides valuable insight to stakeholders in aviation and hospitality, equipping them with an informed perspective on leveraging AI for growth and long-term sustainability in their respective industries
Improving readability of static, straight-line graph drawings: A first look at edge crossing resolution through iterative vertex splitting

We present a novel vertex-splitting approach with which to iteratively resolve edge crossings in order to improve the readability of graph drawings. Dense graphs, even when small in size (10 to 15 nodes in size) quickly become difficult to read with increasing numbers of edges, and form so-called “hairballs”. The readability of a graph drawing is measured using many different quantitative aesthetic metrics. One such metric of particular importance is the number of edge crossings. Classical approaches to improving readability, such as the minimization of the number of edge crossings, focus on providing overviews of the input graph by aggregating or sampling vertices and/or edges. However, this simplification of the graph drawing does not allow for detailed views into the data, as not all vertices or edges are rendered, and also requires sophisticated interaction approaches to perform well. To avoid this, our locally optimal vertex splitting approach aims to minimize the number of remaining edge crossings while also minimizing the number of vertices that need to be split. In each iteration, we identify the vertex contributing the largest number of edge crossings, remove it, locate the embedding locations of said vertex’s two split copies, and determine each copy’s unique adjacency. We conduct a user study with 52 participants to evaluate whether vertex splitting affects users’ abilities to conduct a set of graph analytical tasks on graphs 12 nodes in size. Users were tasked with identifying a vertex’s adjacency, determining the shared neighbors of two vertices, and checking the validity of a set of paths. We ultimately conclude that within the context of small, dense graphs, systematic vertex splitting is preferred by participants and even positively impacts user performance, though at the cost of the time taken per task.