Reviewerly/exHarmony huggingface dataset:
A Novel Image Encryption Technique Using Multi-Coupled Map Lattice System with Generalized Symmetric Map and Adaptive Control Parameter

Abstract Image and video data make up a significant portion of the content shared over the Internet and social media. The use of image and video communication allows more information to be shared while simultaneously presenting higher risks in terms of data security. The traditional encryption schemes are general purpose; however, to encrypt image and video data, application-specific encryption solutions are needed. An image or a video frame comprises a two-dimensional matrix where pixel intensity values are integers in range [0,255], leading to data redundancy problems. Moreover, the bulk amount of image and video data adds another challenge when deploying security primitives. In this paper, a novel coupled map lattice system-based image cryptosystem has been proposed that uses generalised symmetric maps for generation of pseudo-random sequences. The generalization of symmetric maps allows the user to choose the source of pseudo-random sequence generation by varying a single control parameter. Other adaptive control parameters ensure an adequate degree of randomness in the generated sequences. The proposed encryption system relies on three independent sources of pseudo-random sequence generators, which are further re-randomized before the final encryption process. Comprehensive experimentation has been performed to test the proposed system against various attack models on publicly available datasets. A detailed comparative analysis has also been conducted with existing state-of-the-art image encryption techniques. Results show that the proposed algorithm provides high information entropy, negative correlation, large key space, and high sensitivity to key variations, and is resistant to various types of attacks, including chosen-text, statistical, and differential attacks.
Cross-platform sequential recommendation with sharing item-level relevance data

Cross-platform sequential recommendations are a typical solution to the problem of sparse data and cold starts in the field of recommendation systems. Specifically, we utilize data from the auxiliary platform to improve the recommendation performance of the target platform. One typical scenario is the fusion of data from two interaction platforms to perform cross-platform recommendation tasks. Existing approaches assume that interaction data from the auxiliary platform can fully share cross-platforms. However, such a hypothesis is unreasonable as, in the real world, different companies may operate these platforms. Records of user interactions with items are sensitive, and the complete sharing of original data could violate business privacy policies and increase the risk of privacy leaks. This paper considers a more realistic scenario for performing cross-platform sequential recommendations. To avoid compromising users' privacy during data sharing, we contemplate sharing only item-level relevance data and not user-level relevance data. Concretely, we transfer item embedding cross-platform to make it easier for both companies to agree on data sharing (e.g., legal policies), as the data to share is irrelevant to the user and has no explicit semantics. For extracting a valuable signal from the transfer items embedding, we propose a new model, Neural Attention Transfer Sequential Recommendation (abbreviated as NATSR), by exploiting the powerful representation capabilities of neural networks. We perform thorough experiments with two real datasets to verify their performance. The NATSR model achieves the best recommendation performance compared to the traditional cross-platform approach of directly sharing user-level relevance data. We demonstrate further that the NATSR model dramatically mitigates the problem of data sparsity with significant user privacy-preserving.
On better detecting and leveraging noisy samples for learning with severe label noise

Despite the success of learning with noisy labels, existing approaches show limited performance when the noise level is extremely high, since deep neural networks (DNNs) are easily overfit to the training set with corrupted labels. In this paper, we introduce Lipschitz regularization to prevent the DNNs from over-fitting to noisy labels quickly. Meanwhile, to better detect and leverage the noisy samples, we propose a Lipschitz regularization based framework with a combination of adaptive modeling and detection module and improved semi-supervised learning. We propose to adaptively model the real distribution of the training set, and the implicit individual clean/noisy distribution, instead of parametric models. With Bayes' rule, we then compute the posterior probability of a sample being clean, which provides a dynamic threshold for the detection of noisy labels. To reduce training instability caused by less labeled data with severe label noise, we improve the semi-supervised learning by combining the advantages of Mixup and FixMatch. It can not only increase the diversity of unlabeled samples, but also improve the generalization capability of the DNNs to avoid over-fitting. Experiments on several benchmarks demonstrate that our approach achieves comparable results with the state-of-the-art methods in the less-noisy environment, and obtains a substantial improvement (∼ 8% and ∼ 6% in accuracy on CIFAR-10 and CIFAR-100 respectively) with severe noise.
The Effect of Inverse Square Law of Light on ENF in Videos Exposed by Rolling Shutter

Due to a constant imbalance between demand and supply of power, ENF (Electric Network Frequency) fluctuates around a nominal value of 50 or 60 Hz. These variations in ENF cause the luminance intensity of a mains-powered light source, having no AC/DC converter inside, also to fluctuate. As a result, a video of a scene illuminated by a mains-powered light source can be used to estimate these fluctuations. As a consequence, the ENF signal within the time period when the video was captured can be estimated. This work explores the effects of frame rate harmonics that emerge when a rolling shutter based approach is used for ENF estimation from videos captured using CMOS cameras. These harmonics are a problem, especially for videos whose frame rate is a divisor of the nominal ENF because the frame rate harmonics and the ENF harmonics overlap. It is discovered that a key reason for the presence of the harmonics is the inverse square law of light that results in some repeating patterns of luminance variation across frames. This paper presents an analysis of the effect of the inverse square law of light on ENF estimation. A technique for refined ENF-related luminance signal estimation is proposed that attenuates these frame rate harmonics. This enables more accurate ENF estimates. The work also proposes an approach to estimate ENF-related luminance waveform cycles within each video frame, and a method to compute the confidence score for the estimated cycles. It provides insight into the reliability of the extracted ENF signal from a video, in the sense of its usefulness for ENF forensics, and consequently for ENF detection, which is an important precursor to ENF-based video forensics.
An easy Hand Gesture Recognition System for XR-based collaborative purposes

In the last few years, collaborative tools have increased dramatically, partly to compensate for the distance between people due to the pandemic and partly to allow activities (work, entertainment, free time, etc.) to be carried out among people without having to worry about geographical distances. In this scenario, it was necessary to overcome the classic remote meeting tools e.g. video, audio, chat), which have a reduced sense of presence. Extended Reality (XR) represents a Computer Graphics (CG) based innovative technology particularly suited to this purpose. Indeed, XR aims to develop Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (XR) solutions that can transform how people interact by increasing the sense of presence. This last aspect depends not only on the virtual/real objects and scene visualization but also on their interaction. In this regard, human-computer interaction (HCI) techniques represent a possible solution. However, these techniques depend on specific devices, such as Head Mounted Display (HMD), Smart Glasses, Depth and Tracking Cameras, etc., whose costs make access difficult. For this reason, we propose a Hand Gesture Recognition (HGR) system that can be used in XR applications using a simple RGB camera. Our is a deep learning system based on MediaPipe, the state-of-the-art (SOTA) for hand tracking through simple RGB images [1], [2].
Visual question answering model based on the fusion of multimodal features by a two-way co-attention mechanism

Scene Text Visual Question Answering (VQA) needs to understand both the visual contents and the texts in an image to predict an answer for the image-related question. Existing Scene Text VQA models predict an answer by choosing a word from a fixed vocabulary or the extracted text tokens. In this paper, we have strengthened the representation power of the text tokens by using FastText embedding, appearance, bounding box and PHOC features for text tokens. Our model employs two-way co-attention by using self-attention and guided attention mechanisms to obtain the discriminative image features. We compute the text token position and combine this information with the predicted answer embedding for final answer generation. We have achieved an accuracy of 51.27% and 52.09% on the TextVQA validation set and test set. For the ST-VQA dataset, our model predicted an ANLS score of 0.698 on validation set and 0.686 on test set.
Bounds for Privacy-Utility Trade-off with Per-letter Privacy Constraints and Non-zero Leakage

An information theoretic privacy mechanism design problem for two scenarios is studied where the private data is either hidden or observable. In each scenario, privacy leakage constraints are considered using two different measures. In these scenarios the private data is hidden or observable. In the first scenario, an agent observes useful data Y that is correlated with private data X, and wishes to disclose the useful information to a user. A privacy mechanism is designed to generate disclosed data U which maximizes the revealed information about Y while satisfying a per-letter privacy constraint. In the second scenario, the agent has additionally access to the private data. First, the Functional Representation Lemma and Strong Functional Representation Lemma are extended by relaxing the independence condition to find a lower bound considering the second scenario. Next, lower bounds as well as upper bounds on privacy-utility trade-off are derived for both scenarios. In particular, for the case where X is deterministic function of Y, we show that our upper and lower bounds are asymptotically optimal considering the first scenario.
On The Multi-View Information Bottleneck Representation

In this work, we generalize the information bottleneck (IB) approach to the multi-view learning context. The exponentially growing complexity of the optimal representation motivates the development of two novel formulations with more favorable performance-complexity tradeoffs. The first approach is based on forming a stochastic consensus and is suited for scenarios with significant representation overlap between the different views. The second method, relying on incremental updates, is tailored for the other extreme scenario with minimal representation overlap. In both cases, we extend our earlier work on the alternating directional methods of multiplier (ADMM) solver and establish its convergence and scalability. Empirically, we find that the proposed methods outperform state-of-the-art approaches in multi-view classification problems under a broad range of modelling parameters.
Sparse Random Khatri-Rao Product Codes for Distributed Matrix Multiplication

We introduce two generalizations to the paradigm of using Random Khatri-Rao Product (RKRP) codes for distributed matrix multiplication. We first introduce a class of codes called Sparse Random Khatri-Rao Product (SRKRP) codes which have sparse generator matrices. SRKRP codes result in lower encoding, computation and communication costs than RKRP codes when the input matrices are sparse, while they exhibit similar numerical stability to other state of the art schemes. We empirically study the relationship between the probability of the generator matrix (restricted to the set of non-stragglers) of a randomly chosen SRKRP code being rank deficient and various parameters of the coding scheme including the degree of sparsity of the generator matrix and the number of non-stragglers. Secondly, we show that if the master node can perform a very small number of matrix product computations in addition to the computations performed by the workers, the failure probability can be substantially improved.
Lyapunov-based fixed-time stabilization control of quantum systems

In this paper, we consider the fixed-time stabilization control problem of quantum systems modeled by Schrödinger equations. Firstly, the Lyapunov-based fixed-time stability criterion is extended to finite-dimensional closed quantum systems in the form of coherence vectors. Then for a two-level quantum system with single control input, a non-smooth fractional-order control law is designed using the relative state distance. By integrating the fixed-time Lyapunov control technique and the bi-limit homogeneity theory, the quantum system is proved to be stabilized to an eigenstate of the inherent Hamiltonian in a fixed time. Comparing with existing methods in quantum system control, the proposed approach can guarantee stabilization in a fixed time without depending on the initial states.
Exploring the utility of synthetic data to extract more value from sensitive health data assets: A focused example in perinatal epidemiology

Privacy, access and security concerns can hinder the availability of health data for research. The use of synthesised data in place of de-identified electronic health records (EHRs) presents an opportunity to conduct research while minimising privacy concerns.To examine whether synthesised data can replicate two prenatal epidemiological associations: between prenatal smoking and lower birthweight, and between prenatal mood disorders and lower birthweight, using data synthesised from de-identified health administrative data collections.We generated two synthetic datasets, using parametric and non-parametric data generating methods, and examined the synthetic data for evidence of privacy concerns. Next, univariable and multivariable logistic regression was utilised to estimate the associations in both synthetic datasets, with results then compared to the real data.Both synthesised datasets performed well in identifying the reduction in birthweight associated with prenatal smoking, while the non-parametric data underestimated the reduction in birthweight associated with prenatal mood disorders. Improbable relationships between some variables were identified in the parametric synthesised data, however, these can be addressed with simple rules during data synthesis. No duplicate rows (i.e., exact copies of de-identified data) were found in the parametric data, while only 0.6% of the rows in the non-parametric data were duplicated.Both synthesised datasets performed well in replicating the statistical properties of the original data while addressing privacy issues. Data synthesis methods provide an opportunity for researchers to utilise health data while managing privacy and security concerns.
Construction of rice supply chain supervision model driven by blockchain smart contract

Abstract The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain’s entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.
Deep learning and transfer learning for device-free human activity recognition: A survey

Device-free activity recognition plays a crucial role in smart building, security, and human–computer interaction, which shows its strength in its convenience and cost-efficiency. Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models, but it suffers from the limitation of manual feature design. Deep learning overcomes such issues by automatic high-level feature extraction, but its performance degrades due to the requirement of massive annotated data and cross-site issues. To deal with these problems, transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics. This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition. We begin with the motivation of deep learning and transfer learning, and then introduce the major sensor modalities. Then the deep and transfer learning techniques for device-free human activity recognition are introduced. Eventually, insights on existing works and grand challenges are summarized and presented to promote future research.
When Virtual Reality meets destination marketing: The mediating role of presences between vividness and user responses

Virtual Reality (VR) has been widely adopted by destination marketing organizations (DMOs) to promote a destination. However, the existing literature lacks a solid theoretical foundation to interpret the phenomenon of VR experience. To address this research gap, this study aims to advance our understanding of how two distinct presences (telepresence and sense of presence) are formed and how they influence user responses after VR experience. A theoretical framework was proposed including vividness, sense of presence, telepresence, satisfaction, and intention to visit. According to the findings, two distinct presences mediate the positive relationship between vividness and satisfaction, and consequently intention. Additionally, theoretical and managerial insights are provided.
ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors.
Selection framework of visualization methods in designing AR industrial task-support systems

An Augmented reality (AR) task-support system incorporates head-mounted displays (HMDs) to improve human performance in maintenance, assembly, and disassembly tasks by intuitively showing the working procedure to the operator. To achieve the intended AR capability and to enable the operator to work efficiently, a system designer must understand the characteristics of AR and design the information to be shown operators for each subtask, taking into account the working situation represented by the procedure, operator performance skills, workspace, devices, and operating objects (i.e., AR information design). Especially, in the AR information design, the designer needs AR expertize to select the suitable visualization method among the many extant methods. This is difficult for designers (manual writers) to perform AR information design in factories in the future. However, at present, only a few studies have been conducted to select the suitable visualization method for each subtask. This study proposes to define 31 subtask types as criteria for decomposing tasks in AR information design. Furthermore, we classify and define 42 visualization methods in terms of AR information design. Finally, to select the suitable visualization method for each subtask, we construct a selection framework consisting of three selection categories representing the working status and 17 selection conditions. The evaluation experiments of the proposed framework could output an average of 4.3 suitable visualization method candidates, including at least one suitable visualization method selected by AR experts in the subtask working situation. By incorporating the proposed framework into a website and combining it with existing HMDs and services for AR application development, it is expected that designers will be able to develop appropriate AR task-support systems.