Reviewerly/exHarmony huggingface dataset:
Learning Interaction Regions and Motion Trajectories Simultaneously From Egocentric Demonstration Videos

Learning to interact with objects is significant for robots to integrate into human environments. When the interaction semantic is definite, manually guiding the manipulator is a commonly used method to teach robots how to interact with objects. However, the learning results are robot-dependent because the mechanical parameters are different for different robots, which means the learning process must be executed again. Moreover, during the manual guiding process, operators are responsible for recognizing the region being contacted and providing expert motion programming, which limits the robot's intelligence. To enhance the level of automation in object interaction for robots, this letter proposes IRMT-Net (Interaction Region and Motion Trajectory prediction Network) to predict the interaction region and motion trajectory simultaneously based on images. IRMT-Net achieves state-of-the-art interaction region prediction results on Epic-kitchens dataset, generates reasonable motion trajectories and can support robot interaction in actual situations.
Time Is on My Side: Forward-Replay Attacks to TOTP Authentication

Time-based One-Time Password (TOTP) is a widely used method for two-factor authentication, whose operation relies on one-time codes generated from the device’s clock and validated using the servers’ clock. By introducing the notion of forward-replay attack, in this paper we underline an obvious (but somewhat overlooked) fact: a secure server’s time reference is not sufficient when an attacker may maliciously set future time instants over the device, collect the relevant TOTPs, and play them back later on, when these time instants will be reached. Through examining viable attack scenarios, we present a concrete proof-of-concept implementation on Android mobile phones and three applications using TOTP, including the widely used TOTP-based Google Authenticator app. Our findings highlight the practicality of such threat and raise concerns about the security of TOTP, suggesting that hardened TOTP-based methods should be explored.
Detection of Privacy-Harming Social Media Posts in Italian

As many psychological and sociological study reveal, many people disclose too much privacy-harming information in social media in the form of text and multimedia posts, thus exposing themselves and other persons to several security risks. Consequently, many researchers have addressed this problem by investigating on the detection and analysis of the so-called self-disclosure behavior in social media and blogging platforms. Among the others, content sensitivity analysis has emerged as a promising research direction, but, so far, it has only focused on English text posts, although it is well-known that people tend to disclose mostly in their own native languages. Therefore, in this paper, we address this limitation by proposing a new text corpus of Italian posts that we have annotated following to the anonymity assumption. We then apply several language models based on transformers to classify them according to their sensitivity. Moreover, since Italian is a lower-resource language compared to English, we also apply some multilingual zero-shot transfer learning architectures trained on a rich and manually annotated English corpus and tested on the Italian one. We show experimentally that the approaches trained directly on the Italian corpus, still outperform multilingual ones trained on the English data and tested on Italian, although some of them exhibit promising prediction performances.
Feature-Based Image Metamorphosis

A new technique is presented for the metamorphosis of one digital image into another. The approach gives the animator high-level control of the visual effect by providing natural feature-based specification and interaction. When used effectively, this technique can give the illusion that the photographed or computer generated subjects are transforming in a fluid, surrealistic, and often dramatic way. Comparisons with existing methods are drawn, and the advantages and disadvantages of each are examined. The new method is then extended to accommodate keyframed transformations between image sequences for motion image work. Several examples are illustrated with resulting images.
Video Textures

This paper introduces a new type of medium, called a video texture, which has qualities somewhere between those of a photograph and a video. A video texture provides a continuous infinitely varying stream of images. While the individual frames of a video texture may be repeated from time to time, the video sequence as a whole is never repeated exactly.Video textures can be used in place of digital photos to infuse a static image with dynamic qualities and explicit action. We present techniques for analyzing a video clip to extract its structure, and for synthesizing a new, similar looking video of arbitrary length. We combine video textures with view morphing techniques to obtain 3D video textures. We also introduce videobased animation, in which the synthesis of video textures can be guided by a user through high-level interactive controls. Applications of video textures and their extensions include the display of dynamic scenes on web pages, the creation of dynamic backdrops for special effects and games, and the interactive control of video-based animation.
An Improved Sub-pixel Corner Detection Algorithm

Aiming at the problems of low accuracy and poor stability of traditional sub-pixel corner detection algorithm, an improved sub-pixel corner detection algorithm is proposed in this paper. Firstly, the poor sub-pixel corner points are extracted, and then the cluster centered on the sub-pixel angle is constructed, the cluster angle is processed by distance weighting, and finally the least squares method is used to solve it to obtain accurate sub-pixel corner position information. Experimental results show that compared with the traditional angle algorithm, the correct extraction rate and coordinate accuracy of the improved algorithm have been greatly improved. The algorithm can provide reliable data for the research of high-precision vision systems to meet the practical application requirements.
Multiple-Image Encryption Scheme Based on an N-Dimensional Chaotic Modular Model and Overlapping Block Permutation–Diffusion Using Newly Defined Operation

Some existing chaotic maps have the drawbacks of a narrow range of chaotic parameters and discontinuities, which may be inherited by new chaotic systems generated from them as seed maps. We propose a chaotic model that can generate N-dimensional chaotic systems to overcome the problem. By fixing the original parameters of the seed map in the chaotic range, we then introduce new parameters and use modular operations to widen the range of the parameters and increase the complexity. Simulation results show that the generated chaotic system has good chaotic dynamics. Based on this chaotic model, we propose a multiple-image encryption algorithm that is not limited by image type, number, and size. The resistance to plaintext attacks is enhanced by a permutation–diffusion algorithm based on overlapping blocks. We design a newly defined lookup table operation based on Latin squares with enhanced nonlinearity and randomness. By adjusting the overlapping block parameters and the number of Latin squares, users can design different encryption levels to balance encryption efficiency and encryption effectiveness. The experimental results show that the proposed image encryption algorithm can effectively encrypt multiple images, and all the evaluation indexes reach the expected value.
Unstructured Lumigraph Rendering

We describe an image based rendering approach that generalizes many current image based rendering algorithms, including light field rendering and view-dependent texture mapping. In particular, it allows for lumigraph-style rendering from a set of input cameras in arbitrary configurations (i.e., not restricted to a plane or to any specific manifold). In the case of regular and planar input camera positions, our algorithm reduces to a typical lumigraph approach. When presented with fewer cameras and good approximate geometry, our algorithm behaves like view-dependent texture mapping. The algorithm achieves this flexibility because it is designed to meet a set of specific goals that we describe. We demonstrate this flexibility with a variety of examples.
Interactive Digital Photomontage

We describe an interactive, computer-assisted framework for combining parts of a set of photographs into a single composite picture, a process we call "digital photomontage." Our framework makes use of two techniques primarily: graph-cut optimization, to choose good seams within the constituent images so that they can be combined as seamlessly as possible; and gradient-domain fusion, a process based on Poisson equations, to further reduce any remaining visible artifacts in the composite. Also central to the framework is a suite of interactive tools that allow the user to specify a variety of high-level image objectives, either globally across the image, or locally through a painting-style interface. Image objectives are applied independently at each pixel location and generally involve a function of the pixel values (such as "maximum contrast") drawn from that same location in the set of source images. Typically, a user applies a series of image objectives iteratively in order to create a finished composite. The power of this framework lies in its generality; we show how it can be used for a wide variety of applications, including "selective composites" (for instance, group photos in which everyone looks their best), relighting, extended depth of field, panoramic stitching, clean-plate production, stroboscopic visualization of movement, and time-lapse mosaics.
Creating Full View Panoramic Image Mosaics and Environment Maps

This paper presents a novel approach to creating full viewpanoramic mosaics from image sequences. Unlike current panoramic stitching methods, which usually require pure horizontal camera panning, our system does not require any controlled motions or constraints on how the images are taken (as long as there is no strong motion parallax). For example, images taken from a hand-held digital camera can be stitched seamlessly into panoramic mosaics. Because we represent our image mosaics using a set of transforms, there are no singularity problems such as those existing at the top and bottom of cylindrical or spherical maps. Our algorithm is fast and robust because it directly recovers 3D rotations instead of general 8 parameter planar perspective transforms. Methods to recover camera focal length are also presented. We also present an algorithm for efficiently extracting environment maps from our image mosaics. By mapping the mosaic onto an artibrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players.
Gradient Domain High Dynamic Range Compression

We present a new method for rendering high dynamic range images on conventional displays. Our method is conceptually simple, computationally efficient, robust, and easy to use. We manipulate the gradient field of the luminance image by attenuating the magnitudes of large gradients. A new, low dynamic range image is then obtained by solving a Poisson equation on the modified gradient field. Our results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast. The method is also able to significantly enhance ordinary images by bringing out detail in dark regions.
A Comprehensive Approach to Image Protection in Digital Environments

Protecting the integrity of images has become a growing concern due to the ease of manipulation and unauthorized dissemination of visual content. This article presents a comprehensive approach to safeguarding images’ authenticity and reliability through watermarking techniques. The main goal is to develop effective strategies that preserve the visual quality of images and are resistant to various attacks. The work focuses on developing a watermarking algorithm in Python, implemented with embedding in the spatial domain, transformation in the frequency domain, and pixel modification techniques. A thorough evaluation of efficiency, accuracy, and robustness is performed using numerical metrics and visual assessment to validate the embedded watermarks. The results demonstrate the algorithm’s effectiveness in protecting the integrity of the images, although some attacks may cause visible degradation. Likewise, a comparison with related works is made to highlight the relevance and effectiveness of the proposed techniques. It is concluded that watermarks are presented as an additional layer of protection in applications where the authenticity and integrity of the image are essential. In addition, the importance of future research that addresses perspectives for improvement and new applications to strengthen the protection of the goodness of pictures and other digital media is highlighted.
Multi-Party Quantum Private Comparison Based on Bell States

Multi-party quantum private comparison (MQPC) assumes responsibility for overseeing the flow of data and communication among diverse entities, wherein it boasts powerful security capabilities that have garnered substantial attention. Most current MQPC protocols rely on difficult-to-prepare quantum states and are inefficient in their use of resources. In this paper, we propose a novel MQPC protocol without entanglement swapping, thereby building upon the assumption of an ideal channel. This protocol is based on Bell states, which simplifies implementation and addresses the challenges associated with using complex quantum states; it also enables the comparison of secret information by having a trusted party prepare and transmit encoded quantum sequences to participants, thereby facilitating efficient equality comparison among all parties. Our MQPC protocol showcased remarkable efficiency in comparison to existing protocols for quantum private comparison. Furthermore, the incorporation of decoy photon and shared key technologies made external and internal attacks ineffective, thereby ensuring the utmost security and integrity of the protocol.
Engaging publics in imagining the future of engineered living materials

Engineered living materials (ELMs) are technologies that respond to environmental cues and are able to remodel, self-organize, and self-heal. We conducted two workshops with a wide range of stakeholders and identified key themes in open discussion. Our approach enabled participants to engage with dimensions of technologies normally reserved for engineers, scientists, or policy makers and should be used in further public engagement in ELMs as well as other emerging technologies. Engineered living materials (ELMs) are technologies that respond to environmental cues and are able to remodel, self-organize, and self-heal. We conducted two workshops with a wide range of stakeholders and identified key themes in open discussion. Our approach enabled participants to engage with dimensions of technologies normally reserved for engineers, scientists, or policy makers and should be used in further public engagement in ELMs as well as other emerging technologies.
Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation

As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reasons. Self-supervised learning provides a new idea to alleviate the data sparsity problem, but most existing self-supervised recommendation methods are designed for bi-partite graphs or social graphs, and cannot be directly used in the spatio-temporal graph of POI recommendations. In this paper, we propose a new method named SSTGL to combine self-supervised learning and GNN-based POI recommendation for the first time. SSTGL is empowered with spatio-temporal-aware strategies in the data augmentation and pre-text task stages, respectively, so that it can provide high-quality supervision information by incorporating spatio-temporal prior knowledge. By combining self-supervised learning objective with recommendation objectives, SSTGL can improve the performance of GNN-based POI recommendations. Extensive experiments on three POI recommendation datasets demonstrate the effectiveness of SSTGL, which performed better than existing mainstream methods.
OCAP: On-device Class-Aware Pruning for personalized edge DNN models

In this paper, we propose a new on-device class-aware pruning method for edge systems, namely OCAP. The motivation behind is that Deep Neural Network (DNN) models are usually trained with a large dataset so that they can learn more diverse features and be generalized to accurately predict numerous classes. Some works reveal that some features (channels) are only related to some classes. And edge systems are usually implemented in a specific environment, where classes the system detects are limited. As a result, implementing a general-trained model for a specific edge environment leads to unnecessary redundancy. Meanwhile, transferring some data and models to the cloud for personalization will cause privacy issues. Thus, we may have an on-device class-aware pruning method to remove the channels which are irrelevant for the classes the edge system observes mostly, thereby reducing the model’s Floating Point Operations (FLOPs), memory footprint, latency, improving energy efficiency and keeping a relatively high accuracy for the observed classes while protecting the in-situ data privacy. OCAP proposes a novel class-aware pruning method based on the intermediate activation of input images to identify the class-irrelevant channels. Moreover, we propose a method based on KL-divergence to select diverse and representative data for effectively fine-tuning the pruned model. The experimental results show the effectiveness and efficiency of OCAP. In comparison with state-of-the-art class-aware pruning methods, OCAP has better accuracy and higher compression ratio. Additionally, we evaluate OCAP on Nvidia Jetson Nano, Nvidia Jetson TX2 and Nvidia Jetson AGX Xavier in terms of efficiency, where the experimental results demonstrate the applicability of OCAP on edge systems. The code is available at https://github.com/mzd2222/OCAP.
Did the Quality of Digital Communication Skills in Education Improve after the Pandemic? Evidence from HEIs

In the global transition, digital technologies are perceived as important drivers of change. Contemporary IT technologies help to enhance the productivity and efficiency of numerous systems on the way to achieving sustainable development goals. Wider use of digital communication tools in HEIs (higher education institutions) can reduce inequalities and increase inclusiveness of tertiary education. In this paper, we present the results of exploratory research aimed at assessing whether COVID-19 has improved digital communication skills in higher education. We revised the quality of digital direct and indirect communication skills between students and teachers (both ways), by referring to the level of satisfaction of both sides of digital communication (teachers and students). The results indicate that there is a statistically significant improvement in the quality of digital communication skills, in particular, if we compare the position of both students and teachers after the pandemic, relative to the pre-COVID-19 pandemic period. Our investigation confirms that COVID-19 was a shock that enhanced the improvement of digital communication skills in higher education, and the pandemic experience had a positive impact on the more efficient use of digital education technologies.
CENTRAL BANK DIGITAL CURRENCY – APARADIGMSHIFTINMONETARYARCHITECT

Money was a centre of economic gravity from its incepƟ on. It evolved from gold to fi at currency, and now we face a new era of monetary evoluƟ on. Every step of development gives him new power to infl uence society by reaching more and more aspects of social life. With its infl uence were unknown risks and concerns to-ward monetary systems fl exibility and sustainability.Using a promise to pay as money (banknote) has several criƟ cal legal and technical inconsistencies with the token-based CBDC system. One signifi cant limitaƟ on is that promissory notes may not have a unique serial num-ber like physical currency printed by central banks. In the modern hybrid monetary system where only cash has a unique serial number and banks’ electronic payments work on an account-based approach, the incompaƟ bility of promise to pay with real money is not visible widely. Overall, the role of fracƟ onal reserve banking and the money mulƟ plier in a token-based CBDC system will play a diff erent role than in tradiƟ onal banking systems, given the unique characterisƟ cs of digital tokens and the role of central banks in issuing and controlling them. By analysing these key aspects, this paper aims to provide a comprehensive understanding of the potenƟ al eff ects of CBDCs on fracƟ onal reserve banking. The fi ndings of this research will contribute to the ongoing discus-sions and debates surrounding the adopƟ on and implementaƟ on of CBDCs, enabling policymakers, regulators, and banking insƟ tuƟ ons to make informed decisions in this challenging era of digital currencies.
Wide-baseline view synthesis for light-field display based on plane-depth-fused sweep volume

The advancements in three-dimensional (3D) display technology have led to a wide interest in light-field display. However, the need to simultaneously capture a large number of object views made content generation for light-field displays still a bottleneck. In this paper, we propose a method for light-field content generation based on plane-depth-fused sweep volume (PDFSV), focusing on handling wide-baseline views and exhibiting scene generalization when the camera array remains unchanged. Specifically, the proposed PDFSV exploits the prior depth of the images captured by a 4 × 4 spherical camera array to represent 3D information of scenes. Then two optimized sequential convolutional neural networks (CNN) are employed for implicit depth modeling and final color calculation, respectively. By doing these, the prior depth facilitates the synthesis of regions with complex textures in the target view. We produce a Wide-baseline Multi-view Image Set (WMIS) which has a field of view (FOV) angle reaching 54°and could be publicly available for access. In our experiments, we use only the 4 vertex views as input. Results demonstrate that the proposed approach can synthesize high-quality views at arbitrary positions between sparse views, outperforming existing neural-radiance-fields-based (NeRF-based) methods. Finally, we conduct autostereoscopic display experiments, achieving satisfactory results.