Reviewerly/exHarmony huggingface dataset:
Exploring New Idioms for Books as Interactive Experiences in Digital Domain

Books—traditional repositories of knowledge—have been evolving continuously since time immemorial. From clay tablets to hand-painted illuminated manuscripts, and finally, after the Chinese and Gutenberg's printing technology, the idea of mass production of printed volumes has changed immensely, and new technologies have made variations beyond imagination. The introduction of eBooks has further initiated a drastic shift toward the digital platform. Although books in electronic formats have widely been adopted by a diverse range of readers, their overall interactivity has often been debated. Have digital reading experiences reached their zenith? Or are there new unexplored avenues for making them more interactive? This research investigates the many possibilities that can be integrated into a digital reading experience. Focusing on the Ancient Indian script—Brahmi as a reference model, this study aims to investigate the many aspects affecting the translation of traditional text-heavy and fewer image-based subjects into immersive digital experiences. The design processes adopted, rationale behind decision-making, content structuring & aesthetical fine-tuning, and the development of the final design solution reveal crucial leads for creating effective digital reading and learning experiences. This paper also evaluates numerous ways to add 3D technology interactions for an immersive user experience. Also, making use of 3D technology adds an eye-catching visual experience for the readers. The effectiveness of adding 3D virtual museums and virtual learning techniques to enhance digital reading and learning experiences has also been studied during the research. Feedback received from real-time users has conclusively validated the efficacy of the digital experience achieved through its novel approaches, pioneering use of 3D modeling techniques, and layering of multisensory elements. The application of these learnings can be effectively used for transforming traditional knowledge into interactive digital platform learning experiences for enhanced comprehension.
Virtual Reality Based Medical Training Simulator and Robotic Operation System

In the medical sector learning about the human body and practicing surgeries, the medical students need a real human body which is very costly. Repeatedly practice or undone after practice is almost impossible with the human body. Human anatomy, physiology, etc. subjects are very hard to understand and learn from books and images. Learning surgery on living humans is sometimes unethical and trainee surgeons has to struggle to learn rare surgeries. To solve these problems we use virtual reality (VR) to build a medical training simulator that can provide medical students better understanding and learning about the human body through 3D models of the body. Students can get an interactive learning experience and see how organs work from the molecular level and perform several operations. Since this is a simulation everything a new learner will do can be undone and re-practiced. On the other hand skilled surgeons are not available in all places in the country especially in remote places. A patient who needs the critical surgical treatment has to travel to a city to take the treatment by the expert surgeons. To solve this problem in this project we also connect a robot to our VR based system. A skilled surgeon can operate in the virtual environment from a distant place using the robot. The vision system of the robot can be realized by the surgeon through VR headset and he or she feels like, he or she is present there. Surgeons can control the robotic hands and tools necessary to perform the surgery from the remote place. This will reduce the trouble of patients traveling from remote places to city area for surgery. We implement the demo system in the lab and show some results which indicate the applicability of the proposed system.
DIFLD: domain invariant feature learning to detect low-quality compressed face forgery images

Abstract With the rapid development of deep learning, face forgery detection methods have also achieved remarkable progress. However, most methods suffer significant performance degradation on low-quality compressed face images. It is due to: (a) The image artifacts will be blurred in the process of image compression, resulting in the model learning insufficient artifact traces; (b) Low-quality images will introduce a lot of noise information, and minimizing the training error causes the model to absorb all correlations in the training dataset recklessly, leading to the over-fitting problem. To solve the above problems, we consider learning domain invariant representations to inscribe the correct relevance, i.e., artifacts, to improve the robustness of low-quality images. Specifically, we propose a novel face forgery detector, called DIFLD. The model has the following components: (1) a high-frequency invariant feature learning module(hf-IFLM), which effectively retrieves the blurred artifacts in low-quality compressed images; and (2) a high-dimensional feature distribution learning module(hd-FDLM), that guides the network to learn more about the consistent features of distribution. With the above two modules, the whole framework can learn more discriminative correct artifact features in an end-to-end manner. Through extensive experiments, we show that our proposed method is more robust to image quality variations, especially in low-quality images. Our proposed method achieves a 3.67% improvement over the state-of-the-art methods on the challenging dataset NeuralTextures.
eBiBa: A Post-Quantum Hash-Based Signature With Small Signature Size in the Continuous Communication of Large-Scale Data

Abstract We present eBiBa (enhanced BiBa), a hash-based signature scheme with the smallest possible signature size, while ensuring high feasibility and security in a specific application model. Our scheme is tailored to address the communication requirement of a large-scale public data stream continuously disseminated between two participants while ensuring data source and data integrity authentication. To achieve these goals, firstly, we optimized the classical hash tree mode into a hybrid mode to efficiently perform public key authentication and eliminate the need for an authenticated channel to transmit large amounts of data, unlike the initial BiBa-based broadcast authentication protocol. Secondly, we employed a specific tweakable hash chain function to digest a batch of messages, reducing the required conditions for post-quantum existential unforgeability under adaptive chosen message attack (EUCMA) of eBiBa to a second-pre-image-resistance-like property instead of collision resistance. This results in reduced pre-computation in both key and signature generations. Thirdly, we utilized a forward-secure pseudorandom function to achieve forward-secure of the proposed scheme. Finally, we minimize the signature size through a series of procedures. Firstly, we select BiBa few-time signature as the underlying signature scheme since it is currently the few-time hash-based signature with the smallest signature size that we are aware of; in addition, the hybrid approach we employed can also significantly reduce the signature size compared to using a hash tree solely; for the hash tree structure, we design a specific authentication path in combination with the related communication model to further minimize the signature size; finally, we optimize the authentication approach to achieve the minimum signature size in a single transmission. Our construction minimizes the signature size in the aforementioned model, achieving a compression rate of 0.017 to 0.828 based on distinct values of parameters, as compared to XMSS-256. We also demonstrated that eBiBa can achieve post-quantum forward-secure and EUCMA security.
Timbre Realities

Abstract This chapter addresses the concept of timbre in reference to “Anthropogenic Debris,” the first movement of Liza Lim’s Extinction Events and Dawn Chorus (2018). It develops an enactive approach to timbre, demonstrating the reality of timbre as lived sonic experience. The chapter shows how timbral changes over time play a central role in music’s formal design. Further, the chapter entails a phenomenological investigation of timbre experience as the basis for its analysis of Lim’s music. The chapter argues that from the perspective of enactive cognition, timbres are real, qualitative phenomena co-determined by the sensorimotor activity of a perceiving creature and the physical attributes of sound waves in a specific sonic environment at a particular time.
ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing tampering localization methods, however, suffer from severe performance degradation when the images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">re</i> storation-assisted framework for image tampering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loc</i> alization (ReLoc). The ReLoc framework mainly consists of an image restoration module and a tampering localization module. The key idea of ReLoc is to use the restoration module to recover a high-quality counterpart from the distorted tampered image, such that the distorted tampering traces can be re-enhanced, facilitating the tampering localization module to identify the tampered regions. To achieve this, the restoration module is optimized not only with the conventional constraints on image visual quality, but also with a forensics-oriented objective function. Furthermore, the restoration module and the localization module are trained alternately, which can stabilize the training process and is beneficial for improving the performance. The robustness of ReLoc has been evaluated by using several common post-processing operations, including lossy compressions, online social network transmission, and image resizing. Extensive experimental results show that ReLoc can significantly improve the localization performance compared to using a restoration-free model. In addition, we have shown that the restoration module in a well-trained ReLoc model is transferable for different localization modules and across different datasets.
VTON-MP: Multi-Pose Virtual Try-On via Appearance Flow and Feature Filtering

Multi-pose virtual try-on has become a research focus for online clothes shopping due to the fixed-pose virtual try-on methods that cannot provide a different pose try-on effect. The challenge of multi-pose virtual try-on is that the detailed information of a generated image is difficult to obtain in the pose transformation and garment distortion. To solve the issue, we propose a multi-pose virtual try-on method via appearance flow and feature filtering (VTON-MP). First, a segmentation generation network of 2D keypoints about the target pose is used to predict the body semantic distribution of the target pose. Second, the desired garment is distorted to correspond to the body posture using the appearance flow figure alignment network (AFFAN). Third, latent useless feature weights are restrained using a filtering-enhancement block (FEB), and effective appearance feature weights are enhanced. Finally, the spatial relationship of body parts in the resulting image is further optimized using spatially-adaptive instance normalization (SAIN). Compared to state-of-the-art methods of subjective and objective experiments on the MPV dataset, the proposed VTONMP achieves the best performance in terms of SSIM, PSNR and FID. The experimental results demonstrate that the proposed algorithm can better retain image details (head, hands, arms, and trousers).
Secure Cloud-Assisted Data Pub/Sub Service With Fine-Grained Bilateral Access Control

Secure cloud-assisted data publish/subscribe (Pub/Sub) service provides an asynchronous method for publishers and subscribers to non-interactively exchange encrypted messages. Besides performing conjunctive subscription policy, numerous data Pub/Sub systems have recently been proposed to provide dynamic access control enforced from the publisher side to the subscriber side. However, these solutions fail to consider the following properties: (i) bilateral access control for both publishers and subscribers; (ii) the anonymity of the publisher; (iii) high matching time cost between publication and subscription. Therefore, we present P/S-BiAC, a secure and boolean cloud-assisted data Pub/Sub system with attribute-based bilateral access control that achieves authenticity and anonymity of publishers. In particular, P/S-BiAC enables cloud-based brokers to use the subscriber’s trapdoor to match published data with sub-linear time complexity. Technically, we introduce a “BiAC-and-Hidden” technique to refine publication tuples and trapdoor in classic searchable symmetric encryption solutions. Moreover, we implement P/S-BiAC and evaluate its practical performance based on Enron dataset in real cloud environment. To deal with a conjunctive subscription policy, P/S-BiAC runs 27.8× faster for matching time cost (with s-term=10) compared to state-of-the-art solutions, which demonstrates its feasibility in practical data Pub/Sub services with strong security properties.
Real-Time and Robust Video Stabilization Based on Block-Wised Gradient Features

Portable camera devices are commonly used in modern society. However, the videos captured by such consumer electronics cameras usually exhibit heavy shake which affect the consumer experience. Also, consumer cameras often have limited computational resources. Thus, how to efficiently eliminate camera shake has become a hot issue in the field of consumer electronics. This paper proposed a fast video stabilization algorithm based on block-wised gradient features. After extracting the gradient features, the block-based feature descriptors are generated to perform feature point matching. Then, we calculate the affine transformation matrix of two consecutive frames of the video sequence to register the first frame to generate the final stable video. Experiments show that our fast method has excellent real-time performance and stabilization performance. To deal with complex shaky scenes, we further proposed an improved robust method. In our improved algorithm, the camera’s trajectory from the transformation matrix between two frames is extracted and the total camera path is calculated to acquire the final stabilized video. The results of experiments demonstrate that our improved method can perform real-time stabilizing for shaky videos with various complicated scenes.
Adaptive Data Placement in Multi-Cloud Storage: A Non-Stationary Combinatorial Bandit Approach

Multi-cloud storage is recently a viable approach to solve the vendor lock-in, reliability, and security issues in cloud storage systems. As a key concern, data placement influences the cost and performance of storage services. Yet, in practice it remains challenging to address the huge solution space. Previous studies typically focus on constructing efficient data placement schemes based on the predicted pattern of workloads or assuming fully a-priori known network conditions. They cannot be easily applied in multi-cloud storage scenarios, which typically involve dynamic network conditions and time-varying workloads. To this end, we formulate the data placement optimization in a combinatorial multi-arm bandit (CMAB) perspective and solve it by learning placement strategy online. In contrast to a stationary setting where reward distributions are unknown but identical over time, we consider a realistic multi-cloud environment with non-stationary conditions, i.e., reward distributions change over time. To swiftly accommodate this, we propose an adaptive window combinatorial upper confidence bound based data placement (AW-CUCB-DP) scheme to reduce latency and cost. In AW-CUCB-DP, a simple and efficient change detector, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Page-Hinkley test</i> with forgetting mechanism (FM-PHT), is employed to enable variable-size sliding windows to handle both gradual and abrupt variations in network conditions or workloads. We establish that AW-CUCB-DP is asymptotically optimal in the non-stationary multi-cloud environment. Trace-driven experiments further verify that our scheme outperforms alternatives, especially in highly dynamic environments.
A DWT Domain High-Fidelity Anti-Tampering Digital Watermarking Algorithm for Medical Image

In order to prevent medical images from being tampered in network transmission, a high fidelity anti-tampering medical image digital watermarking algorithm in DWT domain is proposed. The algorithm uses Logistic chaotic mapping to generate watermark embedding position sequence and watermark bit sequence with high ergodicity and randomness; Furthermore, the proposed DWT-SIN algorithm is used to embed and extract watermarks in DWT domain. The DWT-SIN algorithm slightly modifies each DWT coefficient corresponding to the embedding position sequence, so that the sine transforming value of the modified DWT coefficient is exactly equal to the corresponding item of the watermark bit sequence, thus achieving high fidelity for the watermarked image. Based on this, a chaotic key adaptive watermarking algorithm is proposed to achieve a given target fidelity for the watermarked image. The algorithm adaptively adjust the chaotic position key and then obtain the ideal watermark embedding position sequence, which effectively reduce the distortion of the watermarked medical image. In order to reduce the computational complexity of the algorithm, a chaotic key segmentation adaptive watermarking algorithm is further proposed. The experimental results show that the algorithm in this paper not only greatly improves the quality of the watermarked image, but also can locate the tampered position and present the tampering rate.
SNR Estimation Method based on SRS and DINet

In conventional SNR estimation, the energy of the useful signal cannot be accurately calculated because of the noise in the received signal. At the same time, because of the random nature of noise, how to accurately estimate the noise is a common challenge in the engineering community. To address this problem, this paper proposes a signal-to-noise ratio (SNR) estimation method that combines the sounding reference signal (SRS) with the deep learning network DINet, where DINet is composed of denoising convolutional neural network (DnCNN) and image restoration convolutional neural network (IRCNN) in parallel. To demonstrate the higher estimation performance of our proposed method, we replicate some advanced algorithms, such as Boumard's algorithm, Qun X et al.'s improved algorithm, and M2M4 algorithm, and in the paper we refer to Boumard's algorithm and Qun X et al.'s improved algorithm as algorithm 1 and algorithm 2, respectively. At the transmitter side, to address the randomness of the noise distribution, we map the SRS into a nine-box grid on the resource block, and this arrangement facilitates a more accurate estimation of the noise and signal. At the receiver side, the SNR of each nine-box grid is calculated firstly using algorithm 1. Then the SNR is put into the corresponding resource unit and linear interpolation is performed on the resource block. Finally, the resource blocks are equated to images and input to DINet for denoising to obtain a more accurate value of the SNR estimate. Experiments show that the proposed method in this paper has a significant performance improvement compared with algorithm 2 and M2M4 algorithm.
Mind in Action: Cognitive Assessment Using Action Recognition

Human action recognition aims at extracting features on top of human skeletons and estimating human pose. It has received increasing attention in recent years. However, existing methods capture only the action information while in a real world application such as cognitive assessment, we need to measure the executive functioning that helps psychiatrists to identify some mental disease such as Alzheimer, Schizophrenia and ADHD. In this paper, we propose a skeleton-based action recognition named Mind-In-Action (MIA) for cognitive assessment. MIA integrates a pose estimator to extract the human body joints and then automatically measures the executive functioning employing the distance and elbow angle calculation. Three score functions were designed to measure the executive functioning: the accuracy score, the rhythm score and the functioning score. We evaluate our model on two different datasets and show that our approach significantly outperforms the existing methods.
Cross Pyramid Transformer makes U-net stronger in medical image segmentation

Accurate auto-medical image segmentation, which is essential in disease diagnosis and treatment planning, has been significantly prospered by recent advances in Convolution Neural Network (CNN) and Transformer. However, pure CNN-based or pure Transformer-base architectures exhibit limitations for segmentation tasks. For example, CNN fails to capture long-range relations due to fix receptive fields, and Transformer ignores pixel-level spatial details of features. To address these challenges, we propose a novel parallel hybrid architecture for medical image segmentation, which is named CPT-Unet (Cross Pyramid Transformer U-shape Network). CPT-Unet may be the first attempt to exploit both the advantages of Pyramid Vision Transformer (PVT) and CNN to the full by integrating them into the standard U-shape network to improve the segmentation performance and inference time. Specifically, we design a parallel dual branch encoder and decoder in CPT-Unet that consists of CNN and PVT. The input image is fed into the encoder of these two branches simultaneously to extract low-level spatial details and global contexts in a much shallower way. We design a novel fusion strategy to adequately utilize the multi-scale features extracted from CNN and PVT. We also add PVT in the decoder to better restore the segmentation map. Experiments on two public segmentation datasets demonstrate the improved performance of the proposed CPT-Unet over the comparison methods. The source code is available at https://github.com/ShengYue007/CPT-Unet.git.
Adversarial Attack and Defense in Breast Cancer Deep Learning Systems

Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women's health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on natural images are vulnerable to attacks that can lead to errors in recognition and classification, raising security concerns about deep systems based on medical images. We used the adversarial attack algorithm FGSM to reveal that breast cancer deep learning systems are vulnerable to attacks and thus misclassify breast cancer pathology images. To address this problem, we built a deep learning system for breast cancer pathology image recognition with better defense performance. Accurate diagnosis of medical images is related to the health status of patients. Therefore, it is very important and meaningful to improve the security and reliability of medical deep learning systems before they are actually deployed.