Reviewerly/exHarmony huggingface dataset:
A Cryptographic Tamper Detection Approach for Storage and Preservation of Forensic Digital Data Based on SHA 384 Hash Function

The current age permits for legal, official, sensitive, and confidential documents to be exchanged using digital channels among stakeholders. In this digital age, new advances in technology are tremendously vital creating more sophisticated and intelligent tools in areas like informatics, electronics, and telecommunication. In the wake of these new advances, any individual can digitize any kind of document, and modern computing tools can alter all these using computational drawing tools, such as GIMP or Paint Shop Pro, without causing any distortions hence, tampered documents can be presented with the same quality as the original documents. Documents tampered, when used or distributed illegally can cause economic, political, legal and moral damages to individuals and organizations. In this paper, we propose to detect the tampering of documents using SHA-384 hash function..
An Algorithm of Scene Information Collection in General Football Matches Based on Web Documents

In order to obtain the scene information of the ordinary football game more comprehensively, an algorithm of collecting the scene information of the ordinary football game based on web documents is proposed. The commonly used T-graph web crawler model is used to collect the sample nodes of a specific topic in the football game scene information and then collect the edge document information of the football game scene information topic after the crawling stage of the web crawler. Using the feature item extraction algorithm of semantic analysis, according to the similarity of the feature items, the feature items of the football game scene information are extracted to form a web document. By constructing a complex network and introducing the local contribution and overlap coefficient of the community discovery feature selection algorithm, the features of the web document are selected to realize the collection of football game scene information. Experimental results show that the algorithm has high topic collection capabilities and low computational cost, the average accuracy of equilibrium is always around 98%, and it has strong quantification capabilities for web crawlers and communities.
Deep Human Dynamics Prior

Motion capture (MoCap) technology aims to provide an accurate record of human motion, with specific potentials in activity analysis, human behavior understanding, as well as multimedia industries of animation production and special effects movies. However, because of joint occlusion and limitation of equipment precision, the raw motion data are often damaged, which severely hinders its downstream applications. The latest method relies on deep neural networks to reconstruct the underlying complete motion from the degraded observation, achieving remarkable results. Unfortunately, due to the non-enumerability of human motion, the trained model from large-scale training data often fails to comprehensively cover incomputable action categories, which may lead to a sharp decline in the performance of deep learning-based methods. To handle these limitations, we propose an untrained deep generative model, in which Graph Convolutional Networks (GCNs) are utilized to efficiently capture complicated topological relationships of human joints. We show that the untrained GCN architecture with randomly-initialized weights is sufficient to extract some low-level statistics for human motion reconstruction without any training process. Notably, the performance of our approach is comparable to that of those trained models, while its application is not restricted by the availability of training data or a pre-trained network. Moreover, the proposed model even surpasses the state-of-the-art methods when encountering unprecedented samples in the human action database, regardless of the tasks of human motion recovery and gap-filling problem.
Deep Reinforcement Learning with Adjustments

Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated problems in recent years. However, their application on real-world physical systems remains limited. Despite the advancements in RL algorithms, the industries often prefer traditional control strategies. Traditional methods are simple, computationally efficient and easy to adjust. In this paper, we first propose a new Q-learning algorithm for continuous action space, which can bridge the control and RL algorithms and bring us the best of both worlds. Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining. Next, we present an approximation of our algorithm which can be applied to address short-term requirements of any pre-trained RL algorithm. The case studies demonstrate that both our proposed method as well as its practical approximation can achieve short-term and long-term goals without complex reward functions.
Blind Super-Resolution on Remote Sensing Images with Blur Kernel Prediction

Single image super-resolution (SISR) is essential in many remote sensing applications. Most of the existing SISR methods on remote sensing images assume that the low resolution (LR) images are synthesized from high-resolution (HR) images by bicubic downscaling. However, the performance of those methods is limited in the real-world remote sensing scenario as the actual degradation is sometimes different from the assumption. Therefore, we introduce the blind super-resolution (SR) concept and propose a super-resolution method with blur kernel prediction (BKPSR). BKPSR first predicts the blur kernel code for an image and then utilizes the blur kernel code to assist the image super-resolution. Experimental results indicate that our method outperforms existing SISR methods on real-world remote sensing images.
A normal form for single-qudit Clifford+T operators

Normal forms play a crucial role in the problem of gate synthesis. We propose a normal form for single-qudit gates composed of Clifford and T-gates for qudits of odd prime dimension $$p\ge 5$$ . We prove that any single-qudit Clifford+T operator can be re-expressed in this normal form in polynomial time. We obtained strong computational evidence that this normal form is unique. Assuming uniqueness, we are able to use this normal form to provide an efficient algorithm for exact synthesis of any single-qudit Clifford+T operator with minimal T-count.
Semantic Segmentation for High-Resolution Remote Sensing Images by Light-Weight Network

Accurate segmentation of high-resolution remote sensing images is increasingly demanded, yet poses significant challenges for algorithm efficiency. Most current approaches pursue accuracy by employing global context information to enhance the overall consistency or utilize multi-scale features or attention mechanisms to optimize object details, without considering the network complexity uniformly. In this paper, we propose a light-weight semantic segmentation network for HRRS images by way of explicitly supervising the objects' body and edge features to optimize the overall consistency and object details of semantic segmentation at the same time. Furthermore, we introduce a score-based feature fusion module to establish the long-range dependency between pixels in the final stage of feature fusion (to combine the body and edge features) effectively. Experiments on ISPRS Vaihingen dataset show an obvious advantage of the proposed approach compared with the existing approaches. Specifically, it achieves 89.60% overall accuracy with only 2.83M parameters and 2.37GFLOPs computation costs.
A quantum-behaved particle swarm optimization algorithm with self-adaptive elitist crossover

A quantum-behaved particle swarm optimization with self-adaptive elitist crossover (EXQPSO) is proposed in this paper. In the proposed EXQPSO algorithm, the crossover operator is performed on the elitist individuals to improve the qualities of the search individuals. Moreover, a self-adaptive scheme is proposed to automatically tune the crossover probability according to the performance of the crossover operator. Experiments on the well-known benchmark test suite showed that the proposed EXQPSO is better than the original QPSO in terms of global search capability and computation efficiency.
Private Cloud Configuration Using Amazon Web Services

The private cloud is characterized as a figuring model that offers administrations over the Web or a private internal network just to a solitary business substance rather than a public organization. Private cloud computing incorporates numerous advantages of public cloud-like versatility, self-administration, and flexibility with extra control and customization. Also, private clouds give a more significant level of security and protection through the organization’s firewalls and internal hosting which guarantees that third-party suppliers could not get access to delicate information and tasks. This paper gives insights into private cloud configuration and also elaborates on how to create a virtual private cloud using Amazon Web Services and how to run elastic compute cloud instances on a private cloud. For the creation of a virtual private cloud, the virtual private cloud (VPC) service of Amazon is used. This virtual private cloud service enables us to configure custom virtual private cloud by setting subnets, route tables, and Internet gateways. All steps of setting subnets, route tables, and Internet gateways are explained in this paper. For demonstration purposes, two virtual machines (instances) are created using elastic compute cloud service (EC2) and deployed on the custom virtual private cloud successfully. Out of these two instances, one instance is made public and another instance as private, and on a public instance, one small Web application is deployed to get its access from anywhere on the Internet.
SBMPO: Sampling Based Model Predictive Optimization for robot trajectory planning

Sampling-Based Model Predictive Control (SBMPO) is a novel nonlinear MPC (NMPC) approach that enables motion planning with dynamic models. This tool is also well suited to solve traditional MPC problems and has been tested in various situations ranging from robotics, task scheduling, resource management, combustion processes, and general optimization.
An End-to-End Demodulation System Based on Convolutional Neural Networks

The demodulation of digital signal plays a key role in the communication system. The traditional demodulator is usually realized by special hardware platform, which has the disadvantages of high cost and long development cycle. In this paper, we propose an end-to-end digital signal demodulator based on convolutional neural network (CNN). It consists of an encoder and a decoder, in which the encoder encodes the input symbol sequence and maps the signal features to the hidden layer space. Then, the decoder decodes the features of the hidden layer space to obtain the demodulation result of the input sequence. The proposed algorithm can automatically learn how to demodulate the received signal without manually extracting the features. Compared with the traditional demodulator, the proposed CNN demodulator has better bit error rate (BER) performance.
Multi-View Attention Network for Remote Sensing Image Captioning

In traditional remote sensing image captioning models, the attention mechanism plays a dominant role and has been used to integrate image features to infer the latent visual-semantic alignment. However, the scenes of remote sensing image are complex and diverse, using only one attention module to capture features often leads to insufficient semantic representation. In our work, we present a novel Multi-view Attention Network (MAN) model to realize feature integration from different views. With MAN, more semantically rich ensemble attended features can be obtained by different attention modules. Specifically, we enforce the weights of attention modules to be diverse through a cosine distance loss. This will provide the model with distinct views to make semantic predictions for each feature. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed model for the task of remote sensing image captioning.
VmAP: A Fair Metric for Video Object Detection

Video object detection is the task of detecting objects in a sequence of frames, typically, with a significant overlap in content among consecutive frames. Mean Average Precision (mAP) was originally proposed for evaluating object detection techniques in independent frames, but has been used for evaluating video based object detectors as well. This is undesirable since the average precision over all frames masks the biases that a certain object detector might have against certain types of objects depending on the number of frames for which the object is present in a video sequence. In this paper we show several disadvantages of mAP as a metric for evaluating video based object detection. Specifically, we show that: (a) some object detectors could be severely biased against some specific kind of objects, such as small, blurred, or low contrast objects, and such differences may not reflect in mAP based evaluation, (b) operating a video based object detector at the best frame based precision/recall value (high F1 score) may lead to many false positives without a significant increase in the number of objects detected. (c) mAP does not take into account that tracking can be potentially used to recover missed detections in the temporal neighborhood while this can be account for while evaluating detectors. As an alternate, we suggest a novel evaluation metric (VmAP) which takes the focus away from evaluating detections on every frame. Unlike mAP, VmAP rewards a high recall of different object views throughout the video. We form sets of bounding boxes having similar views of an object in a temporal neighborhood and use a set-level recall for evaluation. We show that VmAP is able to address all the challenges with the mAP listed above. Our experiments demonstrate hidden biases in object detectors, shows upto 99% reduction in false positives while maintaining similar object recall and shows a 9% improvement in correlation with post-tracking performance.
Machine Learning to Augment the Fusion Process for Data Classification

The fusion of feature data has the ability to greatly aid the task of data classification. However, in most situations, some features are better suited to aid in the classification then others. In this research, we utilize a model-free reinforcement learning approach, coupled with Dempster-Shafer fusion calculus, to learn the subset of features to use for classification of data from multiple classes. Our approach is compared with using all features for data classification on an automobile feature data set, and the results show the benefits of our approach.
The Covid-19 Between State of Emergency and Exception in Italy and Its Consequences on Human Rights

Abstract In mid-December 2019, several atypical cases of pneumonia were detected in hospitals in Wuhan City – Hubei Province – in Inner China. It turns out that the first patients had already fallen ill in early December or even mid-November. However, only on the last day of the year 2019, Chinese doctors were able to officially identify a new virus in a 41-year-old patient admitted 5 days earlier. The virus belongs to the class of “coronavirus”, the same to which the SARS (Severe Acute Respiratory Syndrome) and MERS (Middle East Respiratory Syndrome) viruses belong. It is initially baptized 2019-nCov, or “new 2019 coronavirus”. In February, the official name assigned to the virus is Sars-COV-2 and the associated disease is named COVID-19. The outbreak took on considerable proportions in China and then spread to the rest of the world, leading the World Health Organization to declare the infection a “pandemic” on 11 March 2020. The containment strategies applied in the most affected countries have proved to be very different in effectiveness, to the point that the lethality of the virus appears very different from country to country. This difference in impact has led to different legal, economic and social consequences.
Remote Sensing Image Retrieval by Multi-Scale Attention-Based CNN and Product Quantization

With the development of information technology, all kinds of image information are expanding. It has been a hot issue in computer vision to quickly retrieval interested images from data sets. The complexity of remote sensing images brings new challenges to the retrieval process. This paper presents a new method for remote sensing image retrieval. In our proposed multi-scale attention-based convolutional neural network with improved product quantization method (APQ), we first use deep neural network with visual attention mechanism to extract feature representation of remote sensing images, and then we use an improved product quantization method to reduce the dimension of the features for the purpose of reducing the retrieval computation cost. Experiments on two remote sensing datasets Satellite Remote Sensing and NWPU show that our APQ method can outperform some state-of-the-art remote sensing image retrieval methods.
Safe-Error Analysis of Post-Quantum Cryptography Mechanisms - Short Paper-

The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to use safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.
Constructing Binary Matrices with Good Implementation Properties for Low-Latency Block Ciphers based on Lai-Massey Structure

Abstract Diffusion layers are crucial components for lightweight cryptographic schemes. Optimal binary matrices are widely used diffusion layers that can be easier to achieve the best security/performance trade-off. However, most of the constructions of binary matrices are concentrated in smaller dimensions. Besides, to maximize the number of branches, the performance is often neglected. In this paper, we investigate the diffusion of the Lai-Massey (L-M) structures and propose a series of binary diffusion layers with the best possible branch number and efficient software/hardware implementations as well for feasible parameters (up to 64). Firstly, we prove the lower bound of the circuit depth of a binary matrix with a fixed branch number. Then, we construct binary matrices by L-M structure with cyclic shift as round functions because of taking account of the improvement of software performance and demonstrate that this construction can not get the diffusion layers with branch number &amp;gt;4. Then, we get some 4 $\times $ 4 and 6 $\times $ 6 optimal binary matrices with branch number 4 by one-round L-M structure. Note that the depth of these results is optimal, i. e. they achieve the lowest hardware costs without loss of software efficiency. Secondly, we construct diffusion layers by extended L-M structures to obtain binary matrices with large sizes. We give a list of software/hardware friendly optimal binary matrices with large dimensions, especially for dimensions 48 and 64. In particular, some of the solutions are Maximum Distance Binary Linear matrices. Finally, we also present diffusion layers constructed by the extended generalized L-M structure to improve their applicabilities on other platforms.
Research on Image Classification of Sports Training Video Based on Grey Relational Analysis and Support Vector Machine

Aiming at the problem of poor performance of change detection in traditional sports training video image classification methods, a sports training video image classification method based on gray correlation analysis and support vector machine is designed. The decision denoising algorithm is used to denoise the sports training video image, and the sports training video image is divided into a 3×3 filtering neighborhood window to denoise. Eight neighborhood search method and parallel image algorithm are combined to extract the features of the sports training video image, obtain the specific contour of the feature area of the sports training video image, implement contour tracking for the specific contour of the feature area of the sports training video image, and merge multiple record sequence tables that may be generated when tracking the contour. Based on gray correlation analysis, the correlation degree of sports training video image features is analyzed, and the sports training video image features are classified by support vector machine, so as to realize the classification of sports training video image. In order to prove that this method has better change detection performance, the traditional sports training video image classification method is compared with this method. The experimental results show that this method has better change detection performance than the traditional method.
A reversible data hiding scheme based on multiple residual histogram shifting

In this paper, a reversible data hiding scheme based on multiple residual histogram shifting is proposed. As is known to all, there is a prediction technology which can improve the hiding ability under the premise of ensuring good stego-image quality. It studies the similarity between image pixels and uses the residual histogram of the original coverage image to hide secret data. However, when the residual histogram is used to hide the secret data, some pixels in the original image are not well utilized. To solve this problem, we proposed a novel reversible data hiding scheme based on multiple residual histogram shifting. The basic image composed of basic pixels is extracted and processed by prediction technology, and the residual histogram of the basic image is used to hide the secret data too. The experimental results demonstrate that the proposed scheme has higher hiding capacity and better stego-image quality.
On the Provable Security Against Truncated Impossible Differential Cryptanalysis for AES in the Master-Key Setting

Impossible differential cryptanalysis is a powerful cryptanalysis technique of block ciphers. Length of impossible differentials is important for the security evaluation of a block cipher against impossible differential cryptanalysis. Many previous studies on finding impossible differentials of AES assumed that round keys are independent and uniformly random. There are few results on security evaluation of AES in the master-key setting. In ASIACRYPT 2020, Hu et al. redefined impossible differential with the key schedule considered, and showed that there exists no one-byte active input and one-byte active output impossible differential for 5-round AES-128 even considering the relations of 3-round keys. In this paper, we prove theoretically that even though the relations of all round keys are considered, there do not exist three kinds of truncated impossible differentials for 5-round AES: (1) the input truncated differences are nonzero only in any diagonal and the output truncated differences are nonzero only in any inverse diagonal; (2) the input truncated differences are nonzero only in any two diagonals and the output truncated differences are nonzero only in any inverse diagonal; (3) the input truncated differences are nonzero only in any diagonal and the output truncated differences are nonzero only in any two inverse diagonals. Furthermore, for any given truncated differentials of these three kinds, the lower bounds of the number of master keys such that the truncated differentials are possible for 5-round AES-128 are presented.