Reviewerly/exHarmony huggingface dataset:
Statistical Differential Fault Analysis of the Saturnin Lightweight Cryptosystem in the Mobile Wireless Sensor Networks

Mobile wireless sensor networks (MWSNs) have blended into a new application scenario to create revolutionary intelligent cities and handle numerous challenges of security, adaptability, and robustness as wireless technology has advanced. Exploiting lightweight cryptosystems is considered one of the main approaches for MWSNs to achieve confidentiality, integrity, and authentication to prevent malicious cyberattacks and resource abuses. The Saturnin lightweight cryptosystem, presented at ToSC in 2020, can be applied to protect MWSNs. No literature suggests Saturnin can defend against a ciphertext-only attack, where the attackers are the most susceptible. This study proposes the novel statistical differential fault analysis (SDFA) in this attack scenario with double distinguishers of square Chi-maximum likelihood estimate and Dice-Hamming weight. Following the experiments, it recovered the 256-bit secret key using 656 faults in the fourth-to-last round of Saturnin. Compared to the classic statistical fault analysis (SFA), the novel SDFA can expand fault injections to the deeper round and decrease the faults by half with a reliability of at least 99%. It gives an essential reference for figuring out how secure lightweight cryptosystems in MWSNs are.
CamDec: Advancing Axis P1435-LE video camera security using honeypot-based deception

Abstract The explosion of online video streaming in recent years resulted in advanced services both in terms of efficiency and convenience. However, Internet-connected video cameras are prone to exploitation, leading to information security issues and data privacy concerns. The proliferation of video-capable Internet of Things devices and cloud-managed surveillance systems further extend these security issues and concerns. In this paper, a novel approach is proposed for video camera deception via honeypots, offering increased security measures compared to what is available on conventional Internet-enabled video cameras.
Vehicle tracking in satellite videos: improved SiamRPN++ incorporating bounding box constraint optimization and inertia mechanism

Aiming at the issues of low tracking accuracy and model drift in complex scenes, such as occlusion, for satellite video vehicle tracking, an improved SiamRPN++ tracking method for satellite video is proposed to achieve high-precision sustained vehicle tracking. Firstly, considering the tiny size of satellite video vehicles, the bounding box size constraint of SiamRPN++ is reduced to 5 × 5 pixels, so as to obtain a more accurate target bounding box, which can reduce similar targets and background interference, and improve tracking accuracy. Then, the maximum value (F<sub>max</sub>) and average peak-to-correlation energy (APCE) of the classification score map are calculated to monitor the target state. Finally, when the target is monitored to be occluded, the inertial mechanism is employed to predict and correct the position of the occluded target to achieve continuous tracking. Experiment results based on the expanded XDU-BDSTU dataset show that the precision rate and success rate of the proposed method may reach 90.22% and 55.26%, respectively, which are 15.11% and 15.52% higher than those of the original SiamRPN++, respectively. The proposed method may enable continuous, high-precision tracking of satellite video vehicles at 52 FPS, with excellent real-time performance.
Small ship detection based on YOLOX and modified Gaussian Wasserstein distance in SAR images

Due to the increase in data quantity, ship detection in Synthetic Aperture Radar (SAR) images has attracted numerous studies. As most ship targets are small and cover a few pixels in SAR images, the commonly used intersection-over-union (IoU) metric which is sensitive to the location deviation of the bounding box is not suitable to measure the distance between two small ship boxes. To solve this problem, this paper proposes a small ships-oriented detection method based on YOLOX. First, as an anchor-free one-stage detector, YOLOX can achieve state-of-the-art performance without extra anchor parameters. To make a balance between detection accuracy and speed, YOLOX-tiny is adopted as the baseline network. Then, a modified Gaussian Wasserstein distance is proposed. By modeling the bounding boxes as 2D Gaussian distributions, the Modified Wasserstein Distance (MWD) can be used to measure the similarity between the boxes in network training and post-processing. Finally, the proposed method is verified on Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0), and the experimental results show that the proposed MWD can effectively improve the detection performance on small ships.
Research on point cloud organization method based on KD tree

With the rapid development of computer technology, the requirements of geospatial data are also increasing, and the traditional spatial data measurement methods are then unable to meet the needs of information technology. The emergence of 3D laser scanning technology, with its unique advantages, has been widely used in the collection and processing of information data in various industries, but at the same time, the data obtained by 3D laser scanning technology is a huge amount of point cloud data, and the huge amount of data brings difficulties to the computer storage and query. Therefore, this paper organizes and manages the point cloud data through KD tree, and the experiment proves that KD tree can manage the point cloud data efficiently, and has high efficiency when performing the related radius search and nearest neighbor search.
A scene information integrating network for object detection in remote sensing images

Object detection in remote sensing images is a challenging task in field of computer vision since detection performance is negatively influenced by complicated background and various object size. However, most studies have only focused on object appearance, with only a few taking into account scene information, which is closely related to existence and category of objects. In this paper, we put forward a new method by integrating scene information into detection with aim of generating more powerful feature. Specifically, we made use of GRU cell, a special kind of RNN, in order to enhance object feature. The proposed method was verified through experiments on a challenging dataset, i.e., DOTA. Compared to the baseline model RoI-Transformer, the proposed method has achieved around 2.7% improvement in terms of mAP, which is initial attempt to integrate scene information into object detection.
YOLO-DFD: A Lightweight Method for Dog Feces Detection Based on Improved YOLOv4

Computer vision has been integrated into people’s daily lives, but mainstream target detection algorithms deployed to embedded devices with limited hardware resources are difficult to meet the task requirements in terms of real time and accuracy. So we proposed YOLO-DFD, a light detection algorithm based on improved YOLOv4 to solve the problem of dog feces in our living environment. The main improvement strategies are as follows: the YOLOv4 backbone network is replaced with MobileNetV3, and the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mn>3</mn> </mrow> <mrow> <mo>∗</mo> </mrow> </msup> <mn>3</mn> </math> convolutions in the feature enhancement network are replaced by depthwise separable convolutions to further reduce the number of parameters. To enhance the accuracy of target detection, we introduced the convolutional block attention module (CBAM) in neck network, and the complete intersection over union (CIoU) loss of YOLOv4 is replaced with the SCYLLA intersection over union (SIoU) loss to reduce false detection rate. In this paper, the dataset we used was made up of pictures of dog feces taken in life. For the self-made dog feces dataset, we used data enhancement technology to expand it. The training result shows that the average precision (AP) has reached 98.66%. While maintaining detection performance, the parameter of YOLO-DFD is reduced by 82% and FPS increases 14 compared to YOLOv4. And YOLO-DFD has a lower parameter quantity and a smaller calculation than other algorithms, making it easier to deploy on embedded devices to clean dog feces.
Statistical zero-knowledge and analysis of rank-metric zero-knowledge proofs of knowledge

A series of Stern-like Code-Based Zero-Knowledge Proofs of Knowledge (CBZKPoKs) in the rank setting have been proposed since 2011. These CBZKPoKs (RStern, RJKPT, RVDC, RankId, RCVE, RVéronID) are rank metric adaptations of Stern, JKPT, AGS, CVE, and Véron protocols in the Hamming setting. RVéronID has been analyzed and the witness can be recovered because improper permutation leaks the information of witness. However, there are several open problems in the rest of rank metric CBZKPoKs: (1) statistical zero-knowledge property; (2) security and completeness. In this paper, we deeply analyze rank metric permutation and its cryptographic properties, and rigorously show that RStern and RJKPT can achieve statistical zero-knowledge property. We then analyze RVDC, RankId, and RCVE and show that they do not satisfy completeness and RVDC can be broken by the rank support learning attack. To repair and strengthen security, we reconstruct the rank metric protocols (RAGS and RVéron), in which two protocols work on random linear codes without the limitation of cyclic structure. The performance analysis shows that: their communication costs (20 KB and 26 KB) are the lowest among existing Stern-like CBZKPoKs for 128-bit security. When compared with lattice-based zero-knowledge proofs of knowledge with a non-negligible soundness error such as improved Stern (PKC 2013), Bootle et al. (CRYPTO 2019), and Beullens (EUROCRYPT 2020), our protocols perform a significant advantage in communication costs.
Consolidating economic exchange ontologies for financial reporting standard setting

Several UFO grounded economic exchange ontologies have been developed in the last decade, notably COFRIS, OntoREA, REA2, and ATE. An important question is whether they have reached a level to support financial reporting standard setters. This article first describes the foundational assumptions for exchange conceptualization and consolidates the latest developments in COFRIS - a core ontology for financial reporting information systems, within the most recent versions of the UFO theories and the OntoUML tool. The ontology is evaluated based on competency questions. Furthermore, it is confronted with the conceptual frameworks and standards for accounting and financial reporting and compared with other UFO grounded exchange ontologies. The results show both the maturity level of current exchange ontologies and their applicability to standard setting.
Copyright

The achievements of AI are particularly spectacular in the domain of Art, especially since artistic activities have long been thought to be the preserve of humans alone. This and other creative activities of AIs present copyright law with an unexpectedly new and very difficult dilemma: whether a “work” created by AI can be subject to copyright protection, and if so, who should hold the copyright to it. An effective study of the problem of robot creative works should, and perhaps must, combine perspectives from both private and public law. The authors are of the opinion that an AI should be granted the right of authorship. Then it would be necessary to decide whether, and to what extent, the works of robots would belong to the public domain, and to what extent copyright property rights would be vested in entities we would consider to have a moral title to these rights, be it ownership of the AI-associated robot, or making creative or financial contributions to its abilities. Finally, the chapter lists possible instruments for public redistribution of the profits coming from the creative activity of AI.
Image Cryptosystem for Different Kinds of Image by Using Improved Arnold Map

In this chapter, the methods of image encryption and decryption based on optical transformation are introduced. The extended fractional Fourier transform is constructed by designing an eccentric lens group, and is used in the image encryption system. By using the Fresnel diffraction model, an iterative phase recovery algorithm is constructed, and a color image hiding method and optical system are introduced. A parallel phase retrieval technique in gyrator transform domain is constructed, and is applied to construct an image decryption system.
Fully private and secure coded matrix multiplication with colluding workers

In this paper, we propose a new coded computation scheme that can alleviate straggler effects in distributed computing. We consider data security and master’s privacy for matrix multiplication tasks. The proposed scheme, called fully private and secure coded matrix multiplication (FPSCMM), ensures data security and master’s privacy on two data matrices for multiplication tasks from colluding workers. We also show that the storage overhead at workers can be reduced by FPSCMM, since it is enough for workers to store the encoded matrices with sub-blocks. Lastly, we compare FPSCMM with the existing master’s privacy-preserving coded matrix multiplication schemes.
Detailed Classification of Customer Reviews Using Sentiment Analysis

Abstract: With the rapid of Growth of E-Commerce websites, there is no doubt that people are drawn to online shopping more nowadays. As people are drawn to it, so are the sellers. But sellers can differentiate in quality as well as quantity. To adjust to the needs of the consumer, some sellers provide luxury items and some focus on fulfilling the needs of the consumer at normal rates. To make it easier for consumers to decide which product suits for their demands and needs, customer review on ECommerceWebsites is a proficient way for consumers to get what they arelooking for. In today’s world, customer’s reviews can have bigimpacts on the sales of a product
Kernel Partial Least Squares Feature Selection Based on Maximum Weight Minimum Redundancy

Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are not identical, and thus the feature selection method should have different feature evaluation criteria for all datasets. Additionally, high-dimensional data analysis poses a challenge to enhancing the classification performance of the different feature selection methods. This study presents a kernel partial least squares feature selection method on the basis of the enhanced maximum weight minimum redundancy algorithm to simplify the calculation and improve the classification accuracy of high-dimensional datasets. By introducing a weight factor, the correlation between the maximum weight and the minimum redundancy in the evaluation criterion can be adjusted to develop an improved maximum weight minimum redundancy method. In this study, the proposed KPLS feature selection method considers the redundancy between the features and the feature weighting between any feature and a class label in different datasets. Moreover, the feature selection method proposed in this study has been tested regarding its classification accuracy on data containing noise and several datasets. The experimental findings achieved using different datasets explore the feasibility and effectiveness of the proposed method which can select an optimal feature subset and obtain great classification performance based on three different metrics when compared with other feature selection methods.
DASH: Data Aware Locality Sensitive Hashing

Locality sensitive hashing (LSH) has been extensively employed to solve the problem of c-approximate nearest neighbor search (c-ANNS) in high-dimensional spaces. However, the search performance of LSH is degenerated with the number of data increasing. To this end, we propose an efficient method called Data Aware Sensitive Hashing (DASH) to deal with this drawback. DASH is the data-dependent hashing algorithm under considering the residual distance prior. DASH leverages this prior knowledge and provides theoretical guarantee for search results. Our experimental results with various datasets show that DASH achieves better search performance and the running time can reach up to about 4–40x speedups compared with other state-of-the-art methods.
A Self-training Approach for Few-Shot Named Entity Recognition

Named entity recognition (NER) is a basic task in natural language processing and can be used in a wide range of downstream tasks, such as question answering, text summarization, and machine translation. In recent years, deep-learning based methods achieve great performance in the NER task. It often demands a huge amount of data to train models. However, it is very expensive to collect sufficient training data in many real-world applications. Thus, it is important to develop NER systems for few-shot settings. In this paper, we propose a self-training approach for NER that employs the framework of the machine reading comprehension model when lacking training samples. Experimental results on NER benchmarks demonstrate that the proposed method in this paper outperforms the state-of-the-art methods.
Self-supervised Label-Visual Correlation Hashing for Multi-label Image Retrieval

Perceiving multiple objects within an image without the labels’ supervision is the challenge of multi-label image hashing tasks. Existing unsupervised hashing approaches do reconstruction or contrastive learning for the representation of the object of interest but ignore the other objects in the image. We propose to use pseudo labels to provide candidate objects, making the image match the possible objects’ features by the co-occurrence correlations between labels. As a result, we explore the co-occurrence correlations based on empirical models and design a data augmentation strategy in a self-supervised learning framework to learn label-level embeddings. We also build the image visual correlations and design a dual overlapping group sum-pooling (OGSP) component to fuse label-level and visual-level embeddings into image representations, alleviating noise from empirical models. Extensive experiments on public multi-label image datasets using pseudo labels demonstrate that our self-supervised label-visual correlation hashing framework outperforms state-of-the-art label-free hashing algorithms for retrieval. GitHub address: https://github.com/lzHZWZ/SS-LVH.git.
How to Share Medical Data Belonging to Multiple Owners in a Secure Manner

Over the past few years, we have witnessed that the big data analysis technologies based on machine learning and deep learning have brought great change to medical research. The medical data is a special kind of sensitive data, which is contributed by multiple users. For example, the prescriptions are generated by doctors but also contain the patients’ symptoms and history of diseases. However, the existing data sharing solutions focus on protecting the privacy of the single data owner while ignoring the privacy belonging to multiple owners. In this paper, we first identify this problem and propose a novel secure medical data sharing scheme based on the Chinese remainder theorem to protect the privacy of multiple owners in the medical data. To better support the scenario of medical data sharing, we implement the proposed scheme over a medical blockchain system to build a medical data sharing prototype, and dynamically manage two kinds of subscribers based on logical key hierarchy. The performance analysis validates the security and efficiency of the proposed method for the data sharing scenario of medical data from multiple owners.
MISRec: Multi-Intention Sequential Recommendation

Learning latent user intentions from historical interaction sequences plays a critical role in sequential recommendation. A few recent works have started to recognize that in practice user interaction sequences exhibit multiple user intentions. However, they still suffer from two major limitations: (1) negligence of the dynamic evolution of individual intentions; (2) improper aggregation of multiple intentions. In this paper we propose a novel Multi-Intention Sequential Recommender (MISRec) to address these limitations. We first design a multi-intention extraction module to learn multiple intentions from user interaction sequences. Next, we propose a multi-intention evolution module, which consists of an intention-aware remapping layer and an intention-aware evolution layer. The intention-aware remapping layer incorporates position and temporal information to generate multiple intention-aware sequences, and the intention-aware evolution layer is used to learn the dynamic evolution of each intention-aware sequence. Finally, we produce next-item recommendations by identifying the most relevant intention via a multi-intention aggregation module. Extensive experimental results demonstrate that MISRec consistently outperforms a large number of state-of-the-art competitors on three public benchmark datasets.
User Multi-behavior Enhanced POI Recommendation with Efficient and Informative Negative Sampling

Point-of-Interest (POI) recommendation plays a crucial role in the location-based social networks (LBSNs), while the extreme sparsity of user check-in data severely impedes the further improvement of POI recommendation. Existing works jointly analyse user check-in behaviors (i.e., positive samples) and POI distribution to tackle this issue. However, introducing user multi-modal behaviors (e.g., online map query behaviors), as a supplement of user preference, still has not been explored. Further, they also neglect to exploit why users don’t visit the POIs (i.e., negative samples). To these ends, in this paper, we propose a novel approach, user multi-behavior enhanced POI recommendation with efficient and informative negative sampling, to promote recommendation performance. In particular, we first extract three types of relationships, i.e., POI-query, user-query and POI-POI, from map query and check-in data. After that, a novel approach is proposed to learn user and POI representations in each behavior through these heterogeneous relationships. Moreover, we design a negative sampling method based on geographic information to generate efficient and informative negative samples. Extensive experiments conducted on real-world datasets demonstrate the superiority of our approach compared to state-of-the-art recommenders in terms of different metrics.
Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama

The synchronization of videos is an essential pre-processing step for multi-view reconstruction such as the image mosaic by UAV remote sensing; it is often solved with hardware solutions in motion capture studios. However, traditional synchronization setups rely on manual interventions or software solutions and only fit for a particular domain of motions. In this paper, we propose a self-supervised video synchronization algorithm that attains high accuracy in diverse scenarios without cumbersome manual intervention. At the core is a motion-based video synchronization algorithm that infers temporal offsets from the trajectories of moving objects in the videos. It is complemented by a self-supervised scene decomposition algorithm that detects common parts and their motion tracks in two or more videos, without requiring any manual positional supervision. We evaluate our approach on three different datasets, including the motion of humans, animals, and simulated objects, and use it to build the view panorama of the remote sensing field. All experiments demonstrate that the proposed location-based synchronization is more effective compared to the state-of-the-art methods, and our self-supervised inference approaches the accuracy of supervised solutions, while being much easier to adapt to a new target domain.