Reviewerly/exHarmony huggingface dataset:
URRNet: A Unified Relational Reasoning Network for Vehicle Re-Identification

With the continuous improvement and optimization of security monitoring networks, vehicle Re-Identification (Re-ID) becomes an emerging key technology in the development of intelligent visual surveillance systems. Due to the influence of viewpoint variation and fine-grained differences, vehicle Re-ID is still a research topic worth investigating. To alleviate above problems, a novel end-to-end framework named Unified Relational Reasoning Network (URRNet) is proposed in this paper, which integrates global features with local features to obtain better recognition accuracy. For the proposed framework, to understand the overall semantics of the image, an algorithm based on the global feature graph-structure learning is designed. The pixel-level feature maps are transformed to the node features of graph in the interactive space by projection, then graph reasoning is performed by using the graph convolutional network to improve the representation of global features. Moreover, an algorithm based on multi-scale local feature relational reasoning is designed. Using keypoint and viewpoint to obtain the multi-scale partial characteristics of the vehicle, and the vehicle multi-view features are learned from the single-view vehicle images through relational reasoning and attention mechanism. The two algorithms are combined to obtain the overall model, which not only preserves the details of the vehicle, but also effectively solves the problem of viewpoint variation. Comprehensive experimental results on two public datasets (VeRi-776 and VehicleID) indicate that the proposed URRNet can practically improve the model's representation ability and generalization ability, which is comparable to the state-of-the-art vehicle Re-ID methods.
Sensor based human action recognition and known public datasets a comprehensive survey

Human activity recognition (HAR) using specific information collected from many data acquisition devices such as (camera in video-based activity recognition or sensor in sensor-based activity recognition) which is employed in many types of research domains such as human monitoring, healthcare, and computer-human interaction.This paper provides an overview of the latest papers sensor-based activity recognition and known public datasets used in such papers.Which will gives a diagram of the most recent parts of the accompanying perspectives: significant sign, information catch, and preparing, techniques for managing obscure areas and patterns on the body, choice of fitting highlights, movement models, exercise manuals and measures to evaluate action execution and strategies for surveying the ease of use of the HAR framework. The study covers the identification of repetitive exercises, cases, falls and dormancy.
Nirbhaya Naari: An Artificial Intelligence Tool for Detection of Crime Against Women

Dowry abuse, rape, domestic violence, forced marriage, witchcraft-related abuse, honor killings are just a few of the myriad atrocities women encounter and fight against worldwide. The psychological impacts of abuse on the victim can lead to depression, PTSD, eating disorders, withdrawal from the outside world and society, and low self-esteem to name a few. The physical implications could result in an inability to get to work, wage loss, dearth of involvement in routine activities, not being able to take care of themselves and their families. Our initiative is dedicated to curbing violence against women by providing a forum for women to speak about violence as well as passing a signal about it through a dedicated hand gesture. Our designed solution has three modules namely: Violence/Crime Scene Detection against women using audio and video, help hand signal detection, and multi-label story classification. Our approach uses Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) for video classification along with Support Vector Machine (SVM), and Random forest for audio classification.
CCRFs-NER: Named Entity Recognition Method Based on Cascaded Conditional Random Fields Oriented Chinese EMR

Named Entity Recognition(NER) for Chinese Electronic Medical Record (EMR) is to identify medical entities and entity boundaries. Nested entities exist in Chinese electronic medical records, which makes NER low accuracy. To improve the recognition accuracy, this paper proposes a NER method, named CCRFs-NER. Firstly, the feature set is constructed by word features, part-of-speech features, and entity-identifier features. Then, Bi-LSTM and attention mechanism are used to process the feature set to extract the global and local features. Finally, the features are input into the CCRFs for probability prediction, and the recognition results are obtained. The experimental results show that the CCRFs-NER can improve the accuracy of named entity recognition for Chinese electronic medical records.
Secured E-voting system based on iris identification

Security is one of the most important requirements in the design and implementation of electronic services. Access to and the protection of data during transmission and storage are critical issues. In this work, the security of Electronic Voting System (E-Voting system) is proposed to handled and process. Secured E-voting system is proposed, designed and implemented for private purposes. The system is proposed to protect mainly using iris identification as an authentication method.in addition to protection of transmission channel and stored data. Iris recognition is implemented using one of deep learning neural network model. Special purpose camera is used for iris capturing in addition to CASIA and IITD data set, recognition result is evaluated and compared with other related works. Transmitted data is protected using modified AES algorithm. Stored data is protected using some of MYSQL properties. Security of the system is evaluated, Where the recognition rate for the proposed method was, 96.5, 98.1, for all data types, and for cameras samples respectively. Speed and security level of the modified encryption algorithm also evaluated, where the modified algorithm processing time for encryption and decryption less than the time of the original algorithm processing time. Where the encryption time for original and modified algorithm was 82 and 78 milliseconds respectively for image of size 59.6 Kbyte, modified algorithm had passed histogram and NIST test. The evaluation of the entire system security proved that the system had acceptable security. The results were promised.
Non-silicon Computing with Quantum Superposition Entanglement Using Qubits

The transistors, which can represent either 0 or 1 (bits) at the same time, are computed in classical computing. Even though classical computers have become smaller and faster, they are still incapable of solving issues like factoring huge integers, messages are sent in coded form using large digit prime integers. Currently, hundreds of electrons are used to turn on or off a single transistor on a chip in classical computing. To achieve great performance, we must progress past the limitations of conventional computing using electrons as well as silicon and into the non-silicon era. One solution that is gaining some traction is quantum computing. Quantum computers typically built on quantum bits (qubits) and take advantage of quantum features like quantum superposition and quantum entanglement to overcome the miniaturization issues of classical computing. Quantum superposition simply states that a quantum unit can exist in 2 different places at once. According to this hypothesis, simultaneously qubits can represent a wide range of 1 and 0 combinations. Quantum computers are acquiring processing power at a twice exponential rate, according to this observation. To gain an exponential growth in power scaling, the qubits are replaced with bits. The best part is that there are numerous choices available, from quantum computing to miraculous materials such as nanotechnology, and customized processors. Regardless of which path we choose, the future of computers promises to be exciting.
Privacy-Preserving Parallel Computation of Matrix Determinant With Edge Computing

With the widespread deployment of secure outsourcing computation, the resource-constrained client can delegate intensive computation tasks to powerful servers. Matrix determinant computation is a fundamental mathematical operation that has been widely used in IoT applications. This operation is computationally expensive. Nevertheless, the existing secure outsourcing protocols for matrix determinant are all designed based on one cloud server, which cannot well meet the low-latency and real-time computing requirements for the client. To address this issue, we explore accelerating the computation of matrix determinant by parallel outsourcing based on two nearby edge servers and propose the first practical protocol. We use the matrix blocking technique to split the computation task into multiple subtasks, which are parallel outsourced to edge servers for accelerating the computation. Moreover, we propose a privacy-preserving matrix transformation technique for data privacy protection. This technique only involves the operations of matrix-vector multiplication and matrix-matrix addition. It achieves the lightweight computation for the client and supports computational indistinguishability for the blinded input and a uniform distribution. The correctness, privacy and verifiability of the proposed protocol are analyzed. Finally, the performance advantage of the proposed protocol is demonstrated through simulation experiments.
Democratization Through “Cancel Culture”—Three Levels of Artistic Freedom

While ‘cancel culture’ is commonly regarded as limiting freedom of speech and artistic freedom, this article proposes a new understanding of ‘cancel culture’ as emancipatory norm-setting that is key for democratization. On a non-governmental level of the self-regulation of the art world, the argument for artistic freedom ignores the fact that art is permeated by power. The introduction of ‘politically correct’ norms leads to a justified redistribution of such power. On a parastatal level of public broadcasting and state cultural funding, neutrality is necessary but should be understood materially to include marginalized voices. Restrictions of freedom of speech and artistic freedom do occur on the state-level of hate-speech regulation. Here, the danger of a potential shift from emancipatory regulation to a harmful restriction is particularly virulent, as a discussion of the German BDS ban shows.
Lateral control of an autonomous vehicle based on salp swarm algorithm

An autonomous vehicle or driverless vehicle requires higher criteria of accuracy to track the predefined trajectory. This article proposes an advanced Stanley controller combined with the Salp Swarm Algorithm (SSA) to solve the problem of path tracking during maneuvering at low speeds. This study's basic objective is to adjust the advanced Stanley controller's gains with the proposed algorithm to steer the vehicle correctly on the desired road. However, to investigate from the performance competence of the suggested algorithm, it is compared with the commonly used algorithm, which is the Particle Swarm Optimization (PSO) algorithm. Also, the proposed controller is applied on various paths with large curvatures to evaluate the performance of the adjusted gains. The study found that the SSA based advanced Stanley controller achieved better performance to minimize the lateral error with an enhancement percentage of (93.758%) than the PSO based advanced Stanley controller. Moreover, the controller-based proposed algorithm managed to trace different curved trajectories generated from the motion planning efficiently.
Privacy Protection Scheme for the Internet of Vehicles Based on Collaborative Services

With the development of new-generation mobile communication technology, the Internet of Vehicles is playing an increasingly important role in people’s lives. However, the sensitive information contained in its “data fingerprinting” raises many privacy and security concerns. To better protect the location privacy of users on the Internet of Vehicles, this article proposes a collaborative service-based privacy protection scheme for the Internet of Vehicles. In this scheme, each requesting user first initiates a location service query by generating a pairing index of the location points set. Then a customized pairing result threshold is used to determine the collaborating users that can participate in the service response. And in the process, the location privacy of users is secured by location point generalization and encryption. In addition, redundant location service recommendations are eliminated by the proposed repeatability tests. Security analysis and experiments show that this scheme has good performance and anti-privacy leakages, forgery attacks, collusion attacks, etc.
New hybrid encryption algorithm for cloud computing security using chaotic system and mCrypton-salsa20 algorithms

Cloud computing is among the technologies that have become popular in recent times, due to the increasing demand for their use by individuals, companies and organizations. It offers its resources as an online service upon demand by its users. In order to enhance the trust between the cloud and its users, attention must be paid to addressing the issues that the cloud suffers from, the most outstanding of those are problems relating to the safety and privacy of users' data on the cloud. Encryption is considered one of the successful and efficient ways to raise the security of data kept on the cloud by converting it into data that is incomprehensible to unauthorized persons. In our research paper, we will present a method that provides security and privacy for data stored on the cloud, and this method consists of a combination of two lightweight algorithms (mCrypton and salsa20) to form a new lightweight hybrid algorithm based on the mCrypton algorithm, where stages were added to it and other modifications. Additionally, chaos has been added to generate encryption keys.
Donate Yourself: An AR trail exploring the future of organ, tissue and body data donation

This article explores how communication and interaction design were used in the augmented reality experience, Donate Yourself. It aims to demystify some of the ethical and personal concerns around the donation of organs, tissue and body data for scientific and medical research. The research finds different modes of provoking thought around a central question: can augmented reality be used to open debates on who has access to our biological and digital traces beyond death? Taking into account how COVID-19 has made the public deeply reconsider their biological and data bodies, the paper documents and contextualizes the making of Donate Yourself , which was created in collaboration with interactive design collective body&gt;data&gt;space and scientists from the Human Cell Atlas project (HCA). In doing so, it explores the contested histories of human tissue in research and contemporizes these relations by looking at the way HCA members use and care for human tissue and data in their work. A range of methods that were used to capture diverse public attitudes and ethical concerns about donation using participatory approaches included: the co-creation of design probes with HCA scientists, online workshops, qualitative interviews, a documentation zine and public maker jam. These methodological lenses, and the data they produce, were used to construct a non-linear narrative and digital bricolage that is experienced as a hybrid public walking tour. Five themes were generated for the augmented reality experiences: care, trust, immortal, consent, future. This triangulation of science, technology and the arts was materialized in a webAR walking trail that ran during November 2021, which was accessed by the public via QR codes as part of the larger One Cell At A Time online exhibition, funded by the Wellcome Trust.
Multi-Modal Multi-Grained Embedding Learning for Generalized Zero-Shot Video Classification

Zero-shot learning aims to learn knowledge from existing information to classify new classes with no visual training data. In the current work on zero-shot video classification, only the category name information can be used for unseen classes. While, most of the category names cannot fully describe the entire video information, but are only precise labels assigned by humans to actions, in which the amount of information is very small. In order to make up for the semantic deficiencies of video databases and build relationships between categories, we propose a multi-modal generalized zero-shot video classification framework based on multi-grained semantic information with a proposed video description text database. Our model explores semantic knowledge from accurate but lacking informative category names and exhaustive but redundant description texts, and learns visual knowledge from semantic embeddings of varying granularity. Further, we use the learned semantic and visual knowledge to perform multi-grained classification on test video data with both seen and unseen classes. To describe actions in detail and provide complete semantic information, we propose a description text database. The textual descriptions, including category definitions and explanations, in our proposed textual database effectively help establish relationships between categories, thus providing a more reliable basis for visual feature synthesis. Furthermore, our framework generates synthesized features for unseen classes from both coarse-grained and fine-grained semantic information, which would effectively avoid the bias of generalized zero-shot learning on seen classes. Extensive experimental results on the database prove the validity of our method and the effectiveness of the description texts in generalized zero-shot video classification problems.
Information-Containing Adversarial Perturbation for Combating Facial Manipulation Systems

With the development of deep learning technology, the facial manipulation system has become powerful and easy to use. Such systems can modify the attributes of the given facial images, such as hair color, gender, and age. Malicious applications of such systems pose a serious threat to individuals’ privacy and reputation. Existing studies have proposed various approaches to protect images against facial manipulations. Passive defense methods aim to detect whether the face is real or fake, which works for posterior forensics but can not prevent malicious manipulation. Initiative defense methods protect images upfront by injecting adversarial perturbations into images to disrupt facial manipulation systems but can not identify whether the image is fake. To address the limitation of existing methods, we propose a novel two-tier protection method named Information-containing Adversarial Perturbation (IAP), which provides more comprehensive protection for facial images. We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection. Recovering the message in adversarial examples with a decoder serves passive protection, contributing to provenance tracking and fake image detection. We introduce a feature-level correlation measurement that is more suitable to measure the difference between the facial images than the commonly used mean squared error. Moreover, we propose a spectral diffusion method to spread messages to different frequency channels, thereby improving the robustness of the message against facial manipulation. Extensive experimental results demonstrate that our proposed IAP can recover the messages from the adversarial examples with high average accuracy and effectively disrupt the facial manipulation systems.
An overview of the status, challenges, and trends of the advanced crypto algorithms to enhance the security of wireless networks

In modern applications, such as real-time applications, high-speed end-to-end transmission sometimes conflicts with the requirements of confidentiality and security. One of the most promising study fields in cryptography is Advanced Crypto, which is fast in encryption processing, resistant to attacks, and requires minimal resources. End-to-end communication and the possibility to use Advanced Cryptography in resource-constrained smart devices are the primary arguments for using Advanced Cryptography. There should be a trade-off between security, cost and performance in any cryptographic architecture. Power, energy consumption, delay, and throughput are some of the metrics used to quantify performance. Security requirements, on the other hand, are concerned with ensuring that the system is kept private and secure at all times.
A Min-Pooling Detection Method for Ship Targets in Noisy SAR Images

As the application of deep learning in general optical images becomes more and more widespread, the field of remote sensing images also begins to pay attention to the application of deep learning methods.Although deep learning detection algorithms have achieved better results than traditional detection algorithms, the detection results for poorly imaged SAR images still need improvement, and processing poorly imaged noisy SAR images is still a big challenge for existing algorithms.To address the problem of low precision and recall of existing algorithms for noisy SAR image detection, we propose a convolutional neural network detection algorithm based on min-pooling.First, we design a feature processing layer with min-pooling as the main structure to suppress the noise and then use a feature fusion layer to compensate for the missing information caused by pooling.To avoid problems such as redundancy in computation caused by the anchor-base algorithm, we choose the anchor-free algorithm as the main structure of ship detection.Finally, the model is evaluated using ordinary SAR image datasets and noisy SAR image datasets.Experiment results show that our proposed method has a better detection effect for noisy SAR images than other object detection models.
Metaverse: The Potential Threats in the Virtual World

The virtual world seems like it’s a bit old term, let’s use the metaverse. Neal Stephenson, a science fiction writer, created the term metaverse in 1992. “The concept of a fully immersive virtual world where people assemble to socialize, play, and work,” according to its most basic definition. From basic games and shopping to complex meetings, we can now be organized in the virtual world called the metaverse. In general, we are habituated to working in the physical world but metaverse is the future where we will be playing games, virtual shopping, virtual meetings, creating our virtual world, and investing in stocks. Cryptocurrencies are the fuel for metaverse to keep it working. Non-Fungible Tokens (NFTs) are the cryptocurrencies that are used to stand for real-world objects. These NFTs cannot be hacked since they use blockchain technology that allows the decentralization of data to prevent these hacks. Now comes the billion-dollar question is this metaverse safe? As we know everything cannot be 100% perfect and it’s also had some shortcomings. Anyone can enter into a highly confidential meeting using our unique avatar, and this arises much dubiousness, is our data safe in the metaverse?, privacy concerns, digital boundaries, and social engineering. This makes metaverse a bit scary even though it has some dominance. This requires creating a more secure, robust metaverse, and awareness in the people.