Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Image Retrieval With Unsupervised Double Bit Hashing

Unsupervised image hashing is a widely used technique for large-scale image retrieval. This technique maps an image to a finite length of binary codes without extensive human-annotated data for compact storage and effective semantic retrieval. This study proposes a novel deep unsupervised double-bit hashing method for image retrieval. This approach is based on the double-bit hashing method, which has been shown to better preserve the neighboring structure of binary codes than single-bit hashing. Traditional double-bit hashing methods require the entire dataset to be processed simultaneously to determine optimal thresholding values of binary feature encoding. In contrast, the proposed method trains the hashing layer in a minibatch manner, allowing for adaptive threshold learning through a gradient-based optimization strategy. Additionally, unlike most former methods, which only train the hashing networks on top of fixed pre-trained neural networks backbone. The proposed learning framework trains both hashing and backbone networks alternately asynchronously. This strategy enables the model to maximize the learning capability of the hashing and backbone networks. Furthermore, adopting the lightweight Vision Transformer (ViT) in the proposed method allows the model to capture both local and global relationships between multiple image views exemplar, which lead to better generalization, thus maximizing the retrieval performance of the model. Extensive experiments on CIFAR10, NUS-WIDE, and FLICKR25K datasets validate that the proposed method has superior retrieval quality and computational efficiency than state-of-the-art methods.
Concrete Quantum Cryptanalysis of Binary Elliptic Curves via Addition Chain

Thus far, several papers reported concrete resource estimates of Shor’s quantum algorithm for solving the elliptic curve discrete logarithm problem (ECDLP). In this paper, we study quantum FLT-based inversion algorithms over binary elliptic curves. There are two major algorithms proposed by Banegas et al. and Putranto et al., where the former and latter algorithms achieve fewer numbers of qubits and smaller depths of circuits, respectively. We propose two quantum FLT-based inversion algorithms that essentially outperform previous FLT-based algorithms and compare the performance for NIST curves of the degree n. Specifically, for all n, our first algorithm achieves fewer qubits than Putranto et al.’s one without sacrificing the number of Toffoli gates and the depth of circuits, while our second algorithm achieves smaller depths of circuits without sacrificing the number of qubits and Toffoli gates. For example, when $$n = 571$$ , the number of qubits of our first algorithm is 74% of that of Putranto et al.’s one, while the depth of our second algorithm is 83% of that of Banegas et al.’s one. The improvements stem from the fact that FLT-based inversions can be performed with arbitrary sequences of addition chains for $$n - 1$$ although both Banegas et al. and Putranto et al. follow fixed sequences that were introduced by Itoh and Tsujii’s classical FLT-based inversion. In particular, we analyze how several properties of addition chains, which do not affect the computational resources of classical FLT-based inversions, affect the computational resources of quantum FLT-based inversions and find appropriate sequences.
Optimisations and Tradeoffs for HElib

In this work, we investigate the BGV scheme as implemented in HElib. We begin by performing an implementation-specific noise analysis of BGV. This allows us to derive much tighter bounds than what was previously done. To confirm this, we compare our bounds against the state of the art. We find that, while our bounds are at most 1.8 bits off the experimentally observed values, they are as much as 29 bits tighter than previous work. Finally, to illustrate the importance of our results, we propose new and optimised parameters for HElib. In HElib, the special modulus is chosen to be k times larger than the current ciphertext modulus $$Q_i$$ . For a ratio of subsequent ciphertext moduli $$\log (\frac{Q_i}{Q_{i-1}}) = 54$$ (a very common choice in HElib), we can optimise k by up to 26 bits. This means that we can either enable more multiplications without having to switch to larger parameters, or reduce the size of the evaluation keys, thus reducing on communication costs in relevant applications. We argue that our results are near-optimal.
Fully Automated Differential-Linear Attacks Against ARX Ciphers

In this paper, we present a fully automated tool for differential-linear attacks using Mixed-Integer Linear Programming (MILP) and Mixed-Integer Quadratic Constraint Programming (MIQCP) techniques, which is, to the best of our knowledge, the very first attempt to fully automate such attacks. We use this tool to improve the correlations of the best 9 and 10-round differential-linear distinguishers on Speck32/64, and reach 11 rounds for the first time. Furthermore, we improve the latest 14-round key-recovery attack against Speck32/64, using differential-linear distinguishers obtained with our MILP/MIQCP tool. The techniques we present are generic and can be applied to other ARX ciphers as well.
Fast Novel Efficient S-Boxes with Expanded DNA Codes

IoT is one of the most popular technologies in recent years due to the interconnection of various infrastructures, physical devices, and software. To guarantee the security of Internet of Things (IoT) pervasiveness, lightweight cryptographic solutions are needed and this requires lightweight cryptographic primitives. The choice of S-box in light block ciphers plays an important role in characterizing the security-performance trade-off. The choice of the 4 × 4 S-box for the lightweight constructions results in compact hardware, speeding up the computational capability of the security algorithm unlike the 8 × 8 S-box. This work presents efficient algebraic S-boxes for a fast image cryptosystem based on a strong nonlinear function which is expanded by a biological technique depending on DNA. The robustness of the proposed S-boxes is analysed and tested against various standard attack criteria such as interpolation attacks, avalanche effect, and nonlinearity. The great advantage of introducing S-boxes is that its DSAC is the ideal value which is equal to zero. Also, other tests executed on these S-boxes guaranteed its robustness and excellent security performance. Moreover, the experiments are applied with full description in two different modes; RGB and gray images. The results of all tests proved to have fast and strong effective S-boxes.
PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification

Recent studies have shown that phishers are using phishing kits to deploy phishing attacks faster, easier and more massive.Detecting phishing kits in deployed websites might help to detect phishing campaigns earlier.To the best of our knowledge, there are no datasets providing a set of phishing kits that are used in websites that were attacked by phishing.In this work, we propose PhiKitA, a novel dataset that contains phishing kits and also phishing websites generated using these kits.We have applied MD5 hashes, fingerprints, and graph representation DOM algorithms to obtain baseline results in PhiKitA in three experiments: familiarity analysis of phishing kit samples, phishing website detection and identifying the source of a phishing website.In the familiarity analysis, we find evidence of different types of phishing kits and a small phishing campaign.In the binary classification problem for phishing detection, the graph representation algorithm achieved an accuracy of 92.50%, showing that the phishing kit data contain useful information to classify phishing.Finally, the MD5 hash representation obtained a 39.54% F1 score, which means that this algorithm does not extract enough information to distinguish phishing websites and their phishing kit sources properly.
Challenges of blockchain implementation in SMEs’ supply chains: an integrated IT2F-BWM and IT2F-DEMATEL method

Blockchain technology is one of the prominent advancements of Industry 4.0 (I4.0), although the technology is difficult to be implemented, particularly in relatively small organizations. This study investigates the challenges of blockchain implementation in the supply chain management of small and medium-sized enterprises (SMEs). Therefore, this study investigates whether the first-generation blockchain can satisfy the increasing demands of organizations and companies. To overcome environmental uncertainty, the IT2F-BWM is used to evaluate the weight of the challenges and the IT2F-DEMATEL is used to check the internal relationships of the indices. The research results showed the relative importance of some obstacles such as “scalability”, “a shortage of human resources and implementation expertise/knowledge” and “financial and budgetary obstacles.” Meanwhile, “technical and infrastructural obstacles” imposed the highest degree of influence, and challenges caused by “a shortage of human resources and implementation expertise/knowledge” were identified as the most effective criteria. The novelty of the present study can be the examination of blockchain challenges in SMEs’ supply chain the use of a new method to combine fuzzy techniques. this study not only mentions the challenges but also accurately rank the challenges to the implementation of this emerging technology in SMEs When the main priorities are identified through more accurate techniques, organization managers will be able to design and implement a more accurate strategy to improve the efficiency of blockchain technology.
Increasing Transparency around Facial Recognition Technologies in Law Enforcement: Towards a Model Framework

Law enforcement authorities around the world are increasingly trialing or using facial recognition technologies (FRT). Their use has raised many legal and ethical challenges, one of which is a lack of transparency: Community members do not have sufficient information about what government organizations use FRT, for what purposes, and what safeguards are in place to manage the risks that they pose to human rights. While increased transparency around FRT use has been demanded by policy makers and academics, there are no established guidelines on how much transparency is needed around different FRT (authentication, identification, categorization) and what the barriers are in achieving the expected levels of transparency. This article fills this gap by proposing criteria which would help determine the required levels of transparency for different FRT applications (both lowrisk and high-risk ones) and examines organizational, technical, legal and operational barriers in achieving adequate transparency and how they could be addressed.
Robust flight navigation out of distribution with liquid neural networks

Autonomous robots can learn to perform visual navigation tasks from offline human demonstrations and generalize well to online and unseen scenarios within the same environment they have been trained on. It is challenging for these agents to take a step further and robustly generalize to new environments with drastic scenery changes that they have never encountered. Here, we present a method to create robust flight navigation agents that successfully perform vision-based fly-to-target tasks beyond their training environment under drastic distribution shifts. To this end, we designed an imitation learning framework using liquid neural networks, a brain-inspired class of continuous-time neural models that are causal and adapt to changing conditions. We observed that liquid agents learn to distill the task they are given from visual inputs and drop irrelevant features. Thus, their learned navigation skills transferred to new environments. When compared with several other state-of-the-art deep agents, experiments showed that this level of robustness in decision-making is exclusive to liquid networks, both in their differential equation and closed-form representations.
Quantum multiparameter estimation with multi-mode photon catalysis entangled squeezed state

We propose a method to generate the multi-mode entangled catalysis squeezed vacuum states (MECSVS) by embedding the cross-Kerr nonlinear medium into the Mach—Zehnder interferometer. This method realizes the exchange of quantum states between different modes based on Fredkin gate. In addition, we study the MECSVS as the probe state of multi-arm optical interferometer to realize multi-phase simultaneous estimation. The results show that the quantum Cramer—Rao bound (QCRB) of phase estimation can be improved by increasing the number of catalytic photons or decreasing the transmissivity of the optical beam splitter using for photon catalysis. In addition, we also show that even if there is photon loss, the QCRB of our photon catalysis scheme is lower than that of the ideal entangled squeezed vacuum states (ESVS), which shows that by performing the photon catalytic operation is more robust against photon loss than that without the catalytic operation. The results here can find applications in quantum metrology for multiparatmeter estimation.
Cybersecurity and Prevention in the Quantum Era

Emerging future technologies such as Quantum computer is a serious threat to the privacy and security of user data and organizational security. The speed of outperforming classical computers is a major concern. If quantum computer at scale becomes real it will break current crypto systems such as TDS, DES, AES, etc. While quantum computing has the potential to significantly impact cybersecurity, there are still many challenges that need to be overcome and advancements to be made. One major challenge is producing enough fault-tolerant qubits, which are the basic units of quantum information, to fully utilize the computational power of quantum computers. There are also several key differences between quantum computers and the classical computers and smartphones that are commonly used today. Despite these challenges, quantum computing holds promise for revolutionizing various fields, including cybersecurity.
Real Time CCTV Violence Detection System Using Deep Learning

Violence has been a menace in every city across the world but with the increase in surveillance cameras, it has become easy to keep check on it. Though it is not possible to physically monitor all the locations at once. In this paper, we intend to create a system to raise an alarm in situations of violence for quick intervention of the local police to avert any mishaps and serious escalations. In this paper, we have proposed using a CNN based MobileNetV2 for feature extraction because MobileNetV2 is known to provide a higher accuracy and also has a lower response time after which a deep learning algorithm of LSTM is used. There are various datasets available for violence detection but we aim to create our own dataset for the model The dataset used is created using a collection of videos on violence from various social sites like Youtube, Shareavideo, Mediadrop. Another dataset is used for determining the accuracy of the model further.
Malaria Parasite Detection Using CNN-Based Ensemble Technique on Blood Smear Images

The parasite genus Plasmodium found in red blood cells of an infected person causes malaria, a disease that is transmitted by female Anopheles mosquitoes. The gold standard for diagnosing malaria is by reviewing blood smear under a mi-croscope. By aiding with triage and disease diagnosis, computer-aided diagnostic (CADx) methods and tools incorporating ma-chine learning (ML) algorithms on microscopic blood smear images possess the capability to lessen therapeutic encumbrance. On blood smear images, prominent pre-processing techniques have been performed, and two benchmark architectures have been applied along with ensemble technique. The ensemble approach was used to enhance the accuracy and performance of our suggested method. The NIH Malaria Dataset is used for all experimental evaluations. According to the results, the proposed approach has a 96.71 percent accuracy rate for identifying malaria in microscopic blood smears, with precision of 97.44 percent, specificity of 97.48 percent, F1-score of 96.69 percent, and Cohen's Kappa coefficient of 99.39 percent. Our study supports that convolutional neural networks (CNNs) can be used to quickly diagnose red blood cells (RBCs) with malaria parasite infection from segmented microscopic blood smear images, which is advantageous in regions with a shortage of medical personnel.
KNNTree: A New Method to Ameliorate K-Nearest Neighbour Classification using Decision Tree

Classification in supervised learning is one of the major issues in machine learning and data science. K-Nearest Neighbour (KNN) and Decision Tree (DT) are one of the most widely used classification techniques that are commonly applying for single model and ensemble processes. KNN is known as lazy learner as it doesn't build any decision line from the training data. DT, on the other hand, is a top-down recursive divide-and-conquer technique that used for both classification and regression problems. DT has several advantages e.g, is requires little prior knowledge and non-linear relationship of features don't affect the tree performance. In this paper, we have proposed a new learning algorithm named KNNTree which is a hybrid model of KNN and DT algorithms. The proposed model is basically a decision tree, but leaf nodes are replaced by the KNN classifier. We have tested the proposed method with KNN and DT algorithms on 10 benchmark datasets taken from UC Irvine Machine Learning Repository and found the proposed method outperforms both KNN and DT classifiers.
A Decentralized Secure Blockchain-based Privacy-Preserving Healthcare Clouds and Applications

In recent times, Blockchain (BC) technology has been one of the fastest-growing technologies poised to perform a significant role in the near future. In the age of Cloud Computing (CC) solutions, the Internet of Things (IoT), and big data analytics, BC guarantees data safety, transaction security, and self-regulation development. CC solutions initiated its role in the health industry due to its flexibility and efficient energy consumption paradigm. Nevertheless, CC operations have an issue with preserving confidential data and sharing patients' critical medical data with other healthcare facilities. BC provides solutions in regard to cloud protection and privacy issues of such decentralization features coupled with information protection and privacy, while the cloud contributes towards the resolution of BC's measurability and efficiency difficulties, thereby introducing the idea of an innovative BC-Cloud integration to track and preserve the electronic health records of patients in a reliable manner. In this paper, we present a BC-cloud combination for the electronic healthcare service to provide healthcare officials with the impulses following the rise of this most delinquent model, propose an arrangement of surviving structures, and their applicability for more reliable medical healthcare services. We then evaluate the improvement stages and co-operations and focus on the analysis difficulties for the combined BC-cloud structure, potential solutions, and prospective analysis objectives. This paper's outcome will benefit the health service enterprise to create and improve data administration systems to address patient concerns reliably.
Semisupervised Generative Adversarial Networks With Temporal Convolutions for Human Activity Recognition

Many potential applications of human activity recognition (HAR) can be found in health, surveillance, manufacturing, sports, and so on. For instance, HAR can be exploited in ambient-assisted living (AAL) systems to provide users with assistance services intend to improve their well-being, safety, and autonomy. Data annotation in HAR is a complex and time-consuming process that limits the availability of labeled samples. Furthermore, the classification performance of supervised deep neural networks depends on the availability of large, annotated training data. This article proposes an alternative framework for semisupervised generative adversarial networks (GANs) using temporal convolutions for semisupervised action recognition to address several problems related to conventional approaches in the HAR context, such as high dimensionality, scarcity of annotated data, scalability, and robustness. The proposed framework employs a single architecture on different datasets and its effectiveness is tested under four conditions reflecting real-world semisupervised scenarios to investigate the impact of intersubject training, amount of labeled data, number of classes, and inertial measurement unit (IMU) positions on model performance. The evaluations are performed on the Physical Activity Monitoring for Aging People (PAMAP2), opportunity-locomotion, and Laboratory of Images, Signals and Intelligent Systems (LISSI) HAR datasets to evaluate the generalizability of the framework. The results show the proposed framework’s high classification performance and generalization ability compared with baseline methods, achieving up to 25% improvement when only a small amount of annotated data is available. A comparison with the previous work using the same datasets has also validated the performance of the framework.
Domain adaptation based on feature fusion and multi-attention mechanism

In this paper, we proposed a domain adaptation method based on feature fusion and multi-attention mechanism. First, for extracting shared features, the global domain information was captured using the self-attention module, and feature fusion was used to fully utilize the shallow feature information. Next, the dual domain channel branching method, which is based on channel attention, was used to obtain different weights for the source and target domains through different convolution channels by using the activation function, which can extract identifiable features while aligning the domain. The feature correction module in the advanced stage of the network was then used to align the high-level features of the source and target after the task-specific layer to further reduce the domain differences. Furthermore, several cross-domain experiments were performed on the mainstream deep domain adaptation datasets to demonstrate the superior performance of the proposed method.
Vision Transformer With Enhanced Self-Attention for Few-Shot Ship Target Recognition in Complex Environments

Ship target recognition is essential for maritime transportation, commercial trade, maritime security, and monitoring illegal activity. The majority of previous ship target recognition models are based on fully-supervised learning, which necessitates vast amounts of labeled training data. However, optical lenses capturing ship images in natural scenarios have numerous complex environments, making feature extraction difficult due to hardware, weather, light, waves, and other factors. Moreover, image annotation is also both expensive and time-consuming. How to effectively learn from unbalanced, limited, and sparsely annotated sample data is a significant challenge. In this paper, we propose a new vision transformer with enhanced self-attention(SAVT) that can locate ship targets in complex environments with just a few shots. SAVT consists of a novel enhanced self-attention(ESA) module and a novel measurement self-adjusting computation(MAC) module to differentiate unique ship classes in complex environments with few labeled samples. The ESA module focuses on the features of the target-region at each layer of the backbone network in order to ensure that the contour detailed features are extracted effectively from ship images with complex environments. The model is then enhanced in terms of speed and stability by utilizing the MAC module. CIB-ships and public MAR-ships datasets are used to evaluate the proposed method. In this paper, the results demonstrate that SAVT achieves good performance on few-shot labeled data under both 1-shot and 5-shot settings, while also providing valuable guidance for maritime ship target recognition with limited labeled samples.