Reviewerly/exHarmony huggingface dataset:
Secure Sampling for Approximate Multi-party Query Processing

We study the problem of random sampling in the secure multi-party computation (MPC) model. In MPC, taking a sample securely must have a cost Ω(n) irrespective to the sample size s. This is in stark contrast with the plaintext setting, where a sample can be taken in O(s) time trivially. Thus, the goal of approximate query processing (AQP) with sublinear costs seems unachievable under MPC. To get around this inherent barrier, in this paper we take a two-stage approach: In the offline stage, we generate a batch of n/s samples with (n) total cost, which can then be consumed to answer queries as they arrive online. Such an approach allows us to achieve an Õ(s) amortized cost per query, similar to the plaintext setting. Based on our secure batch sampling algorithms, we build MASQUE, an MPC-AQP system that achieves sublinear online query costs by running an MPC protocol to evaluate the queries on pre-generated samples. MASQUE achieves the strong security guarantee of the MPC model, i.e., nothing is revealed beyond the query result, which itself can be further protected by (amplified) differential privacy
Enriching Recommendation Models with Logic Conditions

This paper proposes RecLogic, a framework for improving the accuracy of machine learning (ML) models for recommendation. It aims to enhance existing ML models with logic conditions to reduce false positives and false negatives, without training a new model. Underlying RecLogic are (a) a class of prediction rules on graphs, denoted by TIEs, (b) a new approach to learning TIEs, and (c) a new paradigm for recommendation with TIEs. TIEs may embed ML recommendation models as predicates; as opposed to prior graph rules, it is tractable to decide whether a graph satisfies a set of TIEs. To enrich ML models, RecLogic iteratively trains a generator with feedback from each round, to learn TIEs with a probabilistic bound. RecLogic also provides a PTIME parallel algorithm for making recommendations with the learned TIEs. Using real-life data, we empirically verify that RecLogic improves the accuracy of ML predictions by 22.89% on average in an area where the prediction strength is neither sufficiently large nor sufficiently small, up to 33.10%.
Robust Image Encryption Algorithm Using a New Variant of Hill Cipher

In this work, a new approach to image encryption is proposed. This crypto system is composed of two stages of the Hill cipher. First, a passage in vector of the original image is carried out, followed by a subdivision in blocks of three pixels, then, an invertible (3×3) order matrix is generated from the chaotic maps will be used for implementation an encryption process based on an affine transformation. Finally, another complete order matrix (3×3) not necessarily invertible is used to install a diffusion phase using a new mode enhancement (CBC) whose purpose is to protect our approach against any differential attack. Simulations on a large number of images prove that our algorithm has excellent security performance.
Leveraging Blockchain for Enhanced Security in IOB Data Management

The Internet of Behaviors (IOB) extends the Internet of Things (IoT) by focusing on individual behaviors through the analysis of data collected from connected devices. However, ensuring the security of IOB data is a major concern due to the increasing number of cyber attacks. To tackle this challenge, we propose an IOB architecture that leverages blockchain technology to enhance data security. In this article, we define IOB, highlight the differences between IOB and IoT, discuss the challenges of IOB, present our proposed IOB architecture with a blockchain-based security framework, and showcase real-life applications and use cases that require a high level of data security to demonstrate the effectiveness of our approach by combining IOB and blockchain.
A comparative study and performance evaluation of discrete Tchebichef moments for image analysis

Orthogonal moments seem to be less affected by noise ensuring minimal information redundancy, which leads to a great efficient capability of feature representation, extraction, and reconstruction. Based on discrete Tchebichef’s polynomials the image moments do not require any discretization assumptions like in Legendre moments for example. However, that makes Tchebichef moments more utilized in a wide range of applications (e.g: image analyses and recognition). This paper is a comparative study of such methods for extracting moments from gray-scale and binary images based on Tchebichef polynomials. The methods are compared and evaluated in terms of accuracy and computational time speed. Which is required in real-time applications, where image reconstruction capability from computed moments is analyzed and evaluated under speckle noise and free-noise cases.
Comparative Analysis of Iterative Decoding Algorithms for QC-MDPC McEliece Cryptosystems

QC-MDPC McEliece cryptosystems are a promising candidate for post-quantum cryptography due to their resistance to quantum attacks. However, the performance of these cryptosystems is heavily reliant on the decoding algorithms used for decryption. In this paper, we give a comparative analysis of iterative decoding algorithms for QC-MDPC McEliece cryptosystems. Specifically, we compare different iterative decoders bit-flipping, black-gray flip decoder with the backflip decoder. We evaluate the performance of each algorithm in terms of decoding failure rate, average number of iterations and running time. Our experimental results demonstrate that the choice of iterative decoding algorithm significantly impacts the efficiency of QC-MDPC McEliece cryptosystems. We identify the most efficient decoding algorithm based on the experimental results and discuss the implications of our findings on the practical use of QC-MDPC McEliece cryptosystems in post-quantum cryptography.
Female or male observers ? - Gender differences in subjective image quality assessment

Objective image quality assessment measures were extensively used to evaluate the performance of different image processing and analysis algorithms. However, they are application-driven. In contrast, subjective assessment measures are general-purpose and considered as the ground truth. Several Subjective Image Quality Assessment (SIQA) methods have been proposed in the literature. These measures require normalized environment and standardized protocols and are mainly based on the visual judgments of image quality. Nevertheless, these judgments are strongly influenced by human factors which might skew the precision of the assessment. Consequently, when selecting observers panel, researchers take into account several criteria such as age, sex, ethnic origin, socio-cultural and professional. Following an in-depth analysis of recent state-of-the-art work, we found that no one has investigated a gender difference in human judgment. In this paper, we aim to highlight the effect of this property in the subjective evaluation tests that are conducted on the TID2008 database. Obtained results show that men and women judge the same distorted images differently, with high precision in favor of women, outperforming in several cases the results provided by the TID2008 database.
Employing multi-agent systems to enhance virtual reality platforms

The use of virtual reality technology in the classroom has the potential to transform the way students learn by fostering immersive and interesting learning experiences that let them explore, discover, and build their own knowledge by giving students realistic and interactive experiences, virtual reality can dramatically influence distance learning. This technology has a positive effect on education. In addition, several research articles show that this technology has a positive impact on the user experience of learners. This paper presents some different virtual reality platforms used in different filed, specifically education. Additionally, there are systems that enable the complete integration of two increasingly important areas: virtual reality for visualization and intelligent multi-agent systems for artificial intelligence. The main goal of this paper is to propose an architecture for our system based on a multi-agent system with Virtual Reality allowing the learner to interact with the virtual scenes.
From Pre-Quantum to Post-Quantum RSA

Quantum computing, however young, challenges the most popular public-key encryption techniques. Due to their ability to solve the key distribution problem and provide high security in insecure communications channels that allow website access, email exchange, financial transactions, digitally signed documents, military communications, and medical data, such systems are vital to Internet security today. RSA, ECC, and Diffie Hellman are increasingly used globally. Internet protocols like Transport Layer Security (TLS) used by ordinary PCs and IoT devices increasingly include these systems. Because many IoT devices are battery-powered and have limited processing power and memory, they need energy-efficient and lightweight algorithms to provide effective security. These constraints become critical when developing cryptosystems that require extensive mathematical operations and significant computing resources, often needed in defence, mission-critical, or intelligent healthcare applications where data privacy must be maintained over time. Scientists are striving to protect IoT devices against quantum computing. This study will demonstrate how quantum computing can break RSA in polynomial time, something conventional computers cannot do due to processing power constraints. It will show how quantum computing could effect blockchain and wallet signatures, which employ the RSA cryptosystem for key encryption. Quantum computers with more qubits will crack factorization cyphers faster.
Authentication in Internet of Things: State of Art

Internet of Things (IoT) is a system of wireless electronics that may visualize and control much of the world that surrounds us. This new concept brings with it a broad range of research challenges. Rather, the highly critical challenge of IoT networks is security and particularly authentication. This paper consists of an exhaustive study about authentication in the IoT environment. Accordingly, we present analysis result of a comparison study between eleven IoT authentication schemes. The main criteria of our comparison are cryptography techniques, security services provided, resistance against attacks and computational cost. At the end, this paper will highlight the weaknesses of current IoT authentication and propose future research directions.
Tweets similarity classification based on Machine Learning Algorithms, TF-IDF and the Dynamic Case Based Reasoning

The research on the field of Twitter sentiment analysis, which aims to extract users’ sentiments through their public opinion about a given topic, has been increased and grown rapidly across a broad range of disciplines in the last decade. In this article, we propose a hybrid approach for Tweets similarity classification Based on Dynamic Case Based Reasoning approach, machine learning algorithms and Multi-Agent System. Our approach proposes a multi-agent adaptive system for Tweets similarity classification. It combines the Dynamic Case-Based Reasoning approach with the scientific measurement of keyword weight (Term Frequency- Inverse Document Frequency, TF-IDF). It consists of gathering and pre-processing tweets about a given topic and use a feature extraction to extract useful features. Machine Learning algorithms are then used for similarity content-based classification. Our approach is general and can be used to follow users’ tweets traces to predict their sentiments and provide them with an individualized content. In this study, Covid-19 tweets have been taken as an example.
Application of Swin-Unet for pointer detection and automatic calculation of readings in pointer-type meters

Abstract Pointer-type meters are widely used in military, industrial, and aerospace applications. In this paper, we propose a method to automatically calculate the readings of pointer-type meters that exhibits strong robustness to various situations, such as complex backgrounds, tilted meters, blurred images, and meter images with uneven illumination. First, the mask maps of scale and pointers are obtained using the Swin-Unet semantic segmentation network. For the mask map of scale, the Swin Transformer image classification network is used to identify the values of the scale and the coordinates of the centroids of the scale, and for the mask map of the pointer, the least skeleton circle method is proposed to fit the linear equation of the pointer. Second, the influence of the pointer and several adjacent scale on the meter reading is considered, and the weighted angle method (WAM) is proposed to calculate the meter reading. In order to verify the robustness of the algorithm in this paper, the pointer detection method is compared with the traditional pointer detection method, and it is found that the pointer detection method in this paper works better, and the pointer detection algorithm in this paper is applied to different semantic segmentation results, and it is verified that the pointer detection algorithm in this paper can be well adapted to different semantic segmentation results. This paper also compares the proposed algorithm with the existing meter reading calculation methods. The experiments show that using WAM on uncorrected meter images reduces the error by 30% compared with the traditional angle method, and using WAM on corrected meter images reduces the error by about 50%, which finally verifies the effectiveness of the algorithm in this paper.
An image inpainting algorithm using exemplar matching and low-rank sparse prior

Abstract Image inpainting is a challenging problem with a wide range of applications such as object removal and old photo restoration. The methods based on low-rank sparse prior have been used for regular or nearly regular texture inpainting. However, since such inpainting results do not synthesize the original pixels, they are usually not sharp especially when the area to be recovered is large. One remedy is to use an exemplar-based method. However, it often produces false matches and cannot obtain globally consistent inpainting results. In this paper, we give a new model to promote low rankness and sparsity and solve this model with a recently proposed Riemannian optimization algorithm. Furthermore, we propose a novel two-stage algorithm by integrating the low-rank sparse model with an exemplar-based method. Numerical experiments demonstrate that the proposed low-rank sparsity-based method and the two-stage algorithm achieve encouraging results compared to state-of-the-art image completion algorithms.
News Classification and Categorization with Smart Function Sentiment Analysis

Search engines are tools used to find information on the Internet. Since the web has a plethora of websites, the engine queries the majority of active sites and builds a database organized according to keywords utilized in the search. Because of this, when a user types a few descriptive words on the home page of the search engine, the search function lists websites corresponding to these keywords. However, there are some problems with this search approach. For instance, if a user wants information about the word Jaguar, most search results are animals and cars. This is a polysemic problem that forces search engines to always provide the most popular but not the most relevant results. This article presents a study of using sentiment technology to help news classification and categorization and improve the classification accuracy. We have introduced a smart search function embedded into a search engine to tackle polysemic issues and record relevant results to determine their sentimentality. Therefore, this study presents a topic that involves several aspects of natural language processing (NLP) and sentiment analysis for news categorization and classification. A web crawler was used to collect British Broadcasting Corporation (BBC) news across the Internet, carried out preprocessing of text by using NLP, and applied sentiment analysis methods to determine the polarity of the processed text data. The sentimentality represents negative, positive, or neutral polarities assigned by the sentiment analysis algorithms. The research utilized the BBC news site to collect different information using a web crawler and a database to explore the sentimentality of BBC news. The natural language toolkit (NLTK) and BM25 indexed and preprocessed patterns in the database. The experimental results depict the proposed search function surpassing normal search with an accuracy rate of 85%. Moreover, the results show a negative polarity of BBC news using the Sentistrength algorithm. Furthermore, the Valence Aware Dictionary and sEntiment Reasoner (VADER) was the best-performing sentiment analysis model for news classification. This model obtained an accuracy of 85% using data collected with the proposed smart function.
Particle number and interactions in the entropic uncertainty relations

Abstract The dependence of the entropic uncertainty relations on particle number and on attractive and repulsive interparticle potential strengths, is examined in the ground state of coupled harmonic oscillators. The reduced entropy sums depend on the normalization constants of the position and momentum densities, which in turn depend on particle number and interaction strength. These sums exhibit maxima as the particle number is varied, when the particles are in the presence of attractive potentials. On the other hand, no maxima are observed for systems with a repulsive potential. All reduced entropy sums increase and move away from the bound with greater interactions. Consistent behaviours are observed for both reduced one- and two-variable entropy sums. Further reductions of the N -particle densities induce that the respective entropy sums are now farther away from the corresponding bounds. The interpretation of these results is that reduction and increased interaction translates into a weakening of the strength of the uncertainty principle statement. The one- and two-variable entropy sums of these systems are shown to approximate the <?CDATA $a+b\mathrm{ln}N$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>a</mml:mi> <mml:mo>+</mml:mo> <mml:mi>b</mml:mi> <mml:mi>ln</mml:mi> <mml:mi>N</mml:mi> </mml:math> relation, when the intensity of the one-body harmonic potential is set at the number of particles.
Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices — the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices — and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications.
Towards an Open-Source Based E-Mail Forensic Tool that uses Headers in Digital Investigation

Email-related incidents/crimes are on the rise owing to the fact that communication by electronic mail (e-mail) has become an important part of our daily lives. The technicality behind e-mail plays an important role when looking for digital evidence that can be used to create a hypothesis that can be used during litigation. During this process, it is needful to have a tool that can help to isolate email incidents as a potential crime scene in the wake of suspected attacks. The problem that this paper is addressing paper, is more centered on realizing an open-source email-forensic tool that used the header analysis approach. One advantage of this approach is that it helps investigators to collect digital evidence from e-mail systems, organize the collected data, analyze and discover any discrepancies in the header fields of an e-mail, and generates an evidence report. The main contribution of this paper focuses on generating a freshly computed hash that is attached to every generated report, to ensure the verifiability, reliability, and integrity of the reports to prove that they have not been modified in any way. Finally, this ensures that the sanctity and forensic soundness of the collected evidence are maintained.
An exploratory study on the contribution of Artificial Intelligence in improving the bank credit analysis process

The world of work is at the dawn of major transformations and is still poorly prepared for it. The current uncertainties on the consequences of the combined development of artificial intelligence (AI) and big data are gradually dissipating by impacting a few business processes in the hope of a future improvement in organizational productivity and social well-being [3].
Optimisation and Resampling methods for Handling Imbalanced Datasets in Credit Card Fraud Detection using Artificial Neural Networks

In recent years, there has been a significant increase in fraud cases, causing financial losses for many companies. Detecting credit card fraud is a challenge due to the infinite and growing number of ways for fraudsters to commit fraud. Machine learning techniques have been proposed to detect fraudulent transactions, but the extremely low rate of fraud cases versus non-fraudulent transactions produces imbalanced data, making it difficult to train machine learning models. This study presents a comparative experimental approach to address the imbalance classification problem by employing several optimization and resampling methods to deal with imbalanced datasets. We examined methods such as binary cross-entropy loss minimization, minimization using weights to adjust for class imbalance, under-sampling, and over-sampling using the SMOTE technique, in combination with artificial neural networks. We applied the experiment to 284,807 anonymous transactions to compare the performance metrics of the listed approaches. It is crucial to consider the impact of class imbalance on model performance and choose appropriate techniques to mitigate its effects, as failure to do so can lead to unreliable models.
Elliptic Curve Cryptography signing algorithms behind blockchain 2.0

Blockchain technology (BCT) has garnered a great deal of attention and interest due to its cutting-edge, revolutionary, and promising features. The concept of blockchain involves linking a series of transaction blocks together in a chain, with each block being encrypted and inaccessible to anyone who lacks unanimous agreement. BC is comprised of five core stages, but the focus of this paper is on BC 2.0, specifically smart contracts. Smart contracts are utilized to ensure appropriate access controls and the enforcement of contracts. Digital signatures are essential for transactions in smart contracts, and various signature algorithms are employed, including ECDSA and EdDSA. The research problem is to compare and contrast the levels of security offered by two digital signature algorithms, ECDSA and EdDSA, which are employed in smart contracts. The primary objective of this study is to determine which digital signature algorithm performs better in terms of security.