Reviewerly/exHarmony huggingface dataset:
GuardBox: A High-Performance Middlebox Providing Confidentiality and Integrity for Packets

The deepening of digital transformation has led to an increasing amount of data from industries being transmitted over the Internet. However, packets in plaintext originally designed for transmission in private networks suffer from significant security threats on the Internet. Unfortunately, existing encryption schemes, such as the representative TLS, are difficult to be applied to these industrial protocols due to their specific requirements and conditions such as low latency requirements and restricted operating environments. In this paper, we present a high-performance encryption/decryption middlebox called GuardBox to provide confidentiality and integrity for packets. GuardBox is expected to transparently encrypt/decrypt packets sent/received by protected industrial equipment with low latency and supports almost any application-layer protocol. To do that, we design a high-performance packet I/O framework and an optimized encryption/decryption scheme for GuardBox. More importantly, we use commodity trusted hardware, Intel SGX, to ensure the security of keys and the encryption/decryption process. Our extensive evaluation demonstrates that GuardBox can provide confidentiality and integrity for packets transmitted over the Internet with low latency and a near-native throughput.
Privacy and correctness trade-offs for information-theoretically secure quantum homomorphic encryption

Quantum homomorphic encryption, which allows computation by a server directly on encrypted data, is a fundamental primitive out of which more complex quantum cryptography protocols can be built. For such constructions to be possible, quantum homomorphic encryption must satisfy two privacy properties: data privacy which ensures that the input data is private from the server, and circuit privacy which ensures that the ciphertext after the computation does not reveal any additional information about the circuit used to perform it, beyond the output of the computation itself. While circuit privacy is well-studied in classical cryptography and many homomorphic encryption schemes can be equipped with it, its quantum analogue has received little attention. Here we establish a definition of circuit privacy for quantum homomorphic encryption with information-theoretic security. Furthermore, we reduce quantum oblivious transfer to quantum homomorphic encryption. By using this reduction, our work unravels fundamental trade-offs between circuit privacy, data privacy and correctness for a broad family of quantum homomorphic encryption protocols, including schemes that allow only the computation of Clifford circuits.
Coupling machine learning and synthetic image DIC-based techniques for the calibration of elastoplastic constitutive models

Abstract. Today, most design tasks are based on simulation tools. However, the success of the simulation depends on the accurate calibration of constitutive models. Inverse-based calibration methods, such as the Finite Element Model Updating and the Virtual Fields Method, have been developed for identifying constitutive parameters. These methods are based on mechanical tests that allow heterogeneous strain fields under the “Material Testing 2.0” paradigm in which digital image correlation plays a vital role. Although these methods have been proven effective, constitutive model calibration is still a complex task. A machine learning approach is developed and implemented to calibrate elastoplastic constitutive models for metal sheets, using datasets populated with finite element simulation results of strain field data from mechanical tests. Feature importance analysis is conducted to understand the importance of the different input features and to reduce the computational cost related with model training. Synthetic image DIC-based techniques were coupled with the numerically generated database, enabling the construction of a virtual experiments database that accounts for sources of uncertainty that can influence experimental DIC measurements. A robustness analysis of the methodology is performed for the boundary conditions of the test.
Several classes of permutation polynomials over the finite field <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

Let Fq denote the finite field with q elements. In this paper, based on the AGW criterion and determining the number of solutions to certain equations over the finite field Fp2m, several classes of permutation polynomials with given forms are proposed. Some of them are the permutation polynomials of the form (xpm−x+δ)s+L(x), where L(x) is a linearized polynomial. Others are the permutation polynomials of the form (xpm−x+δ)s1+(xpm−x+δ)s2+x.
Web-Based Application Development for Training Data Management Using REACTJS

This research was conducted to develop a web-based information system for training data management to improve training programs and business opportunities for companies. The management of training data includes training programs, participant data, invoice data, attendance numbers of participants, certificates and reports that have not been optimally carried out by the company. Based on these problems, the training manager was less effective and efficient. From these problems, this research is aimed at developing a system managing training data. The method used is scrum method. Web-based application design using ReactJS and MySQL as the database, with the Java and JavaScript programming languages. The development of this web-based information system aims to make it easier to manage training data that has been implemented according to the schedule and timeline of the training program. From the results of system integration test (SIT) and user acceptance test (UAT) it can be concluded that information systems developed with the addition of other features increase effectiveness by as much as 90.4%, efficiency is obtained by as much as 91.2%, then for user satisfaction by employees and admin as much as 92.6%. The evaluation results that have been carried out by the application are very feasible to use, namely 91.5%.
SPARSE IMAGE INTERPOLATION METHODS WORKING IN THE FREQUENCY DOMAIN

The work compares the work of sparse image interpolation methods such as: - interpolation method of sequential computation of the Fourier spectrum (IMSCS), the method of projections onto convex sets (projections onto convex sets, POCS), and the method of amplitude iterations (MAI). All calculations necessary for the reconstruction of sparse images are performed only on spatial spectra. As an example, an aerospace digital image is used, which is typical for the tasks of remote sensing of the earth's surface. A high degree of sparseness is modeled (90 percent of the information is missing). The effectiveness of the studied methods was carried out according to several objective criteria.
Federated learning and differential privacy in clinical health: Extensive survey

Federated Learning (FL) is concept that has been adopted in medical field to analyze data in individual devices through aggregation of machine learning model in global server. It also provides data privacy being that the sampled devices are not allowed to share data among themselves. Therefore, it minimizes computation costs and privacy risks to some extent compared to conventional methods of machine learning. However, federation learning provides a different use case in health as compared to other sectors. Preservation of patients’ sensitive information such as electronic health record (EHR) when sharing data among different medical practitioners is of greatest concern. So the question is, how should FL techniques be structured in the current clinical environment where heterogeneity is the order of the day? The EU’s General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act of 1996 (HIPPA) regulations recommends health providers to gain authorizations from patients before sharing their private data for medical analytical progression. This leads to some bottlenecks in clinical analysis. Although attempts have been made to address some of the challenges, privacy, performance, implementation, computation and adversaries still pose some threats. This paper provides a comprehensive review that covers literature, mathematical notations, architecture, process flow, challenges and frameworks used to implement FL with respect to healthcare. Possible solutions on how to address privacy challenges in accordance with HIPPA act and GDPR is discussed. Finally, the study gives future direction of FL in clinical health and a list of practical tools to conduct analysis on patients’ data.
Powering Fine-Tuning: Learning Compatible and Class-Sensitive Representations for Domain Adaption Few-shot Relation Extraction

Relation extraction (RE) is an important task in information extraction that has drawn much attention. Although many RE models have achieved impressive performance, their performance drops dramatically when adapting to the new domain and under few-shot scenarios. One reason is that the huge gap in semantic space between different domains makes the model obtain suboptimal representations in the new domain. The other is the inability to learn class-sensitive information with only a few samples, which makes the instances with confusing factors hard to be distinguished. To address these issues, we propose a Contrastive learning-based Fine-Tuning approach with Knowledge Enhancement (CFTKE) for the Domain Adaptation Few-Shot RE task (DAFSRE). Specifically, we fine-tune the model in a contrastive-learning way to refine the semantic space in the new domain, which can bridge the gap between different domains and obtain better representations. To enhance the stability and learning ability of contrastive learning-based fine-tuning, we design the data augmentation mechanism and type-aware networks to enrich the instances and stand out the class-sensitive features. Extensive experiments on the DAFSRE benchmark dataset demonstrate that our approach significantly outperforms the state-of-the-art models (by 2.73% on average).
CATCL: Joint Cross-Attention Transfer and Contrastive Learning for Cross-Domain Recommendation

Cross-domain recommendation (CDR) improves recommendation accuracy by transferring knowledge from rich domains to sparse domains, which is a significant advancement in the effort to deal with data sparsity. Existing CDR works, however, still have some challenges, including (1) ignoring the user-item interaction long-tail distribution problem and (2) transferring only the domain-shared feature preferences of common users. In this paper, we propose a CDR framework named joint Cross-Attention Transfer and Contrastive Learning for Cross-Domain Recommendation (CATCL). We first add random uniform noise to the original representation to maximize the mutual information between the original representation and its augmented view, and then pre-train to obtain more uniformly distributed user/item representations, in order to address the issues of data sparsity and popularity bias within intra-domain. In addition, we introduce a cross-attention mechanism for extracting user domain-shared and domain-specific features in order to capture the relevance of user preferences between inter-domain. Then we employ an element-wise attention component that dynamically distributes weights between domain-specific and domain-shared features, allowing different features to exhibit different importance in rich and sparse domains. The final experimental results on three public datasets demonstrate the effectiveness of the proposed framework for many powerful state-of-the-art approaches.
Memory-Enhanced Period-Aware Graph Neural Network for General POI Recommendation

As the popularity of Location-based Services increases, Point-of-Interest (POI) recommendations receive higher requirements to characterize the users, POIs and interactions. Although many recent graph neural network-based (GNN-based) studies have tried working on temporal and spatial factors, they still cannot seamlessly handle the temporal locality and spatial consistency. To tackle this issue, we propose a novel Memory-enhanced Period-aware Graph neural network for general POI Recommendation (MPGRec). Specifically, it exploits the advantages of the GNN module in characterizing user preferences. Moreover, we develop a period-aware gate mechanism after the GNN information propagation to characterize the temporal locality, and devise a dynamic memory module to extract, store and disseminate global information for spatial consistency. Furthermore, we propose a reading and writing strategy to merge the GNN module and memory module into a unified framework. Extensive experiments are conducted on four real-world datasets, and the experimental results demonstrate the effectiveness of our method.
Comparing Blockchain Bridges: Poly Network vs. IBC

Blockchain is a system where connected nodes work together to perform various activities within the system, and all of them maintain their own copy of a system ledger that stores transactions.With the emergence of new blockchain networks and the massive growth of users on the network the need for inter blockchain communication is now at an all-time high.At first glance the two existing bridging protocols, the Poly Network and the Inter Blockchain Communication (IBC) protocol, seem to be completely different, but a closer inspection reveals a connection between them that will most likely be seen in future blockchain bridges as well.This paper discusses their similarities and differences and ends by making a prediction about future blockchain bridges.
The Feasibility of Blockchain Technology as a Tool for Zimbabwean Financial Inclusion: An Online Desktop Review

Using the research of
Progressive Latent Replay for Efficient Generative Rehearsal

We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network. While replay strategies mitigate the effects of catastrophic forgetting in neural networks, recent works on generative replay show that performing the rehearsal only on the deeper layers of the network improves the performance in continual learning. However, the generative approach introduces additional computational overhead, limiting its applications. Motivated by the observation that earlier layers of neural networks forget less abruptly, we propose to update network layers with varying frequency using intermediate-level features during replay. This reduces the computational burden by omitting computations for both deeper layers of the generator and earlier layers of the main model. We name our method Progressive Latent Replay and show that it outperforms Internal Replay while using significantly fewer resources.
Learn by Oneself: Exploiting Weight-Sharing Potential in Knowledge Distillation Guided Ensemble Network

Recent CNNs (convolutional neural networks) have become more and more compact.The elegant structure design highly improves the performance of CNNs.With the development of knowledge distillation technique, the performance of CNNs gets further improved.However, existing knowledge distillation guided methods either rely on offline pretrained high-quality large teacher models or online heavy training burden.To solve the above problems, we propose a feature-sharing and weightsharing based ensemble network (training framework) guided by knowledge distillation (EKD-FWSNet) to make baseline models stronger in terms of representation ability with less training computation and memory cost involved.Specifically, motivated by getting rid of the dependence of offline pretrained teacher model, we design an end-to-end online training scheme to optimize EKD-FWSNet.Motivated by decreasing the online training burden, we only introduce one auxiliary classmate branch to construct multiple forward branches, which will then be integrated as ensemble teacher to guide baseline model.Compared to previous online ensemble training frameworks, EKD-FWSNet can provide diverse output predictions without relying on increasing auxiliary classmate branches.Motivated by maximizing the optimization power of EKD-FWSNet, we exploit the representation potential of weight-sharing blocks and design efficient knowledge distillation mechanism in EKD-FWSNet.Extensive comparison experiments and visualization analysis on benchmark datasets (CIFAR-10/100, tiny-ImageNet, CUB-200 and ImageNet) show that self-learned EKD-FWSNet can boost the performance of baseline models by large margin, which has obvious superiority compared to previous related methods.Extensive analysis also proves the interpretability of EKD-FWSNet.Our code is available at https://github.com/cv516Buaa/EKD-FWSNet.
Security and Privacy in Cloud Computing: Challenges and Solutions

This paper provides an overview of the security and privacy challenges that arise in cloud computing and the solutions proposed to address them. Cloud computing has become an essential part of modern business operations, but it also poses significant security and privacy risks. These risks include data breaches, unauthorized access to sensitive information, and compliance issues. The paper discusses the different security and privacy challenges that cloud computing presents, such as data confidentiality, integrity, and availability. It also explores the different solutions that have been proposed to address these challenges, including encryption, access control, and identity management. Additionally, the paper discusses the role of cloud service providers in ensuring security and privacy and the regulatory and compliance issues that organizations must consider. The paper concludes by providing recommendations for organizations to enhance their security and privacy in cloud computing. Keywords : Cloud Computing Security, Cloud Computing Privacy , Data Protection, Access Control, Encryption ,Risk Management
Mineralogical and Structural Analyses of Natural Fluorite from Yantuwaru Mining Site, Nigeria

Experimental studies of natural fluorite have been reported. In this study, combined X-Ray Fluorescence, FTIR and UV-Vis analyses were performed to give mineralogical information about natural fluorspar. The X-ray diffraction technique was also used to determine the crystallographic parameters and structure of the mineral. Results show a variation of Ca contents and low concentration of F, through all fluorite samples due to Ba and Br contents in the samples, causing replacements of impurity ions in the fluorite lattice. REEs in the fluorite samples were to be found responsible for colour. UV-Visible absorption results showed peaks between 209 to 368 nm, which did not affect the fluorite colour. The 224 nm and 365 nm peaks were caused by the electrons and holes trapped in the Ca2+ interstitial and vacancy, respectively. The absorption bands in the visible range relate to the corresponding fluorite samples. The FTIR results showed absorptions causing CO2 and CO32-stretching vibration due to the CaCO3 and OH stretching vibration due to water in the samples. The structural configuration, the lattice parameters, and the planes confirmed a cubic structure. Obtained results confirmed them to be Calcium Fluorite, as expected.
Study on the Effect of Video Conference on the Fatigue of Students Using one-way ANOVA and Regression Analysis

The COVID-19 pandemic pushed for the sudden implementation of online classes; students were forced to work within a new adjustment to continue their education.The prolonged virtual life eventually led to the popularization of video conference fatigue, commonly known as Zoom fatigue.However, literature on the relationship between video conferences and fatigue is lacking.The researchers conducted a one-way ANOVA and regression analysis on data collected through a survey questionnaire in identifying the factors that has a significant effect over the fatigue of students attending online classes.In the failure, modes, and effect analysis, the results indicated that length of synchronous sessions, posture, and temperature are the factors that have a proven relationship with the types of fatigue, namely mental, physical, and physiological fatigue, are present during video conferences.Risk mitigation for the priority failure modes is limited to what could be controlled by education institutions and the availability of immediate resources to students, such as class schedules and meditated breaks.Future researchers should analyze fatigue quantitatively to understand the relationship between video conferences and fatigue.
Cybercrime Awareness on Social Media: A Comparison Study

The popularity of social media has not waned since it gained popularity in the early 2000s. Social networks such as Facebook, YouTube, Twitter, and Snapchat boast billions of active users worldwide. Social media remains an invaluable tool to both organizations and individuals because of the ease of sharing information and media and the ability to both reach and engage specific audiences of interest. Due to its massive user base, communication ease, and data sharing, social media presents fertile ground for the conduct of cybercrime. Cybercriminals actively target social media users, use social media to facilitate their cybercrime activities, and advertise their criminal activities on social media. The potential dangers of cybercrime on social media necessitate that organizations institute cybercrime on social media policies to guard against these threats and provide employees with cybercrime awareness on social media (CASM) training. CASM is important as corporate and personal use of social media becomes increasingly blurred. This study attempted to measure the CASM scores of employees in security-critical sectors and determine if hearing disability had any impact on the CASM scores. Employees of the education, finance, government, information technology, legal, medicine, military, and Policing sectors in the United States were surveyed. Results showed that the CASM score was average across all sectors. No statistically significant difference in CASM score was found between groups with and without hearing difficulties, although CASM scores were slightly lower for employees with hearing difficulties. The results suggested that more CASM training is needed for employees in the surveyed sectors.