Reviewerly/exHarmony huggingface dataset:
Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index. Finally, according to the additive homomorphic property of Paillier homomorphic encryption, a similarity measurement method suitable for computing in the retrieval system’s security is ensured by the encrypted domain. The experimental results, which were obtained on Web Image Database from the National University of Singapore (NUS-WIDE), Microsoft Common Objects in Context (MS COCO), and ImageNet data sets, demonstrate the system’s robust security and precise retrieval, the proposed scheme can achieve efficient image retrieval without revealing user privacy. The retrieval accuracy is improved by at least 37% compared to traditional hashing schemes. At the same time, the retrieval time is saved by at least 9.7% compared to the latest deep hashing schemes.
Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

Elderly or disabled people can be supported by a human activity recognition (HAR) system that monitors their activity intervenes and patterns in case of changes in their behaviors or critical events have occurred. An automated HAR could assist these persons to have a more independent life. Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system. With the fast development of neural networks, computing, and machine learning algorithms, HAR system based on wearable sensors has gained popularity in several areas, such as medical services, smart homes, improving human communication with computers, security systems, healthcare for the elderly, mechanization in industry, robot monitoring system, monitoring athlete training, and rehabilitation systems. In this view, this study develops an improved pelican optimization with deep transfer learning enabled HAR (IPODTL-HAR) system for disabled persons. The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living. The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data. Besides, EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer. Finally, the IPO with deep belief network (DBN) model is utilized for the recognition and classification of human activities. The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively. The experimental validation of the IPODTL-HAR method is tested using benchmark dataset. Extensive comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures.
A Novel Approach for Security Enhancement of Data Encryption Standard

Data Encryption Standard (DES) is a symmetric key cryptosystem that is applied in different cryptosystems of recent times. However, researchers found defects in the main assembling of the DES and declared it insecure against linear and differential cryptanalysis. In this paper, we have studied the faults and made improvements in their internal structure and get the new algorithm for Improved DES. The improvement is being made in the substitution step, which is the only nonlinear component of the algorithm. This alteration provided us with great outcomes and increase the strength of DES. Accordingly, a novel good quality S-box construction scheme has been hired in the substitution phase of the DES. The construction involves the Galois field method and generates robust S-boxes that are used to secure the scheme against linear and differential attacks. Then again, the key space of the improved DES has been enhanced against the brute force attack. The outcomes of different performance analyses depict the strength of our proposed substitution boxes which also guarantees the strength of the overall DES.
Multi-Attribute Couplings-Based Euclidean and Nominal Distances for Unlabeled Nominal Data

Learning unlabeled data is a significant challenge that needs to handle complicated relationships between nominal values and attributes. Increasingly, recent research on learning value relations within and between attributes has shown significant improvement in clustering and outlier detection, etc. However, typical existing work relies on learning pairwise value relations but weakens or overlooks the direct couplings between multiple attributes. This paper thus proposes two novel and flexible multi-attribute couplings-based distance (MCD) metrics, which learn the multi-attribute couplings and their strengths in nominal data based on information theories: self-information, entropy, and mutual information, for measuring both numerical and nominal distances. MCD enables the application of numerical and nominal clustering methods on nominal data and quantifies the influence of involving and filtering multi-attribute couplings on distance learning and clustering performance. Substantial experiments evidence the above conclusions on 15 data sets against seven state-of-the-art distance measures with various feature selection methods for both numerical and nominal clustering.
Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

To secure web applications from Man-In-The-Middle (MITM) and phishing attacks is a challenging task nowadays. For this purpose, authentication protocol plays a vital role in web communication which securely transfers data from one party to another. This authentication works via OpenID, Kerberos, password authentication protocols, etc. However, there are still some limitations present in the reported security protocols. In this paper, the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method. The proposed OpenID-based encrypted Email’s Authentication, Authorization, and Accounting (EAAA) protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String (SSEAS). This string is deployed on both the relying party and the email server, which is unique and trustworthy. The first authentication, OpenID Uniform Resource Locator (URL) identity, is performed on the identity provider side. A second authentication is carried out by the hidden Email’s server side and receives a third authentication link. This Email’s third SSEAS authentication link manages on the relying party (RP). Compared to existing cryptographic single sign-on protocols, the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing attacks. This study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods, such as Single Sign-On (SSO) and OpenID. The experimental sites were examined by 72 information technology (IT) specialists, who found that 88.89% of respondents successfully validated the user authorization provided to them via Email. The proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.
An Efficient Three-Party Authenticated Key Exchange Procedure Using Chebyshev Chaotic Maps with Client Anonymity

Internet of Things (IoT) applications can be found in various industry areas, including critical infrastructure and healthcare, and IoT is one of several technological developments. As a result, tens of billions or possibly hundreds of billions of devices will be linked together. These smart devices will be able to gather data, process it, and even come to decisions on their own. Security is the most essential thing in these situations. In IoT infrastructure, authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit (e.g., via client identification and provision of secure communication). It is still challenging to create secure, authenticated key exchange techniques. The majority of the early authenticated key agreement procedure depended on computationally expensive and resource-intensive pairing, hashing, or modular exponentiation processes. The focus of this paper is to propose an efficient three-party authenticated key exchange procedure (AKEP) using Chebyshev chaotic maps with client anonymity that solves all the problems mentioned above. The proposed three-party AKEP is protected from several attacks. The proposed three-party AKEP can be used in practice for mobile communications and pervasive computing applications, according to statistical experiments and low processing costs. To protect client identification when transferring data over an insecure public network, our three-party AKEP may also offer client anonymity. Finally, the presented procedure offers better security features than the procedures currently available in the literature.
Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and 528 (3%) ‘failure data’. The three independent variables to predict machine failure were pressure indicator, flow indicator, and level indicator. The accuracy of the classifiers is very high and close to 100%, but the sensitivity of all classifiers using the original dataset was close to zero. The performance of the three classifiers was then evaluated for data with different imbalance rates (10% to 50%) generated from the original data using SMOTE, SMOTE-Support Vector Machine (SMOTE-SVM) and SMOTE-Edited Nearest Neighbour (SMOTE-ENN). The classifiers were evaluated based on improvement in sensitivity and F-measure. Results showed that the sensitivity of all classifiers increases as the imbalance rate increases. SVM with radial basis function (RBF) kernel has the highest sensitivity when data is balanced (50:50) using SMOTE (Sensitivity<sub>test = </sub>0.5686, F<sub>test </sub>= 0.6927) compared to Naïve Bayes (Sensitivity<sub>test = </sub>0.4033, F<sub>test </sub>= 0.6218) and Logistic Regression (Sensitivity<sub>test = </sub>0.4194, F<sub>test </sub>= 0.621). Overall, the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases, but the sensitivity is below 50%. The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.
Memory-Occupied Routing Algorithms for Quantum Relay Networks

Quantum transmission experiments have shown that the successful transmission rate of entangled quanta in optical fibers decreases exponentially. Although current quantum networks deploy quantum relays to establish long-distance connections, the increase in transmission distance and entanglement switching costs still need to be considered when selecting the next hop. However, most of the existing quantum network models prefer to consider the parameters of the physical layer, which ignore the influence factors of the network layer. In this paper, we propose a meshy quantum network model based on quantum teleportation, which considers both network layer and physical layer parameters. The proposed model can reflect the realistic transmission characteristics and morphological characteristics of the quantum relay network. Then, we study the network throughput of different routing algorithms with the same given parameters when multiple source-destination pairs are interconnected simultaneously. To solve the challenges of routing competition caused by the simultaneous transmission, we present greedy memory-occupied algorithm Q-GMOA and random memory-occupied algorithm Q-RMOA. The proposed meshy quantum network model and the memory-occupied routing algorithms can improve the utilization rate of resources and the transmission performance of the quantum network. And the evaluation results indicate that the proposed methods embrace a higher transmission rate than the previous methods with repeater occupation.
Cover Enhancement Method for Audio Steganography Based on Universal Adversarial Perturbations with Sample Diversification

Steganography techniques, such as audio steganography, have been widely used in covert communication. However, the deep neural network, especially the convolutional neural network (CNN), has greatly threatened the security of audio steganography. Besides, existing adversarial attacks-based countermeasures cannot provide general perturbation, and the transferability against unknown steganography detection methods is weak. This paper proposes a cover enhancement method for audio steganography based on universal adversarial perturbations with sample diversification to address these issues. Universal adversarial perturbation is constructed by iteratively optimizing adversarial perturbation, which applies adversarial attack techniques, such as Deepfool. Moreover, the sample diversification strategy is designed to improve the transferability of adversarial perturbations in black-box attack scenarios, where two types of common audio-processing operations are considered, including noise addition and moving picture experts group audio layer III (MP3) compression. Furthermore, the perturbation ensemble method is applied to further improve the attacks’ transferability by integrating perturbations of different detection networks with heterogeneous architectures. Consequently, the single universal adversarial perturbation can enhance different cover audios against a CNN-based detection network. Extensive experiments have been conducted, and the results demonstrate that the average missed-detection probabilities of the proposed method are higher than those of the state-of-the-art methods by 7.3% and 16.6% for known and unknown detection networks, respectively. It verifies the efficiency and transferability of the proposed methods for the cover enhancement of audio steganography.
Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

As the development of the modern economy is increasingly inseparable from credit support, the traditional credit investigation mode has yet to meet this demand. Because of the difficulties in conventional credit data sharing among credit investigation agencies, poor data portability, and centralized supervision, this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain. Given the problems such as difficult data sharing, difficult recovery of damaged data, and accessible data leakage between institutions and users with non-traditional credit investigation data other than credit, this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope. Based on the above two solutions, this paper designs a double blockchain credit data-sharing platform based on the “public chain + alliance chain” from credit investigation agencies’ and visiting subjects’ perspectives. The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision, uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus, uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data, data leakage, and other issues. After the upload test, the average upload speed reaches 80.6 M/s. The average download speed of the system is 88.7 M/s after the download test. The multi-thread stress test tests the linkage port on the system package, and the average response time for the hypertext transfer protocol (HTTP) is 0.6 ms. The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.
Text Summarization Using Natural Language Processing

The availability of information today accessible in digital form has accelerated. Retrieving useful document from such large pool of information gets difficult. So, to summarize these text documents is very crucial. Text summarization is a process of minimizing the original source document to get essential information of that document. It eliminates the redundant, less important content and provides you with the vital information in a shorter version usually half a length of the original text. Creating a manual summary is a very time-consuming task. Automatic summarization helps in getting the gist of information present in a particular document in a very short period. In the comparison of all Indian regional languages, there is very less amount of work done for summarization of Hindi documents. This paper presents an effective way to summarize using a Text Rank algorithm. It focuses on summarizing single Hindi text document at a time based on natural language processing (NLP).
Case Study on a High-quality Information Technology Lesson Based on iFIAS

With the rapid development of artificial intelligence, big data, and education informatization, information technology education has been playing an important role in improving student's information competences and cultivating student's computational thinking. Information technology teachers can develop professional skills by the observation and analysis of high-quality information technology lessons. Therefore, this study selects a high-quality information technology lesson from the campaign which is held by Ministry of Education, analyzes its teaching behaviors based on improved Flanders Interaction Analysis System(iFIAS), as well as summarizes the key characteristics and problems, aiming to provide references for information technology teachers to improve teaching quality.
CDHD: Contrastive Dreamer for Hint Distillation

Replaying previous training data is the most effective approach for Class-Incremental Learning (CIL), with its performance bounded by data availability. Therefore, many recent studies consider the Data-Free Class-Incremental Learning (DFCIL) problem that requires no previous data. However, the existing methods do not consider synthesising data of heterogeneity, thus limiting models’ generalizability. Such homogenous images further hinder the knowledge distillation process when regularising only the deeper layers close to the output, resulting in catastrophic forgetting. To address these issues, we present CDHD: a contrastive dreamer for hint distillation. Our approach starts with training a generator for data synthesis. A model inversion technique is introduced to obtain a generator capable of producing heterogeneous images from the classifier by imposing the ContRastive Loss. Moreover, to better transfer the previous knowledge to the current model, we force the teacher network to provide more general knowledge to its students by enforcing the Hint Loss in shallower layers rather than only in deeper ones. We validate the performance of CDHD on CIFAR-100 for various tasks and compare it against the SOTA baseline for DFCIL, demonstrating our superiorities and thus constituting a new benchmark.
Not All Classes are Equal: Adaptively Focus-Aware Confidence for Semi-Supervised Object Detection

Semi-supervised object detection (SSOD) is a significant application of Semi-supervised learning to further improve object detectors but suffers more seriously from confirmation bias and error accumulation caused by the classes imbalance. Existing SSOD approaches have attempted to address this issue but fails to consider dynamically changed detection difficulties of different classes for detectors. In this paper, we propose adaptively focus-aware confidence, which treats object classes differently. Predictions generated from the teacher and student models are stored in a memory dictionary, and the differences between them are utilized to adaptively perceive learning statuses. Based on this, confidence thresholds are flexibly assigned and adjusted for different classes. Extensive experiments are conducted on MS-COCO benchmark dataset with multiple protocols and our SSOD framework out-performs the state-of-the-art competitors by a large margin.
Detail-Aware Uncalibrated Photometric Stereo

Photometric stereo is the problem of jointly inferring the 3D reconstruction, reflectance, lighting and specularities of an object from a set of visual signals. Recently, some variational, uncalibrated, unsupervised and unified formulations have provided robust solutions to the problem while reducing the prior knowledge about the shape geometry or the lighting conditions. Unfortunately, these approaches cannot still produce solutions with an ample variety of details in the shape. That is mainly due to the non-convex and non-linear nature of the problem which requires the best initialization as possible. In this context, we propose a fully interpretable formulation that combines a physically-aware image formation model under perspective projection with a minimal detail-aware initialization and that it can handle general lighting. As a result, our formulation can consider multiple scenarios composed of unknown complex geometries and lighting patterns. Experimental results on challenging synthetic and real datasets show the effectiveness of our approach to capture more fine details, outperforming state-of-the-art techniques in terms of 3D reconstruction.
Robust Watermarking Scheme in Encrypted Domain Based on Integer Lifting Wavelet Transform and Compressed Sensing

Watermarking schemes in plaintext domain usually suffer from high exposure risk of the cover signal. Data hiding in encrypted image (DHEI) can guarantee the security of the cover signal by embedding secret information into encrypted signal. Based on the above, we propose a robust watermarking scheme in encrypted domain based on integer lifting wavelet transform (LWT) and compressed sensing (CS). Firstly, the signal owner uses Kronecker CS progressive reconstruction and global random permutation (GRP) to realize the reserving room and encryption. Subsequently, the watermark is preprocessed by integer LWT, and compressed by CS. Then, the watermark is embedded by multiple embedding method (MEM) and most significant bits (MSBs) replacement in the cover signal. Experiments show that the proposed scheme can extract the watermark stably under a variety of attacks while the cover signal achieves satisfactory reconstruction.