Reviewerly/exHarmony huggingface dataset:
Shrinkable Cryptographic Technique Using Involutory Function for Image Encryption

Cryptography is the technique of hiding and transmitting confidential information via the internet. There are various traditional cryptographic approaches have been presented by different researchers. Still, these techniques have several limitations, including huge computational times (during key generation, encryption, and decryption) and a high level of complexity (during permutation and substitutions). In the current scenario, Lightweight electronic devices (mobile, IoT, Smart Home devices) are becoming increasingly popular today. These devices create massive amounts of data, making it necessary to seek shrinkable and lightweight cryptographic techniques to ensure security. In this paper, we employ the property of an Involutory Function and a Pseudorandom Number Generator (PRNG) for encryption and decryption. The Involutory functions overcome the permutation and combinations encryption process and shrink the cryptographic operations using Functional Based Encryption. The XORed and Bit-Level Encryption processes are used to acquire the encrypted image. The encryption process is examined in terms of Execution Time. The encrypted image is evaluated using standard NIST statistical testing, Correlation coefficient, and Histogram analysis, demonstrating that the encrypted data has excellent statistical properties and security performance in cryptographic applications.
Using Deep Learning to Detecting Deepfakes

In the recent years, social media has grown to become a major source of information for many online users. This has given rise to the spread of misinformation through deepfakes. Deepfakes are videos or images that replace one person's face with another computer-generated face, often a more recognizable person in society. With the recent advances in technology, a person with little technological experience can generate these videos. This enables them to mimic a power figure in society, such as a president or celebrity, creating the potential danger of spreading misinformation and other nefarious uses of deepfakes. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat. This survey focuses on providing a comprehensive overview of the current state of deepfake detection models and the unique approaches many researchers take to solving this problem. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.
Efficient Long-Text Understanding with Short-Text Models

Abstract Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles, and long documents due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch. In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.
Data release for machine learning via correlated differential privacy

Traditional correlated differential privacy technology usually introduces too much noise, reducing data availability. Besides, machine learning often confronts training sets of high-dimensional data, which brings heavy computing overhead. Aiming at the first issue, we design a more reasonable correlation analysis method. This method combines feature matching algorithms with information entropy-based feature importance to accurately calculate the correlated degree of records, reducing data correlation and correlated sensitivity and improving the data's utility. It is a novel evaluation method of the correlation of records that can alleviate the limitations of traditional calculating correlation methods. Based on this method, we provide a data release solution to reduce the data dimensionality and improve the training efficiency of machine learning by combining the maximum information coefficient with differential privacy. Furthermore, we introduce an optimization algorithm based on mutual information to choose the best principal components to improve the efficiency of our data release solution. To demonstrate the proposed solution's effectiveness and performance compared to existing schemes, we conducted experiments on three real-world datasets. The experimental results show that our scheme reduces the data correlation by up to 80% compared to existing schemes. Moreover, the accuracy of machine learning is improved by 10% to 20% for the same privacy budget.
GSN-HVNET: A Lightweight, Multi-Task Deep Learning Framework for Nuclei Segmentation and Classification

Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies accomplish the two tasks by splicing two related neural networks directly, resulting in repetitive computation efforts and a redundant-and-large neural network. Thus, this paper proposes a lightweight deep learning framework (GSN-HVNET) with an encoder-decoder structure for simultaneous segmentation and classification of nuclei. The decoder consists of three branches outputting the semantic segmentation of nuclei, the horizontal and vertical (HV) distances of nuclei pixels to their mass centers, and the class of each nucleus, respectively. The instance segmentation results are obtained by combing the outputs of the first and second branches. To reduce the computational cost and improve the network stability under small batch sizes, we propose two newly designed blocks, Residual-Ghost-SN (RGS) and Dense-Ghost-SN (DGS). Furthermore, considering the practical usage in pathological diagnosis, we redefine the classification principle of the CoNSeP dataset. Experimental results demonstrate that the proposed model outperforms other state-of-the-art models in terms of segmentation and classification accuracy by a significant margin while maintaining high computational efficiency.
Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories, extending from the longest common sub-sequence (LCSS). Moreover, a DBSCAN cluster validity index (DCVI) is proposed to improve the trajectory clustering performance. The DCVI is used to choose the epsilon parameter for DBSCAN. The proposed method is evaluated using two real trajectory datasets: MIT Badge and sCREEN. The experimental results show that the proposed method effectively detects human trajectory anomalies in indoor spaces. With the MIT Badge dataset, the proposed method achieves 89.03% in terms of F1-score for hypothesized anomalies and above 93% for all synthesized anomalies. In the sCREEN dataset, the proposed method also achieves impressive results in F1-score on synthesized anomalies: 89.92% for rare location visit anomalies (τ = 0.5) and 93.63% for other anomalies.
Strengthening evolution-based differential evolution with prediction strategy for multimodal optimization and its application in multi-robot task allocation

Many real-world problems can be considered multimodal optimization problems (MMOPs), which require locating as many global optima as possible and refining the accuracy of the found optima as high as possible. However, there are some issues with existing algorithms for solving MMOPs. For instance, most of the existing methods adopt the greedy selection strategy to select offspring, which may lead some individuals to fall into local optima and the repetitive evaluations for these local optima will exhaust many fitness evaluations (FEs). Moreover, many MMOPs tend to be expensive to evaluate, and the rational allocation of evaluation resources to better deal with MMOPs is a critical challenge within a limited number of FEs. How to allocate FEs reasonably in a whole evolution and how to avoid individuals becoming trapped in local optima are two key problems in solving MMOPs. Therefore, this paper proposes a strengthening evolution-based differential evolution with prediction strategy (SEDE-PS) for solving MMOPs and verifies its performance in a multirobot task allocation (MRTA) problem, which has the following three contributions. First, a neighbour-based evolution prediction (NEP) strategy is proposed to predict the position of individuals in the next generation by using the historical information of individuals as much as possible. Second, a prediction-based mutation (PM) strategy is introduced to accelerate convergence by combining it with the NEP strategy. Third, a strengthening evolution (SE) strategy is proposed to select inferior individuals to evolve them unconditionally several times and make them approach global optima or jump out of local optima. We compare the SEDE-PS with state-of-the-art multimodal optimization algorithms on the widely used CEC’2013 benchmark. The experimental results show that SEDE-PS performs better than, or is competitive with these compared algorithms. Moreover, SEDE-PS is applied to a real-world MRTA problem to further verify the effectiveness of SEDE-PS.
Nine novel ensemble models for solar radiation forecasting in Indian cities based on VMD and DWT integration with the machine and deep learning algorithms

This paper focuses on integrating two popular signal processing techniques, i.e., variational mode decomposition (VMD) and discrete wavelet transform (DWT), with deep learning (DL) and machine learning (ML) algorithms to come up with nine novel ensemble models for solar radiation forecasting. For these ensemble models, we considered five DL algorithms that include gated recurrent units (GRU), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and deep neural network (DNN); two ML algorithms that include artificial neural network (ANN) and support vector regression (SVR). The proposed nine models were tested for seven Indian cities, namely Delhi, Chennai, Hyderabad, Nagpur, Patna, Trivandrum, and Bhubaneshwar. We used root mean square error (RMSE), mean absolute error (MAE), and coefficient-of-determination (R2) metrics for understanding the performance. Observed that the DL-based VMD integration had generated considerably promising results compared to DWT integration. Out of the nine models, VMD-integrated GRU gave the most optimum results for all the cities with RMSE (0.82-1.22), MAE (0.54-1.02), and R2 (0.83-0.93). It's because GRU employs fewer training parameters, which requires less memory and functions faster than any other algorithm. Overall, this study helps Indian cities to forecast solar radiation more effectively with advanced ML and DL algorithms and scope for applying these models elsewhere around the world.
Depth Sensors-Based Action Recognition Using a Modified K-Ary Entropy Classifier

Surveillance system is acquiring an ample interest in the field of computer vision. Existing surveillance system usually relies on optical or wearable sensors for indoor and outdoor activities. These sensors give reasonable performance in a simulation environment. However, when used under realistic settings, they could cause a large number of false alarms. Moreover, in a real-world scenario, positioning a depth camera at too great a distance from the subject could compromise image quality and result in the loss of depth information. Furthermore, depth information in RGB images may be lost when converting a 3D image to a 2D image. Therefore, extensive surveillance system research is moving on fused sensors, which has greatly improved action recognition performance. By taking into account the concept of fused sensors, this paper proposed a novel idea of a modified K-Ary entropy classifier algorithm to map the arbitrary size of vectors to a fixed-size subtree pattern for graph classification and to solve complex feature selection and classification problems using RGB-D data. The main aim of this paper is to increase the space between the intra-substructure nodes of a tree through entropy accumulation. Hence, the likelihood of classifying the minority class as belonging to the majority class has been reduced. The working of the proposed model has been described as follows: First, the depth and RGB images from three benchmark datasets have been taken as the input for the model. Then, using 2.5D cloud point modeling and ridge extraction, full-body features, and point-based features have been retrieved. Finally, for the efficacy of the surveillance system, a modified K-Ary entropy accumulation classifier is optimized by the probability-based incremental learning (PBIL) algorithm has been used. In both qualitative and quantitative experimental results, the testing results have shown 95.05%, 95.56%, and 95.08% performance over SYSU-ACTION, PRECIS HAR, and Northwestern-UCLA (N-UCLA) datasets. The proposed system could apply to various real-world emerging applications like human target tracking, security-critical human event detection, perimeter security, internet security, public safety etc.
Machine Learning Adoption in Blockchain-Based Smart Applications

The development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. Data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. These dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. The assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector Machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). The difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered.
The Integration of Connectivity and System Integrity Approaches using Internet of Things (IoT) for Enhancing Network Security

Machine learning, this same Internet of Things, and other emerging digital technologies aid in improving military uses and aid management in efficiently gathering, analysing, manipulating, and storing enormous volumes of data. Additionally, these technologies help with the storage of information that may be recovered later. However, this poses a serious risk to the organization’s ability to protect the data that is needed to properly increase the network security. It has been stated that IoT may help safeguard the organisation from various cyber-hacks, necessitating the use of various ways to raise security levels. Additionally, the expanded use of IoT improves cooperation and integration across diverse systems and processes in commercial businesses.In order to improve network security, this research is primarily concerned with identifying the critical factors that relate here to integration of connections and system integrity techniques. The scholars used both inferential and descriptive statistics to conduct the study, and it has been further recognised that all the IoT causal factors support strengthening network level of security. The paper is classified in outlining the crucial aspect on which the entity needs to concentrate when using IoT to enhance security by protecting the data.
Training Compact CNNs for Image Classification Using Dynamic-Coded Filter Fusion

The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or to impose a hyperparameter-sensitive sparse constraint on the loss objective to regularize the network training. In this paper, we present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF), to derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Each filter in our DCFF is first given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance. In contrast to simply keeping high-score filters in other methods, we propose the concept of filter fusion, i.e., the weighted averages using the assigned proxies, as our preserved filters. We obtain a one-hot inter-similarity distribution as the temperature parameter approaches infinity. Thus, the relative importance of each filter can vary along with the training of the compact CNN, leading to dynamically changeable fused filters without both the dependency on the pretrained model and the introduction of sparse constraints. Extensive experiments on classification benchmarks demonstrate the superiority of our DCFF over the compared counterparts. For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10. A compact ResNet-50 is obtained with 63.8% FLOPs and 58.6% parameter reductions, retaining 75.60% top-1 accuracy on ILSVRC-2012. Our code, narrower models and training logs are available at https://github.com/lmbxmu/DCFF.
Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal

The development of John Aitchison's approach to compositional data analysis is followed since his paper read to the Royal Statistical Society in 1982. Aitchison's logratio approach, which was proposed to solve the problematic aspects of working with data with a fixed-sum constraint, is summarized and reappraised. It is maintained that the properties on which this approach was originally built, the main one being subcompositional coherence, are not required to be satisfied exactly—quasi-coherence is sufficient, that is near enough to being coherent for all practical purposes. This opens up the field to using simpler data transformations, such as power transformations, that permit zero values in the data. The additional property of exact isometry, which was subsequently introduced and not in Aitchison's original conception, imposed the use of isometric logratio transformations, but these are complicated and problematic to interpret, involving ratios of geometric means. If this property is regarded as important in certain analytical contexts, for example, unsupervised learning, it can be relaxed by showing that regular pairwise logratios, as well as the alternative quasi-coherent transformations, can also be quasi-isometric, meaning they are close enough to exact isometry for all practical purposes. It is concluded that the isometric and related logratio transformations such as pivot logratios are not a prerequisite for good practice, although many authors insist on their obligatory use. This conclusion is fully supported here by case studies in geochemistry and in genomics, where the good performance is demonstrated of pairwise logratios, as originally proposed by Aitchison, or Box–Cox power transforms of the original compositions where no zero replacements are necessary.
Concept drift handling: A domain adaptation perspective

Data stream prediction is challenging when concepts drift, processing time, and memory constraints come into account. Concept drift refers to changes in data distribution over time that reduces prediction systems’ accuracy. We present a method for handling concept drift with a domain adaptation approach (CDDA) in a data stream. The proposed method passively deals with the concept drift by using the domain adaptation approaches with multiple sources while reducing the model execution time and memory consumption. We introduce two variants of CDDA to transfer the information in the multi-source windows to the target window: weighted multi-source CDDA and multi-source feature alignment CDDA. Then, we theoretically study the behavior of CDDA and find the generalization bound of CDDA for the data stream prediction problem. Moreover, an extensive set of experiments conducted on both synthetic and real-world data streams confirms the validity and excellent performance of the proposed approach. Our code is available at https://github.com/mahan66/cdda.
Hacking measurement-device-independent quantum key distribution

Decoy-state measurement-device-independent quantum key distribution (MDI-QKD) is regarded as one of the most promising protocols since it is physically immune to all fatal detection-side attacks. However, the loopholes in the source side still open side channels to eavesdroppers. Previous work has indicated that a system is insecure if its decoy-state modulation is inaccurate, but a practical method for Eve to exploit the inaccurate modulation is still absent. In this work, we propose a practical hacking method and provide a detailed theoretical analysis. Using our method, we experimentally hacked a MDI-QKD system and successfully obtained all final keys. By demonstrating the hacking experiment, we hope to bring attention to source-side security and provide inspiration for the development of protocols with better source-side security.