Reviewerly/exHarmony huggingface dataset:
An Efficient Verifiable Searchable Encryption Scheme With Aggregating Authorization for Blockchain-Enabled IoT

Blockchain-enabled Internet of Things (IoT) provides a secure sharing of data and resources to the various miners of the IoT network, removes centralized control, and can overcome part of the existing challenges in traditional IoT. However, the IoT ecosystem faces some great challenges in terms of security, such as privacy leaking, eavesdropping, and so on, which seriously impede the deployment of the IoT ecosystem. Verifiable searchable encryption (SE) can make data owners (DOs) in the IoT dispel concerns about privacy and make data users (DUs) believe they get correct search results. For the blockchain-enabled IoT, this article proposes an efficient verification SE scheme with aggregation authorization and trusted revocation. With this scheme, DOs are willing to share their data to DUs for reward in a secure, efficient, and trusted way. For the DU, the DO can generate an aggregating key of a subset of encrypted documents, and then the DU has the privilege to search these documents and to verify the search results. By utilizing the trusted execution environment, the DO can revoke the search privilege of the DU. We present the analysis to show our proposed scheme achieves confidentiality, soundness, and fair payment at the same time. With the performance evaluations, we prove our proposal practical for blockchain-enabled IoT in terms of computational and smart contracts overhead.
Oversampling with Reliably Expanding Minority Class Regions for Imbalanced Data Learning

This paper proposes a simple interpolation Oversampling method with the purpose of Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate minority region around each original minority sample, then exploits this region to further identify those clean subregions without distributing any majority sample. The synthetic samples are only allowed to generate in the clean subregions, so that the regions of the minority class can be broadened reliably. Given that the learning from multiclass imbalanced data is more challenging as compared to two-class scenarios, we also extend OREM to handle multiclass imbalance problems by leveraging an iteration procedure of generating synthetic samples, consequently leading to a multiclass oversampling algorithm OREM-M. The key peculiarity of OREM-M is to reduce the class overlapping not only between the synthetic minority and original samples, but also from the synthetic samples of different minority classes. In this way, OREM-M ensures that the data of each class after oversampling can be modeled well. In addition, we embed OREM into boosting framework to develop a new ensemble method OREMBoost addressing class imbalance problems. Extensive experiments demonstrate the effectiveness of the proposed OREM, OREM-M, and OREMBoost.
Combining blockchain and machine learning in healthcare and health informatics: An exploratory study

Blockchain has emerged as the latest technology with a reliable and secure platform. Blockchain has become very popular with these facilities in different application areas such as the food industry, supply chain management, energy, government support facilities, and healthcare. There is so much inaccurate information and fraud in the healthcare industry, so we need to explore a secure and trusting environment to better the system. Nonfinancial blockchain provides secure and immutable data sharing to data management in the diverse medical workflow. The healthcare domain is one of those potential research areas where we can improve various healthcare activities. We will design and involve complex and significant data management in the medical sector by implementing systematic blockchain workflow with a smart contract system; the healthcare system is most feasible for all the stakeholders. The viable way to offer blockchain with healthcare services enabled by the Internet of Things (IoT) is to keep a close watch on patient health parameters by individual and in real time using ever-present health monitoring systems. In these, biosignals are acquired from different sensor nodes, and the data are sent to a gateway through a specific communication protocol. With this feature of blockchain, this healthcare system will offer a more secure and trusting environment to the medical claim department as well as support the government with their different facilities. Based on 5G, this healthcare system will revolutionize the proposed plan, and efficiency and interoperability will improve, especially with IoT-based sensors and communication between different nodes in the network. We are also applying smart contracts in this system to help us with the formal agreement among the hospital, patients, and other nodes (Mediclaim, family members, doctors). We will store and send all data to the device, where we will apply artificial intelligence and machine learning techniques, which will help predict a patient’s diseases, especially for doctors. This blockchain-based healthcare system is very efficient with the built-in features and correctly applying a smart contract algorithm or contract between all the nodes. The sensors based on advanced 5G technology work on access control, interoperability, and data integrity.
A Mobile Gesture Interaction Method for Augmented Reality Games Using Hybrid Filters

In most existing games, the separation of gaming operation and feedback regions results in a lower immersion. Therefore, a mobile gesture interaction method is proposed to unify operation and feedback regions to provide users with more immersive interaction experiences. Specifically, the proposed method integrates a Leap Motion (LM) device with a HoloLens (HL) augmented reality (AR) glasses, which provides a natural mobile interaction interface between real bare hands and virtual objects in a real environment. To obtain an accurate mobile interaction between the real bare hand and virtual objects, the proposed method provides an effective registration method between LM and HL AR glasses. To ensure the accuracy and stability of the gesture data, the Kalman filter (KF) and the Particle filter (PF) are applied to estimate the position and the orientation of the hand. The proposed interaction method provides a gaming interaction method similar to real world interaction in daily life, which significantly improves the immersive experience of games. Game experiments and user subjective experience analysis are performed to evaluate the effectiveness of the proposed method. Results show that the proposed method yields a better accuracy and provide users with a more immersive interaction experience.
Asymmetric PAKE with Low Computation and communication

In Crypto’21 Gu, Jarecki, and Krawczyk [25] showed an asymmetric password authenticated key exchange protocol (aPAKE) whose computational cost matches (symmetric) password authenticated key exchange (PAKE) and plain (i.e. unauthenticated) key exchange (KE). However, this minimal-cost aPAKE did not match prior aPAKE’s in round complexity, using 4 rounds assuming the client initiates compared to 2 rounds in an aPAKE of Bradley et al. [13]. In this paper we show two aPAKE protocols (but not strong aPAKEs like [13, 30]), which achieve optimal computational cost and optimal round complexity. Our protocols can be seen as variants of the Encrypted Key Exchange (EKE) compiler of Bellovin and Merritt [7], which creates password-authenticated key exchange by password-encrypting messages in a key exchange protocol. Whereas Bellovin and Merritt used this method to construct a PAKE by applying password-encryption to KE messages, we construct an aPAKE by password-encrypting messages of a unilaterally authenticated Key Exchange (ua-KE). We present two versions of this compiler. The first uses salted password hash and takes 2 rounds if the server initiates. The second uses unsalted password hash and takes a single simultaneous flow, thus simultaneously matching the minimal computational cost and the minimal round complexity of PAKE and KE. We analyze our aPAKE protocols assuming an Ideal Cipher (IC) on a group, and we analyze them as modular constructions from ua-KE realized via a universally composable Authenticated Key Exchange where the server uses one-time keys (otk-AKE). We also show that one-pass variants of 3DH and HMQV securely realize otk-AKE in the ROM. Interestingly, the two resulting concrete aPAKE’s use the exact same protocol messages as variants of EKE, and the only difference between the symmetric PAKE (EKE) and asymmetric PAKE (our protocols) is in the key derivation equation.
An Algorithm-Hardware Co-Design for Bayesian Neural Network Utilizing SOT-MRAM’s Inherent Stochasticity

Probabilistic machine learning plays a central role in the domains such as decision-making and autonomous control benefitting from its ability of representing and manipulating uncertainty about models and predictions. Until now, there are few hardware considerations to address the intensive computation and true random number generation for Bayesian neural network (BayesNN), whose weights are represented by probability distributions. In this article, we propose to apply the local reparameterization trick to alleviate the burden of random number generators (RNGs), which could be implemented by utilizing the inherent random noise of spin-orbit torque magnetic random access memory (SOT-MRAM). Sampling strategies are discussed to significantly reduce the number of operations and parameters of BayesNN. A device-circuit-system benchmark framework is then developed to evaluate the effects of device nonidealities such as the bias and variation of switching probability. The evaluation on the CIFAR-10 dataset suggests that BayesNN could achieve comparable accuracy as conventional deep neural network (DNN) with acceptable hardware overhead but provide much better uncertainty calibration with respect to out-of-distribution (OOD) inputs (rotated images as the example).
Graph Stream Sketch: Summarizing Graph Streams With High Speed and Accuracy

A graph stream is a continuous sequence of data items, in which each item indicates an edge, including its two endpoints and edge weight. It forms a dynamic graph that changes with every item. Graph streams play important roles in cyber security, social networks, cloud troubleshooting systems and more. Due to the vast volume and high update speed of graph streams, traditional data structures for graph storage such as the adjacency matrix and the adjacency list are no longer sufficient. However, prior art of graph stream summarization either supports limited kinds of queries or suffers from poor accuracy of query results. In this paper, we propose a novel <i>G</i> raph <i>S</i> tream <i>S</i> ketch (GSS for short) to summarize the graph streams, which has linear space cost <inline-formula><tex-math notation="LaTeX">$O(|E|)$</tex-math></inline-formula> (E is the edge set of the graph) and high update speed, and supports most kinds of queries over graph streams with controllable errors. Experimental results show that our solution is up to 142 times faster than the adjacency list when processing updates in graph streams, and its memory consumption is as small as <inline-formula><tex-math notation="LaTeX">$30\%$</tex-math></inline-formula> of the adjacency list. Though error is introduced as a trade off in our solution, both theoretical analysis and experiment results confirm that such error is small and controllable. The relative error is below <inline-formula><tex-math notation="LaTeX">$10^{-2}$</tex-math></inline-formula> in edge weight query, and the precision is above <inline-formula><tex-math notation="LaTeX">$90\%$</tex-math></inline-formula> is 1-hop precursor/successor queries.
CArDIS: A Swedish Historical Handwritten Character and Word Dataset

This paper introduces a new publicly available image-based Swedish historical handwritten character and word dataset named Character Arkiv Digital Sweden (CArDIS) (https://cardisdataset.github. io/CARDIS/). The samples in CArDIS are collected from 64,084 Swedish historical documents written by several anonymous priests between 1800 and 1900. The dataset contains 116,000 Swedish alphabet images in RGB color space with 29 classes, whereas the word dataset contains 30,000 image samples of ten popular Swedish names as well as 1,000 region names in Sweden. To examine the performance of different machine learning classifiers on CArDIS dataset, three different experiments are conducted. In the first experiment, classifiers such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbor (k-NN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Random Forest (RF) are trained on existing character datasets which are Extended Modified National Institute of Standards and Technology (EMNIST), IAM and CVL and tested on CArDIS dataset. In the second and third experiments, the same classifiers as well as two pre-trained VGG-16 and VGG-19 classifiers are trained and tested on CArDIS character and word datasets. The experiments show that the machine learning methods trained on existing handwritten character datasets struggle to recognize characters efficiently on the CArDIS dataset, proving that characters in the CArDIS contain unique features and characteristics. Moreover, in the last two experiments, the deep learning-based classifiers provide the best recognition rates.
Recovering Missing Data via Top-k Repeated Patterns for Fuzzy-Based Abnormal Node Detection in Sensor Networks

The stream data acquired by heterogeneous Internet of Things (IoT) sensors are seldom perfect. Most of the collected data streams include either missing or abnormal values caused by various factors such as failure, malfunction, or integrity attacks. Such unreliable data affect the real-time monitoring and compromise the quality of data analysis. By simply analyzing the sensor data via anomaly detection, applications may still be unreliable over the incomplete sensor data streams. Therefore, a reliable method for recovering the missing data and detecting the abnormal ones is indispensable in the IoT environment. This paper presents FuzHD++, a new method to recover missing sensor data and detect abnormal nodes jointly rather than independently. Both elements, data recovery and abnormal node detection, rely on the observed temporal and spatial correlation of sensor data to effectively achieve reliable recovery estimation and detection performance. In the data recovery process, the system adopts a matrix profile to extract the top-<i>k</i> repeated patterns from different sensor nodes. Furthermore, it utilizes the <i>k</i>-nearest neighbor estimator to recover the missing data based on the extracted pattern information of multiple neighbor nodes. During the abnormal node detection process, the system adopts a refined fuzzy rule-based detection method. The refined fuzzy rule-based inference system integrates the expert rules and the rules obtained from sensor data analysis to treat the ambiguity in the decision-making process. We validated the performance of FuzHD++ by comparing it with existing methods using two real-world datasets. Our results showed that the proposed missing sensor data recovery method achieves more than 20% improved root mean square error results than most existing methods. Furthermore, FuzHD++ achieved an average accuracy of 92% for analyzing the sensor readings and detecting the abnormal ones. According to the results, the proposed mechanisms based on the observed temporal and spatial correlation analysis improve the robustness of IoT against data loss and integrity attacks.
TSINIT: A Two-Stage Inpainting Network for Incomplete Text

Although there are lots of studies on scene text recognition, few of them focus on the recognition of the incomplete text. The recognition performance of existing text recognition algorithms on the incomplete text is far from the expected, and the recognition of the incomplete text is still challenging. In this paper, an end-to-end Two-Stage Inpainting Network for Incomplete Text (TSINIT) is proposed to reconstruct the incomplete text into the complete one even when the text is in various styles and with various backgrounds, and the reconstructed text can be recognized by the existing text recognition algorithms correctly. The proposed TSINIT is divided into text extraction module (TEM) and text reconstruction module (TRM) to make the inpainting only focus on the text. TEM separates the incomplete text from the background and character-like regions at the pixel level, which can reduce the ambiguity of text reconstruction caused by the background. TRM reconstructs the incomplete text towards the most possible text with the consideration of the abstract and semantic structures of the text. Furthermore, we build a synthetic incomplete text dataset (SITD), which contains contaminated and abraded text images. SITD is divided into 6 incomplete levels according to the number of pixels in the incomplete regions and the ratio of the incomplete characters to all characters. The experimental results show that the proposed method has better inpainting ability for the incomplete text compared with traditional image inpainting algorithms on the proposed SITD and real images. When using the same text recognition method, the recognition accuracy of the incomplete text on SITD can be improved much more with the help of the proposed TSINIT than with the traditional image inpainting methods.
Spam Email Detection Using Machine Learning and Deep Learning Techniques

Due to the rising use of social media across the globe, the quantity of unsolicited large number of e-mails has increased, necessitating the deployment of a trustworthy system to filter out such problems. Spam emails are the most common problem on the internet. It is simple for spammers to send an email containing spam messages. Spammers have the ability to steal important information from our devices, such as files and contacts. Various deep learning-based word embedding approaches have been developed in recent years. These developments in the area of word representation may be able to provide a solid solution to such issues. In this research, we'll look at a strategy that employs natural language processing (NLP) to detect Spam and Ham News using the Spam Email Dataset. We have used Dense classifier Sequential Neural Network , LSTM and Bi- LSTM and compared accuracies and results. The dataset's efficacy is determined using metrics such as recall, accuracy, and F1-score.The study shows that by employing Bi-LSTM classification, the dataset's overall accuracy improves. The overall work is done in Python and implemented in a Jupyter notebook.
Leveraging CycleGAN in Lung CT Sinogram-free Kernel Conversion

Cancer screening guidelines recommend annual screening with low-dose Computed Tomography (CT) for high-risk groups to reduce lung cancer mortality. Unfortunately, lung CT effectiveness can be strongly impacted by the considered reconstruction kernel. This selection is (almost) final, implying that it is no longer possible to change the used reconstruction kernel once applied, unless a sinogram for the conversion is available. The aim of this paper was to introduce a new sinogram-free kernel conversion in the contest of lung CT imaging. In particular, we wanted to define a procedure able to deal with different acquisition protocols, able to be used in an unpaired images scenario. To this aim, we leveraged a CycleGAN, considering the CT kernel conversion task as a style transfer problem. Results show that the CT kernel conversion can be effectively addressed as a style transfer problem.
Intelligent Identification Method of Insulator Defects Based on CenterMask

Insulator defect is one of the most important factors for the grid power transmission accidents. However, up till now, traditional insulator defect identification methods cannot meet the requirements of high-speed transmission and high pixel ratio aerial image processing. To solve this problem, in this paper, we proposed a novel method based on CenterMask algorithm to achieve intelligent insulator defect identification. First, the overall architecture of the proposed method that entirely relies on the deep learning models is designed to map the relationship between inputs and outputs. Subsequently, the residual connection and effective Squeeze-Excitation module are introduced to improve the original backbone network, thus overcoming the problem of deep network saturation and channel information loss in the feature layer. Finally, the SAG-Mask with spatial attention mechanism is performed to extract the insulator mask image, while the defect identification and location is realized based on the anchor-free FCOS algorithm. At last, we verify the performance of this proposed method by comparing with other benchmarks, including YOLOv4, SSD and Faster RCNN, which shows high accuracy and good robustness of CenterMask-based insulator defect identification algorithm.
Modified Non-local Means Model for Speckle Noise Reduction in Ultrasound Images

In the modern health care field, various medical imaging modalities play a vital role in diagnosis. Among the modalities, Medical Ultrasound Imaging is the most popular and economic modality. But its vulnerability to multiplicative speckle noise is challenging, which obscure accurate diagnosis. To reduce the influence of the speckle noise, various noise filtering models have been proposed. But while filtering the noise, these filters exhibit limitations like high computational complexity and loss of detailed structures and edges of organs. In this article, a novel Non-local means (NLM)-based model is proposed for the speckle reduction of Ultrasound images. The design parameters of the NLM filter are obtained by applying the Grey Wolf Optimization (GWO) to the input image. The optimized parameters and the noisy image are passed to the NLM filter to get the denoised image. The efficiency of this proposed method is evaluated with standard performance metrics. A comparative analysis with existing methods highlights the merit of the proposal.
Decision-Based Attack to Speaker Recognition System via Local Low-Frequency Perturbation

Despite neural network-based speaker recognition systems (SRS) have enjoyed significant success, they are proved to be quite vulnerable to adversarial examples. In practice, the SRS model parameters are not always available. Attackers have to probe the model only via querying, and such decision-based attacking merely relies on the output label is quite challenging. This letter proposes a two-step query-efficient decision-based attack based on local low-frequency perturbation. Specifically, instead of imposing perturbation on the entire audio sample, a local attacking region is firstly sought, confining the perturbed distortion to a local region. Second, considering that the majority of energy concentrates on the low-frequency bands, the proposed method suggests performing perturbation generation in the low-frequency domain. Experimental results demonstrate that, compared with the recent methods, our method could implement target attacking to SRS with a higher attacking success rate, at the cost of much lower queries and adversarial perturbation.