Reviewerly/exHarmony huggingface dataset:
Research on status information monitoring of power equipment based on Internet of Things

Power equipment is an inseparable and important part of the power system, and its operation largely determines the operation of the power grid. With the continuous promotion of the application of the Internet of Things technology in the power field, the monitoring of the status information of power equipment based on the Internet of Things has high research value. In order to explore the monitoring method of power equipment status information based on the Internet of Things, this article combines work practice to analyze the data information, tasks, and technical methods of power equipment status monitoring, conducts detailed research on primary equipment and secondary equipment, and formulates the power equipment status information monitoring strategy and method based on the Internet of things.
Teacher-student collaborative knowledge distillation for image classification

A single model usually cannot learn all the appropriate features with limited data, thus leading to poor performance when test data are used. To improve model performance, we propose a teacher-student collaborative knowledge distillation (TSKD) method based on knowledge distillation and self-distillation. The method consists of two parts: learning in the teacher network and self-teaching in the student network. Learning in the teacher network allows the student network to use knowledge from the teacher network. Self-teaching in the student network is to build a multi-exit network based on self-distillation and provide deep features as supervised information for training. In the inference stage, we use ensembles to vote on the classification results of multiple sub-models in the student network. The experimental results demonstrate the superior performance of our method compared with a traditional knowledge distillation method and a self-distillation-based multi-exit network.
A Tangible Spherical Proxy for Object Manipulation in Augmented Reality

In this paper, we explore how a familiarly shaped object can serve as a physical proxy to manipulate virtual objects in Augmented Reality (AR) environments. Using the example of a tangible, handheld sphere, we demonstrate how irregularly shaped virtual objects can be selected, transformed, and released. After a brief description of the implementation of the tangible proxy, we present a buttonless interaction technique suited to the characteristics of the sphere. In a user study (N = 30), we compare our approach with three different controller-based methods that increasingly rely on physical buttons. As a use case, we focused on an alignment task that had to be completed in mid-air as well as on a flat surface. Results show that our concept has advantages over two of the controller-based methods regarding task completion time and user ratings. Our findings inform research on integrating tangible interaction into AR experiences.
The effect of innovation components on organisational performance: case of the governorate of Golestan Province

Innovation is a combination of the overall success of an organisation because of its efforts to modernise, improve and apply various aspects of innovation in the organisation. The purpose of this study is to investigate the effect of innovation components on organisational performance in the Golestan Province. This is an applied research, which is descriptive-correlative in terms of data collection. Thus, a 32-item structured questionnaire was adapted from the literature for hypotheses testing. The statistical population of the research consists of the management and staff members of the Golestan provincial government. According to Cochran's formula, the research questionnaire was randomly distributed among 94 individuals in the research population. The data were then analysed with the SmartPLS 3 software using the structural equation modelling technique. Based on research findings, service innovation, administrative process innovation and technological process innovation have a significant impact on organisational performance.
Self-Supervised Low-Light Image Enhancement Using Discrepant Untrained Network Priors

This paper proposes a deep learning method for low-light image enhancement, which exploits the generation capability of Neural Networks (NNs) while requiring no training samples except the input image itself. Based on the Retinex decomposition model, the reflectance and illumination of a low-light image are parameterized by two untrained NNs. The ambiguity between the two layers is resolved by the discrepancy between the two NNs in terms of architecture and capacity, while the complex noise with spatially-varying characteristics is handled by an illumination-adaptive self-supervised denoising module. The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment. Extensive experiments show that the proposed method not only outperforms existing non-learning-based and unsupervised-learning-based methods, but also competes favorably with some supervised-learning-based methods in extreme low-light conditions.
Towards Interpretability of Segmentation Networks by Analyzing DeepDreams

Interpretability of a neural network can be expressed as the identification of patterns or features to which the network can be either sensitive or indifferent. To this aim, a method inspired by DeepDream is proposed, where the activation of a neuron is maximized by performing gradient ascent on an input image. The method outputs curves that show the evolution of features during the maximization. A controlled experiment shows how it enables to assess the robustness to a given feature, or by contrast its sensitivity. The method is illustrated on the task of segmenting tumors in liver CT images.
Shallow and deep feature fusion for digital audio tampering detection

Abstract Digital audio tampering detection can be used to verify the authenticity of digital audio. However, most current methods use standard electronic network frequency (ENF) databases for visual comparison analysis of ENF continuity of digital audio or perform feature extraction for classification by machine learning methods. ENF databases are usually tricky to obtain, visual methods have weak feature representation, and machine learning methods have more information loss in features, resulting in low detection accuracy. This paper proposes a fusion method of shallow and deep features to fully use ENF information by exploiting the complementary nature of features at different levels to more accurately describe the changes in inconsistency produced by tampering operations to raw digital audio. Firstly, the audio signal is band-pass filtered to obtain the ENF component. Then, the discrete Fourier transform (DFT) and Hilbert transform are performed to obtain the phase and instantaneous frequency of the ENF component. Secondly, the mean value of the sequence variation is used as the shallow feature; the feature matrix obtained by framing and reshaping of the ENF sequence is used as the input of the convolutional neural network; the characteristics of the fitted coefficients are obtained by curve fitting. Then, the local details of ENF are obtained from the feature matrix by the convolutional neural network, and the global information of ENF is obtained by fitting coefficient features through deep neural network (DNN). The depth features of ENF are composed of ENF global information and local information together. The shallow and deep features are fused using an attention mechanism to give greater weights to features useful for classification and suppress invalid features. Finally, the tampered audio is detected by downscaling and fitting with a DNN containing two fully connected layers, and classification is performed using a Softmax layer. The method achieves 97.03% accuracy on three classic databases: Carioca 1, Carioca 2, and New Spanish. In addition, we have achieved an accuracy of 88.31% on the newly constructed database GAUDI-DI. Experimental results show that the proposed method is superior to the state-of-the-art method.
A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%.
An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.
Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography

Main coronary segmentation from the X-ray angiography images is important for the computer-aided diagnosis and treatment of coronary disease. However, it confronts the challenge at three different image granularities (the semantic, surrounding, and local levels). The challenge includes the semantic confusion between the main and collateral vessels, low contrast between the foreground vessel and background surroundings, and local ambiguity near the vessel boundaries. The traditional hand-crafted feature-based methods may be insufficient because they may lack the semantic relationship information and may not distinguish the main and collateral vessels. The existing deep learning-based methods seem to have issues due to the deficiency in the long-distance semantic relationship capture, the foreground and background interference adaptability, and the boundary detail information preservation. To solve the main coronary segmentation challenge, we propose the progressive perception learning (PPL) framework to inspect these three different image granularities. Specifically, the PPL contains the context, interference, and boundary perception modules. The context perception is designed to focus on the main coronary vessel based on the semantic dependence capture among different coronary segments. The interference perception is designed to purify the feature maps based on the foreground vessel enhancement and background artifact suppression. The boundary perception is designed to highlight the boundary details based on boundary feature extraction through the intersection between the foreground and background predictions. Extensive experiments on 1085 subjects show that the PPL is effective (e.g., the overall Dice is greater than 95%), and superior to thirteen state-of-the-art coronary segmentation methods.
An Overview of Blockchain-Based Electronic Health Records and Compliance with GDPR and HIPAA

The healthcare sector plays a pivotal role in both generating and relying on vast amounts of data, emphasizing the significance of collecting, managing, and sharing information. Technological advancements have facilitated the transformation of healthcare data into electronic health records (EHRs). These digital records are disseminated among various stakeholders, including patients, healthcare professionals, providers, insurance companies, and pharmacies. Given the sensitivity of healthcare information, the assimilation of new technologies is paramount. Blockchain technology, with its immutable nature and decentralized features, has emerged as a promising solution to instigate changes in the healthcare system. In the healthcare domain, where confidentiality is crucial, strict regulations are in place to safeguard patient privacy. Frameworks like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) are designed to mitigate the risks associated with health data breaches. Although blockchain's characteristics, such as enhanced interoperability, anonymity, and access control, can improve the overall landscape of health data management, it is imperative for blockchain applications to adhere to existing regulatory frameworks for practical implementation. This paper delves into the examination of the compliance of blockchain-based EHR systems with regulations like HIPAA and GDPR. Additionally, it introduces a Blockchain-based EHR model specifically crafted to seamlessly align with regulatory requirements, ensuring its viability and effectiveness in real-world scenarios.
Elitism based Multi-Objective Differential Evolution for feature selection: A filter approach with an efficient redundancy measure

The real world data are complex in nature and addition to that a large number of features add more value to the complexity. However, the features associated with the data may be redundant and erroneous in nature. To deal with such type of features, feature selection plays a vital role in computational learning. The reduction in the dimensionality of the dataset not only reduces the computational time required for classification but also enhances the classification accuracy by removing the misleading features. This paper presents a Filter Approach using Elitism based Multi-objective Differential Evolution algorithm for feature selection (FAEMODE) and the novelty lies in the objective formulation, where both linear and nonlinear dependency among features have been considered to handle the redundant and unwanted features of a dataset. Finally, the selected feature subsets of 23 benchmark datasets are tested using 10-fold cross validation with four well-known classifiers to endorse the result. A comparative analysis of the proposed approach with seven filter approaches and two conventional as well as three metaheuristic based wrapper approaches have been carried out for validation. The result reveals that the proposed approach can be considered as a powerful filter method for feature selection in various fields.
Desktop Action Recognition From First-Person Point-of-View

Desktop action recognition from first-person view (egocentric) video is an important task due to its omnipresence in our daily life, and the ideal first-person viewing perspective for observing hand-object interactions. However, no previous research efforts have been dedicated on the benchmark of the task. In this paper, we first release a dataset of daily desktop actions recorded with a wearable camera and publish it as a benchmark for desktop action recognition. Regular desktop activities of six participants were recorded in egocentric video with a wide-angle head-mounted camera. In particular, we focus on five common desktop actions in which hands are involved. We provide original video data, action annotations at frame-level, and hand masks at pixel-level. We also propose a feature representation for the characterization of different desktop actions based on the spatial and temporal information of hands. In experiments, we illustrate the statistical information about the dataset, and evaluate the action recognition performance of different features as a baseline. The proposed method achieves promising performance for five action classes.
Ranking products with IF-based sentiment word framework and TODIM method

Purpose The purpose of this paper is to rank products by combining sentiment analysis (SA) and multiple attribute decision-making (MADM). Design/methodology/approach This research constructs intuitionistic fuzzy (IF)-based sentiment word framework and corresponding computation rules, which aim to calculate the sentiment score of each sentiment phase. Based on intuitionistic fuzzy weighted averaging operator, the authors aggregate the overall performance of each feature for different products. Then, the MADM method can be used, TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) in this paper, to rank product through online reviews. Findings The results of the research show the superiority and applicability of proposed method in ranking products with online reviews. Originality/value This paper proposes IF-based sentiment word framework and corresponding computation rules, which is a novel idea to express both the sentiment orientations (positive, negative and neutral) and emotional intensities. In addition, this research makes full use of knowledge from both experts and online reviewers. Further, attention degree of each feature is considered in the process of calculating weight of different features, which is rarely seen in current studies. This paper makes full use of SA, fuzzy control theory and MADM theory to handle vague information (online comments) and rank alternative products, which can promote future perspectives and developments.
Repeat after us: Syntactic alignment is not partner-specific

Conversational partners match each other’s speech, a process known as alignment. Such alignment can be partner-specific, when speakers match particular partners’ production distributions, or partner-independent, when speakers match aggregated linguistic statistics across their input. However, partner-specificity has only been assessed in situations where it had clear communicative utility, and non-alignment might cause communicative difficulty. Here, we investigate whether speakers align partner-specifically even without a communicative need, and thus whether the mechanism driving alignment is sensitive to communicative and social factors of the linguistic context. In five experiments, participants interacted with two experimenters, each with unique and systematic syntactic preferences (e.g., Experimenter A only produced double object datives and Experimenter B only produced prepositional datives). Across multiple exposure conditions, participants engaged in partner-independent but not partner-specific alignment. Thus, when partner-specificity does not add communicative utility, speakers align to aggregate, partner-independent statistical distributions, supporting a communicatively-modulated mechanism underlying alignment.
R<sup>2</sup>BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.
Semi-supervised Learning for Face Sketch Synthesis in the Wild

Face sketch synthesis has made great progress in the past few years. Recent methods based on deep neural networks are able to generate high quality sketches from face photos. However, due to the lack of training data (photo-sketch pairs), none of such deep learning based methods can be applied successfully to face photos in the wild. In this paper, we propose a semi-supervised deep learning architecture which extends face sketch synthesis to handle face photos in the wild by exploiting additional face photos in training. Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs. We then compose a pseudo sketch feature representation using the corresponding sketch feature patches to supervise our network. With the proposed approach, we can train our networks using a small reference set of photo-sketch pairs together with a large face photo dataset without ground truth sketches. Experiments show that our method achieves state-of-the-art performance both on public benchmarks and face photos in the wild. Codes are available at https://github.com/chaofengc/Face-Sketch-Wild .