Reviewerly/exHarmony huggingface dataset:
Monitoring method of electricity safety status at customer side based on Internet of Things perception

To solve the problem of low classification accuracy of electricity safety status on the customer side, which affects the monitoring accuracy, this paper studies the monitoring method of electricity safety status on the customer side combined with the perception technology of the Internet of Things. The power consumption monitoring data on the user side is collected by using the IoT perception. The TAN network topology is taken as the core to construct a security state identification model, and the model operation result is output as the monitoring result to realize the power consumption security state monitoring. Through experimental verification, the new monitoring method has higher accuracy in safety state classification and higher monitoring accuracy, which can comprehensively improve the perception, monitoring and service capabilities of customer-side electricity safety, and enhance the level of user electricity safety.
Textual Dissection of Twitter Reviews using Deep learning Algorithms

In the most recent period, the discipline of Analyzing the emotions expressed on Twitter has grown rapidly, with several studies supporting the utilization of algorithms based on machine learning techniques to analyze tweets and extract user sentiments about a certain subject.This work intends to do a comprehensive analysis of the emotional tone of tweets by making use of ordinal regression as well as other machine learning approaches.Following the completion of the preprocessing of the tweets, the suggested technique implements a method for the extraction of features in order to generate a reliable feature.After that, a number of different criteria are used in order to rank and assign weights to these attributes.The emotional states can be detected through the utilization of several techniques, such as multinomial logistic regression using SoftMax, support vector regression (SVR), decision trees (DTs), and random forests (RF).The software makes advantage of NLTK corpora resources that are open to the public, namely a Twitter dataset.The experimental findings indicate that the proposed approach for recognizing ordinal regression using methods derived from machine learning is accurate to a high degree.Additionally, it would seem that Decision Trees performs better than any other algorithm that was tested.
Signals of global symmetry are important for abnormality detection in mammograms

Expert radiologists can detect the "gist of abnormal" in bilateral mammograms even three years prior to onset of cancer. However, their performance decreases if both breasts are not from the same woman, suggesting the ability to detect the abnormality is partly dependent on a global signal present across the two breasts. We aim to detect this implicitly perceived "symmetry" signal by examining its effect on a pre-trained mammography model.A deep neural network (DNN) with four mammogram view inputs was developed to predict whether the mammograms come from one woman, or two different women as the first step in investigating the symmetry signal. Mammograms were balanced by size, age, density, and machine type. We then evaluated a cancer detection DNN's performance on mammograms from the same and different women. Finally, we used textural analysis methods to further explain the symmetry signal.The developed DNN can detect whether a set of mammograms come from the same or different woman with a base accuracy of 61%. Indeed, a DNN shown mammograms swapped either contralateral or abnormal with a normal mammogram from another woman, resulted in performance decreases. Findings indicate that abnormalities induce a disruption in global mammogram structure resulting in the break in the critical symmetry signal.The global symmetry signal is a textural signal embedded in the parenchyma of bilateral mammograms, which can be extracted. The presence of abnormalities alters textural similarities between the left and right breasts and contributes to the "medical gist signal."
HUSP-SP: Faster Utility Mining on Sequence Data

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low-utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this article, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely, discovering high-utility sequential patterns with the seqPro structure (HUSP-SP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns’ utilities and upper-bound values. Furthermore, a new upper bound on utility, namely, tighter reduced sequence utility and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.
The Journal of Visualization and Interaction

The Journal of Visualization and Interaction (JoVI) is a venue for publishing scholarly work related to the fields of visualization and human-computer interaction. Contributions to thejournal include research in:• how people understand and interact with information and technology,• innovations in interaction techniques, interactive systems, or tools,• systematic literature reviews,• replication studies or reinterpretations of existing work,• and commentary on existing publications.Cross-disciplinary work from other fields such as statistics or psychology, which is relevant to the fields of visualization or human-computer interaction is also welcome.
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions

One surprising trait of neural networks is the extent to which their connections can be pruned with little to no effect on accuracy. But when we cross a critical level of parameter sparsity, pruning any further leads to a sudden drop in accuracy. This drop plausibly reflects a loss in model complexity, which we aim to avoid. In this work, we explore how sparsity also affects the geometry of the linear regions defined by a neural network, and consequently reduces the expected maximum number of linear regions based on the architecture. We observe that pruning affects accuracy similarly to how sparsity affects the number of linear regions and our proposed bound for the maximum number. Conversely, we find out that selecting the sparsity across layers to maximize our bound very often improves accuracy in comparison to pruning as much with the same sparsity in all layers, thereby providing us guidance on where to prune.
Joint Optimization of Measurement Point Intelligent Selection and End-to-End Network Traffic Calculation in Datacenters

The effective design and management of data centers needs to follow the end-to-end traffic characteristics of data center networks (DCNs). However, directly measuring the end-to-end traffic of the network requires huge software and hardware costs. Since the particularity of the structure of DCNs, the flow estimation method used in the traditional computer network cannot be applied to existing DCNs. In this paper, we study the end-to-end traffic calculation of cloud computing DCNs. We propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> , which is an intelligent end-to-end traffic inference algorithm based on network tomography. Only using SNMP (simple network management protocol) data generally supported by switches, end-to-end traffic information can be calculated quickly and accurately. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> first devices a network traffic measurement point intelligent selection scheme based on node weighting. It first assigns weight to nodes through node criticality, and then uses node weighted incidence matrix approximation algorithm to calculate initial solution. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> then designs a network tomography method suitable for cloud computing network traffic calculation. It uses the time correlation of data center traffic to model the algorithm problem into a linear state space model, and finally calculates the traffic carried by each path through the improved Kalman filtering algorithm. Our evaluation and analysis demonstrate that <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> can effectively use the extracted coarse-grained traffic characteristics, and greatly improve the accuracy of the calculation on the premise of ensuring the computational efficiency.
Dynamic Adaptive Hierarchical N-Level Hierarchy Optimization for Electric Power System Problems

Many complex problems in the real world that contain a large number of constraints, especially circuit system problems. We design the Dynamic Adaptive Hierarchical N-Level Hierarchy Optimization (DAH-NLHO) algorithm based on the hierarchy affine of populations in nature. Its hierarchy levels can change dynamically and adaptively following the dimensionality of the problem and the number of constraints. Comparative tests were performed on top of the latest CEC2020 benchmark test set. The final experimental results demonstrate the effectiveness and feasibility of dynamic adaptive hierarchy levels.
An adaptive black insertion display method based on human eye activity for virtual reality

Based on the existing time domain convolution module, attention mechanism, relationship network and other artificial intelligence technologies, the existing virtual reality products are aimed at the problems such as picture dragging and eye fatigue caused by low refresh rate, and the existing virtual reality products do not set sleep mode for people with sleep disorders. This paper proposes an adaptive black insertion display technology based on human eye activity. Users can switch game mode and sleep mode according to their own needs.
Integrate the key persona information and context to improve the performance of multi round dialogue generation

In multi-round dialogue systems, we can easily find that the final reply is closely related to two points, one is the context of the dialogue, the other is the persona characteristics. But not all characters and contextual information will affect the final reply, because the final reply may only be related to some crucial characters and contextual information,the indiscriminate use of all information may even have a negative impact on the generated dialogue. So it is necessary to extract and utilize key characters and contextual information to improve the quality of the final generated response. In this paper, we show how to solve this problem through our new model and methods. Specifically, our new model consists of two parts: encoder and decoder. The encoder is mainly used to encode personas, contexts and historical responses, and the decoder generates corresponding words from the vocabulary. Then, the weight of the character and context is updated through the multi-head self-attention mechanism to affect the response generated by the decoder. The experimental results show that compared with the baseline models, our model and methods have improved in terms of metric-based evaluation.
Attaining Real-Time for Microscopic Images Using GAN

In the last years, several deep learning models, especially generative adversarial networks (GAN) have received a lot of attention to perform the task of single image super-resolution (SISR). These methods build up an end-to-end framework, which shows super resolution (SR) for a given low resolution (LR) image in a single step and achieves state-of-the-art performance. This paper develops an application which can perform super-resolution microscopy in real-time in a standard GPU. In this work, we propose a speed up in training process of the network. Furthermore, we suggest changes to an existing super resolution network to improve the results. We then explore the possibility of the trained network to produce high-resolution HR outputs for different domains. Finally, we evaluate our model on various images and videos to show that it performs better than the baseline network and other approaches and can perform inference in real time, thus opening its applications in different areas like low-end benchtop and even mobile microscopy.
Image defogging algorithm and artificial intelligence technology innovation application in digital media image processing

Contemporary digital media have been promoted under the leadership, for video image capture and processing/using artificial intelligence technology to classify video content and other technologies have been effectively applied. Facing the current era of short video outbreak, image processing technology and digital media technology occupy an important position in the application. Image processing technology can recognize video content through video capture and processing. However, it can be found in the practical application research that many short videos have the problem of low definition, so the captured image is not clear enough, and the recognition and resolution of the image bring some adverse effects. In addition, the algorithms of image to fog of digital media can also play the role of "rescue," for some not to retrieve video and image information, through the image to fog algorithm can fix the problems, and make the image more clear, thus improve the precision of image and recognition, image information acquisition. It can be seen that the combination of image dehazing algorithm and artificial intelligence can more effectively strengthen the image processing of digital media, and facilitate the protection and classification of image data.
Asymmetric channel phase matching quantum key distribution

The phase-matching protocol is a practical and promising protocol that can surpass the linear key generation rate boundary. However, classical phase-matching quantum key distribution requires the channel attenuation between communicating parties to be symmetric. In practice, channels used are often asymmetric, owing to geographical reasons in a quantum key distribution network. To enhance the practicality of phase-matching, this paper proposes an asymmetric phase-matching protocol based on the classical framework and establishes a relevant mathematical simulation model to study the influence of channel asymmetry on its performance. The simulation results show that channel asymmetry significantly affects the count rate, error rate, gain, and quantum bit error rate (QBER), ultimately, system performance. As the channel attenuation difference increases, the system performance decreases and the rate of decrease accelerates. Key generation becomes impossible when the channel attenuation difference exceeds 4 dB. Although the decoy-state scheme cannot change the system's tolerance to channel attenuation difference, when the channel attenuation difference is large, the increasing of the number of decoy states significantly can improve system performance, with a three-decoy-state phase-matching protocol outperforming a two-decoy-state protocol. Considering the limited data length, the system performance is improved as the data length increases, and the tolerance to channel attenuation differences gradually increases. When the data length exceeds 10&lt;sup&gt;12&lt;/sup&gt;, this improvement does not continue any more. The system cannot break through the boundary of linear key generation rate when the channel attenuation difference is 2 dB and the data length is less than 10&lt;sup&gt;12&lt;/sup&gt;. Comparing with symmetric channels, the system performance improvement is very significant under asymmetric channel conditions as the data length increases.
CRIMINAL FACIAL DETECTION USING DEEP LEARNING

Deep learning models have enhanced picture-based semantic pattern identification.Facial photos can assess a person's emotional state and character traits.Due to this motive, we are applying a variety of deep learning architectures to try to infer criminal tendency from face pictures.This study used a simple convolutional neural network (CNN) architecture and multiple pre-trained CNN designs, including VGG-16, VGG-19, and InceptionV3.We compared these systems' effectiveness in identifying criminal traits from human faces.Deep learning models were tested using the National Institute of Standards and Technology's public database.(NIST).Our initiative only used men's images to avoid misunderstanding.VGG CNN models performed best, even with low data, detecting criminal faces with 99.5% accuracy.This index includes Image
Enabling tradeoffs in privacy and utility in genomic data Beacons and summary statistics

The collection and sharing of genomic data are becoming increasingly commonplace in research, clinical, and direct-to-consumer settings. The computational protocols typically adopted to protect individual privacy include sharing summary statistics, such as allele frequencies, or limiting query responses to the presence/absence of alleles of interest using web services called Beacons. However, even such limited releases are susceptible to likelihood ratio–based membership-inference attacks. Several approaches have been proposed to preserve privacy, which either suppress a subset of genomic variants or modify query responses for specific variants (e.g., adding noise, as in differential privacy). However, many of these approaches result in a significant utility loss, either suppressing many variants or adding a substantial amount of noise. In this paper, we introduce optimization-based approaches to explicitly trade off the utility of summary data or Beacon responses and privacy with respect to membership-inference attacks based on likelihood ratios, combining variant suppression and modification. We consider two attack models. In the first, an attacker applies a likelihood ratio test to make membership-inference claims. In the second model, an attacker uses a threshold that accounts for the effect of the data release on the separation in scores between individuals in the data set and those who are not. We further introduce highly scalable approaches for approximately solving the privacy–utility tradeoff problem when information is in the form of either summary statistics or presence/absence queries. Finally, we show that the proposed approaches outperform the state of the art in both utility and privacy through an extensive evaluation with public data sets.
Methods and Applications of Graph Neural Networks for Fake News Detection Using AI-Inspired Algorithms

Graph data, which often includes a richness of relational information, are used in a vast variety of instructional puzzles these days. Modelling physics systems, detecting fake news on social media, gaining an understanding of molecular fingerprints, predicting protein interfaces, and categorising illnesses all need graph input models. Reasoning on extracted structures, such as phrase dependency trees and picture scene graphs, is essential research that is necessary for other domains, such as learning from non-structural data such as texts and photos. These types of structures include phrase dependency trees and image scene graphs. Graph reasoning models are used for this kind of investigation. GNNs have the ability to express the dependence of a graph via the use of message forwarding between graph nodes. Graph convolutional networks (GCN), graph attention networks (GAT), and graph recurrent networks (GRN) have all shown improved performance in response to a range of deep learning challenges over the course of the last few years.
TraVaG: Differentially Private Trace Variant Generation Using GANs

Process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant. State-of-the-art research mainly focuses on providing privacy guarantees, e.g., differential privacy, for trace variants that are used by the main process mining techniques, e.g., process discovery. However, privacy preservation techniques for releasing trace variants still do not fulfill all the requirements of industry-scale usage. Moreover, providing privacy guarantees when there exists a high rate of infrequent trace variants is still a challenge. In this paper, we introduce TraVaG as a new approach for releasing differentially private trace variants based on Generative Adversarial Networks (GANs) that provides industry-scale benefits and enhances the level of privacy guarantees when there exists a high ratio of infrequent variants. Moreover, TraVaG overcomes shortcomings of conventional privacy preservation techniques such as bounding the length of variants and introducing fake variants. Experimental results on real-life event data show that our approach outperforms state-of-the-art techniques in terms of privacy guarantees, plain data utility preservation, and result utility preservation.
Coverage in Dutch Newspapers of Earthquakes in Italy and Beyond before Lisbon 1755

This chapter discusses tidings about earthquakes retrieved from the digitised Dutch newspapers before the Lisbon Earthquake of 1755. It focuses on how these newspapers reported about previous earthquakes and how detailed their contents were. Other addressed questions: Which parts of the world did they deal with most frequently, did they explain the causes of earthquakes, and what type of media culture do they represent? This chapter demonstrates that readers of early modern Dutch newspapers were already familiar with earthquake reports long before 1755. These papers mainly treated data, although within the existing societal framework. They represented, as far as possible, a media culture of accuracy, although sometimes with a touch of sensationalism. Readers could read dominant opinions only between the lines and draw conclusions for themselves. The widely accepted idea was that earthquakes were punishments from God and that people should repent their sins.
An Edge Visual Incremental Perception Framework Based on Deep Semi-supervised Learning for Monitoring Power Transmission Lines

In recent years, several efforts have been made to develop power transmission line abnormal target detection models based on edge devices.Typically, updates to these models rely on participation of the cloud, which means that network resource shortages can lead to update failures, followed by unsatisfactory recognition and detection performance in practical use.To address this problem, this article proposes an edge visual incremental perception framework, based on deep semisupervised learning, for monitoring power transmission lines.After generation of the initial model using a small amount of labeled data, models trained using this framework can update themselves based on unlabeled data.A teacher-student joint training strategy, a data augmentation strategy, and a model updating strategy are also designed and adopted to improve the performance of the models trained with this framework.The proposed framework is then examined with various transmission line datasets with 1%, 2%, 5%, and 10% labeled data.General performance enhancement is thus confirmed against traditional supervised learning strategies.With the 10% labeled data training set, the recognition accuracy of the model is improved to exceed 80%, meeting the practical needs of power system operation, and thus clearly validating the effectiveness of the framework.