Reviewerly/exHarmony huggingface dataset:
Compression Noise Reduction via Non-local Filtering with Rectified Regularity for Urban Building Scenes*

In this paper, we propose a novel low-rank based non-local image denoising method for HEVC video compression with the strategy of gathering non-local patches in the rectified domain. Owing to the irreversible quantization, image compression can be considered as adding noises into the original image, causing the distortion between the original image and the de-compressed image. Current non-local collaboration based image denoising methods collect K-nearest patches from image interior with the underlying translational motion model, and restores the corrupted content from the similar image patches. However, these methods are usually not suitable for image content with repeated patterns with different scales due to the viewing perspective. To address this problem, we derive the plane orientation utilizing vanishing points, which can be determined through line segment detection and clustering. Vanishing points are used to construct a specific homographic transformation to model patch correspondence in rectified domain. With this transformation, the regularity in image can be extracted based on SIFT features and mean-shift clustering in the rectified domain, and can provide useful guidance for patch gathering. Thus, our method is especially suitable for urban building scenes. Our experimental results show that the non-local denoising in rectified domain can further improve the average PSNR by up to 0.32dB comparing to those non-local methods on Urban100 dataset compressed by HEVC standard.
Pixel-Wise Quantization for Image Compression

This paper proposes a pixel-wise quantization (PWQ) method, which allows to reduce the quantization parameters (QPs) of simple pixels adaptively for the purpose of enhancing the subjective quality, since the distortions on simple pixels are more noticeable than those on complex pixels. For the pixel-wise prediction in Fig. 1, the pixel-wise reconstruction is implemented and the transformation is disabled, where the symbol “=” (or $^{\prime \prime}\vee^{\prime \prime}/^{\prime \prime}\gt^{\prime \prime}$) means the current prediction is the average value of the left and right reconstructions (or the upper/left reconstruction). And the PWQ method is applied in the same prediction direction and reconstruction order, with adjusting the current pixel QP $(Q_{pixel})$ adaptively by (1), where Q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cb</inf> denotes the current block $\mathrm{Q}\mathrm{P}, T_{pred}$ denotes the predicted texture complexity based on the neighboring reconstruction pixels, and parameters $\delta, Q_{jnd}, Q_{thres}$ and T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">thres</inf> are preseted on the encoder and decoder side. So no additional syntax need to be transmitted in the bitstream. Moreover, for the transformation-off non-pixel-wise prediction, the straightforward extension of the PWQ method is designed to divide the coding block into simple and complex areas based on the above reference pixels, and reduce the pixel QP in simple areas. Qualitative results in Fig. 1 show that, the PWQ method can significantly improve the subjective quality by reducing the distortions on simple pixels, especially in the flat areas near the object edge and between the words on the screen content, and realizes more fine-grained pixel-level quantization compared with the traditional block-level quantization.
Applicability limitations of differentiable full-reference image-quality metrics

More and more visual-quality metrics are being developed to assess the quality of images, but little research has considered their limitations. In this paper, we demonstrate that image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF. We propose a series of neural-network preprocessing models that increase DISTS by up to 34.5%, LPIPS by up to 36.8%, VIF by up to 98.0%, and HaarPSI by up to 22.6% in the case of JPEG-compressed images. However, a subjective comparison of these preprocessed images showed that the visual quality either dropped or remained unchanged, indicating the limited applicability of these metrics. We used a ResNet-like lightweight CNN architecture for preprocessing and the differentiable DiffJPEG algorithm for compression.
An Upgraded Data Security Based on Homomorphic Encryption and Aggregate Signature Method in Wireless Sensor Network

Wireless sensor networks (WSN) have been implemented in nearly every field of use because they offer a solution to practical problems that can also be affordably implemented. The sensor nodes have limited computing resources, weak batteries, and limited storage space. The environmental or physical data collected by these nodes is transmitted straight to the BS. The data transfer cost is raised due to the direct data transmission. In addition, the lifetime of sensor networks is shortened because of the rise in energy required for data exchange. As a result, data aggregation is utilized in WSN to lessen the burden of transmission costs and lengthen the useful life of the sensor networks. Each sensor node's transmission is encrypted with cipher text generated by the Paillier homomorphic cryptosystem. In addition, the Bilinear aggregate signature method is used to create a digital signature at each sensor node. The cluster head BS is where the aggregation takes place once the cipher text and signature have been combined. Before deciding whether to accept or reject the message, the BS checks the aggregate signature. The homomorphic cryptosystem saves power because it does not perform intermediate-level or cluster-head decryption. Data integrity, authenticity, and confidentiality are all maintained while using less power with this technology. The Intel laboratory dataset is used in the implementation. When compared to current systems, the proposed SDA method requires less time and energy to calculate.
A Concentrated Energy Consumption Wireless Sensor Network by Symmetric Encryption and Attribute Based Encryption Technique

Wireless sensor networks (WSNs) are increasingly used in a wide variety of settings, including defence, industry, healthcare, and education. Hundreds or even thousands of sensor nodes are spread out across a given area and linked to a central Base Station (BS) in order to keep tabs on the environment. The BS then sends the data out to the users over the internet. The sensor network's adaptability, portability, dependability, and quickness are driving its widespread use across industries. The suggested SHS evaluates the efficiency of well-established symmetric algorithms to see where it stands in the spectrum of security. The Blowfish encryption algorithm was proven to require the least amount of processing power after extensive benchmarking. Therefore, the Blowfish algorithm is selected to protect sensitive medical information. The medical database receives the encrypted health records. Only those with proper permissions should be able to access them. Therefore, the CP-ABE is implemented to regulate access to patient records. The SHS's results on the dataset are compared to those of other existing systems. With SHS, health data may be transmitted to doctors rapidly and securely because it requires less computing time and energy. In addition to these benefits, SHS also offers privacy, authentication, and authorization.
Exploring the Characteristics of Attribution and Countermeasures for Junior High School Students’ Success or Failure in Physics Examinations

Junior high school students; Physical examination; Characteristics of success or failure attribution
Long-distance Information Filtering Network for Compressed Video Quality Enhancement

Restoring high-quality videos from low-quality compressed ones is a crucial research topic in video coding. Most existing methods do not exploit the information in the long-distance compressed frames. Even when they do, these methods ignore the effect of interference information during reconstruction. As shown in Figure 1, we propose a unique Long-distance Information Filtering (LIF) scheme with the 3D-CNN, which enhances compressed videos by mining filtered and valid information from long-distance frames. Specifically, we propose a practical block, Long-distance Feature Extraction (LFE) block, to model the spatio-temporal relationship within a long temporal range. Furthermore, a progressive Information Filtering (IF) module is proposed in LFE to promote artifact removal and texture restoration by capturing a large effective receptive field, which can significantly boost the effect of LIF. We use MFQE 2.0 dataset for training and compare LIF to the following novel VQE methods on 18 standard test sequences of Joint Collaborative Team on Video Coding (JCT-VC) as the test set in Table 1. Extensive experiments demonstrate that our method achieves state-of-the-art performance with nearly 25% of the parameters and half of the training volume, which indicates that our model is more lightweight and more efficient.
Analysis of College Students’ Safety Education Curriculum Reform in Big Data Era

Big data; College students; Safety education course
Rate-Distortion via Energy-Based Models

Rate-distortion theory provides a framework for understanding the limits of source coding. Energy-based models (EBMs), which have a broad range of applications in fields such as physics, statistics, and machine learning, can be used to estimate these limits. In this work, we demonstrate how EBMs can be used to estimate rate-distortion functions, and show that our empirical estimates agree with known closed-form expressions and bounds.
Low-Latency Lossless Compression of Monochrome Medical Images

A common approach for enabling remote radiologists to view medical images is to provide them with secure access to a cloud instance using a web-based application. Such solutions must transcode input images or videos to low latency, lossless bit streams and make them available to remote users. The heart of the solution is the highly efficient, low-latency, and hardware-agnostic compression scheme. This paper surveys potential codecs for the low-latency, lossless compression of 16-bit RAW monochrome medical images and compares the compression ratio and encode time for each. Based on research and performance testing, the paper recommends optimized High Throughput JPEG 2000 (HTJ2K) for this purpose.
Spatio-Temporal Information Fusion Network for Compressed Video Quality Enhancement

Video is often compressed by standard compression algorithms to facilitate storage and transmission. Compressed video will produce artifacts that affect the video quality. How to improve the quality of compressed video during video post-processing has become an important topic in the multimedia field. This paper proposes a Spatio-Temporal Information Fusion Network for quality enhancement of compressed video, as shown in Fig. 1. The algorithm comprises two parts. In the first part, we use 3D convolution to build a U-shaped network to model the temporal dynamics between input frames. We concatenate features with the same spatial resolution from shallow layers to deep layers by skip-connecting merging channels, which helps the local information of shallow- generated features to reach the output. In the second part, we designed a quality enhancement module to fully mine the spatio-temporal information extracted in the first part, cut the feature map on the time dimension t, and then extract the feature map separately on the spatial dimension and refine the feature. The network is trained in an end-to-end manner, and the data sets are selected from the database Xiph (Xiph.org) and VQEG. We use the H.265/HEVC reference software HM16.5 to compress the video to evaluate the performance of the model under different compression levels. The experimental results show that the average PSNR of 18 HEVC standard sequences is improved by 0.88 dB, 0.86 dB, 0.81 dB and 0.72 dB when the quantization parameters(QP) are equal to 37, 32, 27 and 22, respectively, and the number of parameters are only 0.66 million.
Defending Federated Learning Against Model Poisoning Attacks

Federated Learning (FL) is a machine learning framework that allows multiple clients to contribute their data to a single machine learning model without sacrificing their privacy. Although FL addresses some security issues, it is still susceptible to model poisoning attacks where malicious clients aim to corrupt the main learning model by sending poisoned updates. Byzantinerobust methods are defenses that aim to prevent corruption of the main learning model by tolerating a certain number of malicious clients. However, they can only resist a small number of malicious clients. This proposed method uses Gap statistics to determine the optimal number of clusters to cluster clients. This will improve the detection accuracy of malicious clients while preventing the mislcassification of honest clients in a Federated Learning setting. Our experiments so far show us an improvement over the base method.
Locating disparities in machine learning

Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming legislation, practitioners need to locate such disparate outcomes. However, previous literature typically detects disparities through statistical procedures for when the sensitive attribute is specified a priori. This limits applicability in real-world settings where datasets are high dimensional and, on top of that, sensitive attributes may be unknown. As a remedy, we propose a data-driven framework called Automatic Location of Disparities (ALD) which aims at locating disparities in machine learning. ALD meets several demands from industry: ALD (1) is applicable to arbitrary machine learning classifiers; (2) operates on different definitions of disparities (e.g., statistical parity or equalized odds); (3) deals with both categorical and continuous predictors even if disparities arise from complex and multi-way interactions known as intersectionality (e.g., age above 60 and female). ALD produces interpretable audit reports as output. We demonstrate the effectiveness of ALD based on both synthetic and real-world datasets. As a result, we empower practitioners to effectively locate and mitigate disparities in machine learning algorithms, conduct algorithmic audits, and protect individuals from discrimination.
Preserving the Privacy in IoT Medical data in Edge Computing using Modified AES Model with Discrete-Time Alternating Quantum Walk

The rapid growth of IoT edge devices has caused in an unprecedented explosion of data volume. Extremely often, the created data is gathered and disseminated via edge-based IoT structures. When hostile IoT devices make requests to steal private data from a cloud storage system via edge nodes, the data-sharing privacy exposure issue becomes even more concerning. We provide evolutionary privacy protection learning algorithms for an IoT data sharing scheme based on edge computing as a solution to the stated problem. Deep learning of medical models in an IoT-based healthcare scheme is described here via a federated learning approach. Using a combination of the alternating quantum walk (AQW) and the Advanced encryption standard (AES), this research suggests a new method of picture encryption. A keystream generator associated with AQW limits is used to build a likelihood distribution matrix, and the study makes use of quantum features to enhance the AES algorithm. To unlock the AES method, we must first extract certain singular values from the matrix. Elements of the probability distribution matrix stand in for Rcon in the AES algorithm. The AES algorithm's S-box and ShiftRow transformation mapping rules are then jumbled based on the increasing size of the clone probability distribution matrix. The preprocessing is done with a likelihood distribution matrix and a plaintext XOR operation, and the encryption is done with a modified version of the AES algorithm. To prevent an adversary from concluding confidential medical data via assaults like a perfect reconstruction attack, etc., this encryption technique may be applied to better safeguard local models.
Feature Acquisition Using Monte Carlo Tree Search*

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model predictions and acquisition costs and 3) simultaneously optimizing model prediction and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.
Sorting the Digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection

In contemporary email communication, the ever- expanding volume of digital correspondence has ushered in an era where big data plays a pivotal role in addressing the challenge of distinguishing between legitimate (ham) and unsolicited (spam) emails. The primary objective of this paper is the meticulous identification and establishment of criteria for the discrimination between ham and spam emails. To achieve this, the study harnesses data from three distinct datasets, aiming to identify common attributes shared across all emails, irrespective of their classification, while concurrently devising methodologies for precise spam detection. Central to this endeavor is the evaluation of the effectiveness of feature selection techniques, specifically Chi-Square and Pearson Correlation, in elevating the accuracy of email classification. The investigation extends to assessing how the combination of these feature selection techniques with the broader machine learning framework can be optimized. This optimization entails the application of diverse preprocessing techniques to the datasets, all designed to amplify the precision of email classification. Furthermore, this research scrutinizes the performance evaluation metrics employed in the assessment of email classifiers. By conducting comprehensive experiments, the study identifies optimal classifiers based on rigorous evaluation metrics. This contributes valuable insights to the toolkit of techniques for proficient email classification within the realm of big data analysis.
Research on the Model of Complex Network Based Key Information Extracting in Power Engineering Technical Transformation and Overhaul Project

A huge amount of data is generated in power system every year, which covers the information in different aspects such as grid production, operation, customer service and maintenance, reflecting the actual production and operation of power grid. However, the information contained in it has not been fully mined. Therefore, it becomes a major concern among managers to extract key information, specifically speaking, locating the emphases from the huge information system, "extracting" the information and "getting" the key information so as to improve the working efficiency. In this paper, the author carries out research on key information extraction in power engineering technical transformation and overhaul projects based on relevant knowledge such as text mining and complex network, exactly digging out the key information through "algorithm parsing and data extracting", which provides data basis for the effective implementation of power engineering technical transformation and overhaul in the future.
NLP Based Question Answering System

The proliferation of data in various domains has increased the significance of Question Answering Systems (QAS), which utilize Natural Language Processing (NLP) techniques to comprehend and respond to natural language queries. This review paper provides a comprehensive overview of recent advancements in QAS that employ NLP techniques. However, this thesis concentrates on specific QA scenarios where the answer is explicitly present in the text source. The objective is to build a QA system that can extract answers from plain text sources quickly, precisely, and accurately, using only a single sentence or a fragment of a sentence. The paper discusses the obstacles involved in creating QAS and how NLP techniques can overcome them. The paper proposes a question answering system that combines extractive and abstractive summarization methods to produce brief and accurate answers to user questions. The system uses keyword extraction and classification to identify pertinent information from a collection of documents and then combines extractive and abstractive summarization approaches to create a comprehensive and concise response. The research findings indicate that the proposed system has significant potential to enhance the effectiveness and efficiency of question answering systems in a range of applications.
Method for creating privacy-preserving information using Probabilistic Latent Semantic Analysis

In recent years, rapid digitization and technological advances have enabled governments and companies to utilize various types of personal information, including environment, medical, and lifestyle. Privacy protection is an important issue in the utilization of personal information, and anonymization methods such as k-anonymization and 1-diversity have been proposed in the past. However, to utilize data effectively, it is also important not to lose the usefulness of the information. Privacy protection and preserving the usefulness of information are in a trade-off relationship, and balancing these two aspects has been a difficult problem. Recently, microaggregation using clustering algorithms has been attracting attention as an anonymization method that ensures the usefulness of the data while protecting privacy. In this study, we proposed a new microaggregation method based on iterative processing using probabilistic latent semantic analysis and evaluated the proposed method based on the use case. The use case is a classification of social isolation and an analysis of the relationship with physical frailty risk using the classified groups. Compared to microaggregation using quasi-identifiers and microaggregation based on a conventional clustering algorithm, the proposed method was able to find social isolation characteristics obtained in the raw data with higher accuracy. In addition, in a more detailed analysis focusing on a population with certain characteristics, the proposed method was able to replicate the analysis results obtained using the raw data and to make appropriate assessments of the health risks.