Reviewerly/exHarmony huggingface dataset:
Truthful Incentive Mechanism Design via Internalizing Externalities and LP Relaxation for Vertical Federated Learning

Although vertical federated learning (VFL) has become a new paradigm of distributed machine learning for emerging multiparty joint modeling applications, how to effectively incentivize self-conscious clients to actively and reliably contribute to collaborative learning in VFL has become a critical issue. Existing efforts are inadequate to address this issue since the training sample size needs to be unified before model training in VFL. To this end, selfish clients should unconditionally and honestly declare their private information, such as model training costs and benefits. However, such an assumption is unrealistic. In this article, we develop the first Truthful incEntive mechAnism for VFL, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {TEA}$ </tex-math></inline-formula> , to handle both information self-disclosure and social utility maximization. Specifically, we design a transfer payment rule via internalizing externalities, which bundles the clients’ utilities with the social utility, making truthful reporting by clients be a Nash equilibrium. Theoretically, we prove that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {TEA}$ </tex-math></inline-formula> can achieve truthfulness and social utility maximization, as well as budget balance (BB) or individual rationality (IR). On this basis, we further design a sample size decision rule via linear programming (LP) relaxation to meet the requirements of different scenarios. Finally, extensive experiments on synthetic and real-world datasets validate the theoretical properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {TEA}$ </tex-math></inline-formula> and demonstrate its superiority compared with the state-of-the-art.
Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care

This study used machine learning to develop a 3-year lung cancer risk prediction model with large real-world data in a mostly younger population.Over 4.7 million individuals, aged 45 to 65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment procedures, with an outpatient visit in 2013 were identified in Optum's de-identified Electronic Health Record (EHR) dataset. A least absolute shrinkage and selection operator model was fit using all available data in the 365 days prior. Temporal validation was assessed with recent data. External validation was assessed with data from Mercy Health Systems EHR and Optum's de-identified Clinformatics Data Mart Database. Racial inequities in model discrimination were assessed with xAUCs.The model AUC was 0.76. Top predictors included age, smoking, race, ethnicity, and diagnosis of chronic obstructive pulmonary disease. The model identified a high-risk group with lung cancer incidence 9 times the average cohort incidence, representing 10% of patients with lung cancer. Model performed well temporally and externally, while performance was reduced for Asians and Hispanics.A high-dimensional model trained using big data identified a subset of patients with high lung cancer risk. The model demonstrated transportability to EHR and claims data, while underscoring the need to assess racial disparities when using machine learning methods.This internally and externally validated real-world data-based lung cancer prediction model is available on an open-source platform for broad sharing and application. Model integration into an EHR system could minimize physician burden by automating identification of high-risk patients.
Research on deep learning garbage classification system based on fusion of image classification and object detection classification

With the development of national economy, the output of waste is also increasing. People's living standards are constantly improving, and the problem of garbage pollution is increasingly serious, which has a great impact on the environment. Garbage classification and processing has become the focus of today. This topic studies the garbage classification system based on deep learning convolutional neural network, which integrates the garbage classification and recognition methods of image classification and object detection. First, the data sets and data labels used are made, and then the garbage classification data are trained and tested through ResNet and MobileNetV2 algorithms, Three algorithms of YOLOv5 family are used to train and test garbage object data. Finally, five research results of garbage classification are merged. Through consensus voting algorithm, the recognition rate of image classification is improved to 2%. Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie microcomputer to achieve ideal results.
Quantum <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e836" altimg="si66.svg"><mml:mi>k</mml:mi></mml:math>-fold cross-validation for nearest neighbor classification algorithm

Cross-validation is one of the important tools in machine learning, which is generally used for performance evaluation. It uses different portions of the data to test and train a model on different iterations, which leads to a high computational cost. In this paper, we present a quantum version of k-fold cross-validation to choose a good parameter for the nearest neighbor classification algorithm with a threshold t, where the classification performance is estimated efficiently. With the help of amplitude amplification and estimation, the proposed quantum algorithm achieves a polynomial speedup on the number of samples over its classical counterpart.
Edge-Guided Hyperspectral Image Compression With Interactive Dual Attention

Compressing hyperspectral images (HSIs) into compact representations under the premise of ensuring high-quality reconstruction is an essential task in HSI processing. However, existing compression methods usually encode images by smoothing due to the low-frequency information occupying a prominent component in most images. Consequently, these methods fail to capture sufficient structural information, especially in low bit rates, often causing inferior reconstruction. To address this problem, we propose here an edge-guided hyperspectral compression network, called CENet, to realize high-quality reconstruction. To enhance the structural latent representation ability, the CENet model incorporates an edge extractor neural network into the compression architecture to guide compression optimization by the edge-guided loss. We propose an interactive dual attention module to selectively learn edge features, obtain the most effective edge structure, and avoid additional edge information redundancy at the same time. In the proposed CENet, the edge-guided loss and interactive dual attention module are combined to enhance the comprehensive structure of the latent representation. Concretely, interactive dual attention makes the edge extraction network focus only on moderate boundaries rather than on all edges, which enables savings on the bit rate cost and helps achieve a strong structural representation. As a result, the reconstruction quality is significantly improved. The extensive experiments on seven HSI datasets verify that our model can effectively raise the rate–distortion performance for HSIs of any type or resolution (e.g., yielding an average peak signal-to-noise ratio (PSNR) of 30.59 dB at 0.2382 bpppb, which exceeds the baseline for Chikusei by 10.99%).
mURLi: A Tool for Detection of Malicious URLs and Injection Attacks

SQL databases have been a staple for storing data in enterprises to meet their database requirements. SQL injection has also become a major security threat due to the growth of SQL. Injection attacks occur when the query in the database is modified by malicious data from an unsecure user. The use of NoSQL databases has been growing steadily due to advancements in Big Data and similar domains, and as an alternative to SQL databases. But recent studies have proved that even NoSQL is susceptible to injection attacks. Malicious websites can harm a person using the internet in various ways. Phishing attacks, Spam attacks, Defacement Attacks can occur once a user clicks on a URL. In this research, we propose the tool mURLi, for early detection of Malicious URLs, SQLi and NoSQLi, which pose a large threat to the end-users. To build mURLi, we have used various deep learning models (ANN, BiLSTM, BERT) and machine learning models (Random Forest, XGBoost, Adaboost, KNN) in an attempt to detect these attacks (SQLi, NoSQLi and Malicious URLs) more accurately than traditional methods and have presented a comparative study of the same. The results show that BERT model outperforms the existing tools for SQL and NoSQL attacks with an accuracy of 99.6% and 99.01% respectively, which was achievable due to its pre-training and deep language knowledge. BiLSTM proves to be the best tool for detection of Malicious URL attacks, with the results being 95.2% accurate.
An efficient approach for improving customer Sentiment Analysis in the Arabic language using an Ensemble machine learning technique

The complexity of the Arabic language in terms of morphology, orthography, and dialects renders it more difficult to conduct sentiment analysis for the Arabic language. This issue is made much more challenging by the practice of extracting text features from short communications to evaluate the tone of the communication. On the other hand, the technique for analyzing and assessing sentiment faces a great deal of difficulty. These issues might be hampered by the accurate interpretation of sentiments and identifying the appropriate polarity of sentiment. Sentiment analysis can recognize and extract subjective information from the text. This study intends to investigate the effectiveness of various Machine Learning (ML) techniques in understanding the sentiments conveyed by the Arabic language. In addition, the feature extraction from the dataset was carried out with the help of the Term Frequency-Inverse Document Frequency (TF-IDF). As a consequence of this, the techniques of Adaboost classifier (AC), Support Vector Machine (SVM), Maximum Entropy (ME), Decision tree (DT), and K-Nearest Neighbors (KNN) are utilized in the process of sentiment analysis (SA). In conclusion, a model for ensemble-based sentiment analysis was developed. Compared to other machine learning classifiers cited earlier, we achieved better performance in terms of accuracy, precision, kappa, and ROC AUC-score for ensemble classifiers with 10-fold cross-validation.
PPVerifier: A Privacy-Preserving and Verifiable Federated Learning Method in Cloud-Edge Collaborative Computing Environment

Federated Learning is a distributed machine learning framework, which mainly adopts cloud-edge collaborative computing mode and supports multiple participants to train models without directly sharing local data. However, participants’ sensitive information may still be leaked through their gradients. Besides, incorrect aggregated results returned by the aggregation server may reduce the effect of joint modeling. This article proposes a privacy-preserving and verifiable federated learning method called PPVerifier to support privacy protection and verification of aggregated results in the cloud-edge collaborative computing environment. By the integrating Paillier homomorphic encryption and random number generation technique, all gradients and their ciphertexts can be protected. Meanwhile, an additive secret-sharing scheme is introduced to resist potential collusion attacks among the aggregation server, malicious participants, and edge nodes. Moreover, a verification scheme based on discrete logarithm is proposed, which can not only verify the correctness of aggregated results, but also discover lazy aggregation servers, and the verification overhead can be reduced by over half compared with a bilinear aggregate signature method. Finally, theoretical analysis and experiments conducted on the MNIST data set prove that our proposed method can achieve gradient protection and correctness verification of the aggregated results with higher efficiency.
On MDS codes with galois hulls of arbitrary dimensions

The Galois hulls of linear codes are a generalization of the Euclidean and Hermitian hulls of linear codes. In this paper, we study the Galois hulls of (extended) GRS codes and present several new constructions of MDS codes with Galois hulls of arbitrary dimensions via (extended) GRS codes. Four general methods of constructing MDS codes with Galois hulls of arbitrary dimensions by Hermitian or general Galois self-orthogonal (extended) GRS codes are given. Using these methods, some MDS codes with larger dimensions and Galois hulls of arbitrary dimensions can be obtained. In addition, two new classes of MDS codes with explicit parameters and with Galois hulls of arbitrary dimensions are also constructed. One of the two classes can yield 1-Galois self-orthogonal GRS codes, and more new classes of MDS codes with Galois hulls of arbitrary dimensions can be derived from one of the four general methods we give.
A systematic review on effective energy utilization management strategies in cloud data centers

Abstract Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people to access computer resources on demand. It provides amenities on the pay-as-you-go basis across the data center locations spread over the world. Consequently, cloud data centers consume a lot of electricity and leave a proportional carbon impact on the environment. There is a need to investigate efficient energy-saving approaches to reduce the massive energy usage in cloud servers. This review paper focuses on identifying the research done in the field of energy consumption (EC) using different techniques of machine learning, heuristics, metaheuristics, and statistical methods. Host CPU utilization prediction, underload/overload detection, virtual machine selection, migration, and placement have been performed to manage the resources and achieve efficient energy utilization. In this review, energy savings achieved by different techniques are compared. Many researchers have tried various methods to reduce energy usage and service level agreement violations (SLAV) in cloud data centers. By using the heuristic approach, researchers have saved 5.4% to 90% of energy with their proposed methods compared with the existing methods. Similarly, the metaheuristic approaches reduce energy consumption from 7.68% to 97%, the machine learning methods from 1.6% to 88.5%, and the statistical methods from 5.4% to 84% when compared to the benchmark approaches for a variety of settings and parameters. So, making energy use more efficient could cut down the air pollution, greenhouse gas (GHG) emissions, and even the amount of water needed to make power. The overall outcome of this review work is to understand different methods used by researchers to save energy in cloud data centers.
Towards Single Camera Human 3D-Kinematics

Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future.
Chinese named entity recognition method for the finance domain based on enhanced features and pretrained language models

For some named entities in the Chinese finance domain that are long, with difficult to delineate boundaries and diverse forms of expression, we propose a method based on pretrained language models for named entity recognition with enhanced features. First, the method considers entity boundary delineation and entity classification as two separate tasks and learns enhanced Chinese character features by introducing a gating-based multi-channel attention mechanism to delineate financial entity boundaries on the basis of a pretrained language model. Then, the boundary demarcation results are input into the pretrained language model in the form of mask units for data enhancement. Subsequently, document-level entity-based enhancement features are introduced to construct a finance entity classification model. We experimentally identified the best-performing Chinese pretrained language model from several state-of-the-art models and then embedded it into our method to compare against other benchmark models. The experimental results showed that our model is superior to other benchmark models on the named entity recognition task in the finance domain.
Online relational tracking with camera motion suppression

To overcome challenges in multiple-object tracking (MOT) tasks, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks or transformers to extract interaction features that lead to high computation costs. In this paper, a novel interaction cue based on geometric features is presented aiming to detect occlusion and reidentify lost targets with low computational costs. Moreover, in the majority of algorithms, camera motion is considered negligible, which is a strong assumption that is not always true and can lead to identity (ID) switching or mismatching of targets. In this paper, a method for measuring camera motion is presented that efficiently reduces its effect on tracking. The proposed algorithm is evaluated on MOT17 and MOT20 datasets and achieves state-of-the-art performance on MOT17 with comparable results on MOT20. The code is also publicly available.1
MTNet: Mutual tri-training network for unsupervised domain adaptation on person re-identification

The existing unsupervised domain adaptation (UDA) methods on person re-identification (re-ID) often employ clustering to assign pseudo labels for unlabeled target domain samples. However, it is difficult to give accurate pseudo labels to unlabeled samples in the clustering process. To solve this problem, we propose a novel mutual tri-training network, termed MTNet, for UDA person re-ID. The MTNet method can avoid noisy labels and enhance the complementarity of multiple branches by collaboratively training the three different branch networks. Specifically, the high-confidence pseudo labels are used to update each network branch according to the joint decisions of the other two branches. Moreover, inspired by self-paced learning, we employ a sample filtering scheme to feed unlabeled samples into the network from easy to hard, so as to avoid trapping in the local optimal solution. Extensive experiments show that the proposed method can achieve competitive performance compared with the state-of-the-art person re-ID methods.
Entropic Hardness of Module-LWE from Module-NTRU

The Module Learning With Errors problem ( ) has gained popularity in recent years for its security-efficiency balance,and its hardness has been established for a number of variants. In this paper, we focus on proving the hardness of (search) for general secret distributions, provided they carry sufficient min-entropy. This is called entropic hardness of . First, we adapt the line of proof of Brakerski and Döttling on (TCC’20) to prove that the existence of certain distributions implies the entropic hardness of . Then, we provide one such distribution whose required properties rely on the hardness of the decisional Module- $$\textrm{NTRU}$$ problem.
Digital Assets in the Eyes of Generation Z: Perceptions, Outlooks, Concerns

The recent decade saw an explosion of digital assets and digitalisation of financial services. The present contribution poses several research questions incorporated into a survey questionnaire and grouped into two categories: (1) associations with, knowledge of, and familiarity with notions relevant to digital assets and (2) perceptions of digital assets and attitude towards investing in them. Invitations to participate were sent to a group of 570 random representatives of Generation Z with 387 correctly completed questionnaires employed in the study. The research demonstrated that it was not insufficient funds that posed the greatest barrier to the growth in digital assets investments. The respondents justified their concerns about digital assets with poor knowledge of cryptocurrencies and non-fungible tokens (NFTs). The scepticism is fuelled mostly by the nontangible nature of digital assets (approx. 23%). The respondents most commonly (123, approx. 47%) associated NFTs with digital works of art, virtual objects, and NFT graphics. Blockchain most often brought to the minds of the respondents databases, algorithms, data recording, transaction data transfer, data cloud transactions, cryptocurrencies, cryptography, and decentralised financial systems. The research seems to suggest a certain difficulty with representing (characterising) the digital ecosystem and virtual reality. The media narrative emphasises the intangible nature of the digital ecosystem, often depicting it as impalpable and unreal, which does not help with how prospective investors view it. Some recommendations emerge from the research that should be considered when drawing a strategy for presenting digital assets, cryptocurrencies, and NFT markets.