Reviewerly/exHarmony huggingface dataset:
Modelling of Smart Sensing Device for Sensitive Nanocluster

The report claims that nanocomposites have lately been used to create several smart gadgets with innovative characteristics. Because of their distinctive features, composites oxides have now been studied for many years as air gas sensors. Air testing relies heavily on biosensor. They meet the demand for portable analytical equipment with qualities like sensitivity and robustness, amongst many others, and provide considerable benefits over conventional approaches. The physical features of the capable of detecting materials employed in the manufacturing of nano sensing are essential for determining the effectiveness of the finished product. Trustable nano sensor nodes with important realistic necessities and practicability in the purview of increasing environmental detection and monitoring are expected to be developed. Current micro sensing have become more sophisticated and sensitive as a consequence of recent developments in the area of materials research. Often, they outperform conventional methods in terms of effectiveness.
A Skeleton-Based Deep Learning Approach for Recognizing Violent Actions in Surveillance Scenarios

A novel skeleton-based approach that recognizes specific violent actions (VAs) such as kicking and punching, which are highly relevant in surveillance scenarios, is presented. The method uses a depth sensor for more efficient and accurate depth data acquisition and classifies an action by utilizing the forecasts of an ensemble of Long Short-Term Memory (LSTM) networks, each trained to predict a specific VA. The proposed method offers the advantages of requiring a smaller dataset for training (since only data for a few specific VAs is required and data for non-VAs is not needed) and a lower risk of misclassification (since a separate LSTM network is trained for each VA). The utilization of a compact skeletal representation and a distributed architecture allows the system to operate efficiently bolstering its potential to be practically used in real-world scenarios.
Information Extraction from Web Pages Using Hyperlinks

Web mining is a well-studied topic nowadays. Web mining is a data processing tool that is useful for gathering information from the internet. With the expansion of the Web, people now have access to vast amounts of data. Online structure mining deals with the analysis of web linkages and content. An essential component of web structure mining is the web crawler. A web crawler is an automated software that searches the internet to locate new web pages and to update the content of sites that have already been downloaded. With the aim of helping web crawlers extract fresh connections and data from the sites, we provide the fundamental method for web crawling utilizing a set of seed Urls in this work. We take into account a scenario that concentrates on both the difficulties of redundant Urls and metadata gathering. This study makes use of an algorithm that downloads and browses websites at random. The necessity to rapidly download pages, store them efficiently in a database, and traverse pages is just one of the issues with the design of a web crawler. The architecture of Web Crawler, several of its capabilities, and a basic crawling technique are all described in this article.
A Data Cleaning Method for Industrial Data Flow Based on Multistage Combinational Optimization of Rule Set

With the development of the era of big data, the quality of data has become a growing concern of people. Improving data quality has become a very hot topic at present. In this paper, we propose a data cleaning method for industrial data flow based on multistage combinational optimization of rule set. According to the characteristics of the data, excellent cleaning algorithm is selected for the data. The data is evaluated and the cleaning rules are updated. In the first step, feature detection is carried out on the data, and high-quality data is selected as training samples to match the optimal data cleaning algorithm for them. In the second step, the model uses a random forest algorithm to learn the relationship between data features and data cleansing algorithms, and constructs multi-level filtering rules. In the third step, the data is cleansed and iterated to ensure that the rules are updated automatically. Finally, the model can automatically clean the data with a good cleaning effect. The results show that the method presented in this paper can achieve automatic cleaning effect on real industrial data sets, and the cleaning effect can reach 99% accuracy. This method effectively solves the problem of automatic data cleaning and can be used in the actual industrial data system.
Efficient Text-based Person Search via Single-stage Identity-guided Attribute Parsing and Alignment

Cross-modal text-based person search aims at retrieving target person in a large image gallery by natural language description. This task is quite challenging due to the complex environment of person image acquisition and the semantic gap between different modalities. The existing popular deep learning based models rely heavily on a large amount of labeled data to obtain good performance, which is labor-consuming and not always available in real applications. In order to achieve an effective alignment within and between modalities, additional semantic information or pre-trained network is often introduced to assist human body positioning, which will further shape the increase in training parameters and the decrease in efficiency. To address these problems, we propose a single-stage Identity-guided image-text Attribute Parsing and Alignment network (IAPA). IAPA realizes cross-modal alignment of human body parts in an unsupervised manner through image itself, resulting in great efficiency improvement while maintaining promising accuracy. This is also the first attempt to apply pixel-level supervision to cross-modal person retrieval task. The experiments on the CUHK-PEDES data-set validate the effectiveness and the efficiency of IAPA compared to the other state-of-the-art methods.
Automatic Attendance System Using Artificial Intelligence

The traditional attendance method involves instructors marking registers, which leads to human error and a lot of upkeep. In the existing system, time consumption is a significant problem. We have considered revolutionising it by using new digital techniques, such as FACE RECOGNITION. The proposed framework will result in increased accuracy and little manual labour. To address the issues of the traditional system, the project has been modernised. The proposed system is on face recognition and then recording attendance. The database of all the students in the class is maintained, and attendance is noted when the individual student's face matches one of the faces stored images; otherwise, the face is disregarded, and attendance is not marked. The multiple faces are detected in the camera simultaneously which helps in real time marking system. The proposed framework employed the HAAR features to find the face in the image using the HAAR cascading classifiers and it is implemented in the python.
Collision-free Path Planning in Multi-vehicle Deployments – A Quantum Approach

A crucial factor that can severely limit the success and performance of Multi-Robot Systems (MRS) is the possibility of collisions among the robots in a deployment. The MRS deployment in real-world applications has increased almost exponentially in recent years. Thus, finding efficient and fast methods to identify collision-free paths for MRS environments becomes very important.A common objective in such applications is to generate a collision-free path for all the vehicles between two points and minimize the total distance covered while reaching the destination. Since these problems are known to be NP-hard (i.e., no classical algorithm can solve the problem in polynomial time), a heuristics-based approach is usually applied to obtain an acceptable solution.We derive a novel Quantum Alternating Operator Ansatz (QAOA) construction for collision-free path planning in MRS deployment. A heuristic approach such as the QAOA is well suited for combinatorial optimization problems that need to satisfy multiple hard constraints. We also derive the approximate resource estimates in terms of universal quantum gates for our QAOA constructions.
Passive User Authentication Utilizing Two-Dimensional Features for IIoT Systems

Passive user authentication is critical for the secure operation of Industrial Internet of Things (IIoT) systems. By jointly utilizing both the time-varying characteristics of the user sequential operation actions and spatial variation characteristics of channel state information (CSI) caused by these actions, this paper proposes a novel two-dimensional passive authentication framework for IIoT systems. In particular, we construct the time-varying operation action sequences from the routine work process of a user and apply the Hidden Markov Model to characterize behavioral biometric characteristics of the user, and also employ the eXtreme Gradient Boosting model to depict the spatial variation characteristics of CSI related to the user. By designing two classifiers corresponding these two characteristics and assigning each classifier an appropriate weight, we propose a two-dimensional user authentication framework for continuous and non-intrusive user authentication in IIoT scenarios. Extensive experiments are conducted to illustrate the authentication performance of the proposed authentication framework in terms of false acceptance rate, false rejection rate and equal-error rate. We further investigate the related authentication efficiency issues like the sensitivity to the weights for classifiers, the sensitivity to authentication time and the capability of resisting against impersonation attacks.
Active Pointly-Supervised Instance Segmentation

The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The key of APIS is to find the most desirable points to maximize the segmentation accuracy with limited annotation budgets. We formulate this setting and propose several uncertainty-based sampling strategies. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset, compared against other learning strategies. The results suggest that APIS, integrating the advantages of active learning and point-based supervision, is an effective learning paradigm for label-efficient instance segmentation.
Coherence Based Automatic Essay Scoring Using Sentence Embedding and Recurrent Neural Networks

Automatic essay scoring (AES) is an essential educational application using natural language processing and deep learning. However, enacting advanced deep learning models and natural language processing has improved AES systems' accuracy and performance. Though the current AES systems failed in certain areas like content-based assessment, the models are not tested on adversarial responses and giving feedback on the essay. In this paper, we proposed a sentence-based embedding to capture text's coherence and cohesion into a vector. Then, we trained these vectors on LSTM and Bi-LSTM to find the sentence connectivity internally and externally to check the sentence framing, its association with other sentences, and its relevance to the prompt. Finally, we tested our trained model on different adversarial responses and observed decent outcomes. We used two different data sets: one is the standard ASAP Kaggle dataset, and another is a collection of responses from the operating system domain. The result shows that the LSTM model outperformed with baseline model quadratic weighted Kappa (QWK) by 0.76. Moreover, the testing on adversarial responses enhances the efficiency of the proposed model in different domains.
Remote Sensing Image Super-Resolution via Dilated Convolution Network with Gradient Prior

Due to the limitations of the imaging sensor, the spatial resolution of satellite imagery is often insufficient, namely, low resolution (LR). Therefore, super-resolution (SR) is proposed, which strives to improve image resolution, perfectly to compensate for the shortcomings of satellite sensor imaging. In this study, we develop a unique dilated convolution network with gradient prior (DCNG) for remote sensing SR, aiming to extract powerful low-level features with gradient prior and efficitive network and then reconstruct the high-level feature details. The DCNG is built of two components: the Multi-Scale Feature Extraction Network and the Feature Reconstruction Network. In the Multi-Scale Feature Extraction Network, the Double-Path Dilated Residual Block (DPDRB) is designed with the dilation convolution operation to obtain the multi-scale features and increase the receptive field, the Global Self-attention Module (GSA) to catch the long-range dependency among picture patches, and a Gradient Propagation Network (GPN) is proposed to extract high-level gradient information. In the Feature Reconstruction Network, the Pixel Shuffle is introduced to reconstruct the feature by combining characteristics of different frequency bands. Experiments using Massachusetts_Roads and 3K VEHICLE_SR data sets indicate that our DCNG surpasses state-of-the-art algorithms in terms of quantitative and qualitative evaluations.
An XR Optical Camouflage Technology

In recent years, remarkable progress in science and technology has enabled the realization of many technologies previously imagined in fiction and popular culture. At present, optical camouflage technology designed to render objects transparent and invisible is being studied in various fields. Some optical camouflage methods have been proposed and implemented using special materials and equipment such as projectors. In this study, we developed a system integrating real-world person recognition using augmented reality (AR) and virtual space construction using virtual reality (VR). Our system is designed to recognize a human silhouette and replace it with a background image. The images used to cloak the silhouettes were based on scans of real three-dimensional spaces. Using these technologies, we constructed a system that displays a scene in which a person is rendered transparent using a camera in real time. We then realized optical camouflage as a mobile device application.
$$\log ^*$$-Round Game-Theoretically-Fair Leader Election

It is well-known that in the presence of majority coalitions, strongly fair coin toss is impossible. A line of recent works have shown that by relaxing the fairness notion to game theoretic, we can overcome this classical lower bound. In particular, Chung et al. (CRYPTO’21) showed how to achieve approximately (game-theoretically) fair leader election in the presence of majority coalitions, with round complexity as small as $$O(\log \log n)$$ rounds. In this paper, we revisit the round complexity of game-theoretically fair leader election. We construct $$O(\log ^* n)$$ rounds leader election protocols that achieve $$(1-o(1))$$ -approximate fairness in the presence of $$(1-o(1)) n$$ -sized coalitions. Our protocols achieve the same round-fairness trade-offs as Chung et al.’s and have the advantage of being conceptually simpler. Finally, we also obtain game-theoretically fair protocols for committee election which might be of independent interest.
Credit Card Fraud Detection via Machine Learning

Credit card fraud has recently been a more severe problem since fraudsters are continuously developing more sophisticated fraud techniques. In this work, we have developed a software-based machine learning (ML) system for detecting credit card fraud. Data pre-processing was the first step at which Pearson correlation coefficient was used to decide which features are worth using in our model. Then, k-folds cross validation was used to evaluate the performance of different classifiers: k-Nearest Neighbor (k-NN), Naïve Bayes, Support Vector Machine (SVM), Bagging, Random Forest and Multilayer Perceptron (MLP). To overcome the imbalanced data issue, synthetic minority oversampling technique (SMOTE) was applied to the dataset to generate more fraud data points. To evaluate model's performance, Precision, recall, accuracy and F1 score evaluation metrics were used. With feature correlation of 0.1, SVM had the highest recall score: 88.55%. Also, when implementing SMOTE, the k-NN classifier showed the highest F1 score and precision.
Evaluation Design and Improvement on Higher Vocational College Teaching Evaluation System

In this paper, a large number of higher vocational college teaching data norm extraction with the appropriate model design and improvement. So as to improve the effectiveness of data analysis, it provides powerful classroom analysis support for classroom teaching evaluation in higher vocational education. The concept of overall distribution is used to describe the overall data state by presenting the distribution diagram. The big data simulation analysis results can provide reference or basis for the construction of normal model resources of classroom teaching quality of teachers in vocational colleges as a whole.
Extended StyleGAN Encoder for Image Restoration

Great success has been achieved in reusing Style-GAN, but there are still challenges in reusing StyleGAN to image restoration tasks. We believe that the limited expression of StyleGAN’s latent code hinders its effective use in image restoration tasks. Therefore, we propose to extend StyleGAN’s latent code to improve its expressiveness. Specifically, we propose a new model called Extended StyleGAN Encoder (ESE). ESE is based on Feature Pyramid Network, which can encode input images both in coarse-grain and fine-grain, and generate H × W × 2C feature maps instead of 2C-dimensional style vectors. In this way, ESE can provide more information of the input images to StyleGAN, which can achieve a more accurate reconstruction. Therefore, ESE can be applied to different image restoration tasks. Experiments show that ESE can surpass some models specifically designed for regular and irregular mask inpainting tasks, while it is difficult for other GAN Inversion methods. And experiments also show that ESE achieved good performance on colorization and denoising tasks. Besides, we further analyze the impact of the extended latent code by visualizing the outputs of ESE and the intermediate outputs of StyleGAN, which demonstrates that ESE does really reuse the knowledge of StyleGAN.
The Performance of Logistic Regression and Discriminant Analysis in Spam E-mail Classification

In this study, the performance of a proposed discriminant analysisDiscriminant analysis was compared to that of logistic regressionLogistic regression in classifying spam e-mailsSpam e-mails. While it is believed that the weight of the terms in e-mails is able to give insights into how it affects the probability of an e-mail being spam in the mailbox, logistic regression was also tasked to identify these terms. The performance of the two classifiers evaluated at their respective optimal classificationClassification thresholds was compared; this included accuracyAccuracy, precisionPrecision and recallRecall. The findings of this study show that terms like money, http, question and thank were among those that affected the probability of an e-mail to be spam after applying two layers of variable selectionVariable selection. Coincidently, this agrees with the general perception of a spam e-mail which is typically associated with money-related issues and URLs (Uniform Resource Locators). The results also revealed that logistic regression outperformed quadratic discriminant analysis despite its violation of the linearity statistical assumption. From the perspective of spam e-mail filtering, this study emphasises statistical approaches and provides an alternative option of a classifier in spam e-mail classification.
Domain Generalization via Selective Consistency Regularization for Time Series Classification

Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training. Popular domain alignment methods for domain generalization seek to extract domain-invariant features by minimizing the discrepancy between feature distributions across all domains, disregarding inter-domain relationships. In this paper, we instead propose a novel representation learning methodology that selectively enforces prediction consistency between source domains estimated to be closely-related. Specifically, we hypothesize that domains share different class-informative representations, so instead of aligning all domains which can cause negative transfer, we only regularize the discrepancy between closely-related domains. We apply our method to time-series classification tasks and conduct comprehensive experiments on three public real-world datasets. Our method significantly improves over the baseline and achieves better or competitive performance in comparison with state-of-the-art methods in terms of both accuracy and model calibration.
Authorized Update in Multi-user Homomorphic Encrypted Cloud Database

Cloud Computing is a promising solution in distributed internet computing for IT and scientific research. However, data storage and computing in the cloud domain raise added questions in terms of security. Data encrypted with traditional encryption schemes may confirm confidentiality, but computation in cloud domain becomes infeasible. This paper focuses on designing an encrypted database considering homomorphic encryption (HE) as an underlying scheme so that query execution is carried out on encrypted version of the database without any need of intermediate decryption. Existing encrypted databases are either based on partial HE or deterministic HE to achieve practical performance; hence, they are either limited in terms of types of query execution or prone to known attacks. To mitigate these issues, we explore the practical challenges of a fully homomorphic encryption (FHE)-based database design. FHE theoretically promises to perform arbitrary operations on encrypted data. However, realizing any algorithm in homomorphic domain requires a circuit-based representation of that specific algorithm, which is a non-trivial task. In this work, we explore the practical challenges of FHE database design, mostly in the case of multi-user organizational scenarios, and propose a scheme for secure modification or conditional update of the encrypted database. Moreover, when an organization outsources a database to the cloud, a single FHE key is used to encrypt all columns of the database. However, all users(or employees) of the same organization should not have equal read and write access permission to the whole database. In this work, we propose an architecture to apply Attribute-Based Access Control (ABAC) on FHE databases with minimum overhead in terms of performance and storage. We propose required changes in registration, login, and user revocation phases for our scheme to perform conditional SQL query processing on FHE encrypted database (EDB). Our proposed framework is capable of performing end-to-end encrypted conditional UPDATE with suitable access control within 17 minutes on a multi-core processor platform for 769 rows and 9 columns of database size with 16-bit size of data. To the best of our knowledge, our proposed technique is the first one in literature to support arbitrary secure encrypted SQL query execution with suitable access control.