Reviewerly/exHarmony huggingface dataset:
Evaluation of Feature Detection and Extraction Methods for Landmark Separability in Visual Localization

In this study, the utilization of feature detector methods in landmark-based visual localization research has been evaluated. In contrast to comparative studies of existing feature detection methods, their performance has been assessed in detecting and discerning similarities among environments at distinct locations that pose challenges for perception. Additionally, variables such as varying lighting conditions, camera exposure time, and sensor light sensitivity (ISO value), as well as focus range, have been incorporated into the evaluation. BRISK, Fast, Harris, MinEigen, MSER, ORB, SIFT, SURF feature detector, and descriptor methods have been considered in this assessment. BRISK and MinEigen, notably, have demonstrated superior performance in detecting objects that could serve as landmarks in extremely unambiguous environments compared to other methods. Furthermore, it has been observed that in situations with excessive measurements in camera exposure time, more features can be detected. Additionally, an increase in image sensor light sensitivity has led to a reduction in the number of detected features across all methods. Another noteworthy finding is the dramatic decrease in matched features observed in images captured under varying lighting conditions and the same positions.
Calibrated Uncertainty-Guided Multi-task Framework for Medical Image Segmentation

Medical image segmentation is a crucial part of computer-aided diagnosis. Due to the enormous cost of labeling medical images, researchers have turned to exploring semi-supervised learning. However, the lack of supervisory information makes it difficult to accurately segment the fuzzy regions (e.g., complex edges or corners of organs). In this paper, we propose a novel method called Multi-task Consistency Segmentation Network based on Calibrated Uncertainty (CU-MCSNet). This model incorporates calibrated uncertainty to guide the network’s learning process. In addition, the model consists of two tasks: i) semantic segmentation as the primary task and ii) signed distance regression as the auxiliary task. To enhance the accuracy of edge segmentation, we propose the Edge Calibration Network for the primary task. This network integrates essential spatial and channel features, employing gradient complementation to hinder the accumulation of defective information and supply pertinent data to fuzzy regions. We also use the inter-task consistency loss to explore the underlying information of the images. In the multi-task domain, it is tough to balance each task manually, and we note that homoscedastic uncertainty focuses on inter-task variation. However, its numerical estimation may still be subject to bias. Therefore, we propose an adaptive loss-balancing strategy based on calibrated homoscedastic uncertainty. Extensive experiments show that our proposed method achieves state-of-the-art performance.
Putting fraudsters on notice

Hardly a day goes by without a warning or headline about scams. Employee frauds, push payment or errant suppliers short-changing customers – the level of activity is rising, despite the actions of authorities to stamp out the problem
Construction of personalized learning service system based on deep learning and knowledge graph

Abstract With the development of information technology, it is difficult for people to find effective information from the massive Internet information, and personalized services are in short supply. This paper explores the essential association between knowledge graphs and deep learning and examines the construction process of knowledge graphs. Based on the content and collaborative filtering recommendation method, the theoretical method of personalized learning service recommendation is being studied. Knowledge graphs are introduced in collaborative filtering to accurately portray user and item vector representations. The knowledge graph is used to represent the TransR model, forming an entity space and a multivariate relationship space. The students’ demand for personalized learning service systems is analyzed from the user’s point of view, the personalized learning service system is constructed, and the accuracy of the personalized learning service system constructed in this paper is analyzed. The accuracy rate of DLKG is 0.1425 when K=10, which is the highest among the five models. In the effect of personalized teaching, the mean value of the metacognitive ability of the students in the experimental class is higher than that of the control class by 0.4155, 0.0261, 0.3995, 0.7967, respectively, and the experiment proves that the personalized learning service system based on the DLKG model proposed in this paper is more effective.
Research on the Path of High-Quality Development of Student Management under the Explanatory Structural Model Oriented to Solve the Dilemma of Collaborative Governance of Student Management in Colleges and Universities

Abstract This paper firstly studies the student management model, analyzes the mechanism of explaining the operation of the structural model, and proposes the DEMATEL method. Secondly, the ISM model is established on the basis of DEMATEL, and the cross-matrix multiplication method is introduced to explore the diffusion of interrelationships among factors in the system. And finally, the construction of the DEMATEL-ISM model is completed. Then, 11 experts were selected to set up an ISM group to construct the relevant reachability matrix after determining the key factors of the problem, and a multi-layer recursive order structure model was established based on the high-quality management influencing factors. Finally, an empirical examination was conducted to examine the overall effect of student management quality in S colleges and universities. Among the key factors of student management, classroom management was identified as the most critical factor affecting the quality of student management, with the ratio of the most important and the important summed up to 76.1%. Among the classroom student participation in higher education, the highest score was given to participatory behavior, with an interval between [2,5] and a mean value of 3.4808. The quality of student management in higher education was characterized by a high level of dissatisfaction among students and teachers, which was 41.94% and 51.28%, respectively. It can be seen that daily management, moral education, teacher management, and teacher competence are insufficient. Colleges and universities need to strengthen the construction of these 4 aspects and put forward relative coping strategies for the deficiencies that exist.
Review the Amazon Products Using Data Science Tools to Enhance Online Feedback and Services

Many websites provide their services officially and unofficially, leading to many customer problems, such as fraud, delays in the services offered, and many other issues. Millions of consumers are victims of Scams every year—online fraud daily in different ways. The research-collected data set from the Kaggle Platform contains different products from the Amazon website and is analyzed. A quantitative Descriptive Method followed the research—a list of over 3,400 more client reviews for Amazon products. The Kindle, hearth TV Stick, and additionally provided by Datafiniti's Product info. The dataset includes every product's basic product info, rating, review text, and additional information. It has introduced a model to obtain reviews from online users. Finally, it was paying more attention to evaluating and developing services because, unfortunately, many institutions use them unprofessional to bring them profits. However, with the idea of introducing a platform specializing in evaluation processes for the various services provided by multiple institutions and its further development in the future, it becomes challenging to manipulate the consumer and society. Service evaluation has become very important, especially these days, so adding features that greatly help avoid fraud, deception, and manipulation is a good idea. Unfortunately, evaluation has become insufficient to obtain the service effectively, so it is necessary to develop a practical Platform work, especially in this field in the future, effectively helping the community.
Identification of Nervosa Disease using Case-Based Reasoning

Among teenagers or adults, body shape greatly affects a person's confidence. Most people will be very confident if their body shape is ideal, where the person will be very confident speaking in public. Different things with people who have a body shape that is too thin/fat, so their confidence is less to appear in public. To get the ideal body shape they will do everything possible, one of which is dieting, taking weight loss capsules, and so on. They do not know the dangers of dieting too strictly and taking weight loss capsules carelessly which can make them contract Nervosa disease. To get the results of the expert system, the case-based reasoning method will be used. After this application is built, the result obtained is the type of nervosa disease based on the symptoms felt by the user.
Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.
A quantum algorithm for computing multiplicative order of integers modulo a prime

One of the challenging problems in number theory is to compute efficiently multiplicative order of integers modulo a prime. This problem was listed under open problems in the number theoretic complexity II proposed by Leonard M. Adleman and Kevin S.McCurley in 1994. This paper offers a polynomial-time quantum algorithm for computing the order k = min {x|x∈, ax ≡ 1(modp)}, if p∈ Primes and gcd(a,p) = 1, where a, p∈, and it requires 2 (1) 2 ((log p) +O )  quantum operations.
VCSEL Based Neuromorphic Computing

We report on Vertical-Cavity Surface-Emitting Lasers (VCSELs) for high-speed and energy-efficiency systems for photonic neuromorphic computing, yielding excellent performance in complex processing tasks whilst benefitting from hardware-friendly implementations and full compatibility with optical communication technologies.
Semantically Adaptive JND Modeling with Object-wise Feature Characterization and Cross-object Interaction

This work proposed a spatio-temporal JND model based on semantic attention. Firstly, the principal semantic features affecting visual attention are extracted, including the semantic sensitivity, objective area and shape, central bias and contextual complexity, and the HVS responses of these four features are explored and quantified. Secondly, the semantic attention model is constructed by inscribing the attentional competition model, considering the interaction between different objects with limited perception resources. Finally, the obtained semantic attention weighting factor is combined with the basic spatial attention model to develop an improved transform domain JND model. Detailed performance results of different JND models are shown in Tab. 1. The simulation results validate that the proposed JND profile is highly consistent with HVS, with strong competitiveness among the state-of-the-art models.
Exploring the Distinctive Tweeting Patterns of Toxic Twitter Users

In the pursuit of bolstering user safety, social media platforms deploy active moderation strategies, including content removal and user suspension. These measures target users engaged in discussions marked by hate speech or toxicity, often linked to specific keywords or hashtags. Nonetheless, the increasing prevalence of toxicity indicates that certain users adeptly circumvent these measures.This study examines consistently toxic users on Twitter (rebranded as X) Rather than relying on traditional methods based on specific topics or hashtags, we employ a novel approach based on patterns of toxic tweets, yielding deeper insights into their behavior.We analyzed 38 million tweets from the timelines of 12,148 Twitter users and identified the top 1,457 users who consistently exhibit toxic behavior, relying on metrics like the Gini index and Toxicity score. By comparing their posting patterns to those of non-consistently toxic users, we have uncovered distinctive temporal patterns, including contiguous activity spans, inter-tweet intervals (referred to as “Burstiness”), and churn analysis. These findings provide strong evidence for the existence of a unique tweeting pattern associated with toxic behavior on Twitter.Crucially, our methodology transcends Twitter and can be adapted to various social media platforms, facilitating the identification of consistently toxic users based on their posting behavior. This research contributes to ongoing efforts to combat online toxicity and offers insights for refining moderation strategies in the digital realm. We are committed to open research and will provide our code and data to the research community.
Private Continuous Survival Analysis with Distributed Multi-Site Data

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results.
TreeBERT: Advanced Representation Learning for Relation Extraction

As the COVID-19 pandemic has subsided, it remains crucial to analyze the vast amount of research produced during this period to advance our understanding of effective medical treatments and therapies. The wealth of biomedical literature generated during the pandemic presents a challenge for researchers who need to quickly assimilate new information. To tackle this issue, we introduce Tree-BERT, a novel representation learning language model designed for the automatic extraction of biomedical relationships from medical publications. Unlike traditional BERT models and their variants, which solely focus on the contextual relationship between words in input sentences, Tree-BERT also incorporates sentence structure information, enhancing word embeddings. Our experiments reveal that Tree-BERT outperforms alternative methods in biomedical relationship extraction. We will also demonstrate the model’s ability to identify new relationships from Covid-19 publications, showcasing its potential to contribute to the ongoing analysis of pandemic research and its broader applications in the field of bio-medicine.
Sentence Meaning Similarity Detector Using FAISS

Sentence meaning similarity in machine learning (ML), which is automatically done by calculating the semantic similarity between sentences. In this study, word embeddings are used to depict sentences as numerical vectors in a high-dimensional space. It is then discussed how different similarity measures may be applied to evaluate these vectors and determine how similar the sentences are. The FAISS (Facebook AI Similarity Search) method is covered in the study. An open-source library developed by Facebook AI Research for similarity search and clustering of dense vectors, can be used to efficiently calculate sentence similarity by leveraging its efficient nearest neighbor search capabilities. Searching exhaustively increases the number of comparisons to decrease that we proposed in this paper.
Data Governance Method and Application for Agency Power Purchase Business

Agency power purchase refers to the role of power supply enterprises acting as user agent service providers, purchasing the electricity required by users from the electricity trading platform at market prices, and settling electricity fees according to the monthly published agency electricity purchase price. As an important part of promoting the marketization of electricity, agency power purchase can promote the high-quality development of the power industry and support the construction of new power systems. In order to normalize the agency power purchase business and ensure the smooth operation of the power purchase mechanism, taking the data of Jiangsu Province from 2013 to 2022 as the indicator, a data governance method for the agency power purchase business is proposed, which predicts the data and analyzes the deviation rate, so as to realize multi-source data fusion governance, and approve the monthly electricity, daily electricity, time-based electricity and load data to ensure that the average monthly forecast accuracy rate reaches 95% or more, realize dynamic monitoring and analysis of key users, and ensure the steady progress and accurate implementation of the agency power purchase business.
Improving Image Quality of Noisy Images Through Denoising and Style GAN Technique

This research proposes an approach to enhance the denoising and upscaling performance of noisy images using Generative Adversarial Networks (GANs), particularly Style GAN architecture. Denoising and upscaling noisy images are crucial in many computer vision applications, and GANs have shown remarkable effectiveness in creating high-quality images. However, training Style GAN requires huge amount of data and is computationally expensive. To address this issue, this study proposes using various filters such as mean, median, and weighted median to pre-process the noisy images before feeding them to Style GAN. The proposed approach achieves superior denoising and up scaling compared with other system in terms of FID and inception score, and further exploration of hyperparameters and variations of the Style GAN architecture can lead to even better results.
Voice Cloning and Forgery Detection Using WaveGAN and SpecGAN

This paper presents a comparative analysis of Deep Convolutional GAN (DCGAN), WaveGAN, and SpecGAN for voice cloning and forgery detection. The aim is to assess how well each GAN performs in producing excellent in quality synthetic recordings of speech and identifying artificial voices. Experimental results show that SpecGAN outperforms both DCGAN and WaveGAN in generating high-quality synthetic voice samples. In addition, the paper proposes an audio forgery detection method that combines copy-move forgery detection, CQSS-GA-SVM analysis and audio forgery detection using SpecGAN achieving 98% accuracy in detecting synthetic voices. The findings provide insights into the state-of-the-art techniques for voice cloning and forgery detection.
Feature Selection on Big Data using Masked Sparse Bottleneck Centroid-Encoder

We introduce a nonlinear model, Masked Sparse Bottleneck Centroid-Encoder (MSBCE), for determining the features that discriminate between two or more classes. The algorithm aims to extract discriminatory features in groups while reconstructing the class centroids in the ambient space and simultaneously use additional penalty terms in the bottleneck layer to decrease within-class scatter and increase the separation of different class centroids. The model has a sparsity-promoting layer (SPL) with a one-to-one connection to the input layer. Along with the primary objective, we minimize the $l_{2,1}$-norm of the sparse layer, which filters out unnecessary features from input data. During training, we update class centroids by taking the Hadamard product of the centroids and weights of the sparse layer, thus masking the irrelevant features from the target. Therefore the proposed method learns to reconstruct the critical features of the class centroids. The algorithm is applied to various real-world data sets, including high-dimensional biological, image, speech, and accelerometer sensor data. We compared our method to different state-of-the-art feature selection techniques, including supervised Concrete Autoencoders (SCAE), Feature Selection Networks (FsNet), Stochastic Gates (STG), and LassoNet. We empirically showed that MSBCE features often produced better classification accuracy than other methods on the sequester test sets, setting new state-of-the-art results. Apart from achieving state-of-the-art results, this is the first time feature selection is done on two high-energy particle physics data sets, SUSY and HIGGS, with 4.5 million and 10 million samples, respectively. The selected features, which are only 25-30% of the total number of variables, attained almost similar prediction rates compared to all features.