Reviewerly/exHarmony huggingface dataset:
Multi-task Cooperative Monocular 3D Object Detection Using Surface Center Keypoints

Detecting 3D objects from a single monocular image without spatial information is a challenging task. In this paper, we propose a 3D object detection model which directly regresses the center point, depth, size and orientation of the 3D bounding box using a single monocular RGB image. In particular, we obtain multiple surface center points of each 3D box in two ways, one of which is directly predicting them, and the other is indirectly obtaining them through geometry computation. The error between the results in two ways acts as a new unified loss item to improve the convergence of the prediction on surface center points. The proposed approach has been verified on the public nuScenes dataset and experiments demonstrate that the additional keypoints estimation and the unified loss improve some aspects of 3D object detection.
Continuous Release of Location Data Based on Differential Privacy

Nowadays, location data sharing has become an important way for people to share their lives and exchange for location-based services. The release of location data maximizes the value of the data, but also leads to personal privacy leakage. Existing research work on differential privacy-based data publish scheme use a grouping mechanism to improve the utility of release data. However, it requires more pre-defined parameters, besides, its data protection process does not adequately consider data variation characteristics. In this paper, we propose a location data continuous release privacy protection framework, called LDCR, which provides <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> -event privacy protection for the release of location aggregated data. We define data change rate, which captures the data trends utilizing the change in the tilt angle of the data slope at adjacent moments. Meanwhile, we design a grouping mechanism based on data change rate to reduce the number of pre-defined parameters, and a privacy budget allocation mechanism that adapt to data changes to improve the rationality of privacy budget application. Experimental results show that our proposed mechanism can provide privacy protection for the continuous release of location data while ensuring data utility.
Implementing Machine Learning Adoption and Blockchain in the Health Care System

Insider attacks on the electronic healthcare device category can result in a deceptive inspection of patients' fitness information, ensuing in unaccountability of records intake along with huge low-budget prices as an outcome of facts fissures in the Digital-Healthcare device without an effective detection technique. As a result, several health centers have confronted prison and reputational implications. A green technique must be proposed to address this challenge, especially for eHealth structures inside the cloud surroundings, in which activities are now carried out via cloud be cloud-based as a result of it. Even as anticipating such options proposed, health records may be centered, which may additionally bring about poorly affected person remedy thanks to disinformation, and accordingly individual mortality. This research is being driven by way of this necessity. This paper proposes a brand-new context for figuring out insider threats upon the usage of extraction in watermarking and detection in logging strategies which is primarily based on Cloud-based health care systems. The method generated a file that included numerous acts carried out through users as well as an audit path of criminal and un legal entries into the device. The technique displayed an immoderate degree of precision, recall, and accuracy inside the assessment finished at the cease of the studies, indicating that its performance is extremely good to undertake. Keywords: Watermarking Extraction, Logging Detection technique.
Image Processing based Smart, Economical Blood Cancer Identification System

By separating young white blood cells from red blood cells, blood leukaemia can be detected with more accuracy and specificity. The only method to determine if someone has a blood problem is to take skin photos, calculate, shade, and measure them. The World Health Organization ranks leukaemia as the sixth most common cause of death globally. For a therapy to be effective and have a positive outcome, infection must be identified and detected early. In this research, it is believed that the cells that are connected to leukaemia would be able to be found and tested. Whether or not immature cells are present, as well as the degree of chronic or severe leukaemia, can be used to categories this illness. By combining morphological methods like region opening, region closing, disintegration, and expansion with histogram levelling and straight difference extending, it is possible to obtain a more even distribution of data points. As a result, the Proposed Method is more efficient than other earlier techniques.
A Supervisioned Approach for Semantic Resolution in Portuguese with Neighborhood Analysis Integrated to the Falibras Translator

Deafness makes social interaction considerably more difficult since it inhibits the individual from communicating through the oral-auditory pathway. To facilitate communication between deaf people and listeners, machine translation tools to sign languages can be used. However, according to reports in the literature, about 75% of the deaf community feels dissatisfied with the translation of the existing tools. Among the main causes of this dissatisfaction is the use of inappropriate signs for words with semantic ambiguity. In this work, we propose the addition of a new semantic module to the translation process of the Falibras System, with the objective of improving the quality of the translation with respect to semantic disambiguation of the input text (in Portuguese). The main objectives of the proposed approach are: (1) to know the state of the art regarding the resolution of ambiguities in Portuguese; and (2) to combine existing techniques to develop an ambiguity resolution module for Falibras. The evaluation of the results showed a gain in accuracy, in a comparison with existing work in the literature. The evaluation was conducted based on the Goal-Question-Metric model.
Cyber Security and Data Mining Techniques

For a long time, security and privacy have been a source of concern for the general population. Nonetheless, rapid technological advances, the rapid growth of the internet and electronic commerce, and the development of more contemporary methods for collecting, investigating, and using private information have elevated privacy to the forefront of public and government concerns. Because of the ease with which data can be collected and stored on PC systems, the area of data mining is growing insignificance. Data mining techniques, while allowing individuals to extract hidden information, on the one hand, offer a variety of privacy risks on the other. This article provides an overview of the various data mining methods that may be utilized in cyber security to identify intrusions. This article provides an overview of data mining in the context of cyber security.
Optimization Framework of Digital Online Model for Vocal Music Training based on Computer Data Perception and Fusion

Optimization framework of digital online model for vocal music training based on computer data perception and fusion is studied in the paper. Usually, the training in pattern recognition refers to statistical learning, that is, it requires a large number of training examples to obtain the estimated values of the parameters in the model, while the high-level voice semantic training is based on example learning. With the mentioned models, the signal fusion ideas are considered. With the development of general communication technology, audio AGC technology has also developed from analog circuits to the era of the digital signal processing. With these ideas, the digital online model for vocal music training will be then considered as the novel understanding model. Through the simulation, the framework of digital online model for vocal music training is tested and validated.
Ada-MIP: Adaptive Self-supervised Graph Representation Learning via Mutual Information and Proximity Optimization

Self-supervised graph-level representation learning has recently received considerable attention. Given varied input distributions, jointly learning graphs’ unique and common features is vital to downstream tasks. Inspired by graph contrastive learning (GCL), which targets maximizing the agreement between graph representations from different views, we propose an Ada ptive self-supervised framework, Ada-MIP, considering both M utual I nformation between views (unique features) and inter-graph P roximity (common features). Specifically, Ada-MIP learns graphs’ unique information through a learnable and probably injective augmenter, which can acquire more adaptive views compared to the augmentation strategies applied by existing GCL methods; to learn graphs’ common information, we employ graph kernels to calculate graphs’ proximity and learn graph representations among which the precomputed proximity is preserved. By sharing a global encoder, graphs’ unique and common information can be well integrated into the graph representations learned by Ada-MIP. Ada-MIP is also extendable to semi-supervised scenarios, with our experiments confirming its superior performance in both unsupervised and semi-supervised tasks.
Optimal Additive C-Fuzzy Regression Trees for Software Development Effort Prediction

There is evidence that precise prediction of the software development effort plays a crucial role in properly monitoring and managing software projects. Researchers have suggested many software effort estimation techniques. Nonetheless, none of these methods performed well in all circumstances. Ensemble models have been recently proposed in the literature to overcome the significant drawbacks of single machine learning approaches. In this study, we proposed a novel model, the ensemble of optimal additive cluster-based fuzzy regression trees for software development effort prediction. We performed an empirical evaluation using four datasets and the 30% holdout cross-validation technique. We compared the performance of our proposed ensemble model to the c-fuzzy regression tree, the bagged c-fuzzy regression tree model, the ensemble of optimal trees, random forest, and regression trees. Our suggested model outperforms all the compared models in Pred (25%), MMRE, and MdMRE in all employed datasets.
Combined Approach of ML and Blockchain for Fraudulent Detection in Insurance Claim

Since the beginning of the insurance business, dishonest practices have plagued the sector. Each year, many crimes are not investigated, which costs the insurance industry billions of dollars. It is anticipated that by the year 2020, the Indian insurance business would have grown to a value of 21,715 billion dollars thanks to improvements in both knowledge and distribution methods. India comes up at number 10 for the amount of gross premiums collected by life insurance companies and number 15 for the total amount earned by non-life insurance companies. As a result, a blockchain technology-based architecture is developed to enable safe transactions and the data interchange between many agents make up the insurance network. The blockchain is a decentralized peer-to-peer technology that enables the secure, unchangeable, and open-source authentication of healthcare claims. Consider the ways in which blockchain technology and smart contracts could make corporate operations more effective. It will demonstrate how these technologies may be integrated to construct a system that makes use of machine learning to identify and prevent fraud in a variety of fields, including automobile, healthcare, and life insurance claims, among others.
Methodology for Quantifying the Flicker Effect in Video Compression

The present tendency is for streaming video to consume increasingly more bandwidth on the Internet. In recent years, interest toward the development of models for objectively assessing video quality has been on a highly growing trend. Objective assessments of video quality developed on models of the human visual system have been successful in describing static spatiotemporal coding. However, much less attention has been paid to the question concerning the way the visual areas of the brain process temporary information masking in motion. Stroboscopic effects in television have been well studied for lower frequencies, but knowledge of higher frequencies is needed for streaming video as well as the complex temporal spectra, which occur with simultaneous motion and flicker of objects. In the presented research, a methodology and equipment were developed to quantify the flicker effect in video compression, specifically, information on the response to motion masking for high temporal frequencies, or, in other words, information hiding in motion in today’s video content presentation environment.
A closer look at organizational cybersecurity research trending topics and limitations

Purpose In identifying both the topics of interest and key limitations of the extant organizational security research, both opportunities for future research as well as some underlying challenges for conducting this research may be revealed. Design/methodology/approach To identify the leading organizational cybersecurity research topics of interest and their key limitations, the author conducted a topic modeling analysis of the organizational level studies published in the Association for Information Systems (AIS) senior scholars' “basket of eight journals” (Association for Information Systems, 2022) over the past five years. Findings Leading topics include (1) organizational security research concerns governance and strategic level decision-making and their role in shaping organizational security successes and failures, (2) cybercriminals and organizations' ability to monitor and detect them from both within and outside the firm; (3) cost, liability and security negligence, (4) organizations' innovation dispositions for security products and services and (5) organizational breach response efficacy; while key limitations of this study include the following: (1) scholars' ability to propose and assess strategic and operational level threat response recommendations, (2) their understanding how influence is formed and maintained among employees and groups and (3) their measurement instruments and models. Originality/value Organizations remained plagued by an ever-emerging set of threats to the security of their digital and informational assets. New threats are regularly discovered and remedies to existing threats are continually proven ineffective against these new threats. Providing an orientation to the current research on organizational security can help advance their security efforts.
News Curation, Abstract, and Recommender App using Deep Learning Attention Models

It is important to keep ourselves updated on the latest news. People skip reading the news because of the lack of time or if the news articles are too lengthy. The aforementioned issue is addressed by making the news articles more concise and the interests of the user can be catered to by providing them with a personalized news feed. Existing applications either personalize the news feed or provide abstract news. The proposed system aims to provide abstract news and personalize the news feed. For the proposed system, the main components are collecting and extracting the news, news abstraction, recommender system, and the application backend. To collect and extract the contents of the articles, NEWS API and newspaper3k Library are employed. Abstract of the news article is created by employing the pre-trained BART model and for categorizing the news, the DistilBERT model which can categorize an article within 4ms has been fine-tuned. A content-based approach has been used to personalize the news feed. Application backend tracks the data required for personalizing the news and retrieves the recommended articles for the news feed. The proposed system can deliver personalized, curated, and abstract news to each user.
A Deep Learning Approach to Enhance Underwater Images with Low Contrast, Blurriness and Degraded Color

This paper presents how to improve underwater images with non-uniform lighting, low contrast, blurriness, and degraded color using a Physical Neural Network (PNN)-based image-enhancing approach. The suggested method is built on the deep learning principle and focuses on a damaged or noisy underwater image's input images, weight & weight maps, and white balance data. The proposed method employs a variety of weight maps, including luminance, contrast, chromatic, and saliency, to create an image that overcomes the limits of the initial or noised image, which lacks distinct clarity. Reduced noise levels and better exposed dark regions, as well as increased global contrast and finer features and edges, can be found in the underwater image, created utilizing the aforementioned processes. The experiments are carried out on the EUVP dataset, and it is observed that the proposed method surpasses other state-of-the-art methods in terms of efficiency.
A Sequenced Approach for Customer Data Processing in Banking Applications between Various Services with Novel Decision Tree and K nearest Neighbour

The objective of this work is to conduct a sequenced approach for customer data processing in banking applications between various services with decision tree and k nearest neighbour. Materials and Methods: It is considered as two gatherings, for example, decision tree and k nearest neighbor algorithms where N=10 sample iterations to test the accuracy of the model for customer data processing in banking applications for various services. Result: The accuracy results of the Novel Decision Tree classifier model has potential up to (98%) and the K Nearest Neighbour model has an accuracy of (88.98%). It’s been observed that there is a statistical remarkable perfection between the Decision Tree and K Nearest Neighbour (p=0.002). Conclusion: The work results for customer data processing in banking applications between various services show that the Decision Tree algorithm has shown higher results and Significance than K Nearest Neighbour algorithm.
A Novel Approach Towards News Category Prediction using NLP

The manner that people receive information has changed substantially as a result of technology’s enormous influence on society. A well-known and common service is news. The way news is created, received, and broadcast has significantly changed as a result of recent technology breakthroughs. It has made it possible for cellphones to get more frequent and real-time news information at all times. People now anticipate hearing about topics that interest them immediately. Based on content/information type, the news category predictor seeks to identify and classify various articles. Processing a large number of articles requires the automatic news categorization to be used effectively. In order to swiftly determine where they are concentrated, it can categorize and label the news pieces by evaluating the content (i.e., extracting feature values), enabling effective and rapid news delivery. Furthermore, news websites may improve their exposure by creating a recommendation system that proposes or recommends pertinent content to draw in more visitors.The main motive of the paper is to use natural language processing and find the category of the news from the data using the intrinsic NLP accuracy metrics.
Emotion Classification and Intensity Prediction using Hybrid Deep Learning Models

The study of people’s feelings, emotions, and opinions toward a specific event or issue is known as sentiment analysis. Individuals can utilize social media to share their views and opinions on any subject or issue, resulting in massive amounts of unstructured data. By processing and evaluating these feelings, you can get insights and understand them. Multiple Machine Learning (ML) based approaches were used to study various points of view. Due to changes in the sequential order, stream lengths, and advanced logic, determining the exact thoughts expressed in the consumer comments is still difficult. This paper proposed the emotion analysis model by Vectorizing all the training data and creating hybrid models with the Conv 1D model with the LSTM model. The proposed outcomes show outstanding performance as compared to the other base models.
Development and Implementation of Counselor Work Management Information System based on Hadoop and Distributed Data Backup Algorithms

The big data technology has been the trend for the combination of the MIS optimization, hence, this paper studies the recent development and implementation of counselor work management information system based on the Hadoop and distributed data backup algorithms. Hibernate is an open-source object-relational mapping framework. It encapsulates JDBC with very lightweight objects. It establishes a mapping relationship between POJOs and database tables. It is a fully automatic ORM framework. Hence, this is considered as the basis for finding information about missing data from a dialectical point of view. It also has its own limitations, because all the information and data information of computer equipment must be saved every day, which brings the second repetition of data information, increases the core system storage pressure, and reduces the use space. With this idea, the novel system is designed. Through the experimental analysis, the performance is then validated.
SwinTransTrack: Multi-object Tracking Using Shifted Window Transformers

With the great popularity of Transformers, there has been many works using Transformers to explore the temporal association properties of objects between different video frames. However, due to the large-scale variation of visual entities and the high resolution of pixels in images, the original Transformers take so long time for both training and inference. Based on Swin Transformer, we propose SwinTransTrack, a novel shift-window encoder and decoder model. Different from the original model, we fuse low-rank adaptation to achieve feature dimension enhancement and propose a new shifted-window decoder network to obtain accurate displacement to associate trajectories. Finally, We conducted extensive quantitative experiments on different MOT datasets, MOT17 and MOT20. The experimental results show that SwinTransTrack achieves 75.5 MOTA on MOT17 and 67.5 MOTA on MOT20, leading both MOT competitions.
Computer-Aided Analysis Software of the Influence Mechanism of Students' Employment Pressure on Their Own Emotions based on Data Mining of Brain Signal Spectrum

In this paper, the spectral estimation theory and u pattern recognition theory are used to establish the Fuzzy pattern recognition system y of the EEG signal, and the EEG signal is automatically analyzed and diagnosed with the help of a computer. Employment stress and anxiety levels are significantly correlated, and employment stress and anxiety levels are significantly correlated. Psychological resilience is negatively correlated, and psychological resilience and anxiety level are significantly negatively correlated. Anxiety level of high stress, high heart and resilience: low anxiety level, high melon power, low heart and resilience. Pearman correlation was performed on the basis of collected data The analysis shows that there is a significant negative correlation between employment anxiety and social work professional identity.