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Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs

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arXiv 2024年

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. © 2024, CC BY.

关键词： Graph neural networks

A HOLISTIC VIEW ON DATA PROTECTION FOR SHARING, COMMUNICATING, AND COMPUTING ENVIRONMENTS: TAXONOMY AND FUTURE DIRECTIONS

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arXiv 2022年

作者： Gupta, Ishu Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System’s analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection. © 2022, CC BY.

关键词： Digital storage

HISA-SMFM: HISTORICAL AND SENTIMENT ANALYSIS BASED STOCK MARKET FORECASTING MODEL

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arXiv 2022年

作者： Gupta, Ishu Madan, Tarun Kumar Singh, Sukhman Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately. © 2022, CC BY.

关键词： Sentiment analysis

Auxiliary Informatics System: An Advancement Towards A Smart Home Environment

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TechRxiv

TechRxiv 2022年

作者： Gupta, Ishu Yadav, Preetesh K. Pareek, Sourav Shakeel, Saif Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The main problem that we face today is not about getting services, but how efficiently we manage them. We are living in an era where we cannot neglect the fact that we are now depending on various electronic devices which we use daily in our life. These devices have become an inseparable part of our life, in the present scenario technology is evolving so rapidly that it’s a fact if we do not accept any upcoming trend then we lag behind in the eye of others. Managing all these devices and remembering to tune them is quite a hectic task, as we have to do other main tasks too. Using every device efficiently at the same time is not possible. Hence, we came up with a solution, with a system that will manage all these devices according to predefined rules or instructions. Current smart home systems work in a way, to establish a connection between nodes and a hub and are controlled by smartphones, computers, remote, etc. In this research paper, we have proposed a model that will not only establish a connection but will work smartly according to instructions and algorithms saved earlier. This Auxiliary Informatics System (AuxIS) will serve the requirement of managing all the devices in a parallel fashion with very little human intervention and hence, will make a smarter home environment. © 2022, CC BY.

关键词： Internet of things

A Facial Recognition and Detection System using openVC

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TechRxiv

TechRxiv 2022年

作者： Goel, Yaman Gupta, Ishu Rani, Pooja Singh, Ashutosh Kumar Department of Computer Applications National Institute of Technology Kurukshetra136119 India Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

Detection of the human face and matching it against user data, saved in our databases, with the intent of authenticating a person’s identity through the computer is an engaging and challenging task. With the development in the field of robotics and computer vision technologies, real-time human face recognition has also emerged as a key ongoing area. So, we proposed a novel scheme to process the real-time images according to requirements, captured in a computer web camera. By making use of open source computer vision library (OpenCV) and applying its in-built functions, we can easily detect human faces and then match them against the training sets (face images stored in the database) to recognize a human’s identity. Image handling and processing are done using basic library methods. These library functions are able to capture images from a webcam, create windows to hold the images at run time, save them in the database and process them to check exact likeness. CascadeClassifier is used for face detection, and HaarCascade is used for this purpose. FaceRecognizer class is used for face recognition. EigenFaces, FisherFaces and Local Binary Patterns are the basic methods that can be used for this. Images are stored in form of a CSV database. © 2022, CC BY.

关键词： Face recognition

TIDF-DLPM: TERM AND INVERSE DOCUMENT FREQUENCY BASED DATA LEAKAGE PREVENTION MODEL A PREPRINT

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arXiv 2022年

作者： Gupta, Ishu Mittal, Sloni Tiwari, Ankit Agarwal, Priya Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

Confidentiality of the data is being endangered as it has been categorized into false categories which might get leaked to an unauthorized party. For this reason, various organizations are mainly implementing data leakage prevention systems (DLPs). Firewalls and intrusion detection systems are being outdated versions of security mechanisms. The data which are being used, in sending state or are rest are being monitored by DLPs. The confidential data is prevented with the help of neighboring contexts and contents of DLPs. In this paper, a semantic-based approach is used to classify data based on the statistical data leakage prevention model. To detect involved private data, statistical analysis is being used to contribute secure mechanisms in the environment of data leakage. The favored Frequency-Inverse Document Frequency (TF-IDF) is the facts and details recapture function to arrange documents under particular topics. The results showcase that a similar statistical DLP approach could appropriately classify documents in case of extent alteration as well as interchanged documents. © 2022, CC BY.

关键词： Semantics

Momentum Decoding: Open-ended Text Generation As Graph Exploration

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arXiv 2022年

作者： Lan, Tian Su, Yixuan Liu, Shuhang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., greedy/beam search) often lead to the degeneration problem, i.e., the generated text is unnatural and contains undesirable repetitions. Existing solutions to this problem either introduce randomness prone to incoherence or require a look-ahead mechanism that demands extra computational overhead. In this study, we formulate open-ended text generation from a new perspective, i.e., we view it as an exploration process within a directed graph. Thereby, we understand the phenomenon of degeneration as circular loops within the directed graph. Based on our formulation, we propose a novel decoding method—momentum decoding—which encourages the LM to greedily explore new nodes outside the current graph. Meanwhile, it also allows the LM to return to the existing nodes with a momentum downgraded by a pre-defined resistance function. We extensively test our approach on three benchmarks from different domains through automatic and human evaluations. The results show that momentum decoding performs comparably with the current state of the art while enjoying notably improved inference speed and computation FLOPs. Furthermore, we conduct a detailed analysis to reveal the merits and inner workings of our approach. © 2022, CC BY.

关键词： Momentum

MLRM: A MULTIPLE LINEAR REGRESSION BASED MODEL FOR AVERAGE TEMPERATURE PREDICTION OF A DAY

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arXiv 2022年

作者： Gupta, Ishu Mittal, Harsh Rikhari, Deepak Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

Weather is a phenomenon that affects everything and everyone around us on a daily basis. Weather prediction has been an important point of study for decades as researchers have tried to predict the weather and climatic changes using traditional meteorological techniques. With the advent of modern technologies and computing power, we can do so with the help of machine learning techniques. We aim to predict the weather of an area using past meteorological data and features using the Multiple Linear Regression Model. The performance of the model is evaluated and a conclusion is drawn. The model is successfully able to predict the average temperature of a day with an error of 2.8 degrees Celsius. © 2022, CC BY.

关键词： Weather forecasting

PCA-RF: AN EFFICIENT PARKINSON'S DISEASE PREDICTION MODEL BASED ON RANDOM FOREST CLASSIFICATION

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arXiv 2022年

作者： Gupta, Ishu Kaur, Sizman Sharma, Vartika Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has become quite easy. However, the complex and the selection of an optimal machine learning technique for the given dataset greatly affects the accuracy of the model. A large amount of datasets exists globally but there is no effective use of it due to its unstructured format. Hence, a lot of different techniques are available to extract something useful for the real world to implement. Therefore, accuracy becomes a major metric in evaluating the model. In this paper, a disease prediction approach is proposed that implements a random forest classifier on Parkinson's disease. We compared the accuracy of this model with the Principal Component Analysis (PCA) applied Artificial Neural Network (ANN) model and captured a visible difference. The model secured a significant accuracy of up to 90%. © 2022, CC BY.

关键词： Neural networks