Reviewerly/exHarmony huggingface dataset:
DeKeDVer: A deep learning-based multi-type software vulnerability classification framework using vulnerability description and source code

Software vulnerabilities have confused software developers for a long time. Vulnerability classification is thus crucial, through which we can know the specific type of vulnerability and then conduct targeted repair. Stack of papers have looked into deep learning-based multi-type vulnerability classification, among which most are based on vulnerability descriptions and some are based on source code. While vulnerability descriptions can sometimes mislead vulnerability classification and source code-based approaches have been rarely explored in multi-type vulnerability classification. We design DeKeDVer (Vulnerability Descriptions and Key Domain based Vulnerability Classifier) with two objectives: (i) to extract more useful information from vulnerability descriptions; (ii) to better utilize the information source code can reflect. In this work, we propose a multi-type vulnerability classifier which combine vulnerability descriptions and source code together. We process vulnerability descriptions and source code of each project separately. For the vulnerability description of a sample, we preprocess it using a specified way we design based on our observations on numerous descriptions and then select text features. After that, Text Recurrent Convolutional Neural Network (TextRCNN) is applied to learn text information. For source code, we leverage its Code Property Graph (CPG) and extract key domain from it which are then embedded. Acquired feature vectors are then fed into Relational Graph Attention Network (RGAT). Result vectors gained from TextRCNN and RGAT are combined together as the feature vector of the current sample. A Multi-Layer Perceptron (MLP) layer is further added to undertake classification. We conduct our experiments on C/C++ projects from NVD. Experimental results show that our work achieves 84.49% in weighted F1-measure which proves our work to be more effective. Our work utilizes information reflected both from vulnerability descriptions and source code to facilitate vulnerability classification and achieves higher weighted F1-measure than existing vulnerability classification tools.
Skeleton-based action recognition with local dynamic spatial–temporal aggregation

Skeleton-based action recognition has attracted more and more attention due to its advantage in avoiding environmental noise including light transformations and viewpoint changing. Most current approaches consist of interleaving spatial-only modules and temporal-only modules, where messages are first spatially aggregated between human joints and then temporally delivered. Although efficient, such factorized design hinders direct information flow among joints in adjacent frames, thus failing to (1) effectively convey information in a larger neighborhood and (2) be inferior to capture short-term motion to distinguish similar action pairs. To handle these limitations, we propose spatial–temporal graph attention network (STGAT) to dynamically model local cross-spacetime information. Keeping the temporal-only modules unchanged, it inflates the spatial-only modules to perform spatial–temporal modeling from the local spatial–temporal neighborhood without a second transmission cost. Especially, different from previous spatial–temporal modeling methods, graph edges are adaptively computed to dynamically aggregate beneficial information to capture short-term movements, getting rid of predefined fixed connectivity. This ensures STGAT could dynamically attend to important joints in a local spatial–temporal neighborhood to capture critical short-term movements. While STGAT is theoretically effective in introducing local spatial–temporal information by comprising a large spatial–temporal neighborhood, we find its effectiveness is impeded by the inherent redundancy in local features. We propose three simple modules to reduce local feature redundancy to calibrate the feature modeling schema and further release the potential of STGAT, which (1) narrow the scope of self-attention operators, (2) dynamically weight joints along the temporal dimension, and (3) separate subtle motion from static features, respectively. As a result, STGAT achieves state-of-the-art performance on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400. Thanks to special care on local cross-spacetime information, STGAT generalizes better upon classifying hard similar actions than previous methods. Visualizations demonstrate that STGAT could intelligently attend to critical joints when dealing with different actions. Code is available at https://github.com/hulianyuyy/STGAT.
Critical Success Factors of Agile Transformation Initiatives from a Project Management Perspective

Context: Agile methods have been adopted as an alternative to meet the frequent changes in the IT industry and to address issues of quality, cost, speed, and efficiency in systems development processes. Problem: Managers are responsible for the appropriate application of knowledge, skills, tools, and techniques that can impact the success or failure of an agile transformation initiative. However, there is not much visibility into the critical success factor (CSF) associated with it. Solution: We aim to identify the CSF that affect agile transformation initiatives from a project management perspective. IS Theory: The General Systems Theory provides the basis for this study since the organizational aspects involved in the agile transformation are interrelated and influenced by factors such as environment, culture, information technology, internal management systems, and other elements that may hinder its adoption. Method: We performed a systematic mapping study in the following digital databases: EI Compendex, IEEE, ISI Web of Science, and SCOPUS. Summary of Results: We identified 12 CFS based on 12 studies. The most cited ones were top management support, team empowerment, adapting the process to agile, and customer focus. Adoption of participatory management, good communication, and building strong teams are among the least mentioned CSF. Contributions and Impact in the IS Area: Our results can help organizations in new agile transformation initiatives. Such CSF are interrelated and, if taken into consideration, have an essential effect on the success of the agile transformation. Moreover, management should consider the business through a systemic and holistic view because those CSF can positively impact agile transformation.
Explainable Artificial Intelligence in Medical Image Analysis: State of the Art and Prospects

to increase transparency in the work of artificial intelligence systems in the analysis of medical images is called to use methods of explainable artificial intelligence. Our study provides an overview of the current state of application of explainable artificial intelligence methods for medical image analysis, and also considers potentially promising approaches to improving the technology.
Gas Turbine Selective Control System

The object of the research is a system of selective control of a gas turbine engine. The paper presents a model of the standard selector. The shortcomings of the existing system of selective control were identified. The main disadvantage is operation in static mode. The analysis of modern approaches to constructing a selector control system of a gas turbine engine was carried out. Alternatively, it is proposed to use model of collective behavior using a threshold model of behavior agents. The simulation results are presented. The task of improving the selector model is defined.
Proposal and Preliminary Evaluation of a Usability and UX Multi-Touch Evaluation Technology

Context: For multi-touch Information Systems (ISs) to reach the desired level of quality, it is necessary to have an evaluation of it. Two important quality aspects are usability and User eXperience (UX). A good assessment must take into account the specifics of the evaluated context. Problem: Through a Systematic Mapping Study (SMS), it was realized that no usability and UX evaluation technologies take into account the unique characteristics of the multi-touch context. Solution: To solve this gap, the User eXperience and Usability Multi-touch Evaluation Technology (UXUMEQ) was proposed. IS Theory: To fulfill this gap, UXUMEQ was based on Design Theory, which concerns the development and refinement of an artifact. Method: The usability and UX aspects and the evaluation technologies found in the SMS results were selected and evaluated to compose the UXUMEQ. The questions of evaluation technologies found considered relevant were filtered and adapted for the multi-touch context. Results: Therefore, the purpose of UXUMEQ takes into account the specifics of multi-touch, in the form of a modular questionnaire, covering the aspects of usability and UX. Moreover, a preliminary evaluation with three experts in Human-Computer Interaction provided improvements for the UXUMEQ. Contribution: In this way, the existence of usability and UX evaluation technology of ISs that uses multi-touch interaction can contribute to reaching a better quality of this kind of system.
Impact of Hyperparameter tuning on Solar PV Performance using Machine Learning for Moscow, Russia

The usage of solar photovoltaic (PV) systems has become a viable option for supplying the worlds expanding energy needs while lowering greenhouse gas emissions. The efficiency and cost-effectiveness of the system must be designed and optimized, which requires accurate modeling of solar PV performance. In order to choose the best hyperparameters for a particular situation, it is important to have a deeper grasp of how hyperparameter tuning affects solar PV model performance. In this paper, the impact of hyperparameter tuning on the effectiveness of solar PV modeling for Moscow, Russia, using machine learning approaches is investigated. For the purpose of predicting the performance of the solar PV system, the data and various machine learning models are being analyzed. These models performance is assessed using a variety of criteria while contrasting its results during hyper tuning. The accuracy and dependability of the solar PV performance models can be greatly increased, according to the research, by hyperparameter tuning. We outline the implications and prospective effects of our study on the design and optimization of solar PV systems in Moscow, Russia, and make suggestions for further study. The findings of this study would help Russia and other regions design solar PV systems that are more effective and sustainable.
Multi-class Traffic Sign Recognition System Using One-Stage Detector YOLOv5s

One of the crucial software elements in the upcoming generation of autonomous vehicles is image recognition. Traditional approaches to image recognition using computer vision and machine learning typically have a lengthy response time. Modern artificial neural network-based methods and designs, including the YOLOv5s algorithm, are able to tackle this issue without suffering precision losses. In this study, we demonstrate how to use the most recent YOLOv5s algorithm to identify traffic signs. We showed the reliability of the method by training the network for 4 traffic sign classes (speed limit, traffic light, crosswalks, stop,).
Hazardous Asteroid Prediction Using Machine Learning

With the help of machine learning algorithms, make a prediction of potentially hazardous asteroids (PHAs) which are having different combinations of orbital parameters. The suggested method intends to identify all major groupings of PHA-rich near-Earth asteroids (NEAs) and their subgroups. we applied different machine learning methods namely Random Forest, decision tree, k nearest neighbours, Naive bayes, Logistic Regression, Support Vector Machine (SVM) - for identifying potential hazardous and non-hazardous asteroids and features responsible for claiming an asteroid to be hazardous. so far as, asteroids are considered, there are many asteroids which are nearer to the earth, but all are not hazardous. So, our target in this project is to identify the asteroids which are hazardous and classify them with non-hazardous types. For this, we choose machine learning models. We used the data attributes to train these models, and then we compared the results to choose the model that provided the greatest accurate categorization.
Design of Biped Robot Using Reinforcement Learning and Asynchronous Actor-Critical Agent (A3C) Algorithm

The creation of a humanoid robot necessitates a remarkable interdisciplinary effort spanning engineering, mathematics, software, and machine learning. In this work, we investigate the policy-based algorithm known as Reinforce, which is a deep reinforcement method. The goal of policy-based approaches is to directly optimize the policy without the utilizes of a value function. Reinforce specifically belongs to the Policy-Gradient techniques subclass of Policy-Based techniques. This subclass uses gradient ascent to estimate the weights of the ideal policy, directly optimizing the policy. In order to stabilize the training by lowering the variance, a hybrid architecture combining policy-based and value-based methodologies is proposed in this paper. Asynchronous Advantage Actor-Critic (A3C), a hybrid technique, trains agents in robotic environments by employing Stable-Baselines3. It trains two agents to walk, one on two legs and the other on a spider moment. According to the experimental findings, both robots are able to recognize the target's orientation, move to the proper location, and then successfully raise the target together.
Named Entity Recognition-based Hospital Recommendation

Extracting the intended information from raw text input remains a potential challenge in the biomedical industry. An NER based pre-diagnosis model that uses a general medical description from the patient is proposed to extract the symptoms. In addition, a Machine Learning model that predicts the disease and recommends a hospital is discussed. To develop this pipeline, we collected the BC5CDR dataset, supplemented with manually annotated data, and trained it using several models, including the spaCy blank model, pre-trained BC5CDR model, and transformer models such as the BERT case and RoBERTa. After evaluating the models' performance, we found that RoBERTa outperforms other models with a higher F1 Score of 0.8586 and precisely identifies symptoms, diseases, and chemicals from the medical description. These physical symptoms are used by ML techniques to predict possible disease. Then, using the hospital's data and predicted results, the K-nearest neighbor algorithm suggests nearby, highly rated hospitals based on the user's location, distance, and ratings. This study can potentially enhance medical diagnoses and treatments and provide a promising foundation for future medical consultations and self-diagnosis.
Web-based chatbot for basic financial and mortgage services

A Chatbot often also called as conversational interfaces, enable users a new method to converse with the computers. Traditionally, any mundane query would require a human to resolve them and therefore there were limitations to it, like time and knowledge of that particular human. But with the onset of the chatbot system the experience is like getting your queries resolved by a human but there are no common limitations to the same. Many businesses have developed their own chatbots to help their customers with their needs. Our chatbot is a financial services chatbot which specializes in lending and mortgage services.
Blockchain Fundraising and Charity Platform

Charity is regarded as a moral imperative throughout the world, and enormous sums of money are spent in its name. In most situations, the charity collection systems are not clear, and owing to this, charitable organisations struggle to acquire contributors' confidence and attention. This study introduces a crowdfunding platform built on the blockchain to increase the transparency of the fundraising process and grant makers' expenses and to overcome the drawbacks that exist in the present distribution system. Donations can only be withdrawn by the people or organizations that organized the charity event, enabling registered charities to post their identities for investor verification. Since no single entity is in charge of the blockchain for charities and fundraising, blockchain technology can help prevent data breaches and promote openness. The platform serves as a gateway for fundraising and charitable work.
Dual Access Control for Cloud-Based Data Storage and Sharing

Cloud-based storage capacity has seen a significant increase in interest from academics and corporations in recent years due to its effective and cost-effective administration. Because services are provided via a public channel, network operators must adopt secure data exchange and storage mechanisms to preserve user privacy and data confidentiality. Cryptography represents the most commonly used method for restricting sensitive data exposure. Yet, data encryption alone cannot fully address the requirement for data management. To combat EDoS, assaults that impede users from using the service, strict access restrictions for download requests must additionally be taken into consideration. In the sense of developing a system of control over both network connectivity and download operations without sacrificing security and efficacy, this paper covers dual access controls connected to cloud-based storage. For each testing environment, a single dual admission control system was built in this study. The system's safety and experimental investigations are also presented.
Ant Colony System for Efficient Virtual Machine Placement in Cloud Computing

Cloud computing data centers host millions of Virtual Machines across the world resulting in an extremely secure and flexible environment. Virtual machine placement is solitary of major challenging problems in cloud infrastructure organization. It introduces problems such as excessive energy consumption by physical machines and greenhouse effect. Most cloud servers consume only about 11% to 50% of their total resources. A study based upon the different methods responsible for achieving energy efficient cloud computing was conducted. Many algorithms like the First Fit-Decreasing Algorithm and the Modified Ant Colony Optimization have been discovered to achieve energy efficient virtual machine placement but the consumption rate is still high. An algorithm based upon the Ant Colony System approach was analyzed. This, combined among Order Exchange and Migration confined look for technique, resulted the Communication Ant Colony System. It helps in assigning virtual equipment to suitable hosts. In this manuscript, we discuss the working and implications of the Order Exchange and Migration Ant Colony System. It is applied to numerous Virtual Machine Placement problems under different circumstances using the Clouds simulator. The results, compared with other algorithms, assure that Communication in Ant Colony System consumes the least amount of energy and is reliable.
Research on Sentiment Classification of MOOC User Comments Based on Machine Learning

The increasing use of information technology in education has led to the emergence of online teaching methods, such as MOOC, that plays a vital role in promoting the reform and development of basic education. MOOC primarily uses micro-lessons to present learning content, and students can collaborate, transfer and share information by exchanging questions in the comment section. MOOC comments reflect the learners' evaluation and attitude towards the course. However, comment text is unstructured data, so obtaining accurate information about learners' emotional feedback from comment text is challenging for both users and platforms, and improving MOOC lessons using feedback from comments proves difficult. In this study, we used the "Chinese University MOOC" platform as a case study, utilizing artificial labeling to create experimental datasets. We compared the sentiment classification of four conventional machine learning models, explored error cases, and proposed a model optimization scheme. The results serve as a reference for future research on sentiment classification of spoken text data.
Evaluating Off-chain Transaction Queueing Delay to Ensure Data Integrity by Blockchain

In recent years, society has been promoting the use of big data to improve our daily lives. At the same time, there is a demand to verify the legitimacy of data. For this reason, there have been many proposals to ensure data integrity by a blockchain consensus. However, the blockchain-based system cannot process a lot of transactions in a short period. Therefore, the blockchain technology cannot be simply applied into systems where large amounts of data are added and updated.Off-chain protocols have been proposed to scale blockchains. They guarantee the integrity of data outside the blockchain and reduce the on-chain replication cost by not directly storing data in the blockchain storage. Meanwhile, an off-chain queueing delay is newly introduced since off-chain transactions (OTXs) are processed in a batch manner by periodically posting the OTX summary data on the underlying blockchain. This delay cannot be ignored, especially in the case of frequently updated data, such as IoT data.In this paper, we propose and analyze a queueing model that considers OTX processing time in order to estimate the mean OTX queueing delay. This analytical values help us to previously determine whether the adopted off-chain approach is effective to ensure the integrity of the given data. In numerical examples, we show how the parameters, such as the OTX batch size and the OTX rate, affect the OTX queueing delay.
Federated Learning Differential Privacy Preservation Method Based on Differentiated Noise Addition

Differential privacy is an essential tool in federated learning privacy preservation. However, existing differential privacy-preserving techniques introduce excessive noise perturbations for privacy-preserving purposes, which makes federated learning models less usable. Under the condition of satisfying differential privacy, this paper proposes a new noise addition method, differential privacy preservation by federated learning based on differentiated noise addition (DDPFL), to reduce the loss of model usability. Specifically, the method derives importance coefficients for each model parameter by analyzing the value of the gradient update size, the magnitude of the absolute value of the weight parameter, and the relationship between the global model and the local model gradient update trend during the training process for each client. Then, noise is added to the model parameters using a differentiated noise addition mechanism according to the magnitude of the importance coefficients to achieve noise perturbation of the local model. Finally, the effectiveness of the proposed method is validated by an experimental comparison of the MNIST dataset and the Fashion-Mnist dataset. The usability of the model improves under the same differential privacy condition as the federated learning differential privacy preserving method before the improvement.