Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis to Review Products based on Machine Learning

In this study, a method is proposed to represent the corresponding viewpoints and features in the form of graphs for observing the sentiment of the subscribers based on product review. The aim is to observe and empathize the situations based on real time with sentimental analysis using deep learning and machine learning. Nowadays machine learning and deep learning is applied for the solutions of the problems in computer vision, voice recognition, image processing and convolutional neural network. In this work, a convolutional neural network model has been proposed which attains a better outcome than other methods.
A reconfigurable rack-scale interconnect architecture based on PCIe fabric

Today cloud computation center has become an important component of modern information system. How to achieve the cloud computation center resource sharing and improve resource utilization is an important research topic. In order to improve the resource utilization, rack-scale host (also called converged infrastructure system) is usually used by cloud computation center. This paper proposes a reconfigurable interconnect architecture and resource allocate algorithm for the rack-scale host. The architecture includes CPU node, IO node, PCIe switch node and Ethernet switch node. PCIe switch node is used to connect CPU and PCIe device, and Ethernet switch node is used to configure PCIe fabric. The resource allocation algorithm includes the following steps: request for CPU nodes and IO nodes, initialize PCIe devices, establish device configuration space map, discover new devices and allocate device address space, establish device address space map, establish a map of PCIe device interrupt vectors and CPU interrupt vector tables, and at last load device drivers. The interconnect between CPU and PCIe device is transparent to CPU and OS, so all CPU and OS that can connect to the PCIe device are supported.
Utilization of AI for Streamlining and Optimizing Credit Decision Process and Security Access Loan Risks in the Banking Sector

The rapid growth of credit card service in banking is a challenge because of user level decision to provide loan risk. More specific real-time data analysis through Artificial Intelligence (AI), banking, and malware penetration application data analysis through other areas needs security identification. The efficiency gain of the method has been verified by experiments on real data sets from commercial banks. Representative behavior models can help improve online banking credit fraud detection performance significantly. Previous approaches of individual and population models find the single level of detecting fraud deposits, access levels, loan deployment etc. It will not efficient in containing customer behavior faced with online payment fraud detection. The proposed TRUBSV rule-based fraud detection is carried out by a precision algorithm and behavior-based group-level or general-level calculation system to provide loans. The recommended method is to evaluate by doing behavioral factors depends on security analysis in user level to provide loan. It can be easily discovered that the original and fraudulent transactions. Differential analysis is used to gather Group level evidence where fraud indicates significant deviations from normal behavior. Evaluating fraud possibilities is a fraud detection method based on effective identification by tracking the number of different accounts, accessing our main contributing accounts, and accessing each other's devices. Provide a systematic and detailed overview of these issues and challenges that may disrupt Target Request Based User Behavioral Session Verification (TRUBSV). The TRUBSVs promising for the e-commerce system. These e-commerce systems have been introduced closely to the types of fraud that are widely used. Most of the e-commerce systems have been able to process financial transactions. Behavior-based methods are considered a promising method for online payment fraud detection. It is to develop a high-resolution behavior model using the details of low-quality behavior.
UAVDet: A Lightweight Fast Detection Model for Marine Ships based on Edge Mobile Devices

Recently, Unmanned Aerial Vehicles have been widely used in the fields of water traffic supervision and maritime sovereignty inspection, becoming an important means of data acquisition. It is crucial to apply deep learning-based target detection technology to UAV edge devices. Traditional detectors are often underperformed when deployed directly to UAVs. One reason is that the amount of UAV imagery data is often limited and insufficient to support the training of deep learning algorithms. The second is that deep learning-based detectors often have huge models and huge amounts of parameters with high computational complexity, making it difficult to deploy them to work effectively on edge mobile devices with extremely limited computational resources and memory. To solve these problems, we proposed a new detection model for UAV view ship target based on YOLOv4. To this end, first, we constructed a satellite remote sensing ship image dataset and used transfer learning to reduce the reliance on model training data. Second, we lightened the model by sparsity training, channel and layer pruning, and then used knowledge distillation techniques to rebound the accuracy. In the end, the model size is reduced by 97.19% and the detection time of a single image is reduced by 39.73% while maintaining high detection accuracy, achieving high precision real-time detection suitable for deployment on edge devices such as UAVs.
Design Feature Selection and Classifiers for Hybrid Feature Selection - Particle Swarm Optimization (HFS-PSO)

In image classification, the feature selection process is critical for reducing overhead and improving accuracy. In light of the increasing growth of data, the concept of integrating classifiers with feature selection approaches has lately emerged as a new trend for boosting the performance of classification systems. There are several benchmark algorithms that use wrapper, filter, or hybrid approaches. These algorithms use a variety of strategies, ranging from basic search -based totally strategies to more complex nature-stimulated set of rules-based totally strategies. In this paper, a hybrid feature selection set of rules based on image classification is proposed. The suggested framework's performance was compared to many benchmark supervised and unsupervised feature choice techniques and found to be advanced. Moreover, the advised algorithm is much less computationally extensive and therefore took much less time to execute for the publicly available datasets used within the trials, which covered high-dimensional datasets.
Blind Superresolution of Satellite Videos by Ghost Module-Based Convolutional Networks

Deep learning (DL)-based video satellite superresolution (SR) methods have recently yielded superior performance over traditional model-based methods by using an end-to-end manner. Existing DL-based methods usually assume that the blur kernels are known and, thus, do not model the blur kernels during restoration. However, this assumption is rarely held for real satellite videos and leads to oversmoothed results. In this article, we propose a Ghost module-based convolution network model for blind SR of satellite videos. The proposed Ghost module-based video SR (GVSR) method, which assumes that the blur kernel is unknown, consists of two main modules, i.e., the preliminary image generation module and the SR results’ reconstruction module. First, the motion information from adjacent video frames and the wrapped images are explored by an optical flow estimation network, the blur kernel is flexibly obtained by a blur kernel estimation network, and the preliminary high-resolution image is generated by feeding both blur kernel and wrapped images. Second, a reconstruction network consisting of three paths with attention-based Ghost (AG) bottlenecks is designed to remove artifacts in the preliminary image and obtain the final high-quality SR results. Experiments conducted on Jilin-1 and OVS-1 satellite videos demonstrate that the qualitative and quantitative performance of our proposed method is superior to current state-of-the-art methods.
Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis

Federated learning enables multiple clients to learn a general model without sharing local data, and the federated learning system also improves information security and advances responsible artificial intelligence (AI). However, the data of different clients in the system are non-independently and identically distributed (IID), which results in weight divergence, especially for complex graph data extraction. This article proposes a novel feature-contrastive graph federated (FcgFed) learning approach to improve the robustness of the federated learning system in graph data. First, we design an architecture for FcgFed learning systems to analyze graph information. Furthermore, we present a graph federated learning method based on contrastive learning to alleviate the weight divergence in federated learning. The experiments in node classification and graph classification demonstrate that our method achieves better performance than model-contrastive federated learning (MOON) and federated average (FedAvg). We also test the adaptability of our method in image classification, and the results demonstrate that weight similarity evaluation works for other frameworks and tasks.
Artificially developed intelligent system using Python

ADINA stands for ‘Artificially Developed Intelligent Assistant’ which is developed as an application software. The purpose is to fulfill the basic requirements of its users and based on the usage of the user to predict its behavior. The basic requirement of this user that this application covers include making web-based as well as Wikipedia searches, event scheduling of users (calendar and planner), exploring real time news headlines throughout the globe as well as exploring new places. We have developed our own artificial intelligence assistant ‘ADINA’ using python language. This paper provides what services a modern assistant can provide along with scalability to add more existing services and also incorporate the services of the future. It can also be used to incorporate and control ‘IOT’ applications as they majorly use Raspberry Pi processor which can be easily integrated.
Developing 3d flashcard-augmented reality learning media for pre-service teachers

The advancement of technology in the field of education is currently accelerating. Today's media must keep pace with technological advancements. The development of technology-based learning media needs to be developed to increase student motivation. One of the media that can be used is augmented reality technology. Augmented reality is an innovation from computer graphics that present visualization and animation of objects. The augmented reality application in the world of education has a strong appeal to students and provides freedom for students to carry out the process of observing themselves. The research method used is R&D with a 4-D model and has four main stages, namely Define, Design, Develop and Disseminate. Product feasibility is reviewed from the material and media expert's assessment and student responses. The results showed that the 3d flashcard-augmented reality learning media was feasible to use in learning mathematics, especially in geometry material. The following results are that each of them gets a score of 85% from material experts, 87% from media experts, and 90% from student responses.
Tech Worker Perspectives on Considering the Interpersonal Implications of Communication Technologies

Communication technologies, from social media to video conferencing, are used by billions of people globally and contribute to shaping relationships between people. As these technologies become increasingly ubiquitous, the tech workers building them are increasingly making product decisions that can have far-reaching interpersonal ramifications. At the same time, few workplace tools and support exist to help tech workers understand and navigate these potential ramifications, and tech worker perspectives on such tools are not fully understood. In this work, we explore the needs, challenges, and opportunities encountered by tech workers in thinking through the interpersonal implications of their products. To do this, we ran a semi-structured interview study with 10 diverse tech workers. To ground the discussion, study participants interacted with a design probe prototype, InterAct, which provides research-grounded information about interpersonal implications of product features. Our findings suggest a desire by tech workers to consider the social implications of the technologies they build, and the potential for structured tooling to help provide the required knowledge and build organizational support. Based on these findings, we provide design considerations for creating future workplace tools to support thinking about the social implications of technologies.
An Efficiency–Accuracy Balanced Power Leakage Evaluation Framework Utilizing Principal Component Analysis and Test Vector Leakage Assessment

The test vector leakage assessment (TVLA) is a widely used side-channel power leakage detection technology which requires evaluators to collect as many power traces as possible to ensure accuracy. However, as the total sample size of the power traces increases, the amount of redundant information will also increase, thus limiting the detection efficiency. To address this issue, we propose a principal component analysis (PCA)-TVLA-based leakage detection framework which realizes a more advanced balance of accuracy and efficiency. Before implementing TVLA to detect leakage, we project the original power data onto their most significant feature dimensions extracted by the PCA procedure and screen power traces according to the magnitude of their corresponding components in the variance of the projection vector. We verified the overall performance of the proposed framework by measuring the detection capability and efficiency with t-values and the required time, respectively. The results show that compared with similar existing schemes, under the best circumstances, the proposed framework decreases the t-value by 4.3% while saving time by 25.2% on the MCU platform and decreases the t-value by 2.4% while saving time by 38.0% on the FPGA platform.
A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data

Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping heterogeneous fuzzy data to a unified distribution space by adjusting the mean and variance of data from each information source. As a result of combining the normalized descriptions from various sources for an object, we can obtain a fused representation of that object. We then derive an adaptive Gaussian-shaped membership function based on the addition law of the Gaussian distribution. GFIS uses it to dynamically granulate fusion inputs and to design inference rules. This proposed membership function has the advantage of being able to adapt to changing information sources. Finally, we integrate the normalization method and adaptive membership function to the Takagi–Sugeno (T–S) model and present a modified fuzzy inference framework. Applying our methodology to four datasets, we confirm that the data do lend support to the theory implying the improved performance and effectiveness.
Intelligent Agents in Educational Institutions: NEdBOT - NLP-based Chatbot for Administrative Support Using DialogFlow

Artificial intelligence (AI)-based chatbot systems have seen increased adaption in the educational domain in recent years owing to increased sophistication in the AI domain. However, most of the communication between students and educational institutions is still performed physically and causes major administrative overhead, especially during the time of admission. Contemporary pattern-matching-based and generative-based chatbots underperform to queries outside a limited scope, grammatically and structurally ambiguous inputs, outliers to pre-defined rule-set, and longer response times for a huge knowledge base. We proposed a NEdBOT-An NLP-based Educational Bot, developed by Natural Language Processing models integrated within the DialogFlow platform utilizing a Retrieval-based approach. We evaluate the developed chatbot on a custom dataset generated for the admissions use case of a prominent university. We used an objective evaluation criterion with real-world users to achieve an intent classification accuracy of 76.8% at an average mean response time of 216.43ms per query and a user-friendliness score of 72% on the System Usability Scale (SUS). The results demonstrate the proposed approach's ability to create robust, reliable, responsive, and user-friendly web-based smart chatbots that are highly scalable with the capability to handle wider scopes and vague inputs with ease.
Agent for Recommending Information Relevant to Web-based Discussion by Generating Query Terms using GPT-3

In Web discussions, which have become mainstream with COVID-19, the amount of information possessed and the level of understanding of the discussion differ among participants. As a result, some participants may not be able to speak up satisfactorily, and this can hinder consensus building in the discussion as a whole. Therefore, we develop an agent that automatically recommends information related to the discussion as information that facilitates participants to speak up. The agent first obtains necessary discussion data from on-going Web discussions. The information to be recommended is determined by real-time search. Query words for the search are generated using a pre-trained query-term-generation model. When selecting information to recommend from the information obtained in the search, a model that classifies the acquired information according to the discussion phase is used. The results of a discussion experiment in which an agent intervened in a Web-based discussion showed many results indicating the effectiveness of the agent, although there are some points that need to be improved. However, since the scale of the discussion experiment was small, it will be necessary to validate the agent in large-scale discussions in the future.
SVGraph: Learning Semantic Graphs from Instructional Videos

In this work, we focus on generating graphical representations of instructional videos. We propose a self-supervised, interpretable approach that does not require any annotations for graphical representations, which would be expensive and time consuming to collect. We attempt to overcome ‘black box’ learning limitations by presenting Semantic Video Graph or SVGraph, a multi-modal approach that utilizes narrations for semantic interpretability of the learned graphs. SVGraph 1) relies on the agreement between multiple modalities to learn a unified graphical structure with the help of cross-modal attention, and 2) assigns semantic interpretation with the help of Semantic-Assignment, which captures the semantics from narration. We perform experiments on multiple datasets and demonstrate the interpretability of SVGraph in semantic graph learning.
A Channel Modified Underwater Image Enhancement Algorithm

The influence of the medium and particles in the water leads to the attenuation and scattering of light wave propagation. The underwater image will appear blur and color deviation in the imaging process, which makes the underwater target recognition, target tracking, feature extraction and other applications difficult. In view of the above problems, this paper proposes a channel modified underwater image enhancement algorithm. First, the underwater image with color deviation is modified and equalized, a mapping is made by using the intensity center of the histogram. The mapped three channel information is fused into the adaptive histogram equalization with limited contrast, which improves the color deviation and insufficient contrast of the image. Second, the image is deblurred by a priori algorithm of dark channel. Experiments on underwater image data sets show that the proposed method is more effective than existing algorithms in dealing with the degradation of underwater image imaging, and achieves better image quality indicators.
Proactive Push Research on Personalized Learning Resources Based on Machine Learning

The personalized learning resource push is to respect learners' interests and preferences, behavioral habits, and other differences, so that improving the speed and accuracy of resource acquisition, enhancing learners' learning interests and efficiency, and finally promoting personalized learning. This article analyzes the role of traditional big data technology and machine learning technology in recommending personalized learning resources in educational assistance systems. It combines the background, significance, and technical means of personalized learning resource push to explore the advantages of personalized learning resource push based on machine learning technology, the problems that need to be solved and development paths in the future. Finally, the study finds that machine learning technology, whose model is more stable, has better performance in personalized learning resource delivery. At the same time, machine learning technology can help develop the field of “Artificial Intelligence + Education” rapidly and efficiently.
USV Path Planning Based on Sparse Visibility Graph

The path planning of unmanned surface vehicle is the key to realize the intelligent driving of unmanned vehicle. Aiming at the problem of low search efficiency caused by the increase of vertex connections in the existing Visibility Graph, this paper presents a path planning algorithm based on Sparse Visibility Graph, which improves the planning efficiency of Visibility Graph by reducing the complexity of visibility graph and improving the search algorithm. Firstly, Sparse Visibility Graph construction is introduced, which reduces the complexity of the Visibility Graph by clipping unnecessary edges to reduce the degree of vertices. Secondly, the improved Lazy Theta* is introduced, the weighted valuation function is introduced to analyze the influence of the actual cost and the estimated cost on the planning effect. Aiming at the problem that the basic A * search path is constrained by the grid and the Theta* planning path is not optimal and the search efficiency is low. Through delayed the line-of-sight check and improvement in checking generations limits, the improved Lazy Theta <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm improves the efficiency of planning and the authenticity of the path. Finally, simulation experiments are carried out in a two-dimensional grid environment. The results show that, compared with the search algorithm based on Visibility Graph, the path planning based on Sparse Visibility Graph has a shorter search time, can achieve more efficient local path planning, and the path is more reasonable.