Reviewerly/exHarmony huggingface dataset:
Toward Developing a National Customer Satisfaction Index in Jordan

Purpose: This research aims to develop a national index to measure customer satisfaction in Jordan. Based on householders' perspectives and assessments, this index will form a national measurement of customer satisfaction with goods and services offered in Jordan.&#x0D; Design/methodology/approach: T he r esearchers p erformed a s ystematic literature r eview (SL R) b y reading and analyzing the previous primary studies, using pre-specified search and inclusion criteria. In this study customer satisfaction index was defined and developed to the same degree as those that adopted specified customer satisfaction index principles in their development and application.&#x0D; Findings: Using the developed Jordanian Customer Satisfaction Index (JCSI), each sector, industry, and company included in the index will get a customer satisfaction score. The JCSI will measure customer satisfaction in 15 major economic sectors in Jordan. It contains three items to measure and track customer satisfaction using three facets of satisfaction―an overall rating of satisfaction, performance against expectations, and performance against the customer's ideal service.&#x0D; Research limitations/implications: The JCSI will encourage all Jordanian household consumers to give customer satisfaction feedback on purchasing and using goods and services. In this regard, this proposed national index will be helpful for consumers, managers, and policymakers.&#x0D; Originality/value: This paper developed a national customer satisfaction index in Jordan by using two perspectives(i.e., micro and macro levels). The proposed index in this paper is going to offer a multi-industry index to measure and benchmark Jordanian consumers’ satisfaction with goods and services produced and delivered in the Jordanian market.
A Machine Learning Approach to Predict Breast Cancer Using Boosting Classifiers

Breast cancer is a prevalent disease, with the second highest incidence rate among all types of cancer.The risk of death from breast cancer is increasing due to rapid population growth, and a dependable and quick diagnostic system can assist medical professionals in disease diagnosis and lower the mortality rate.In this study, various machine-learning algorithms are examined for predicting the stages of breast cancer, and most especially in the medical field, where those methods are widely used in diagnosis and analysis for decision-making.We focused on boosting classification models and evaluated the performance of XGBoost, AdaBoost, and Gradient Boosting.Our goal is to achieve higher accuracy by using boosting classifiers with hyperparameter tuning for the prediction of breast cancer stages, precisely the distinction between "Benign" and "Malignant" types of breast cancer.The Wisconsin breast cancer dataset is employed from the UCI machine learning database.The performance of our model was evaluated using metrics such as accuracy, sensitivity, precision, specificity, AUC, and ROC curves for various strategies.After implementing the model, this study achieved the best model accuracy, and 98.60% was achieved on AdaBoost.
An IoT Based Attendance System using PI Cam and RFID Controller

The primary focus of our country's education system is on attendance, particularly during graduation and post-graduation, as it is considered indicative of a student's commitment to their education. This project specifically centers around improving the attendance system. In the past, attendance used to be recorded in a traditional manner. However, over time, various attendance systems have been introduced, many of which rely on a single authentication method using a single sensor. Currently, there are attendance systems that utilize two sensors, like the "attendance system using biometric and RFID controller," for capturing attendance. Nevertheless, a drawback of the current system is the potential problem faced by management if one of the sensors fails to record data, making it difficult to obtain accurate attendance records. Moreover, injuries to the hand can hinder the use of the biometric attendance system results in a reliance on conventional techniques. An ESP32 camera is used in our project to take pictures of students or staff. for attendance purposes. This camera is capable of capturing facial images as well, ensuring attendance can still be recorded even in cases of facial injuries. Alternatively, an RFID card can be used as a backup method for attendance. The materials required for this project include Node MCU or ESP32, ESP32 camera, and an RFID controller.
Modality Direct Image Contrast Enhancement for Liver Tumour Detection

Medical image analysis relies heavily on segmenting medical images. Numerous methods based on edge or area characteristics have been developed for segmenting medical images. These are reliant on the image’s quality. When detecting a region of interest, such as a lesion, on a CT or MRI scan, contrast is crucial (s). Manual histogram adjustment techniques are used for improving the contrast of image where contrast enhancement is based on cross-modality in a 2D histogram specification technique. Our proposed multi-modality guided histogram specification approach improves the liver CT images by MRI images for input data. The optimization scheme is used in our technique which is based on guided picture enhancement as well as image quality management. In a two-step process, the suggested Improved Supervised Contrast Enhancement Technique (ISCET) scheme makes use of both structural and contextual information. Applying a two-dimensional histogram definition while utilizing contextual data from the matching guiding image, such as a magnetic resonance image, is the initial step (MRI). The second stage involves using an optimization strategy that keeps the original image’s structural information while employing a structural similarity metric. The results show that the performance is improved by the increase in entropy and MIGLCM values.
MDPFL:A Multiple Differential Privacy Protection Method based on Federated Learning

With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are lots of problems in federated learning, differential privacy and other machine learning privacy protection schemes, such as high loss of availability and the need of a fully trusted third-party server. In order to solve these problems, we propose MDPFL(Multiple Differential Privacy based on Federated Learning) algorithm. The algorithm combines with the differential privacy model in each stage of federated learning to solve the problem of curious third-party data collectors obtaining users’ original data in the process of machine learning. Meanwhile, the algorithm does not adopt a decentralized machine learning scheme directly, but uses a double noise adding mode with the existence of the third-party data collectors. We use Laplacian mechanism in the central server and GRR mechanism in local clients to ensure the goal of stability of the effect of the machine ¡earning model. The algorithm is compared with FedAvg, CDP, LDP algorithm in accuracy and loss rate which based on EMNIST data sets. In different global sensitivities, the model training effect is consistent with FedAvg algorithm while comparing with LDP algorithm, the speed of model convergence is improved.
Implementation of Artificial Intelligence in Education

Artificial intelligence in education (AIED) applications have been made easier to implement largely to the quick development of computing technologies. In order to improve teaching, learning, or decision-making, AIED refers to the employment of AI technology or application programmes in educational contexts. This study analyses every facet of the education sector's use of AI tools and technologies in great detail. The findings show how we may integrate AI development into these teaching, learning, student assistance, and management technologies. This study attempts to show how AI is being used in learning and teaching in the digital age.
ZoIE: A Zero-Shot Open Information Extraction Model Based on Language Model

Open Information Extraction (Open IE) provides a method to extract triplets from text, which has become a research frontier and hot topic in recent years. However, the traditional open information extraction approaches have low precision and significant costs, which heavily rely on the artificially defined extraction paradigm. In this paper, we propose ZoIE, a zero-shot open information extraction model. First, we set positive and negative samples and use the pretraining method based on contrastive learning to train a language model. Then, we use the self-attention weight matrix to perform triplet extraction by using the method of nucleus sampling beam search. Finally, we construct a triplet fine-sorting strategy based on the loss function and select the optimal triplet from the candidate triplets. Experimental results on serveral datasets show that our proposed approach achieves better performance than other baselines.
DevFinder: An approach to finding expert software developers considering desired technologies and industry domains

Finding software development experts who address the specific needs of companies is essential in software development environments. However, companies have struggled to find experts who master specific technical skills aligned with the experience of acting in specific industry domains. To address this issue, we propose a recommendation system approach that helps to identify and classify eligible experts. In order to achieve this goal, data from repository databases are extracted, and a complex network is modeled considering a combination of three main aspects: i) the impact of a developer on the repositories of the industry context, ii) the technologies considered in the search terms, and iii) the date when the developer contributed to the project. A final recommendation experts list is presented in three contexts considering different technologies and industry domains, followed by a collaboration trends discussion.
Curvature-Driven Knowledge Graph Embedding for Link Prediction

Knowledge Graph Embedding (KGE) aims to learn how to represent the low-dimensional vectors for entities and relations based on the observed triplets in knowledge graph. Most of the existing models use simple structural features, such as node degrees and directed edges, and pay little attention to advanced inherent information of structured knowledge. In this paper, we propose CD-GCN, a curvature-driven KGE method for link prediction. Specifically, we first apply Ricci curvature to knowledge graph. Then, we use curvature information to drive the state update, which aims to further exploit the graph-structured information. Finally, we use a ConvE scoring function to output the link prediction results. Through extensive experiments on public datasets FB15k-237 and WN18RR, CD-GCN has achieved state-of-the-art results compared with all baseline models.
Singular Value Decomposition Based Pedestrian Trajectory Prediction

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory.
An Answer Summarization Scheme Based on Multilayer Attention Model

At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence" of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system.
Abnormal Crowd Behavior Detection Based on the Fusion of Macro and Micro Features

There are two types of research on abnormal crowd behavior detection: micro modeling methods (e.g., crowd pose) and macro modeling methods (e.g., optical flow methods). In crowd evacuation, however, micro methods fail to solve the occlusion problem, while macro methods fail to address issues such as poor real-time performance and non-adaptive motion target detection. To address these issues, we propose a method that combines macro and micro methods to detect abnormal crowd behavior. Firstly, we extract the two-dimensional poses of moving people using the human pose estimation algorithm (OpenPose) and get the corresponding micro motion features. Secondly, we create an optical flow map of the video using the dense optical flow algorithm (Farneback) and a scene activity map by comparing the optical flow sizes between two consecutive frames. Then we derive the entropy change curve, which represents the macro motion features. Finally, we fuse micro and macro motion features and put the fused hybrid feature vectors into our classification network to train our model, and then find the transition time from micro to macro abnormal states in crowd evacuation videos. The experimental findings demonstrate that our proposed method can more accurately and quickly identify the abnormal behaviors that appear in evacuation scenes.
Towards Secure Transactions with IoT: An Advanced Smart Payment Solution

Since the spread of the Corona virus, the financial sector has seen rapid growth in online stores and the use of credit cards has become increasingly popular. However, the banking industry is adversely affected by the phenomenon of online payment fraud which can have a significant impact on both customer trust and the reputation of the bank. Many customers around the world and Moroccan customers in particular, prefer to stay away from online or credit card payments by using traditional payment methods. Furthermore, cybercriminals and fraudulent activity are becoming more creative. According to recent studies, cumulative commercial losses from fraud worldwide between 2023 and 2027 will exceed $343 billion. Therefore, it is imperative to provide the necessary infrastructure for customers to make fast and secure financial transactions. To prevent fraud and boost customer confidence, banks should expand contactless payment options and offer new online payment methods. This article presents a method for making secure and fast online payments using the Internet of Things (IoT). The main objective is to improve bank quality and customer satisfaction by reducing waiting times and improving customer interaction during the payment process.
Realistic Manipulations in Mixed Reality: Application to the Aeronautical Maintenance Review

The main objective of this study is to improve maintenance activities using mixed reality. Co-design work with Support Engineers was undertaken to develop a mixed reality solution that corresponded to the different needs of task sequencing feasibility. Multiple versions of the solution were tested using Microsoft HoloLens hardware to achieve continuous improvement. The study compared the two latest versions of the solution: the CAD version and the PHYS version. The CAD version was an immersive version inspired by CATIA CAD software, while the PHYS version implemented a realistic approach to parts handling (including gravity, continuous presence, and spatial organization of physical parts on a real table). Support Engineers carried out an experiment, performing a disassembly/assembly task using both versions of the solution. Results showed that the overall workload was significantly reduced, along with task completion time, using the PHYS solution. The PHYS version was also judged to be more usable than the CAD version. Adding 'pseudo-natural' manipulation of virtual objects to a maintenance task simulation better suits the needs of Support Engineers.
The Decision System of Language and Literature Education in Vocational Schools Based on Apriori Algorithm

The study of language and literature education in vocational schools is a new phenomenon in the field of education. This paper will discuss the research of language and literature education decision system in vocational schools based on Apriori algorithm. Apriori algorithm is a method to find the most possible word combination from a given set by using frequency counting. This algorithm can be used to determine whether students have acquired some knowledge or skills through lectures. It uses observation, interview, document review and field work as the main methods of investigation. In this case, researchers will use observation to collect data from different sources, such as books, newspapers, magazines, etc., and interview experts will be used to obtain their opinions on the research issues. Due to the large number of students in the school and the large number of subjects, there are certain difficulties in optimizing the allocation of teaching resources. For each student, there is a great correlation between their grades in various subjects, and this correlation, because of the differences in schools and vocational schools, shows relatively obvious differences. In view of such correlation and difference, Apriori algorithm combined with cloud computing can carry out better analysis and research. For each school and vocational school, the corresponding optimal allocation scheme of teaching resources in line with the collective is obtained.
A Two-Level Fuzzy Model for Filtering Signals of the Automatic Dependent Surveillance-Broadcast

A two-level fuzzy model for filtering complex signals such as automatic dependent surveillance-broadcast is presented in the article. The first and second levels of the fuzzy model consist of three operations: fuzzification, fuzzy composition and defuzzification. Input variables of two levels are given by trapezoidal membership functions that are formed automatically, depending on the characteristics of the complex signal. The output function at the first level is given by a singleton function, and the defuzzification is carried out using a simplified center of gravity model. The proposed two-level fuzzy model makes it possible to increase the sensitivity of the ADS-B signal receiver and correctly detect the received signal.
Using Forms and Group Boxes

When you fill out paper forms, you may notice that the paper forms group related items together. For example, the form may ask for your name, address, and phone number in one area of the form and ask for your gender, racial background, and marital status in another area of the form. Paper forms make it easy to enter related data in one area.
CDGCN: An Effective and Efficient Algorithm Based on Community Detection for Training Deep and Large Graph Convolutional Networks

Graph convolution neural network (GCN) has become a critical tool to capture representations of graph nodes. At present, the graph convolution model for large scale graphs is trained by full-batch stochastic gradient descent, which causes two problems: over-smoothing and neighborhood expansion, which may lead to loss of model accuracy and high memory and computational overhead. To alleviate these two challenges, we propose CDGCN, a novel GCN algorithm. Through partitioning of the original graph with a community detection algorithm, the original graph is decoupled into many different communities. The decoupled communities restrict the message passing between certain communities, and the model can be trained by using mini-batch stochastic gradient descent. Meanwhile, to prevent the prediction accuracy of the model from decreasing due to over-decoupling, a shallow full-batch stochastic gradient descent GCN is added to the model. We have conducted extensive experiments on node classification tasks of multiple datasets. The experiments show that the model has excellent performance, especially in the dataset with a higher average clustering coefficient, and can achieve higher prediction accuracy.
Horizontal Federating Decision Tree Learning From Data Streams: Building Intelligence in IoT Edge Networks

The paper presents Horizontal Federated Decision Tree Learning (HFDTL) system which is capable of building a collaborative model from evolving data streams in federated environment such as that of edge network where IoT gateways play the role of edge nodes processing data locally and edge server aggregates the local models using newly proposed majority-based aggregation algorithm. The popular VFDT algorithm is modified for edge nodes. The performance analysis of the HFDTL system reveals that it results in more accuracy compared to centralized VFDT for balanced partitioned data streams whereas the accuracy remains lower for unbalanced partitioned data. The communication cost also remains slightly higher for unbalanced data streams. The deployment of HFDTL system is expected to bring intelligence in IoT edge networks.