Reviewerly/exHarmony huggingface dataset:
Damage detection method of long-span bridge cable structure based on wavelet packet analysis

In order to improve the effectiveness of damage detection of long-span bridge cable structure, a damage detection method of long-span bridge cable structure based on wavelet packet analysis is proposed. After clarifying the causes and types of cable structure damage, the damage location of cable structure is determined by wavelet packet analysis method. The main content is to construct the extended two-scale equation of wavelet packet based on scale vector and wavelet vector, so as to obtain the damage location detection function of cable structure. After the damage location detection, the damage degree detection of cable structure is completed by using the classification principle of support vector machine. The experimental results show that compared with the traditional detection methods, this method can detect the damage position and degree of cable structure more accurately.
Face detection and recognition in organic video: A comparative study for sport celebrities database

The present paper reports on an experimental study carried out under the applicative field of organic video processing and related to the possibility of identifying soccer celebrities in video content.In contrast to common state-of-the-art studies, special attention is paid to the cases in which the face is not completely included in the frame (lateral views, partial occlusions, etc.) and/or in which arbitrarily lighting conditions occur.To this aim, after a state-of-theart study, we consider two conventional types of face detection algorithms (Haar Cascade Classifier, and MMOD -Max-Margin object detection) coupled to two conventional face recognition models (LSBH -Local binary pattern histogram, and CNN-based Pruned ResNet).The experimental work consists of evaluating the performances of the four possible combinations among the abovementioned two face detection and two face recognition methods.An organic video database of about 1 hour is organized for this study.In addition, a public image database with 31 celebrity's frontal face images is also considered.As an overall conclusion, we brought to light that the MMOD coupled to a Pruned ResNet model seems to better suit the organic video processing use-case constraints, being able to reach an accuracy of 85%.The study also brings to light and discusses the differences in the quantitative results obtained for the two types of databases content (organic video content vs. celebrity's face images.
Sentence Extraction Using Machine Learning Algorithms for Bug Reports

Automatic Summarization is one of the very important tasks that are performed to improve the searching experience in the internet world. Software Repositories are one of the greatest sources of information for the software development community as it contains varied information like the team behavior, intentions, emotions, the bugs, the project style, project management information, etc. The paper is an extension to the previous work where we have used just the feature-based technique to generate the summary for the Bug Reports. Here in this paper, we have used machine-learning approaches along with the Features to find out how the results vary. For the machine learning approaches, as there are many approaches which are available, we use the very popular approaches KNN, CART, NB and SVM for the observation. We observed that when the machine learning approaches are integrated with the feature-based approach, the results improve.
Detecting Hybrid Anomalies Using an Unsupervised Approach in Online Social Networks

Over the last decade, there has been significant interest in detecting hybrid anomalies in Online Social Networks (OSN). However, there remain several questions regarding the evaluation of these systems and the nature of anomaly. In order to answer these questions, further research must be conducted on anomaly definitions and development for social networks. This is achieved through datasets that represent a balanced testing environment and contains anomalies with selected characteristics. In this paper, we propose an artificial injection agent as a security solution for evaluating detecting users’ abnormal behavior in OSN. The proposed agent creates synthetic anomaly and injects it into the data to utilize the detection accuracy in a Bayesian Network Classifier. We evaluated the effectiveness of the proposed technique concerning the detection of the injected anomalies. Using our approach, we were able to detect the injected anomalies with a success rate of 94% and no false alarm rates.
Methods of Ensuring Privacy in a Decentralized Environment

The section discusses all currently available methods and mechanisms used in cryptocurrency blockchains to ensure user privacy. Each of these methods and mechanisms is described in detail, their comparative analysis is performed. It has been determined which of them are most suitable for maintaining the confidentiality of certain private personal information. The methods and cryptographic primitives used in blockchain technologies of such largest market capitalizations and “most private” digital currencies as Monero, Dash and Zcash are analyzed. In addition, these cryptocurrencies are the object of close attention and ban by many intelligence agencies and financial institutions around the world. This attention also indicates the level of their anonymity. However, only the Monero cryptocurrency is anonymous, of course, and the Dash and Zcash cryptocurrencies only allow the choice of the anonymity option at the user's request. The results of the analysis show how the information obtained can be used to create a network that will operate on a blockchain basis and store, process and transmit confidential information, such as personal data, while ensuring its privacy. It is concluded that to ensure a given level of privacy in the processing, storage, transmission and receipt of confidential personal information, it is possible to use only the appropriate mathematical apparatus, such as ring signatures (such as Monero) or SNARKs, such as Zcash. The example of the most common protocol Groth16 shows how the information is structured before constructing the SNARK-proof, how the preliminary calculations are performed (the so-called ceremony, or Setup), how the proof itself is built and how its correctness is verified. It also shows how to build recursive SNARK-proofs and why and in what situations they are necessary.
Image Processing: Image Compression Using Compressed Sensing, Discrete Cosine Transform and Wavelet Transform

Recently, the image quality and the speed of acquisition are very studied, particularly in the medical field. We try to find in our paper the most efficient compression method, which allows having good image quality with a short compression period. Reducing the compression time amounts to reducing the acquisition time. In our article, we proposed three compression methods applied to the medical image: the discrete cosine transform (DCT) method, the wavelet transformation (DWT) and the compressed sensing method (CS). We studied also the acquisition time. In our results, we found that the DWT method gives better image quality compared to other methods. We found also that the CS method is faster than other methods.
Skillsets and attributes for enhanced teaching–learning outcomes at higher educational institutions in disruptive times

This chapter elucidates the obligatory need for the academic fraternity to upskill and align with the emerging landscape of the teaching–learning processes (TLP) at Higher Educational Institutions (HEIs) across the globe. The disruptions in Education 4.0 have been accelerated by the ongoing pandemic caused by COVID-19. As a result, it is a well-appreciated fact that both academics and learners must engage actively in holistic development at HEIs. Teachers, who are the facilitators, can lead to enhance the teaching–learning (TL) outcomes by acquiring contemporary pedagogical skillsets and attributes and by embracing Digitech. The key roles of academics: designing curriculum, teaching (pedagogy), researching, assessing, evaluating, and contributing to developing new and conducive learning opportunities for students’ call for realignment with enduring changes. Being agile, learning, unlearning, and relearning, and uplifting their emotional intelligence is fast becoming part of the professional culture at academia. Interestingly, the roles of the teaching community, who are primarily digital immigrants, are becoming more widened and multifaceted than ever before; calling for cohesive collaborations from all corners of the academia for enhanced TL outcomes via adopting holistic teaching means. Taking these points into consideration, this chapter provides a roadmap for academics to strategise their learning and development to realign their roles in a Digitech dominated environment and to surpass embedded challenges.
A Survey on Privacy-Preserving Deep Learning with Differential Privacy

Deep learning has been increasingly applied in a wide variety of domains and has achieved remarkable results. Generally, a large amount of data is needed to train a deep learning model. The data might contain sensitive information, leading to the risk of privacy leakage in the process of model training and model application. As a privacy definition with strict mathematical guarantee, differential privacy has gained great attention and has been widely studied in recent years. However, applying differential privacy to deep learning still faces a big challenge to reduce the impact on model accuracy. In this paper, we first analyze the privacy threats that exist in deep learning from the perspective of privacy attacks, including membership inference attacks and reconstruction attacks. We also introduce some basic theories necessary to apply differential privacy to deep learning. Second, to summarize how existing works apply differential privacy to deep learning, we divide perturbations into four categories based on different perturbation stages of deep learning with differential privacy: input perturbation, parameter perturbation, objective function perturbation, and output perturbation. Finally, the challenges and future research directions on deep learning with differential privacy are summarized.
Optimizing View Synthesis Approach for TerraSAR-X Image Registration Using Decision Maker Framework

Image registration using iterative view synthesis algorithm is experimentally shown to solve a wide range of registration problems, albeit at the cost of additional memory and time to be spent on generation of views and feature extraction across all the views. This paper tries to optimize the iterative algorithm of image registration using prior knowledge of possible deformations between the images. The proposed approach incorporates a trained classifier model into the registration pipeline. This classifier model can predetermine the possibility of registering the input image pairs and also predict the minimum number of synthetic views necessary to register the input image pair. Hence for the images that have been registered, an additional time requirement for the proposed approach is tolerable. However for the images that are not registered, the gain in time because of classifier model is extremely significant.
Artificial Intelligence (AI) Prediction of Atari Game Strategy by using Reinforcement Learning Algorithms

Video games produce reactionary, resilient, or clever behavior, mostly on non-player characters (NPCs), who resemble Artificial Intelligence (AI). Since the launch of video games in the 1950s AI has become an important component. AI is a separate subfield in computer games that varies from AI. Instead of learning the machine or determining it is used to enhance the player experience. The concept of an AI opponent was popularized during the golden age of arcade videogames in the form of graded levels of difficulty, distinct action styles and events based on the player’s involvement. Modern games also apply current strategies such as path-finding and decision-making bodies to control NPCs’ actions. AI is used often in mechanisms, such as data mining and process content creation, which is not immediately accessible to the user. We were creating an AI Organization to use the same hyper parameter to learn how to play a variety of Atari games. Over time it became a more theory-oriented project, in which we discussed numerous ways to use our methods for deep learning, and Put them on a game, Pong, rather than a game package.
Session-aware Item-combination Recommendation with Transformer Network

In this paper, we detailedly describe our solution for the IEEE BigData Cup 2021: RL-based RecSys (Track 1: Item Combination Prediction) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We first conduct an exploratory data analysis on the dataset and then utilize the findings to design our framework. Specifically, we use a two-headed transformer-based network to predict user feedback and unlocked sessions, along with the proposed session-aware reweighted loss, multitasking with click behavior prediction, and randomness-in-session augmentation. In the final private leaderboard on Kaggle, our method ranked 2nd with a categorization accuracy of 0.39224. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
Domain Adversarial Entity Alignment Model between Knowledge Graphs

Entity alignment (EA) is the task of finding equivalent entities in various knowledge graphs (KGs) that represent the same object in the real world, which aims at building a unified large-scale knowledge graph with rich and high-quality content. Existing EA works have achieved promising performance. These works mainly focus on learning the entity embeddings by capturing the structural semantics of entities on the relational triples in KG. Some works also take utilization of attributes information in entity embeddings. However, they are still hindered by the lack of prior seed alignment known as labeled training data. In this paper, we propose a novel approach for KG entity alignment based on transfer learning named DAEA, which incorporates both attributes triples and relational triples to generate the entity embeddings. DAEA first uses the J-GCN model to learn the entity representation model in source domain KG. Then, it combines domain adversarial network to learn entity representation model of the target domain. Since DAEA can transfer common entity information from the source domain to the target domain, it mitigates the problem of poor alignment performance due to lack of prior seed alignment. Experiments results show that DAEA significantly outperforms the state-of-the-art EA approaches on both monolingual and cross-lingual entity alignment datasets. Ablation studies on different components further demonstrate the effectiveness of our approach.
Reader Response to Text Adaptation: A Study of Female Emirati Undergraduate Students

Current research explores the response of female Emirati undergraduate students to text adaptation. Incorporated at the time of developing learning aids, students’ response can serve as one of the tools for enhancing students’ language skills required for their academic success. The analysis is based on the response of two groups of students (24 and 25 students respectively) obtained through a test and a text difficulty survey addressing their comprehension skills of authentic versus adapted texts. The effect of the text and sentence length, grammatical complexity, vocabulary, and cohesion on students’ comprehension were considered in the study. The research identifies requirements for amending the text for Emirati female students, and the areas of concern that helps material designers working in the Arab world to choose the most appropriate approach to text manipulation.
Signet Ring Cell Detection from Histological Images Using Deep Learning

Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases.Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task.Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research.In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN).In the first step, the input image is fed to Resnet-101 for feature extraction.The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign.Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers.The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset.Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials.We aim to release the employed database soon to assist the improvement in the SRC recognition research area.
Fusion of Radar- and Lidar-Data for Object-Tracking-Applications at Feature Level

The topic of automation can be found in almost every area these days. In Industry 4.0 in particular, the exclusion of hazards to people, for example in a human-robot collaboration, is a central issue. Object tracking is an option for workspace monitoring in this context. To improve object tracking, a fusion is to be designed and implemented in this work in order to combine the various measurement properties of a Radar- and a Lidar-sensor. In this paper, the Kalman filter and the fusion in general are briefly introduced. Object tracking, the concept of the implemented fusion and the arrangement and synchronization of the sensors are then described in more detail. A final evaluation leads to the conclusion that especially the availability and the reliability in object tracking can be improved. Furthermore, it can be shown that feature-level fusion has a significant advantage over symbol-level fusion.
Simulation and Optimization of Instrument Mechanism Based on ADAMS and Genetic Algorithm

Aiming at the difficult problem of parameter design of an instrument mechanism, the method of digital simulation is adopted to solve it. Firstly, the simulation model is constructed based on ADAMS, and the simulation results are verified. Further, through experimental design, the relationship between stiffness coefficient, damping coefficient and maximum displacement is studied, and the response surface is obtained. Finally, based on response surface, genetic algorithm is used to find the best parameters that meet the design requirements.
High Utility Item-Set Mining From Retail Market Data Stream With Various Discount Strategies

High Utility Item-set Mining (HUIM) is the futuristic remodel version of Frequent Item Mining (FIM). It has the ability to discover customer purchase trends in the retail market. By using that knowledge, retailers can incorporate innovative schemes (discounts, cross-marketing, seasonal sales offers,... etc) to enhance profit. Even though many HUIM algorithms are proposed to detect profitable patterns, most of them cannot be applied to all kinds of retail market data sets due to certain assumptions. The first assumption is that the items always produce a positive profit. But in reality, even though overall profit could be positive, some of the items make negative profit. The second one is they are developed for static transactional data. Those are useful to take decisions at some intervals like quarterly, half-yearly, yearly. But, to take decisions at any time by analyzing the present sales trend, it is required to process the data stream.
Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees' performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.
Analysis and Comparison of Neural Network Models for Software Development Effort Estimation

Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.
Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering

The volume of web search data stored in search engine log is increasing and has become big search log data. The web search log has been the source of data for mining based on web document clustering techniques to improve the efficiency and effectiveness of information retrieval. In this paper Deep Learning Model Convolution Neural Network(CNN) is used in big web search log data mining to learn the semantic representation of a document. These semantic documents vectors are clustered using K-means to group relevant documents for effective web document clustering. Experiment was done on the data set of web search query and associated clicked URLs to measure the quality of clusters based on document semantic representation using Deep learning model CNN. The clusters analysis was performed based on WCSS(the sum of squared distances of documents samples to their closest cluster center) and decrease in the WCSS in comparison to TF.IDF keyword based clusters confirm the effectiveness of CNN in web search log mining for effective web document clustering.