Reviewerly/exHarmony huggingface dataset:
Mechanism of Network Traffic Detection using Count Vectorizer

The main objective of the research work is to categories the network traffic using a count vectorizer with an improved accuracy rate by using Comparing the novel Support Vector Machine (SVM) to the Naive Bayes (NB) Classifier. The data set in this work utilizes the publicly available Kaggle network traffic data set and UCI machine learning repository. The sample size of classifying the network traffic with improved accuracy rate was sample 5000 (Group 1=2500 and Group 2 =2500) and the calculation is carried out utilizing G-power 0.8 with alpha and beta qualities are 0.05, 0.2 with a confidence interval at 95%. Classifying the network traffic using a count vectorizer with improved accuracy rate is carried out by Support Vector Machine (SVM), whereas various samples and Naive Bayes (NB) where the (N = 10) Sample size. The Novel Support Vector Machine (SVM) classifier has 92.123 higher accuracy rate when compared to the accuracy rate of Naive Bayes (NB) is 89.123. There exist a statistical major distinction between the two groups (p=0.013; p<0.05) with confidence interval 95%. Novel Support Vector Machine (SVM) provides better outcome inaccuracy rate with improved accuracy rate.
A Method for Automatic Color Correction of Images

The article presents a method for color correction of images. A Colorchecker Passport - X-rite reference card was used to report the reference values of each color. After measuring the colors from a captured image on the reference card, average deviations from each color were calculated. The color correction method involves adding the calculated deviations to the measured values. Image quality and brightness have been shown to improve after applying color correction.
Webots-Based: Design of Collision Free Trajectory of Wheeled Mobile Robot Using Artificial Potential Field

Wheeled mobile robot (WMR) is widely used in everyday life. The problem that arises in its implementation is navigation. Besides that, integrating navigation components, especially path planning, in a WMR kinematic model is considered very important to produce natural robot motion. One of the reliable path planning algorithms that can be used in real-time is Artificial Potential Field (APF). The weakness of this algorithm is that it can be stuck at the local minimum. Modification of APF (MAPF) which several researchers have proposed, serves to solve the drawbacks of this algorithm. This study aims to integrate and implement navigation capabilities in the WMR kinematics model, especially path planning. The test was carried out in a simulation using the Webots simulator. The robot used is an E-puck robot. E-puck is a differential drive mobile robot (DDMR) that uses two drivers to move. The results show that the MAPF path planning algorithm can be implemented well in the WMR kinematics model. Local minimum problems in the form of symmetrically aligned robot-obstacle-goal (SAROG) and goal non-reachable due to obstacle nearby (GNRON) in real conditions can be solved properly.
Elastic Optimized Edge Federated Learning

To fully exploit the enormous data generated by the devices in edge computing, edge federated learning (EFL) is envisioned as a promising solution. The distributed collaborative training in EFL deals with the delay and privacy issues as compared to the traditional model training. However, the existence of straggling devices degrades the model performance. The stragglers are manifested due to the data or system heterogeneity. In this paper, we introduce elastic optimized edge federated learning (FedEN) approach to mitigate the straggling-effect due to the data heterogeneity. This issue can be alleviated by the reinforced device selection by the edge server which can solve device heterogeneity to some extent. But, the statistical heterogeneity remains unsolved. Specifically, we define the problem of stragglers in EFL. Then, we formulate the optimization problem to be solved at the edge devices. We experimented on the MNIST and CIFAR-10 datasets for the proposed model. Simulated experiments demonstrates that the proposed approach improves the training performance. The results confirm the improved performance of FedEN approach over the baselines.
Arabic Speech Recognition Using Deep Learning and Common Voice Dataset

Speech recognition is critical in creating a natural voice interface for human-to-human communication with modern digital life equipment. Smart homes, vehicles, autonomous devices in the Internet of Things, and others need to recognize various spoken languages. Meanwhile, the Arabic language has a shortage of speech recognition systems. This study comes to develop an Arabic speech-to-text tool for Arabic language. Our solution uses DeepSpeech model which is a deep learning approach and uses a data set from the Common Voice Mozilla project. The results showed a 24.3 percent Word Error Rate and a 17.6 percent character error rate. So, the proposed model reduces the Word Error Rate by 11.7% compared to Bakheet’s Wav2Vec model.
Automating Arabic Tags Creation for Annotating Web Contents

Web Annotation becomes an important collaboration technique for users of the web. In order to increase universal collaboration, it is vital for annotators to contact the most related people that share the same interests. Although annotations themselves can be used to exchange ideas and experts, the improper writing of their attached notes could decrease the intended collaboration due to the lack of expressing ideas clearly. Adorning annotations with proper tags makes it easier for annotators to express their feelings, emotions, and interests through their submitted annotations. The ability to use the tags in searching for annotations leads to reaching the most related tags to one’s interests and this indeed increases changing ideas between annotators and hence their collaboration. However, expressing the ideas in annotations with proper tags is not an easy task for most annotators. On the opposite side, attaching improper tags loses the soul and the intention of creating annotations which decreases the amount of collaboration. This work is related to automating the process of tags’ suggestion by studying the texts selected by annotators over the web. Suggesting proper tags takes into account the part of speech of the annotated texts. The experimental results conducted in the work are related to finding the most suitable threshold for the percentage number of tag suggestions.
Speaker Identification through Gender Detection

In modern technological advancement, voice recognition has played an integral part in many machine learning algorithms, having diversified application areas such as speech recognition, building better access control systems and security systems, etc. The goal of this research is to compare the results of two voice recognition methods, the first method involves identifying a speaker by first determining his/her gender using various machine learning techniques and then cross-matching the voice sample with the detected genders in the dataset using a pattern recognition algorithm. The second method uses the same pattern recognition algorithm on the voice sample without performing gender detection. To do so, we have utilized Mel Frequency Cepstral Coefficients (MFCC) for extracting audio features and used machine learning algorithms such as MLP, RBFN, Random Forest, KNN, Gradient Boosting, Decision Tree, Naive Bayes, Logistic Regression, and SVM for gender detection then compared the results between the nine classifiers. Then, pattern recognition algorithm GMM is applied for the detection of an individual. A dataset of our own and a readymade dataset from Kaggle were used for the research. Gradient Boosting and Random Forest showed high performance in both datasets in case of gender detection. Subsequently, combining gender detection algorithm with GMM led to improved accuracy in some instances.
Comparison of Feature Extraction Techniques for Ambient Sensor-based In-home Activity Recognition

Ambient sensor-based in-home activity recognition plays a crucial role in the design and development of a smart home to better and actively respond to population aging. From the perspective of machine learning, how to extract features from sensor data largely determines the power of a data-driven human activity recognizer. However, few studies systematically investigate how to encode streaming sensor events. To this end, we herein conduct a comparison of different feature extraction techniques for activity recognition. Specifically, we explore two types of feature representations (i.e., statistical features and structural features) and evaluate their single use and joint use. Besides, we experimentally analyze the impact of window size on prediction accuracy. Finally, we perform experiments on three public datasets with 15 different feature encodings and 6 classifiers. Results show that the joint use of different features generally obtains enhanced accuracy and that the interval 60s of window size achieves a better accuracy-speed tradeoff.
Interactive Real-time Leader Follower Control System for UAV and UGV

This article is aimed at creating a leader-follower system for Unmanned Aerial vehicles (UAV) and Unmanned Ground vehicles (UGV) with obstacle avoidance. In this system, the UAV acts as a leader, which is given the coordinates of a predetermined path, along which it must follow according to its algorithm. While the UGV is a follower, that needs to follow the leader by avoiding obstacles. QDrone and QBot from QUANSER were used as vehicles, and the OptitTack tracking system was applied for determining their locations. The article presents simple algorithms created for both vehicles and the results of the experiment. The experiment showed that all algorithms work flawlessly. However, problems arose with the accuracy of placing obstacles due to the limited power of the follower's onboard computer.
Learning Implicit Depth Information for Monocular 3D Object Detection

Detecting 3D traffic participants in the surrounding of an autonomous vehicle is a challenging task. But as long as the vehicle is equipped with expensive light detection and ranging (LiDAR) sensors and the weather conditions are sufficient, the task seems to be easily solvable. As soon as one of these preconditions is no longer satisfied, camera sensors become an important factor. Obtaining 3D spatial information from a 2D image is a challenging task and most 3D object detectors do not deliver adequate results as long as they solely depend on camera images. For this reason, more input information needs to be provided to such systems. But as long as there are no other sensors available, it is not trivial to gain additional information. As depth reconstruction from monocular images started to provide solid results, camera based 3D detection algorithms began to use these generated depth maps as additional input information. However, this comes at the cost of an additional algorithm that requires valuable on-board resources. To circumvent this issue, we propose an architecture that learns the concept of depth during training based on additional ground truth. In operational mode, the 3D object detector no longer relies on depth maps as input. On the KITTI 3D object detection dataset the proposed architecture achieves comparable results to architectures using additional depth inputs and outperforms image only methods.
Characterization and Reconstruction of Hypergraphic Pattern Sequences

The notion of hypergraph has been introduced as a generalization of graphs so that each hyperedge is a subset of the set of vertices, without constraints on its cardinality. Our study focuses on 3-uniform hypergraphs, i.e., those hypergraphs whose (hyper)edges have three as common cardinality. A widely investigated problem related both to graphs and to hypergraphs concerns their characterization and reconstruction from their degree sequences. Concerning graphs, this problem has been efficiently solved in 1960 by Erdös and Gallai, while no efficient solutions are possible in the case of hypergraphs, even in the simple case of 3-uniform hypergraphs, as shown in 2018 by Deza et al. [4]. These problems are among the most studied in the field of Discrete Tomography (see [11, 12] for a complete survey) and, in a more general fashion, of Image Analysis. So, to reduce the NP-hard core of the hypergraph reconstruction problem, we consider a class of degree sequences defined in [4] that show interesting properties. Here, in particular, we characterize the subclass $$\mathcal {P}$$ by using the new notion of pattern and pattern sequence. First, we focus on t-pattern sequences, i.e., sequences with constant pattern $$t \ge 1$$ , and we study the remarkable behaviour of their last elements, called tails. In particular, for any fixed t, we show that the tails tend to a fixed point when increasing the sequences’ lengths. The elements of these fixed points, on varying t, are the same and form the sequence A002620 in [13], generalizing the results in [6]. Finally, we provide a fast algorithm to reconstruct the hypergraphs that realize the sequences in $$\mathcal {P}$$ by iteratively discovering the elements of the characterizing pattern sequence.
A Graph-Based Service Composition Method for Science and Technology Resources

Science and technology resources can be regarded as web services on the Internet, in order to realize the reuse of science and technology resources, the industry provides services for Internet users in multiple web service methods. In order to reuse of the web services, multiple web services need to be combined according to certain rules and business logic to solve the problem of limited functions of a single web service. The web service composition algorithm focuses on finding a service composition scheme with the best service quality. A single service composition scheme cannot cope with the dynamic changes of the network environment in real time, such as service failures. This paper proposes a graph-based service composition method, which uses the service dependency graph to establish the relationship between services, and combines the functional and non-functional attributes of the service. In the service selection stage, a formula for calculating the matching degree between the service and the target parameters is proposed to evaluate the current matching degree between the service and the target parameters, and the service with the best matching degree is selected.
Solving a Cloze Test for Generative Commonsense Question Answering

Commonsense question answering has always been a challenging task due to the wide-domain coverage and the implicity of commonsense knowledge. Few works are tackling the answer generation of commonsense questions, which is more difficult than multiple-choice. This motivates us to delve into the answer generation ability of pre-trained language models (PLMs). Other than utilizing knowledge bases to extract commonsense-related knowledge to answer commonsense questions, we exploit the latent knowledge within PLMs to solve this task. In this work, we reformulate this generative task into a masked token prediction task and experiment with masked language models (MLMs) and generative language models (GLMs). Experimental results on the ProtoQA dataset demonstrate the effectiveness of our proposed method. Our work finds that both MLMs and GLMs are good at masked token prediction and that PLMs have acquired commonsense knowledge through large-corpus pre-training.
A Scheduled Mask Method for TextVQA

At present, many successful applications use deep learning method in the field of Visual Question Answering (VQA). With the introduction of Optical Character Recognition (OCR), Text-based Visual Question Answering (TextVQA) tasks have a mature basic structure, a transformer-based iterative decoding prediction module. However, there is a problem in the current models: the training process of the model is inconsistent with the inference process. This inconsistency is shown in the different input and the different iteration prediction steps. We propose a scheduled mask method. After using this method, our model can gradually adapt to the situation without the ground truth answer input in the training process. We have verified the effectiveness of our method on the TextVQA dataset and exceeded the performance of other models previously proposed.
Course Recommendation System Based on SSM Framework

With the continuous increase of online courses and the rapid growth of network data, how to improve the recommendation accuracy and real-time performance of personalized recommendation system is a key issue. In order to improve the recommendation quality and real-time performance of the recommendation system, this paper uses SSM (Spring, Spring MVC, Mybatis) Framework, the most popular and latest framework of the enterprise, as the framework of the recommendation system, and uses the hybrid recommendation algorithm (Socialized Recommendation algorithm) that integrates Collaborative Filtering recommendation algorithm and social relationship to make course recommendation. In this paper, the course and relational data of scholar.com are used as data sets for research, so as to provide richer social relationships and more references for experiments.
A Pedestrian Detection Method Based on Improved ResNet for Container Terminal

Pedestrian detection in the scene of automated container terminal can bring massive benefits to improving operation safety of the terminal. However, pedestrian detection in container terminal environment exists small pedestrian target size, extreme foregroung-background class imbalance problems. To solve these problems, this paper proposes a one-stage pedestrian detection model based on the improved ResNet. For small target pedestrian detection, we introduce the Channel attention module and spatial attention module to ResNet, enhancing the network’s attention to small target pedestrians. Moreover, the feature pyramid network is added to achieve semantic fusion of different scales to enhance the detection of small-scale targets. Finally, the focal loss function is employed as the loss function to suppress the weight influence of simple samples and relieve the problem of extreme foregroung-background class imbalance. The experimental results on the virtual container terminal pedestrian dataset show that the average accuracy of the model is 74.3%, which is 3.9% higher than that of ResNet. The detection accuracy is also improved by comparing with traditional pedestrian detection models. In addition, the effectiveness of the model is verified on Caltech and COCO2017 datasets.
Self-embedding and Variable Authentication Approach for Fragile Image Watermarking Using SVD and DCT

In this paper, we propose a self-embedding fragile image watermarking technique based on Singular Value Decomposition (SVD) and Discrete Cosine Transform (DCT). To improve security and robustness a novel block separation technique is presented in which an input image is divided into non-overlapping blocks and subsequently SVD is applied on each block. Entropy value of the resulting vector of singular values is measured. Mean of entropy of all blocks is considered as a threshold for deciding two sets of blocks. For the first set of blocks, Entropy measures of singular value based authentication codes are generated and for second set of blocks, DCT DC value based authentication codes are obtained for embedding information. To enhance visual quality of watermarked and recovery image DCT based watermark embedding technique is proposed. Experiments have been conducted on gray scale images and it is observed that proposed scheme gives peak signal to noise ratio (PSNR) of 58 dB which ensures high performance in terms of imperceptibility. Experimental results showed that 99% of watermarked blocks are correctly detected during recovery phase. The image tampering is precisely identified and tampered images are recovered with very good quality. To the best of our knowledge the presented fragile image watermarking scheme is superior to all existing scheme.
FinTech and Its Implementation

The article presents an overview of current implementations in the FinTech field. FinTech is an abbreviation for the term Financial Technology. This article shows the general characteristics of some of the most popular FinTech technologies and their implementation. FinTech has no implications only for the financial industry. It includes virtually all forms of e-commerce and spreads to other industry fields, especially in blockchain technology. The article presents four significant areas of FinTech such as insurance, banking services, trading on capital markets, and risk management. It also covers key technologies in use in FinTech, such as artificial intelligence (AI), Big Data, advanced analytics, robotic process automation (RPA), and blockchain technology. There are two case studies of FinTech implementation presented in the article. The first case study discusses applying artificial intelligence and advanced data analytics in payment fraud prevention and detection. Blockchain implementation is illustrated in more detail in the Dubai Government’s Blockchain Strategy case study. The connection of FinTech to ontology theory is given at the end of the article.
Nuclei Segmentation of Microscopic Images from Multiple Organs Using Deep Learning

Digital Pathology and examination of microscopy images is broadly used for the investigation of cell morphology or tissue structure. Manual assessment of the images is labour concentrated and inclined to inter-observer and intra-observer variations. The detection and segmentation of cell nuclei is the first step in quantitative analysis of biomedical microscopy images which helps in cancer diagnosis and prognosis. Many methods are available to segment nuclei in the images but a single method does not work on different imaging experiments. It needs to be chosen and designed for every experiment. Here we describe a deep learning approach for segmentation, that could be applied to different types of images and experimental conditions by not adjusting the parameters manually.
A Communication-Efficient Federated Learning: A Probabilistic Approach

Federated learning (FL) empowers edge gadgets, similar to the IoT gadgets, servers, and industries that cooperatively prepare a model in the absence of respective privacy information. It expects gadgets that they trade their ML parameters. Subsequently, it expects to be together get familiar with a solid model relies upon the quantity of preparing ventures and boundary time for transmission per each step. Practically, It’s transmissions are regularly overseen at a large number of partaking gadgets on asset-restricted correspondence organizations, for example, remote organizations with restricted data transfer capacity and power. Along these lines, the rehashed FL boundary transmission from edge gadgets instigates a remarkable postponement, which might be bigger than the ML model preparation time by significant degrees. Thus, correspondence delay establishes a genuine issue in FL. A correspondence proficient FL system is proposed to together further develop the FL union time, hence the preparation misfortune. During this system, a probabilistic gadget choice plan is implied such as the gadgets which will altogether further develop the union speed and prepare misfortune to get the high probabilities to choose for the transmission model. We propose a novel technique to additionally decrease the transmission time and to downsize the amount of the model boundaries traded by gadgets, and an effective remote asset designation conspire is created. Reproduction results show that the proposed FL system can further develop identification exactness.