Reviewerly/exHarmony huggingface dataset:
Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning Techniques

Android-based applications are widely used by almost everyone around the globe. Due to the availability of the Internet almost everywhere at no charge, almost half of the globe is engaged with social networking, social media surfing, messaging, browsing and plugins. In the Google Play Store, which is one of the most popular Internet application stores, users are encouraged to download thousands of applications and various types of software. In this research study, we have scraped thousands of user reviews and the ratings of different applications. We scraped 148 application reviews from 14 different categories. A total of 506,259 reviews were accumulated and assessed. Based on the semantics of reviews of the applications, the results of the reviews were classified negative, positive or neutral. In this research, different machine-learning algorithms such as logistic regression, random forest and naïve Bayes were tuned and tested. We also evaluated the outcome of term frequency (TF) and inverse document frequency (IDF), measured different parameters such as accuracy, precision, recall and F1 score (F1) and present the results in the form of a bar graph. In conclusion, we compared the outcome of each algorithm and found that logistic regression is one of the best algorithms for the review-analysis of the Google Play Store from an accuracy perspective. Furthermore, we were able to prove and demonstrate that logistic regression is better in terms of speed, rate of accuracy, recall and F1 perspective. This conclusion was achieved after preprocessing a number of data values from these data sets.
Text Similarity Computation Model for Identifying Rumor Based on Bayesian Network in Microblog

The research of text similarity, especially for rumor texts, which constructed the calculation model by known rumors and calculated its similarity. From which, people can recognize the rumor in advance, and improve their vigilance to effectively block and control rumors dissemination. Based on the Bayesian network, the similarity calculation model of microblog rumor texts was built. At the same time, taking into account not only the rumor texts have similar characters, but also the rumor producers have similar characters, and therefore the similarity calculation model of rumor texts makers was constructed. Then, the similarity between the text and the user was integrated, and the microblog similarity calculation model was established. Finally, also experimentally studied the performance of the proposed model on the microblog rumor text and the user data set. The experimental results indicated that the similarity algorithm proposed in this paper could be used to identify the rumors of texts and predict the characters of users more accurately and effectively
On the Information Leakage in Private Information Retrieval Systems

We consider information leakage to the user in private information retrieval (PIR) systems. Information leakage can be measured in terms of individual message leakage or total leakage. Individual message leakage, or simply individual leakage, is defined as the amount of information that the user can obtain on any individual message that is not being requested, and the total leakage is defined as the amount of information that the user can obtain about all the other messages except the one being requested. In this work, we characterize the tradeoff between the minimum download cost and the individual leakage, and that for the total leakage, respectively. Coding schemes are proposed to achieve these optimal tradeoffs, which are also shown to be optimal in terms of the message size. We further characterize the optimal tradeoff between the minimum amount of common randomness and the total leakage. Moreover, we show that under individual leakage, common randomness is in fact unnecessary when there are more than two messages.
An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

Data quality is a significant research subject for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT infrastructure but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based on for any data processing activity. Our aim is to secure data quality for those features, at least, that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining ones in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently selecting the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism.
Differential Neural Networks (DNN)

In this work, we propose an artificial neural network topology to estimate the derivative of a function.This topology is called a differential neural network because it allows the estimation of the derivative of any of the network outputs with respect to any of its inputs.The main advantage of a differential neural network is that it uses some of the weights of a multilayer neural network.Therefore, a differential neural network does not need to be trained.First, a multilayer neural network is trained to find the best set of weights that minimize an error function.Second, the weights of the trained network and its neuron activations are used to build a differential neural network.Consequently, a multilayer artificial neural can produce a specific output, and simultaneously, estimate the derivative of any of its outputs with respect to any of its inputs.Several computer simulations were carried out to validate the performance of the proposed method.The computer simulation results showed that differential neural networks are capable of estimating with good accuracy the derivative of a function.The method was developed for an artificial neural network with two layers; however, the method can be extended to more than two layers.Similarly, the analysis in this study is presented for two common activation functions.Nonetheless, other activation functions can be used as long as the derivative of the activation function can be computed.
A New Object Detection Algorithm Based on YOLOv3 for Lung Nodules

Lung cancer has always threatened people's health and life. Lung nodules, as early features of lung cancer, have very important clinical significance and research value for the diagnosis of lung cancer. The features captured by the traditional convolutional neural network are limited, in addition, traditional YOLO method has the problems of low accuracy and inaccurate positioning. Aiming at this problem, this paper proposes a new algorithm based on YOLOv3 for detecting lung nodules. The Inception ResBlocks are added to the feature network of YOLOv3, so that the network can extract richer feature information, furthermore, a new bounding box regression loss function is proposed. The loss function GDIoU loss makes the prediction of bounding box regression more accurate and further improves the performance of lung nodule detection. After experimental verification, the AP of this model can reach 83.5%, and the sensitivity can reach 92.6%. The proposed method has a good performance in terms of positioning accuracy and detection rate, and can avoid the problems of false detection and missed detection to a certain extent. It provides a new idea for the detection of lung nodules.
The One-Stage Detector Algorithm Based on Background Prediction and Group Normalization for Vehicle Detection

Vehicle detection in intelligent transportation systems (ITS) is a very important and challenging task in traffic monitoring. The difficulty of this task is to accurately locate and classify relatively small vehicles in complex scenes. To solve these problems, this paper proposes a modified one-stage detector based on background prediction and group normalization to realize real-time and accurate detection of traffic vehicles. The one-stage detector firstly adds a module to adjust the width and height of anchors and predict the target background, which avoids the problem of the target vehicle missing detection or wrong detection due to the influence of the complicated environments. Then, group normalization and the loss function based on weight attenuation can improve the one-stage detector performance in the training process. The experimental results on traffic monitoring datasets indicate that the improved one-stage detector is superior to the other neural network models in terms of precision at 95.78%.
Building Recommender Systems with PyTorch

In this tutorial we show how to build deep learning recommendation systems and resolve the associated interpretability, integrity and privacy challenges. We start with an overview of the PyTorch framework, features that it offers and a brief review of the evolution of recommendation models. We delineate their typical components and build a proxy deep learning recommendation model (DLRM) in PyTorch. Then, we discuss how to interpret recommendation system results as well as how to address the corresponding integrity and quality challenges.
Semantic Segmentation for Aerial Mapping

Mobile robots commonly have to traverse rough terrains. One way to find the easiest traversable path is by determining the types of terrains in the environment. The result of this process can be used by the path planning algorithms to find the best traversable path. In this work, we present an approach for terrain classification from aerial images while using a Convolutional Neural Networks at the pixel level. The segmented images can be used in robot mapping and navigation tasks. The performance of two different Convolutional Neural Networks is analyzed in order to choose the best architecture.
Assessing information synthesis within and across multiple texts with verification tasks: a signal detection theory approach

To extend the existing research examining multiple text comprehension and its assessment, we developed a verification task approach to assessing of information that was explicitly and implicitly presented within and across nine texts. A nonparametric form of signal detection theory was used to analyse the performance of 47 middle school students using a rating response scale to judge the truth value of specific statements. The results showed that the four types of statements in the verification task (explicit and implicit; within- and across- text) behaved systematically: within-text explicit statements were the easiest for students to verify as true and across-text implicit statements were the most difficult. Practical implications of these results regarding assessment of complex comprehension processes are discussed.
Extraction of Core Web Content from Web Pages using Noise Elimination

Due to the emergent of technological development, Web has evolved as the most powerful digital weapon for mankind in recent days.As the size of the web is increasing rapidly, extracting the interesting content from the web become the supreme challenge.In the meantime, the retrieved web pages have many uninteresting content blocks that are not useful for the user which also degrades the performance of content extraction.These uninteresting blocks include advertisements, banners, copyrights, navigation bars etc., and are normally named as web page noise.Removing these noises from the web pages is considered to be the primary task in pre-processing.This paper presents an approach that eliminates the noise and near duplicates for extracting significant content from the web page.The proposed method has three steps.Initially, the web page is divided into various blocks and the block which is considered as noise is removed using tag analysis and Document Object Model Tree.Secondly, the elimination of redundant blocks is carried out by computing fingerprints using modified simhash algorithm with proximity measure.From the distinct blocks, several parameters such as Titlewords, Linkwords and Contentwords are extracted.Thus, the extraction of significant content is carried out by computing the scores for each block using a weighted block scoring mechanism.The blocks having higher score values are extracted and finally, the core content is extracted from the web page.The experimental analysis has been performed and the results show that the proposed method eliminates noise in an efficient way.
Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

Summary This work proposes a strategy to create an embedded genetic algorithms (GAs) for low‐power, low‐cost, and low‐size‐memory devices. This strategy aims to provide the means of GAs to run as a low‐cost and low‐power consumption embedded system, where microcontrollers (Cs) are commonly used. The implementation details are presented, emphasizing the limitations and restrictions imposed to turn it more compact and efficient. In addition, data related to the algorithm effectiveness, processing time, and memory consumption were obtained from simulations, oscilloscope measurements, and using the hardware‐in‐loop technique. Finally, this implementation is compared with other implementation from the literature and the results show that 8‐bits Cs can run GAs for several practical applications.
HUBUNGAN MINAT BELAJAR IPA SISWA KELAS V SD NEGERI 2 PELEMKEREP TERHADAP HASIL BELAJAR SELAMAPEMBELAJARAN DARING

The purpose of this study was to analyze the students' interest in learning science class V SD Negeri 2 Pelemkerep during online learning and to analyze the relationship between interest in learning science and learning outcomes for students at SD Negeri 2 Pelemkerep during online learning. This study uses a quantitative approach to the type of correlation research. Correlation research is a study conducted to find the level of relationship between variables and to find out how big the relationship between each variable is used. This research was conducted in class V SD Negeri 2 Pelemkerep Jepara. The independent variable in this study is the interest in learning science, while the dependent variable is the result of learning science. The data analysis technique used is the product moment correlation test and one sample t test. The results of this study indicate that (1) there is a relationship between interest in learning science and learning outcomes of students at SD Negeri 2 Pelemkerep during online learning with a correlation coefficient value of 0.905 in the very strong category and (2) the average interest in learning science for students at SD Negeri 2 Pelemkerep during online learning&gt; 50% with an average value of the questionnaire on learning interest of 86.04 and an average value of learning outcomes in science learning of 83.78. Based on the results of the calculation, the average percentage of the answers to the questionnaire on the students' interest in learning science as a whole was 54.36%, meaning that most students had an interest in learning science.
Fast and Improved Real-Time Vehicle Anti-Tracking System

Safety on roads and the prevention of accidents have become major problems in the world. Intelligent cars are now a standard in the future of transportation. Drivers will benefit from the increased support for driving assistance. This means relying on the development of integrated systems that can provide real-time information to help drivers make decisions. Therefore, computer vision systems and algorithms are needed to detect and track vehicles. This helps traffic management and driving assistance. This paper focuses on developing a reliable vehicle tracking system to detect the vehicle that is following the host vehicle. The proposed system uses a unique approach consisting of a mixture of background removal techniques, Haar features in a modified Adaboost algorithm in a cascade configuration, and SURF descriptors for tracking. From the camera mounted at the rear of the host vehicle, videos are captured. The results presented in this paper demonstrate the potential and efficiency of the system.
A knowledge-data framework and geospatial fuzzy logic-based approach to model and predict structural complexity

Prediction of structural complexity for geohazard and subsurface resource applications requires constraining and interpreting data that are often ambiguous or lack key information. Moreover, structural complexity is a subjective term, requiring context for quantification. Recognizing this, a new knowledge-data framework and a geospatial fuzzy logic method is developed to represent and predict structural complexity in the subsurface. A conceptual model for known structural complexity serves as a basis for associating geospatial representations with types of damage zones. A second conceptual model for zones of structural complexity facilitates its prediction, notably in areas with limited explicit structural data. For each conceptual model, a fuzzy logic inference model is developed to incorporate geospatial data and estimate structural complexity potential. This approach is demonstrated using several public geospatial datasets within the state of Oklahoma. Explicit fault and earthquake location data were integrated using a fuzzy model of known structural complexity to train topographic, lithologic, and geophysical proxy datasets, applied to a fuzzy model to predict structural complexity, and evaluated with Receiver Operating Characteristic analyses and error classification. The final model output, displayed in maps and cross sections, offers comparison with interpreted structural data for validation. Together, these results demonstrate the effectiveness and limitations of the new approach as a screening tool for predicting structurally complex areas.
Landmark-free head pose estimation using fusion inception deep neural network

Head pose estimation is a typical computer vision task that has been applied to many helpful applications. It is still challenging due to occlusions, low resolution, and extreme pose changes. A method is proposed for landmark-free head pose estimation in a fusion inception deep neural network. The fusion inception network is developed to predict head pose angles without landmarks in RGB images only, which avoids the error caused by landmark location faults. A three-channel prediction module in the developed network is designed to perform classification and regression jointly. A data augmentation method is proposed for sample expansion to alleviate overfitting. A balance loss strategy is proposed to take place of cross-entropy for pose angle classification. The developed balance loss can be applied to deal with class imbalance. The proposed method has excellent performance in head pose estimation by comparison with state-of-the-art methods on some challenging datasets.
Research on Interactively Digital Display for Cultural Heritage- Discovering the Hall of Mental Cultivation: A Digital Experience Exhibition

The digital heritage display breaks through the limitations of exhibition space, cultural relic vulnerability, and supported by the integration of techniques such as computer animation, AR, VR and others.In order to better achieve the effective dissemination of knowledge and culture, digital construction of cultural heritage museum focus on the interactive design of the exhibition items.The Discovering the Hall of Mental Cultivation: A Digital Experience at the Beijing Duanmen Digital Museum is a famous digital exhibition of cultural heritage in China, using touch-controlled interaction, augmented reality, virtual reality, simulation, and motion capture, which maximize technical and artistic levels.Based on the research and scholars' experience in the field of digital cultural heritage and interactive design, the digital presentation and interaction of touchscreens, AR and VR in this digital exhibition is reviewed.Digital presentation and human-computer interaction such as technical concepts, interactive forms, operational details, characteristics, and advantages are discussed.In addition, the paper explores how the interaction of cultural heritage narrates the distance between the audience and historical relics.Finally, considering the challenging factors and future trends in the field of digital cultural heritage.
SankeyVis: Visualizing active relationship from emails based on multiple dimensions and topic classification methods

The explosive growth of email has led to the rapid development of e-mail-based forensics, and at the same time, it has brought enormous challenges. As a special kind of data, email consists of structured metadata and unstructured email Body. Most attention is paid to visualization forensics of metadata at present, which is more focused on the mining of social network relationships between senders and recipients. These methods limit the range of email visualization forensics. Visual forensics of semantic analysis of the email body is relatively rare and difficult to connect semantic analysis with visualization. In recent years, the booming development of machine learning has extended the focus of forensics to the email body. This paper proposed SankeyVis: a visualization model for email forensics of active relation based on multiple dimensions and LDA topic classification methods, focusing on mining social relationships and semantic patterns in emails. SankeyVis conducts forensic work from the four data attributes of the email, “From,” “To,” “Date,” and “Body,” and the data is divided into two parts, the email header and email body according to the structure. The email header is used to get address pair with the working relationship after selecting and for the email body. Then introduced the Latent Dirichlet Allocation model to classify the topic of the email body discussed and adopt the adaption Sankey diagram to conduct forensic work from the topic semantic. It is proved that tested well by adapting to the Enron corpus. SankeyVis integrates structured and unstructured data in the visualization of email forensics, achieving visual forensics of email content. It breaks the limitations of dimensions and supports adding more than four attributes for forensics, extending the breadth of email forensics. SankeyVis reveals the topics of email senders and recipients with active relationships discussed at different time units and supports for forensics of email content to varying levels of relationships, extending the depth of email forensics.