Reviewerly/exHarmony huggingface dataset:
A Reputation Ranking Method based on Rating Patterns and Rating Deviation

In the e-commerce platform, user purchase behavior often depends on personal experience and the objective ratings of others. Various businesses employ a large number of spammers to obtain illegal benefits by distorting the ranking of goods, which seriously affects the market order. How to design a high- speed and effective ranking method to remove these spammers is necessary and significant. In this paper, a novel reputation ranking method is proposed based on users' Rating Patterns and Rating Deviation (RPRD) because users' rating preference and historical behavior differ significantly with spammers. We compare RPRD method with three classical methods Deviation- based Ranking (DR), Iterative Group-based Ranking (IGR) and Iterative Balance Ranking (IBR) on three real datasets. Experimental results show that the RPRD method can effectively resist spammers attack and identification, especially in detecting random spammers. On the other hand, this method always has high accuracy and robustness even if the network is relatively sparse. It can also be applied in large and sparse bipartite rating networks in a short time.
Passive Triangulation Attack on ORide

Privacy preservation in Ride Hailing Services is intended to protect privacy of drivers and riders. ORide is one of the early RHS proposals published at USENIX Security Symposium 2017. In the ORide protocol, riders and drivers, operating in a zone, encrypt their locations using a Somewhat Homomorphic Encryption scheme (SHE) and forward them to the Service Provider (SP). SP homomorphically computes the squared Euclidean distance between riders and available drivers. Rider receives the encrypted distances and selects the optimal rider after decryption. In order to prevent a triangulation attack, SP randomly permutes the distances before sending them to the rider. In this work, we use propose a passive attack that uses triangulation to determine coordinates of all participating drivers whose permuted distances are available from the points of view of multiple honest-but-curious adversary riders. An attack on ORide was published at SAC 2021. The same paper proposes a countermeasure using noisy Euclidean distances to thwart their attack. We extend our attack to determine locations of drivers when given their permuted and noisy Euclidean distances from multiple points of reference, where the noise perturbation comes from a uniform distribution. We conduct experiments with different number of drivers and for different perturbation values. Our experiments show that we can determine locations of all drivers participating in the ORide protocol. For the perturbed distance version of the ORide protocol, our algorithm reveals locations of about 25% to 50% of participating drivers. Our algorithm runs in time polynomial in the number of drivers.
CADE: The Missing Benchmark in Evaluating Dataset Requirements of AI-enabled Software

The inductive nature of artificial neural models makes dataset quality a key factor of their proper functionality. For this reason, multiple research studies proposed metrics to assess the quality of the models’ datasets, such as dataset correctness, completeness, and consistency. However, these studies commonly lack a point of reference against which the proposed quality metrics could be assessed. To this end, this paper proposes a generic process that extracts the necessary knowledge to build a reliable reference point for the purpose of explanation, assessment, and augmentation of the AI-software dataset. This process automatically builds a benchmark specific to the software operational domain, interprets the training and validation datasets of AI-enabled perception software systems, and evaluates the dataset semantic quality and completeness relative to the benchmark. We implemented this process within a framework called Concept Augmentation and Dataset Evaluation (CADE), which leverages a series of novel natural language and image processing techniques to construct a semantic benchmark with respect to the domain specifications. The application of CADE to three commonly-used autonomous driving datasets showed several common weaknesses present in the arbitrarily-collected datasets against the encoded domain specifications, demonstrating dataset divergence from the domain concepts and under-represented variances of the concepts in the data. The qualitative evaluation results showed an average of about 75% relevancy of CADE generated topics.
Career Prediction Using Naive Bayes

In today’s world a lot of people find it difficult to pick their career in the IT field. So, we proposed a system to suggest the user a career in the IT field, this model suggests a career for that user based on his educational background, interest, and current education. The more information the user provides the more accurate the career will be predicted for that user.
Enabling Identity-Based Integrity Auditing and Data Sharing with Sensitive Information Hiding for Secure Cloud Storage

With the increase in the data growth, where data storage becomes difficult if the user wants to store it on a local drive, which is why many organizations and people prefer storing their data on a cloud storage. With accessibility of cloud storage, people can remotely use cloud storage and avail data storage. Remote data integrity has been suggested as a way to ensure the integrity of data stored in clouds. The health care system is one example of cloud storage, and the cloud file there may include sensitive data. This sensitive information should not be shared with others. One solution would be to encrypt the whole file to avoid sensitive data sharing, but then this way the file would be unusable to others. The method for implementing data sharing with sensitive information disguised in remote data integrity audits is one topic that hasn't been looked into yet. This technique does this by creating a sanitizer that will clean up data blocks that correspond to the sensitive information in the file and change those signatures into valid ones for the cleansed file. These signatures are utilised during the integrity auditing procedure to confirm the cleaned-up file. As a result, this scheme allows files to be shared with others, while also maintaining data security by sanitizing blocks of sensitive data, with the data integrity auditing still able to be executed efficiently without any problems. Identity-based cryptography, which the proposed solution is built on, makes complex certificate administration simple. The proposed method is efficient and secure, according to the performance analysis and security analysis.
MPC: Multi-view Probabilistic Clustering

Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance. In this paper, we propose a novel unified framework for incomplete and complete MVC named multi-view probabilistic clustering (MPC). MPC equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, MPC also equivalently transforms probabilistic clustering's objective to avoid complete pairwise computation and adjusts clustering assignments by maximizing joint probability iteratively. Extensive experiments on multiple benchmarks for incomplete and complete MVC show that MPC significantly outperforms previous state-of-the-art methods in both effectiveness and efficiency.
GPU-Parallelized Iterative LQR with Input Constraints for Fast Collision Avoidance of Autonomous Vehicles

Collision avoidance in emergency situations is a crucial and challenging task in motion planning for autonomous vehicles. Especially in the field of optimization-based planning using nonlinear model predictive control, many efforts to achieve real-time performance are still ongoing. Among various approaches, the iterative linear quadratic regulator (iLQR) is known as an efficient means of nonlinear optimization. Additionally, parallel computing architectures, such as GPUs, are more widely applied in autonomous vehicles. In this paper, we propose 1) a parallel computing framework for iLQR with input constraints considering the characteristics of the problem and 2) a proper environmental formulation that can be covered with single-precision floating-point computation of the GPU. The GPU-accelerated framework was tested on a real-time simulation-in-the-loop system using CarMaker and ROS at a 20 Hz sampling rate on a low-performance mobile computer and was compared against the same framework realized with a CPU.
An Efficient Machine Learning based Cervical Cancer Detection and Classification

Cervical cancer (CC) is the fourth commonly occurring cancer among females over the globe. It accounts for 7.9% of woman cancer as identified by world health organization (WHO). The most important reason for increased mortality due to cervical cancer is the deficiency of effective initial treatment. The asymptomatic nature is a main problem faced in the analysis of CC from initial stage. Recently, computer aided diagnosis (CAD) model has gained significant attention in the disease diagnostic process. At the same time, machine learning (ML) finds its use in several medical applications and is utilized as classifier for the initial detection of cancerous cells occurs from cervix area of uterus. With this motivation, this study introduces an intelligent ML based CAD (IML-CAD) technique to classify cervix cancer. The IML-CAD technique involves different stages of operations to detect and classify the cancerous cervix cells. In addition, the IML-CAD technique involves histogram based segmentation to determine the affected regions. Moreover, local binary patterns (LBP) based feature extractor and least squares support vector machine (LS-SVM) based classifier is designed for CC classification. To showcase the better performance of the IML-CAD technique, a series of simulations is performed and the experimental results highlighted the superior performance of the IML-CAD technique over the other techniques.
Designing and Making UI/UX Designs on The Official Website with The Design Thinking Method

The digital era is not new and has spread to various lines of people's lives. The digital age makes everything easy to access and use anywhere and is captured, thus making human life more manageable. The impact of the digital era is not only on the community but also companies, both MSMEs and well-known ones. Companies need to adapt to the digitization process so that sales and the company's existence are well maintained; one example is a website. Mahakarya Mulya is a company originating from Pati, Central Java. The company is engaged in the Processing of patchwork waste into a variety of crafts. Among them are synthetic cotton or patal, pillows, bolsters, mattresses, dolls, and accessories. The company owner needs a medium for promoting and introducing his company that can easily access anywhere and anytime. The answer to the problem is the website. Website is one of the technologies produced in the digital era. The website reviews an attractive appearance and makes it easier for users to find all the necessary information. Everyone in the world has become familiar with websites and made them part of the necessities of life. Designing a website must require a method, for the method used is Design Thinking. Design Thinking is a creative and practical method of solving problems focused on the user. By using this method, it can design the User Interface of the website briefly, not verbosely, and can meet client and user requests.
Object Tracking and Anomaly Detection in Full Motion Video

High volume of Full Motion Videos (FMVs) require development of automated tools to help reduce the cognitive burden of the analysts. The number of algorithms for object detection, classification, tracking and anomaly detection in FMV were investigated. The object detection and classification was performed using YOLOv4 technique. Two approaches for object tracking were analyzed: (i) short-term tracking approach based on DeepSORT and (ii) persistent tracking based on template matching and structural similarity index. Anomaly detection and pattern of life analysis algorithms based on trajectory clustering and time series analysis were tested on simulated data and real FMV over a highway with traffic.
An overview of cloud computing technology and its application on library services

Cloud computing is completely a new model in information and communication technology and it is regarded as the third revolution following the Personal computer and the Internet. This paper provides a brief overview of cloud computing, including its definition, characteristics, and models. The paper also discusses the advantages and disadvantages of cloud computing technology, as well as the use of cloud computing to improve library services.
Improving the Students’ Pronunciation Ability by Using Elsa Speak App

The purpose of this study was to determine the improvement of students' pronunciation skills (Supra-segmental) in English using the ELSA Speak application and to determine student perceptions of the use of the ELSA Speak application. The location of this research is at the University of West Sulawesi. The approach used in this research is quantitative. The methodology used in this study is the One Group Pre-experimental design. The population in this study was class D class 2021 majoring in English at the University of West Sulawesi. The sampling method used in this study is a simple random sampling method. The research sample used was 20 people. The research data was collected through a pronunciation test given to students using pre-test and post-test as well as distributing perception questionnaires to students. The data in this studywere analyzed using non-parametric analysis in the form of a T-test through SPSS. The students' pre-test means the score is 52.30 while the post-test mean score is 65.60. while the results of the T-test showed that sig. (2-tailed = 0.000) &lt;0.05. That means the ELSA Speak application can significantly improve students' pronunciation skills in English. The results of the perception questionnaire are 82% which is included in the good category. This means that students respond well to the use of the ELSA Speak application.
Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement

Estimating 3D poses and shapes in the form of meshes from monocular RGB images is challenging. Obviously, it is more difficult than estimating 3D poses only in the form of skeletons or heatmaps. When interacting persons are involved, the 3D mesh reconstruction becomes more challenging due to the ambiguity introduced by person-to-person occlusions. To tackle the challenges, we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics from the occlusion-robust 3D skeleton estimation and 2) Transformer-based relation-aware refinement techniques. In our pipeline, we first obtain occlusion-robust 3D skeletons for multiple persons from an RGB image. Then, we apply inverse kinematics to convert the estimated skeletons to deformable 3D mesh parameters. Finally, we apply the Transformer-based mesh refinement that refines the obtained mesh parameters considering intra- and inter-person relations of 3D meshes. Via extensive experiments, we demonstrate the effectiveness of our method, outperforming state-of-the-arts on 3DPW, MuPoTS and AGORA datasets.
Vulnerable Line Identification of Cascading Failure in Power Grid Based on New Electrical Betweenness

In this brief, a new electrical betweenness method is proposed to identify the vulnerable lines of the power grid. This method can better balance the accuracy and efficiency of identifying vulnerable lines. The proposed power percentage of generator-load is used as an indicator to evaluate the stability of power grid, and the dynamic influence of generator and load node removal on power grid is considered. The nonlinear cascading failure model of complex networks with overload and weighted edges is introduced, and the three states of normal-failure-overload of lines are fully considered. The model used is more in line with the actual power system. The simulation analysis on IEEE 39 and 118-bus system shows that the change of power loss caused by removing a certain proportion of vulnerable lines and deliberately attacking high new electrical betweenness in turn is compared. The feasibility and effectiveness of this method in identifying vulnerable lines in cascading failure model can be verified.
Self-supervised Learning as a Means to Reduce the Need for Labeled Data in Medical Image Analysis

One of the largest problems in medical image processing is the lack of annotated data.Labeling medical images often requires highly trained experts and can be a time-consuming process.In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining.We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies.The networks are pretrained on a percentage of the dataset without labels and then finetuned on the rest of the dataset.We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.We also show that it is possible to increase the maximum performance of a fully-supervised model by adding a self-supervised pretraining step, and this effect can be observed with even a small amount of unlabeled data for pretraining.
Deep Residual Network for Image Super-Resolution Reconstruction

In view of the fact that traditional neural networks mostly only use the spatial domain information of images over a period of single image super-resolution reconstruction, the generated images are prone to lose important details. We propose a method based on deep residual networks. The sub-image is used as the input of the deep residual network, and then the residual network is improved by combining both local and global residual learning, which reduces the phenomenon of gradient disappearance and improves the efficiency of information transfer. The multi-scale convolution kernel is used to extract rich feature texture information for obtaining the reconstructed image. The experimental results indicate that the reconstruction effect of this method achieves better subjective visual experience and objective evaluation indicators than the contrasting algorithms.
Sentiment Analysis on Online Learning for Higher Education During Covid-19

Distance learning has become a necessary option to the education system for almost every nation because of the COVID-19 outbreak. Due to the closure of educational institutions, which creates obstacles to students' learning in the current COVID 19 pandemic environment, the role of information technology has gained momentum. With the current technology advancements, online learning is made possible for everyone. Although this method appears to be helpful to students and instructors, the effectiveness depended on several factors. For instance, the availability of internet services and the economic capacity of the users may affect the user experience of online learning. The student's reaction is desperately needed to enhance the university's effectiveness and acquire insight into student wants. However, it is difficult to collect and evaluate all the text data on social media, particularly Twitter. In this research, the analysis regarding perception students towards online learning is done through the Natural Language Processing technique which is sentiment analysis. The aim for this study is to determine the terms or keywords used for online learning and identify which machine learning classifiers work best with a large dataset. This research used a Twitter dataset that consisted of 38,602 tweets posted between 23rd July and 14th August 2020. Firstly, pre-processed the data to remove irrelevant tweets. Second, classify the tweet into three classes namely positive, negative, and neutral using rapid miner. Consequently, several machine learning classifiers are trained using different techniques which are Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DCT), and Random Forest (RF). Support Vector Machine classifier with a percentage split 80:20 using VADER Lexicon managed to get the highest accuracy which is approximately 90.41%. Finally, the result of the sentiment analysis has been shown using Power BI data visualization for better understanding. For future work, it would better if this project can provide a real time sentiment using the data from different platforms such as Facebook, Instagram and so on
AF-GCN: Completing various graph tasks efficiently via adaptive quadratic frequency response function in graph spectral domain

Graph neural network is a breakthrough in applying deep learning to non-Euclidean space. It is widely used for tasks such as social network analysis, molecular function inference, drug repositioning and protein modeling, achieving outstanding performance on relational models. Despite the great success of graph neural networks, most of them cannot be generalized to various scenarios. This is because graph information needs variation for different tasks and fixed models limit the flexibility of feature extraction. To address this challenge, we design a graph filter that can be adaptively adjusted according to graph tasks. This filter combines the multi-view strategy with the learnable quadratic frequency response function, using the crests of the quadratic functions to adaptively emphasize the required information. We further design the graph convolutional network model base on this adaptive filter, named AF-GCN. Extensive experiments are performed with 13 SOTA models on 12 different real-world datasets, including homogeneous and heterogeneous datasets in the node classification task, biological datasets, and social network datasets in the graph classification task. AF-GCN achieves state-of-the-art results in various scenarios. In addition, AF-GCN has high interpretability in the graph spatial domain. The development process from Graph Convolutional Networks (GCN) to AF-GCN has historical similarities with the development of Convolution Neural Networks (CNN).
Fast Distance Transforms in Graphs and in Gmaps

Distance Transform (DT) as a fundamental operation in pattern recognition computes how far inside a shape a point is located. In this paper, at first a novel method is proposed to compute the DT in a graph. By using the edge classification and a total order [1], the spanning forest of the foreground is created where distances are propagated through it. Second, in contrast to common linear DT methods, by exploiting the hierarchical structure of the irregular pyramid, the geodesic DT (GDT) is calculated with parallel logarithmic complexity. Third, we introduce the DT in the nD generalized map (n-Gmap) leading to a more precise and smoother DT. Forth, in the n-Gmap we define n different distances and the relation between these distances. Finally, we sketch how the newly introduced concepts can be used to simulate gas propagation in 2D sections of plant leaves.