Reviewerly/exHarmony huggingface dataset:
Preliminary Design on Modular Electrical Autonomous Vehicle

Automation has been used in many industrial applications, such as process in any factory which includes the inventory management of products. Autonomous vehicle can help with inventory management by deliver inventories to its desired destination. Electric vehicle (EV) has less complexity of engines and pollution free comparing to internal combustion engine vehicle. Modular design leads trend of using this system in the factory as it is simple to scale up the size and quantity of the system. Lidar with computer vision (line tracking) is used to compute the robot localization, path planning and obstacle avoidance which integrate with Robot Operating System (ROS). This paper describes the Modular Electric Autonomous Vehicle (MEAV) is study and designed and tested in the real factory.
Simultaneous communication of relevant parameters

The aim of this work is to investigate bidirectional quantum teleportation (BQT) of information, where a Bell state is considered as the quantum channel of the teleportation process. We consider that the Bell state is distributed by a noisy amplitude damping channel. We analyze the behavior of quantum Fisher information(QFI), which provides a tool to quantify the efficiency of bidirectional teleportation. Our results show that by using the weak measurement technique it is possible to mitigate the effect of the noise and enhance the bidirectional teleportation of relevant parameters.
Detecting Distributional Shift Responsible for Predictive Model's Failure

Any predictive software deployed with this hypothesis that test data distribution will not differ from training data distribution. Real time scenario does not follow this rule, which results inconsistent and non-transferable observation in various cases. This makes the dataset shift, a growing concern. In this paper, we&#x0027;ve explored the recent concept of Label shift detection and classifier correction with the help of Black Box shift detection(BBSD), Black Box shift estimation(BBSE) and Black Box shift correction(BBSC). Digits dataset from &#x201C;sklearn&#x201D; and &#x201C;LogisticRegression&#x201D; classifier have been used for this investigation. Knock out shift was clearly detected by applying Kolmogorov-Smirnov test for BBSD. Performance of the classifier got improved after applying BBSE and BBSC from 91&#x0025; to 97&#x0025; in terms of overall accuracy.
Blockchain-Based Crowdsourcing Framework with Distributed Task Assignment and Solution Verification

Internet backboned crowdsourcing utilizes network-wide resources to solve complicated and large-scale tasks, which are not accomplishable for independent individuals. Existing crowdsourcing platforms are mostly centralized solutions with reliability and trustworthiness fragile to single-point failures on the central servers. The innovation of distributed ledgers as blockchain inspires us to optimize the traditional crowdsourcing procedure with distributed sustainability. We propose a blockchain-based design of the distributed secure crowdsourcing scheme for task distribution and result verification without relying on any third trusted institution. A preference-based task distribution (PTD) mechanism is presented which guarantees the percentage of task distribution and the satisfaction of the chosen workers. Task works are continuously assessed for reputations based on their historical behaviors. Task completion correctness is verified by blockchain consensus in two different scenarios after workers submit their results with reputations. We implement a prototype system based on the Ethereum chain with PTD and solution verification components. With various tasks and scenarios evaluated in the system, the proposed distributed crowdsourcing framework shows system reliability, data security, and scenario feasibility.
Application of SA-KNN combined with SVM based on different kernel functions in pattern knowledge mining

Pattern knowledge plays an important role in text recognition. The classification and segmentation system under a large concept system needs to focus on the problem of pattern knowledge mining. The relationship between labels and concepts is divided into three types: equal, belong, and irrelevant. There is a need for a classification method to accurately classify the relationships between label and concept, and between concepts. In this paper, the classification algorithm proposed is SA-KNN-SVM, which can retain the advantages of fast K-nearest neighbor (KNN) model training time and good prediction effect while improving accuracy. The SA-KNN algorithm aims to continuously adjust the parameters and determine the number of the iterations through loop iterations, and then quickly find the accuracy rate corresponding to the different K values. SVM has many kinds of kernel functions, select the kernel function that makes the classification accuracy higher. And find the best parameters by looping an iterative grid search. The experimental results of the proposed algorithm show significant improvement in classification accuracy and processing time.
Hierarchical Clustering Algorithm Based on Fast and Uniform Segmentation

Hierarchical clustering algorithm is a very important method in data mining. The disadvantage of hierarchical clustering lies in the time complexity of the algorithm and the one-way irreversibility of the algorithm. The inaccuracy of the conditions for cluster termination is another major disadvantage of hierarchical clustering. Hierarchical clustering requires the final cluster number. But for most datasets, the number of clusters cannot be known in advance. Therefore, a method is proposed to combine the split-based and agglomeration-based hierarchical clustering algorithms to first quickly and uniformly partition the original dataset, and then make similar partitions adaptively merge based on the partition density and partition distance on the basis of these partitions. In this paper, aiming at these defects of hierarchical clustering, a hierarchical clustering algorithm based on fast and uniform segmentation is proposed.
Regional importance detection of 3D mesh via fusion of local color difference and curvature entropy

Predicting relative visual salient regions on three-dimensional (3D) meshes benefits many computer graphics applications. Most computation models for mesh saliency focus on geometrical information alone. Nevertheless, the ignored texture, lighting and material also provide more detailed appearance information, especially in the context of static scene rendering. In this paper, we propose a mesh saliency detection algorithm considering both geometrical and colorimetric information to address this challenge. Our model first computes the local curvature entropy at multi-scale to capture the geometrical details. Second, a set of images are projected onto the screen at several viewpoints with specified material and lighting model. Potentially salient regions on the rendered images are detected by fusion of multiple color difference maps measured with an approximated multi-scale Laplacian of Gaussian filter. A Gaussian distribution-based central bias model is applied to the image saliency map to emphasize the global rarity of salient regions. Third, the saliency maps of rendered images are projected back to the 3D mesh via the ray casting method. In the end, both saliency maps are combined linearly as the saliency map of 3D mesh. Experiment on the human fixation database demonstrates the performance of our method compared to the classic methods in terms of linear correlation coefficient and AUC.
Testing a Prototype Earthquake Early Warning System: A Retrospective Study of the 2021 Mw 7.4 Maduo, Tibet, Earthquake

Abstract On 21 May 2021, an Mw 7.4 earthquake occurred in Maduo County, Qinghai Province, China. A dense network of high-rate (1 Hz) Global Navigation Satellite System (GNSS) stations around the epicenter provided one of the most complete recordings of GNSS kinematic displacements in Tibet to date. We used these data retrospectively to test a prototype earthquake early warning (EEW) system. Here, we present the results of that test, in which the EEW system archived high-rate GNSS displacement streams, and then used them to track evolving magnitude and slip. The geodetic module in the EEW system issued the first alert 34 s after the earthquake onset. The first alert had a moment magnitude (Mw) of 6.65, which then increased until reaching a stable value (Mw 7.4) 61 s after the earthquake onset. Testing results show good agreement with rapid GNSS displacements and postprocessed GNSS displacements; moreover, the rapid finite-fault inversion is consistent with postinversion magnitude and rupture dimensions. Our findings confirm the viability and usefulness of this EEW system in quasi-real-time magnitude estimation and finite-fault slip inversion.
Minotaur - A Single Lane Navigation System

Autonomous driving has been a major field for re-search, with recent developments in self-driving vehicles, various studies are being done for building affordable autonomous rovers and bots. In this paper we propose Minotaur, an autonomous differential drive rover with perception and control abilities that have been designed on the ROS(Robot Operating System) platform. We have introduced a deep learning based solution for lane detection, replacing the previously used classical methods and sensors. For safeguarding, a rollover protection system and an M-Stop Button are incorporated, along with hand-curated hardware interfacing and High-Level PID tuning. We discuss our navigation stack that has been successfully deployed on a hardware system capable of Single Lane Navigation and obstacle avoidance along with waypoint Navigation. Improvements from traditional methods and results have also been presented.
A Novel Cuckoo Search with Levy Distribution-Optimized Density-Based Clustering Model on MapReduce for Big Data Environment

In recent years, big data have been valuable in a variety of applications, including e-commerce, banking, industries, healthcare, and so on, and it has become difficult to manage the processing of enormous amounts of data. At the same time, data clustering is a significant analytics tool for data mining and is mainly employed for data analytics. Besides, a commonly employed technique to effectually handle big data is the MapReduce model. Several metaheuristic optimizations-based clustering algorithms were presented to manage big data analytics. In this view, this paper presents a novel cuckoo search with levy distribution-optimized (CSLD) density-based clustering (CSLD-ODBC) model on MapReduce for big data environment. The DBSCAN method is a popular density-based clustering model approach that can recognize random forms with changing clusters and noisy data. But, the DBSCAN algorithm encounters a significant problem of setting the optimal parameter values involved in it. To resolve this issue, the presented CSLD-ODBC model makes use of CSLD algorithm to search the parameter space and determine the optimal parameters for the DBSCAN. Besides, a fitness function is derived to decide the cluster count involved in the DBSCAN algorithm. A series of simulations were run to validate the CSLD-ODBC technique's competent clustering outcomes on MapReduce, and the results were analyzed in terms of several performance indicators.
Geothermal Exploration of the Baia Mare Region (Romania) with Magnetotellurics &amp;#8211; Responses, Analysis and 1D Models

&amp;lt;p&amp;gt;Geothermal energy extracted from hydrothermal systems can play a key role in mitigating the effects of climate change, while meeting the world&amp;amp;#8217;s increasing energy demand. Moreover, not only does it represent a highly economic and adaptive source of renewable energy, but it is genetically related with the formation of ore deposits which are necessary to fuel our energy transition.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The presented study is part of a larger multidisciplinary and international project that aims at investigating the geothermal potential of the Baia Mare region, in northern Romania and for which geological, geochemical, hydrogeological and geophysical data have been. In this framework, the magnetotelluric (MT) method is used to study the hydrothermal system and more specifically to locate heat sources, highlight the presence of fluids and identify the system&amp;amp;#8217;s structure.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The largest cluster of measurements with anomalously high heat flow values in Romania (100-160 mW/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;) is situated in the greater Baia Mare region, revealing this area as a prime interest for geothermal exploration. The study area is located within the Neogene Inner Carpathians volcanic arc. Crustal hydraulic properties and associated hydrothermal systems are possibly controlled by the regional Dragos-Voda strike-slip fault zone, which could also provide a pathway for late Miocene magmatic intrusions and lava flows. The associated magmatic plumbing system crosscuts the Neogene sedimentary deposits of the Pannonian Basin. The region is known for surface hot springs, salt and metal mining, which all suggest the presence of a hydrothermal system that could be exploited for geothermal energy.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Broadband magnetotelluric transfer functions have been obtained at 24 sites in the Baia Mare region ranging from 300 Hz up to around 1000s using two MTU instruments from Lule&amp;amp;#229; University of Technology. In addition, we collected continuous telluric broadband recordings at the Surlari National Geomagnetic Observatory to be used as remote reference data together with data from other INTERMAGNET observatories. We applied non-stationary, robust remote processing, which allowed to improve poorly constrained estimates of the impedance tensor in the MT dead band (1 to 10s). Phase tensor and impedance maps provide data overview and a first glimpse of subsurface structures. The preliminary 3D inversion results are presented.&amp;lt;/p&amp;gt;
Image super-resolution for endangered animals using refined super-resolution generative adversarial network

In the nature and wildlife protection field, biologists want to learn about endangered animals' behavior, environment, and activities. The hope is to get more information about endangered animals, such as their diets, predators, and surroundings. With the help of digital devices, biologists use drones to capture information about the changing environment of endangered animals. Therefore, high-resolution images with precise details are essential in the research process. Recently, there have been many deep learning models for improving image resolution in the computer science field. For example, super-resolution convolutional neural networks (SRCNN), efficient sub-pixel convolutional neural network (ESPCN), deeply-recursive convolutional network (DRCN), and super-resolution generative adversarial network (SRGAN). SRGAN is a deep learning network model based on generative adversarial networks. It has a good effect on improving image resolution. Based on the SRGAN model, the paper refined the loss function and structure of the SRGAN networks, which optimized the details of the generated high-resolution images, and provided more accurate high-resolution image results. The refined SRGAN aims to give better results in animal image analysis. We selected DIV2K dataset as the training and testing data and compared the refined SRGAN method with the original SRGAN method. The quantitative evaluation used the peak signal-to-noise ratio (PNSR) and structural similarity index measurement (SSIM). Compared with the SRGAN model with one convolution layer, the model improves the average PSNR value of 0.93dB, and the SSIM value reaches 0.8723. With the help of the improved SRGAN network, biologists can obtain better super-resolution images under the premise that other conditions remain unchanged to protect wild animals.
Shining a light on dark places: A comprehensive analysis of open proxy ecosystem

Open proxies provide free relay services and are widely used to anonymously browse the Internet, avoid geographic restrictions, and circumvent censorship. To shed light on the ecosystem of open proxies and characterize the behaviors of open proxies, we conduct a large-scale, comprehensive study on over 436 thousand identified proxies, including 104 thousand responsive proxies in nine months. We characterize open proxies based on active and passive measurements and examine their network and geographic distributions, performance, and deployment. In particular, to obtain a more in-depth and broader understanding of open proxies, we analyze two particular groups of open proxies — cloud-based proxies and long-term proxies. To process and analyze the enormous amount of responses, we design a lightweight method that classifies and labels the proxies based on DOM structure which defines the logical structure of Web documents. We identify that 7.17% of responsive proxies modify the page content, and 76.42% of those proxies perform malicious actions. Furthermore, we parse the contents to extract information to identify the owners of proxies and track their activities for deploying malicious proxies. To this end, we reveal that some owners regularly change the proxy deployment to avoid being blocked and deploy more proxies to expand their malicious attacks.
Fault Prediction for Large Scale Projects Using Deep Learning Techniques

Fault detection in any industry at an early level is important. However, not that much research can use historical data collection to provide the technical analysis for software fault. Therefore, considerable exertions are needed to demonstrate how software improvement is attained with the maintenance of the historical fault database. Finding new patterns from data forms the structure of algorithms and assists in feature extraction and prediction. Fault data extraction plays an important role in rework-related problems. The success of the fault model is only possible by finding the origin of a fault. Genetic programming makes this process optimal. This classified fault domain knowledge learns the patterns and identifies the effective strategies for rework reduction by hybridization with deep learning. In this current study, we present analytical information known as fault taxonomy obtained from the fault commit history to construct a regression model to predict the fault in software using the labels of the fault and its URL shows considerable correlation with the number of faults. The approach also motivates the investigation and comparison of the software quality before and after the software fault taxonomy, data preprocessing and the level of entropy in the datasets.
Multi-Attribute Group Decision Making Using Fuzzy Numbers at Arithmetic Intervals for Determining Thesis Examination

In general, thesis examiners have opposing viewpoints. Despite the fact that the assessment parameters are the same, each examiner can provide a different standard of evaluation. Group decision making can be used to solve problems in the assessment of thesis exams involving a large number of examiners. Group decision making can be used to solve problems in the assessment of thesis exams involving a large number of examiners. In the group's decision-making process, this paper provided fuzzy numbers at the arithmetic interval. Decisions that generally give a vague knowledge of decision-making information and cannot estimate their decision-making information with exact numerical values are used to determine the thesis exam. According to the case study, the fuzzy approach is used to solve the problem, and evaluation alternatives are used to determine the thesis exam. According to the results of the triangular fuzzy approach, the option to repeat the exam (A2) has the highest value and recommended to be a decision.
Detection and handling outliers in longitudinal data: wavelets decomposition as a solution

Wavelets analysis has become a powerful mathematical tool to decompose a series and to provide its frequent and temporal features. Despite the fact that many researchers have begun testing this approach on financial and economic data to detect and treat outliers, it still not widely applied for longitudinal data. Therefore, this article proposes two algorithms for improving the accuracy of outlier detection in longitudinal data using Wavelets Decomposition, namely, Wavelets Decomposition for Outliers Detection and Handling across Subjects (WDODHAS), and Wavelets Decomposition for Outliers Detection and Handling within Subjects (WDODHWIS). The results show that Wavelets Decomposition is capable of detecting and handling outliers without erasing them, as well as highlighting hypothetical scenarios when these observations cannot be handled.
Generative adversarial network for coronary artery plaque synthesis in coronary CT angiography

Detection and quantification of atherosclerotic plaque in the coronary arteries is important in cardiovascular risk analysis. Atherosclerotic plaque can be visualized using coronary computed tomography angiography (CCTA). Manual identification and segmentation of plaque is a complex task that requires high level of expertise. Hence, several automatic approaches have been designed. Automatic methods employing deep learning have shown to outperform conventional approaches, but they are hampered by the requirement on the availability of large and diverse training data. To address this, we designed a method that synthesizes calcified and non-calcified atherosclerotic plaque in the coronary arteries. First, we generate plaque geometry using conventional image analysis approach that varies the radius, length and angle of a plaque and we use this to generate a crude inpainting of a plaque on a target artery. Thereafter, we employ a conditional generative adversarial network (GAN) to synthesize the plaque texture in CCTA. The generator is trained to generate fake images with realistic appearance. The discriminator is trained to distinguish the synthesized fake and the real images. The data set for training and evaluation of the plaque synthesis contained CCTA scans of 102 patients (50 training, 52 testing) with manually annotated calcified and non-calcified plaque. To evaluate performance of the synthesis method, we compared CCTA patches with real and synthesized plaque. The evaluation resulted in mean (standard deviation) structural similarity index of 0.99 (0.01), peak signal noise ratio of 73.99 (5.52) and mean absolute error of 5.56 (3.23) HU. To evaluate whether synthesized data enables plaque segmentation, an additional set of CCTA scans of 92 patients without visible plaque was collected. In these scans, plaque was synthesized using the developed approach, containing in total 615 calcified and 544 non-calcified plaque lesions. The synthesized data was used to train a 3D UNet for segmenting calcified and non-calcified plaque lesions. Automatic segmentation which was trained with real data only resulted in Dice coefficients of 0.68 and 0.35 for calcified and non-calcified plaque, respectively. This was significantly improved by pretraining the network with synthetic data and refining it with real data, which resulted in a Dice coefficients of 0.70 (p=0.03) and 0.36 (p=0.02) for calcified and non-calcified plaque, respectively. The results demonstrate that training with CCTA scans with automatically synthesized calcified and non-calcified plaque improves the performance of plaque segmentation.