Reviewerly/exHarmony huggingface dataset:
Real-Time Inspection of Rod Straightness and Appearance by Non-Telecentric Camera Array

In this paper, we propose a measurement system that employs a camera array based on a non-telecentric optical system and an accompanying measurement algorithm to measure the straightness, length, diameter, and appearance of a rod. Measurements using telecentric optical systems, which employ orthogonal projection to preserve the dimensional ratios regardless of distance, are common in image-based inspection of the dimensional or geometrical tolerances of industrial products. However, some cases depend on the size of the target or inspection item, wherein it is difficult to configure a measurement system using telecentric optical systems. As an example, this study considers the measurement of straightness of a long rod and shows that it is possible to achieve high measurement accuracy using non-telecentric optical systems by introducing methods to calibrate miniscule errors and distortions that remain uncorrected in conventional image calibration methods. We also show that the same measurement allows for the measurement of other inspection items and evaluation of their respective measurement accuracies, thereby proving that a flexible image-based inspection process can be constructed using the proposed system and method.
Tracking of Overlapped Vehicles with Spatio-Temporal Shared Filter for High-Speed Stereo Vision

In this study, we propose a method for measuring the distance and velocity of overlapping vehicles using high-speed stereo vision. The method is based on the correlation filter called MOSSE and is improved by balancing the computation cost and accuracy using the features of high-frame-rate images. To achieve stable tracking, the tracking window was adjusted for each vehicle according to the vehicle distance and the overlapping state. Experiments on the images acquired by the cameras mounted on an actual vehicle were performed to validate the proposed method.
Zariski dense orbits for endomorphisms of a power of the additive group scheme defined over finite fields

We prove the Zariski dense orbit conjecture in positive characteristic for endomorphisms of GaN defined over Fp‾.
High-Speed Depth-Normal Measurement and Fusion Based on Multiband Sensing and Block Parallelization

A wide range of research areas have high expectations for the technology to measure 3D shapes, and to reconstruct the shape of a target object in detail from multiple data. In this study, we consider a high-speed shape measurement technology that realizes accurate measurements in dynamic scenes in which the target object is in motion or deforms, or where the measurement system itself is moving. We propose a measurement method that sacrifices neither measurement density nor accuracy while realizing high speed. Many conventional 3D shape measurement systems employ only depth information to reconstruct a shape, which makes it difficult to capture the irregularities of an object’s surface in detail. Meanwhile, methods that measure the surface normal to capture 3D shapes can reconstruct high-frequency components, although low-frequency components tend to include integration errors. Thus, depth information and surface normal information have a complementary relationship in 3D shape measurements. This study proposes a novel optical system that simultaneously measures the depth and normal information at high speed by waveband separation, and a method that reconstructs the high-density, high-accuracy 3D shape at high speed from the two obtained data types by block division. This paper describes the proposed optical system and reconstruction method, and it evaluates the computation time and the accuracy of reconstruction using an actual measurement system. The results confirm that the high-speed measurement was conducted at 400 fps with pixel-wise measurement density, and a measurement accuracy with an average error of 1.61 mm.
X-ray fluorescence scanning of soft and wet-sediment cores in terrestrial environments; A robust blind source separation approach

X-ray fluorescence (XRF) scanning of split cores using automated cores scanners is rapid but expensive and semi-quantitative method in sedimentary geochemistry. While searching for a low-cost and quantitative solution in environmental geochemistry, we investigated the application of hand-held XRF device in scanning on two siliciclastic-sediment cores from dam-reservoirs in Slovakia. The core-scan data were compared with XRF data from dry, powdered sample aliquots, grain size, water content, and magnetic susceptibility data, which were used as independent variables. The aim of the manuscript is to investigate the applicability of time-series statistical methods to reveal the physical and chemical signals from hand-held XRF scanning of wet-sediment cores. The geochemical data were processed using the centred log-ratio (clr) technique, and then by the Second-Order Blind Identification (SOBI) method, which is a subtype of robust Blind Source Separation (BSS) techniques commonly used to enhance the signal-to-noise ratio in time series analysis of data in medicine or acoustics. So far, BSS has been rarely applied in geochemistry. Raw element concentrations (ppm/wt%) of core scans and sediment powders are essentially incomparable, but their clr coefficients show statistically significant correlation for Al, P, K, Ca, Mn, Fe, Cu, As, Sr, Zr, and Pb, which reduces the difference between the two measurement methods and justifies previous attempts to calibrate the core-scanning data using log-ratio techniques. Scores of three of eight latent components (IC2, IC4, and IC7) found by SOBI in the core scan- and powder data show significant statistical correlation demonstrating the applicability of the core-scan technique. Their geochemical interpretation is based on the IC loadings by element clr, and the stratigraphic correlation with the sediment grain size and water content. These ICs explain geochemical variability due to grain-size contrast between sandy and silty layers, input of biogenic productivity-sensitive elements (Ca and P), eutrophication, and accumulation of anthropogenic elements. The robust BSS approach represents a promising method of processing of large datasets in environmental geochemistry.
Behind the Mix-Zones Scenes: On the Evaluation of the Anonymization Quality

In the flowering of ubiquitous computing, networks like the Internet of Things and the Internet of Vehicles have contributed to connecting objects and sharing location services in broad environments like smart cities bringing many benefits to citizens. However, these services yield massive and unrestricted mobility data of citizens that pose privacy concerns, among them recovering the identity of citizens with linking attacks. Although several privacy mechanisms have been proposed to solve anonymization problems, there are few studies about their behavior and analysis of the data quality anonymized by these techniques. In this paper, we introduce the anonymization quality. For this, we propose analyzing the mix-zones metrics Amount of Cars on Mix-zone (ACM), Interval of Arrival Time between Cars on Mix-zones (ITM), and Activation Time of the Mix-zone (ATM) for characterizing and evaluating the impacts of anonymization quality over time and space in mobility data. The results showed that mix-zone metrics reflect anonymization behavior and can measure the anonymization quality over time. This insight can contribute significantly to building privacy proposes based on anonymization more effectively than based only on traffic flow. To our knowledge, this is the first work that uses mix-zones metrics analysis to observe the anonymization behavior in quality terms.
Performance Evaluation and Testbed for Delivering SRT Live Content using DASH Low Latency Streaming Systems

The work presented in this paper focuses on the implementation of a testbed for the evaluation of content distribution systems using LL-DASH (Low Latency DASH -Dynamic Adaptive Streaming over HTTP-) and devices that provide real-time sources or servers using real-time protocols, such as RTSP (Real Time Streaming Protocol) or SRT (Secure Reliable Transport). These protocols are widely used by IP (Internet Protocol) cameras or by production and transmission systems, such as OBS Studio or vMix. The objective is to show the necessary processes in detail (so they can be reproduced in future works related to low latency services) and to minimize the end-to-end delay, obtaining values in the order of 2 seconds or less. The implementation has been done using FFmpeg software, players like Dash.js or Shaka-Player and implementing a Python web server with LL-DASH support to optimize the transmission delay.
XuILVQ: A River Implementation of the Incremental Learning Vector Quantization for IoT

In machine learning, incremental learning algorithms provide a solution for models that need to dynamically adapt and react to their context by analyzing samples from data streams. These algorithms are especially suitable for Internet of Things (IoT) solutions where devices (like sensors/actuators) have low memory and computation capability. Within this context, we propose an implementation of an Incremental Learning Vector Quantization (ILVQ) algorithm compliant with the well-known River library standards. Our implementation was assessed according to its predicting capacity and also according to its memory and execution time consumption, showing outstanding results.
Toward a Competence Model for Graphical Modeling

Modeling is an integral part of many computing-related disciplines and thus also represents a curricular core component in computing education in tertiary education. Competence models in which modeling is integrated at least to some extent already exist in some of these disciplines. However, for the core component of graphical modeling, a competence model that illuminates the relevant competences in detail is still lacking. Therefore, we develop a competence model for graphical modeling with the aim to make teaching and especially assessments in the field more competence-oriented. This article reports on the first two studies conducted to develop and validate the competence model for graphical modeling. In the first study, the structure of the competence model was developed based on theories and approaches of educational science. Competences relevant for graphical modeling were deductively derived from literature and existing university course descriptions using techniques of qualitative content analysis. The result of the first study is a preliminary competence model. In the second study, the preliminary competence model was reviewed by means of an expert rating in the modeling community. The competence model was revised and refined based on these findings and subsequent expert discussions. The main result of the investigation represents the competence model for graphical modeling, which includes a total of 74 competence facets at different cognitive process levels in the five content areas of “model understanding and interpreting,” “model building and modifying,” “values, attitudes, and beliefs,” “metacognitive knowledge and skills,” and “social-communicative skills.”
Design of a Protein with Improved Thermal Stability by an Evolution‐Based Generative Model

Abstract Efficient design of functional proteins with higher thermal stability remains challenging especially for highly diverse sequence variants. Considering the evolutionary pressure on protein folds, sequence design optimizing evolutionary fitness could help designing folds with higher stability. Using a generative evolution fitness model trained to capture variation patterns in natural sequences, we designed artificial sequences of a proteinaceous inhibitor of pectin methylesterase enzymes. These inhibitors have considerable industrial interest to avoid phase separation in fruit juice manufacturing or reduce methanol in distillates, averting chromatographic passages triggering unwanted aroma loss. Six out of seven designs with up to 30 % divergence to other inhibitor sequences are functional and two have improved thermal stability. This method can improve protein stability expanding functional protein sequence space, with traits valuable for industrial applications and scientific research.
Cloud Computing for Big Data Analysis

With the spread of the Internet of Things, large amounts of digital data are generated and collected from different sources, such as sensors, cameras, in-vehicle infotainment, smart meters, mobile devices, applications, and web services [...]
Using a Convolutional Neural Network to Recognize Text Elements in Poor Quality Scanned Images

The paper proposes a method for recognizing the content of scanned images of poor quality using convolutional neural networks (CNNs). The method involves the implementation of three main stages. At the first stage, image preprocessing is implemented, which consists of identifying the contours of its alphabetic and numeric elements and basic punctuation marks. At the second stage, the content of the image fragments inside the identified contours is sequentially fed to the input of the CNN, which implements a multiclass classification. At the third and final stage, the post-processing of the set of SNA responses and the formation of a text document with recognition results are implemented. An experimental study of all stages was carried out in Python using the Keras deep learning libraries and OpenCV computer vision and showed fairly good results for the main types of deterioration in the quality of a scanned image: geometric distortions, blurring of borders, the appearance of extra lines and spots during scanning, etc.
Role of digitalization, digital competence, and parental support on performance of sports education in low-income college students

Educational institutions have failed to achieve desired goals due to the lack of technology adoption, and this situation needs researchers' emphasis. Hence, the current study examines the impact of digitalization and digital competencies among students on the educational performance of low-income college students in China. The article also investigates the moderating impact of parental support at the nexus of digitalization, digital competencies among students, and educational performance in low-income college students in China. The questionnaires were used by the researchers to gather the data from the selected respondents. The article has applied the smart-PLS to check the linkage among understudy constructs and test the hypotheses. The results revealed that digitalization and digital competencies among students positively link educational performance. The results also exposed that parental support significantly moderates digitalization, digital competencies, and educational performance in low-income college students in China. This article helps policymakers develop policies to improve educational performance using technology adoption.
fSEAD: A Composable FPGA-based Streaming Ensemble Anomaly Detection Library

Machine learning ensembles combine multiple base models to produce a more accurate output. They can be applied to a range of machine learning problems, including anomaly detection. In this article, we investigate how to maximize the composability and scalability of an FPGA-based streaming ensemble anomaly detector (fSEAD). To achieve this, we propose a flexible computing architecture consisting of multiple partially reconfigurable regions, pblocks, which each implement anomaly detectors. Our proof-of-concept design supports three state-of-the-art anomaly detection algorithms: Loda, RS-Hash, and xStream. Each algorithm is scalable, meaning multiple instances can be placed within a pblock to improve performance. Moreover, fSEAD is implemented using High-level synthesis (HLS), meaning further custom anomaly detectors can be supported. Pblocks are interconnected via an AXI-switch, enabling them to be composed in an arbitrary fashion before combining and merging results at runtime to create an ensemble that maximizes the use of FPGA resources and accuracy. Through utilizing reconfigurable Dynamic Function eXchange (DFX), the detector can be modified at runtime to adapt to changing environmental conditions. We compare fSEAD to an equivalent central processing unit (CPU) implementation using four standard datasets, with speedups ranging from 3× to 8×.
Mutual Supervised Fusion &amp; Transfer Learning with Interpretable Linguistic Meaning for Social Data Analytics

Social data analytics is often taken as the most commonly used method for community discovery, product recommendations, knowledge graph, and so on. In this study, social data are firstly represented in different feature spaces by using various feature extraction algorithms. Then we build a transfer learning model to leverage knowledge from multiple feature spaces. During modeling, since the assumption that the training and the testing data have the same distribution is always true, we give a theorem and its proof which asserts the necessary and sufficient condition for achieving a minimum testing error. We also theoretically demonstrate that maximizing the classification error consistency across different feature spaces can improve the classification performance. Additionally, the cluster assumption derived from semi-supervised learning is introduced to enhance knowledge transfer. Finally, a Tagaki-Sugeno-Kang (TSK) fuzzy system-based learning algorithm is proposed, which can generate interpretable fuzzy rules. Experimental results not only demonstrate the promising social data classification performance of our proposed approach but also show its interpretability which is missing in many other models.
Solving a Generalized Network Design Problem Using the Archimedes Optimization Algorithm

Network design decisions in strategic communication networks have attracted widespread interest from the scientific community in the last decades. Since real-world cases are complex and computationally challenging systems that require effective optimization techniques, therefore recently, approximate resolution methods, specifically metaheuristics frameworks are in focus. In this research, we investigate a specific variant of Network Design Problems (NDPs), namely the Generalized Discrete Cost Multicommodity Network Design Problem (GDCMNDP), which seeks to find a network that optimizes the total costs while fulfilling several design restrictions. A promising newly developed solution approach based on the Archimedes Optimization Algorithm (AOA) that has not been tackled in the previous studies for solving NDPs is proposed. To assess its performance, the proposed approach is applied to benchmark network scenarios from the literature. The obtained solutions are compared with well-known state-of-the-art metaheuristic solutions. Results highlight the promising performance of the AOA for solving such NP-hard Network Design Problems.
On Coupling a Content-Based Routing Process with a Multi-label Residual Convolutional Neural Network Text Classifier

The number of complicated documents and texts has grown exponentially in recent years, demanding a deeper comprehension of machine learning techniques in order to effectively categorize texts in numerous applications. In natural language processing, several machine learning techniques have shown astounding outcomes. These learning algorithms’ effectiveness depends on their ability to comprehend intricate models and non-linear correlations in data. However, it might be difficult for researchers to locate appropriate structures, architectures, and methods for text categorization. In this paper, we will outline a Natural Language Processing (NLP) application that makes use of a content-based router to categorize text. This article’s overarching objective is to foresee the occurrence that a legal ad describes from the ad’s plain wording. The Multi-label Residual Convolutional Neural Network will be used for text classification in this challenge, which is solely a supervised task, along with traditional modeling approaches. We will discuss the strategy used to address the issue of classified advertisements, the challenges faced, and the outcomes of the experimental work.
Lossy Compression Effect on Color and Texture Based Image Retrieval Performance

Image retrieval and compression are rigorous research field to solve the problem of storage and management of digital image. Several digital compression techniques are available for digital compression such as lossy and lossless compression methods. JPEG, an international organization issues an effective digital image compression standard. CBIR system have been proposed to reduce the storage of digital multimedia collections. Content Based Indexing and Retrieval method provides some advantages in compression of digital image. However, lossy compression technique reduce the visual appearance of images and also the values of real pixels have been altered. There is a filtering effect on pictorial qualities due to data loss. This purpose of this research is find an efficient retrieval and compression technique of image based on color and texture. This experiment investigates the compression effects on image retrieval using color and texture features and presents retrieval results of a Content Based Image Retrieval (CBIR) system. By using some performance metrics the system's output can be evaluated. Each one is evaluated by different measurement. In this experiment only Two performance metric has been used namely F1 measure and Average Normalized Modified Retrieval Rank (ANMRR). Here four visual descriptors are used and measure performance individually. Two of the color feature and other two is texture features. The system is tested with two different image database color and gray images with visual feature vectors.
Comparative Performance of Tree Based Machine Learning Classifiers in Product Backorder Prediction

Early prediction of whether a product will go to backorder or not is necessary for optimal management of inventory that can reduce the losses in sales, establish a good relationship between the supplier and customer and maximize the revenues. In this study, we have investigated the performance and effectiveness of tree based machine learning algorithms to predict the backorder of a product. The research methodology consists of preprocessing of data, feature selection using statistical hypothesis test, imbalanced learning using the random undersampling method and performance evaluating and comparing of four tree based machine learning algorithms including decision tree, random forest, adaptive boosting and gradient boosting in terms of accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve and area under the precision and recall curve. Three main findings of this study are (1) random forest model without feature selection and with random undersampling method achieved the highest performance in terms of all performance measure metrics, (2) feature selection cannot contribute to the performance enhancement of the tree based classifiers, and (3) random undersampling method significantly improves performance of tree based classifiers in product backorder prediction.
A Transfer Learning Approach to Detect Face Mask in COVID-19 Pandemic

COVID-19 is tumultuous creating our life so unpredictable. There has no solution of this contagious disease rather than vaccination and prevention. The first and foremost preventative step is using face masks. Face mask can hindrance its droplet from one to another. So this paper has focused the detection of facial mask from image processing using Transfer Learning. For this purpose, total 1376 images have been collected where 690 images of with mask and 686 images of without a mask. Here transfer learning is chosen for the reason of its capability to produce best accurate regardless the limited size of the image dataset. Here, multifarious transfer learning models have been trained to find out the best fitting model. Finally, We have found the VGG16 model with the best accuracy where training accuracy is 98.25% and testing accuracy is 96.38%.