Reviewerly/exHarmony huggingface dataset:
Digital image technology based on PCA and SVM for detection and recognition of foreign bodies in lyophilized powder

Background Digital image technology has made great progress in the field of foreign body detection and classification, which is of great help to drug purity extraction and impurity analysis and classification. Objective The detection and classification of foreign bodies in lyophilized powder are important. The method which can obtain a higher accuracy of recognition needs to be proposed. Methods We used digital image technology to detect and classify foreign bodies in lyophilized powder, and studied the process of image preprocessing, median filtering, Wiener filtering and average filtering balance to better detect and classify foreign bodies in lyophilized powder. Results Through industrial small sample data simulation, test results show that in the process of image preprocessing, 3 × 3 median filtering is best. In the aspect of foreign body recognition, the recognition based on principal component analysis (PCA) and support vector machine (SVM) algorithm and the recognition based on PCA and Third-Nearest Neighbor classification algorithm are compared and results show that the PCA+SVM algorithm is better. Conclusion We demonstrated that integrating PCA and SVM to classify foreign bodies in lyophilized powder. Our proposed method is effective for the prediction of essential proteins.
Factors of sustainable development of energy technologies in the formation of digital economy

Efficiency in the use of energy resources is a priority for the “smart cities” trajectory of sustainable development. This is an essential condition for improving the economic, social and environmental well-being of citizens in general. The importance of life systems orientation to the consumer as an effective user is emphasized. The factors of sustainable development of technologies were identified: updating and modernization of fixed assets in the fuel and energy complex and other sectors of the economy, social and industrial infrastructure on a new technological and energy-efficient basis; provision of forecasting, full accounting and regulation of energy resources consumption, as well as reduction of their losses. The directions of increasing the energy systems efficiency in various sectors are generalized. The necessity of developing human resources through digital skills training was emphasized.
Mental models in nuclear emergency management

This paper reports on the research of mental models of uncertainties management in an emergency situation which was carried out in the framework of the European CONFIDENCE (COping with uNcertainties For Improved modelling and DEcision making in Nuclear emergenCiEs) Project. The methodology included the mapping of mental models among several emergency preparedness and response experts and then performing interviews based on structured protocol with lay people in five countries: Germany, Greece, Slovak Republic, Slovenia and Spain. The aim of these investigations was to trace the concepts and understandings of emergency preparedness and response and to identify possible gaps between experts and lay people. The article presents the main results of this research and suggestions for the improvement of EP &amp; R planning.
A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.
Identifying Near-Miss Traffic Incidents in Event Recorder Data

Front video and sensor data captured by vehicle-mounted event recorders are used for not only traffic accident evidence but also safe-driving education as near-miss traffic incident data. However, most event recorder (ER) data shows only regular driving events. To utilize near-miss data for safe-driving education, we need to be able to easily and rapidly locate the appropriate data from large amounts of ER data through labels attached to the scenes/events of interest. This paper proposes a method that can automatically identify near-misses with objects such as pedestrians and bicycles by processing the ER data. The proposed method extracts two deep feature representations that consider car status and the environment surrounding the car. The first feature representation is generated by considering the temporal transitions of car status. The second one can extract the positional relationship between the car and surrounding objects by processing object detection results. Experiments on actual ER data demonstrate that the proposed method can accurately identify and tag near-miss events.
Using Bandits for Effective Database Activity Monitoring

Database activity monitoring systems aim to protect organizational data by logging users’ activity to Identify and document malicious activity. High-velocity streams and operating costs, restrict these systems to examining only a sample of the activity. Current solutions use manual policies to decide which transactions to monitor. This limits the diversity of the data collected, creating a “filter bubble” over representing specific subsets of the data such as high-risk users and under-representing the rest of the population which may never be sampled. In recommendation systems, Bandit algorithms have recently been used to address this problem. We propose addressing the sampling for database activity monitoring problem as a recommender system. In this work, we redefine the data sampling problem as a special case of the multi-armed bandit problem and present a novel algorithm, C–\(\epsilon \)–Greedy, which combines expert knowledge with random exploration. We analyze the effect of diversity on coverage and downstream event detection using simulated data. In doing so, we find that adding diversity to the sampling using the bandit-based approach works well for this task, maximizing population coverage without decreasing the quality in terms of issuing alerts about events, and outperforming policies manually crafted by experts and other sampling methods.
Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies

Mutual Information (MI) based feature selection methods are popular due to their ability to capture the nonlinear relationship among variables. However, existing works rarely address the error (bias) that occurs due to the use of finite samples during the estimation of MI. To the best of our knowledge, none of the existing methods address the bias issue for the high-order interaction term which is essential for better approximation of joint MI. In this paper, we first calculate the amount of bias of this term. Moreover, to select features using \(\chi ^2\) based search, we also show that this term follows \(\chi ^2\) distribution. Based on these two theoretical results, we propose Discretization and feature Selection based on bias corrected Mutual information (DSbM). DSbM is extended by adding simultaneous forward selection and backward elimination (DSbM\(_\mathrm{fb}\)). We demonstrate the superiority of DSbM over four state-of-the-art methods in terms of accuracy and the number of selected features on twenty benchmark datasets. Experimental results also demonstrate that DSbM outperforms the existing methods in terms of accuracy, Pareto Optimality and Friedman test. We also observe that compared to DSbM, in some dataset DSbM\(_\mathrm{fb}\) selects fewer features and increases accuracy.
Support Vector Machines: Introduction and the Dual Formulation

Machine Learning means “learning through Machines”. We make machine learn and predict the behavior in order to find a solution to a problem. Machine is set to make various predictions based on the learning mechanisms that have been incorporated in them. There are various techniques through which machines can learn. Learning can be supervised, Unsupervised or Semi-supervised. Under these learning schemes we have various classifications. Under Supervised learning we have Classification and Regression. Classification works on continuous values and Regression works on discrete values. Support Vector Machine is an efficient classifier which are mostly sort of linear and comes under supervised method of learning. SVM also find its application in real life for Face Detection, Bioinformatics, Handwriting recognition, image classification and many others.
Technology as a Tool in STEM Teaching and Learning

STEM (science, technology, engineering, and mathematics) education nowadays becomes more and more topical; however, there are still few students who choose to study these sciences therefore it is important to develop students' interest in these subjects already at school.Meaningful use of technology in the teaching and learning process of STEM could be one of the ways how to attract students' interest.The aim of the research was to identify the role of technologies in the teaching and learning process of STEM.One hundred and twenty-eight (128) STEM teachers and 257 students in general comprehensive schools of Latvia have been surveyed with the help of the QuestionPro e-platform.The obtained data showed that teachers and students had similar and different views on the use of technologies.Both teachers and students mainly use technologies reproductively as consumers of information not as productive developers of knowledge.There are problems with the specific STEM technology: the sensordata registering and processing system, the use of the computer-adjusted microscope and computer laboratory simulations for carrying out virtual experiments.This means that the impact of specific technologies in STEM education is still largely provisional.Exploring the obstacles that prevent teachers from applying technologies it has been found that both institutional and personal factors are important.
A new flexible laser beam profiler for the inspection of train wheels

In this paper, a flexible laser beam profiler is proposed to easily measure the profile of a train wheel for railway inspection. It only requires two laser beams (together and in parallel) to obtain two three-dimensional point-clouds based on the laser triangulation principle. Either the laser beam profiler or the wheel can be freely moved. The motion need not be known. The wheel profile will be obtained in two steps. First, the wheel axis position and orientation are obtained by minimizing the distance between one of the point-clouds and the other translated point-cloud, and translation is defined as a rotation of any point on the point-cloud around the wheel axis until it lies on the other point-cloud's laser plane. In the second step, the wheel profile is extracted by selecting one of the point-clouds and rotating it about the wheel axis and by finding the intersection of rotating points and a perpendicular plane, the perpendicular plane is defined as any arbitrary plane which passes through the wheel axis. This method is useful particularly for obtaining geometrical parameters of a wheel such as flange height, flange slope and flange thickness. In order to commission the proposed method, a prototype system was designed and manufactured. The performance of the system, evaluated in different circumstances, shows a measurement error of up to 2%. Compared with classical methods utilizing a caliper or those which use expensive equipment or additional parts such as reference guides, the proposed method is easy to use and flexible. Also, a novel calibration method is utilized to calibrate the system accurately and freely.
Entanglement and quantum discord in optically coupled coherent Ising machines

We present analytical and numerical simulation results for squeezing, entanglement, and quantum discord in a dissipatively coupled coherent Ising machine (CIM). Both analytical solutions and numerical simulation results, which are obtained with positive-P, truncated-Wigner and truncated-Husimi representations for the density operator, predict the presence of entanglement and quantum discord, below and above the threshold of CIM. The entanglement criteria and quantum discord are evaluated as a function of the dissipative coupling strength relative to the background loss. For coupled two DOPOs, while entanglement disappears as the background loss exceeds the Ising coupling strength, the quantum discord remains finite even with a large linear loss. For one-dimensional lattice of DOPOs, while entanglement disappears for DOPO pair with large distance, quantum discord remains finite.
Deep Multimodal Clustering with Cross Reconstruction

Recently, there has been surging interests in multimodal clustering. And extracting common features plays a critical role in these methods. However, since the ignorance of the fact that data in different modalities shares similar distributions in feature space, most works did not mining the inter-modal distribution relationships completely, which eventually leads to unacceptable common features. To address this issue, we propose the deep multimodal clustering with cross reconstruction method, which firstly focuses on multimodal feature extraction in an unsupervised way and then clusters these extracted features. The proposed cross reconstruction aims to build latent connections among different modalities, which effectively reduces the distribution differences in feature space. The theoretical analysis shows that the cross reconstruction reduces the Wasserstein distance of multimodal feature distributions. Experimental results on six benchmark datasets demonstrate that our method achieves obviously improvement over several state-of-arts.
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made available
Optimization Design of LQR Controller Based on Improved Whale Optimization Algorithm

Whale optimization algorithm (WOA) is a typical swarm intelligence algorithm. It has been applied to many different optimization problems due to its advantages of simple structure, few parameters and strong optimization ability. However, it has not been used in the optimization of LQR controller. In this paper, we apply the mproved WOA to this problem. It is compared with the traditional optimization algorithm (Particle swarm optimization, genetic algorithm, Grey Wolf optimization algorithm). Experimental results show that the improved WOA performs well in the optimization of LQR controller.
About one synthesis method for adaptive control systems with reference models

Abstract This article describes the synthesis technique of static and recurrent algorithms for adaptive parametric and structurally optimized controllers for systems with a reference model. As models, systems are used that are described in the form of a state space, transfer functions, and vector difference equations. And also considered the task of developing adaptive-interval algorithms for the synthesis of control systems for technological objects.
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the bitrate. Towards this goal, we first encode the LiDAR points into an octree, a data-efficient structure suitable for sparse point clouds. We then design a tree-structured conditional entropy model that models the probabilities of the octree symbols to encode the octree into a compact bitstream. We validate the effectiveness of our method over two large-scale datasets. The results demonstrate that our approach reduces the bitrate by 10-20% at the same reconstruction quality, compared to the previous state-of-the-art. Importantly, we also show that for the same bitrate, our approach outperforms other compression algorithms when performing downstream 3D segmentation and detection tasks using compressed representations. Our algorithm can be used to reduce the onboard and offboard storage of LiDAR points for applications such as self-driving cars, where a single vehicle captures 84 billion points per day
Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations

Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze and draw conclusions from these data using emerging computational tools, such as augmented and artificial intelligence (AI) and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.
Motivation and Employee Effectiveness in Online Learning Environments: Leadership Strategies of New Generation and Emotional Intellect

The article aims at analyzing the impact of leadership technologies and emotional intellect within the motivation systems in educational organizations in the EU and CIS countries on employee effectiveness in online learning environments. During research implementation, it has been also identified the direct relationship be-tween these technologies and profitability of these organizations. The research has shown that the issue of effective motivation and incentive system which would ensure a real professional development for the employees of educational organizations and makes the organization a leading one is of rather in-depth conceptual character and needs development of an adequate employee strategy by using more contemporary methods of influencing them. The achieved results are unique and can be used within the study of the issues related to online learning and motivation as of employees of educational organizations, so as employees in overall in their professional activity.
Inertial Sensor Based Solution for Finger Motion Tracking

Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as a comparison. The proposed algorithms utilize the three-link finger model and provide complete information about the position and orientation of the metacarpus. The numerical experiment shows that this approach is feasible and overcomes some of the major limitations of inertial motion tracking. The paper’s proposed solution was created in order to track a user’s pointing and grasping movements during the interaction with the virtual reconstruction of the cultural heritage of historical cities.
PENERAPAN SISTEM INFORMASI PEMBELAJARAN ONLINE DI SMK AL-HUDA LAMPUNG SELATAN

The teaching and learning process needs to be done well and directed. Good quality of learning will certainly produce good learning outcomes. The learning process at SMK Al-Huda, South Lampung is still done through face-to-face. For this reason, Al-Huda Vocational School needs to develop an online learning system to facilitate teachers in sharing material, assignments and conducting examinations. And can facilitate students in obtaining material, collecting assignments and doing exam questions. The design of this system is made using the PHP programming language, MySQL database and usecase diagrams. The results of testing the system using the blackbox method involved 8 respondents and the results showed that the system built was in accordance with user requirements and could run very well.
Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning

Abstract The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooperative systems in various safety–critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications.