Reviewerly/exHarmony huggingface dataset:
A high precision stereo-vision robotic system for large-sized object measurement and grasping in non-structured environment

Handling and loading of large-sized objects represent a challenging task in industrial environments, especially when the object is a metal object with reflective surface features. Active stereo vision technology is not good for measuring the posture of reflective metal object, a high-precision pose measurement system based on passive stereo vision is proposed to automatic measurement, handling and loading of large objects by industrial robots in industrial environments. The system adopts advanced coarse and fine stereo vision positioning strategy, and realises the high-precision positioning of the measured target for the premise of ensuring stability. For coarse positioning, an improved multi-models template matching method based on machine learning is proposed to robust recognition of the multiple objects in complex background. A RANSAC-based method for the ellipse fitting and multi-points plane fitting is proposed for the 6-DOF pose of object accurately obtained in fine positioning step. Compared with the classical CAD-views method, experiments show that the method proposed in this paper has better performance in positioning accuracy and recognition robustness.
Diverse Adaboost Through Heterogenous Component Classifiers

Homogenous component classifiers in the Adaboost algorithm decrease the diversity among component classifiers, which degrades ensemble performance for classification. Aiming to increase the diversity, we propose heterogenous component classifiers for the Adaboost algorithm (termed as He-Adaboost). During each round, three diverse models based on totally different principles are used for learning from the distribution, and three hypotheses are obtained. The diversity of each hypothesis is measured, and the hypothesis with the maximum diversity is selected as the component classifier in this round. To evaluate the ensemble performance of the proposed He-Adaboost method, some experiments are carried out on the standard datasets. Experimental results consistently confirm the superiority of proposed method. It indicates that this study provides an encouraging methodology for improving ensemble performance of the Adaboost algorithm.
STORYTELLING AS A METHOD OF DIGITAL CULTURE FORMATION IN THE PROCESS OF STUDYING LEGAL DISCIPLINES

The article discusses the concepts of storytelling, digital storytelling and, digital culture. Storytelling is proposed as a topical and potentially effective method of studying legal disciplines. The essence and possibilities of this method of digital culture formation in the process of studying legal disciplines are determined. The possibilities of storytelling as a practice-oriented method in the aspect of improving the educational system are investigated. The types of storytelling are determined. The effectiveness of the above method in the process of digital culture formation in the study of legal disciplines is evaluated.
ConnectedUNets++: Mass Segmentation from Whole Mammographic Images

Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, UNet is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimodal medical images, the traditional U-Net structure appears to be inadequate in various ways. There are certain U-Net design modifications, such as MultiResUNet, Connected-UNets and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have replaced the simple skip connections of Connected-UNets architecture with residual skip connections, while in ConnectedUNets++, we have modified the encoder decoder structure along with employing residual skip connections. We have evaluated our proposed architectures on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast.
MSISR : Modified Single Image Super-Resolution Using Relu Based 2D CNN For Satellite Images

In this research, we presented a Modified Single Image Super-Resolution (MSISR) using enhanced very deep super resolution (VDSR). The suggested technique is based on convolutional neural networks and uses up-sampling and residual inputs for training (an essential part of SISR) with a level of 20. The proposed method hybrid fusion of the enhanced bi-cubic method. The proposed MSISR shows better result in terms of peak signal to noise ratio as well as structural similarity index measurement. The proposed method also show good visual outcomes as compare to other previous techniques in terms of butter and resolution. These two factors play a significant role in the outcome analysis of image super resolution (ISR). For the analysis of proposed method use standard data sets like the UC Mecred Field. Data Set are available for the training and testing of the proposed approach. For the simulation of proposed method used well know tool that is matrix laboratory version R2020B.
Tools for automatic collection of IT assets supporting information security process

Information and communication technologies (ICT) and various computing resources, such as network devices, end devices, cloud services and storage and last, but not least, application software are an integral part of every organization. All these key information assets play an important role in organizations. IT assets serve to fulfil business goals and achieve the company's profit. For these reasons, great emphasis and priority has been placed on information and cyber security in recent years. At a time when IT extends in every domain, it is important for organizations to acquire and maintain a strong information and cyber security profile. Critical IT assets must be protected, and organizations see this as a complex problem. To ensure this goal, organizations are forced to use multiple tools that focuses on different aspects of security, including security processes as a security audit. Tools that cover the field of information and cyber security can be divided into several categories such as risk management tools, inventory tools, network scanning tools, and information gathering tools. The audit of information systems is becoming more and more demanding. This happens mainly due to the development of technologies and the emergence of new technological threats and associated risks. Therefore, it is necessary for auditors to have a wide theoretical knowledge and practical skills. And for the purpose of auditing, they are forced to use various available tools that cover various sub-processes. All this increases the complexity of the process and the demands placed on auditors. Risk management can be defined as pre-set and coordinated activities, the aim of which is to control the level of risk affecting the organization. A cyber security audit verifies and assesses the compliance of the security measures taken with the requirements, whether according to the law, standards, or special regulations. These paper presents our expanding research and further describes possible areas of automation of the risk management and audit process, which will make it easier for auditors to perform IT audits more often. We mainly focus on a comparative analysis of available software tools that cover one of the main sub-processes of the risk management. Namely information gathering and network scanning. The analysis outputs point to available tools suitable for automatization and that provides required information as inputs to mentioned subprocesses. The paper presents which of investigated tools are the most effective from the point of view of speed, the amount of information obtained, and the amount of knowledge needed to operate the tool.
Public-Key Encryption from Homogeneous CLWE

The homogeneous continuous LWE (hCLWE) problem is to distinguish samples of a specific high-dimensional Gaussian mixture from standard normal samples. It was shown to be at least as hard as Learning with Errors, but no reduction in the other direction is currently known. We present four new public-key encryption schemes based on the hardness of hCLWE, with varying tradeoffs between decryption and security errors, and different discretization techniques. Our schemes yield a polynomial-time algorithm for solving hCLWE using a Statistical Zero-Knowledge oracle.
Intention to Use of Security as a Service in Cloud Technology

This study proposes a theoretical model explaining the security factors that determine user intentions to choose a cloud technology service, especially on Software as a Service. By understanding the antecedents of using cloud technology software as a service, users will become more alert and careful in choosing cloud technology software as a service in the future. Knowledge of security factors in cloud technology services in the current era is fundamental and significantly impacts data and information used by anyone and anywhere. The D&M model use and data were tested through a structural equation approach and the Partial Least Square (PLS) technique with snowball sampling as a data collection technique. The study was conducted with 424 people who had previously used cloud technology software services for 12 months, of which 273 were male (64.4%), and 151 were female (35.6%). The study's conclusion revealed that the intention to use software services on cloud technology is influenced directly or indirectly by the warranty, including the License of the Software used. (License), antivirus, device usage records (Log History), then the information given to users (Notification) and access control (Access Control), as well as authenticity of user identity (Authentication User).
EXPLORING THE INFLUENCE OF SELF-EFFICACY TO UNDERGRADUATE STUDENTS' VOCABULARY MASTERY

Self-efficacy has become one of the important factor in learning, especially learning language, self-efficacy has brought essential effect to learners themselves that could influence the outcome of study development well. Vocabulary carries essential role in mastering a language, so therefore, it is important to find out the influence between both self-efficacy and vocabulary mastery. The objective of this research was to find out the influence of self-efficacy to undergraduate students' vocabulary mastery. The correlation research design was applied in this research. The population of this research was 54 seventh semester students of English education study program at University of PGRI Palembang in academic year 2020/2021. As the sample of the research, 24 seventh semester students of PGRI University of Palembang were selected through purposive sampling, the judgment of selecting the sample was consideration of students’ vocabulary score and the discussion between researcher and the lecturer. The data were collected by using questionnaire and test, they are self-efficacy questionnaire and vocabulary test. The result of the test and questionnaire were analyzed by using simple linear regression analysis. The result analysis showed that the Sig value of simple linear regression was 0.01 which is lower than α 0.05. Based on the regression analysis result, there was a significant influence of self-efficacy to undergraduate students' vocabulary mastery at University of PGRI Palembang. Keywords : self-efficacy , vocabulary mastery
A Quality Metric for K-Means Clustering Based on Centroid Locations

K-Means clustering algorithm does not offer a clear methodology to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. In this paper, we present a new metric for clustering quality and describe its use for K selection. The proposed metric, based on the locations of the centroids, as well as the desired properties of the clusters, is developed in two stages. In the initial stage, we take into account the full covariance matrix of the clustering variables, thereby making it mathematically similar to a reduced $$\chi ^2$$ . We then extend it to account for how well the clustering results comply with the underlying assumptions of the K-Means algorithm (namely, balanced clusters in terms of variance and membership), and define our final metric ( $$\mathcal {M}_C$$ ). We demonstrate, using synthetic and real data sets, how well our metric performs in determining the right number of clusters to form. We also present detailed comparisons with existing quality indexes for automatic determination of the number of clusters.
Image Presentation Method for Improving Convenience in Remote Control Using VR

The objective of this research is to improve the convenience of teleoperation of a small 4-wheeled robot. To this end, we adopt a teleoperation method using a steering wheel type controller and aim to improve the convenience of teleoperation by presenting new images to the operator. After creating a virtual space and displaying images from a omnidirectional camera on a Virtual Reality Head Mounted Display, a small 4-wheeled robot is teleoperated with a handle-type controller. In the experiment, a virtual mirror was set up on the virtual space calculated from the human's viewing angle and neck range of motion, and the image behind the moving direction was transmitted.
Hospital Recommender System

Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support timely and appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.
The acoustic profiles of vocal emotions in Japanese: A corpus study with generalized additive mixed modeling

This study investigated the vocal emotions in Japanese by analyzing acoustic features from emotional utterances in the Online Gaming Voice Chat Corpus with Emotional Label (Arimoto and Kawatsu, 2013). The corpus contains the recorded sentences produced in 8 emotions by four native Japanese speakers who are professional actors. For acoustic feature extraction, Praat script ProsodyPro was used. Principle component analysis (PCA) was conducted to evaluate the contribution of each acoustic feature. In addition, a linear discriminant classifier (LDA) was trained with the extracted acoustic features to predict the emotion category and intensity. A generalized additive mixed model (GAMM) was performed to examine the effect of gender, emotional category, and emotional intensity on the time-normalized f0 values. The GAMM’s results suggested the effects of gender, emotion, and emotional intensity on the time-normalized f0 values of vocal emotions in Japanese. The recognition accuracy of the LDA classifier reached about 60%, suggesting that although pitch-related measures are important to differentiate vocal emotions, bio-informational features (e.g., jitter, shimmer, and harmonicity) are also informative. In addition, our correlation analysis suggested that vocal emotions could be conveyed by a set of features rather than some individual features alone.
A data-driven medical knowledge discovery framework to predict the length of ICU stay for patients undergoing craniotomy based on electronic medical records

Craniotomy is an invasive operation with great trauma and many complications, and patients undergoing craniotomy should enter the ICU for monitoring and treatment. Based on electronic medical records (EMR), the discovery of high-risk multi-biomarkers rather than a single biomarker that may affect the length of ICU stay (LoICUS) can provide better decision-making or intervention suggestions for clinicians in ICU to reduce the high medical expenses of these patients and the medical burden as much as possible. The multi-biomarkers or medical decision rules can be discovered according to some interpretable predictive models, such as tree-based methods. Our study aimed to develop an interpretable framework based on real-world EMRs to predict the LoICUS and discover some high-risk medical rules of patients undergoing craniotomy. The EMR datasets of patients undergoing craniotomy in ICU were separated into preoperative and postoperative features. The paper proposes a framework called Rules-TabNet (RTN) based on the datasets. RTN is a rule-based classification model. High-risk medical rules can be discovered from RTN, and a risk analysis process is implemented to validate the rules discovered by RTN. The performance of the postoperative model was considerably better than that of the preoperative model. The postoperative RTN model had a better performance compared with the baseline model and achieved an accuracy of 0.76 and an AUC of 0.85 for the task. Twenty-four key decision rules that may have impact on the LoICUS of patients undergoing craniotomy are discovered and validated by our framework. The proposed postoperative RTN model in our framework can precisely predict whether the patients undergoing craniotomy are hospitalized for too long (more than 15 days) in the ICU. We also discovered and validated some key medical decision rules from our framework.
Analysis and Improvement of Multi-Scaled Unscented Kalman-filters to Increase Accuracy

The paper analyses the so-called Unscented Kalman-filter with a set of scaling parameters and points out its limitations. Furthermore, it defines additional parameters that can highly improve the efficiency of the method. A comprehensive numerical simulation shows the effect of these parameters in computational time and accuracy. With appropriately chosen parameters the RMSE is reduced to 15% of the original value.
READING STRATEGIES USED BY EFL STUDENTS FOR ACADEMIC TEXTS

Reading an academic text might pose a certain challenge for an English as a Foreign Language (EFL) learner with its formal form and specific jargons. Thus, finding the best strategies to face the challenge that work best for the learner is crucial. The current study aimed at finding the most used reading strategies used by 186 university students in an English Education Department of one private university in Indonesia and the different choice made by male and female students. Applying a cross-sectional survey design, the study utilized Survey of Reading Strategies (SORS) questionnaire by Mokhtari and Sheorey (2001) to collect the data. The study found that Problem-Solving Strategies were the most used strategy with the value of M=3.9881. Meanwhile, an independent t-test showed the value of sig 2-tailed of 0.006 &lt; 0.05, which purports that there was a statistically significant difference between male and female students in their choice of reading strategies although the strategies mostly chosen was the same, namely the Problem-Solving Strategies. This gave an implication that problem-solving strategies might be the ones students felt comfortable using and likely to be the ones used and encouraged in the teaching and learning process. The significant difference between the male and female students also implies that the latter have more awareness of using reading strategies that they needed.
Investigation of automorphism group for code associated with optimal curve of genus three

The main result of this paper is contained in two theorems. In the first theorem, it is proved that the mapping λ : ℒ(mP∞) → ℒ(mP∞) has the multiplicative property on the corresponding Riemann - Roch space associated with the divisor mP∞ which defines some algebraic-geometric code if the number of points of degree one in the function field of genus three optimal curve over finite field with a discriminant {-19, -43, -67, -163} has the lower bound 12m/(m-3). Using an explicit calculation with the valuations of the pole divisors of the images of the basis functions x, y, z in the function field of the curve via the mapping λ, we have proved that the automorphism group of the function field of our curve is a subgroup in the automorphism group of the corresponding algebraic-geometric code. In the second theorem, it is proved that if m≥ 4 and n &gt; 12m/(m - 3), then the automorphism group of the function field of our curve is isomorphic to the automorphism group of the algebraic-geometric code n associated with divisors ∑ Pi и mP∞, where Pi are points of the degree one.
Fast Adaptive Machine Vision Positioning Algorithm Based on Relative Threshold Features

With the vigorous development of machine vision, simultaneous positioning and methods based on vision have received extensive attention and research and are now commonly used positioning methods. In order to realize the problem of low efficiency and slow operation speed, a visual positioning algorithm is proposed. First, the algorithm uses relative thresholds for templates and sampled images, which effectively overcomes the impact and reduces the data. Secondly, the algorithm reduces the amount of calculation of the machine vision positioning algorithm, and adopts some methods to speed up and increase the speed, and can set some other conditions to further speed up. The algorithm can quickly locate the target in some ways, and then locate the center of the target faster, which could, to a large extent, accelerate machine vision and ensure the accuracy of the algorithm. The algorithm is fast and accurate, and can meet real-time requirements. Experimental research results show that pose estimation based on visual methods, compared with traditional pose estimation methods, uses relative threshold features as the main or only source of information, which can give a more detailed description of the environment in which the machine is located, and has good on-site performance characteristic.
Unmanned Aerial Vehicle Application in Mining User case in Rwanda

Drones have piqued the interest of the mining industry, which has expressed a strong interest in the usage of UAVs/drones for regular tasks Unmanned Aerial Vehicles(UAVs)/drones, sometimes known as Micro Air Vehicles (MAVs), are mostly drones that are used for a number of commercial and military applications, including surveillance and reconnaissance. These unmanned aerial vehicles (UAVs)/drones are capable of transporting of a wide range sensors depending on the nature of their missions, including acoustic, optical, biochemical, and bio sensors. In order to improve the performance and efficiency of drones/UAV, researchers have concentrated on the design optimization of drones, which has resulted in the creation and construction of a variety of Aerial Vehicles/drones with diverse abilities and capabilities. As a consequence, previous research as well as information from firms that supply drones for the mining industry are being explored further. An investigation of the application of drone/UAVs in surface and subsurface mines is presented in this research. The usage of drones/UAVs in abandoned mines, both on the surface and below, is also discussed. It also includes a thorough discussion of the instruments or sensors that are frequently used in mining drones. In this paper/article, we address the difficulties linked with the usage of drones technologies in underground mines, as well as potential solutions to these difficulties.