Reviewerly/exHarmony huggingface dataset:
THE PROBLEMS OF PROFESSIONAL BECOMING OF LEARNING PERSONALITY IN THE AGE OF SOCIAL INTEGRATION AND GLOBALIZATION

Purpose of the study: The article aims at revealing the peculiarities of integration processes in the field of professional and higher education against the background of integration, internationalization, and globalization of world social processes in general. &#x0D; Methodology: The methodological basis of the study was the philosophical and anthropological approach - in solving the problem of forming a learning personality in the system of modern social relations, activity and system approaches contributed to the identification of the place and role of a learning personality (future employee) in the modern economic and social system as a whole.&#x0D; Results: The most important problems of modern vocational education are revealed: the formation and becoming of a learning personality in the dynamically changing world of professions and the labor market. An analysis of the concepts of trans-professionalism and poly-professionalism is presented in the context of changing professionalism and changes in the nature and list of traditional professions. Objective and subjective factors of the development of a competently developed personality in the system of professional and higher education are revealed. The objective factors are presented as the laws of social development - divisions and changes of labor, as well as the law of labor universality, the meaning of which is to form a future specialist with universal skills and competencies - not only with a certain level of professional education, but also personal properties of creativity, mobility, communicativeness, deep responsibility for making difficult decisions, focusing on the result of his own work and, most important, with the formed skill of self-education and self-development. Subjective factors are considered to be the formation of professional thinking which should include signs of objectivity and practical functionality.&#x0D; Applications of this study: The materials of this article can be useful for teachers of the vocational education system in the points of a strategy of the learning personality development, building an individual path of its development, planning development and the formation of general cultural and professional competencies, etc. The results of the study are important for lawmakers in the field of vocational education in creating and upgrading educational and professional standards, bringing them in line with educational standards according to with the objective factors in the development of vocational education and subjective factors - the requirements for learning personality and for the formation of professional thinking.&#x0D; Novelty/Originality of this study: The interrelations of economics and vocational education are revealed on the level of educational and professional standards, requirements of employers and professional training of specialists in the system of professional and higher education.
Image Cleaning and Enhancement Technique for Underwater Mining

The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this paper, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior(DCP) and then taking the converted images and modifying them into the Long, Medium and Short(LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at INESC TEC robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. The paper describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation.
Proposal of a user interaction model in CASE tools based on metaphors and touch screen technology

The CASE tools (Computer-Aided Software Engineering) aim to increase productivity during the software development process. However, their use by professionals is not as extensive as it could be. Among the reasons for the low adoption of this kind of tool are their complexity and learning curve. The main goal of this paper is to investigate user interaction metaphors for CASE tools based on the touch screen technology. It is expected that after this study the usability of these tools could be improved, minimizing the learning curve.
Boosting Monocular Depth Estimation with Channel Attention and Mutual Learning

We propose a novel learning-based method for monocular depth estimation with channel attention and mutual learning. First, we design a channel attention module, called cascaded channel attention module (CCAM). By applying channel attention modules in a cascade manner, CCAM produces multi-scale feature maps which are well-suited for representing 3D shapes. Then, we develop a two-branch depth prediction network (TBDP-Net) containing CCAM, and train it by mutual learning. By sharing the knowledge of each branch during training, mutual learning enables the TBDP-Net to boost the performance from the baseline which is the state-of-the-art method. Taking advantage of channel attention and mutual learning, TBDP-Net can estimate depth from feature maps which focus on features related to 3D shapes. The experimental results show that our proposed method improves the performance in all of the evaluation metrics of depth estimation with the same computational cost as the baseline.
Restoration of hyperspectral images using iterative regularization based on higher order singular value decomposition

Denoising is an essential task in hyperspectral image preprocessing that can improve the performance of subsequent applications. We propose an iterative hyperspectral images denoising method that results in two algorithms, one global and one nonlocal. Both are based on a higher order singular value decomposition (HOSVD) sparse model and realize a regularization in each iteration. The proposed global algorithm treats the hyperspectral image as a whole entity that is able to jointly consider both spatial and spectral information and then uses an iterative regularization framework to mitigate the noise, while the nonlocal algorithm takes the advantages of a patch-based HOSVD sparse model and is more efficient. The experiments with both synthetic noisy and real hyperspectral images show that the proposed iterative method improves the hyperspectral image quality. The subsequent classification results further validate the effectiveness of the proposed hyperspectral image noise reduction.
On the impact of cryptography on complexity theory

research-article Share on On the impact of cryptography on complexity theory Author: Oded Goldreich View Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 497–526https://doi.org/10.1145/3335741.3335760Published:04 October 2019Publication History 0citation65DownloadsMetricsTotal Citations0Total Downloads65Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

In the actual face recognition applications, the sample sets are updated constantly. However, most of the face recognition models with learning strategy do not consider this fact and using a fixed training set to learn the face recognition models for once. Besides that, the testing samples are discarded after the testing process is completed. Namely, the training and testing processes are separated and the later does not give a feedback to the former for better recognition results. To attenuate these problems, this paper proposed an online sparse learning method for face recognition. It can update the salience evaluation vector in real time to construct a dynamical facial feature description model. Also, a strategy for updating the gallery set is proposed in this proposed method. Both the dynamical facial feature description model and the gallery set are employed to recognize faces. Experimental results show that the proposed method improves the face recognition accuracy, comparing with the classical learning models and other state-of-the-art face recognition methods.
Analysis of Pulse Diagnosis Data from a TCM Doctor and a Device by Random Forest

Pulse diagnosis is a typical diagnosis of Traditional Chinese Medicine (TCM). However, it is not clear if there is any relationship between the result of pulse diagnosis and other health related data. In this study, we investigate this and analyze pulse diagnosis data from a TCM doctor and a pulse diagnostic instrument (PDI) by Random Forest. Subjects’ vital signs and pulse diagnosis data from a TCM doctor are used as training data. We classify vital signs which have the PDI’s diagnoses labels. As a result, classification accuracies were over 60% in all cases. Our experiment results imply that better pulse diagnosis may be made with assistance of personal health data analysis.
Research on the Teaching Model of Oral English Training Based on Digital Network

This paper describes and introduces the scheme of Speech Matching Analysis Recognition Technology(SMART) used in the network language learning platform for learning spoken language. The scheme is aimed at single computer users and Internet users, which provides a new teaching mode for the development of cheaper, convenient oral learning and large-scale oral assessment technology. The speech matching analysis and recognition technology adopted in this paper avoids insurmountable difficulties in speech recognition, and it adopts certain product-oriented technologies in speech recognition, based on user's pronunciation and original sound matching, as well as supplemented by recognition, so that users can practice oral English to the greatest extent, and achieve the same or the same original sound. Finally, through experimental data, the feasibility and effectiveness of the language platform design with oral training as the main part are verified.
Development of Machine Vision System for Tracking Crane Mechanisms

In this paper, the issue of the technical possibility of a new method of the speed measuring of hoisting and transport equipment is revealed. The research is carried out for a bridge crane mechanical system. It is proposed to replace the system of absolute and incremental encoders with a system of computer vision. This technical solution makes it possible to replace several speed measurement devices with one camera. In addition, the computer vision system creates conditions for the development of a system for positioning of the bridge crane mechanisms. The main purpose of the research is to validate the possibility of technical implementation of the speed measurement system by the described method. This method of mechanism speed calculation is new for this application in the field of hoisting and transport equipment. As a result, the computer vision system was implemented to determine the speed and position of bridge crane mechanisms. The results of the speed measurement by two methods are compared: traditional methods are compared with the use of encoders and the proposed one with a computer vision system.
Energy Efficient Self-Steering Mechanism for an Autonomous Sailing Vessel

This paper presents the development and experimental evaluation of a self-steering mechanism for an autonomous sailing vessel. The steering mechanism is designed and tailored to relief the commonly used electro-mechanical steering during majority of the mission by the use of pure mechanical coupling between the self-trimming rig and the rudder. This significantly reduces the need for electric power for steering at constant apparent wind angle. Added to the steering, a two-layer navigation system is proposed for path-planning and navigation with algorithms tailored for low-power, low-memory microcontroller. We present experimental results from a total of 19 days of autonomous sailing in Stockholm's Archipelago. The experiments enabled us to compare the sailing performance with both active and self-steering systems for different apparent wind angles. On average, the active steering keeps a heading within ±5.1° of the target while the self-steering is able to maintain the heading within ±8.1° of the target. Another conclusion that can be drawn is that the apparent wind angle doesn't influence the steering performance.
A Method to Input Secret Information Using an Eye Tracking Device

Passwords are an authentication method used for identity verification in secure information services. However, people with physical disabilities may struggle to input a password because they cannot use a normal keyboard to input a password. Other forms of password input exist, but these come with security drawbacks. People with disabilities can use voice recognition to enter their password, but this is vulnerable to bystanders eavesdropping on them. Additionally, people who cannot move their arms can enter their password by pressing the touchscreen with their toes. When you use a touchscreen, a person nearby can look at the display and recognize what you input. Instead, we propose an input method using an eye-tracking system to keep the passwords secret. Eye-tracking enables a person to control a device using their eye movements. Using this method, people who cannot move their limbs can input a password securely by gazing and blinking. This method of password protection is robust against shoulder surfing, which can occur when the person turns their back on a bystander.
Allocation of Information Granularity: A Multi-Objective Evolutionary Optimization Using Conflict Information

Granular Computing (GrC) and its related granular modeling methodologies have received much attention recently to take advantage of information granules. The principle of justifiable granularity fundamentally guides allocation of information granularity along with its optimization. In the existing literature, two conflict criteria coverage and specificity are optimized by aggregating them as one indicator. In this study, we thoughtfully select evolutionary multi-objective optimization (EMO) approaches for the allocation of information granularity and pave a way to design a new EMO framework considering an analysis of the conflict information of these two objectives. To demonstrate the usefulness of EMO and our proposed algorithms, we present a series of experimental studies based on a typical example of granular fuzzy rule-based models. The experimental results indicate that EMO is more efficient to find optimal solutions of allocation of information granularity. Moreover, the proposed EMO method is more feasible to find a set of solutions which exhibits superior coverage or specificity as possible.
Efficient Detection of Spam Messages Using OBF and CBF Blocking Techniques

Emails are the fundamental unit of web applications. There is an exponential growth in sending and receiving emails online. However, spam mail has turned into an intense issue in email correspondence condition. There are number of substance based channel systems accessible to be specific content based filter(CBF), picture based sifting and many other systems to channel spam messages. The existing technological solution consists of a combination of porter stemer algorithm(PSA) and k means clustering which is adaptive in nature. These procedures are more expensive in regard of the calculation and system assets as they required the examination of entire spam message and calculation of the entire substance of the server. These are the channels must additionally not powerful in nature life on the grounds that the idea of spam block mail and spamming changes much of the time. We propose a starting point based spam mail-sifting system benefit, which works considering top head notcher data of the mail message paying little respect to the body substance of the mail. It streamlines the system and server execution by increasing the precision, recall and accuracy than the existing methods. To design an effective and efficient of autonomous and efficient spam detection system to improve network performance from unknown privileged user attacks.
Stacked lossless deconvolutional network for remote sensing image super-resolution

Super-resolving a satellite imagery from its low-resolution one has a significant impact on the remote sensing industry. There are many potential applications that can directly benefit from this technique. A convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR). However, most deep CNN architectures do not properly handle the inherent trade-off between localization accuracy and the use of global context. In this paper, we propose a stacked lossless deconvolutional network (SLDN) for remote sensing SR. We fully exploit global context information while guaranteeing the recovery of fine details. Specifically, we design a lossless pooling by reformulating the pixel shuffle operator, and incorporate it with a shallow deconvolutional network. The resulting lossless deconvolution blocks (LDBs) are stacked one by one to enlarge the receptive fields without any information loss. We further design an attentive skip connection to improve gradient flows throughout the LDB. The SLDN can reconstruct high-quality satellite images without noticeable artifacts. We also provide an extensive ablation study showing that all the components proposed in this paper are useful for the remote sensing SR. Experimental comparisons demonstrate the superiority of the proposed method over state-of-the-art methods both qualitatively and quantitatively.
A semi-supervised deep-thinking concept based classifier for handwritten digit image recognition

A new semi-supervised intelligent classifier based on human beings' thinking process is developed. Unlike the learning processes in conventional classifiers, the complex mathematical optimization process is avoided. Instead, following the human beings' thinking process, the design of the classifier is greatly simplified. In intuitive-imagery thinking layer, pattern clusters are initialized by the labelled samples and then trained via the unsupervised correlation principle. After that, a middle-level feature vector is generated for each pattern cluster via the dot-product operation. In the abstract-logical thinking layer, the moments' information of the middle-level feature vectors is extracted to generate the advanced feature vector. The Bayesian inference based decision-making algorithm is then applied for classification. Like the human beings' experience accumulation, a self-augmenting mechanism in the final layer is developed for adjusting the pattern clusters in real-time. An experiment for the classification of the handwritten digit images from the MNIST database is performed to show that the proposed intelligent pattern classifier can achieve high classification accuracy when the size of the labelled dataset is small.
Development of a naive bayes classifier for image quality assessment in biometric face images

Currently, the use of applications that requiring biometric facial authentication is increasing. However, the performance of these systems is compromised when the images do not present the quality required for their operation. Considering this, in the present work, a Naive Bayes classifier was developed for image quality assessment (IQA) dedicated to facial biometry systems. The metrics that characterize the image according to blur, sharpness and focus are used as input variables. A bibliographical study is carried out on the subject, deepening in the characteristics of the Bayesian classifiers. A public database is used to create the statistical data with which the Naive Bayes classifier is developed. Finally, the results of the classifier are presented for a set of conditions of the predictor variables.
Discovering patterns in sentimental analysis

Users are continually using text-based social networks and messaging applications to express their sentiments throughout the day. Each post can be interpreted as an event with a message and a time-stamp associated with it. With these considerations, interesting information can be extracted with pattern mining techniques. A question that arises from such techniques is: How strong are the correlations between the time of day and emotions? This work aims to analyze sentiment intensity variation in social network users. The objective is to evaluate if pattern mining techniques applied over events enriched with sentiment analysis metadata can be used to address such a question. The results show patterns with higher measures of quality between negative sentiments and the afternoon and late morning hours. These results also lead to the possibility of future work applied to health care and social market interventions.
An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation

Depth estimation from a single image plays an important role in 3D scene perception. Owing to the development of deep convolutional neural networks (CNNs), monocular depth estimation models have achieved a large number of exciting results. However, the requirement for the manual per-pixel labeled dataset (ground truth) limits the application of these supervised methods. Basing on a geometric constraint between the consecutive stereo images, we propose an unsupervised method to infer the scene structure. We train the model with consecutive stereo images as input while only a single image is required at test time. In contrast to previous works, this paper presents an adaptive loss function to tackle the regions which are non-overlapping between consecutive images. Moreover, by exploiting the pixels’ discontinuity in the edge region and the continuity in the non-edge region of a depth image, we propose a novel depth smoothness loss to improve the accuracy of the model. In addition, as an auxiliary task, our model also obtains the camera motion between consecutive images. Experimental results on the KITTI and Cityscapes datasets show that our model outperforms other unsupervised frameworks and some supervised frameworks.
Evaluating Networks of Arguments: A Case Study in Mīmāṃsā Dialectics

We formalize networks of authored arguments. These networks are then mapped to $$ASPIC{^+}$$ theories that subsequently instantiate Extended Argumentation Frameworks. Evaluation of arguments in the latter determines the status of the arguments in the source networks. The methodology is illustrated through a collaboration between scholars of South Asian philosophy, logicians and formal argumentation theorists, analyzing excerpts of Sanskrit texts concerning a controversial normative debate within the philosophical school of Mīmāṃsā.