Reviewerly/exHarmony huggingface dataset:
FORMATION OF IC-COMPETENCE OF THE FUTURE MASTER OF EDUCATIONAL AND PEDAGOGICAL SCIENCES

The article is substantiated the practical aspects of the formation of IC-competence of the future Master of Educational and Pedagogical Sciences (for students of the second (master's) level of higher education of the educational-professional program 011 Educational and Pedagogical Sciences. Educational policy in terms of informatization of higher education; the role of Google services (for example, Google Classroom, Google Docs &amp; SpreadSheets, Google Forms, Google Calendar) in the formation of IC-competence of the future Master of Educational and Pedagogical Sciences; their characteristics are outlined: 1) saving money on the purchase of software; 2) reducing the need for specialized classrooms, as in any classroom you can organize a modern educational process using laptops and wireless network; 3) the ability to perform many activities, monitor and evaluate student achievement online; 4) openness of the educational environment for those who teaches (teacher) and those who studied (student); examples of pedagogical using of elements of Google services for the formation of IС-competence of the future master of public administration in higher education institutions in the process of studying the discipline (educational component) "Management of the IT-structure of the educational institution".
When is a line of inquiry ‘reasonable’? - a focus on digital devices

Many of the inquiries now made by law enforcement into suspected criminal conduct involve the interrogation of digital data generated by those parties subject to an investigation. This data often exists in large quantities where inquiry-irrelevant information is likely to be in abundance. When conducting an investigation that contains digital device/data, an investigatory team in England and Wales is under an obligation to pursue all reasonable lines of inquiry, however, determining ‘reasonableness’ is not straightforward where unfettered access to all available data should not be a default position in all cases and a suspect’s right to privacy respected. This work examines when a line of inquiry is ‘reasonable’ if it involves a digital device, with the ‘reasonable line of inquiry framework’ offered to support investigatory teams to determine this. This approach is designed to support the production of transparent, robust and defensible decisions regarding the assessment of reasonableness.
On Consistency of the Bayes Estimator of the Density

Under mild conditions, strong consistency of the Bayes estimator of the density is proved. Moreover, the Bayes risk (for some common loss functions) of the Bayes estimator of the density (i.e., the posterior predictive density) goes to zero as the sample size goes to ∞. In passing, a similar result is obtained for the estimation of the sampling distribution.
Renewable Energy Technology Selection for Iran by Using Multi Criteria Decision Making

Energy is required for wide variety of applications including industrial activities, transportation, heating etc. Regarding the issues raised due the consumption of fossil fuels such as emission of greenhouse gases, renewable energy utilization has been expanded in recent decades. Power generation is a key element in the energy systems of countries and significant parts of primary energy consumption is applied for this purpose. Renewable energy-based systems, e.g. solar systems, wind turbines and geothermal power plants are applicable for this purpose. In the present study, o multicriteria decision making approach, TOPSIS, is employed to prioritize conventional renewable energy systems for power generation in Iran. In this way, different criteria are considered to rank the technologies for power generation for the case study. The criteria are weighted on the basis of the questionnaire completed by the experts in this field. Results revealed that applying solar PV is the most preferable technology for power generation in utility-scale in Iran.
Inverse modeling of cancellous bone using artificial neural networks

Abstract Artificial neural networks are used to solve different tasks of daily life, engineering and medicine. In this work, we investigate its suitability for the examination of simulation results of cancellous bone with the aim to evaluate whether the bone is affected by osteoporosis. This bone disease is characterized by a reduction of the cortical bone phase, one of the two main components of the bone. The neural network predicts the simulated volume fraction in different parts of a cylinder, which models the bone. As a basis for its calculations, the neural network gets the information about the magnetic field inside the cylinder from finite element simulations. Examinations show that it is possible to train neural networks on solving that task with very high accuracies.
Smart Bottine for autistic people

Autism affects a person's cognitive abilities and ability to interact with others. They can get easily disturbed by surrounding changes and can affect their health. It's not always possible for the caretaker to be present with the patient at all times, so this product is aimed at providing help to such people. One of the key features of this project is to detect worms in the shoe which might have entered while the patient is out. Some worms have proved to be fatal to health and have caused so many deaths. This paper gives assurance to caretakers of patients having autism for real time information regarding the status of patients such as tracking their location, fall detection, insect detection and obstacle detection based on a smart shoe that wirelessly sends information to the caretakers by sensing from the environment.
A research on submarine cable path planning

Submarine optical fiber cables are essential carriers of international data transmission, and their laying and maintenance require significant investments. However, the failure of the submarine cable may cause severe socioeconomic damages, costs and disruptions. Therefore, it is important to build a cost-effective and resilient submarine cable network that can withstand potential natural or human-made disasters. This paper first discusses the current research progress on submarine cable path planning and cable network design. Next, it focuses on our approach based on a triangulated manifold is used to model the earth’s surface based on real-world geographic data. Then, for submarine cable path planning between two given end-points, the fast marching method (FMM) is used to solve a multi-objective path planning problem that takes into account a variety of design considerations aiming to optimize the survivability and cost of submarine cables. This multi-objective problem is then transformed into a single-objective problem by weighted sum method for solution. Finally, to overcome the difficulties associated with the massive data size that are prevalent in practical scalability and high-precision cable path planning design, two methods named multi-resolution and parallel FMM are proposed. Experimental results show the cable paths obtained by the multi-resolution and parallel FMM have lower cost, shorter length, and higher resolution than the results obtained from the sequential FMM method, which help to achieve scalability and high-quality cable path planning in the future.
Information Extraction and Analysis on Certificates and Medical Receipts

Document digitalization has become a trend in recent years. It provides fast analysis and search because the information in the documents can be easily managed. However, in real applications, while digitalization is in progress, lots of information has not yet been digitalized and only stored on papers. A common demand of analyzing a large amount of documents would be a time-consuming mission because they need massive human labor. Nowadays, some computer vision algorithms have emerged and they can be applied in such a scenario. In this paper, we propose an automatic information extraction and analysis system for mandarin documents. It consists of three main steps. Firstly, the text regions in documents under natural scenes are detected. Secondly, these text regions are recognized and converted into digital forms. Finally, heuristic rules are designed and integrated into the system to improve the recognition accuracy. The proposed system is expected to eliminate the time-consuming problem of document information extraction.
Deep Learning in Computational Linguistics for Chinese Language Translation

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.
Computer-Aided English Pronunciation Accuracy Detection Based on Lip Action Recognition Algorithm

This paper studies computer-aided English pronunciation accuracy detection based on lip recognition. First, the face is detected using the skin color model and the geometric features of the face, and then a multi-level structure of the lip region detection algorithm is proposed to detect the lips, which is to achieve the lips. The coarse positioning to the fine positioning. The optical flow method is used to calculate the movement information of the lips between adjacent frames to accurately mark the time corresponding to the change of the lips. The marking task is completed by calculating the similarity between the learner's pronunciation and the standard speech, and Comparing the scores of learners' pronunciation with experts, calculating the correlation between the two, verifying the feasibility and effectiveness of the model increased by 7.8%.
Traffic Sign Classification by Using Learning Methods: Deep Learning and SIFT Based Learning Algorithm

Automatic detection of road signs is of significance to automated driver assistance systems contributing to the safety of the drivers. With the development of different traffic sign recognition systems, several detection methods appeared: color-based, shape-based, and learning-based methods. In this paper, we consider learning-based methods and compare the performance of deep learning methods and other learning methods based on hand-crafted features (Scale-Invariant Feature Transform(SIFT)) in Landmark recognition. We also compare several algorithms based on deep learning, including a convolutional neural network with 2 and 3 layers, and LeNet. [1] pointed that SIFT is outperformed other learning methods based on hand-crafted features like SURF and BRISK, therefore we choose the SIFT with the best road sign recognition rate to compare with the two basic deep learning methods CNN and Lenet in order to know which is more suitable for road sign recognition. In the SIFT -based classification algorithm, we extract features from images, use K-means, Bag of Words Model to generate feature vectors, and the SVM model is then trained by feature vectors and their labels. The same operation will be done in the test images to feature vectors that are put into the well-trained SVM model and a traffic sign is detected, while in deep learning methods, we use two different convolutional neural network architects. All the results show that all deep learning algorithms perform well and LeNet has higher accuracy than CNN while the SIFT -based classification algorithm has the lowest accuracy even though it has the highest speed.
Unequal error protection transmission for federated learning

Communication has been recognized as one of the primary challenges of federated learning (FL), but the actual communication algorithm or protocol design is still rarely involved in the existing studies. In the paper, viewing the model exchange in FL as a special kind of traffic, an unequal error protection (UEP) scheme is designed based on multi-rate channel coding and multi-layer modulation for it. To answer the question of how to make error control for FL when the wireless channel is no longer simplified as a pipeline, this paper firstly theoretically analyzes the impact of transmission error on machine leanring (ML) model, which reveals that the dynamic range of the weights should be taken into consideration. Guided by the analysis, the UEP scheme is applied to FL in multiple perspectives including parameter, network and time. Furthermore, a UEP-based adaptive coding method is developed for the case with dynamic signal-to-noise ratio (SNR) to ensure faster and more stable convergence of the FL model while saving as much bandwidth as possible. Comprehensive numerical simulation on several real-world datasets verifies that the proposed UEP transmission schemes can indeed bring significant benefits in accuracy, robustness and efficiency, especially when the channel condition is poor.
Research on Detection Method of Insulator Image Based on Improved Faster R-CNN

Abstract This study proposes the use of Improved Faster Region-Convolutional Neural Network (R-CNN) in target detection for insulator images in power systems. Faster R-CNN is essentially a combination of the Fast R-CNN and the Regional Proposal Network (RPN). The Faster R-CNN method, which is being used today, is an extension and improvement of the Fast R-CNN. The Improved Faster R-CNN is particularly highly effective in the detection of occluded targets and those with different aspect ratios and scales. Through the various experiments conducted in the study, it has been demonstrated that the improved Faster R-CNN is highly effective in detecting insulator images with different scales and with different aspect ratios. Furthermore, the improved Faster R-CNN is also very effective in detecting mutually occluded insulator images.
A New hybrid approach of Data Hiding Using LSB Steganography and Caesar cipher and RSA algorithm (S-ccr)

Steganography differs from encryption, but using both sets help to raise the level of security of protected information and prevent the discovery of secret communications. Thus, in this framework we propose a hybrid shorthand scheme based on LSB technique and RSA and Caesar Cipher encryption algorithms. The experimental results show the effectiveness of our scheme in terms of security and the quality of cover image which cannot be distinguished visually from the original image.
The Cost of Reinforcement Learning for Game Engines

Although utilising computers to play board games has been a topic of research for many decades, the recent rapid developments in the field of reinforcement learning - like AlphaZero and variants - brought unprecedented progress in games such as chess and Go. However, the efficiency of this process remains unknown. In this work, we analyse the cost and efficiency of the AlphaZero approach when building a new game engine. Thus, we present our experience building AZ-Hive, an AlphaZero-based playing engine for the game of Hive. Using only the rules of the game and a quality of play assessment, AZ-Hive learns to play the game from scratch. Getting AZ-Hive up and running requires encoding the game in AlphaZero, i.e., capturing the board, the game state, the rules and the assessment of play-quality. And different encodings lead to significantly different AZ-Hive engines, with very different performance results. Thus, we propose a design space for configuring AZ-Hive, and demonstrate the costs and benefits of different configurations in this space. We find that different configurations lead to a less or more competitive playing-engine, but the training and evaluation for different such engines is prohibitively expensive. Moreover, no systematic, efficient exploration or pruning of the space is possible. In turn, an exhaustive exploration can easily take tens of training-years.
High Precision Visual Dimension Measurement Method with Large Range Based on Multi-Prism and M-Array Coding

The visual dimension measurement method based on non-splicing single lens has the contradiction between accuracy and range of measurement, which cannot be considered simultaneously. In this paper, a multi-camera cooperative measurement method without mechanical motion is proposed for the dimension measurement of thin slice workpiece. After the calibration of the multi-camera imaging system is achieved through a simple and efficient scheme, the high-precision dimension measurement with a large field of view can be completed through a single exposure. First, the images of the edges of the workpiece are compressed and combined by splitting and merging light through the multi-prism system, and the results are distributed to multiple cameras by changing the light path. Then, the mapping relationship between the global measurement coordinates and the image coordinates of each camera is established based on the globally unique M-array coding, and the image distortion is corrected by the coding unit composed of black and white blocks. Finally, the edge is located accurately by edge point detection at the sub-pixel level and curve fitting. The results of measuring a test workpiece with the dimension of 24 mm × 12 mm × 2 mm through a single exposure show that the repeated measurement accuracy can reach 0.2 µm and the absolute accuracy can reach 0.5 µm. Compared with other methods, our method can achieve the large-field measurement through only one exposure and without the mechanical movement of cameras. The measurement precision is higher and the speed is faster.
Leveraging graph algorithms to speed up the annotation of large rhymed corpora

Abstract Rhyming patterns play a crucial role in the phonological reconstruction of earlier stages of Chinese. The past few years have seen the emergence of the use of graphs to model rhyming patterns, notably with List’s (2016) proposal to use graph community detection as a way to go beyond the limits of the link-and-bind method and test new hypotheses regarding phonological reconstruction. List’s approach requires the existence of a rhyme-annotated corpus; such corpora are rare and prohibitively expensive to produce. The present paper solves this problem by introducing several strategies to automate annotation. Among others, the main contribution is the use of graph community detection itself to build an automatic annotator. This annotator requires no previous annotation, no knowledge of phonology, and automatically adapts to corpora of different periods by learning their rhyme categories. Through a series of case studies, we demonstrate the viability of the approach in quickly annotating hundreds of thousands of poems with high accuracy.
Automatic characterization of breast lesions using multi-scale attention-guided deep learning of digital histology images

A multi-scale attention-guided deep learning model is proposed to characterise breast tissue in digital histology images (H&E stained) according to four different histological types including normal, benign, in situ carcinoma and invasive carcinoma. The framework includes two parallel convolutional neural networks with modified VGG16 architecture. The first network analyzes the whole-sample images at low magnification. The second network focuses on the patches extracted from the whole-sample images at high magnification. In the low-magnification network, a global average pooling layer was added at the end of the network to extract the class activation maps for the attention model. A long short-term memory network was adapted as a recurrent attention mechanism to increase the contribution of the relevant parts of each image for classification. In the high-magnification network, the probability vectors were averaged over all patches extracted from an image to obtain the probability vectors associated with the four histological types for each sample. The probability vectors for each sample from the high-magnification network and the attention model were fused using a multilayer perceptron network to generate a classification label. Obtained results on an independent test set demonstrated an average accuracy of 97.5% ± 1.0% for the proposed model. An average accuracy of 94.5%, 93.5%, and 96.3% was obtained, respectively, for the separate high- and low-magnification networks, and the multi-scale model without an attention mechanism. The results suggested that a multi-scale strategy coupled with an attention mechanism can improve the accuracy of deep learning models in classifying digital histology images.
First Order Motion Model for Image Animation and Deep Fake Detection: Using Deep Learning

Image animation involves animating an image using a video. In our case, the image contains the face of a person. This face is animated (creation of facial expression) based on the motion of the video frame (may contain a person talking). Current methods involve animating an image with respect to different videos. Our findings show that these methods lack certain features &#x2013; such as audio capabilities (mimicking the voice of the person in the video). We propose that the chosen image be trained with a variety of videos which contain items of the same classification &#x2013; say facial features of humans such as eyes, lips, nose etc. and along with the audio output of these items. Self-supervised formulation approach is adapted for the training purpose. Mapping between the motion of the item in the video and the image can be achieved with the help of generator network models. The result is a generation of a model that can mimic any video of the specified classification with the desired audio output.