Reviewerly/exHarmony huggingface dataset:
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer the latent structure and then apply a GNN to the inferred graph. Unfortunately, the space of possible graph structures grows super-exponentially with the number of nodes and so the available node labels may be insufficient for learning both the structure and the GNN parameters. In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure. This approach consists of training a denoising autoencoder GNN in parallel with the task-specific GNN. The autoencoder is trained to reconstruct the initial node features given noisy node features as well as a structure provided by a learnable graph generator. We explore the design space of SLAPS by comparing different graph generation and symmetrization approaches. A comprehensive experimental study demonstrates that SLAPS scales to large graphs with hundreds of thousands of nodes and outperforms several models that have been proposed to learn a task-specific graph structure on established benchmarks.
Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland

In the mining industry, many reports are generated in the project management process. These past documents are a great resource of knowledge for future success. However, it would be a tedious and challenging task to retrieve the necessary information if the documents are unorganized and unstructured. Document clustering is a powerful approach to cope with the problem, and many methods have been introduced in past studies. Nonetheless, there is no silver bullet that can perform the best for any types of documents. Thus, exploratory studies are required to apply the clustering methods for new datasets. In this study, we will investigate multiple topic modelling (TM) methods. The objectives are finding the appropriate approach for the mining project reports using the dataset of the Geological Survey of Queensland, Department of Resources, Queensland Government, and understanding the contents to get the idea of how to organise them. Three TM methods, Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Nonnegative Tensor Factorization (NTF) are compared statistically and qualitatively. After the evaluation, we conclude that the LDA performs the best for the dataset; however, the possibility remains that the other methods could be adopted with some improvements.
A generic approach to detect design patterns in model transformations using a string-matching algorithm

Maintaining software artifacts is a complex and time-consuming task. Like any other program, model transformations are subject to maintenance. In a maintenance process, much effort is dedicated to the comprehension of programs. To this end, several techniques are used, such as feature location and design pattern detection. In the particular case of model transformations, detecting design patterns contributes to a better comprehension as they carry valuable information on the transformation structure. In this paper, we propose a generic approach to detect, semi-automatically, design patterns and their variations in model transformations. Our approach encodes both design patterns and transformations as strings and use a string-matching algorithm for the detection. The approach is able to detect complete and partial implementations of design patterns in transformations, which is useful to refactoring and improving model transformations.
A new method for creating Mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems

Abstract This research presents a methodology for creating mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems. At its core, this methodology is kind of instruction for analysts facing the task of formalizing knowledge in a given subject area. The result of this formalization is a “knowledge map” created according to a special proposed template. In the future, this template allows forming a knowledge base for a given subject area in the formalism of bipartite oriented mivar networks. As an example, the subject area of ground-based intelligent vehicle control systems is used as a template. The proposed methodology of knowledge formalization makes it possible to simplify the process of creating models in Wi!Mi “Razumator-Consultant” 2.1 and also to level the probability of logical collisions when designing a knowledge model in the formalism of bipartite oriented mivar networks.
Objects Relativity Bias: for Detected Objects if they are in the range of their Bigger Compositions

As with the access of high-speed internet and powerful handheld smartphones, we are generating enormous amount of video data at unprecedented rate. It will never be possible for human race to process and consume all these videos manually. As technology has equipped us to generate, store and share that amount of data, then technology only can help us to process and consume this much data in sensible duration. So that the processed information can be used when it's required. To do so, computer vision plays the most important role in finding out the useful information from tons of tons of newly created videos by leveraging the computing advancements. Object detection is core requirement for any Computer Vision task. To detect the objects efficiently and effectively, there exists many object detection algorithms. In real world smaller objects combined to form bigger objects and the bigger objects can form some compositions of bigger objects. Hence, Objects Relativity Bias, the term which we used in this paper is to show the importance of the placements of smaller objects with respect to the bigger objects. For example, if we see an image which only contains a wheel, then we get some confidence that this is a wheel is totally dependent on the algorithm which we are using. But if we see two horizontally placed wheels connected through some iron frame with a seat on the frame, paddles between the wheels and a handle above one of the wheels, then we are more confident that the portion of the image which contains wheel are definitely some kind of wheels and specifically wheels of a bicycle. Therefore, this relativity between these objects makes the object detection easier along with higher confidence. So, we termed this relationship confidence as Objects Relativity Bias.
Path Planning and Collision Prevention based on Computer Vision applied to a Mini-sized Multi-robot Testbed

A practical testbed is crucial to reduce the gap between theory and real-world applications. Path-planning and collision prevention routines are essential for a reliable operation of these multi-robot platforms, so increasingly complex control algorithms can be proposed, designed and tested. This paper describes the recent progress made on the functionality of a mini-sized multi-robot testbed that runs based on ROS (Robotic Operating System). A high-level path planner, a collision prevention routine and motion controllers are proposed as part of the expansion to more complex applications that can run on the testbed. A PID-based orientation controller and an Inverse Kinematics position controller are designed and compared as viable solutions for following the waypoints generated by the high-level path planner. Improvements on software modularity, scalability and the addition of an extra mobile agent also comprise the main improvements for this new iteration of the testbed. The results of the path-planning routine, as well as the performance of the motion controllers are presented and discussed.
The role of Karl K. Koststyushko-Valyuzhinich in developing scientific library of Tauric Chersonese State Museum and Heritage Site

On July 15 – September 5, 2020, the exhibition “Non-archeological values of the archeological museum” took place at Tauric Chersonese State Museum and Heritage Site. The exhibition revealed the history of the book collection of Karl Kazimirovich Koststyushko-Valyuzhinich (1847−1907), museum founder and first head of excavations. Museum researchers investigate into the origins of the museum scientific library, both the books purchased by the founder and individual publications in the museum book collection. The authors conclude that the museum library was formed, in large part, on the basis Koststyushko Valyuzhinich’s book collection and down to his book preferences. In fact, the unique museum book collection was formed during the lifetime of the outstanding figure. That is the reason for the museum library, and precisely, its part acquired by Koststyushko-Valyuzhinich, to be included into the register of Russian book monuments.
Extending CohereNet to Retain Physical Features when Classifying Benign or Malignant Breast Masses

Breast ultrasound is often used as a supplement to mammography. However, standard breast ultrasound imaging often has a high false positive rate which limits its use as a screening tool. The spatial coherence of ultrasound signals has particularly impactful implications for breast mass diagnoses when considering both quantitative ultrasound and coherence-based ultrasound beamforming techniques. However, coherence can be computationally expensive to compute. Our recent work demonstrated a novel deep neural network architecture, named CohereNet, which has the ability to estimate coherence features by leveraging the universal approximation properties of deep neural networks. The work in this paper extends the CohereNet architecture to perform binary classification and extract unique features from raw ultrasound data that differentiate benign from malignant breast masses. This extended network classified breast masses as benign or malignant with 83% classification accuracy when tested with data that was not used during training. With the CohereNet architecture as the backbone of this extended network, we leverage coherence features during training and ultimately display network-modified coherence functions. Overall, our extended network architecture and training strategy are promising for a new class of deep learning methods that simultaneously extract features from raw ultrasound data, retain physical interpretations, and diagnose breast masses as benign or malignant.
Using Wikipedia's Big Data for creation of Knowledge Bases

Wikipedia is among the most prominent and comprehensive sources of information available on the WWW. However, its unstructured form impedes direct interpretation by machines. Knowledge Base (KB) creation is a line of research that enables interpretation of Wikipedia's concealed knowledge by machines. In light of the efficacy of KBs for the storage and efficient retrieval of semantic information required for powering several IT applications such Question-Answering System, many large-scale knowledge bases have been developed. These KBs have employed different approaches for data curation and storage. The retrieval mechanism facilitated by these KBs is also different. Further, they differ in their depth and breadth of knowledge. This paper endeavours to explicate the process of KB creation using Wikipedia and compare the prominent KBs developed using the big data of Wikipedia.
An Algorithm of INS Error Correction Base on the Intermittent Label Measurement and the Analysis of Its Influencing Factors

Integrated navigation technology is widely used in aviation, aerospace, sailing and other ground-based navigation equipment, the inertial navigation system (INS) is the most common tool. In practice, the key to navigation is precise position, and the position is cooperated with the image matching. According to the label results after image matching, the position of sensor can be calculated. However, the label results cannot be get in every period, what’ more, he output results of the inertial navigation system (INS) is with big error, thus the article studied an algorithm of INS error correction based on the intermittent label measurement, in which the cubature kalman filter is used. After that, it also studied the influencing factor of the labels, such as the label error by match, the count of label, the area of label, thus the INS positioning error can be reduced in line with the engineering guarantee.
Soil fertility status in tidal land of Tirtomarto reservoir, Central Java, Indonesia

Abstract Reservoir tidal land used for agriculture is generally done when the reservoir water level begins to shrink. This land is used as a moor in dryland or lowland rice fields in the wetland. The purpose of the study was to determine the soil fertility status of the tidal land of the Tirtomarto reservoir, Central Java. Parameters tested include cation exchange capacity (CEC); base saturation (BS); C-Organic; total soil P and K levels according to the technical instructions for evaluating soil fertility. Soil samples were taken from three location points by the purposive sampling method. The results showed that most of the soil samples contained low to very low nutrients, except for the total P content which had a high value. Therefore, the fertility status in the tidal land of the Tirtomarto reservoir, Central Java, can be categorized as low. The low fertility status of the soil at the study site was due to the limiting factors, namely the low C-organic content of the soil, low K total soil, and low cation exchange capacity and base saturation.
Suryanamaskar Pose Identification and Estimation Using No Code Computer Vision

Suryanamaskar, also known as a salute to the sun, is a yoga exercise that comprises seven different poses and 12 asanas dedicated to the Hindu God of the Sun, Surya. Suryanamaskar provides a range of health benefits like making muscles stronger and helping in regulating blood sugar levels. Here Microsoft Lobe has been used to analyze Suryanamaskar poses. The poses are detected in real-time by the developed software, as one does Suryanamaskar in front of the camera. The classifier identifies the pose as one of the following: Pranamasana, Hastauttanasana, Hasta Padasana, Ashwa Sanchalanasana, Dandasana, Ashtanga Namaskara, or Bhujangasana.
Differential properties of authenticated encryption mode based on universal hash function (XTSMAC)

A description of an authenticated encryption with associated data (AEAD) block cypher mode, called XTSMAC, is presented. The results of the cryptanalysis, including the non-applicability of a new attack, based on differential properties of non-linear permutations, is also presented. The comparison with other AEAD modes and the results of practical software implementation on Intel’s CPU conclude the article.
Research on Adaptive Video Steganography Algorithm for Clustering and Dispersing DCT Coefficient Difference Histogram

Histogram migration is a method of the information hiding, the advantage of traditional data hiding has large amount of data, but there are many problems, such as poor security and embedding rate changes. The reason is that generate invalid migration when embedding data. The histogram has a larger numerical range after migration. An optimization algorithm is proposed for this problem. Through data difference, Spread out The range of coefficient modifications. The deformation dispersed in each coefficient domain. Then through the K-Means algorithm, according to the number of embedding build suitable embedded space, the embedded fields are evenly distributed throughout the whole video. Finally, the data uniformly embedded through aggregation and dispersion. Through the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), security and bit growth rate tests. Compared with the two similar algorithms, the bit growth rate decreased by 0.14% on average and the detection error rate increased by 0.2% on average under the same embedding capacity and PSNR and SIMM basically remained stable. Experimental results show that the proposed algorithm can effectively improve the security of video steganography and improve the situation of bit rate increase.
Internet design in a modern educational environment

Purpose of the article: analysis of the process of Internet design in the modern educational environment. Methodology: The article presents the results of a survey of students of higher educational institutions “Assessment of the impact of Internet projects on professional self-development", about the dynamics of the participation of students from professional educational institutions in the design of the Internet for three years. Conclusions. According to the research, the number of participants in Internet projects is growing, and the popularity of such contests is increasing. Participation in the Internet project provides the student an opportunity for promising employments.
The Improvement of the Judge’s Performance Appraisal System from the Perspective of the Marketization of Data Elements

As the form of factors of production continues to change with economic development. Land and labor were important production factors in the agricultural era. In recent years, with the development of the information economy, information resources represented by big data have evolved to the form of production factors. Data has been integrated into the process of economic value creation along with other factors, and has a wide-ranging impact on the development of productivity. Reflecting on the performance appraisal system for judges in my country, there are problems such as unreasonable performance indicators, unscientific performance appraisal methods, undisclosed performance appraisal processes, and insufficient use of performance appraisal results. Accordingly, it is necessary to apply evolutionary game theory, comprehensive qualitative and quantitative analysis methods, to judge judges. Dynamic evolution of the incentive mechanism is analyzed, so that the system tends to be perfected.
A Low-cost and Stand-alone Ground Based Sensing Station for Efficient Solar Forecasting in PV Power Systems

The fast growth of solar PV power generation weighs more on fluctuating power and causes grid disturbance due to unpredictable weather conditions. Industry relies on either energy storage or solar forecasting to mitigate the power intermittency and maintain grid stability. The forecasting scheme requires sensors dispatched to report solar irradiance in real-time. This paper presents a novel sensing solution and demonstrates the advantages in terms of high bandwidth and cost effectiveness. The development shows significant improvement in terms of mobility and performance. The advance of Internet of Things (IoT) and wireless communication is adopted and dedicated to the newly developed sensor station.
Predicting Depression by Analysing User Tweets

Even in the modern era, where communication with other Humans does not seem to be a costly affair, Depression still serves as a global problem. Despite Depression being recognised as a severe disease by many international bodies and organisations, counteractions against it do not even solve the problem by a fractional per cent. The core problem lies in predicting Depression since it lacks any specific test, unlike other diseases that show physical changes. Social media is a great tool to analyse the thought process and individual opinions when addressing a large part of the human population. A supervised classifier model has been developed, which helps predict Depression by analysing the user's textual data over Twitter.
Characterization of Meaningful Learning Associated with Feedback in a Digital Transformation

Teaching-learning at the higher university level has radically changed in universities, from face-to-face teaching to virtual teaching, which, forced by circumstances, originates the use of digital tools and connectivity through the internet as fundamental sources in the process. of student learning, since by not being in contact in a face-to-face way, the teacher must generate significant learning situations, ranging from promoting successful study practices with innovative approaches to an effective and constructive feedback process, which answers the questions of the student What did I do right? What can i improve? How can I improve? which we will analyze in this article. The methodology used is quasi-experimental with a descriptive-explanatory correlational design, whose study objective was the association of formative feedback related to meaningful learning, in which the application of technology allowed the development of abilities, aptitudes and skills in students. The study sample was made up of students of the Academic Mention of Primary Education, of the Faculty of Education Sciences and Humanities of the public university of the city of Ica - Peru. Data collection was applied through the observation card instrument, using the Hattie and Timperley feedback model. The results of the analysis have shown that there is a significant relationship between the association of feedback and the characterization of meaningful learning in students at the university level.
Handprinted Character and Online Signature Recognition Using Residual Convolutional Network: A Comparative Study

Residual convolutional networks proved superiority in the image recognition field in addition to many other pattern recognitions related problems. A special benefit of residual convolutional networks has been proven in the field of handwriting recognition, especially in handprinted characters and online signature recognition. Two published research papers addressed the two problems with highly accurate results that compete with the state-of-the-art techniques. Both problems have two well-known challenges in object classification: intra-class variability and inter-class similarity. In the character recognition problem, different users produce different shapes for the same character in addition to the user himself, who produces different shapes for the same character resulting in high intra-class variability. Inter-class similarity exists due to the similarity between different characters. In handwritten signature recognition, intra- class variability is introduced by the same person where no two signatures of the same person may coincide. On the other hand, persons may have similar signatures resulting in inter-class similarity. Solving the two pattern recognition problems using deep learning techniques requires the selection of the best technique that can model the handwriting process, but at the same time, it requires a large training data. In the character recognition case, large training samples are available from many datasets resources. A recent data set representing this challenge is the EMINST dataset. An optimized residual architecture has been introduced to give an excellent solution for this problem in one of the comparison papers. In the case of signature recognition, a different solution based on a residual network has been introduced in a second paper because practically training samples that can be collected from new users are very rare. In this paper, the two techniques are compared, and conclusions that may generalize well in other problem domains are stated.