Reviewerly/exHarmony huggingface dataset:
Visual Network Analysis Based on Stereo Vision and Feature Matching Algorithm

Functions such as Internet browsing and online shopping have a great impact on people's lives. The footprints of people browsing various information and news on the web page are also increasing year by year. More and more people begin to pay attention to the visual communication in web design. Network information is widely loved by people because of its convenience, quickness, and simplicity. In order to study the visual problem in network information, this paper proposes to use a feature matching algorithm to study the visual information transmission in web design. Using stereo vision and feature matching algorithm, the target recognition function in the visual communication of web design is realized. The content of web design is defined from the perspective of visual beauty and overall harmony. By extracting the feature points in the data, the two-dimensional vision is transformed into a three-dimensional vision. It not only can accurately extract feature points from data but also can convert 2D vision to 3D vision. Finally, in order to optimize the feature matching speed of web design images, the epipolar constraint algorithm is used to optimize the feature matching function. The experimental results show that the content of this paper can truly show the virtual effect in web design and can intuitively upload and transmit simple and rich information content. This paper not only meets the aesthetic needs of the public for web design but also improves the problem of fuzzy information in the process of visual information transmission.
PopHybrid: a novel item popularity-aware hybrid approach for long-tail recommendation

Producing recommendations where tail items have been included more is usually considered one crucial task of recommender systems to improve the diversification ability of the platform. The final recommendations are generated using only a single algorithm in traditional settings; however, algorithms can show varying performances for users differentiating in terms of particular characteristics. Based on this fact, we propose a novel popularity-aware recommendation technique, namely PopHybrid, focusing on increasing the visibility of the items in the long-tail in the final recommendations by hybridizing several algorithms suitably. In particular, this technique chooses the most suitable recommendation output based on specific criteria related to item popularity among multiple algorithms for each user. Thus, it helps control the popularity bias problem and improves the system's overall ability to provide long-tail recommendations. The experiments performed on two popular datasets demonstrate that combining seven collaborative filtering algorithms via our PopHybrid strategy significantly decreases the average popularity of the recommended items and increases the ratio of tail items in the suggested ranked lists.
Constructing Symbolic Abstractions From Data Without Model Assumptions

Research at the interface between formal methods and control theory has given rise to symbolic control [1], [2], [3], which deals with control of dynamical systems with logic specifications. A key ingredient of symbolic control is a finite abstraction, i.e. a dynamical system with a finite number of states and inputs, also called symbolic model, constructed from the original system. When the concrete and abstract systems are related by a behavioral relation, such as an approximate alternating simulation relation [1], showing that the trajectories of the abstraction mimic the ones of the original system, the discrete controller synthesized for the abstraction can be refined into a hybrid controller for the original system.
An Innovative Vortex based Complex Potential Signature for Shape based Object Recognition

An Innovative approach for representation and description of shape components for object recognition based on complex potential is proposed. In the complex plane, the flow of velocity is a crucial factor to discriminate different shapes. Hence, the present paper computes the potential flow by transforming the shape of the input object into complex plane. The present paper computes the Vortex based Complex Potential signature (VCP) by considering the radial lines as Equipotential lines and the circles as streamlines. The proposed VCP signature is described with the Fourier transformation for the generation of feature vector. The Chebyshev distance measure is used in the shape toning stage. The efficiency of the proposed descriptor is evaluated with the MPEG-CE-1 Set B database. The results prove the competency of the proposed descriptor than the benchmark descriptors
Metallic-Mineral Prospecting Using Integrated Geophysical and Geochemical Techniques: A Case Study from the Bela Ophiolitic Complex, Baluchistan, Pakistan

An integrated geophysical and geochemical investigation was conducted to investigate the metallic minerals hosted in the mafic and ultramafic rocks in the Bela Ophiolitic Complex. Two thousand magnetic observations were made along with six vertical electrical soundings, with Induced Polarization (IP) targeting the anomalous magnetic zones. The magnetic raw field data were interpreted qualitatively and quantitatively, and two anomalous zones (A1 and A2) were identified on the magnetic maps. The residual magnetic values in the high-magnetic-anomalous zone (A2) ranged from 310 nT to 550 nT, while the magnetic signatures in the low-magnetic zone (A1) ranged from –190 nT to 50 nT. The high-anomalous zone (A2) was distinguished by a high IP value ranging from 3.5 mV/V to 15.1 mV/V and a low apparent and true resistivity signature of 50 ohm·m. Whereas, the low-anomalous zone (A1) was distinguished by very low IP values ranging from 0.78 mV/V to 4.1 mV/V and a very high apparent and true resistivity of 100 ohm·m. The Euler deconvolution was used to determine the depth of the promising zone, which for A1 and A2 was in the 100 m range. The statistical analysis was carried out using hierarchical classification to distinguish between background and anomalous data. The high-magnetic anomalous signature of probable mineralization was in the range of 46,181 nT–46,628 nT, with a total intensity range of 783 nT–1166 nT. The major and trace-element analysis of the 22 rock and stream sediments collected from the high-magnetic-anomalous zone confirmed the mineralization type. The geomagnetic and geophysical cross sections revealed that anomalous mineralization was concentrated with the anticlinal Bela Ophiolitic Complex. The generated results also aided in the identification of rock boundaries, depth, and hidden faults in the area. The findings revealed that the study area has excellent mineralization associated with the ultramafic-rock sequence.
Perceived digital competence of primary and secondary school teachers - a pilot study

Digital competence is nowadays one of the essential facets of every individual’s literacy. Affected by technological advances and the recent pandemic, new breeds of teaching have emerged thus putting the digital competence of teachers to the test. To examine the digital competence of teachers, an empirical study was carried out. Drawing on the DigComp framework proposed by the European Commission, a measuring instrument in the form of a questionnaire has been designed. The representative sample of study participants was composed of teachers from Brod-Posavina county in Croatia. Reported findings uncovered to what extent primary and secondary school teachers differ when particular dimensions of digital competence are considered.
Integrating Local Search Methods in Metaheuristic Algorithms for Combinatorial Optimization: The Traveling Salesman Problem and its Variants

Metaheuristic Algorithms (MAs) have demonstrated exceptional competence in solving Combinatorial Optimization Problems (COP) such as the Minimum Spanning Tree Problem (MSTP), the Features Selection Problem (FSP) with the most popular and oldest being the Traveling Salesman Problem (TSP). However, this class of algorithms many a time suffers from local optima stagnation leading to sub-optimal performance. Thus, there is a need for such algorithms to be supported with specific Local Search (LS) procedures either as an inner component or as a post-processing mechanism to enhance the search process for better performance. This paper presents a comprehensive review of the integration of LS methods in metaheuristic algorithms for solving single, multi, and many-objective COP with a focus on the TSP and its variants. The LS methods reviewed in this study were classified into one-way and two-way based on their mode of operation. In addition, practical suggestions were discussed and possible future directions were pointed out.
On the Feasibility of T-PKI as an Anonymization Protocol for TPMs in Trusted Computing Environments

The feasibility of the Transactional Public Key Infrastructure (T-PKI) cryptographic scheme as the means to anonymize attestation in a trusted computing environment is assessed. T-PKI encryption scheme creates a new public key for every transaction from the same private key. T-PKI would allow a TPM to send anonymous attestations to a verifier, thus not revealing any information about the origin of the attestation. A functional implementation of the T-PKI encryption scheme based on the Python programming language along with General Multi-Precision Arithmetic (GMP) library for large arithmetic operations, and Numeric Python (NumPy) for matrix calculations is presented. An implementation of the Learning with Errors using the same interface is also presented to compare the output from both cryptographic schemes. Both methods utilize a 384-bit secret for encryption and can encrypt a character (7-bits) simultaneously.
Design Attention Awareness Among Robots for Uncertainty-Adaptive Heterogeneous Teaming

Attracted by team size and robot function diversity, heterogeneous multi-robot system (MRS), where multiple robots with different capabilities are coordinated to perform tasks, has been widely used for large-scale scenarios, including disaster search and rescue, site surveillance, and social security. However, the availability of robot members is influenced by real-world factors such as being far away from the target position, occupied by other missions, and robot failure. In a decentralized scenario without human supervisory, due to the availability of robot members is uncertain, it is challenging to flexibly team robots with complementary capabilities to optimally balance reasonable task allocations and maximum utilization of robot capability. To solve this problem, in this paper, a novel flexible teaming method, inner attention (innerATT), is developed to flexibly team available robot members to execute tasks as task types and available robot members change. innerATT is designed by integrating a novel cooperation likelihood calculation method into a multi-agent actor-critic reinforcement learning architecture. With the innerATT, robot availability and capability will be analyzed to calculate the cooperation likelihood among robot members. Based on the cooperation likelihood, robots can flexibly form teams to meet task requirements. Scenarios with different task varieties ("Single Task", "Double Task", and "Mixed Task") and different available robot members were designed. The effectiveness of the innerATT was validated by its accuracy in flexible cooperation.
An introduction to <i>Recent Advances in Understanding Gold Deposits: from Orogeny to Alluvium:</i> the importance of multi-method approaches and developing a characterization

Abstract Gold occurs in many settings but the dynamic nature of Earth's crust means overlapping and overprinting deposit styles are common. Characterization of mineralization from an early stage becomes important particularly where the mineralization is complex, in order to maximize exploration and project development success, and mining productivity. Various techniques are used at different stages of a project to characterize gold deposits. This Special Publication Recent Advances in Understanding Gold Deposits: from Orogeny to Alluvium offers a cross-section of some specific techniques to investigate a variety of gold deposit types. In this Introduction, we briefly compare the most common gold deposit types, summarize the techniques used to investigate them and discuss some of the most important outstanding questions regarding understanding gold deposits. This is followed by a summary of each paper in this Special Publication. Whilst the 15 papers in this book can only showcase some of the many approaches available, they do highlight both the breadth of the available techniques and their utility in deposit characterization. Several papers include suggestions for avenues for fruitful further research, including a paper suggesting a new approach to classifying orogenic gold deposits, and a paper describing archaeological applications of natural gold analyses.
Knowledge graph embedding based on semantic hierarchy

In view of the current knowledge graph embedding, it mainly focuses on symmetry/opposition, inversion and combination of relationship patterns, and does not fully consider the structure of the knowledge graph. We propose a Knowledge Graph Embedding Based on Semantic Hierarchy (SHKE), which fully considers the information of knowledge graph by fusing the semantic information of the knowledge graph and the hierarchical information. The knowledge graph is mapped to a polar coordinate system, where concentric circles naturally reflect the hierarchy, and entities can be divided into modulus parts and phase parts, and then the modulus part of the polar coordinate system is mapped to the relational vector space through the relational vector, thus the modulus part takes into account the semantic information of the knowledge graph, and the phase part takes into account the hierarchical information. Experiments show that compared with other models, the proposed model improves the knowledge graph link prediction index [email protected]% by about 10% and the accuracy of the triple group classification experiment by about 10%.
A Study Regarding Deep Q-Learning Algorithm for Creating Intelligent Characters in a Graphic Engine

The development of Artificial Intelligence (AI) and Deep Learning technology has resulted in an increase in the number of ongoing research projects examining the performance potential rating that an application can reach through applying these techniques in a variety of sectors. The papers concept is centered around a Deep Q-Network (DQN), which is a convolutional neural network that has been received training using Q-Learning. The authors propose the integration of an algorithm that will allow a virtual character to learn how to fulfill a specific task in a manner similar to a human, by trial and error. Such an approach can be useful in situations where it is desired to have an interaction as close as natural between a human and a virtual entity. The paper represents just a small step on a path that leads to obtaining a self-learning virtual character that can interact with a real person. The outcome of this research will present a notion that can serve as the foundation for the automation and optimization of a time-consuming activity carried out by an entity that does not have a preset or predetermined intellectual capability. It is the authors' intention to demonstrate a learning process that is analogous to a psychological test defined as a "match to sample task," where the character is required to recall precisely what set of tasks he or she was awarded for; the more he repeats those tasks as many times as possible, the more he will begin to create a "habit" of looking for the same events in which he is rewarded. This paper demonstrates a concept that can be the basis for automation and optimization of a laborious process performed by an entity without a predefined or preprogrammed intellectual capacity.
High-Performance Deployment of Text Detection Model: Compression and Hardware Platform considerations

Network compression is often adopted for high throughput implementation on commercial accelerators. We propose a heuristic based approach to obtain compressed networks with a hardware-friendly architecture as an alternative to conventional NAS algorithms that are computationally expensive. The proposed compressed network introduces 142 $\times$ memory-footprint reduction and provide throughput improvement of 5-8 $\times$ on target hardware platforms, while retaining accuracy within 5% of the baseline trained model. We report performance acceleration on CPU, GPU, and FPGAs for a text detection task.
The Use of Interactive Educational Forms in a Technical University

The article discusses the students’ improvement in the university, i.e. interactive forms of education. The main methodological innovations today are associated with the use of precisely interactive learning technologies. Interactive technologies help to solve a rather important problem of education; this is the formation of professional qualities by integrating the educational, scientific and practical activities of future specialists.Interactive learning is a special form of organizing the cognitive activity of students. One of the goals of interactive learning is to create a comfortable learning environment in which students feel their success, intellectual competence, which makes the learning process productive.The use of interactive learning involves life situations’ modeling, the use of role-playing games, and joint problem solving. An interactive way of learning means the ability to interact or be in a mode of a conversation, a dialogue, with something or someone.The article considers the following tasks: to identify the essential characteristics of interactive learning, to substantiate the need to use interactive methods in higher education, to identify the most used methods in a technical university, to analyze the advantages of using interactive technologies in higher education.
The Art of Machine Learning as Fashion Stylish for Designing Clothes

Over the years, designers have come to the fore with their originality and personal styles and have shaped the fashion industry with their designs. However, due to the progress of time, designers have become unable to meet the demands of all consumers. Since it takes a lot of time to produce an original design, the production process progresses slowly, and customers are uncomfortable with this situation. As in many other industries, designers are trying to solve this problem with the help of artificial intelligence, which is indispensable in the fields of commerce, art, and security. It first entered the fashion sector with drawing programs in the 1950s and has started to change the fashion sector since the 2000s. In the 1950s, artificial intelligence was used only to create a virtual drawing environment When the designer makes a mistake, he can simply erase the mistake and continue working on the design without having to start the whole design from scratch. These programs have greatly facilitated the work of designers. Designers are now able to draw their designs in a much shorter time. But even this shortened period is not enough for the whole fashion industry. Designers could still not keep up with the demands of all customers. Thanks to researchers who added different perspectives to artificial intelligence in the early 2000s with its usage not only for drawing but also for designing, Therefore, in this paper, it is aimed at producing some original designs by preserving the designer's style with the use of Aí techniques. With the proposed model, it has become able to produce ready-made designs by using features such as object detection and visual processing. The experimental results showed that Aí techniques are very successful for combining different patterns for producing an original fashion style.
An Improved A-Star Algorithm for Path Planning of Outdoor Distribution Robots

Time cost is an important factor that cannot be ignored in the distribution process. Outdoor distribution robots will be influenced by environmental factors during the distribution process, such as traffic jams and construction. The traditional A-Star algorithm ignores these factors in path planning. Aiming at this problem, this paper combines the idea of Ant Colony algorithm with the traditional A-Star algorithm and proposes an improved A-Star algorithm. First, the extra time cost caused by environmental factors in the historical distribution process is recorded. Second, the weights of historical data are set based on the idea of pheromone volatilization. Finally, the distance cost and the time cost are comprehensively considered to obtain the optimal path. A simulation test is carried out in the anaconda environment by using python's 'pygame' module to test the traditional A-Star algorithm and the improved A-Star algorithm. The result shows that the time cost of the path obtained by the improved A-Star algorithm is averagely reduced by 11.2%. Consequently, the improved A-Star algorithm executes path planning more effectively than the traditional A-Star algorithm.
Potentialities and limitations of machine learning to solve cut-and-shuffle mixing problems: A case study

Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well studied, and a simple mixing methodology, which we refer to as the longest segment (LS) method, works well under a broad range of situations. We use supervised learning to train a neural network (NN) to emulate the LS mixing algorithm for mixing a one-dimensional domain of two species. We find that a generic deep NN can emulate the LS method with good accuracy but cannot generalize to conditions significantly outside its training repertoire. The challenges in defining the mixing problem and generalizing a ML mixing approach are indicative of those expected for more complex systems where optimal or near optimal mixing methods remain unknown.
Certificateless Group to Many Broadcast Proxy Reencryptions for Data Sharing towards Multiple Parties in IoTs

Proxy reencryption delegates encrypted data stored in a proxy to a third party. This proxy reencryption takes the form of one sender providing data to one receiver. However, this method incurs a significant overhead for both the sender and proxy as the number of users receiving the same data increases. In addition, in a large-scale environment, such as an Internet of Things or big data environment, a scenario where several workers jointly create and own an output may exist. In such an environment, ownership disputes can arise when only one operator owns a piece data used by other operators. In this study, to solve this problem, we propose a technique in which multiple users can jointly own one piece of data, and multiple recipients can receive the same data through proxy reencryption.