Reviewerly/exHarmony huggingface dataset:
Robust Object Positioning for Visual Robotics in Automatic Assembly Line under Data-Scarce Environments

Object positioning is a basic need for visual robotics in automatic assembly lines. An assembly line requires fast transfer to new object positioning tasks with few or no training data for deep learning algorithms, and the captured visual images usually suffer from partial missing and cropping and environmental lighting interference. These features call for efficient and robust arbitrary shape positioning algorithms under data-scarce and shape distortion cases. To this end, this paper proposes the Random Verify Generalised Hough Transform (RV-GHT). The RV-GHT builds a much more concise shape dictionary than traditional GHT methods with just a single training image. The location, orientation, and scaling of multiple target objects are given simultaneously during positioning. Experiments were carried out on a dataset in an automatic assembly line with real shape distortions, and the performance was improved greatly compared to the state-of-the art methods. Although the RV-GHT was initially designed for vision robotics in an automatic assembly line, it works for other object positioning mechatronics systems, which can be modelled as shape distortion on a standard reference object.
Analysis of the Possibilities of Parsing Electronic Text Documents for the Automation of Standard Control

The article analyzes the possibilities of parsing documents for organizing automated document standard control system, using the example of final qualification works. For this purpose the most relevant formats of text documents have been determined. The features of docx, pdf, odt documents have been compared. It turned out that the docx and odt formats have a similar structure and ways of storing information about the content. At the same time, the pdf format has completely different principles for this. Basic information about the structure of styles in odt documents was collected. They are necessary elements for parsing since they store formatting characteristics. A comparative analysis showed the possibility of parsing all of these three most relevant text document formats for automated control of document processing. To achieve this goal, the possibilities of the Python and Java languages were investigated.
Meningkatkkan Kemampuan Literasi Numerasi Dengan Model Problem Based Learning (PBL) Ditinjau Dari Self Confidence Siswa SMK

&#x0D; &#x0D; &#x0D; &#x0D; The purpose of this study was to determine the increase in student numeracy literacy taught using the Problem-Based Learning (PBL) Learning Model in terms of Self-Confidence. The low numeracy literacy ability of students so that it is necessary to apply a learning model that trains students' abilities as well as fosters student confidence. The method used in this study is a quasi-experimental method that uses a treatment by level 2x2 design design. The consideration of using this design is to see the answers to research questions from two different treatments, namely: the treatment of problem-based learning (PBL) models in the experimental class and conventional models in the control class. The results of the study revealed that the numeracy literacy skills of students who were taught using the PBL learning model were better than students who were taught using the conventional learning model, which had a significant difference; The numeracy literacy ability of students who have high self-confidence is higher than students who have low self-confidence. The results of the study reveal that (1) there are differences in numeracy literacy skills between students who study using the problem-based learning (PBL) learning model and students who study conventionally; (2) There are differences in numeracy literacy skills between students who have high self-confidence and students who have low self-confidence; (3) There is no interaction effect between the application of learning models and self-confidence on students' numeracy literacy abilities; (4) There are differences in numeracy literacy skills between students who learn to use the problem based learning (PBL) learning model and high self-confidence and students who learn to use conventional and high self-confidence; (5) There are differences in numeracy literacy skills between students who learn to use problem based learning (PBL) and low self-confidence and students who learn to use conventional and low self-confidence;Keywords : Literacy Numercy, PBL, Self Confidence&#x0D; &#x0D; &#x0D; &#x0D;
A Fast Graph Minor Embedding Heuristic for Oscillator Based Ising Machines

The demand for high-efficient computing power is constantly increasing. While digital CMOS designs are dom-inating, more research is focusing on novel computing ar-chitectures. Recently, so-called Oscillator-based Ising Machines (OIMs) have become a promising computing approach, which can solve optimization problems using configurable coupling between oscillators. OIMs are based on well-established CMOS silicon technologies and can even be integrated with a processor as hard-ware accelerators. Similarly to quantum computing, OIMs solve graph problems by directly representing them in their hardware. Consequently, the nodes and edges of a problem graph must be mapped to the available hardware-specific network. Since such graph embedding is required before every computation, it has a crucial impact on performance. We propose a heuristic embedding algorithm to transform quickly and efficiently an input problem onto a target hardware. The heuristic is optimized for OIM systems and supports various hardware topologies including routing channels. Using the heuristic algorithm, we analyze different hardware topologies to further improve OIM designs.
(Augmented) Broadcast Encryption from Identity Based Encryption with Wildcard

Several broadcast encryption (BE) constructions have been proposed since Fiat and Naor introduced the concept, some achieving short parameters size while others achieve better security. Since 1994, a lot of alternatives to BE have moreover been additionally proposed, such as the broadcast and trace (BT) primitive which is a combination of broadcast encryption and traitor tracing. Among the other variants of BE, the notion of augmented BE (AugBE), introduced by Boneh and Waters in 2006, corresponds to a BE scheme with the particularity that the encryption algorithm takes an index as an additional parameter. If an AugBE scheme is both message and index hiding, it has been proved that it can generically be used to construct a secure BT scheme. Hence, any new result related to the former gives an improvement to the latter. In this paper, we first show that both BE and AugBE can be obtained by using an identity-based encryption scheme with wildcard (WIBE). We also introduce the new notion of anonymous AugBE, where the used users set is hidden, and prove that it implies index hiding. We then provide two different WIBE constructions. The first one has constant size ciphertext and used to construct a new constant size ciphertext BE scheme with adaptive CPA security, in the standard model (under the $$\textsf {SXDH}{}$$ assumption). The second WIBE provides pattern-hiding, a new definition we introduced, and serves as a basis for the first anonymous AugBE scheme (and subsequently a BT scheme since our scheme is also index hiding by nature) in the literature, with adaptive security in the standard model (under the $$\textsf {XDLin} {}$$ assumption).
Guidance, Navigation and Control for Autonomous Quadrotor Flight in an Agricultural Field: The Case of Vineyards

In this paper, we present a complete and efficient solution of guidance, navigation and control for a quadrotor platform to accomplish 3D coverage flight missions in mapped vineyard terrains. Firstly, an occupancy grid map of the terrain is used to generate a safe guiding coverage path using an Iterative Structured Orientation planning algorithm. Secondly, way-points are extracted from the generated path and added to them trajectory's velocities and accelerations constraints. The constrained way-points are fed into a Linear Quadratic Regulator algorithm so as to generate global minimum snap optimal trajectory while satisfying both the pointing and the corridor constraints. Then, when facing unexpected obstacles, the quadrotor tends to re-plan its path in real-time locally using an Improved Artificial Potential Field algorithm. Finally, a geometric trajectory tracking controller is developed on the Special Euclidean group SE(3). The aim of this controller is to track the generated trajectory while pointing towards predetermined direction using the vector measurements provided by the inertial unit. The performance of the proposed method is demonstrated through several simulation results. In particular, safe guiding paths are achieved. Obstacle-free optimal trajectories that satisfy the way-point position, the pointing direction, and the corridor constraints, are successfully generated with optimized platform snap. Besides, the implemented geometric controller can achieve higher trajectory tracking accuracy with an absolute value of the maximum error in the order of 10-3 m.
Effective Online Knowledge Distillation via Attention-Based Model Ensembling

Large-scale deep learning models have achieved impressive results on a variety of tasks; however, their deployment on edge or mobile devices is still a challenge due to the limited available memory and computational capability. Knowledge distillation is an effective model compression technique, which can boost the performance of a lightweight student network by transferring the knowledge from a more complex model or an ensemble of models. Due to its reduced size, this lightweight model is more suitable for deployment on edge devices. In this paper, we introduce an online knowledge distillation framework, which relies on an original attention mechanism to effectively combine the predictions of a cohort of lightweight (student) networks into a powerful ensemble, and use this as a distillation signal. The proposed aggregation strategy uses the predictions of the individual students as well as ground truth data to determine a set of weights needed for ensembling these predictions. This mechanism is solely used during system training. When testing or at inference time, a single, lightweight student is extracted and used. The extensive experiments we performed on several image classification benchmarks, both by training models from scratch (on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets) and using transfer learning (on Oxford Pets and Oxford Flowers datasets), showed that the proposed framework always leads to an improvement in the accuracy of knowledge-distilled students and demonstrates the effectiveness of the proposed solution. Moreover, in the case of ResNet architecture, we observed that the knowledge-distilled model achieves a higher accuracy than a deeper, individually trained ResNet model.
Variational Quantum Algorithms for Chemical Simulation and Drug Discovery

Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest problems of biochemistry. Each protein folds distinctively, and the difficulty of finding its stable shape rapidly increases with an increase in the number of amino acids in the chain. A moderate protein has about 100 amino acids, and the number of combinations one needs to verify to find the stable structure is enormous. At some point, the number of these combinations will be so vast that classical computers cannot even attempt to solve them. In this paper, we examine how this problem can be solved with the help of quantum computing using two different algorithms, Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), using Qiskit Nature. We compare the results of different quantum hardware and simulators and check how error mitigation affects the performance. Further, we make comparisons with SoTA algorithms and evaluate the reliability of the method.
Design of Mobile 3D Augmented Reality on Marker Visual Inspection Mask

Augmented reality is a ground-breaking technology that allows users to communicate with an enticing real-world experience to receive messages or knowledge. Today, mask inspection is essential to relay information to society through an augmented reality where extensive information about masks is required and identifying them is challenging. Furthermore, with augmented reality, society may have access to the fascinating way of getting information on the masks, assisting in spreading knowledge, particularly during this pandemic. The augmented reality on a visual inspection mask (ARVIM) was created utilizing 3D images on an Android smartphone platform. The models were created using Blender Animation and Unity 3D software. Vuforia Engine and Android Studio applications were used to build a personalized mobile Augmented Reality application. Google Cloud was used to store data that are accessible to users. ARVIM has facilitated teaching and interaction with 3D AR effects enjoyable for users. The analysis results on the use of ARVIM revealed that ARVIM obtained a high easiness rating of 80%. While 83% of respondents say ARVIM can help distinguish between different types of masks, 88% of respondents agreed that ARVIM's design is acceptable and approachable. This initiative enhances society's education in interactive and mixed-reality innovation settings through a long-distance and innovative teaching method that gives a comprehensive Google Platform experience.
Non-verbal communication in hate and sexist speeches

Communication processes might be completely free of any kind of bias, including non-verbal communication, in efforts to eliminate inequalities. The laws on equality between men and women are bringing about the desired situation. Right now, other groups, such as LGBTIQ+, are also asking for their equal rights. The laws recognise these rights for all as equal individuals in law. Sometimes, sexism and hate of any kind is avoided when speaking verbally. But non-verbal communication shows the other side of the coin. Discrimination still happens, and is shown through gestures, tone of voice, postures... this is what informs non-verbal communication.
Improving Academic Writing with a Method for Text Revision Supported by Text Mining

In spite of the undisputed importance of writing, especially in the learning context of higher education, we still find many students with difficulties to write, present arguments, and keep their writing coherent. Therefore, this research aims to investigate how a learning tool with text mining functions can support students in the process of academic writing, based on certain criteria for text analysis. A quasi-experimental study with control and experimental groups was carried out in order to evaluate the tool's capacity to help students revise and improve their texts. The experimental group had access to the mining tool, which guided them in the revision of global features of their texts according to four principles: continuity, congruency, progression and non-contradiction. A group of lecturers corrected the students’ tasks using the same correction grid. Results showed that the students who used the mining tool achieved higher grades when compared to the students who did not use it. Providing one-to-one support for the revision of certain text facets at the global level has been one of the main contributions of this research.
Intelligent Object Tracking with an Automatic Image Zoom Algorithm for a Camera Sensing Surveillance System

Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically detecting, tracking and zooming in on active targets. The object tracking system is connected to a camera that has a 360° horizontal and 90° vertical movement range. The combination of tracking, movement identification and zoom means that the system is able to effectively improve the resolution of small or distant objects. The object detection system allows for the disk space to be conserved as the system ceases recording when no valid targets are detected. Using an adaptive object segmentation algorithm, it is possible to detect the shape of moving objects efficiently. When processing multiple targets, each target is assigned a color and is treated separately. The tracking algorithm is able to adapt to targets moving at different speeds and is able to control the camera according to a predictive formula to prevent the loss of image quality due to camera trail. In the test environment, the zoom can sufficiently lock onto the head of a moving human; however, simultaneous tracking and zooming occasionally results in a failure to track. If this system is deployed with a facial recognition algorithm, the recognition accuracy can be effectively improved.
First-Passage-Time Analysis of the Pulse-Timing Statistics in a Two-Section Semiconductor Laser under Excitable and Noisy Conditions

A two-section semiconductor laser can exhibit excitability for certain parameter settings. When used as a photonic spiking neuron, it is relevant to investigate its sensitivity to noise due to, e.g., spontaneous emission. Under excitable conditions, the system emits irregularly timed noise-triggered pulses. Their statistics is analyzed in terms of a first-passage time distribution for the fluctuating intensity to reach the threshold for excitable response. Two analytic approximations valid for short and long times, respectively, are derived which very well explain measured and simulated pulse-repetition time distributions. This provides physical insight into the noise-triggered spiking mechanism.
Prototype of Project AI4Math: Interdisciplinary and Innovative Technology for Accelerated Learning of Mathematics

As the demand for sustainable education increases, teaching and learning strategies also change and evolve. They are becoming more innovative, creative and technology-based. It is mathematics that is one of the most important subjects with horizontal influence on other sectors, which poses the greatest challenges to educators with the question "How to teach students in such a way that they become interested in mathematics?" As an answer to the question, a new product was created. Using a carefully developed methodology and taking into account the needs of students, the post-doctoral research project "Artificial Intelligence (AI) Support for Approach of Accelerated Learning of Mathematics (AI4Math)”, through the cooperation of an interdisciplinary team in the fields of information technology, mathematics, mechatronics, sports and artificial intelligence, a prototype was developed for an innovative technology that arouses emotions in students of different ages, creates a competitive spirit and promotes accelerated learning of mathematics. The AI4Math prototype combines 3 components: 1) a pressure-sensitive floor platform for jumping out the solutions of mathematical expressions, 2) application for calculation of math expressions, evaluation of answers, accumulation and processing of Big Data, 3) an AI solution for emotion control. The study makes a valuable contribution to researchers, entrepreneurs and the education industry, as it reveals the conditions necessary for modern and sustainable accelerated learning of mathematics. Given that the AI4Math prototype provides an innovative approach to learning mathematics through physical activity, the survey data indicated support and future development prospects for the interdisciplinary technology.
Effect of Nursing Student’s Academic Self-efficacy, Learning Agility, Self-Leadership on Learning Satisfaction: Focused on non-face-to-face class experience during COVID-19

Objectives The purpose of this study was to determine the effects of academic self-efficacy, learning agility, and self-leadership on learning satisfaction of nursing students who experienced non-face-to-face classes due to COVID-19. Methods This study conducted an online survey of 180 nursing students in 3rd and 4th grade. Data collection was conducted using a self-report questionnaire measuring academic self-efficacy, learning agility, self-leadership, and academic satisfaction. Data analysis was performed using descriptive statistics, t-test and ANOVA, Pearson correlatiom coefficients, and stepwise multiple regression using the SPSS 25.0 program. Results According to the study, academic self-efficacy, learning agility, self-leadership and learning satisfaction were significant static correlation. The variables that had a significant effect on learning satisfaction were academic self-efficacy, self-leadership, and average grades, and explanatory ability is 49%. Conclusions Based on the results of this study, it is necessary to develop and apply various teaching and learning strategies and programs that can improve academic self-efficacy, self-leadership, and grades for the academic satisfaction of nursing students in non-face-to-face classes during COVID-19.
A Secure Certificateless Signature Scheme for Space-Based Internet of Things

Space-based Internet of things (S-IoT) can provide global services and connection capabilities. It has broad emerging application, including marine monitoring, forest monitoring, animal monitoring, disaster emergency response and other fields. However, owing to the openness of satellite communications, S-IoT is vulnerable to hijacking attacks, and malicious attackers can tamper with or forge transmitted messages. More seriously, due to limited S-IoT node resources, it is difficult to directly apply existing security solutions to terrestrial networks to the S-IoT. In this study, we propose CSP, a novel, secure, and efficient scheme based on certificateless signatures and bilinear pairings for S-IoT. CSP consists of six phases: system setup, partial private key settings, private key settings, public key settings, signing and verifying. In CSP, we especially design that part of the private key comes from the ground KGC and the other part is generated by the communication entity itself. We theoretically prove CSP is secure, and it is able to resist tampering or forgery attacks. Moreover, it can also ensure the authenticity, integrity, unforgeability, and nonrepudiation of transmitted messages. We also conducted extensive experiments and compared CSP with the existing schemes. The experimental results demonstrate that CSP can significantly reduce the total scheme time consumption. Especially, it can reduce about 50–60% of the time in the signature verification phase.
Topic Modelling with Bag-of-concepts Document Representation

Traditionally, text mining tasks have been implemented by applying topic models like Latent Dirichlet Allocation (LDA). These topic models occasionally produce noisy words in illogical topics with a high probability. The problem is that topic model-based approaches are sparse, have binary weighting for terms, and lack semantic data. The topic model technique is combined with a document representation technique called Bag-of-Concepts to solve these problems. The bag-of-concepts approach groups word vectors from word2vec to create concepts, which are subsequently represented in document vectors by these concept cluster occurrences. The performance of document proximity preservation is taken into account by Bag-of-concepts when using the suitable weighting formula concept frequency-inverse document frequency. Latent Dirichlet Allocation is adjusted for use in document clustering and quality tasks for topics. The results are compared with different LDA frameworks on text documents, as well as the bag-of-concepts representation of documents. LDA with Bag-of-concepts representation generates more cohesive themes in comparison to the other techniques.