Reviewerly/exHarmony huggingface dataset:
Vision-Based Distributed Heterogeneous Swarm Navigation Method

The centralized structure commonly used in the current heterogeneous swarm navigation methods is unreliable, has poor scalability, and relies excessively on communication and external location information. A vision-based distributed heterogeneous swarm navigation method is proposed in this paper, a deep convolutional neural network is adopted to detect nearby agents, the accuracy and speed of detection is enhanced through data enhancement and model pruning. It is shown from simulation experiments that the heterogeneous swarm does not rely on communication and external location information, but only relies on vision, which can achieve safe navigation and formation maintenance.
Improving flexibility in cloud computing using optimal multipurpose particle swarm algorithm with auction rules

Using exact algorithms in complex environments with multi-objective criteria cannot be considered an appropriate method. For this reason, nature-inspired meta-heuristic methods have been proposed to solve problems such as flexibility in the cloud computing environment. In this research, we use the particle swarm optimisation algorithm and auction approach for two-way load balancing. A cloud computing centre comprises several physical machines called data centres, and data centres consume a lot of energy. To increase efficiency, there is a need to increase flexibility in these systems. The researchers proposed Various methods such as static and dynamic management of virtual machines and automatic processor frequency change systems to reduce energy consumption. In this article, we presented an algorithm based on under-load hosts and particle swarm optimisation and mass movement of virtual machines according to the auction rule to reduce energy as the dynamic management of cloud resources. The proposed method has been evaluated by the cloudsim simulator with PlanetLab real dataset. The experimental results show that, in comparison to the other algorithms, energy consumption has improved and SLA violation has decreased. As well as migration of VMs decreased significantly.
Research on Cross-Chain Data Integration Technology Based on Trust Computing

With the rapid development of blockchain technology, a large number of blockchain projects have been designed to meet the needs of different scenarios. However, they are developed by different teams based on different application scenarios and design concepts and using different technical architectures. Each project forms an isolated blockchain P2P network, just like isolated information islands. Cross chain technology is an important technical means to realize the interconnection between chains. At present, the main cross chain mechanisms include Hash TimeLock Contract(HTLC), sidechains, notary mechanisms and communication protocol suite. However, they all have their own disadvantages, such as vulnerability and centralized risk, and only focus on the cross chain transfer of value. This paper proposes a data integration mechanism based on the trust of blockchain nodes: firstly, the blockchain network initiating the integration randomly selects several nodes as the evaluation committee to join the blockchain network providing data sources, and then the evaluation committee evaluates the credibility of the chain according to the model. Finally, if the credibility is higher than the threshold, the integration is initiated. In this paper, the above scheme is applied to the domain name cross organization access, and the experimental test proves the feasibility, efficiency and accuracy of the scheme.
<i>T</i> (<i>A</i>)- Prime gamma subacts

We consider an -act is a Gamma act over a γ-monoid with zero and is the -endomorphism of . A Gamma Act (S-set, operand, S-action, S-system) theory was introduced in 2016. And the concept of prime gamma subcat was introduced in 2016. And also this concept had been studied on Act theory. We introduced -Prime gamma subacts as a proper weakness of prime gamma subacts. We have given several equivalents to this concept and examples. We have found conditions to satisfy some properties of -Prime Gamma subacts.
A Memory Based Approach for Digital Implementation of Tanh using LUT and RALUT

Tangent Hyperbolic (Tanh) has been used as a preferred activation function in implementing a multi-layer neural network. The differentiability of this function makes it suitable for derivativebased learning algorithm such as error back propagation technique. In this paper two different memory-based techniques for accurate approximation and digital implementation of the Tanh function using Look Up Table (LUT) and Range Addressable Look Up Table (RALUT) are given. A thorough comparative study of the two techniques in terms of their hardware resource usage on FPGA and their accuracies are explained. The schematic of the synthesized design for special cased are given as an example.
Low light color balancing and denoising by machine learning based approximation for underwater images

It is difficult for underwater archaeologists to recover the fine details of a captured image on the seabed when the image quality worsens due to the presence of more noisy artefacts, a mismatched device colour map, and a blurry image. To resolve this problem, we present a machine learning-based image restoration model (ML-IRM) for improving the visual quality of underwater images that have been deteriorated. Using this model, a home-made bowl set-up is created in which a different liquid concentration is used to replicate seabed water variation, and an object is dipped, or a video is played behind the bowl to recognise the object texture captured image in high-resolution for training the image restoration model is proposed. Gaussian and bidirectional pre-processing filters are used to both the high and low frequency components of the training image, respectively. To improve the clarity of the high-frequency channel background, soft-thresholding decreases the presence of distracting artefacts. On the other hand, the ML-IRM model can effectively keep the object textures on a low frequency channel. Experiment findings show that the proposed ML-IRM model improves the quality of seabed images, eliminates colour mismatches, and allows for more detailed information extraction. Blue shadow, green shadow, hazy, and low light test samples are randomly selected from all five datasets including U45 [1], EUVP [2], DUIE [3], UIEB [4], UM-ImageNet [5], and the proposed model. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are computed for each condition separately. We list the values of PSNR (at 16.99 dB, 15.96 dB, 18.09 dB, 15.67 dB, 9.39 dB, 17.98 dB, 19.32 dB, 14.27 dB, 12.07 dB, and 25.47 dB) and SSIM (at 0.52, 0.57, 0.33, 0.47, 0.44, and 0.23, respectively. Similarly, it demonstrates that the proposed ML-IRM achieves a satisfactory result in terms of colour correction and contrast adjustment when applied to the problem of improving underwater images captured in low light. To do so, high-resolution images were captured in two low-light conditions (after 6 p.m. and again at 6 a.m.) for the training image datasets, and the results of their observations were compared to those of other existing state-of-the-art-methods.
Augmented Reality for teaching historical time

One of the fundamental aspects of teaching history is the chronology of important events and facts that have occurred. However, this involves an effort of memorization that produces rejection in students, affecting their learning outcomes. This paper describes an experience with Primary and Secondary students, using Augmented Reality with gamification in timeline activities in the subject of History. The results show that there was improvement in learning in several aspects: the chronological location of events in relation to BC and AD, the relationship of different historical events with ages in History and the ability to order different historical milestones. In addition, it was found that students experienced positive emotions during the experience such as joy, curiosity, and interest.
9 Energy Awareness

In this contribution, we study how knowledge about the underlying compute architecture can be incorporated directly into the learning problem. More precisely, we consider the arithmetic limitations of Ultra-Low Power (ULP) Micro-Controller Units (MCU). Such systems do not contain arithmetic co-processors, which implies that most arithmetic computations must be emulated via integer logic. However, this creates a large performance penalty for any machine learning method that relies heavily on floating-point arithmetic. To mitigate this issue, we show how the model itself can be rephrased with integer-only operations such that floating point co-processors are no longer required. We exemplify this procedure with probabilistic undirected models, so-called Markov random fields. All steps of learning and inference are discussed. An approximate but integer-only probabilistic inference procedure called bit-length propagation (BL-Prop) is presented. BL-Prop is based on belief propagation, where instead of the full messages, only their bit-length is propagated along the models’ conditional independence structure. We analyze the algorithm and show what factors have the largest influence on the approximation quality. Furthermore,we derive IntGD-anumerical optimization method for convex objective functions with integrality constraints. The method is based on an accelerated proximal algorithm for non-smooth and non-convex penalty terms. For integer gradients computed via BL-Prop, IntGD is guaranteed to deliver an integer learning procedure in which the final parameter vector as well as all intermediate results are integers. Numerical experiments on benchmark data show that integer models allow us to achieve a competitive prediction quality on low-end hardware while maintaining a large speedup compared with its double precision counterpart-thus, completely mitigating the performance penalty that arose from the missing floating point unit.
Fine-Tuning BERT for Question and Answering Using PubMed Abstract Dataset

The coronavirus, which first originated in China in 2019, spread worldwide and eventually reached a pandemic situation. In the interest of many people, misinformation about the coronavirus has been pouring out on the Internet. We developed a Q&A processing technique by building a dataset based on the PubMed paper abstract for people to easily get the right information. We fine-tuned BioBERT among the BERT models that reached SOTA performance in the biomedical Q&A task. It answered questions about coronavirus with high accuracy. In the future, we will develop our technology that can handle Q&A not only in English but also in multiple languages. This work will contribute to helping people who speak different languages easily obtain correct information amidst confusing data.
Enhancing Web Authentication Security Using Random Forest

A web browser extension that enhances web security by selectively applying cookie attributes to authentication cookies detected using random forest binary classifier.
Automated Water Meter Reading Through Image Recognition

Water meter readings within the Philippine setting are primarily done manually, which is error-prone and work-intensive. Existing smart solutions for the problem, such as smart water metering, are costly to implement and challenging to scale up as it requires replacing each water meter. The most cost-effective alternative is to utilize image capture and deep learning methods to perform image recognition on water meters. This work created an end-to-end automated water meter reader system with image capture via mobile phone that a human meter reader can use to perform meter readings. This reduces the typical multi-step operation of manual transcription to taking a photo of the meter with a mobile application, with additional features to ensure secure and reliable image capture of the meter in case there is a dispute in the reading. The system consists of a mobile application, a cloud database, an image recognition pipeline, and an automated process that retrieves, reads and logs the result. The system performs well on foreign meters that are similar to the training data but performed comparatively poorly on meters used locally in the Philippines, with a reading accuracy of 91.5% and 75%, respectively for the whole digits of a meter counter.
Overcoming exponential volume scaling in quantum simulations of lattice gauge theories

Real-time evolution of quantum field theories using classical computers requires resources that scale exponentially with the number of lattice sites.Because of a fundamentally different computational strategy, quantum computers can in principle be used to perform detailed studies of these dynamics from first principles.Before performing such calculations, it is important to ensure that the quantum algorithms used do not have a cost that scales exponentially with the volume.In these proceedings, we present an interesting test case: a formulation of a compact U(1) gauge theory in 2+1 dimensions free of gauge redundancies.A naive implementation onto a quantum circuit has a gate count that scales exponentially with the volume.We discuss how to break this exponential scaling by performing an operator redefinition that reduces the non-locality of the Hamiltonian.While we study only one theory as a test case, it is possible that the exponential gate scaling will persist for formulations of other gauge theories, including non-Abelian theories in higher dimensions.
Deciphering Conti and the need for effective SOCs

Conti has proven to be one of the most virulent forms of ransomware. It has caused untold damage, and many organisations struggle to combat this kind of threat on their own. This is where a security partner, with an effective and experienced security operations centre (SOC), can play a key role.
AAE: An active auto-estimator for improving graph storage

Graph has currently become an increasingly popular model in many real applications. The efficiency of graph storage is crucial for these applications. Generally speaking, the tuning task of graph storage relies on the database administrators to find the best graph storage. However, DBAs make the tuning decisions by mainly relying on their experiences and intuition. Due to the limitations of the DBAs, the tuning may have an uncertain performance and worse efficiency. In this paper, we observed that an estimator of the graph workload has the potential ability to guarantee the performance of tuning operations. Unfortunately, because of complex characteristics of the graph evaluation task, there exists no mature estimator for graph workload. We formulate the evaluation task of graph workload as a classification task and carefully design the feature engineering process, including graph data features, graph workload features and graph storage features. For this evaluation task, we propose an active auto-estimator (AAE) for the graph workload evaluation by combining the active learning and deep learning. We test the time efficiency and evaluation accuracy of AAE with two open source graph data, LDBC and Freebase. The experimental results show that our estimator could efficiently complete the graph workload evaluation in milliseconds.
An Efficient Unsupervised Learning-based Monocular Depth Estimation Processor with Partial-Switchable Systolic Array Architecture in Edge Devices

With the development of deep learning, many edge devices adopt monocular depth estimation (MDE) to produce reliable 3D RGB-D data due to its low power and low costs compared to an RGB-D sensor [1]. In a user domain, the MDE’s deep neural network has to be re-trained for the domain adaptation [2], [3]. However, the conventional training system requires expensive labeled dataset collection, which is obtained by a high-cost RGB-D sensor with additional raw data preprocessing [3] such as depth alignment, depth synchronization, and depth inpainting. As a result, the proposed processor performs the unsupervised learning-based MDE system, which does not rely on additional sensors and preprocessing for data collection for the training.
Key frames extraction for train roof video based on saliency detection

Roof monitoring of running trains can effectively monitor the key components of the roof. In order to check the status of key components on the roof, it is necessary to check the roof surveillance video, which is often time-consuming and labor-intensive. In order to improve the efficiency of status inspection of key components, this paper proposes a key frame extraction algorithm for roof video surveillance based on saliency detection. The algorithm first designs a saliency detection network, which is used to extract candidate video frames containing key components. Then, through feature clustering analysis, the candidate video frames are classified into frame sequences containing components attached different classes, and finally the correlation is calculated for all frames in each frame sequence and the frame with the largest correlation corresponds to the key frame of the current frame sequence.
Application of Behavioral Science Management Theory based on Intelligent Retrieval Algorithm

This paper proposes an intelligent search algorithm for mobile applications. The algorithm is mainly composed of application semantic scoring module, collaborative scoring module and hybrid scoring module H. In the application of semantic scoring module, DBSCAN algorithm, Bayesian algorithm and similarity algorithm are combined. In order to improve the quality of practice teaching in the post-internship link, our school has developed and constructed the “postinternship management network platform” through the development of network information technology. The application of the post-internship management system to assist the management of off-campus post-internships aims to build a realistic and feasible post-internship management platform, strengthen post-internship management in colleges and universities, and improve the practical role and competitiveness of students’ post-internships.
Application of AI-Enhanced Analytic Hierarchy Process in the Online PHP System

Therefore, in the teaching process of developing PHP business website development courses, students are gradually guided to use PHP language to complete an online examination system with relatively complete functions. By analyzing the current situation and existing problems of teachers' quality in higher normal schools, it is necessary to further recognize and cultivate innovative talents in the 21st century. The requirements of talents for the quality of teachers in higher normal schools, and recognize the very urgent practical problem of improving their own quality of teachers in higher normal schools, combine traditional teaching evaluation methods with modern educational technology, and use existing 1T technology to design a comprehensive teaching ability based on B/S model. The evaluation system changes the traditional manual evaluation into a paperless and networked process.
Real-time analysis of Intra-pulse characteristics based on instantaneous frequency

The analysis of intra-pulse characteristics of radar signal is an important part of radar reconnaissance, real-time analysis of intra-pulse features based on instantaneous frequency can efficiently recognize signals of various modulation types and extract parameters. This method has a high recognition rate under certain signal-to-noise ratio, and the algorithm is simple. It can be implemented at high speed on radar reconnaissance digital receiver.
Dynamic Hand Gesture Recognition from Egocentric Videos based on SlowFast Architecture

Recently, thanks to a large number of lightweight digital recording devices used in different applications, the amount of egocentric data has increased overtime. Compared with videos captured by ambient cameras, egocentric videos have their own challenges as they may contain large, non-linear and unpredictable motion. This paper presents an approach for hand gesture recognition from egocentric videos based on SlowFast network architecture. The model involves Slow and Fast pathway in which a Slow pathway operates at low frame rate while a Fast pathway performs at high frame rate. In egocentric videos, some hand movements happen faster or slower than others depending on the actor or surrounding context. In order to retain egocentric-based attribute of the video, we perform extensive experiments on two branches of information by dividing input frames into Slow pathway and Fast pathway. As a result, our method has achieved better classification accuracy scores in EgoGesture dataset compared with other state-of-the-art frameworks such as VGG-16+LSTM, C3D+LSTM+RSTTM models.
THE IMPACT OF A SOCIAL ROBOT'S VOICE ON CHILDREN'S LEARNING

This study employed a single factor experiment to investigate how different voices from a social robot affect the learning outcomes and motivation of 46 children.The learning motivation for the participants was measured using the ARCS questionnaire, which tested for four subcategories: attention, relevance, confidence, and satisfaction.The results showed that a social robot's voice significantly affected children's motivation to learn, with the children preferring a social robot with a child's voice.However, the voice of a social robot exhibited no significant impact on their learning outcomes.The findings of this experiment are of value to those involved in designing children's learning material that uses social robots and to those who work on interaction design with children.
Research on laparoscopic surgical instrument detection technology based on multi-attention-enhanced feature pyramid network

Laparoscopic surgery is a very active area of research in clinical medicine. Detection of tools in surgical videos can help physicians operate surgical instruments, reduce complications, and ensure patient safety. However, the size of laparoscopic surgical instruments is highly variable, leading to poor detection. Feature pyramid networks (FPNs) can effectively solve the problem of multi-scale target detection, but FPNs still have some problems that limit the full utilization of multi-scale features. By analyzing the FPN design problem, we propose the Multi-Attention Augmented Feature Pyramid Network (MAFPN), which can fully utilize the multi-scale features. First, we replace the convolutional block with feature selection module (FSM) that combines channel attention and global attention, which selectively maintains important information and enhances the expressiveness of features at each scale. Second, the global contextual information is captured by the self-attentive augmented fusion module (AAFM), which enriches the high-level feature information in the FPN and enhances the feature fusion effect. Finally, we use Dynamic Convolution Decomposition (DCD) to alleviate the impact of upsampling while enhancing the feature expression ability. The experimental results on the laparoscopic surgical instrument detection dataset m2cai16-tool-locations show that the average precision of MAFPN is 96.5 when the IOU is 0.5, which is 1.8% better than the baseline method RetinaNet, and the average precision is more than 1.6% better than the comparison network. Compared with the state-of-the-art method, the performance of laparoscopic surgical instrument detection is superior.
CONSUMER PROTECTION AGAINST ONLINE PURCHASE FRAUD IN MALAYSIA: AN ANALYSIS OF THE CONSUMER PROTECTION ACT 1999

There has been an increase in complaints received by the authorities of late concerning fraudulent online purchase cases. Consumers are faced with various modus operandi and fraudulent tactics when making online purchases even though the authorities have provided a safe-purchase guideline for online purchases. Hence, this qualitative study aims to analyse the legal framework surrounding consumer protection involving online shopping in Malaysia. Data were obtained using the content analysis and semi-structured interview methods. Findings indicate that online consumers are protected under the Consumer Protection Act 1999 in cases related to fraudulent online purchases.