Reviewerly/exHarmony huggingface dataset:
ICT-Assisted Growth Management System for Mud crabs (ICT-AGMAS): Deployment and Evaluation

This study provided the concept of applying information technology in the aquaculture sector that offers an appropriate growth management system for mud crabs (Scylla spp.) through the utilization of a patented device and software application known as Information and Communication Technology-Assisted Growth Management System (ICT-AGMAS). This utility model (UM) is intended for real-time monitoring of the morphometric development of mud crabs. The automated nature of this technology avoids the farmers from manually checking the crabs within the pond, thus saving time and labor costs. To test its performance, the system was deployed at one of the polyculture aquaculture farms at San Juan I, Ternate, Cavite for five months. A comparison of the actual number of sampled crabs per unit time between the conventional sampling method (manual) and ICT-AGMAS was made by applying deep learning. Evaluation results revealed that the system can offer a faster and easier way of sampling mud crab with a sampling time difference of 143.25 seconds compared to the conventional sampling protocol. In addition, statistics and analysis of the use of the system disclosed a positive user acceptance of applying the technology based on identified descriptive variables. Estimated financial parameters such as payback period, the return of investment (ROI), and net present value (NPV) were also found to be positive and viable.
Automatic Classification of Software Requirements in Vietnamese based on Machine Learning Techniques

Requirements engineering is often the first stage in the software process to understand the problem statement. Finding mistakes earlier in requirements helps reduce the development cost. One activity contributing to defining clear, complete and precise requirements is classifying requirement items in the specification. This paper presents a classification approach of functional and non-functional requirementsin Vietnamese using different supervised machine learning techniques. Five supervised machine learning algorithms, including Na¨ıve Bayes (NB), Support Vector Machine (SVM), Logistics Regression (LR), Multi-layer Perceptron Neural Network (MLP), and FastText, are implemented, trained, tested and compared using a dataset. The experimental results show that NB is the best model in terms of accuracy.
TopKmer: Parallel High Frequency K-mer Counting on Distributed Memory

High-throughput DNA sequencing is a crucial technology for genomics research. As genetic data grows to hundreds of gigabytes or even terabytes that traditional devices cannot support, high-performance computing plays an important role. However, current high-performance approaches to extracting k-mers cost a large memory footprint due to the high error rate of short-read sequences. This paper proposes TopKmer, a parallel k-mer counting workflow that indexes high-frequency k-mers within a tiny counting structure. On the 2048 cores of Tianhe-2, we construct k-mer index tables in 18 s for 174 GB fastq files and complete queries in 1 s for 1 billion k-mers, with a scaling efficiency of 95%. Compared with the state of the art, the counting table’s memory usage is reduced by 50% with no performance degradation.
Comprehensive analysis and classification of natural language questions based on Bi-LSTM-CRF

This paper aims to study the reasoning and understanding of natural language questions. First, for the knowledge of the learning process, we convert the reasoning and understanding of questions into named entity extraction and relation classification of questions. Thus, a named entity recognition model based on Bi-LSTM-CRF is proposed. Due to the specificity of natural language questions and in view of the fact that contextual information plays a decisive role in interrogative relation, we propose a relation classification model based on Bi-LSTM+Attention mechanism to capture the contextual information. Then, using the already established knowledge graph, the corresponding comparative experiments are designed. The F1 value of the named entity recognition experiment based on Bi-LSTM-CRF could reach 84.63%, and the F1-score of the relationship classification experiment based on Bi-LSTM+Attention mechanism could reach 71.94%. Both of them are better than the traditional model.
Development of an Octocopter Drone for Accompanying and Carrying Objects

In this paper, an octocopter drone for accompanying and carry the user's personal belongings is developed, which can be used during daily life, such as walking or jogging. This drone is designed with a four-layer structure and can carry objects up to 300[g]. Its size is reduced by dividing the eight brushless motors on the planes in two layers. It is equipped with two ESCs to drive eight brushless motors and one six-axis attitude sensor to control its posture. The effectiveness of the drone was confirmed through flight experiments.
Blockchain technology used in medicine. A brief survey

Blockchain is the technology that allows people to make transactions without intermediaries. Bitcoin was the first cryptocurrency launched. Cryptocurrencies are digital tokens that can be used for transactions. They use cryptography to establish their authenticity and are not linked to a central authority. Blockchain has many advantages for the healthcare industry and can be used in various fields such as: implementation and improvement of electronic patient records, in clinical trials, neuroscience, pharmaceutical industry and research. Its security can help to improve the confidentiality of patient data and can also help secure the supply chain of medicines. The security and transparency of the blockchain will play a crucial role in the medical industry. This will allow companies to register their products and conduct secure transactions. QR codes can also be placed on the back of medicine containers to help customers identify the authenticity of the products they purchase. The exchange of health information through the blockchain will also have various challenges, such as maintaining the confidentiality of patient data. At the same time, due to the different regulations in different countries, it can be difficult to establish an efficient and secure exchange. Unlike other cryptocurrencies, blockchains are usually immutable, which means that the data added in the chain will always remain. This eliminates the risk of data loss
HW/SW Co-Design of TFHE Homomorphic OR Gate via NTT-based Polynomial Multiplication on a programmable SoC

Secure computing is possible with fully homomor-phic encryption (FHE) schemes. However, such schemes re-quire computationally heavy and memory intensive operations. Hardware acceleration of their arithmetic operations is required to perform the encrypted computation within a shorter and reasonable time for the application. In this work, we address this challenge and present an FHE-specific System-on-Chip (SoC) de-sign with HW/SW co-design methodology for homomorphic OR gate of Torus Fully Homomorphic Encryption (TFHE) library, which is one of the open-source FHE library in the literature. TFHE utilizes fast fourier transform (FFT) and it mainly involves floating point calculations. We integrate the Number Theoretic Transform (NTT) based polynomial multiplication into the TFHE in order to use integer-based arithmetic. We conducted several experiments with SW and HW implementations on a pro-grammable SoC platform, a Zynq-based ZedBoard development board. Compared to the pure SW implementation, our NTT-based HW accelerator for homomorphic OR gate yielded better results in terms of computation time.
WSSL: WEIGHTED SELF-SUPERVISED LEARNING FRAMEWORK FOR IMAGE-INPAINTING

Image inpainting is the process of regenerating lost parts of the image.Supervised algorithm-based methods have shown excellent results but have two significant drawbacks.They do not perform well when tested with unseen data.They fail to capture the global context of the image, resulting in a visually unappealing result.We propose a novel self-supervised learning framework for image-inpainting: Weighted Self-Supervised Learning (WSSL) to tackle these problems.We designed WSSL to learn features from multiple weighted pretext tasks.These features are then utilized for the downstream task, image-inpainting.To improve the performance of our framework and produce more visually appealing images, we also present a novel loss function for image inpainting.The loss function takes advantage of both reconstruction loss and perceptual loss functions to regenerate the image.Our experimentation shows WSSL outperforms previous methods, and our loss function helps produce better results.
Dual SVD++ Recommendation Model Based on Attention Convolutional Neural Network

In recent years, various recommender system algorithms have emerged one after another, such as traditional matrix factorization (MF) techniques and deep learning (DL) techniques, which have greatly improved the performance of recommender systems. For fully mine user preferences and further study the important role of review texts, this paper presents a dual SVD++ recommendation model based on attentional convolutional neural network, which uses the user's rating data for items to mine the implicit feedback of users and items, through the convolutional neural network (CNN) of the attention mechanism is used to represent the important comment text features of users and items, Using dual SVD++, the learned review text features are fused to generate recommendations. The model uses mean square error (MSE) and mean absolute error (MAE) as evaluation indicators. On the Amazon public datasets, it is compared with five typical recommendation models, and the feasibility of the model is illustrated.
Interdisciplinary connections in education as a pedagogical problem

The topic of interdisciplinary connections has recently become the subject of wide discussions in journals, monographs, educational websites, and program documents of government publications. Along with this topic, this article examines the role of interdisciplinary connections in the process of improving the quality of education. It was determined that the main task of modern pedagogy is to improve the quality of education and ensure the comprehensive development of the younger generation. These qualities were determined not only because students learned facts, but also because they needed to be able to acquire and select the necessary knowledge, synthesize, generalize and unite them around scientific ideas, objects, and processes taking place in real life. The analysis showed that over the centuries, researchers have been looking for more efficient and rational solutions to the problems of ensuring the synthesis, integration, and merging of parts. One of them is solving the problem of knowledge synthesis through interdisciplinary communication. Interdisciplinary communication is a multifaceted problem.
An Ant Colony Optimization Parameter Tuning Method Based on Uniform Design for Path Planning of Mobile Robots

Ant Colony optimization (ACO) has been widely used in mobile robot path planning in recent years. The parameters of ACO have great influence on the global search ability and convergence speed of the algorithm. The existing parameter tuning methods of the ACO mostly depends on experience or cannot evaluate the interaction between parameters, which make it very time-consuming and difficult to obtain excellent parameter combination. An experimental design method Uniform Design (UD) is applied for the ACO offline tuning of mobile robot path planning algorithm in this paper. The results show that the UD method can rapidly obtain excellent parameter combination from few of experiment simulation. The simulation results and experimental results show that the optimal parameter combination of ACO obtained by the UD method can rank in the top 10% of the valid results of the enumeration method sorted by the path length. In addition, the algorithm running time of the optimal parameter combination is shorter than that of most parameter combinations obtained by enumeration method.
The more cost-effective e-paper solution

Printed e-paper displays from Ynvisible (Vancouver, Canada) are cost-effective, ultra-low-power, thin &amp; flexible, and highly customisable. Dynamic information where you need it, when you need it, at a price you can afford
Authentication model and trust management for secured internet of things enabled smart grid using blockchain framework

Flexible power trading is an important capability brought by smart grid to traditional electric distribution networks. Flexible power trading necessitates trust among smart grid entities, security and integrity for the transaction among entities for reliable power trading. In this work, a novel blockchain framework over internet of things enabled smart grid is proposed to facilitate secure and reliable power trading. Towards this aim, a cooperative trust-based reputation score model is proposed to provide reputation score to the entities. The communications between entities involved in power trading are authenticated using a three-party mutual authentication model. The transactions between the entities are integrity verified and secured in a blockchain framework. Use of blockchain provides an immutable storage for transactions and ensures consensus across all the entities involved in power trading, so that no entity can deny the participation in trading.
Sheepdog type navigation for multiple mobile robots based on two-wheeled vehicle model

Shepherding problem is to guide a large number of moving objects (sheep flock) with a small number of controllers (shepherd, sheepdog). Exploring the shepherd guidance model is useful for controlling multiple robot and guiding real swarm of organisms. In this paper, to demonstrate sheepdog system in the real world, we proposed a control system for a sheep robot and a sheepdog robot based on a two-wheeled vehicle model. Through simulation validation, we have verified the validity of the proposed system.
INFORMATION EDUCATIONAL ENVIRONMENT: ANALYSIS, PROBLEMS, PROSPECTS

The article deals with the theoretical issues of informatization in the field of education, current problems and prospects of informatization of education. The influence of the use of information technologies on the educational process and its transformation is discussed. Topical issues of informatization of education are considered. The main problems arising in connection with the introduction of information technologies in the educational process are proposed and substantiated.
Hardware/Software Co-Exploration for Graph Neural Architectures on FPGAs

The success of graph neural networks (GNNs) in the past years has aroused growing interest and effort in designing the best models to handle graph-structured data. Meanwhile, the neural architecture search (NAS) technique has been witnessed to rival against human experts in discovering efficient network topology. Most recently, it has been applied to the field of GNN engineering. However, despite the growing interest in GNN accelerator designs, existing works on graph neural architecture search all concentrate on software (SW) and do not consider hardware (HW) constraints at all, which often leads to sub-optimal system performance when the resulting networks are deployed on hardware accelerators. To address this problem, in this paper we propose a SW-HW co-design framework, namely FGNAS, for automating the search and deployment of GNNs on FPGAs. Experimental results on common benchmark datasets Cora, CiteCeer, and PubMed show that compared with a two-step method built from the state-of-the-art graph NAS framework, FGNAS achieves up to 4.8% improvement accuracy and 3x speedup under the same hardware constraint.
BLOCKCHAIN FOR ENTERPRISES: BUILDING A RESEARCH FRAMEWORK

In recent decades, blockchain has enjoyed fast development in numerous enterprises as seen in various applications.However, there lacks a comprehensive delineation in the scope of blockchain for enterprises.To provide a thorough landscape of blockchain research and practice for enterprises, this research employs a systematic literature review to unveil the current status of enterprise blockchain, as well as its major factors, characteristics, and implications, in order to uncover the potentials and directions of enterprise blockchain use and development in the future.
LAJU PERTUMBUHAN DAPHNIA MAGNA DENGAN PEMBERIAN PUPUK ORGANIK BERBEDA

This study aims to determine the growth rate of Daphnia magna with different organic fertilizers. This research is an experimental study using CRD (completely randomized design) with 3 treatments (p) and 3 replications (u). The organic fertilizer treatments applied to daphnia culture media were: P0 = Control, P1 = Cow dung, P2 = Swallow Manure, and P3 = Chicken Manure. Parameters measured included specific growth rate, and population density of daphnia. Data were analyzed statistically (Anova). The results showed that the application of organic cow dung, swallow and chicken manure had a significant effect on the specific growth rate and population density of daphnia (P&lt;0.05). The best treatment was on P2 (swallow droppings) with a specific growth rate of 19.9%/day and a population density of 1635 ind/L
SD-DCRN: SALIENCY DRIVEN DOUBLE-CHANNEL RESIDUAL NETWORK FOR SUPER-RESOLUTION OF REMOTE SENSING IMAGES

Traditional methods of super-resolution of remote sensing images generally ignore the fact that significant areas usually have a higher demand for super-resolution compared to nonsignificant areas. According to this feature of remote sensing images, we propose a new model of super-resolutio-n based on double-channel residual dense network driven by saliency analysis. Firstly, we use a cascaded partial decoder model to obtain the saliency image of remote sensing images which contributes to distinguishing significant areas and background areas. Secondly, we adopt different super-resolution strategies for regions with different salient values and texture complexity. For the non-salient regions, we adopt the smaller number of RDBs and their internal convolution layers to save computer resources. For the salient regions, we increase the number of RDBs and layers to extract more complex features for super-resolution of the salient regions, which is conducive to the reconstruction of complex texture. Finally, the reconstructed salient regions and non-salient regions are superimposed to obtain the complete super-resolution results. Our experimental results show that the comprehensive perform of our method outperforms other super-resolution models in terms of metrics based on the peak signal-to-noise ratio and structural similarity.
Vision-based Activity Recognition: Present Status &amp; Future Challenges

Video, skeleton joint points are widely explored for human activity recognition (HAR).On the other hand, various sensors are engaged in human activity and behavior understanding.Vision-based human action or activity recognition approaches are based on RGB video sequences, depth maps, or skeleton data -taken from normal video cameras or depth cameras.On the other hand, sensor-based activity recognition methods are basically based on the data collected from wearable sensors having accelerometers, gyroscopes, and so on.There are numerous applications on HAR, however, healthcare, elderly support, and related applications become very important arenas with huge social and financial impacts.Due to the advent of various IoT sensors, it becomes more competitive as well as easier to explore different applications.The keynote will cover our works related to HAR approaches, highlighting healthcare perspectives and methods.
A Proof-of-Weighted-Planned-Behavior Consensus for Efficient and Reliable Cyber-Physical Systems

Recently, blockchain was introduced into the cyber-physical systems, which provides services of privacy and trust. However, reliability and system performance issues exist when blockchain and cyber-physical systems are integrated. In this paper, we design a blockchain-enabled cyber-physical system, where a new blockchain consensus is used to solve the problems of reliability and system performance. Firstly, an autonomous consensus mechanism called Proof-of-Weighted-Planned-Behavior is established based on the theory of planned behavior. Then, the behavior of consensus participants gets further explained by introducing credit evaluation and vulnerable node analysis. Moreover, considering the Jain fairness index, a dynamic authorizer group mechanism that coordinates reliability and decentralization is developed. By optimizing the credit threshold of the authorization group, the security and reliability of our designed mechanism are guaranteed. Finally, the experimental simulation results prove that compared with the traditional consensus, our proposed consensus improves the reliability and the system performance of the blockchain-enabled cyber-physical systems.
Young people with SEND create video about education changes they want to see

Children and Young People NowVol. 2022, No. 6 PracticeYoung people with SEND create video about education changes they want to seeDerren HayesDerren HayesSearch for more papers by this authorDerren HayesPublished Online:6 Sep 2022https://doi.org/10.12968/cypn.2022.6.49AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 June 2022Volume 2022Issue 6ISSN (print): 1755-8093ISSN (online): 2515-7582 Metrics History Published online 6 September 2022 Published in print 2 June 2022 Information© MA Education LimitedPDF download
The Evolution of Research in Resources, Conservation &amp; Recycling Revealed by Word2vec-Enhanced Data Mining

Highlights• A comprehensive text mining was conducted to understand topical patterns in RCR• New word2vec model was developed for scientific literature mining• Trending up and emerging topics, topical interconnections were revealed• Resources-based topics and their interconnections were identified• Publication similarity to different resources was discerned based on classification
GIS-Box Improving Data Literacy in Spatial Disciplines - Integrating spatial data modeling, processing and visualization in spatial study programs

Data modelling, processing, and visualization are crucial competencies for geospatial study programs.Students of different geospatial study programs need to be strengthened in the use and application of digital tools of spatial analysis and visualization within the digitization of teaching.This paper presents an approach on how digital tools for spatial analysis and visualization can be introduced into the curricula of architecture, urban planning and geography studies, strengthening the interdisciplinary exchange and students' data literacy.As a result, an interdisciplinary methodological teaching format for spatial analysis, the "GIS-Box" is introduced.The GIS-Box is developed as a modular toolbox to provide material for collaborative and self-taught learning in different Master and Bachelor degree programs.It offers video lectures as well as practical tutorials, including an introduction to data modelling and programming, with the aim of improving students' data literacy.Students also learn to use QGIS to create maps for applied spatial research.In order to provide a uniform technical basis for teaching Python programming, Jupyter Notebooks are used.The integration of Jupyter Notebooks allows combining theoretical and practical programming content interactively.In this paper, we present the implementation of the class, statistically assess student results and experiences from teaching.In addition, positive and negative aspects of integrating GIS-Box with digital tools in teaching are discussed and further opportunities to improving data literacy in teaching are outlined.