Reviewerly/exHarmony huggingface dataset:
A Framework of Distributed Solar Blockchain as a Service

Abstract Blockchain was combined with several other information technologies and computing concepts to create modern cryptocurrencies protecting through cryptographic mechanisms instead of a central repository or authority. A Framework of distributed solar Blockchain Service (DSBaaS) platform as a Service is designed to manage distributed solar energy system by adopting Blockchain as a Service (BaaS) platform. Base on the ability operation and maintenance features of BaaS, it is available to solve operational management, safety management, material management, project management, financial transactions management, transaction settlement and other related multi-agent transaction pain points. Providing new business models for implementing distributed solar energy production, transmission and utilization of effective coordination and management, it is a key component to forming clean, efficient, flexible and intelligent distributed solar energy system.
Determining Optimal Solar Power Plant Location in Melaka, Malaysia: A GIS-Based Solutions

Abstract The energy demand has risen dramatically in recent years as a result of population growth and the fast expansion of the global economy. Due to rising carbon dioxide (CO2) emissions and increasing energy needs, Malaysia has recently intensified its efforts to encourage the development of renewable energy supplies. Despite the abundance of solar resources, the PV growth in underdeveloped countries such as Malaysia is moving slower. Malaysia has the advantage in solar energy generation since it is geographically close to the equator and has a significant solar generation potential due to its hot and sunny weather all year. Therefore the question here is, where is the most suitable place to build more solar power plants? What methods will we be using to find the most suitable place to build the solar power plant? In picking a place for such development, several factors must be examined, such as how effective the PV power station site is and how to decrease the overall cost of the project by minimising proximity to existing infrastructures while improving solar panel power production. A geographic information system (GIS) is a tool that can help to solve this challenge. In this case, we integrate economic, environmental, and technical variables such as solar radiation intensity, local physical terrain, environment, climate, and placement criteria such as distance from roads and rivers. As additional input factors, other geographical information data were used (solar radiation, digital elevation models (DEM), land cover, and temperature). Further analysis using the Analytic Hierarchy Process (AHP) based Multi-Criteria Decision Making (MCDM) was then applied to this study to get the suitable location based on the importance of the criteria. In order to build a cost-effective and high-performing solar project, a complete solar site assessment is required. The results of the study should give a decision support system model and map on determining the optimal locations of solar power plants in Melaka.
Keypad entry inference with sensor fusion from mobile and smart wearables

This paper investigates the threat of password leakage from sensors on mobile phones and smart wearables. We investigate attack methods for recovering a short random number entered on PEDs using a combination of data collected by from microphone, accelerometer and gyroscope in devices on the person entering the number. We take into consideration features based on keypress sounds, from either phone and/or smart watch, in addition to hand movement acceleration and angular velocity captured by the smart watch. We used the fusion features from these three sensor sources to train a Neural Network to recover key entries on a keypad. Our method based on linear acceleration and angular velocity, with acoustic data used to segment individual keystrokes, showed an improved 74% probability to predict a single keystroke in one guess and 52% for a 6-digit PIN in three guesses.
Optimizing the evaluation of ℓ-isogenous curve for isogeny-based cryptography

In ASIACRYPT 2017, Costello and Hisil proposed two methods of evaluating ℓ-isogenous curves: the usage of Vélu formulas and the recovery from special isogenous points. In isogeny-based cryptography, such as the supersingular isogeny Diffie–Hellman (SIDH)/supersingular isogeny key encapsulation protocol, small-degree isogenies are typically prioritized, because the costs of evaluating points and curves through isogenies using Vélu's formulas will increase with the increase of isogeny degrees. Compared with the use of Vélu formulas, the recovery from special isogenous points is independent from ℓ and runs in constant time under certain conditions. Therefore, it could be efficient in the evaluation of the ℓ-isogenous curve in some variants of SIDH (such as SIDH using twisted torsion (B-SIDH)). In this study, we optimize the evaluation of the ℓ-isogenous curve when three differential isogenous points are provided in advance. We also propose the optimized evaluation in projective coordinates on different elliptic curve models such as the Montgomery, Edwards, Jacobi, and Huff models. Finally, we propose an acceleration of B-SIDH by applying our new function to evaluate the ℓ-isogenous curve in the Montgomery model.
Land Movement Vulnerability in South Padang District Using Geospatial Information System (GIS)

Abstract Based on data from the Center for Volcanology and Geological Hazard Mitigation in April 2021, South Padang District has the potential for high land movement vulnerability. This is due to the high intensity of rainfall, geological conditions and the area is generally a hilly area. The location of South Padang, especially the Gado-Gado Hill area, is a residential area and a tourist attraction. Therefore, it is necessary to know the potential for land movement vulnerability in the South Padang District to minimize the occurrence of landslides which can result in material and no material losses. Research on land movement vulnerability in South Padang District uses Geospatial Information System (GIS) software with parameters such as rainfall data, earthquakes, land slope, vegetation, soil type, water slope, and rock geology. From this research, the results will be obtained in the form of rainfall maps, earthquake maps, slope maps, vegetation maps, soil maps, slope water maps, geological maps and vulnerability maps in South Padang District. From the results of processing the vulnerability map, the area with low vulnerability is 390.99 ha, middle vulnerability is 528.08 ha and high vulnerability is 274.78 ha.
Siamese Transformer Network: Building an autonomous real-time target tracking system for UAV

Currently, the common used vision-based tracking system faces two major challenges: (1) the trade-off between speed and accuracy of the tracker; (2) the robustness of the tracking servo control. In this paper, we propose a new framework composed of a transformer attached to the siamese-type feature extraction networks called Siamese Transformer Network (SiamTrans) to balance the speed and accuracy, avoiding complicated hand-designed components and tedious post-processing of most existing siamese-type trackers with pre-defined anchor boxes or anchor-free schemes. SiamTrans forces the final set of predictions via bipartite matching, significantly reducing hyper-parameters associated with the candidate boxes. Moreover, to enhance the robustness of the servo control, the high-level control part is also redesigned by fusing all the bounding box information and with the Tracking Drift Suppression Strategy (TDSS). The TDSS is mainly used to judge the target’s loss. If the target is lost, it will feedback the previous information to reinitialize the tracker to track and update the template patch of SiamTrans, making the whole system more robust. Extensive experiments on visual tracking benchmarks, including GOT-10K, UAV123, demonstrate that SiamTrans achieves competitive performance and runs at 50 FPS. Specifically, SiamTrans outperforms the leading anchor-based tracker SiamRPN++ in the GOT-10K benchmark, confirming its effectiveness and efficiency. Furthermore, SiamTrans is deployed on the embedded device in which the algorithm can be run at 30FPS or 54FPS with TensorRT meeting the real-time requirements. In addition, we design the complete tracking system demo that can accurately track the target for multiple categories. The actual experimental results also show that the whole system is efficient and robust. The demo video link is as follows: https://youtu.be/UK37Q-M9ET4 .
Enhanced implicit function-based network for arbitrary-scale image super-resolution

With the great development of deep learning, the performance of single image super-resolution (SR) has achieved tremendous progress. As an emerging and promising branch of the SR task, the arbitrary-scale SR task is receiving increasing attention from researchers due to its efficiency and practicality. Although the recent work learning implicit image function opened a solution for arbitrary-scale image SR, its reconstructed images contained structural distortions caused by defective prediction of high-frequency textures. To overcome this problem and further improve the performance of arbitrary-scale image SR, we propose an effective arbitrary-scale SR network, namely, enhanced arbitrary-scale super-resolution, which achieves the arbitrary-scale SR task in a single model by introducing a local-global encoder and enhanced implicit image function. Unlike conventional SR methods, which only stack up convolutional blocks to extract the local feature, the local-global encoder has two branches in parallel, including the local feature branch and the global prior branch. The former effectively extracts the local feature from a low-resolution image and the latter extracts the global prior to assist in the high-resolution image reconstruction. Next, we redesigned an enhanced implicit image function in the form of a dual modulation multiplayer perceptron (MLP) by replacing the implicit image function with a vanilla MLP. Moreover, we introduce the spatial encoding to further reduce structural distortions of reconstructed images. Extensive experiments were conducted to evaluate the performance and demonstrate the superiority of our proposed model.
Quantification of the spatial arrangement of structural lineaments and deformation bands: Implications for the tectonic evolution of the eastern border of the Araripe Basin, NE Brazil

Spatial arrangement regards the spatial position of brittle structures. Even though they are important for several industrial applications, quantification of their spatial arrangement remains a challenge. In this contribution, we quantify and compare the spatial arrangement of regional lineaments and deformation bands in the Eastern Araripe Basin, NE Brazil using the normalized correlation count method on topographic, aeromagnetic, and outcrop data. We have noticed irregular and random arrangements of topographic and aeromagnetic clusters of lineaments, while deformation bands are grouped. The use of topographic and aeromagnetic data represents a reliable way to investigate regions with limitations (topographic, climatic, or social) that make field data hard or impossible to acquire. Furthermore, we proposed a preliminary tectonic evolution model for the eastern border of the Araripe Basin. • Statistical analysis of structural lineaments using topographic and aeromagnetic data. • Quantification of the special arrangement of regional data and deformation bands. • The topography of the basin is marked by brittle deformation. • Observation scale and resolution can affect spatial arrangement analysis.
Penggunaan E-Modul Berbasis Discovery Learning Melalui Pendekatan Lesson Study Terhadap Kemampuan Kognitif Peserta Didik

This research was a pre-experimental study with a one-group pretest-posttest design to assess the improvement in students' cognitive abilities through the use of Discovery Learning-based interactive E-modules with a lesson study approach in students of XI IIS 2 of SMA Negeri 9 Makassar. There were 28 students from XII IIS 2 as the research sample. Based on descriptive statistical analysis, the yield pre-test showed an average of 63, while the posttest had an average of 87. The results from the N-Gain test showed a high-quality improvement in student learning outcomes.This confirmations that the use of Discovery Learning-based e-modules through a lesson study approach is effectively used to improve the cognitive abilities of students.&#x0D;
Development of Instruction Analysis Tools for Elementary Schools Problem-based Learning Instruction Consulting

The purpose of this study is to develop instruction analysis tools that can be used when conducting problem-based learning classes and conducting instruction consulting in elementary schools. The research methods carried out for the realization of the research purpose were case study, needs analysis, expert evaluation, and content validity verification. Case studies were analyzed by collecting and analyzing literature-centered cases in fields related to the research topic. The needs analysis collected the needs for the items necessary for the development of class analysis tools, centering on the field teachers. For expert evaluation, opinions for the development of the final tool were collected from experts in the relevant field. Content validity verification was conducted to verify the validity of the final draft of the tool developed as a result of this study. The instruction analysis tools developed as a result of the research are largely divided into pre-class tools, in-class tools, and post-class tools. The pre-class tool is centered on the content to check the items to be considered in the class design stage. In class, the tools were created to check whether the teacher's and student's activities are properly organized for each procedure of problem-based learning. The activities after instruction were developed to confirm the evaluation and reflection of the entire activity. Instruction analysis tools for elementary schools problem-based learning instruction consulting should be developed as pre-, middle-, and post-class activities according to the flow of the instruction. have to lose In addition, instruction analysis tools should be designed to have systematic, scientific and objectivity.&#x0D;
M-Learning - Before and During COVID-19

The report describes a study of the opinion of students from the University of Ruse on their use of mobile learning before and during the COVID-19 pandemic. The research uses the survey methodology and for this purpose an online questionnaire was developed. A total number of 469 students from four academic years were surveyed - two before COVID-19 and two during the pandemic. Of the total number of surveyed students, 143 are in the academic year 2021-2022 and 133 are in the academic year 2020-2021, when most of the education was online. 72 are in the academic year 2019-2020 and 121 are in the academic year 2018-2019. An analysis and comparison of the results of these three academic years was performed. Relevant conclusions about the impact of COVID-19 on the use of mobile learning are made.
Color Restoration Method for Endoscope Image Using Multiscale Discriminator Based Model Compression Strategy

Color restoration of endoscopic images is an urgent clinical need during photodynamic surgery. In recent years, deep learning methods achieved notable results in the fields of image processing. The model compression algorithm and hardware performance enhancement improved the model inference speed. It is possible to apply deep learning methods to the task of endoscopic image color restoration during surgery. However, experiments show that model compression can lead to image deterioration. To solve this issue, we propose a fast color restoration method for endoscopic images, which use multiscale discriminator based on model compression. Initially, we train a CycleGAN teacher network with multiscale discriminator. Then, we obtain the student model through knowledge distillation and neural architecture search. We use the trained teacher discriminator to guide the student model and add feature matching loss to stabilize the training process. Experiments show that our method ameliorates the performance of compressed models.
Research on Internal Parameter Filtering of Natural Gas Pipeline under Duty Cycle Transmission Mechanism

In recent years, with the continuous growth of China's long-distance oil and gas pipeline network, wireless communication network applications have become more and more extensive with the continuous promotion of intelligent oilfield construction, while the energy-saving problem in the process of information transmission has become increasingly prominent. In order to ensure the sustainable development of China's oil and gas industry, low-power wireless transmission has become a basic requirement for the current intelligent oil and gas pipeline communication construction. For the monitoring of pipeline operating conditions, pressure and flow rate values are important parameters. If the accuracy and comprehensiveness of pipeline operation data can be ensured simultaneously under an energy-efficient transmission mechanism, it will provide strong support for the green and safe operation of pipelines. Therefore, in this paper, a recursive filtering study is conducted for natural gas gathering pipeline flow system under duty cycle transmission mechanism to complete the reliable monitoring of pipeline fluid transportation status.
BUAS: A Knowledge-based Representation of the Border Surveillance Domain

In this study, we propose a methodology for developing a knowledge-based model that defines the entities of the land border surveillance domain and establishes potential associations among them. A practical ontology was devised to be integrated into a video analysis system, operating on data generated by visual sensors. Its deployment aims to infer complex scenarios and determine advanced spatio-temporal relationships between events. In that way, hidden, upper-level knowledge will be exposed as a set of low-level events ordered in a semantic sequence based on their causal relations. We analyze the core concepts and the general framework of the proposed model, and we spell out the data flow within the ontology. Some crucial parts of the introduced approach are based on the EUCISE-OWL ontology architecture, which was created for a similar purpose in the maritime domain. Additionally, we present the methodology of incorporating our model into a real-time data pipeline with the intention of raising an alert upon the occurrence of an agglomeration of persons. We break down the main components of the system that lead to the population of the ontology, and we semantically decompose the reasoning rules aggregated within the relational database. Finally, the developed ontology is evaluated in terms of expressiveness and completeness, and its major metrics are compared with the EUCISE-OWL ontology,
Joint model for learning state recognition with combining action detection and object detection

How younger students, such as primary and secondary school students, can improve their concentration when studying alone has been the subject of research by education experts. In this paper, we develop a learning-state-based tomato-clock system that can help improve concentration. We propose a joint model to detect students' learning states and thus control whether the tasks of the tomato-clock can be completed properly, which in turn motivates students to focus on their learning. In the joint model, the temporal action detection model SlowFast detects the video and identifies the base action category and the state action category. In cases where the actions are similar, such as the states of reading and watching video on mobile phone, we calculate the student's head pose information to determine his or her field of view and use the FPN-Faster RCNN model to detect the key items within his or her field of view to detect the real action. Finally, their learning state was identified based on the duration of the action, with mAP of 85.28%.
Research on image synthesis of fabric replacement in suit customization

With the development of the Internet, online clothing customization has become a trend. Fabric replacement is an important part of clothing customization. If the high-reso-lution image of clothes can be rendered after replacing a speci-fied fabric instantly online, it can greatly enhance the consum-er's purchase. To address this need, this paper proposes an image synthesis method based on pix2pixHD network for suit fab-ric replacement. In this paper, we modify the generator to sepa-rate texture generation and color generation of suit images to reduce the interference of color information to texture infor-mation generation. Meanwhile, we generate and discriminate the image in brightness channel and color channel, and intro-duce additional texture loss and color loss in the discriminator to guide the generator to generate images with higher quality and restore the real fabric texture and color. The effect of a se-ries of different loss functions on the quality of image synthesis is also compared and analyzed. The experimental results show that the suit image synthesized by this method has high resolution and good visual impression, and can restore the genuine texture of the fabric well, which meets the needs of suit customization system.
A Sequence-selective Fine-grained Image Recognition Strategy Using Vision Transformer

Aiming at precise sub-category classification of images, fine-grained image recognition requires the algorithms to enjoy a remarkable ability of subtle feature extraction. Recently, the architecture of Transformer has been successfully applied in vision tasks, bringing a novel approach to improve feature extraction performance of fine-grained image recognition algorithms. However, fine-grained image datasets are usually quite limited in capacity, which are unfavorable for the data-consuming training process of Transformers. In order to increase the available amount of data for training, in this paper we firstly introduce a stochastic image data augmentation method for Vision Transformer (ViT), which uses a Dense-DETR model to extract feature regions and performs random insertion and removal for the transformed patch sequence. To select the most informative sequence elements in the forward propagation pro-cess, we implement a feature patch selection strategy by applying an additional convolutional network structure to ViT encoders. Inspired from active learning, a contrastive loss utilizing the posterior information of paired images is also introduced as a penalty item of ViT's cross-entropy loss objective. Such strategies can make the ViT extract the most discriminative feature information from its input. Extensive experiments have supported that the proposed sequence-selective Vision Transformer reaches the highest recognition accuracies on several frequently-used fine-grained image datasets.