Reviewerly/exHarmony huggingface dataset:
Blockchain-based Charging Pile Detection Data Uploading and Sharing

Aiming at the problems that the existing charging pile detection data is easy to lose, difficult to query, and difficult to ensure the uniqueness and integrity in the process of offline transfer between multiple departments, this paper proposes a charging pile detection data uploading method based on blockchain technology. Shared platform implementation. The charging pile first detects and analyzes the data through a third-party testing agency, and transmits abnormal data to the chain in a timely manner, and ensures the validity of the data detection. Secondly, the charging pile operator manages later operations and services, handles faults, etc., to ensure the quality and service of the charging pile. Finally, use the operation and maintenance organization to optimize and improve the test data to control the quality of the charging pile. Through this platform, the online flow of test data between different departments is effectively realized, which ensures the safety protection and efficient sharing of test data.
Access Control Model Based on the Privacy of Crowdsourcing Task Content Protection in the Blockchain Environment

The booming development of the Internet of Things has prompted the widespread application of crowdsourcing. It’s task allocation is mainly dependent on a centralized platform. Once attacked by distributed denial of service attack, the centralized server will be completely paralyzed.In addition, in crowdsourcing plaforms, the rationality of task allocation, high platform service fee, and privacy protection strategy also need to be considered simultaneously. This paper presents a crowdsourced model based on localized differential privacy methods and a fine-grained implementation of task-authorized access in the blockchain environment. The decentralized blockchain is used to replace the original centralized platform, construct ciphertext-policy attribute-based encryption to realize the fine-grained authorization, and strengthen the privacy protection of the task content.
BPrice: An Optimal Price Recommendation Framework Based on Product Identification

The price of a single product can vary significantly from one retail store to another in the Hong Kong market. The gap between the wealthy and the poor in Hong Kong continues to be large, leading to much of people having to look for bargains daily. While one may be able to search the web for the lowest price, the information on the web is scattered, and it is necessary to compare product names word-for-word. This process is exceptionally inconvenient for some older adults and people with disabilities. The Hong Kong society is pressing for an application that can tackle this problem. In this paper, we propose a framework to recommend the optimal price of the product in the Hong Kong market. The framework aims to speed up the price search with high accuracy. The problem is decomposed into name recognition and database query. The former is accomplished by utilizing the VGG-16 Convolutional Neural Network model pre-trained on the ImageNet dataset via transfer learning and feature matching with Scale-Invariant Features Transform (SIFT) and the colour histogram. The latter is performed on a daily updated MySQL database. The results of the recommendations were satisfactory in the experiments, with the accuracy of validating the CNN model reaching 90.614%.
Real-Time Image Filtering and Edge Detection Method Based on FPGA

This paper mainly studies the real-time image filtering and edge detection method based on FPGA, through the analysis and research of image edge detection filtering, look-up table instead of multiplier and distributed algorithm. In terms of hardware, FPGA series development boards are the main ones, and cameras are used as real-time image acquisition sources. In terms of software, in the Quartus II software, grayscale conversion is performed on the image information obtained by the camera, and then edge detection is performed on it, and finally the display of the image after edge detection and filtering is completed. The experimental results show that the researched distributed algorithm and look-up table can effectively reduce the logic resources required for operation, and can reduce the economic cost in practical application.
IoT Warehouse Management System Based on ACO Path Planning

Current storage management processes involving storage equipment suffer from Automated Guided Vehicle (AGV) path planning problems and rely on recording cargo and information manually. Hence, storage facilities present a low equipment operation efficiency due to recording errors and preserving storage data in paper, which is easy to lose. Therefore, this paper develops an Ant Colony Optimization (ACO) path planning-based IoT warehouse management system, relying on an ant colony algorithm and IoT technology. The proposed system collects and processes the forklift’s data through the vehicle’s intelligent terminal and exploits an optimized ant colony algorithm to realize the forklift’s real-time positioning, over-speed alarm, and path planning. Data transmission between the vehicle terminal and the warehouse management platform is established through the MQTT protocol. Finally, the entire system is monitored and managed through a WEB browser, with the MySQL database employed for persistent data storage. Adopting the developed non-contact warehouse management method centralizes and networks the scattered data in the warehouse management process, improving warehouse logistics efficiency.
An Object Detector based on Bi-directional Feature Pyramid Network and Faster R-CNN

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. The proposal of Feature Pyramid Networks(FPN) has achieved great success in the field of object detection and greatly improved the detection accuracy, but FPN is only a simple top-down single feature fusion.Therefore, in order to build a higher-level feature pyramid with both semantic information and location-spatial information, we propose a top-down and bottom-up bi-directional Feature Pyramid Network(BiFPN). Using BiFPN in a basic Faster R-CNN system, our method can achieve a state-of-the-art mAP of 82.2% on PASCAL VOC dataset, surpassing all improved methods based on the Faster R-CNN system.
MU-Net: An efficient algorithm for foreign objects detection in electric equipment

The detection of foreign objects inside the electric equipment is an important task to maintain the normal operation of the power grid. Aiming at the problems of low efficiency and easily missed detection in the current manual inspection, this paper proposes an improved multi-channel electric equipment foreign objects segmentation method MU-Net based on U-Net. It fuses information from multimodal foreign objects images and uses this information to segment foreign objects. Two different networks of MU-Net perform shallow feature extraction and feature fusion on the original image and the filtered image respectively; at the same time, we use Transformer for deep feature extraction. This attention mechanism can enhance the feature learning of foreign pixels, make the feature extraction more perfect, thereby improving the accuracy of foreign objects segmentation in electric equipment. The experimental results show that MU-Net achieves 92.5 in F1 and 85.24 in mIou on our dataset. Compared with other excellent segmentation networks, the segmentation accuracy has been improved, which fully verifies that MU-Net can effectively Perform foreign objects detection in electric equipment.
Sparse DARTS with Various Recovery Algorithms

Designing an efficient neural architecture search method is an open and challenging problem over the last few years. A typical and well-performed strategy is gradient-based methods (i.e., Differentiable Architecture Search (DARTS)), which mainly searches the target sparse child graph from a trainable dense super graph. However, during the searching phrase, training the dense super graph usually requires excessively computational resources. Besides, the training based on a dense graph is excessively inefficient, and the memory consumption is prohibitively high. To alleviate this shortcoming, recently Iterative Shrinkage Thresholding Algorithm (ISTA), a sparse coding recovery algorithm, has been applied to DARTS, which directly optimizes the compressed representation of the super graph, and saves the memory and time consumption. Indeed, there are several such kinds of sparse coding recovery algorithms, and ISTA is not the best one in terms of recovery efficiency and effectiveness. To investigate the impact of different sparse coding recovery algorithm on performance in DARTS and provide some insights. Firstly, we design several sparse DARTS based on different sparse coding recovery algorithms (i.e., LISTA, CoD, and Lars). Then a series of controlled experiments on selected algorithms are conducted. The accuracy, search time and other indicators of the model are collected and compared. Sufficient theoretical analysis and experimental exploration reveal that the different compression algorithms show different characteristics on the sparse DARTS. Specifically, Lars-NAS tends to choose the operation with fewer parameters, while Cod-NAS is the simplest of the four recovery algorithms, and its consuming time is very short, but the CoD-NAS model is unstable. Particularly, LISTA-NAS achieves the accurate results with stable recovery time. Thus, it can be seen that all compression algorithms are available to utilized according to different environments and requirements.
Construction of an Online Education Platform Based on SOA Architecture and Multimedia Technology

With the deepening of basic education information work, the establishment of regional basic teaching platform, in order to integrate educational resources, unified teaching platform, centralized management of teaching information, education information development is an urgent matter. In view of the above situation, the multimedia teaching system for primary and secondary schools is designed and implemented based on the actual project requirements. In view of the characteristics of wide distribution and many applications of multimedia teaching platform in primary and secondary schools, the current mainstream architecture is adopted to realize the efficient application, flexible expansion and convenient maintenance of the system through centralized management and distributed deployment.
Reversible Data Hiding Based on Multiple Strategies

A reversible data hiding method based on multiple strategies is proposed in this method. To fully exploit strong correlations among pixels, the third maximum pixel is used for predicting the second maximum pixel, and simultaneously, the second maximum pixel is employed to the maximum pixel. To keep the numerical order of the modified three largest pixels unchanged, pixel-value-order (PVO) and improved PVO (IPVO) are combined to modify three largest pixels such that three largest pixels can be embedded with 2 bits at most. Besides, multiple scanning ways are exploited to further improve the number of prediction errors valued 1 and 0. Experimental results also demonstrate the proposed method is effective.
Face detection using Haar cascade classifier

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C-Libras: A Gesture Recognition App for the Brazilian Sign Language

Sign languages are visual representations used by hearing or speech impaired people to communicate between themselves and with other people. There are over 140 sign languages globally, and they can be developed by deaf communities or derived from other existing sign languages. The signs made in this context are not considered gestures but words articulated primarily by the hands, while possibly involving facial expressions and trunk movements, making it far from trivial to understand them. Thus, automatic sign language recognition supported by machine learning has shown significant advancement in recent years. This paper presents a mobile application capable of recognizing gestures representing the letters of the Brazilian Sign Language’s (Libras) alphabet. Our methodology has three steps: the construction of the gesture dataset containing all the letters of the Libras alphabet; the training of the machine learning model used in the gesture classification; and the development of the desktop/mobile application used to capture the gestures to be classified.
Exploring Neural Embeddings and Transformers for Isolation of Offensive and Hate Speech in South African Social Media Space

Our previous study on classification and detection of abusive language in South African social media space has shown the high prospect of surface level features and classical machine learning models in terms of accuracy. However, much improvement is still needed in the aspect of F1-score. Therefore, the state-of-the-arts such as neural embeddings (Word2Vec, Doc2Vec, GloVe) and neural network-based transformer models (BERT and mBERT) which have performed well in many hate speech isolation tasks are explored in this work. In the evaluation of classical machine learning algorithms, Word2Vec with Support Vector Machine outperformed the previous models, Doc2Vec and GloVe in terms of F1-score. In the evaluation of neural networks, all the neural embedding and transformer models performed worse than the previous models in terms of F1-score. In conclusion, the impressive performance of Word2Vec neural embedding with classical machine learning algorithms in terms of best F1-score of 0.62 and accuracy of 0.86 shows its good prospect in the isolation of abusive language and hate speech in South African social media space.
Comparative Compression Robustness Evaluation of Digital Image Forensics

The robustness of two important digital image forensic tasks (i.e. SIFT-based copy-move forgery detection and PRNU-based camera sensor identification) against four different lossy compression techniques is investigated (while typically, only JPEG compression is considered) to identify the best suited technique for this application scenario. Overall, we find that the accuracy of forensic tasks is reduced for increasing compression strength as expected, however, the relative performance of the compression schemes is different for the two tasks. While JPEG is superior for realistic application settings (where accuracy is in an acceptable range) in SIFT-based copy-move forgery detection, JPEG XR and BPG provide the best option for PRNU-based camera sensor identification, whereas JPEG is clearly impacting this forensic application most severely.
A Survey on Technologies Which Make Bitcoin Greener or More Justified

According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactions per second without increasing the mining power and the energy consumption. Bitcoin is not only &#x201C;digital money for hackers&#x201D;. It has very promising future potential as a global reserve currency and a method to make the World Wide Web (WWW) immune to cyberattacks such as the Distributed Denial-of-Service attacks. This survey approached cryptocurrencies&#x2019; various technological and environmental issues from many different perspectives. To make various cryptocurrencies, including bitcoin (BTC) and ether (ETH), greener and more justified, what technological solutions do we have? We found that cryptocurrency mining might be cleaner than is generally expected. There is also a plan to make a vast renewable energy source available by combining Ocean Thermal Energy Conversion and Bitcoin mining. There are plans to use unconventional computing methods (quantum computing, reversible computing, ternary computing, optical computing, analog computing) to solve some of the issues regarding the vast energy consumption of conventional computing (including cryptocurrency mining). We think using spare computing cycles for grid computing efforts is justified. For example, there are billions of smartphones in the world. Many smartphones are being recharged every day. If this daily recharging period of twenty to sixty minutes would be used for grid computing, for example, finding new cures to cancer, it would probably be a significant breakthrough for medical research simulations. We call cryptocurrency communities to research and develop grid computing and unconventional computing methods for the most significant cryptocurrencies: bitcoin (BTC) and ether (ETH).
See No Evil: Discovering Covert Surveillance Devices Using Thermal Imaging

Covert surveillance devices ranging from miniature cameras to voice recorders are increasingly affordable and accessible on the market, raising concerns about surreptitious and unauthorized observation of people. This article contributes an innovative method for discovering covert surveillance devices using thermal imaging integrated with off-the-shelf consumer devices, such as smartphones. We develop a simple yet efficient processing pipeline for identifying covert devices and demonstrate its effectiveness through extensive and systematic evaluations that consider different types of covert cameras. Our results show robustness against a wide range of factors, including distance to other electrical objects, the environment and luminosity of the space where measurements are taken, the type of camera, and partial occlusion of the hidden devices.
Automated Quantization and Retraining for Neural Network Models Without Labeled Data

Deploying neural network models to edge devices is becoming increasingly popular because such deployment decreases the response time and ensures better data privacy of services. However, running large models on edge devices poses challenges because of limited computing resources and storage space. Researchers have therefore proposed various model compression methods to reduce the model size. To balance the trade-off between model size and accuracy, conventional model compression methods require manual effort to find the optimal configuration that reduces the model size without significant degradation of accuracy. In this article, we propose a method to automatically find the optimal configurations for quantization. The proposed method suggests multiple compression configurations that produce models with different size and accuracy, from which users can select the configurations that suit their use cases. Additionally, we propose a retraining method that does not require any labeled datasets for retraining. We evaluated the proposed method using various neural network models for classification, regression and semantic similarity tasks, and demonstrated that the proposed method reduced the size of models by at least 30% while maintaining less than 1% loss of accuracy.We compared the proposed method with state-of-the-art automated compression methods, and showed that it can provide better compression configurations than existing methods.
Depth-Aware and Semantic Guided Relational Attention Network for Visual Question Answering

Visual relationship understanding plays an indispensable role in grounded language tasks like visual question answering (VQA), which often requires precisely reasoning about relations among objects depicted in the given question. However, prior works generally suffer from the deficiencies as follows, (1) <b>spatial-relation inference ambiguity</b>, it is challenging to accurately estimate the distance of a pair of visual objects in 2D space if there is a visual-overlap between their 2D bounding-boxes, and (2) language-visual relational alignment missing, it is insufficient to generate a high-quality answer to the question if there is a lack of alignment in the language-visual relations of objects during fusion, even using a powerful fusion model like Transformer. To this end, we first model the spatial relation of a pair of objects in 3D space by augmenting the original 2D bounding-box with 1D depth information, and then propose a novel model named <b><u>D</u></b>epth-aware <b><u>S</u></b>emantic <b><u>G</u></b>uided Relational <b><u>A</u></b>ttention Network (DSGANet), to explicitly exploit the formed 3D spatial relations of objects in an intra-/inter-modality manner for precise relational alignment. Extensive experiments conducted on the benchmarks (VQA v2.0 and GQA) demonstrate DSGANet achieves competitive performance compared to pretrained and non-pretrained models, such as 72.7&#x0025; vs. 74.6&#x0025; based on the learned grid features on VQA v2.0.
Ontology Quality Evaluation Methodology

Lack of methodologies for ontology quality evaluation causes a challenge in producing good quality ontologies. Thus, we developed an iterative quality methodology to address this gap by analyzing the existing quality theories defined in ontology engineering, as well as, the theories in software engineering. Accordingly, this paper presents the developed methodology including how the other ontology quality theories get associated with it. Moreover, a use case in the agriculture domain has been demonstrated in order to provide an understanding of how the methodology can be applied in a real context. In the future, many experiments are expected to be carried out to fine-tune the methodology and to illustrate its usefulness.
Core-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

Localizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instance (i.e., sub-texts). In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue. CORE first leverages a vanilla relation block to model the relations among all text proposals (sub-texts of multiple text instances) and further enhances relational reasoning via instance-level sub-text discrimination in a contrastive manner. Such way naturally learns instance-aware representations of text proposals and thus facilitates scene text detection. We integrate the CORE module into a two-stage text detector of Mask R-CNN and devise our text detector CORE-Text. Extensive experiments on four benchmarks demonstrate the superiority of CORE-Text.
Extraction of editing processes for modifying a program to another purpose based on representation learning

The purpose of this paper is to clarify the editing process in creating computer programs. We do not record those processes of many people, but propose a way to find the process of editing a program from a set of submitted programs for a wide range of program tasks. In this way, we would like to clarify how to edit and modify a simple program to another more complex program. In the proposed method, we find the programmers&#x0027; editing process by training a representation learning model that converts the tree structure into a vector representation, and clustering with the distance between programs that are embedded in a vector space. As a result, we show a transition diagram of the editing process we extract from various submitted programs.