Reviewerly/exHarmony huggingface dataset:
FBPB-SE: A Forward and Backward Private Blockchain-based Searchable Encryption with Fuzzy Multi-keyword

Searching directly on encrypted data is a fundamental problem with many real-world applications, including cloud storage retrieval and image recognition. Most traditional searchable encryption schemes assume that cloud servers are honest and curious. However, in real applications, cloud servers can also be malicious. We establish a trust system through blockchain technology to enable fair transactions between several untrusted entities. In this paper, we design a reliable smart contract to perform ciphertext retrieval instead of a central cloud server to ensure the correctness of retrieval results and fairness of transactions. We further utilize Word2Vec to train the semantic model to transfer each keyword to the corresponding vector and map synonyms to the approximate vector to support synonym search. We propose a blockchain-based forward and backward private fuzzy multi-keyword searchable encryption (FBPB-SE). In terms of security, we show that FBBP-SE can provide both forward and backward security proofs. We experimentally show the feasibility and efficiency of the scheme.
The System of Personalized Learning Resource Recommendation and Experimental Teaching Based on Collaborative Filtering

“Electronic system design” is an important course closely related to electronic design competition. A wide range of knowledge modules are involved in the class, but some modules are not studied by students. Due to the constraints of classroom time, they need to preview independently before class under the guidance of teachers. We design a recommendation system based on improved collaborative filtering algorithm, so that students can learn by themselves according to the learning resources pushed by teachers. To increase the accuracy of collaborative filtering algorithm, the user's attribute similarity is combined with the traditional collaborative filtering recommendation algorithm to improve the cold start problem and data sparsity of the algorithm. Then we build an experimental teaching platform of "Electronic System Design" curriculum to achieve automatic recommendation of experimental learning resources under teachers’ guidance.
LigNet: Lightweight Hand Tracking for Edge Intelligence

In recent years, as the rise of edge intelligence hand tracking applications have emerged in various IoT systems and applications, such as human-computer interaction, sign language translation, and motion rehabilitation, etc. Due to the hardware and energy limitation of embedded IoT devices, hand tracking is difficult to achieve real-time continuous operation. To achieve this goal, in this paper we propose a set of optimization methods such as space-based error loss and Gaussian process constraints to design a lightweight hand tracking model with low storage overhead and high recognition efficiency. We design and implement the system, and the effectiveness of our proposed methods is verified in the evaluation comparing with the state-of-the-art.
Toward Predictive Modeling of Solar Power Generation for Multiple Power Plants

Solar power is the most widely used renewable energy source, which reduces pollution consequences from using conventional fossil fuels. However, supplying stable power from solar power generation remains challenging because it is difficult to forecast power generation. Accurate prediction of solar power generation would allow effective control of the amount of electricity stored in batteries, leading in a stable supply of electricity. Although the number of power plants is increasing, building a solar power prediction model for a newly constructed power plant usually requires collecting a new training dataset for the new power plant, which takes time to collect a sufficient amount of data. This paper aims to develop a highly accurate solar power prediction model for multiple power plants available for both new and existing power plants. The proposed method trains the model on existing multiple power plants to generate a general prediction model, and then uses it for a new power plant while waiting for the data to be collected. In addition, the proposed method tunes the general prediction model on the newly collected dataset and improves the accuracy for the new power plant. We evaluated the proposed method on 55 power plants in Japan with the dataset collected for two and a half years. As a result, the pre-trained models of our proposed method significantly reduces the average RMSE of the baseline method by 73.19%. This indicates that the model can generalize over multiple power plants, and training using datasets from other power plants is effective in reducing the RMSE. Fine-tuning the pre-trained model further reduces the RMSE by 8.12%.
Design Study for a Virtual Work Station for Aerodrome Air Traffic Service Officers

Driven by the need for cost efficiency and a need for increasing the air traffic service (ATS) level the “Remote Tower” concept was developed and implemented the last years. By a relayed video stream from the airport to a remote location it allows the provision of ATS from a centralized Remote Tower Center, where valuable resources can be pooled and used more efficiently. The display of the video streams is usually realized by large panorama monitors but for cost and space reasons could also be performed via virtual reality head worn glasses for certain use cases. This design study investigates possible interface concepts for such virtual reality environments. The aim was to design a complete air control traffic (ATC) tower work station for a virtual reality environment. Previous research has primely relied on representation of the video stream and first interaction concepts in a virtual environment. By means of a contextual interview with an ATS officer and a subsequent usage context analysis, requirements for the system were defined. As a result, a basic design structure of an ATC Tower work station in VR was developed in form of mock ups. The design and its interaction concept were tested in a user survey with very positive feedback from ATS officers. In further studies an implementation of the prototypes in VR will be sought and further evaluated.
Towards an Internet of Things application for the prognosis of Coronary Artery Disease using Machine Learning and Fuzzy Logic

Internet of Things (IoT) is a field of growing interest and reputation. It is a result of the advances of computer science and electronics, which enable the researchers and industries to design and create devices that connect with each other. In this work, we propose a novel application design for the prognosis of Coronary Artery Disease (CAD), which can exploit the capabilities of IoT devices for interacting with the user to gather data. We focus our work on the decision framework of the pipeline, which is implemented by Artificial Neural Networks, Decision Trees, and Fuzzy Cognitive Maps. The inner architecture of the model is powered by the real-time patient data. Its accuracy may be improved by taking advantage of heart rate signals, measured from wearable devices. The user is informed of the risk in several ways and is the one to monitor the functionality of the application through a graphical user interface (GUI). The simulation results demonstrate the effective functionality of the decision-making framework, since it manages to aggregate conflicting predictions and improve the accuracy. The proposed framework could be utilized for evaluation tests with the appropriate supplement of data.
Bitwise Signature Comparison: Enabling More Efficient Similarity Estimation

Estimating the similarity of sets of data is a common operation in computing. Minhash is widely used to estimate similarity by computing a signature for each set and then comparing their signatures. Therefore, signature comparison is an important part of similarity estimation. To make the comparison efficient, the size of the signature components is commonly set to the word size of the processor or to one half or one fourth of it. This enables efficient data manipulation and comparison but is not optimal in terms of storage. For example, 48-bit signatures may be more than enough in many applications but since that size cannot be easily manipulated by most processors, 64-bit signatures are used. This implies a 33.3% memory overhead. In this paper, Bitwise Signature Comparison (BSC), a method that enables the efficient comparison of signature components of any bitwidth is presented and evaluated. The results show that BSC achieves a similar speed to that of the traditional comparison implementation regardless of the size of the signature components. This enables the use of any signature component size enabling better trade-offs in the implementation of similarity estimation sketches.
Topological modelling of comb graph in terms of order

Many applications of Mathematics like topology and graph theory are seen in Engineering and Material Science. In this paper, we have established the results showcasing association of graph theory and topology. The order of elements of a topological space with topology as incidence topology generated by Comb graph has been presented in this paper. Also, the order of edges with respect to respective incidence topology of this special graph have been studied. Various results based on order of these nodes and edges with respect to the incidence topology has been established and generalized for the comb graph and supporting examples are illustrated along with the results.
Research and Implementation of Unsupervised Image Stitching Algorithm

In today’s era, the field of image stitching is developing rapidly, and more and more image stitching has been proposed one after another. The effect of image stitching often determines the development of many downstream tasks, and the quality of images often plays a decisive role in the final stitching effect. However, the traditional feature-based image stitching technology depends on the quality of feature detection to a great extent and usually cannot stitching images with few features or low resolution. The lack of labeled image stitching data,it makes supervised methods unreliable. In order to solve the above problems, we proposed a new image stitching framework, the framework is composed of three parts, respectively is the transformation of homography, the stitching of structural and the optimization of content, the problems of large parallax, few features, low resolution and inconsistent color brightness in the process of image stitching are solved respectively. Finally, comparing the image stitching in this paper with SIFT and deep learning, it is proved that our method still has the best image mosaic effect in different scenes.
Credit Card Fraud Detection: Challenges and Techniques

Credit card usage has risen dramatically as a result of rapid advancements in electronic commerce and the unexpected circumstance of COVID. With credit cards becoming the most popular payment method for both offline and online transactions, the number of cases of fraud associated with them is rapidly increasing. In case of online fraud, it is not necessary for the perpetrators to be present at the scene of crime. The fraudulent activities can be accomplished by them in the seclusion of their homes through a multitude of methods for disguising their identities. VPNs are one way to obscure one's identity, as is routing communication through any Tor network for the victim, making it difficult to track back the culprit.
Web Application Based Text Encryption

In today’s world we are using cryptography everywhere. Cryptography ensures that the information is secured and can only be accessed by the sender and the receiver of the message. We use it to securely send passwords over vast networks for online purchases. During recent time, we have seen a rise in the number of cyberattacks. Our data is becoming less and less secure. Also, the encryption and decryption process has become really cumbersome which has made it really difficult for it to be used in daily life. The aim of our paper is to protect the privacy of users. We have proposed a novel RKJ algorithm which is based on keyless encryption. In this algorithm the message is converted to binary and divided into groups of 32 bits. Every group is used as its own key after round shifting the bits by a random amount. This is done for a number of times (rounds). Apart from the proposed algorithms, we have also provided the users to create an id and use our proposed web application to store important data in the form of notes. We have used the blowfish algorithm to store these passwords in our database. The proposed RKJ algorithm has been used to encrypt these notes. To read the notes we would have to use login and password. Finally, we have hosted proposed web application on Heroku.
Technologies of the information transformation of the education during the COVID-19 pandemic

To date, the information technology is an integral part of te aching students of a technical university. In a pandemic condition, it gives the opportunity to build an effective educational process due to constant communication between teachers and students in a general chat, constant access to theoretical and practical materials and to improve your results (retake the assignment, test). Kazan National Research Technological University teaches students using distance technologies. LMS MOODLE is used as a learning management system at Kazan National research Technological University. The alternative systems are also Google Meet, Zoom, MS Teams. A variety of learning platforms at the same time allows to satisfy all possible requests of students and teachers and organizes online and distance learning at a higher level.
Human Gender Detection from Facial Image using Global and Local Feature

Identifying gender is frequently difficult when physical characteristics show different facts, which can lead to public controversy. This paper describes a method for determining a person's gender solely based on physical characteristics, particularly facial shape and features. The method used combines global and local facial features. Face landmark detection is a global feature that displays a face's shape or contour in 2D. While the Histogram of Oriented Gradient (HOG) is a local feature that shows the detailed shape of facial components like eyebrows, eyes, nose, and mouth. The combined information from global and local features is fed into the Support Vector Machine (SVM) and classified as male or female. Based on the experimental results, our system was able to identify gender with an accuracy of 80% with an average F-measure is 0.8.
Towards Well-Being Management with Automated Qualitative Data Analysis

This paper reports on using qualitative data analysis to understand aspects of the well-being of dwellers in underserved communities, by applying machine learning algorithms to identify specific themes from unstructured interview data. The work involved data translation, transcription, pre-processing as well as developing Word2Vec and FastText algorithms and ultimately a combined analysis engine. The reported experiments are conducted on field data captured from communities in India, hence offering a unique opportunity to examine automated context-based qualitative data analysis. The approach is proven feasible despite the dominant limitations on technology infrastructure and community awareness. The machine learning results identify themes from the interview data within minutes as opposed to hours of manual investigations through conventional qualitative analysis techniques. The outcomes from the analysis engine can be used for creating a grounded theory for further studies, hence facilitating an evidence-based approach to the evaluation of underserved communities.
DesignerGAN: Sketch Your Own Photo

Person image generation is a challenging problem due to the complexity of human body structure and the richness of clothing texture. Recent works have made great progress on pose transfer by using keypoints, but cannot characterize the personalized shape attributes. Hence, they have limited person image editing ability, especially in respect of shape editing. In this paper, we propose to use sketches as the expression of the target image, which can not only represent the pose and shape simultaneously but is also flexible to manipulate at the semantic level. We propose DesignerGAN, a novel two-stage model for pose transfer and shape-related attributes editing. The first stage predicts the target semantic parsing using the target sketch and obtains parsing feature maps. In the second stage, with the parsing feature maps and the scaled target sketch, we devise a domain-matching spatially-adaptive normalization method to guide target image generation in multi-level. Qualitative and quantitative comparison results demonstrate our method’s superiority over state-of-the-arts on pose transfer. Besides, we achieve flexible person image editing through simple hand-drawings on sketches.
Deep Learning for Detecting Tilt Angle and Orientation of Photovoltaic Panels on Satellite Imagery

The goal of this research is to accomplish two tasks that increase the accuracy of the process of estimating solar power generation in real time for different regions around the world. Specifically, we explain a method for detecting the tilt angle and installation orientation of photovoltaic panels on rooftops using satellite imagery only. The method for detecting tilt angles is based on their dependence on the roof shapes. As for the architectures used in this research, we chose MobileNetV2 and Yolov4 since both require only medium hardware resources, without the need for graphics processing units (GPUs). Since it was difficult to find a suitable data set, we had to create our own, which, although not large, was proven to be sufficient to confirm the capabilities of our method. As for the final results, our approach provides good predictions for the tilt angle and the orientation of photovoltaic panels based on a data set of images from six different locations in Europe collected via Google Maps.
A Cross-Area Knowledge Learning Method for Status Monitoring of Transportation-Oriented Energy Interconnected System

In the status monitoring study of transportation-oriented energy interconnected system, the labor-intensive nature of collecting enough labeled samples tremendously limits the actual applications of deep learning-based methods. To address it, a cross-area knowledge learning method with domain-invariant information of system status is proposed in this paper. First, considering the spatial-temporal correlation change of transportation-oriented energy interconnected system, convolutional neural network with global feature enhancement, as feature extraction module, is proposed to extract and separate features of different system status. Second, to improve the learning ability of the proposed model on the condition of unlabeled samples, sample classify module including two classifiers is proposed to make feature extraction module capture the intrinsic similarity features between different systems. Third, for improving the classification accuracy of status monitoring, virtual adversarial training item is added in the loss function of the proposed method to reduce the disturbance influence. The proposed method is evaluated by three different datasets, and ten typical methods are selected for comparison. The comprehensive results demonstrate effectiveness and superiority of the proposed method for status monitoring of transportation-oriented energy interconnected system with unlabeled samples.
LRDE:Long-Range Dependency and Salient Feature Enhancement for Co-SOD

Different from other salient object detection (SOD) for the task of extracting salient objects in a image, collaborative saliency object detection (Co-SOD) targets a set of related image groups, and uses algorithms to find and locate the common collaborative salient objects in related image groups. The current SOD has achieved remarkable results, but most of the previous researches focus on how to obtain the consistency between images, while the contextual semantic information of images and the features of salient objects are less utilized. To solve these problems, we propose a novel network to capture long-range dependencies on images and enhance (LRDE) the representation of salient objects. Specifically, In LRDE, we capture the long-range dependencies by the Non-local module to obtain contextual semantic information. Furthermore, in order to highlight the features of salient objects and enhance the representation of salient objects, we propose the Coordinate Attention module, which embeds the location-attention channel attention and encodes the feature maps as orientation-aware and position-aware attention maps, and It is applied complementary to the input feature map, thereby enhancing the representation of salient objects. Experiments show that the method outperforms twelve improved methods on four publicly available datasets.
Using the Field Survey Support Platform for Drainage Canal Obstruction Problem Management

This paper presents the field survey support platform for drainage canal obstruction problem management. The platform is developed to gather the surveyed information about drainage canal obstruction problems, including the specialist's recommendation solution. Because of the complexity and more detail of the obstruction problems, the platform is designed by applying the user interface design guideline and uses the chatbot technology to support the user in successfully performing the obstruction problem management planning. The system usability score is used to evaluate the platform. The average index score is 71.75, which means the platform usability is accepted. This platform has been practically applied as the information system to support flooding management planning for local government in Chiang Mai province, Thailand.
Individual Weighting of Unlabeled Data Points via Confidence-Awareness in Semi-Supervised Learning

In existing semi-supervised learning (SSL) approaches, the contributions of labeled and unlabeled data points to the training objective are expressed as two separated terms for the respective sets. A single weight specifies how much the unlabeled samples contribute to the overall loss and is carefully scaled in the course of the training. In this work, we propose a SSL framework in which the contribution of each unlabeled sample is individually scaled based on the confidence generated by a teacher model. We demonstrate that our scaling mechanism enables the student model to efficiently learn from heavily interpolated and augmented samples and achieve state-of-the-art performance.
Mapping the History

This article presents an intermedia design of the East Sea Frontier project, part of a design project commissioned by Xiangshan County for its cultural tourism and travel applications in the coastal area of Zhejiang province, east China. The concept is inspired by the coastal defense history of Changguo Wei, an ancient town in the county in the Ming and Qing dynasty. The East Sea Frontier project consists of four interactive installations: Light Belt, Virtual Armor, Mapping Ancient Xiangshan County, and an indoor intermedia exhibition, Meta Space Tour of Changguo Wei. Each part allows tourists to experience an aspect of the local history by applying visual and historical elements from relevant maps and paintings. The design integrated storytelling and interaction. The narration simulated the historical scene of a sea battle; the installations applied Touch Designer to realize the interaction between viewers and the design. The implementation of interactive installations and immersive experience design gives another positive solution as a tourism strategy aims to attract a broader class of visitors. Furthermore, this project delivers a cultural identity of the East Sea Frontier that embodies the intangible value of the area and would become a benchmark.