Reviewerly/exHarmony huggingface dataset:
Enhancing SRTM digital elevation models with deep-learning-based super-resolution image generation

Digital elevation models are responsible for providing altimetric information on a surface to be mapped.While global models of low and medium spatial resolution are available open source by several space agencies, the highresolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive.Here we address this limitation by the utilization of deep learning algorithms coupled with SISR techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs.The development of a GAN-based methodology enables the improvement of the initial spatial resolution of low-resolution images.A dataset with different pairs of digital elevation models was created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained.It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased.Furthermore, the visual analysis of the generated image against the high-and low-resolution ones showed a great similarity between the first two.
An approach for recommending relevant articles in news portal based on Doc2Vec

News portals are among the most popular websites, and their main goal is to bring the latest news to their readers. Also, it is important to provide relevant content to various types of readers. In this article, we propose an approach for recommending relevant articles on the news portal based on the content of a specific article. The proposed approach is based on Doc2Vec. The main steps of the proposed approach and training of the Doc2Vec model are described. The article also deals with text similarity problems and limitations of the Czech language in the context of recommending relevant articles. For experiment verification of our approach, random articles from the selected news portal were selected. For each article, our approach recommends the most relevant similar articles. Then, the relevant and irrelevant articles were marked. And finally, the ratio of proposed relevant articles for each random article was calculated. The experimental results show the accuracy and relevancy of the proposed approach.
Cosine Similarity based Few-Shot Video Classifier with Attention-based Aggregation

Meta learning algorithms for few-shot video recognition use complex, episodic training but they often fail to learn effective feature representations. In contrast, we propose a new and simpler few-shot video recognition method that does not use meta-learning, but its performance compares well with the best meta-learning proposals. Our new few-shot video classification pipeline consists of two distinct phases. In the pre-training phase, we learn a good video feature extraction network that generates a feature vector for each video. After a sparse sampling strategy selects frames from the video, we generate a video feature vector from the sampled frames. Our proposed video feature extractor network, which consists of an image feature extraction network followed by a new transformer encoder, is trained end-to-end by including a classifier head that uses cosine similarity layer instead of the traditional linear layer to classify a corpus of labeled video examples. Unlike prior work in meta learning, we do not use episodic training to learn the image feature vector. Also, unlike prior work that averages frame-level feature vectors into a single video feature vector, we combine individual frame-level feature vectors by using a new Transformer encoder that explicitly captures the key, temporal properties in the sequence of sampled frames. End-to-end training of the video feature extractor ensures that the proposed Transformer encoder captures important temporal properties in the video, while the cosine similarity layer explicitly reduces the intra-class variance of videos that belong to the same class. Next, in the few-shot adaptation phase, we use the learned video feature extractor to train a new video classifier by using the few available examples from novel classes. Results on SSV2-100 and Kinetics-100 benchmarks show that our proposed few-shot video classifier outperforms the meta-learning-based methods and achieves the best state-of-the-art accuracy. We also show that our method can easily discern between actions and their inverse (for example, picking something up vs. putting something down), while prior art, which averages image feature vectors, is unable to do so.
Automatic Control for Adaptive Lighting System for Underwater Photogrammetry Image Acquisition

Underwater photogrammetry has been used for multiple applications in research, analysis and entertainment activities. It allows the generation of three-dimensional (3D) representation of underwater ecosystems, which provides helpful information. In addition, according to technological developments, this technique has become an inexpensive and simple alternative to generate a 3D model. However, underwater photogrammetry has some challenges related to illumination in medium-depth and large-depth zones, and how it influences image quality. Therefore, this paper presents the design and testing of automatic control for an adaptive lighting system. The purpose is to generate adequate illumination over the area of interest and obtain images with optimal illumination, which could allow building better 3D models. From the results, we will analyze the control signal convergence, stabilization time, appearance of the real image and robustness to different values of the control parameters. Particularly, according to the control parameters selected and the considerations over the tests, a 22.5% and 15.3% illumination capacity is required for underwater and ground image acquisition, respectively.
Financial Fraud Detection Based on Ensemble Machine Learning

Machine learning plays an important role in credit card fraud detection. Credit card fraud detection is a huge challenge today, mainly because it has two problems – changes in fraud and normal behavior and the highly skewed dataset used which also means the ratio of fraud to normal transactions is very small. To solve the problem of unbalanced classification of data sets, this paper explores a combination of the Synthetic Minority Oversampling Technique (SMOTE) algorithm and random under-sampling for data enhancement, while using Principal Component Analysis (PCA) dimensionality reduction and removing extreme outliers in the influencing factors related to classification to reduce data redundancy. In this paper, we compare eight different machine learning algorithms and select three machine learning algorithms with better prediction results for integration learning, and find that ensemble learning can improve recall by 2%, effectively improving the accuracy of financial fraud detection.
Cognitive Engineering Modeling of Phishing

Using signal detection theory (SDT) and fuzzy SDT, the influence of familiarity with phishing and having a background in cybersecurity on phishing behavior was examined. The results from SDT analysis indicated that familiarity with phishing only accounted for 11% of the variance in sensitivity and 5% in bias. When examining the same using Fuzzy SDT, familiarity with phishing accounted for 6% of the variance in bias. Background in cybersecurity had a statistically significant effect on sensitivity and bias in classical SDT but only on bias in fuzzy SDT. A confusion matrix revealed that the percentage of successfully transmitted information from the stimuli to the judgements made by participants was only 26%. Participants most frequently identified requests for personal information in stimulus emails as phishing cues. Future research should continue to explore application of the different cognitive engineering models to phishing identification.
Dealing with Inconsistencies in $${ASPIC}^+$$

Inconsistencies in argumentation theory have been a recurrent topic in the literature. The $$ ASPIC ^+$$ being one of the most well know argumentation formalisms is frequently used to deal with inconsistencies. However, the existing approaches consider a limited version of $$ ASPIC ^+$$ . In our work, we managed to deal with inconsistencies in a very general scenario. To do this, we impose in $$ ASPIC ^+$$ some reasonable conditions to the relations between arguments to adjust how arguments interact with each other. As consequence, we avoid inconsistent arguments interfere with consistent arguments by neutralizing the inconsistent argument. Then we show that under simple conditions, $$ ASPIC ^+$$ preserves current results on the satisfaction of fundamental properties of consistency and logical closure.
Handle the Traces: Revisiting the Attack on ECDSA with EHNP

The Elliptic Curves Digital Signature Algorithm (ECDSA) is a standard public key signature protocol. It has become essential to the security of blockchain, digital currency, and many more Internet applications. Currently, it is still one of the most popular algorithms used for digital signing and SSL/TLS transport. Among the possible attacks on ECDSA, side-channel attack poses a serious threat against hardware and software implementations. In particular, Extended Hidden Number Problem can be used when ECDSA leaks side-channel information about the double-and-add chains. The problem is then converted to the shortest vector problem and solved with lattice algorithms. In this paper, we analyze the Extended Hidden Number Problem and present an improved EHNP lattice attack on ECDSA implementations that adopt leaky scalar multiplication. Our attack requires information of double-and-add chains or traces extracted from side-channel results. In addition to methods such as elimination and merging that have been introduced, we make further improvements according to the specific structure of the lattice basis. In fact, the specific property of EHNP allows us to find a sublattice that contains the target vector. We simulate the attack to the secp256k1, and the result shows that three signatures are enough to lead to a success rate greater than 0.8. When 4 or 5 traces are known, the success rate is close to 1. The new algorithm significantly improves the performance of attacks using EHNP methods.
Automatic Modulation Mode Recognition of Communication Signals Based on Complex-Valued Neural Network

Intelligent transportation systems (ITS) are designed to provide efficient and comfortable transportation. The development of ITS has brought new communication challenges, which require faster and more reliable transmission of information. In this paper, we investigate the modulation mode recognition method of communication signals based on a complex-valued neural network (CVNN). By combining a complex-valued convolutional neural network (CVCNN) with complex-valued long short-term memory (CVLSTM) and adding a residual learning unit, a modulation recognition model is established. The model can automatically learn from complex-valued signals without manual feature extraction and can recognize 11 modulation modes (3 analog modulation modes and 8 digital modulation modes) with a signal-to-noise ratio (SNR) between −20 dB and 18 dB. We design a Gaussian filter, and divide the signal to be identified into high SNR signal and low SNR signal through SNR estimation. The low SNR signal is Gaussian filtered before modulation recognition, so as to improve its modulation recognition accuracy. The algorithm proposed in this paper directly recognizes the modulation mode of the complex-valued signal without any preprocessing, and the recognition accuracy is better than the existing algorithms. This work is of great significance to the improvement of information transmission speed and the construction of ITS.
Feature selection approach to detect phishing website using machine learning algorithm

Phishing is a form of scam and social engineering attack that often used to steal user information including login credentials, social security number, and credit card number. For the past few years, the most common phishing activities were conducted using email phishing, Short Message Service (SMS) phishing, phone call phishing, and website phishing. According to Google's Transparency Report, an average of 46,000 new phishing websites are detected every week in 2020. To overcome the issues, machine learning algorithm is used to show the differences between the legitimate link and the phishing link. In this paper, we consider hyperlink-based approach to detect phishing website. We focused on hyperlink as it is the main contributor in phishing related to email, SMS, and website phishing. These hyperlink features selection approach used Correlation, Gain Ratio, Information Gain, OneR, ReliefR, and Symmetrical Uncertainty algorithm. The features selected are tested on machine learning algorithm using WEKA tools. The performance of features selection approach is shown in terms of accuracy, precision, true positive, and false positive. As a result, Information Gain is the best classification algorithm using top 5 features and top 20 features tested on Random Forest algorithm with 94 percent and 95.6 percent respectively. For 10 features ranked using ReliefR algorithm recorded its best tested on Random Forest algorithm with 94.6 percent.
An Empirical Study on the Fault-Inducing Effect of Functional Constructs in Python

Functional programming is expected to introduce several benefits to programs, including fewer side effects, easier parallelization, and even, in some circumstances, better comprehensibility. This paper investigates the extent to which the addition/modification of certain programming language constructs, i.e., lambdas, comprehensions, and map/filter/reduce, have higher chances to induce fixes than other changes. To this extent, we analyze the change history of 200 popular open-source programs written in Python, accounting for ≃ 630k commits and 6M changes. The study results show that changes to functional constructs have higher odds to induce fixes than other changes, and that some functional constructs, such as lambdas and comprehensions, have higher odds to induce fixes than others. Finally, a qualitative analysis revealed different scenarios in which functional constructs have been fixed. Results of this study may trigger better development support when using functional constructs during development, and prioritize code review and testing on certain areas of the source code.
iMask—An Artificial Intelligence Based Redaction Engine

The industrial use cases in Computer Vision and Artificial Intelligence have seen a lot of progression in the last few years. The advancements in the processing power of machines and the availability of data have its own share for many successful implementations in Computer vision technology. The tightened regulations and restrictions like GDPR on the usage of personal data of customers have increased the complexity of how we ship, store, and process these data in images. In these circumstances, the Personally Identifiable Information (PII) of customers like Date of Birth, SSN, PAN card#, Mobile number, Aadhaar number, etc. that are shared in image format as additional proofs for applications need to be safeguarded from identity theft and misuse. iMask is an Artificial Intelligence-based framework/tool that has a great potential to identify such Personally Identifiable Information from variety of multiple images automatically using Computer vision technology to either encrypt or redact them from original image. These redacted images can be shipped outside its controlled environment across geographies and use them in non-production like environment for any further backend processing. This automated approach to remove the personal information from images ensures no to very less manual intervention for masking them.
Lightweight architecture for face recognition system

In view of the current world market demand, “Face ID Recognition System” has developed to the highest level. In “Facial Recognition” field, the applications used are generally similar. Now the only thing we can change is how to adjust image resolution and how to speed up image processing. This paper adopts the existing rapid identification system architecture of “Paddle Paddle”. It is an open source deep learning framework platform developed by Baidu. It contains a lot about lightweight architecture and lightweight networking. The “PP-Sin Tu” architecture is one of the lightweight identifications in “Paddle Paddle”. It can use a few data to achieve the identification effect. The fast identification system and the accuracy of the identification effect, both performances are excellent, It’s just that this architecture is not used for face recognition. Therefore, this paper wants to create a system that can quickly identify and achieved real-time identity by using a few identity data.
Application of Virtual Simulation in Digital Exhibition and Performance

With the rapid development of science and technology, the combination of art and technology has given new possibilities for the artistic creation and presentation of digital exhibitions and performances. In the virtual reality space, which is on-site, interactive and three-dimensional, it shows the symbiosis and coexistence of people and technology, art and technology in the digital era. Created a new audio-visual experience and performance experience. Virtual simulation enhances users’ multi-sensory experience by creating and simulating virtual scenes in which people are immersed. Through the addition of a variety of interactive devices, the whole creative process reflects the basic characteristics of immersion, interactivity and simulation. Whether exhibition space, stage design or image design, with the integration of virtual simulation, there are new ways of presentation. So that the staff can preview the on-site effect in advance, which greatly shortens the cycle of site construction and image creation, so as to improve the creative efficiency and effect of digital exhibitions and performances.
Object Detection and Tracking for Autonomous Vehicle using AI in CARLA

The main objective of autonomous driving is to make vehicle understand about its surrounding environment. CARLA simulator provides great simulated platform for testing autonomous vehicle with different AI techniques. Initially, short-est path between user defined source and destination is found among all the possible paths using A star algorithm. Further, instance segmentation is used to conduct road detection to deter-mine drivable area. For this scenario, Detectron2 is implemented with achieved accuracy of 98.3%. To locate obstacles in its way, object detection and classification is performed using YOLOv4. The proposed YOLOv4 model has achieved mAP accuracy of 92.6%. Finally, tracking of moving cars is accomplished which employs DeepSORT algorithm. Therefore, proposed research work has a unique capability for conducting important tasks of autonomous vehicle such as finding shortest path, road detection, object detection, classification and tracking in single module.
UAV Fast Signal Detection Algorithm with Convolutional Neural Network

As UAVs tend to be miniaturized and invisible, the successful recognition rate of traditional UAV identification methods such as time-frequency analysis, video surveillance, and radio interference is getting lower and lower. Aiming at this problem, Firstly, the traditional time-frequency analysis method is used to preprocess the data, obtain the time-frequency spectrum of the data, and construct the training set of convolutional neural network. Then build VGG network and residual network model based on maximum pooling, and use sample training set to train the sample model. Finally, the time-frequency spectrum of the obtained remote control signal is input to the learning model, and the classification and recognition results can be output.Finally, based on the Y550 software radio platform, three UAVs including parrot, DJ-m100 and DJ-a3 were measured. The experimental results show that when the learning rate is 0.1, the recognition rate of the method proposed in this paper can reach more than 97%. Under different learning rates, the recognition rate is still above 95%, which is greatly improved compared to the traditional time-frequency analysis and recognition method, and has a strong application prospect.
Improving the Bag-of-words Model Using the Hubness Phenomenon for Skeleton-based Action Recognition

This paper proposes a new bag-of-words (BoW) approach for human skeletal action recognition based on the hubness phenomenon. Many previous action recognition methods adopt a BoW method that ignores the high dimensions of the feature space. Hence, this paper presents a visual vocabulary construction approach for action recognition. The action feature descriptors including the spatial-temporal joint information are first extracted from action sequences. Various factors are considered in the spatial descriptors, including the three-dimensional coordinates of the joints, angles between body segments, and direction, as well as elevation angles. The feature vectors with higher hubness scores are then selected as candidates. Finally, the visual words are selected from the candidates using the maximin method. We evaluated the effectiveness of the proposed method on two public datasets, the CAD-60 and UTKinect datasets. The experimental results show that the proposed method performs better than several state-of-the-art algorithms.
Innovation and Prospect of Digital Scene Setting in the Context of Metaverse

Based on the analysis of meta-scene concept, this paper analyzed the needs and changes of digital scene setting from the perspective of the metaverse, and clarified its innovative development and future trends. Through the concept of metaverse, the development status of digital scene setting, the needs of future digital scene setting, technological innovation and prospect analysis, and the possibility and new challenges of virtual world were sorted out. Building the metaverse through digital scene setting can explore the innovative direction of future digital scene setting. The digital scene is set on the basis of the theory, aesthetics, methods and principles of the traditional scene, and the continuous innovation of digital technology can be widely used in various application environments, creating an opportunity for the rapid development of the metaverse concept.
Context-Aware Support of Dexterity Skills in Cross-Reality Environments

Within our work, we apply context-awareness to determine how AR/VR technology should adapt instructions based on the context to suit user needs. We focus on situations where the user must carry out a complex manual activity that requires additional information to be present during the activity to achieve the desired result. To this end, the emphasis is on activities that require fine-motor skills and in-depth expertise and training, for which XR is a powerful tool to support and guide users performing these tasks. The contexts we detect include user intentions, environmental conditions, and activity progressions. Our work builds on these contexts with the main focus on determining how XR should adapt for the end-user from a usability perspective. The feedback we request from ISMAR consists of input in detection, usability, and simulation categories, together with how to balance these categories to create real-time and user-friendly systems. The next steps of our work will consider how to content should adjust based on the cognitive load, activity space, and environmental conditions.