Reviewerly/exHarmony huggingface dataset:
Lattice-Based Fault Attacks on Deterministic Signature Schemes of ECDSA and EdDSA

The deterministic ECDSA and EdDSA signature schemes have found plenty of applications since their publication, e.g., block chain and Internet of Thing, and have been stated in RFC 6979 and RFC 8032 by IETF respectively. Their theoretical security can be guaranteed within certain well-defined models, and since no randomness is required by the algorithms anymore their practical risks from the flaw of random number generators are mitigated. However, the situation is not really optimistic, since it has been gradually found that delicately designed fault attacks can threaten the practical security of the schemes. In this paper, based on the random fault models of intermediate values during signature generation, we propose a lattice-based fault analysis method to the deterministic ECDSA and EdDSA algorithms. By virtue of the algebraic structures of the deterministic algorithms, we show that, when providing with some faulty signatures and an associated correct signature of the same input message, some instances of SVP or CVP problems in some lattice can be constructed to recover the signing key. The allowed faulty bits in the method are close to the size of the signing key, and obviously bigger than that allowed by the existing differential fault attacks. In addition, the lattice-based approach supports more alternative targets of fault injection, which further improves its applicability when comparing with the existing approaches. We perform some experiments to demonstrate the effectiveness of the key recovery method. In particular, for deterministic ECDSA/EdDSA algorithm with 256-bit signing key, the key can be recovered efficiently with significant probability even if the targets are affected by 250/247 faulty bits. However, this is impractical for the existing enumerating approaches.
Forecasting the 10.7-cm Solar Radio Flux Using Deep CNN-LSTM Neural Networks

Predicting the time series of 10.7-cm solar radio flux is a challenging task because of its daily variability. This paper proposed a non-linear method, a convolutional and recurrent neural network combined model to achieve end-to-end F10.7 forecasts. The network consists of a one-dimensional convolutional neural network and a long short-term memory network. The CNN network extracted features from F10.7 original data, then trained the feature signals in the long short-term memory network, and outputted the predicted values. The F10.7 daily data during 2003–2014 are used for the testing set. The mean absolute percentage error values of approximately 2.04%, 2.78%, and 4.66% for 1-day, 3-day, and 7-day forecasts, respectively. The statistical results of evaluating the root mean square error, spearman correlation coefficient shows a superior effect as a whole for the 1–27 days forecast, compared with the ordinary single neural network and combination models.
Collaborative Data Use between Private and Public Stakeholders—A Regional Case Study

Research and development are facilitated by sharing knowledge bases, and the innovation process benefits from collaborative efforts that involve the collective utilization of data. Until now, most companies and organizations have produced and collected various types of data, and stored them in data silos that still have to be integrated with one another in order to enable knowledge creation. For this to happen, both public and private actors must adopt a flexible approach to achieve the necessary transition to break data silos and create collaborative data sharing between data producers and users. In this paper, we investigate several factors influencing cooperative data usage and explore the challenges posed by the participation in cross-organizational data ecosystems by performing an interview study among stakeholders from private and public organizations in the context of the project IDE@S, which aims at fostering the cooperation in data science in the Austrian federal state of Styria. We highlight technological and organizational requirements of data infrastructure, expertise, and practises towards collaborative data usage.
Evolutionary Weighted Broad Learning and Its Application to Fault Diagnosis in Self-Organizing Cellular Networks

As a novel neural network-based learning framework, a broad learning system (BLS) has attracted much attention due to its excellent performance on regression and balanced classification problems. However, it is found to be unsuitable for imbalanced data classification problems because it treats each class in an imbalanced dataset equally. To address this issue, this work proposes a weighted BLS (WBLS) in which the weight assigned to each class depends on the number of samples in it. In order to further boost its classification performance, an improved differential evolution algorithm is proposed to automatically optimize its parameters, including the ones in BLS and newly generated weights. We first optimize the parameters with a training dataset, and then apply them to WBLS on a test dataset. The experiments on 20 imbalanced classification problems have shown that our proposed method can achieve higher classification accuracy than the other methods in terms of several widely used performance metrics. Finally, it is applied to fault diagnosis in self-organizing cellular networks to further show its applicability to industrial application problems.
Named entity recognition in material field based on Bert-BILSTM-Attention-CRF

There are many data from published scientific literature in the professional database of titanium alloy corrosion. The current problem is that the sample size of annotated documents is insufficient, which leads to insufficient knowledge digging in the material field, and the low accuracy of named entity recognition. Therefore, this paper proposes a domain-adaptive named entity recognition method based on attention mechanism. First, a bidirectional long and short-term memory conditional random field (BERT-BiLSTM-CRF) named entity recognition model based on the BERT (bidirectional encoder representations from transformers) pre-trained language model is constructed on the general domain data set. The network is improved, and an attention network is added between the BiLSTM and CRF layers to assist CRF in the serialization of named entities; the results of comparative experiments show that the model can fully extract the feature information of the material field and improve the accuracy of sequence annotation, and then improve the effect of named entity recognition in the field of materials science.
Native Mobile Applications UI Code Conversion

With the widespread use of mobile applications in daily life, it has become crucial for software companies to develop the applications for the most popular platforms like Android and iOS. Using a native development is time consuming and costly. Cross-platform mobile development like Xamarin and React native emerged as a solution to the mentioned problem of native development for the time and cost. Meanwhile it requires the developers to learn a new language. Other tools are converting the mobile apps of specific platform to the corresponding platform, but most of them still lack the mobile application user interface code conversion. In this paper, we present a native mobile application user interface code converter based on trans-compilation approach. Our experiment shows that the proposed trans-compiler user interface code converter has an accuracy of 88.9% when tested using the BLEU evaluation tool on 5 applications.
Social Robots in Education: Conceptual Overview and Case Study of Use

Social robots are increasingly being used in education. They can take over various roles including teaching assistant, tutor, and novice. This chapter aims to provide a conceptual overview of the phenomenon. A classification of social robots is outlined; the criteria are visual appearance, social capabilities, and autonomy and intelligence. The majority of robots used in education are humanoid; Nao from SoftBank Robotics is a quasi-standard type. An important social capability is empathy; a model illustrating how a robot can show empathy is discussed. A taxonomy is presented in order to capture the various degrees of robot autonomy. To achieve autonomy, artificial intelligence is necessary. This chapter advocates for a symbiotic design approach where tasks are collaboratively carried out by the teacher and the social robot, utilizing the complementary strength of both parties. This may be in line with the concept of hybrid intelligence. The ethical aspects of social robot use are explored, including privacy, control, responsibility, and the role of teachers. Moreover, the acceptance of social robots is discussed. Overall, attitudes toward social robots seem to be positive; however, there are also contrary findings. Finally, results are presented from a technology acceptance study with a sample of N = 462 university students from the social sciences. The chapter closes with suggestions for further research.
CDANet: Contextual Detail-Aware Network for High-Spatial-Resolution Remote-Sensing Imagery Shadow Detection

Shadow detection automatically marks shadow pixels in high-spatial-resolution (HSR) imagery with specific categories based on meaningful colorific features. Accurate shadow mapping is crucial in interpreting images and recovering radiometric information. Recent studies have demonstrated the superiority of deep learning in very-high-resolution satellite imagery shadow detection. Previous methods usually overlap convolutional layers but cause the loss of spatial information. In addition, the scale and shape of shadows vary, and the small and irregular shadows are challenging to detect. In addition, the unbalanced distribution of the foreground and the background causes the common binary cross-entropy loss function to be biased, which seriously affects model training. A contextual detail-aware network (CDANet), a novel framework for extracting accurate and complete shadows, is proposed for shadow detection to remedy these issues. In CDANet, a double branch module is embedded in the encoder–decoder structure to effectively alleviate low-level local information loss during convolution. The contextual semantic fusion connection with the residual dilation module is proposed to provide multiscale contextual information of diverse shadows. A hybrid loss function is designed to retain the detailed information of the tiny shadows, which per-pixel calculates the distribution of shadows and improves the robustness of the model. The performance of the proposed method is validated on two distinct shadow detection datasets, and the proposed CDANet reveals higher portability and robustness than other methods.
Activity Detection using 2D LIDAR for Healthcare and Monitoring

Monitoring elderly people living alone is of the utmost importance given the amount of risk they are exposed to. Being aware of the activities of the elderly person in real time could help prevent/detect dangerous event that might occur such as falling. In this paper, we propose a method for activity detection using a 2D LIght Detection and Ranging (LIDAR) and deep learning. Unlike conventional work, where an activity refers to moving from one position to another, we use the term &#x201C;activity&#x201D; to refer to a set of movements including walking, standing, falling and sitting. Not only does our approach detect these activities, but it also identifies a given person from his gait, and identifies unsteady gait (i.e., when he is about to fall or feeling dizzy). Throughout our experiments, we show that the proposed approach could reach an accuracy equal to 92.3&#x0025; and 91.3&#x0025; in activity and unsteady gait detection, respectively. It is also capable of identifying up to 3 people&#x0027;s gait with an accuracy equal to 92.4&#x0025; using 10 seconds of walking data.
A Trigger Exploration Method for Backdoor Attacks on Deep Learning-Based Traffic Control Systems

Deep learning methods are in the forefront of techniques used to perform complex controls in autonomous vehicles (AVs). Such methods are vulnerable to nuanced types of adversarial attacks, and can have sever safety implications. Specifically, backdoors are an emerging kind of adversarial attacks on deep neural networks (DNNs), where a secret backdoor is injected into the DNNs by an attacker and activated in the presence of well-designed triggers, which necessitate a systematic exploration to enable the study of effective defenses. In this paper, we learn an adversarial distribution for trigger samples by reinforcement learning with the objective that the difference between the adversarial and genuine distributions are minimized. This bypasses many detection algorithms that are designed based on the difference between the adversarial and genuine input samples. Specifically, the difference between two distributions are evaluated by the Jensen-Shannon (JS)-divergence. The adversarial samples generated by the learned adversarial distribution are used for manipulating benign models in two complex traffic control systems. Our results show that our method renders the backdoor attack stealthy overriding the benign control objectives and potentially causing vehicle collisions.
MLCHECK– Property-Driven Testing of Machine Learning Classifiers

An increasing amount of software with machine learning components is being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize in a single type of machine learning model (e.g., neural networks). We propose the property-driven testing of machine learning models. Our approach MLCHECK encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. The test case generation employs an elaborate verification method for a systematic, property-dependent construction of test suites, without additional user-supplied generator functions. We evaluate MLCHECK using requirements and data sets from three different application areas (software discrimination, learning on knowledge graphs and security). Our evaluation shows that in addition to its generality, MLCHECK can outperform specialised testing approaches while having a comparable runtime.
Personalized Recommendation of Learning Resources Based on Knowledge Graph

Personalized learning resources recommendation is one of the crucial problems under the background of internet plus education. The current personalized learning resources recommendation methods are mostly based on the learner's basic information and learning behavior, without considering the logical relation between the learning resources. With above consideration, we use knowledge graph to construct the class model. And then we propose an efficient personalized recommendation algorithm based on the interest similarity and knowledge connection degree and design a related recommendation system. Taking the discrete mathematics as an instance, we verify the correctness and effectiveness of our recommendation algorithm by experiment.
A self-alignment XOR coding strategy resistant to global illumination

The code-based method is a popular technique in fringe projection profilometry. However, the lens defocusing and ambient noise can cause the jump errors in the absolute phase. Furthermore, harsh measuring environments, such as global illumination, might aggravate the errors further. In this paper, a phase-based XOR coding strategy (PBXOR) is designed to settle the above problems simultaneously without additional patterns. Especially, the high-frequency characteristic of the wrapped phase is converted into a complementary pattern. By adopting a new coding method, the common low-frequency binary code is embedded with high-frequency information, which can suppress the errors caused by global illumination. Moreover, by utilizing the complementary pattern to optimize the decoding process, the defocusing errors are transferred from the edges of the coded patterns to their monochromatic regions, which are easy to eliminate. The results of the contrast experiments prove that PBXOR can effectively settle defocusing and global illumination. • The frequencies of coded patterns are increased to suppress global illumination. • The frequencies of the patterns are reduced in decoding to suppress the defocusing. • No additional auxiliary patterns are required. • The high-frequency auxiliary pattern is extracted from the wrapped phase.
Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs

Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture, engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However, research on KG updates in the industry is scarce, with most current research focusing on text-based KG updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the potential of computer vision technology for explicit relationship extraction in KG updates is yet to be explored. This paper combines zero-shot human-object interaction detection techniques with general KGs to propose a novel framework called Image2Triplets that can extract explicit visual relationships from images to update the construction activity KG. Comprehensive experiments on the images of architectural decoration processes have been performed to validate the proposed framework. The results and insights will contribute new knowledge and evidence to human-object interaction detection, KG update and construction informatics from the theoretical perspective.
Comparison of Machine Learning Classifiers for Breast Cancer Diagnosis

Cancer is a fatal disease and it has several types in the world. Among all the types of cancer, Breast Cancer is a frequent type of cancer in women. In 2020, there were 2.3 million women diagnosed with breast cancer and 6,85000 deaths globally. Therefore, early prediction of breast cancer is important for proper treatment and healthy life. Machine learning approaches contribute a lot to cancer disease diagnosis as well as other fatal diseases. In this paper, Breast Cancer Wisconsin dataset is considered with different machine learning techniques of cancer classification. Techniques are evaluating with evaluation matrices mainly included Accuracy, Precision, Recall, F1 Score, and AUC are considered to evaluate the robustness of the machine learning model. We have compared different machine learning methods with each other on the behalf of evaluation metrics and we have achieved 96.5&#x0025; accuracy with the &#x2019;Gradient Boosted Tree&#x2019; Machine learning classifier.
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Generative adversarial networks (GAN) training is subject to problems including mode collapse, gradient vanishing, and instability. Although many different losses have been proposed to alleviate these shortcomings, they heavily rely on a fixed-value function with limited expressive power in terms of robustness, whereby failing to perform consistently over multiple data sets. To solve this problem, we propose a parametric and robust αβ-loss function that can improve the performances of GAN on different data sets. Specifically, unlike standard GAN loss function it exploits the αβ-divergence (AB-divergence) to weigh the likelihood ratio associated with each data point. This weighing mechanism makes the model robust to noises and yields better models in terms of FID score. To reduce the cost of searching for the optimal α and β, we further propose an adaptive version to systematically update these parameters according to statistics of the discriminator's output. Moreover, αβ-loss can be reduced to Least Square GAN (LS-GAN) and standard GAN (SGAN) loss function as special cases. We conduct extensive experiments on both synthetic and real-world data sets. Experimental results over the synthetic data sets (2D Gaussian ring and grid) demonstrate that our approach can significantly alleviate the issue of mode collapse. Additionally, by constraining the gradient of the discriminator that is fed back to the generator via finely adjusting the hyper-parameters α and β, our approach can improve the quality of synthetic images, as can be seen from the decrease of FID from 40 to 23.71 on the data set CIFAR10 using the SN-DCGAN architecture.
A privacy preserving homomorphic computing toolkit for predictive computation

Predictive computation now is a more and more popular paradigm for artificial intelligence. In this article, we discuss how to design a privacy preserving computing toolkit for secure predictive computation in smart cities. Predictive computation technology is very important in the management of cloud data in smart cities, which can realize intelligent computing and efficient management of cloud data in the city. Concretely, we propose a homomorphic outsourcing computing toolkit to protect the privacy of multiple users for predictive computation. It can meet the needs of large-scale users to securely outsource their data to cloud servers for storage, management and processing of their own data. This toolkit, using the Paillier encryption system and Lagrangian interpolation law, can implement most commonly basic calculations such as addition, subtraction, multiplication and division etc. It can also implement secure comparison of user data in the encrypted domain. In addition, we discuss how to implement the derivative of polynomial functions using our homomorphic computing encryption tool. We also introduce its application in neural networks. Finally, we demonstrate the security and efficiency of all our protocols through rigorous mathematical analysis and performance analysis. The results show that our toolkit is efficient and secure.
Multiclass Colorectal Cancer Histology Images Classification Using Vision Transformers

Colorectal cancer (CRC) is the third most diagnosed cancer form globally and the second leading cause of cancer-related death after lung cancer. A precise histological categorization of CRC tissue is critical for diagnosis and patient management decisions. However, the variety of tissue patterns in CRC histological images makes the classification a challenging problem. This study applies Vision Transformers, a new class of deep-learning models in computer vision, to perform a multiclass tissue classification of a publicly available CRC histology images dataset. The data set consists of 5000 images with eight categories of tissue. We trained two variants of Transformers, namely Vision Transformer and Compact Convolutional Transformer, and achieved 93.3% and 95% accuracy, respectively. Our results outperform the original paper (87.4%) on the same dataset. Furthermore, our study highlights the opportunities of using Transformers in the histopathological image domain.