Reviewerly/exHarmony huggingface dataset:
Differential SfM and image correction for a rolling shutter stereo rig

Most modern consumer-grade cameras are equipped with an electronic rolling shutter (RS), leading to image distortions when the camera moves during image acquisition. We explore the first structure and motion estimation problem of a dynamic generalized RS stereo camera. Such a general configuration is commonplace in robots and autonomous driving applications. We propose a tractable RS stereo differential structure from motion (SfM) algorithm, taking into account the RS effect during consecutive imaging, which effectively compensates for the RS-stereo image distortion by a linear scaling operation on each optical flow. We further propose embedding the cheirality into RANSAC and develop a robust RS-stereo-aware full-motion estimation framework. We demonstrate that the RS stereo motion and depth map refined by our non-linear optimization schemes within the maximum likelihood criterion can be used for image correction to recover high-quality global shutter (GS) stereo images. Moreover, using the proposed generalized RS stereo differential SfM pipeline, the corrected images produce an accurate 3D scene structure as the ground-truth structure. Extensive experiments on both synthetic and real RS stereo data demonstrate the effectiveness of our model and method in various configurations.
Contour deformation network for instance segmentation

• GCN-based contour deformation network is proposed. • The refined contour of an object mask is achieved for instance segmentation . • To deal with various sizes of objects in scenes, adaptive deformation-scale selection strategy presented. • Automatically constructs the local neighborhood graph and selects multiscale features. • Extensive experimental results provided to demonstrate the performance of the proposed network. To improve the precision of the contour in instance segmentation, this study proposes an iterative contour deformation network (CD-Net) based on a graph convolutional network (GCN). The proposed method treats the segmentation results of the Mask R-CNN model as the initial contours and refines the instances contour iteratively. Specifically, a contour point set is first sampled from the initial contour. Considering the various sizes of the instances, and according to the size of corresponding bounding boxes determined by the Mask R-CNN, a local neighborhood graph is constructed for each selected contour point. Subsequently, multi-scales features are automatically selected and combined with features learned in Mask R-CNN for each point in the local neighborhood graph. The local neighborhood graphs with features are then fed into the GCN to learn the deformation vectors, and the instance contours are refined accordingly. Finally, the refined contour is treated as the initial contour, and the above process is repeated to obtain the final instance contours. The experimental results on the COCO and Cityscapes datasets demonstrate that the proposed method achieves state-of-the-art performance.
Notes on the polytomous generalization of knowledge space theory

Stefanutti et al. (2020) and Heller (2021) have recently done significant work on the polytomous extensions of knowledge space theory (KST), which opens the field for systematically generalizing many KST concepts to the polytomous case. Following these developments, the paper provides a first counterexample showing that the assumptions in Heller (2021) do not guarantee component-directed joins to be defined item-wise. This leads to an incomplete characterization of the closed elements of the Galois connection in Proposition 8 of Heller (2021), an issue which is resolved in the present paper. A second counterexample in the paper shows that the equivalence between atoms and ⨆-irreducible elements of the polytomous structure stated in Stefanutti et al. (2020) may not hold in general. This paper provides theoretical results showing that the equivalence still holds if the response categories form a linear order or the structure happens to be factorial.
Axiomatic hardware-software contracts for security

We propose leakage containment models (LCMs)---novel axiomatic security contracts which support formally reasoning about the security guarantees of programs when they run on particular microarchitectures. Our core contribution is an axiomatic vocabulary for formalizing LCMs, derived from the established axiomatic vocabulary for formalizing processor memory consistency models. Using this vocabulary, we formalize microarchitectural leakage---focusing on leakage through hardware memory systems---so that it can be automatically detected in programs and provide a taxonomy for classifying said leakage by severity. To illustrate the efficacy of LCMs, we first demonstrate that our leakage definition faithfully captures a sampling of (transient and non-transient) microarchitectural attacks from the literature. Second, we develop a static analysis tool based on LCMs which automatically identifies Spectre vulnerabilities in programs and scales to analyze real-world crypto-libraries.
Chatterjee Correlation Coefficient: A robust alternative for classic correlation methods in geochemical studies- (including “TripleCpy” Python package)

Correlation coefficients (CC) are statistical tools that measure how strong a relationship is between two variables. In geochemical studies, these variables could be different elements’ concentrations per sample, and the correlation between the concentrations provides a better perspective for the relevant elements in any type of mineralization and commodities. The most common classical CCs in geochemical studies are Pearson’s and Spearman’s (PCC and SCC). The advantage of such CCs is their simplicity, however, their efficiency could be limited while dealing with non-normal, noisy, closed, or open data (i.e., even after applying log-ratios to the data). Geochemists usually apply SCC to non-normal data, and PCC to normal and open data. What if we need a simple CC that is invariant under monotone transformations of the data, and robust enough to deal with both types of data, including normal/non-normal, noisy, and outliers? Chatterjee CC (CCC: Chatterjee, 2021), as a function of ranks correlation, is a new correlation method with a very simple and understandable formula, and quick computing, but significantly robust to deal with the aforementioned data types without having any assumptions for the variables’ distributions. In this research, CCC is introduced to geoscientists further. To apply CCC to datasets, “TripleCpy” Python package was developed and introduced in this research, then applied to a data sample to demonstrate CCC’s efficiency compared to PCC and SCC. This method is highly recommended for future geochemical studies.
Effective Attention-Based Mechanism for Masked Face Recognition

Research on facial recognition has recently been flourishing, which has led to the introduction of many robust methods. However, since the worldwide outbreak of COVID-19, people have had to regularly wear facial masks, thus making existing face recognition methods less reliable. Although normal face recognition methods are nearly complete, masked face recognition (MFR)—which refers to recognizing the identity of an individual when people wear a facial mask—remains the most challenging topic in this area. To overcome the difficulties involved in MFR, a novel deep learning method based on the convolutional block attention module (CBAM) and angular margin ArcFace loss is proposed. In the method, CBAM is integrated with convolutional neural networks (CNNs) to extract the input image feature maps, particularly of the region around the eyes. Meanwhile, ArcFace is used as a training loss function to optimize the feature embedding and enhance the discriminative feature for MFR. Because of the insufficient availability of masked face images for model training, this study used the data augmentation method to generate masked face images from a common face recognition dataset. The proposed method was evaluated using the well-known masked image version of LFW, AgeDB-30, CFP-FP, and real mask image MFR2 verification datasets. A variety of experiments confirmed that the proposed method offers improvements for MFR compared to the current state-of-the-art methods.
Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

The amount of text people need to read and understand grows daily. Software defaults, designers, or publishers often choose the fonts people read in. However, matching individuals with a faster font could help them cope with information overload. We collaborated with typographers to (1) select eight fonts designed for digital reading to systematically compare their effectiveness and to (2) understand how font and reader characteristics affect reading speed. We collected font preferences, reading speeds, and characteristics from 252 crowdsourced participants in a remote readability study. We use font and reader characteristics to train FontMART, a learning to rank model that automatically orders a set of eight fonts per participant by predicted reading speed. FontMART’s fastest font prediction shows an average increase of 14–25 WPM compared to other font defaults, without hindering comprehension. This encouraging evidence provides motivation for adding our personalized font recommendation to future interactive systems.
Increasing ising machine capacity with multi-chip architectures

Nature has inspired a lot of problem solving techniques over the decades. More recently, researchers have increasingly turned to harnessing nature to solve problems directly. Ising machines are a good example and there are numerous research prototypes as well as many design concepts. They can map a family of NP-complete problems and derive competitive solutions at speeds much greater than conventional algorithms and in some cases, at a fraction of the energy cost of a von Neumann computer.
Adaptive search space to generate a per-instance genetic algorithm for the permutation flow shop problem

This study introduces, HHABS, a new hyper-heuristic for permutation-based problems. It is a high-level local search that generates tailored genetic algorithms for considered problem instances. The motivation of this work is to reduce the time needed to design a dedicated genetic algorithm for a new instance increasing the chance to explore undiscovered search spaces. It uses three search spaces to build genetic algorithms. In the first one, standard blind operators are used. In the second one, problem-oriented ones are used and finally, in the last one, knowledge extracted during the search process is taken into consideration through diversification and intensification strategies. HHABS's solving process explores the three search spaces starting from the standard one and jumps to the next search spaces until it gets the best found solution so far, for the given instance, or all search spaces are covered. Extensive experiments have been conducted on the well-known PFSP. The performance comparison, on the Taillard instances, against state-of-the-art algorithms verified the reliability of the proposed organization of the search space on its performance. Besides, it allowed us to classify instances into easy, medium and difficult.
Fraud Detection Using Neural Networks: A Case Study of Income Tax

Detecting tax fraud is a top objective for practically all tax agencies in order to maximize revenues and maintain a high level of compliance. Data mining, machine learning, and other approaches such as traditional random auditing have been used in many studies to deal with tax fraud. The goal of this study is to use Artificial Neural Networks to identify factors of tax fraud in income tax data. The results show that Artificial Neural Networks perform well in identifying tax fraud with an accuracy of 92%, a precision of 85%, a recall score of 99%, and an AUC-ROC of 95%. All businesses, either cross-border or domestic, the period of the business, small businesses, and corporate businesses, are among the factors identified by the model to be more relevant to income tax fraud detection. This study is consistent with the previous closely related work in terms of features related to tax fraud where it covered all tax types together using different machine learning models. To the best of our knowledge, this study is the first to use Artificial Neural Networks to detect income tax fraud in Rwanda by comparing different parameters such as layers, batch size, and epochs and choosing the optimal ones that give better accuracy than others. For this study, a simple model with no hidden layers, softsign activation function performs better. The evidence from this study will help auditors in understanding the factors that contribute to income tax fraud which will reduce the audit time and cost, as well as recover money foregone in income tax fraud.
Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition

Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven regional Bangla dialects with the goal of detecting synthesized Bangla speech and categorizing it. To categorize the regional language spoken by the speaker in the Bangla speech and determine its authenticity, the proposed model was created; a Stacked Convolutional Autoencoder (SCAE) and a Sequence of Multi-Label Extreme Learning machines (MLELM). SCAE creates a detailed feature map by identifying the spatial and temporal salient qualities from MFEC input data. The feature map is then sent to MLELM networks to generate soft labels and then hard labels. As aging generates physiological changes in the brain that alter the processing of aural information, the model took age class into account while generating dialect class labels, increasing classification accuracy from 85% to 95% without and with age class consideration, respectively. The classification accuracy for synthesized Bangla speech labels is 95%. The proposed methodology works well with English speaking audio sets as well.
Enhanced distance-aware self-attention and multi-level match for sentence semantic matching

Sentence semantic matching is a core research area in natural language processing, which is widely used in various natural language tasks. In recent years, attention mechanism has shown good performance in deep neural networks for sentence semantic matching. Most of the attention-based deep neural networks focus on sentences interaction which ignore modeling the core semantic of the sentence. In other words, they do not consider the importance of the relative distance of words when modeling the sentence semantics, which leads to deviations in modeling the core semantics of the sentence and unstable sentence interaction. Usually, people tend to associate words that are relatively close together when they read and believe that there is a deeper connection between them. Besides, the current interactive matching method after sentence modeling is relatively simple and it may be inadequate. In this paper, we build a well-performed distance-aware self-attention and multi-level matching model (DSSTM) for sentence semantic matching tasks. By considering the importance of different distance tokens, it can get the better original semantics of sentences and hold interactive matching method in multiple level after sentence modeling. To be specific, given two input sentences, we first encode them as contextual embeddings. Then, the contextual embeddings are handled by enhanced distance-aware self-attention to further strengthen the sentence semantic modeling from the whole and local aspect. At the same time, we apply the co-attention layer to extract cross-sentence interaction features while simplifying all the remaining components. Finally, we fuse them into the multi-level matching function to obtain the aggregation vector and learn divers matching representations, which is helpful to capture the diversity of sentence pairs. We conduct experiments on three sentence semantic matching tasks. Experimental results on these public datasets demonstrate that our model outperforms competitive baseline methods and our model has fewer parameters. Our source code is publicly available at https://github.com/xiaodeng-1/DSSTM.
Using ZMET to explore consumers’ cognitive model in virtual reality: take the tourism experience as an example

Due to its growing application in various industries as a marketing tool, the travel industry is increasingly perceiving the potential of virtual reality (VR) in the tourism arena. Although VR has been widely discussed in practice management research, there is, however, little knowledge on how VR technology can be applied in themed tourism environments. Hence, in this study, we use the Zaltman Metaphor Elicitation Technique (ZMET) to empirically study how visitors express their inner values and meanings through VR-themed tourism. We invited 26 respondents to participate in a VR-themed tourism experience, and we organized our results based on three key themes. First, characteristics of VR-themed tourism such as 'freedom of virtual reality', 'escapism of virtual reality' and 'anxiety of virtual reality' were summarized. Second, an objective consensus map was created using the 'conservative', 'average' and 'optimistic' calculation procedures. Third, we determined the relevance of the integrated linking constructs. We found that 'imagination' and 'fantasy dream' have the highest relevance value, followed by 'illusion' and 'illusionary situation'. These themes could thus provide new insights into the possible intertwined integration and complexity of 'virtual' and 'physical' issues that may arise when tourists experience VR-themed tourism.
A Vulnerability Detection Framework for Hyperledger Fabric Smart Contracts Based on Dynamic and Static Analysis

Hyperledger Fabric is another development of blockchain technology after Ethereum, which is more suitable as an operating platform for smart contracts. However, the testing technology of Hyperledger Fabric smart contracts (also known as chaincode) is not yet mature currently. Based on this, this paper studies the vulnerability detection of Golang chaincodes. Firstly, we summarize 17 kinds of Golang chaincode vulnerabilities by investigating existing research. Secondly, taking the high accuracy of dynamic detection and the high efficiency of static detection into consideration, we propose a chaincode vulnerability detection framework that combines the dynamic symbolic execution and the static abstract syntax tree analysis technology. We also implement a supporting-tool that can detect the above 15 types of vulnerabilities. Finally, we test the tool by 15 chaincodes collected from GitHub and unknown vulnerabilities were detected in 13 projects. The precision turned out to be 91% after manual inspection. In order to verify the recall rate, we manually inject 30 vulnerabilities into the collected chaincodes and all of them are detected. The evaluation results show the accuracy of the proposed vulnerability detection method for Hyperledger Fabric smart contracts.
Governance and societal impact of blockchain-based self-sovereign identities

Abstract Traditionally, governments and companies store data to identify persons for services provision and interactions. The rise of self-sovereign identities (SSIs) based on blockchain technologies provides individuals with ownership and control over their personal data and allows them to share their data with others using a sort of “digital safe.” Fundamentally, people have the sole ownership of their identity data and control when and how it is shared, protecting their privacy. As these data need to be validated to be trusted, they may become a more important data source for digital information sharing and transactions than the formal source of identity controlled by governments. Furthermore, SSIs can be used for interacting digitally with any organization. These developments change the relationship between government, companies, and individuals. We explore information sharing and governance in the digital society using blockchain-based SSIs. In addition, the impact of SSIs on data storage in the digital world is assessed. Technology enactment might result in no greater control or privacy and might only reinforce current practices. Finally, we argue that regulation and a combination of centralized and decentralized governance are still required to avoid misuse and ensure that envisaged benefits are realized.
GWMA: the parallel implementation of woodpecker mating algorithm on the GPU

The Woodpecker Mating Algorithm (WMA) is a new metaheuristic algorithm (MA) with high efficiency in solving optimization problems. Nevertheless, WMA needs many search agents to explore the problem space when handling complex problems, resulting in a long computational time. This paper focuses on the acceleration of WMA for solving optimization problems with parallelization in the graphics processing unit (GPU) to improve the speedup and decrease the execution time of the WMA, thereby retaining the quality of the optimal outcomes. The proposed algorithm provides various mechanisms for generating random factors on the host system, bulk transfer of these factors to the GPU, parallel initialization and evaluation of the initial population on GPU, updating and evaluating the search agents’ position. It also uses a decreasing mechanism to determine the best agent. The comparative evaluations of the parallel and serial WMA are based on a classical benchmark and CEC2017 test functions. In addition, the execution time of the GWMA is measured by other parallel MAs to represent the speed of the GWMA. The ultimate outcomes prove that the GWMA is run about 10–82 times quicker than the sequential WMA. Also, the GWMA is faster than other parallel algorithms in solving optimization problems.
Blockchain Technology

The entry of new technologies has upgraded the dynamics of various marketing strategies and enables it to tap a wider range of uncovered markets. In the present scenario, technology helps businesses to deeply penetrate the existing market and fetch more precise consumer information. It has much emphasis on increasing service quality, efficient management systems, and customer loyalty. In the 21st century, blockchain technology has the potential to upgrade business models across various industries. In this chapter, much emphasis has been given to how blockchain technology can potentially impact a firm's marketing strategies and present some of the applications of blockchain in the marketing domain. Applications of blockchain in marketing and its comprehensive discourse on integrating the applications of IoT system with this technology have also been covered in this chapter. It can be concluded that blockchain technology has a huge impact on a firm's marketing strategies. It will not only provide the features to consumers but assist many businesses to frame their strategies.
Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects

Database management systems are facing growing data volumes. Previous research suggests that GPUs are well-equipped to quickly process joins and similar stateful operators, as GPUs feature high-bandwidth on-board memory. However, GPUs cannot scale joins to large data volumes due to two limiting factors: (1)~large state does not fit into the on-board memory, and (2)~spilling state to main memory is constrained by the interconnect bandwidth. Thus, CPUs are often the better choice for scalable data processing.