Reviewerly/exHarmony huggingface dataset:
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets

Natural language processing models often exploit spurious correlations between taskindependent features and labels in datasets to perform well only within the distributions they are trained on, while not generalising to different task distributions.We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model, by simply replacing its training data.Our approach consists of 1) a method for training data generators to generate high-quality, label-consistent data samples; and 2) a filtering mechanism for removing data points that contribute to spurious correlations, measured in terms of z-statistics.We generate debiased versions of the SNLI and MNLI datasets, 1 and we evaluate on a large suite of debiased, outof-distribution, and adversarial test sets.Results show that models trained on our debiased datasets generalise better than those trained on the original datasets in all settings.On the majority of the datasets, our method outperforms or performs comparably to previous state-ofthe-art debiasing strategies, and when combined with an orthogonal technique, productof-experts, it improves further and outperforms previous best results of SNLI-hard and MNLI-hard.
Application of lithogeochemical and pyrite trace element data for the determination of vectors to ore in the Raja Au–Co prospect, northern Finland

Abstract. Discovering ore deposits is becoming increasingly difficult, and this is particularly true in areas of glaciated terrains. As a potential exploration tool for such terrains, we test the vectoring capacities of trace element and sulfur isotope characteristics of pyrite, combined with quantitative statistical methods of whole-rock geochemical datasets. Our target is the Rajapalot gold–cobalt project in northern Finland, where metamorphosed Paleoproterozoic volcanic and sedimentary rocks of the Peräpohja belt host recently discovered gold prospects, which also have significant cobalt enrichment. The focus is particularly put on a single gold–cobalt prospect, known as Raja, an excellent example of this unusual cobalt-enriched gold deposit, common in the metamorphosed terranes of northern Finland. The major lithologies at Rajapalot comprise amphibolite facies metamorphosed and polydeformed calcsilicate rocks that alternate with albitic units, mafic volcanic rocks, mica schist and quartzite. Mineralization at Rajapalot prospects is characterized by an older Co-mineralizing event and a younger high-grade Au mineralization with re-mobilization and re-deposition of Co. Detailed in situ laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a powerful technique that produces robust trace element and sulfur isotope databases from paragenetically and texturally well-characterized pyrite from the Raja prospect. The results are treated with appropriate log-ratio transformations and used for multivariate statistical data analysis, such as the computation of principal components. Application of these methods revealed that elements such as Co, Ni, Cu, Au, As, Ag, Mo, Bi, Te, Se, Sn, U, Tl and W have high vectoring capacities to discriminate between Co-only and Au–Co zones, as well as between mineralization stages. The systematic pyrite study suggests that homogenous sulfur isotopic characteristics (+1.3 ‰ to +5.9 ‰) and positive loadings of Co, Se, As, Te, Bi and Au onto PC1 are reflective of an early stage of Co mineralization, while the opposing negative loadings of Mo, Ni, W, Tl, Cu and Ag along PC1 are associated with pyrites from the Au-mineralizing event. The sulfur isotopic signature of the latter pyrite type is between −1.2 ‰ and +7.4 ‰. Subtle patterns recognized from the whole-rock geochemistry favor an As–Au–Se–Te–W–U signature along the positive axis of PC1 for the localization of high-grade Au–Co zones, whereas the element group Ni, Cu, Co, Te, Se and As, which has negative loadings onto PC2, will predict Co-only zones. This study shows the efficiency of trace element geochemistry in mineral exploration targeting, which has the capacity to define future targets by characterizing the metallogenic potential of a host rock, as well as distinguishing various stages of mineralization.
Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation

In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training. In this case, traditional unsupervised domain adaptation models usually fail since they cannot adapt to the target domain with over-fitting to one (or few) target samples. To address this problem, existing OSUDA methods usually integrate a style-transfer module to perform domain randomization based on the unlabeled target sample, with which multiple domains around the target sample can be explored during training. However, such a style-transfer module relies on an additional set of images as style reference for pre-training and also increases the memory demand for domain adaptation. Here we propose a new OSUDA method that can effectively relieve such computational burden. Specifically, we integrate several style-mixing layers into the segmentor which play the role of style-transfer module to stylize the source images without introducing any learned parameters. Moreover, we propose a patchwise prototypical matching (PPM) method to weighted consider the importance of source pixels during the supervised training to relieve the negative adaptation. Experimental results show that our method achieves new state-of-the-art performance on two commonly used benchmarks for domain adaptive semantic segmentation under the one-shot setting and is more efficient than all comparison approaches.
Dynamic Viewport Selection-Based Prioritized Bitrate Adaptation for Tile-Based 360° Video Streaming

Interactive 360° remote video applications have seen booming advancements due to the proliferation of smart display devices that enable a truly immersive experience. Compared to regular monoscopic videos, 360° videos have different requirements related to content preparation, packaging, transmission, specialized viewing equipment, and display factors (e.g., brightness, contrast, delay, frame rate, resolution, image quality, etc. In addition, 360° video requires substantial network and computational resources, which are challenging to achieve with conventional transmission and rendering infrastructure. Viewport-adaptive streaming is a common way to ensure visual quality under limited bandwidth resources. However, identifying, extracting, and rendering the true viewport in response to drastic head rotations can adversely affect user experience. This paper proposes two dynamic viewport selection approaches, which adapt the streamed regions based on content complexity variations and positional information to ensure viewport availability and smooth visual angles for VR users. They incorporate content information as well as user head movement patterns to support tile-based prioritized 360° video streaming. Moreover, a practical, prioritized bitrate adaptation approach, which requests selected tiles at appropriate quality levels, is also proposed to reduce the impact of inefficient bandwidth utilization in the VR scene. Experimental evaluations under real 4G bandwidth logs demonstrate that the proposed solutions outperform the closest state-of-the-art algorithms across multiple performance metrics, i.e., viewport overlap, perceived quality levels, quality fluctuations, and viewport bandwidth utilization.
The circumstances of literacy numeracy skill: Between notion and fact from elementary school students

Literacy activities in Indonesia are intensively developed to form literate citizens. Literacy is defined as a basic ability to develop individual potential in achieving goals. One of the literacies developed is numeracy literacy, which was coined in 2017. Numerical literacy is a branch of mathematical literacy. This ability guides individuals to recognize the role of numeracy competencies in everyday life. The knowledge of numeracy literacy is an urgent need for students. The student at least has received numeracy literacy development. This study aims to describe numeracy literacy in the field and compare numeracy literacy targets with reality in the area. The research method is descriptive quantitative. The data collection technique used a test with a research instrument of numeracy literacy skills and used non-test data, namely observation, and open interview guidelines. The research findings show that the students' numeracy literacy skills are still in the poor category. Numeracy literacy has not been developed in the implementation of the Gerakan Literasi Sekolah (GLS). Basic literacy activities have not been implemented properly.
Unsupervised Structure-Texture Separation Network for Oracle Character Recognition

Oracle bone script is the earliest-known Chinese writing system of the Shang dynasty and is precious to archeology and philology. However, real-world scanned oracle data are rare and few experts are available for annotation which make the automatic recognition of scanned oracle characters become a challenging task. Therefore, we aim to explore unsupervised domain adaptation to transfer knowledge from handprinted oracle data, which are easy to acquire, to scanned domain. We propose a structure-texture separation network (STSN), which is an end-to-end learning framework for joint disentanglement, transformation, adaptation and recognition. First, STSN disentangles features into structure (glyph) and texture (noise) components by generative models, and then aligns handprinted and scanned data in structure feature space such that the negative influence caused by serious noises can be avoided when adapting. Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters. This not only guarantees the absolute separation, but also enhances the discriminative ability of the learned features. Extensive experiments on Oracle-241 dataset show that STSN outperforms other adaptation methods and successfully improves recognition performance on scanned data even when they are contaminated by long burial and careless excavation.
FedHe: Heterogeneous Models and Communication-Efficient Federated Learning

Federated learning (FL) is able to manage edge devices to cooperatively train a model while maintaining the training data local and private. One common assumption in FL is that all edge devices share the same machine learning model in training, for example, identical neural network architecture. However, the computation and store capability of different devices may not be the same. Moreover, reducing communication overheads can improve the training efficiency though it is still a challenging problem in FL. In this paper, we propose a novel FL method, called FedHe, inspired by knowledge distillation, which can train heterogeneous models and support asynchronous training processes with significantly reduced communication overheads. Our analysis and experimental results demonstrate that the performance of our proposed method is better than the state-of-the-art algorithms in terms of communication overheads and model accuracy.
CNN-Bi LSTM Neural Network for Simulating Groundwater Level

Managing groundwater resources affected by varying climatic conditions requires applying reliable and precise forecasts of groundwater levels.Hence, we investigated the implementation of deep learning neural network called CNN-Bi LTM, which combines convolutional neural network layers and bidirectional long-short term memory layers (Bi LSTM) models for forecasts of groundwater levels in a well affected by pumping for irrigation.The CNN-BiLSTM model was trained with hourly groundwater level data for Jan 2021 -Dec 2021, and the data was divided into 70% for training and 30% for testing.Besides, Bayesian optimization was used to find the best range of variables for the model, such as the number of Bi LSTM units, the number of Bi LSTM layers, and the initial learning rate.Also, the Adaptive Moment Estimation (Adam) is used to calculate adaptive learning rates.As a result, the model showed promising results in the taring stage with a regression value equal to 0.9173.In comparison, the model showed acceptable results in the testing stage with regression equal to 0.6324, and the optimization duration lasted for 21 hours.Further, the optimization method showed that the best number of Bi LSTM units is 192, the best number of Bi LSTM layers is two layers, and the best initial learning rate is 0.01.
Microsoft teams’ acceptance for the e-learning purposes during Covid-19 outbreak: A case study of UAE

The brisk spread of Covid-19 led educational policymakers and organizations to opt for better alternatives to resume the students' educational journey. In this context, Microsoft Teams remained one of the most devoted and credible online platforms that greatly facilitated the educational process across the globe. Current research also analyzed Microsoft Teams acceptance using the self-proposed conceptual model supported by the Technology Acceptance Model by Davis. We employed the survey method and examined the gathered using the applied Structural Equation Modelling (SEM). Results indicated that there is a significant impact of Covid-19 on the Perceived Ease of Use (p&gt; 0.000) and Perceived Usefulness (p&gt; 0.000). Besides, the relationship between Perceived Ease of Use and Perceived Usefulness also remained significant (p&gt; 0.000). Moreover, the proposed relationship between Attitude, Perceived Ease of Use, and Perceived Usefulness also remained substantial (p&gt; 0.009 and p&gt; 0.000). However, the relationship between Attitude and Behavioral Intention remained insignificant (p&gt; 0.556). Finally, the relationship between Behavioral Intention and Microsoft Teams Acceptance remained significant (p&gt; 0.088). Thus, we concluded that Microsoft Teams is an effective study tool that unites students and instructors in the United Arab Emirates. It governs eLearning experiences and, therefore, provides a virtual environment to students and directly influences our perceptions and behavior.
Wavelet-Based Texture Reformation Network for Image Super-Resolution

Most reference-based image super-resolution (RefSR) methods directly leverage the raw features extracted from a pretrained VGG encoder to transfer the matched texture information from a reference image to a low-resolution image. We argue that simply operating on these raw features neglects the influence of irrelevant and redundant information and the importance of abundant high-frequency representations, leading to undesirable texture matching and transfer results. Taking the advantages of wavelet transformation, which represents the contextual and textural information of features at different scales, we propose a Wavelet-based Texture Reformation Network (WTRN) for RefSR. We first decompose the extracted texture features into low-frequency and high-frequency sub-bands and conduct feature matching on the low-frequency component. Based on the correlation map obtained from the feature matching process, we then separately swap and transfer wavelet-domain features at different stages of the network. Furthermore, a wavelet-based texture adversarial loss is proposed to make the network generate more visually plausible textures. Experiments on four benchmark datasets demonstrate that our proposed method outperforms previous RefSR methods both quantitatively and qualitatively. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zskuang58/WTRN-TIP</uri> .
Deepfake Video Detection Based on MesoNet with Preprocessing Module

With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%.
Combining Multi-Feature Regions for FineGrained Image Recognition

Fine-grained visual classification(FGVC) is challenging task duo to the subtle discriminative features.Recently, RA-CNN selects a single feature region of the image, and recursively learns the discriminative features.However, RA-CNN abandons most of feature regions, which is not only the inefficient but aslo ineffective.To address above issues,we design a noval fine-grained visual recognition model MRA-CNN,which associates multi-feature regions.To improve the feature representation,attention blocks are integrated into the backbone to reinforce significant features;To improve the classification accuracy, we design the feature scale dependent(FSD) algorithm to select the optimal outputs as the classifier inputs;To avoid missing features, we adopt the k-means algorithm to select multiple feature regions.We demonstrate the value of MRA-CNN by expensive experiments on three popular fine-grained benchmarks:CUB-200-2011,Cars196 and Aircrafts100 where we achieve state-of-the-art performance.Our codes can be found at https://github.com/dlearing/MRA-CNN.git.
Hamiltonian open quantum system toolkit

Abstract We present an open-source software package called “Hamiltonian Open Quantum System Toolkit" (HOQST), a collection of tools for the investigation of open quantum system dynamics in Hamiltonian quantum computing, including both quantum annealing and the gate-model of quantum computing. It features the key master equations (MEs) used in the field, suitable for describing the reduced system dynamics of an arbitrary time-dependent Hamiltonian with either weak or strong coupling to infinite-dimensional quantum baths. We present an overview of the theories behind the various MEs and provide examples to illustrate typical workflows in HOQST. We present an example that shows that HOQST can provide order of magnitude speedups compared to “Quantum Toolbox in Python" (QuTiP), for problems with time-dependent Hamiltonians. The package is ready to be deployed on high performance computing (HPC) clusters and is aimed at providing reliable open-system analysis tools for noisy intermediate-scale quantum (NISQ) devices.
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking

Dialogue State Tracking is central to multidomain task-oriented dialogue systems, responsible for extracting information from user utterances.We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attention Networks in such a way to allow causal, sequential prediction of slot values.The model architecture captures inter-slot relationships and dependencies across domains that otherwise can be lost in sequential prediction.We report improvements in state tracking performance in Mul-tiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified sparse training scenario in which DST models are trained only on session-level annotations but evaluated at the turn level.We further report detailed analyses to demonstrate the effectiveness of graph models in DST by showing that the proposed graph modules capture inter-slot dependencies and improve the predictions of values that are common to multiple domains.
Accurate Detection of Breast Cancer Metastasis Using a Hybrid Model of Artificial Intelligence Algorithm

Background: Breast cancer (BC) is a prevalent disease and a major cause of mortality among women worldwide. A substantial number of BC patients experience metastasis which in turn leads to treatment failure and death. The survival rate has been significantly increased due to more rapid detection and substantial improvements in adjuvant therapies including newer chemotherapeutic and targeted agents, and better radiotherapy techniques.Methods: In this study, we cross-compared the application of advanced artificial intelligence algorithms such as Logistic Regression, K-Nearest Neighbors, Discrete Cosine Transform, Random Forest Classifier, Support Vector Machines, Multilayer Perceptron, and Ensemble to diagnose BC metastasis. We further combined MLP with genetic algorithm (GA) as a hybrid method of intelligent analysis. The core data we used for comparison belonged to the images of both benign and malignant tumors collected from Wisconsin Breast Cancer dataset from the UCI repository.Results: The application of several different algorithms to the collection of BC data indicated that these algorithms have comparable accuracy rate in detecting and predicting cancer. However, our hybrid algorithm showed superior accuracy, sensitivity and specificity compared to the individual algorithms. Two methods of comparison (Cross-Validation and Holdout) were applied to this study which produced consistent results.Conclusion: Our findings indicate that our MLP-GA hybrid algorithm can speed up diagnosis with higher accuracy rate than the individual patterns of algorithm.
Continuous Smartphone Authentication using Wristbands

Many users find current smartphone authentication methods (PINs, swipe patterns) to be burdensome, leading them to weaken or disable the authentication.Although some phones support methods to ease the burden (such as fingerprint readers), these methods require active participation by the user and do not verify the user's identity after the phone is unlocked.We propose CSAW, a continuous smartphone authentication method that leverages wristbands to verify that the phone is in the hands of its owner.In CSAW, users wear a wristband (a smartwatch or a fitness band) with built-in motion sensors, and by comparing the wristband's motion with the phone's motion, CSAW continuously produces a score indicating its confidence that the person holding (and using) the phone is the person wearing the wristband.This score provides the foundation for a wide range of authentication decisions (e.g., unlocking phone, deauthentication, or limiting phone access).Through two user studies (N = 27, 11) we evaluated CSAW's accuracy, usability, and security.Our experimental evaluation demonstrates that CSAW was able to conduct initial authentication with over 99% accuracy and continuous authentication with over 96.5% accuracy.
Privacy-Preserving Internet of Things: Techniques and Applications

Privacy has become an imperative term in the recent technology developments. Lots of data are being collected through every digital activity of users. The expeditious development of IoT applications have raised the concern about the privacy of the IoT systems. The data collected via IoT sensors can reveal the daily behavior of the users, location, and other sensitive information. Hence, it is necessary to preserve the privacy of data collected by IoT devices. A large number of techniques and approaches have been implemented and used in different IoT based applications such as cloud computing based IoT, fog computing based IoT, blockchain based IoT and trajectory applications. In this paper, we present a detailed investigation of the existing approaches to preserve the privacy of data in IoT applications. The techniques like k-anonymity, secure multiparty computation, attribute based encryption and homomorphic encryption are analyzed. Finally, a comparative analysis of privacy preserving techniques with its applications are presented.