Reviewerly/exHarmony huggingface dataset:
Blockchain for Transparent Data Management Toward 6G

The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks (6G). Due to data heterogeneity and dynamics, decentralized data management (DM) is desirable for achieving transparent data operations across network domains, and blockchain can be a promising solution. However, the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency, efficiency, and privacy requirements in decentralized blockchain-based DM. In this paper, we investigate blockchain solutions to address the challenge. First, we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures. Second, we investigate the authentication and authorization requirements for DM stakeholders. Third, we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing. Subsequently, we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives. Finally, we conclude this paper and discuss future research directions.
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment

Table structure recognition is a challenging task due to the various structures and complicated cell spanning relations. Previous methods handled the problem starting from elements in different granularities (rows/columns, text regions), which somehow fell into the issues like lossy heuristic rules or neglect of empty cell division. Based on table structure characteristics, we find that obtaining the aligned bounding boxes of text region can effectively maintain the entire relevant range of different cells. However, the aligned bounding boxes are hard to be accurately predicted due to the visual ambiguities. In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features. Specifically, we propose the framework of Local and Global Pyramid Mask Alignment, which adopts the soft pyramid mask learning mechanism in both the local and global feature maps. It allows the predicted boundaries of bounding boxes to break through the limitation of original proposals. A pyramid mask re-scoring module is then integrated to compromise the local and global information and refine the predicted boundaries. Finally, we propose a robust table structure recovery pipeline to obtain the final structure, in which we also effectively solve the problems of empty cells locating and division. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on several public benchmarks.
Contrast and Order Representations for Video Self-supervised Learning

This paper studies the problem of learning self-supervised representations on videos. In contrast to image modality that only requires appearance information on objects or scenes, video needs to further explore the relations between multiple frames/clips along the temporal dimension. However, the recent proposed contrastive-based self-supervised frameworks do not grasp such relations explicitly since they simply utilize two augmented clips from the same video and compare their distance without referring to their temporal relation. To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames. In particular, given two video clips, our model first predicts whether they come from the same input video, and then predict the temporal ordering of the clips if they come from the same video. We also propose a novel decoupling attention method to learn symmetric similarity (contrast) and anti-symmetric patterns (order). Such design involves neither extra parameters nor computation, but can speed up the learning process and improve accuracy compared to the vanilla multi-head attention. We extensively validate the representation ability of our learned video features for the downstream action recognition task on Kinetics-400 and Something-something V2. Our method outperforms previous state-of-the-arts by a significant margin.
Determination of the urban rooftop photovoltaic potential: A state of the art

In today's world, the necessity of reducing greenhouse gas emissions to meet the global warming regulations has increased the demand for renewable energy sources. A notable portion of energy consumption is dedicated to urban environments. While solar energy is the most promising sustainable energy, urban environments can be considered as high-potential electricity producers by using rooftop-mounted photovoltaic systems. However, effective guidelines for optimal installation of solar photovoltaics remains a challenge. Although in recent years there has been a vast development of methods as well as improvement in availability of data sources. Since it is not always possible to apply the same techniques, specific approaches are needed for local, regional, or continental scales. It still remains to develop a uniform accurate multi-factor method that uses uniform open data sources to determine urban rooftop's photovoltaic potential. The aim of this paper is to make a complete systematic review of various developed methodologies published in the current state of the art, and identify vital factors for urban rooftop solar photovoltaic potential assessment as well as to detect the best available methods to create a complete global basis for future studies.
C-Net: A reliable convolutional neural network for biomedical image classification

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics.
MALL language learning outcomes: A comprehensive meta-analysis 1994–2019

Abstract The aim of this study is to comprehensively evaluate quantitative experimental mobile-assisted language learning (MALL) studies published between 1994 and 2019 that meet minimal conditions of research design and statistical analysis. Starting with a bibliographical database of 1,144 references to experimental MALL implementations, of which there were 700 objectively substantiated by quantitative experimental language learning outcomes, only 84 experimental MALL studies met the inclusion requirements. Their analysis addresses two critical sets of research questions. First, what are the general characteristics of the selected studies and, second, what are their language learning outcomes in terms of measured effect size. Nine general characteristics are considered: publication source, chronological distribution, country of origin, institutional environment, sample size, intervention duration, targeted language, language learner competence level, and learning focus. Effect size was calculated separately for between-group (independent, experimental) and within-group (quasi-experimental) treatment studies. In both cases, the overall results were quite large: 0.72 for the former and 1.16 for the latter. An analysis of four critical moderator variables (language learner competence level, language area focus, institutional environment, and intervention duration) revealed similarly large effect sizes. Notwithstanding, analysis of the data also confirmed obvious publication bias and a very high level of heterogeneity that frequently approached 100%. The relevance of positive language learning outcome conclusions thus needs to be tempered by these shortcomings.
Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction

3D hand-mesh reconstruction from RGB images facilitates many applications, including augmented reality (AR). However, this requires not only real-time speed and accurate hand pose and shape but also plausible mesh-image alignment. While existing works already achieve promising results, meeting all three requirements is very challenging. This paper presents a novel pipeline by decoupling the hand-mesh reconstruction task into three stages: a joint stage to predict hand joints and segmentation; a mesh stage to predict a rough hand mesh; and a refine stage to fine-tune it with an offset mesh for mesh-image alignment. With careful design in the network structure and in the loss functions, we can promote high-quality finger-level mesh-image alignment and drive the models together to deliver real-time predictions. Extensive quantitative and qualitative results on benchmark datasets demonstrate that the quality of our results outperforms the state-of-the-art methods on hand-mesh/pose precision and hand-image alignment. In the end, we also showcase several real-time AR scenarios.
Discriminative adversarial domain generalization with meta-learning based cross-domain validation

The generalization capability of machine learning models, which refers to generalizing the knowledge for an “unseen” domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions. Domain Generalization (DG) techniques aim to enhance such generalization capability of machine learning models, where the learnt feature representation and the classifier are two crucial factors to improve generalization and make decisions. In this paper, we propose Discriminative Adversarial Domain Generalization (DADG) with meta-learning-based cross-domain validation. Our proposed framework tries to learn a domain-invariant feature representation from source domains and generalize it to the unseen domains. It contains two main components that work synergistically to build a domain-generalized Deep Neural Network (DNN) model: (i) discriminative adversarial learning, which proactively learns a generalized feature representation on multiple “seen” domains, and (ii) meta-learning based cross domain validation, which simulates train/test domain shift via applying meta-learning techniques in the training process. In the experimental evaluation, a comprehensive comparison has been made among our proposed approach and other existing approaches on three benchmark datasets. The results shown that DADG consistently outperforms a strong baseline DeepAll, and outperforms the other existing DG algorithms in most of the evaluation cases.
Machine Learning Classifiers on Breast Cancer Recurrences

Breast cancer remains to be a leading cause of cancer-related deaths among women. Mortality is mainly attributed to metastasis and recurrence. Hence, early detection of breast cancer recurrence has become a real-world medical problem. Using data mining approaches, we compared four popular machine learning models (Logistic Regression, Naïve Bayes, K-Nearest Neighbors, and Support Vector Machines) on a high-dimensional but very small dataset, the Wisconsin Prognostic Breast Cancer Data Set for classifying breast cancer recurrences on four different configurations: a) only scaling applied, b) scaling with PCA, c) scaling with PCA and oversampling of minority class, and d) only with selected features (i.e. choose only one from each set of features that have high correlation with each other). Our results showed that Logistic Regression provided the best scores in almost all metrics (precision, recall, accuracy, f1 score (weighted), AUROC, AUPROC, and Cohen Kappa statistic in all four configurations, followed by Support Vector Machines, and then by K-Nearest Neighbors. Naive Bayes performed poorly especially in the scaling with PCA configuration, however, when we retained only one of many features that have high correlations with each other, Naïve Bayed performance improved. KNN improved its recall with oversampling while SVM’s accuracy score has been fairly constant in all four configurations. Based on this study, the Logistic Regression model can serve as a potential model for predicting breast cancer recurrence that would enable clinicians to propose treatment options based on whether patient’s features correspond to a good or bad prognosis (recurrence). This indicates the clinical utility of data mining methods for the early detection of breast cancer recurrence in post-surgical patients to save lives.
SoK: Lending Pools in Decentralized Finance

Lending pools are decentralized applications which allow mutually untrusted users to lend and borrow crypto-assets. These applications feature complex, highly parametric incentive mechanisms to equilibrate the loan market. This complexity makes the behaviour of lending pools difficult to understand and to predict: indeed, ineffective incentives and attacks could potentially lead to emergent unwanted behaviours. Reasoning about lending pools is made even harder by the lack of executable models of their behaviour: to precisely understand how users interact with lending pools, eventually one has to inspect their implementations, where the incentive mechanisms are intertwined with low-level implementation details. Further, the variety of existing implementations makes it difficult to distill the common aspects of lending pools. We systematize the existing knowledge about lending pools, leveraging a new formal model of interactions with users, which reflects the archetypal features of mainstream implementations. This enables us to prove some general properties of lending pools, and to precisely describe vulnerabilities and attacks. We also discuss the role of lending pools in the broader context of decentralized finance and identify relevant research challenges.
Self-Mutual Distillation Learning for Continuous Sign Language Recognition

In recent years, deep learning moves video-based Continuous Sign Language Recognition (CSLR) significantly forward. Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal information, and the Connectionist Temporal Classification (CTC) loss is adopted to train the network. However, due to the limitation of chain rules in back-propagation, the visual module is hard to adjust for seeking optimized visual features. As a result, it enforces that the contextual module focuses on contextual information optimization only rather than balancing efficient visual and contextual information. In this paper, we propose a Self-Mutual Knowledge Distillation (SMKD) method, which enforces the visual and contextual modules to focus on short-term and long-term information and enhances the discriminative power of both modules simultaneously. Specifically, the visual and contextual modules share the weights of their corresponding classifiers, and train with CTC loss simultaneously. Moreover, the spike phenomenon widely exists with CTC loss. Although it can help us choose a few of the key frames of a gloss, it does drop other frames in a gloss and makes the visual feature saturation in the early stage. A gloss segmentation is developed to relieve the spike phenomenon and decrease saturation in the visual module. We conduct experiments on two CSLR bench-marks: PHOENIX14 and PHOENIX14-T. Experimental results demonstrate the effectiveness of the SMKD.
Privacy-Preserving federated learning in medical diagnosis with homomorphic re-Encryption

Unlike traditional centralized machine learning, distributed machine learning provides more efficient and useful application scenarios. However, distributed learning may not meet some security requirements. For example, in medical treatment and diagnosis, an increasing number of people are using IoT devices to record their personal data, when training medical data, the users are not willing to reveal their private data to the training party. How to collect and train the data securely has become the main problem to be resolved. Federated learning can combine a large amount of scattered data for training, and protect user data. Compared with general distributed learning, federated learning is more suitable for training on scattered data. In this paper, we propose a privacy-preserving federated learning scheme that is based on the cryptographic primitive of homomorphic re-encryption, which can protect user data through homomorphic re-encryption and trains user data through batch gradient descent (BGD). In our scheme, we use the IoT device to encrypt and upload user data, the fog node to collect user data, and the server to complete data aggregation and re-encrypting. Besides, the security analysis and experimental results show that our scheme can complete model training while preserving user data and local models.
OFFSEG: A Semantic Segmentation Framework For Off-Road Driving

Off-road image semantic segmentation is challenging due to the presence of uneven terrain, unstructured class boundaries, irregular features and strong textures. These aspects affect the vehicle perception. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a framework for off-road semantic segmentation (OFFSEG) that involves (i) a pooled class semantic segmentation with four classes (sky, traversable region, non-traversable region and obstacle) using state-of-the-art deep learning architectures (ii) a color segmentation methodology to segment out specific sub-classes (grass, puddle, dirt, gravel, etc.) from the traversable region for better scene understanding. The evaluation of the framework is carried out on two off-road driving datasets, namely, RELLIS-3D and RUGD. We have also tested the proposed framework on IISERB campus data. The results show that OFFSEG achieves good performance and also provides detailed information on the traversable region.
Self-Supervised Video Representation Learning with Meta-Contrastive Network

Self-supervised learning has been successfully applied to pre-train video representations, which aims at efficient adaptation from pre-training domain to downstream tasks. Existing approaches merely leverage contrastive loss to learn instance-level discrimination. However, lack of category information will lead to hard-positive problem that constrains the generalization ability of this kind of methods. We find that the multi-task process of meta learning can provide a solution to this problem. In this paper, we propose a Meta-Contrastive Network (MCN), which combines the contrastive learning and meta learning, to enhance the learning ability of existing self-supervised approaches. Our method contains two training stages based on model-agnostic meta learning (MAML), each of which consists of a contrastive branch and a meta branch. Extensive evaluations demonstrate the effectiveness of our method. For two downstream tasks, i.e., video action recognition and video retrieval, MCN outperforms state-of-the-art approaches on UCF101 and HMDB51 datasets. To be more specific, with R(2+1)D backbone, MCN achieves Top-1 accuracies of 84.8% and 54.5% for video action recognition, as well as 52.5% and 23.7% for video retrieval.
Gradient-Leakage Resilient Federated Learning

Federated learning(FL) is an emerging distributed learning paradigm with default client privacy because clients can keep sensitive data on their devices and only share local training parameter updates with the federated server. However, recent studies reveal that gradient leakages in FL may compromise the privacy of client training data. This paper presents a gradient leakage resilient approach to privacy-preserving federated learning with per training example-based client differential privacy, coined as Fed-CDP. It makes three original contributions. First, we identify three types of client gradient leakage threats in federated learning even with encrypted client-server communications. We articulate when and why the conventional server coordinated differential privacy approach, coined as Fed-SDP, is insufficient to protect the privacy of the training data. Second, we introduce Fed-CDP, the per example-based client differential privacy algorithm, and provide a formal analysis of Fed-CDP with the (∊,δ) differential privacy guarantee, and a formal comparison between Fed-CDP and Fed-SDP in terms of privacy accounting. Third, we formally analyze the privacy-utility tradeoff for providing differential privacy guarantee by Fed-CDP and present a dynamic decay noise-injection policy to further improve the accuracy and resiliency of Fed-CDP. We evaluate and compare Fed-CDP and Fed-CDP(decay) with Fed-SDP in terms of differential privacy guarantee and gradient leakage resilience over five benchmark datasets. The results show that the Fed-CDP approach outperforms conventional Fed-SDP in terms of resilience to client gradient leakages while offering competitive accuracy performance in federated learning.
The Effect of Azimuth and Tilt Angle Changes on the Energy Balance of Photovoltaic System Installed in the Southern Slovakia Region

Energy balance of the photovoltaic system is influenced by many factors. In this article the effect of tilt and azimuth angle changes of the photovoltaic system energy production is analyzed. These parameters have significant impact on the amount of solar radiation which hits on the photovoltaic panel surface and therefore also on the energy absorbed by the module surface. The main aim of research was identification of the optimal position of photovoltaic system installation in the southern Slovakia regions. The experimental apparatus had two setups consisting of polycrystalline photovoltaic modules. The first setup was used for identification of the tilt angle changes in the range (0–90°). The second one was focused on the detection of the azimuth angle effect to the energy production. The measurement results were statistically processed and mathematically analyzed. Obtained dependencies are presented as two-dimensional and three-dimensional graphical relations. Regression equations characterize time relations between the tilt or azimuth angle and the energy produced by the photovoltaic system in Southern Slovakia. Obtained simplified mathematical model was verified by analytical model. Presented models can be used for the dimensioning and optimization of the photovoltaic system energy production.