Reviewerly/exHarmony huggingface dataset:
Graph Deep Learning Based Anomaly Detection in Ethereum Blockchain Network

Ethereum is one of the largest blockchain networks in the world. Its feature of smart contracts is unique among the other crypto-currencies and gained wider attention. However, smart contracts are vulnerable to attacks and financial fraud within the network. Identifying anomalies in this massive network is challenging because of anonymity. Using traditional machine learning-based techniques, such as One-Class Support Vector Machine and Isolation Forest are ineffective in Identifying anomalies in the Ethereum transactions because of its limitations in terms of capturing the internode or account relationship information in the transactions. Ethereum transactions can be effectively represented using an attributed graph with nodes and edges capturing the inter-dependencies. Hence, in this paper, we propose to use a One-Class Graph Neural Network-based anomaly detection framework for detecting anomalies in the Ethereum blockchain network. Empirical evaluation demonstrates that the proposed method is able to achieve higher anomaly detection accuracy than traditional non-graph based machine learning algorithms.
Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study

This paper conducts a comprehensive review of various word and sentence semantic similarity techniques proposed in the literature. Corpus-based, Knowledge-based, and Feature-based are categorized under word semantic similarity techniques. String and set-based, Word Order-based Similarity, POSbased, Syntactic dependency-based are categorized as sentence semantic similarity techniques. Using these techniques, we propose a model for computing the overall accuracy of the twitter dataset. The proposed model has been tested on the following four measures: Atish’s measure, Li’s measure, Mihalcea’s measure with path similarity, and Mihalcea’s measure with Wu and Palmer’s (WuP) similarity. Finally, we evaluate the proposed method on three real-world twitter datasets. The proposed model based on Atish’s measure seems to offer good results in all datasets when compared with the proposed model based on other sentence similarity measures.
A distributed perspective on reading: implications for education

The aim of this paper is to argue for an interdisciplinary research agenda to the study of reading. We discuss the methodological and educational/practical challenges and opportunities that an embodied cognitive and distributed language perspective entails for literacy and education research. Although an increasing body of research pivots on the embodiment of reading, reading still tends to be theorised as a sequence of internal processes used to interpret texts. A distributed perspective replaces this code view with an embodied and multi-scalar description of the activity. This ‘embodied turn’ generates valuable results that involve not only the whole body (as a multi-sensory organ), but a historical, skilled body that affects the reading ecology. Consequently, an embodied and distributed perspective is not only a matter of drawing on multiple sensory modalities in the here-and-now that conglomerate in the human sensorium, it also highlights the interconnected habits that enable readers to enact cultural practices based on writing systems. Skilled linguistic action allows readers to construe symbolizations, re-evoking parts of their individual life experience. Embodiment thus involves a perspective that links multiple timescales, thereby moving beyond local coordination of body and artefact. While the sensorial richness of our body is a prerequisite for engaging with a text, the reading activity becomes even more complex than interconnecting the many bodily modalities, when we think of the body in terms of lived experience. Building on this multi-scalar view, the paper seeks to demonstrate the integrative potential of a distributed approach to reading as well as some initial educational implications of such a distributed perspective.
An Interpretable Solar Photovoltaic Power Generation Forecasting Approach Using An Explainable Artificial Intelligence Tool

The spread of artificial intelligence (AI) over diverse industries provides many benefits as well as challenges. The inner working of an AI system still behaves like a black-box, and its adoption depends on converting it to a more glass-box structure. Recent developments in solar photovoltaic (PV) power generation forecasting indicate that AI has great potential for predicting solar power output. Interpretation of a PV power generation forecasting will enhance the efficiency and the adoption of PV energy further. This paper presents the use case of PV energy forecasting utilizing an explainable AI (XAI) tool on a high-resolution dataset. The forecasting of power generation is done using the XGBoost algorithm, and feature contributions are explained with the ELI5 XAI tool. XGBoost and ELI5 together provide simple, fast, and efficient forecasting to facilitate straightforward deployment. The proposed models are trained and tested using all features, as well as a subset of features. The results of these two models are evaluated in terms of root mean squared error (RMSE) scores.
Older Adults’ Attitudes Toward Virtual Volunteering During the COVID-19 Pandemic

This study explored older adults’ technology use patterns and attitudes toward virtual volunteering during the COVID-19 pandemic. A 22-item survey was administered to 229 volunteers in the St. Louis region who tutor children through the Oasis Intergenerational Tutoring program. Although most respondents are familiar with technology and expressed that they are likely to volunteer virtually, their responses varied significantly by age, education, gender, income, and school districts. Some tutors expressed that virtual volunteering may eliminate barriers to in-person volunteering, while others were concerned with establishing a personal connection with students online. These findings suggest that tutors anticipate both benefits and challenges with virtual volunteering and that efforts to engage older adults during the pandemic should factor in prior use of technology and ensure that different subgroups are not marginalized.
TinyVIRAT: Low-resolution Video Action Recognition

The existing research in action recognition is mostly focused on high-quality videos where the action is distinctly visible. In real-world surveillance environments, the actions in videos are captured at a wide range of resolutions. Most activities occur at a distance with a small resolution and recognizing such activities is a challenging problem. In this work, we focus on recognizing tiny actions in videos. We introduce a benchmark dataset, Tiny VIRAT, which contains natural low-resolution activities. The actions in Tiny VIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. We propose a novel method for recognizing tiny actions in videos which utilizes a progressive generative approach to improve the quality of low-resolution actions. The proposed method also consists of a weakly trained attention mechanism which helps in focusing on the activity regions in the video. We perform extensive experiments to benchmark the proposed Tiny VIRAT dataset and observe that the proposed method significantly improves the action recognition performance over baselines. We also evaluate the proposed approach on synthetically resized action recognition datasets and achieve state-of-the-art results when compared with existing methods. The dataset and code are publicly available at https://github.com/UgurDemir/Tiny-VIRAT.
Synthesizer: Rethinking Self-Attention for Transformer Models

The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importance and contribution of the dot product-based self-attention mechanism on the performance of Transformer models. Via extensive experiments, we find that (1) random alignment matrices surprisingly perform quite competitively and (2) learning attention weights from token-token (query-key) interactions is useful but not that important after all. To this end, we propose \textsc{Synthesizer}, a model that learns synthetic attention weights without token-token interactions. In our experiments, we first show that simple Synthesizers achieve highly competitive performance when compared against vanilla Transformer models across a range of tasks, including machine translation, language modeling, text generation and GLUE/SuperGLUE benchmarks. When composed with dot product attention, we find that Synthesizers consistently outperform Transformers. Moreover, we conduct additional comparisons of Synthesizers against Dynamic Convolutions, showing that simple Random Synthesizer is not only 60% faster but also improves perplexity by a relative 3.5%. Finally, we show that simple factorized Synthesizers can outperform Linformers on encoding only tasks.
Gated Spatio-Temporal Attention-Guided Video Deblurring

Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur. Most of the existing works depend on implicit or explicit alignment for temporal information fusion, which either increases the computational cost or results in suboptimal performance due to misalignment. In this work, we investigate two key factors responsible for deblurring quality: how to fuse spatio-temporal information and from where to collect it. We propose a factorized gated spatio-temporal attention module to perform non-local operations across space and time to fully utilize the available information without depending on alignment. First, we perform spatial aggregation followed by a temporal aggregation step. Next, we adaptively distribute the global spatio-temporal information to each pixel. It shows superior performance compared to existing non-local fusion techniques while being considerably more efficient. To complement the attention module, we propose a reinforcement learning-based framework for selecting keyframes from the neighborhood with the most complementary and useful information. Moreover, our adaptive approach can increase or decrease the frame usage at inference time, depending on the user’s need. Extensive experiments on multiple datasets demonstrate the superiority of our method.
CLD-Net: A Network Combining CNN and LSTM for Internet Encrypted Traffic Classification

The development of the Internet has led to the complexity of network encrypted traffic. Identifying the specific classes of network encryption traffic is an important part of maintaining information security. The traditional traffic classification based on machine learning largely requires expert experience. As an end-to-end model, deep neural networks can minimize human intervention. This paper proposes the CLD-Net model, which can effectively distinguish network encrypted traffic. By segmenting and recombining the packet payload of the raw flow, it can automatically extract the features related to the packet payload, and by changing the expression of the packet interval, it integrates the packet interval information into the model. We use the ability of Convolutional Neural Network (CNN) to distinguish image classes, learn and classify the grayscale images that the raw flow has been preprocessed into, and then use the effectiveness of Long Short-Term Memory (LSTM) network on time series data to further enhance the model’s ability to classify. Finally, through feature reduction, the high-dimensional features learned by the neural network are converted into 8 dimensions to distinguish 8 different classes of network encrypted traffic. In order to verify the effectiveness of the CLD-Net model, we use the ISCX public dataset to conduct experiments. The results show that our proposed model can distinguish whether the unknown network traffic uses Virtual Private Network (VPN) with an accuracy of 98% and can accurately identify the specific traffic (chats, audio, or file) of Facebook and Skype applications with an accuracy of 92.89%.
Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects

Deep neural networks for automatic image colorization often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. Such color-bleeding artifacts debase the reality of generated outputs, limiting the applicability of colorization models in practice. Although previous approaches have attempted to address this problem in an automatic manner, they tend to work only in limited cases where a high contrast of gray-scale values are given in an input image. Alternatively, leveraging user interactions would be a promising approach for solving this color-breeding artifacts. In this paper, we propose a novel edge-enhancing network for the regions of interest via simple user scribbles indicating where to enhance. In addition, our method requires a minimal amount of effort from users for their satisfactory enhancement. Experimental results demonstrate that our interactive edge-enhancing approach effectively improves the color-bleeding artifacts compared to the existing baselines across various datasets.
A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Abstract Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions.
Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised training sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts' efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our.
Privacy-preserving credit evaluation system based on blockchain

In digital intelligence era, the authenticity of data and the privacy protection of data sharing and multiparty collaborative computing are key factors in building a good credit evaluation system. Although blockchain-based credit evaluation system considered this point, more studies on data privacy protection are either only the design of the framework or the proposal of the concept, which is insufficient for privacy protection. To provide a more comprehensive and reliable privacy-preserving scheme, this paper proposes a novel credit evaluation system with secure sharing and multiparty computation based on blockchain. The system consists of five modules: data, access control, data encryption, secure computation and model storage modules. The Hyperledger Fabric blockchain-based data module ensures the authenticity and traceability of the data source. The raw data are encrypted by a linear transformation algorithm in the data encryption module, which minimizes the output and utilization of data when leaving local storage and prevents potential privacy leakage in data sharing to the greatest extent. The phillie homomorphic encryption-based secure computation algorithm in the secure computation module achieves secure data sharing while applying the secure multiparty computation, which makes it possible to data sharing and privacy protection in multiparty computing. The system obtains final summary statistical results without exposing the raw data. Additionally, the final statistical results of the raw data can be inferred from the encrypted data, and their results are consistent. The correctness and accuracy of the linear conversion encryption mechanism and homomorphic encryption algorithm are proven by theoretical analysis. Security analysis and calculation case show the security of the proposed credit evaluation system and the correctness and effectiveness of the proposed encryption scheme.
Distance Teaching During the COVID-19 Crisis: Social Connectedness Matters Most for Teaching Quality and Students’ Learning

In spring 2020, school closures were enacted to slow the spread of the COVID-19 pandemic. Students and teachers faced the challenge of organizing digital teaching and learning without sufficient time to prepare. In this study, we investigated how teachers implemented teaching from a distance and how these different implementations were associated with students’ and parents’ perceptions of teaching quality and students’ social involvement, enjoyment of learning, academic effort, and perceived competence. To this end, we examined data from 277 teachers, 3,159 students in Grades 5 to 10, and 1,688 parents who rated classes in mathematics, German language arts, and English as a foreign language during the school closures. The results showed that teachers’ implementations of distance teaching varied greatly. Teaching methods enabling social connectedness (e.g., video meetings, learning videos created by the teacher) revealed the most consistent positive associations with students’ and parents’ teaching quality ratings and students’ learning experiences.
Content and Style Aware Generation of Text-Line Images for Handwriting Recognition

Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art.
Stablecoins: Growth Potential and Impact on Banking

<ns2:p>Stablecoins have experienced tremendous growth in the past year, serving as a possible breakthrough innovation in the future of payments. In this paper, we discuss the current use cases and growth opportunities of stablecoins, and we analyze the potential for stablecoins to broadly impact the banking system. The impact of stablecoin adoption on traditional banking and credit provision can vary depending on the sources of inflow and the composition of stablecoin reserves. Among the various scenarios, a two-tiered banking system can both support stablecoin issuance and maintain traditional forms of credit creation. In contrast, a narrow bank approach for digital currencies can lead to disintermediation of traditional banking, but may provide the most stable peg to fiat currencies. Additionally, dollar-pegged stablecoins backed by adequately safe and liquid collateral can potentially serve as a digital safe haven currency during periods of crypto market distress.</ns2:p>
Two-party secure semiquantum summation against the collective-dephasing noise

In this paper, we propose a two-party semiquantum summation protocol, where two classical users can accomplish the summation of their private binary sequences with the assistance of a quantum semi-honest third party (TP). The term 'semi-honest' implies that TP cannot conspire with others but is able to implement all kinds oof attacks. This protocol employs logical qubits as traveling particles to overcome the negative influence of collective-dephasing noise and needn't make any two parties pre-share a random secret key. The security analysis turns out that this protocol can effectively prevent the outside attacks from Eve and the participant attacks from TP. Moreover, TP has no knowledge about the summation results.
Method for solar resource assessment using numerical weather prediction and artificial neural network models based on typical meteorological data: Application to the south of Portugal

In this work a method for regional solar resource assessment based on numerical weather prediction (NWP) and artificial neural network (ANN) models is presented. The method was developed using typical meteorological and solar radiation data and applied to the location of Évora, Portugal with the goal of assessing solar global horizontal (GHI) and direct normal (DNI) irradiations with 1.25 km of horizontal resolution in the south of Portugal. The NWP model used was the research model Meso-NH and a site adaptation model was developed based in ANNs and using as inputs the simulated meteorological variables from Meso-NH and aerosol data from Copernicus Atmospheric Monitoring Services (CAMS) for the observation site. The resulting annual relative mean bias errors for GHI and DNI at Évora and typical meteorological year are of 0.55 % and 0.98 %, respectively, while the values for the original Meso-NH simulations are of 8.24 % for GHI and 31.71 % for DNI. The developed site adaptation model is applied to the region for the purpose of solar radiation assessment and validated using data from a network of solar radiation measuring stations scattered throughout the south of Portugal, showing relative mean bias errors of 2.34 % for GHI and 3.41 % for DNI, while the original Meso-NH simulations presents relative mean bias errors of 8.50 % and 29.54 %, respectively. These results allowed the generation of improved solar resource availability maps which are a very useful tool in solar resource assessment, the study of shortwave radiative climate, as well as project planning and solar system design and operation.