Reviewerly/exHarmony huggingface dataset:
Proactively Screening Machine Learning Pipelines with ARGUSEYES

Software systems that learn from data with machine learning (ML) are ubiquitous. ML pipelines in these applications often suffer from a variety of data-related issues, such as data leakage, label errors or fairness violations, which require reasoning about complex dependencies between their inputs and outputs. These issues are usually only detected in hindsight after deployment, after they caused harm in production. We demonstrate ArgusEyes, a system which enables data scientists to proactively screen their ML pipelines for data-related issues as part of continuous integration. ArgusEyes instruments, executes and screens ML pipelines for declaratively specified pipeline issues, and analyzes data artifacts and their provenance to catch potential problems early before deployment to production. We demonstrate our system for three scenarios: detecting mislabeled images in a computer vision pipeline, spotting data leakage in a price prediction pipeline, and addressing fairness violations in a credit scoring pipeline.
Introduction to Apache Spark for Large-Scale Data Analytics

Apache Spark started as a research project at the UC Berkeley AMPLab in 2009. It became open source in 2010 and was transferred to the Apache Software Foundation in 2013 and boasts the largest open source big data community.
Bitcoin Under the Microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for economically meaningful interpretations of user behavior. We identify and divide wallets into user categories -- miners, exchanges, services, retail wallets and receiving-only addresses -- and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. By matching historical transactions with minute-level price data, we estimate wallets' realized financial return and find that these user-types not only exhibit different transaction patterns and balances, but also different levels of financial performance. Our paper highlights opportunities for novel empirical research that exploits Bitcoin wallet-level data on individual user characteristics.
A Blockchain-Based Semantic Exchange Framework for Web 3.0 Toward Participatory Economy

Web 3.0 is the next-generation Internet that enables participants to read, write, and own contents in a decentralized manner. It is mainly driven by blockchain, semantic communication, edge computing, and artificial intelligence, which can construct value networks to achieve participatory economics based on participatory decision making. Web 3.0 can capture the characteristics of blockchain, semantic extraction, and communication to achieve decentralized semantic sharing and transfer information precisely. However, current Web 3.0 solutions focus on the blockchain while overlooking other new technologies' roles in Web 3.0. To further unleash the advantages of semantic extraction and communication in Web 3.0, in this article, we propose a blockchain-based semantic exchange framework to realize fair and efficient interactions. In this framework, we first attempt to tokenize semantic information into Non-Fungible Token (NFT) for semantic exchange. Then we utilize a Stackelberg game to maximize buying and pricing strategies for semantic trading. We also leverage Zero-Knowledge Proof to share authentic semantic information without publishing it before receiving payments, which can achieve a fair and privacy-preserving trading compared with current NFT marketplaces. A case study about urban planning is given to show clearly the proposed mechanisms. Finally, several challenges and opportunities are identified.
MRPFA-Net for Shadow Detection in Remote-Sensing Images

The presence of shadows in high-resolution (HR) remote-sensing images reduces object detection accuracy. To address this problem, in this paper, we proposed a deep neural network algorithm for shadow detection by using the AISD and SSAD remote-sensing shadow image datasets. To improve the ability to extract spatial information from feature maps, we developed a cross-spatial attention module that focuses on semantic information in the horizontal and vertical directions at each position point on the remote-sensing image. This module overcomes the limitations of existing technologies in accurately judging small areas and suspected shadow areas and in missing or incorrectly detected shadow areas. In addition, to improve the ability to extract shadow features and the accuracy of shadow detection in remote-sensing images, we developed a channel attention module that assigns more attention to channels that conform to the shadow color characteristics. The network architecture comprises an encoder – decoder structure, with ResNeXt50 used as the backbone for the encoder and a multi resolution parallel fusion (MRPF) designed for the decoder; cross-spatial and channel attention were incorporated into the decoder unit. Experimental results demonstrated the superior performance of the proposed algorithm, with an F1 score of 92.6% for the shadow category on the test set, thus, outperforming other algorithms and making the proposed method an effective solution for shadow detection in HR remote-sensing images.
End-To-End Compression for Surveillance Video With Unsupervised Foreground-Background Separation

With the exponential growth of surveillance video, efficient video coding method is in great demand. The learning-based methods emerge which either directly use a general video compression framework, or separate the foreground and background and then compress them in two stages. However, they do not take into account the relatively static background fact of surveillance video, or simply separate foreground and background in offline mode which reduces the separation performance because the temporal domain correlation is not considered very well. In this paper, we propose an end-to-end Unsupervised foreground-background separation based Video Compression neural Networks, dubbed as UVCNet. Our method mainly consists of three parts. First, the Mask Net unsupervisely separates foreground and background online which sufficiently uses the temporal correlation prior. Then, a traditional motion estimation-based residual coding module is applied to foreground compression. Simultaneously, a background compression module is applied to compress background residual and update the background by sufficiently using the relatively static property. Compared with previous approaches, our method does not separate foreground and background in advance but in an end-to-end manner. So we can not only use the relatively static background property to save bit rate, but also achieve end-to-end online video compression. Experimental results demonstrate that the proposed UVCNet achieves superior performance compared with the state-of-the-art methods. Specifically, UVCNet can achieve 2.11 dB average improvement on Peak Signal-to-Noise Ratio (PSNR) compared with H.265 on surveillance datasets.
BiAIT*: Symmetrical Bidirectional Optimal Path Planning With Adaptive Heuristic

Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially when collision checking is expensive. However, the heuristic estimation in AIT* consumes lots of computational resources, and its asymmetric bidirectional searching strategy cannot fully exploit the potential of the bidirectional method. In this article, we propose an extension of AIT* called BiAIT*. Unlike AIT*, BiAIT* uses symmetrical bidirectional search for both the heuristic and space searching. The proposed method allows BiAIT* to find the initial solution faster than AIT*, and update the heuristic with less computation when a collision occurs. We evaluated the performance of BiAIT* through simulations and experiments, and the results show that BiAIT* can find the solution faster than state-of-the-art methods. We also analyze the reasons for the different performances between BiAIT* and AIT*. Furthermore, we discuss two simple but effective modifications to fully exploit the potential of the adaptively heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is inspired by the adaptively heuristic method and the symmetrical bidirectional searching method. The article introduces a novel algorithm that uses the symmetrical bidirectional method to calculate the adaptive heuristic and efficiently search the state space. The problem-specific heuristic in BiAIT* is derived from a lazy-forward tree and a lazy-reverse tree, which are constructed without collision checking. The lazy-forward and lazy-reverse trees are enabled to meet in the middle, thus generating the effective and accurate heuristic. In BiAIT*, the lazy-forward and lazy-reverse trees share heuristic information and jointly guide the growth of the forward and reverse trees, which conduct collision checking and guarantee the feasibility of their edges. Compared with state-of-the-art methods, BiAIT* finds the initial heuristic and updates the heuristic more quickly. The proposed algorithm can be applied to industrial robots, medical robots, or service robots to achieve efficient path planning. The implementation of BiAIT* is available at https://github.com/Licmjy-CU/BiAITstar.
Grouped People Counting Using mm-Wave FMCW MIMO Radar

The problem of radar-based counting of multiple individuals moving as a single group is addressed using an mm-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar. This problem is challenging because the different individuals are closer to each other than the range/azimuth resolution, and their bulk Doppler signatures are difficult to distinguish, as they tend to move together. A processing pipeline is proposed, based on the combination of a multiple target tracking algorithm with a classifier to track each group and count the number of people within. Specific salient features are defined for the classifier and extracted from range–azimuth maps and cadence velocity diagrams (CVDs). The proposed pipeline has been experimentally validated in several outdoor scenarios with grouped people. The results show that the combination of tracking algorithm and classifier in the proposed pipeline outperforms alternative methods from the literature as well as a commercial toolbox for people counting.
What are the priorities and the points to be addressed by a legal framework for quantum technologies?

Quantum technologies (QTechs) have the potential to have revolutionary effects in scientific, technological and commercial areas in equal measure. This includes major transformations boosted by quantum computing, quantum communication and quantum sensing, with applications ranging from information technologies to chemistry or finance.
SymBChainSim: A Novel Simulation Tool for Dynamic and Adaptive Blockchain Management and its Trilemma Tradeoff

Despite the recent increase in the popularity of blockchain, the technology suffers from the trilemma trade-off between security decentralisation and scalability prohibiting adoption, and limiting the efficiency and effectiveness of the induced system. Addressing the trilemma trade-off calls for dynamic management and configuration of the blockchain system. In particular, choosing an effective and efficient consensus protocol for balancing the trilemma trade-off when inducing the blockchain-based system is acknowledged to be a challenging problem given the dynamic and complex nature of the blockchain environment. DDDAS approaches are particularly suitable for this challenge, and in previous work, the authors presented a novel DDDAS-based blockchain architecture and demonstrated that it offers a promising approach for dynamically adjusting the parameters of a system and optimising for the trade-off. This paper presents a novel simulation tool that can support and satisfy the DDDAS requirements for a dynamically re-configurable blockchain system. The tool supports the simulation and the dynamic switching of consensus protocols, analysing their trilemma trade-off. The simulator design is modular and allows the implementation and analysis of a wide range of consensus protocols and their implementation scenarios, along with the ability for parallelization. The paper also presents a quantitative evaluation of the tool.
Machine Learning-Based Prediction of Users' Involvement on Social Media

Useful information can be extracted through the analysis of Facebook posts. Text analysis and image analysis can play a vital role towards this. To predict the users' involvement, text data and image data can be incorporated using some machine learning models. These models can be used to perform testing on advertisements that are posted on Facebook for users' involvement prediction. Count of share and comments with sentiment analysis are included as users' involvement. This chapter contributes to understand the users' involvement on social media along with finding out the best machine learning model for prediction of users' involvement. The procedure of prediction with both text data and image data by suitable models is also discussed. This chapter produces a predictive model for posts of Facebook to predict users' involvement that will be based on the number of shares and comments on the post. The best models are obtained by using the combination of image data and text data. Further, it demonstrated that random models are surpassed by the models that are integrated for prediction.
Is My Pruned Model Trustworthy? PE-Score: A New CAM-Based Evaluation Metric

One of the strategies adopted to compress CNN models for image classification tasks is pruning, where some elements, channels or filters of the network are discarded. Typically, pruning methods present results in terms of model performance before and after pruning (assessed by accuracy or a related parameter such as the F1-score), assuming that if the difference is less than a certain value (e.g., 2%), the pruned model is trustworthy. However, state-of-the-art models are not concerned with measuring the actual impact of pruning on the network by evaluating the pixels used by the model to make the decision, or the confidence of the class itself. Consequently, this paper presents a new metric, called the Pruning Efficiency score (PE-score), which allows us to identify whether a pruned model preserves the behavior (i.e., the extracted patterns) of the unpruned model, through visualization and interpretation with CAM-based methods. With the proposed metric, it will be possible to better compare pruning methods for CNN-based image classification models, as well as to verify whether the pruned model is efficient by focusing on the same patterns (pixels) as those of the original model, even if it has reduced the number of parameters and FLOPs.
Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks

For the last two decades, artificial neural networks (ANNs) of the third generation, also known as spiking neural networks (SNN), have remained a subject of interest for researchers. A significant difficulty for the practical application of SNNs is their poor suitability for von Neumann computer architecture, so many researchers are currently focusing on the development of alternative hardware. Nevertheless, today several experimental libraries implementing SNNs for conventional computers are available. In this paper, using the RCNet library, we compare the performance of reservoir computing architectures based on artificial and spiking neural networks. We explicitly show that, despite the higher execution time, SNNs can demonstrate outstanding classification accuracy in the case of complicated datasets, such as data from industrial sensors used for the fault detection of bearings and gears. For one of the test problems, namely, ball bearing diagnosis using an accelerometer, the accuracy of the classification using reservoir SNN almost reached 100%, while the reservoir ANN was able to achieve recognition accuracy up to only 61%. The results of the study clearly demonstrate the superiority and benefits of SNN classificators.
Fostering students’ mathematical critical thinking skills on number patterns through digital book STEM PjBL

21st-century learning requires students to have the competency problem-solving and critical thinking, creative thinking, collaboration and communication. However, the mathematical critical thinking skills of Indonesian junior high school students remain relatively low. Therefore, the study aims to develop a digital book STEM PjBL to foster the students’ mathematical critical thinking skills. To achieve this aim development and research of the Borg and Gall (Gall et al., 2007) model continued with the experimental research conducted using one group pre- and post-test design. Instrument tests of mathematical critical thinking skills and student perception questionnaires were employed to collect data. The findings indicate that the digital book STEM PjBL is valid and significant in fostering students’ critical thinking skills and has a positive impact on other 21st-century learning skills. Furthermore, the data shows that students are satisfied and interested in learning through active teaching and learning methods that incorporate ICT tools and STEM-PjBL aspects.
Machine learning and galaxy morphology: for what purpose?

Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come renders this task inhuman and Machine Learning (mainly Deep Learning) has been called to the rescue for more than a decade. However, the results look mitigate and there seems to be a shift away from the paradigm of the traditional morphological classification of galaxies. In this paper, I want to show that the algorithms indeed are very sensitive to the features present in images, features that do not necessarily correspond to the Hubble or de Vaucouleurs vision of a galaxy. However, this does not preclude to get the correct insights into the physics of galaxies. I have applied a state-of-the-art ''traditional'' Machine Learning clustering tool, called Fisher-EM, a latent discriminant subspace Gaussian Mixture Model algorithm, to 4458 galaxies carefully classified into 18 types by the EFIGI project. The optimum number of clusters given by the Integrated Complete Likelihood criterion is 47. The correspondence with the EFIGI classification is correct, but it appears that the Fisher-EM algorithm gives a great importance to the distribution of light which translates to characteristics such as the bulge to disk ratio, the inclination or the presence of foreground stars. The discrimination of some physical parameters (bulge-to-total luminosity ratio, $(B -- B)T$ , intrinsic diameter, presence of flocculence or dust, arm strength) is very comparable in the two classifications.
A collaborative auditing scheme with dynamic data updates based on blockchain

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Categorization of Arabic posts using Artificial Neural Network and hash features

Sentiment analysis is an important study topic with diverse application domains including social network monitoring and automatic analysis of the body of natural language communication. Existing research on sentiment analysis has already utilised substantial domain knowledge available online comprising users' opinion in various areas such as business, education, and social media. There is however limited literature available on Arabic language sentiment analysis. Furthermore, datasets used in majority of these studies have poor classification. In the present study, we utilised a primary dataset comprising 2122 sentences and 15,331 words compiled from 206 publicly available online posts to perform sentiment classification by using advanced machine learning technique based on Artificial Neural Networks. Unlike lexicon-based techniques that suffer from low accuracy due to their computational nature and parameter configuration, Artificial Neural Networks were used to classify people opinion posts into three categories including conservative, reform and revolution, accompanied by multiple hasher vector size to benchmark the performance of the proposed model. Extensive simulation results indicated an accuracy of 93.33%, 100%, and 100% for the classification of conservation, reform, and revolutionary classes, respectively.
Integration of Machine Learning Solutions in the Building Automation System

This publication presents a system for integrating machine learning and artificial intelligence solutions with building automation systems. The platform is based on cloud solutions and can integrate with one of the most popular virtual building management solutions, HomeAssistant. The System uses communication based on the Message Queue Telemetry Transport (MQTT) protocol. The example machine learning function described in this publication detects anomalies in the electricity waveforms and raises the alarm. This information determines power quality and detects system faults or unusual power consumption. Recently, increasing electricity prices on global markets have meant that buildings must significantly reduce consumption. Therefore, a fundamental element of energy consumption diagnostics requires detecting unusual forms of energy consumption to optimise the use of individual devices in home and office installations.
NON-CONTRACTIVE LOGICS, PARADOXES, AND MULTIPLICATIVE QUANTIFIERS

Abstract The paper investigates from a proof-theoretic perspective various non-contractive logical systems, which circumvent logical and semantic paradoxes. Until recently, such systems only displayed additive quantifiers (Grišin and Cantini). Systems with multiplicative quantifiers were proposed in the 2010s (Zardini), but they turned out to be inconsistent with the naive rules for truth or comprehension. We start by presenting a first-order system for disquotational truth with additive quantifiers and compare it with Grišin set theory. We then analyze the reasons behind the inconsistency phenomenon affecting multiplicative quantifiers. After interpreting the exponentials in affine logic as vacuous quantifiers, we show how such a logic can be simulated within a truth-free fragment of a system with multiplicative quantifiers. Finally, we establish that the logic for these multiplicative quantifiers (but without disquotational truth) is consistent, by proving that an infinitary version of the cut rule can be eliminated. This paves the way to a syntactic approach to the proof theory of infinitary logic with infinite sequents.