Reviewerly/exHarmony huggingface dataset:
Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.
RLCard: A Platform for Reinforcement Learning in Card Games

We present RLCard, a Python platform for reinforcement learning research and development in card games. RLCard supports various card environments and several baseline algorithms with unified easy-to-use interfaces, aiming at bridging reinforcement learning and imperfect information games. The platform provides flexible configurations of state representation, action encoding, and reward design. RLCard also supports visualizations for algorithm debugging. In this demo, we showcase two representative environments and their visualization results. We conclude this demo with challenges and research opportunities brought by RLCard. A video is available on YouTube.
Microcontrollers on the Edge – Is ESP32 with Camera Ready for Machine Learning?

For most machine learning tasks big computing power is needed, but some tasks can be done with microcontrollers. In this paper well-known SoC ESP32 has been analyzed. It is usually used in IoT devices for data measurement, but some authors started to use simple machine learning algorithms with them. Generally, this paper will analyze the possibility of using ESP32 with a built-in camera for machine learning algorithms. Focus of research will be on durations of photographing and photograph processing, because that can be a bottleneck of a machine learning tasks. For this purpose, logistic regression has been implemented on ESP32 with camera. It has been used to differentiate two handwritten letters on the greyscale pictures ("o" and "x"). Logistic regression weights have been calculated on the cloud, but then they have been transferred to an ESP32. The output results have been analyzed. The duration of photographing and processing were analyzed as well as the impact of implemented PSRAM memory on performances. It can be concluded that ESP32 with camera can be used for some simple machine learning tasks and for camera picture taking and preparing for other more powerful processors. Arduino IDE still does not provide enough level of optimization for implemented PSRAM memory.
Active Learning of Timed Automata with Unobservable Resets

Active learning of timed languages is concerned with the inference of timed automata by observing some of the timed words in their languages. The learner can query for the membership of words in the language, or propose a candidate model and ask if it is equivalent to the target. The major difficulty of this framework is the inference of clock resets, which are central to the dynamics of timed automata but not directly observable. Interesting first steps have already been made by restricting to the subclass of event-recording automata, where clock resets are tied to observations. In order to advance towards learning of general timed automata, we generalize this method to a new class, called reset-free event-recording automata, where some transitions may reset no clocks. Central to our contribution is the notion of invalidity, and the algorithm and data structures to deal with it, allowing on-the-fly detection and pruning of reset hypotheses that contradict observations. This notion is a key to any efficient active-learning procedure for generic timed automata.
Hyperbolic Feature-based Sarcasm Detection in Telugu Conversation Sentences

Abstract Recognition of sarcastic statements has been a challenge in the process of sentiment analysis. A sarcastic sentence contains only positive words conveying a negative sentiment. Therefore, it is tough for any automated machine to identify the exact sentiment of the text in the presence of sarcasm. The existing systems for sarcastic sentiment detection are limited to the text scripted in English. Nowadays, researchers have shown greater interest in low resourced languages such as Hindi, Telugu, Tamil, Arabic, Chinese, Dutch, Indonesian, etc. To analyse these low resource languages, the biggest challenge is the lack of available resources, especially in the context of Indian languages. Indian languages are very rich in morphology which pose a greater challenge for the automated machines. Telugu is one of the most popular languages after Hindi among Indian languages. In this article, we have collected and annotated a corpus of Telugu conversation sentences in the form of a question followed by a reply for sarcasm detection. Further, a set of algorithms are proposed for the analysis of sarcasm in the corpus of Telugu conversation sentences. The proposed algorithms are based on hyperbolic features namely, Interjection, Intensifier, Question mark and Exclamation symbol. The achieved accuracy is 94%.
A distributed sensing data anomaly detection scheme

With the continuous development of Internet of Things, the information society has gradually entered a new era of the Internet of everything. Sensor nodes are important sources of data in the Internet of Things. The abnormal and failure of sensing data in the Internet of Things will affect the connectivity of the network. If the accuracy and reliability of the corresponding perception data can be effectively improved, we can timely and accurately find out the emergency and monitor the working status of the network. Therefore, it is of great significance to detect the abnormal data of data streams in the sensor network nodes and confirm its source. For the low quality of sensor data collected in real time in IoT, this paper proposes an anomaly detection method for sensing data streams based on edge computing. In this algorithm, the sensor data is expressed in the form of time series. On the edge computing based sensor data anomaly detection model, the improved confidence interval is used to detect whether the data is abnormal. The concept of interval difference is proposed as the judgment of the source of the anomaly. The accuracy and effectiveness of the algorithm are verified by experiments. The results show that the detection rate of abnormal data is above 98%, which indicates that the algorithm has certain practicability.
HEX: Hyperbolic Event eXtractor, a Seismic Phase Associator for Highly Active Seismic Regions

Abstract The task of seismic phase association is to correlate the onsets of radiated seismic energy with an underlying source. Commonly applied within seismic monitoring networks for event detection, it forms a vital component of many seismic processing pipelines. With the complexity of this task naturally increasing with the number of phases to simultaneously correlate, rapid advancements in the number of sensors per seismic deployment, along with improved picking algorithms have greatly increased the volume of phases now recorded across seismic networks. Although traditional phase association methods work well for historic catalogs, they become unreliable when tasked with associating the frequent smaller events recorded in the latest seismic datasets. Accurately correlating such events is crucial if seismologists are to better understand the underlying physical processes. The phase association problem is, therefore, being revisited with novel techniques now being applied to improve performance. We present a new technique for associating seismic phases, Hyperbolic Event eXtractor (HEX). HEX adapts the logic of Random Sample Consensus, a model estimation approach widely used in the computer vision community and specifically designed to deal with high proportions of noise in the data distribution. We demonstrate the performance of HEX in associating phases over a synthetic dataset for a regional seismic network in northern Chile. Synthetic testing reveals that HEX can correlate seismic phases when events have up to a ∼15 s average spacing.
Accelerating wireless channel autoencoders for short coherence-time communications

Traditional wireless communication theory is based on complex probabilistic models and fixed conjectures, which limit the optimal utilization of spectrum resources.Deep learning has been used to design end-to-end communication systems using an encoder to replace the transmitter and a decoder for the receiver.We address the challenge to update the parameters of a wireless channel autoencoder (AE) under a time-varying channel with short coherence-time.We suggest an optimized training algorithm that updates the learning rate value on a per-dimension basis, restricting the past gradients instead of accumulating them.We also scale the initial weights of our AE by sampling them from a normalized uniform distribution.While recently proposed AE configurations might fail to converge at a few number of epochs, our setting attains a fast convergence maintaining its robustness to large gradients, oscillations, and vanishing problems.By simulation results, we demonstrate that our proposed AE configuration improves the bit reconstruction accuracy in shorter training time.
Out-of-equilibrium steady states of a locally driven lossy qubit array

This work shows that localised dissipation in a qubit array can establish long-range coherence and density-wave order in a class of resonant geometries, which can be understood from simple rate equations.
Guiding Inferences in Connection Tableau by Recurrent Neural Networks

We present a dataset and experiments on applying recurrent neural networks (RNNs) for guiding clause selection in the connection tableau proof calculus. The RNN encodes a sequence of literals from the current branch of the partial proof tree to a hidden vector state; using it, the system selects a clause for extending the proof tree. The training data and learning setup are described, and the results are discussed and compared with state of the art using gradient boosted trees. Additionally, we perform a conjecturing experiment in which the RNN does not just select an existing clause, but completely constructs the next tableau goal.
Multispectral image denoising using sparse and graph Laplacian Tucker decomposition

Abstract Multispectral image denoising is a basic problem whose results affect subsequent processes such as target detection and classification. Numerous approaches have been proposed, but there are still many challenges, particularly in using prior knowledge of multispectral images, which is crucial for solving the ill-posed problem of noise removal. This paper considers both non-local self-similarity in space and global correlation in spectrum. We propose a novel low-rank Tucker decomposition model for removing the noise, in which sparse and graph Laplacian regularization terms are employed to encode this prior knowledge. It can jointly learn a sparse and low-rank representation while preserving the local geometrical structure between spectral bands, so as to better capture simultaneously the correlation in spatial and spectral directions. We adopt the alternating direction method of multipliers to solve the resulting problem. Experiments demonstrate that the proposed method outperforms the state-of-the-art, such as cube-based and tensor-based methods, both quantitatively and qualitatively.
Discriminant Analysis for Compositional Data Incorporating Cell-Wise Uncertainties

Abstract In the geosciences it is still uncommon to include measurement uncertainties into statistical methods such as discriminant analysis, but, especially for trace elements, measurement uncertainties are frequently of relevant size. Uncertainties can be reported by each measured variable, by each observation or by individual cells (i.e., each observation has an individual uncertainty for each variable). Most methods incorporating uncertainties use the uncertainties as weights for the variables or observations of the data set. The method proposed in this contribution uses variance additivity properties and generalised least squares to calculate better estimates of group variances and group means, which then influence the decision rules of linear and quadratic discrimination algorithms. This methodological framework allows incorporation of cell-wise uncertainties, and would be largely valid if the information about co-dependency between variable errors within each observation were reported. The method is also appropriate for incorporating uncertainties into compositional data sets—for example, those formed by concentrations, proportions, percentages or any other form of information about the relative abundance of a set of components forming a whole—even if such uncertainties are nearly never reported considering this compositional nature. The methods are illustrated by means of case studies with simulated data. Graphical abstract
Approximate Counting of Minimal Unsatisfiable Subsets

Given an unsatisfiable formula F in CNF, i.e. a set of clauses, the problem of Minimal Unsatisfiable Subset (MUS) seeks to identify a minimal subset of clauses $$N \subseteq F$$ such that N is unsatisfiable. The emerging viewpoint of MUSes as the root causes of unsatisfiability has led MUSes to find applications in a wide variety of diagnostic approaches. Recent advances in identification and enumeration of MUSes have motivated researchers to discover applications that can benefit from rich information about the set of MUSes. One such extension is that of counting the number of MUSes. The current best approach for MUS counting is to employ a MUS enumeration algorithm, which often does not scale for the cases with a reasonably large number of MUSes. Motivated by the success of hashing-based techniques in the context of model counting, we design the first approximate MUS counting procedure with $$(\varepsilon ,\delta )$$ guarantees, called $$\mathsf {AMUSIC}$$ . Our approach avoids exhaustive MUS enumeration by combining the classical technique of universal hashing with advances in QBF solvers along with a novel usage of union and intersection of MUSes to achieve runtime efficiency. Our prototype implementation of $$\mathsf {AMUSIC}$$ is shown to scale to instances that were clearly beyond the realm of enumeration-based approaches.
DPoSB: Delegated Proof of Stake with node's behavior and Borda Count

Blockchain was known by Satoshi Nakamoto in 2008 because of Bitcoin. After more than a decade of development, blockchain has been widely used in medical, digital finance, and electronic evidence, etc. Consensus algorithm is the core part of blockchain and has become the bottleneck of blockchain performance. At present, there are dozens of different consensus algorithms for different application context, such as Proof of Work (PoW), Proof of Stake (PoS), Delegate Proof of Stake (DPoS), Proof of Reputation (PoR), etc. But in Public blockchains, their performance, security and stability are difficult to support a large project. This paper introduces an improved consensus algorithm named Delegated Proof of Stake with node's behavior and Borda Count (DPoSB) which based on the node behavior monitoring in DPoS and Borda count voting. The node's behavior of generating blocks is used as a reference index for node elections, and it will increase the difficulty for nodes that have bad behavior. Then, combined with the Borda Count to elect nodes that is more in line with the other nodes' wishes. So, the elected nodes are more secure and fairer, thereby ensuring the stability of the system and the interests of blockchain nodes. Finally, the feasibility and correctness of the consensus algorithm is proved by simulation experiments of the blockchain system.
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round. Naive weighted aggregation of such models causes objective inconsistency, that is, the global model converges to a stationary point of a mismatched objective function which can be arbitrarily different from the true objective. This paper provides a general framework to analyze the convergence of federated heterogeneous optimization algorithms. It subsumes previously proposed methods such as FedAvg and FedProx and provides the first principled understanding of the solution bias and the convergence slowdown due to objective inconsistency. Using insights from this analysis, we propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.
Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring

The research paper focuses on a challenging task faced in blind image deblurring (BID). It relates to the estimation of arbitrarily shaped (nonparametric or complex shaped) point spread functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring, in this case, requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on genetic algorithm and utilizes the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other state-of-the-art single image motion deblurring schemes as benchmarks. Validation has been carried out on the standard real-life blurred images. Results of both benchmark and real images are presented. For real-life blurred images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions. However, the benchmark schemes fail to effectively restore the real blurred images. The proposed scheme surpasses on average of seven percent higher image quality as compared to the benchmark schemes.
Data Association via Set Packing for Computer Vision Applications

Significant progress has been made in the field of computer vision because of the development of supervised machine learning algorithms, which efficiently extract information from high-dimensional data such as images and videos. Such techniques are particularly effective at recognizing the presence or absence of entities in the domains where labeled data are abundant. However, supervised learning is not sufficient in applications where one needs to annotate each unique entity in crowded scenes respecting known domain-specific structures of those entities. This problem, known as data association, provides fertile ground for the application of combinatorial optimization. In this review paper, we present a unified framework based on column generation for some computer vision applications, namely multiperson tracking, multiperson pose estimation, and multicell segmentation, which can be formulated as set packing problems with a massive number of variables. To solve them, column generation algorithms are applied to circumvent the need to enumerate all variables explicitly. To enhance the solution process, we provide a general approach for applying subset-row inequalities to tighten the formulations and introduce novel dual-optimal inequalities to reduce the dual search space. The proposed algorithms and their enhancements are successfully applied to solve the three aforementioned computer vision problems and achieve superior performance over benchmark approaches. The common framework presented allows us to leverage operations research methodologies to efficiently tackle computer vision problems.
2D geometric shapes dataset – for machine learning and pattern recognition

In this paper, we present a data article that describes a dataset of nine 2D geometric shapes, and each shape is drawn randomly on a 200 × 200 RGB image. During the generation of this dataset, the perimeter and the position of each shape are selected randomly and independently for each image. The rotation angle of each shape is chosen randomly for each image within an interval between -180° and 180°, as well as the background colour of each image and the filling colour of each shape are selected randomly and independently. The published dataset is composed of 9 classes of data, and each class represent a type of geometric shape (Triangle, Square, Pentagon, Hexagon, Heptagon, Octagon, Nonagon, Circle and Star). Each class is composed of 10k generated images. This paper also includes a GitHub URL to the generator source code used for the generation, which can be reused to generate any desired size of data. The proposed dataset aims to provide a perfectly clean dataset, for classification as well as clustering purposes. The fact that this dataset is generated synthetically provides the ability to use it to study the behaviour of machine learning models independently of the nature of the dataset or the possible noise or data leak that can be found in any other datasets. Moreover, the choice of a 2D geometrical shape dataset provides the ability to understand as well to have good knowledge of the number of patterns stored inside each data class.
Automata Tutor v3

Computer science class enrollments have rapidly risen in the past decade. With current class sizes, standard approaches to grading and providing personalized feedback are no longer possible and new techniques become both feasible and necessary. In this paper, we present the third version of Automata Tutor, a tool for helping teachers and students in large courses on automata and formal languages. The second version of Automata Tutor supported automatic grading and feedback for finite-automata constructions and has already been used by thousands of users in dozens of countries. This new version of Automata Tutor supports automated grading and feedback generation for a greatly extended variety of new problems, including problems that ask students to create regular expressions, context-free grammars, pushdown automata and Turing machines corresponding to a given description, and problems about converting between equivalent models - e.g., from regular expressions to nondeterministic finite automata. Moreover, for several problems, this new version also enables teachers and students to automatically generate new problem instances. We also present the results of a survey run on a class of 950 students, which shows very positive results about the usability and usefulness of the tool.