Reviewerly/exHarmony huggingface dataset:
An exploration on text classification using machine learning techniques

This paper provides an exploration of the applicability and effectiveness of Machine Learning models on text classification. In particular traditional machine learning such as Support Vector Machines, Naïve Bayes and Random Forests are scrutinized. Apart from a comparison of their performance on text classification for real-world corpora, the paper also describes and highlights the significance of other steps involved in the text classification workflow, such as preprocessing, lemmatization and use of n-grams in the discussed datasets.
Integrating blockchain with Enterprise Resource Planning systems: benefits and challenges

Enterprise Resource Planning (ERP) systems seamlessly support business activities by controlling both Supply Chain (SC) operations and relevant information. By serving as consolidation platforms, ERP systems offer significant benefits in data visibility and back-office functions and enhance decision-making processes in modern business. Blockchain technology is driving Industry 4.0, and its integration with current ERP systems seems almost mandatory for the years to come. Based on a systematic survey of the current literature, we classify the benefits of blockchain and ERP systems integration, particularly in terms of improved operational efficiency, workflow automation, security and regulatory compliance. In addition, we discuss various challenges for integrating blockchain with ERP systems, such as possible infrastructure upgrades, organizational and cultural aspects, standardization, regulatory issues and high adoption and maintenance costs. We believe that the main results of this study could be of significant value to both researchers and practitioners and could serve as a starting point for better portraying the interrelationships between ERP systems and blockchain technology.
Quantum Machine Learning: Current State and Challenges

In recent years, machine learning has penetrated a large part of our daily lives, which creates special challenges and impressive progress in this area. Nevertheless, as the amount of daily data is grown, learning time is increased. Quantum machine learning (QML) may speed up the processing of information and provide great promise in machine learning. However, it is not used in practice yet, because quantum software and hardware challenges are still unsurmountable. This paper provides current research of quantum computing and quantum machine learning algorithms. Also, the quantum vendors, their frameworks, and their platforms are presented. A few fully implemented versions of quantum machine learning are presented, which are easier to be evaluated. Finally, QML's challenges, and problems are discussed.
Collegiate Social Engineering Capture the Flag Competition

Social engineering (SE) is an essential, yet often over-looked, field within cybersecurity, particularly in the context of education, training, and awareness. While there are investments in cybersecurity education programs, they tend to have a primarily technical focus, including within classroom curricula and Capture the Flag (CTF) competitions. Because the current technical CTFs do not emphasize the relevance of the human-socio-psychological aspects of cyberattacks and cybersecurity, the researchers organized and hosted a Collegiate SECTF grounded in the social sciences, which offered a timely and unique platform for students to learn about social engineering topics, such as OSINT, phishing, and vishing, in a hands-on, engaging, and ethical manner. This paper details the planning and logistics of the virtual SECTF event which took place October 2020 at Temple University and hosted 6 teams of undergraduate students from across the world. Students&#x2019; experiences while participating in this event are described in detail, with insight on teams&#x2019; preparations, group formation and dynamics, strategies and adaptations, learning benefits, and thoughts on each individual flag. The success and positive student responses from the inaugural SECTF provide a proof of concept, demonstrating that experiential learning can be used to teach students about SE.
Cross-Modal feature description for remote sensing image matching

Effective feature description for cross-modal remote sensing matching is challenging due to the complex geometric and radiometric differences between multimodal images. Currently, Siamese or pseudo-Siamese networks directly describe features from multimodal remote sensing images at the fully connected layer, however, the similarity of cross-modal features during feature extraction is barely considered. Therefore, we construct a cross-modal feature description matching network (CM-Net) for remote sensing image matching in this paper. First, a contextual self-attention module is proposed to add semantic global dependency information using candidate and non-candidate keypoint patches. Then, a cross-fusion module is designed to obtain cross-modal feature descriptions through information interaction. Finally, a similarity matching loss function is presented to optimize discriminative feature representations, converting a matching task into a classification task. The proposed CM-Net model is evaluated by qualitative and quantitative experiments on four multimodal image datasets, which achieves the average Matching score (M.S.), Mean Matching Accuracy (MMA), and average Root-mean-square error (aRMSE) of 0.781, 0.275, and 1.726, respectively. The comparative study demonstrates the superior performance of the proposed CM-Net for the remote sensing image matching.
Binary Subspace Chirps

We describe in detail the interplay between binary symplectic geometry and notions from quantum computation, with the ultimate goal of constructing highly structured codebooks. The Binary Chirps (BCs) are Complex Grassmannian Lines in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N = 2^{m}$ </tex-math></inline-formula> dimensions used in deterministic compressed sensing and random/unsourced multiple access in wireless networks. Their entries are fourth roots of unity and can be described in terms of second order Reed-Muller codes. The Binary Subspace Chirps (BSSCs) are a unique collection of BCs of ranks ranging from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r=0$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r = m$ </tex-math></inline-formula> , embedded in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> dimensions according to an on-off pattern determined by a rank <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> binary subspace. This yields a codebook that is asymptotically 2.38 times larger than the codebook of BCs, has the same minimum chordal distance as the codebook of BCs, and the alphabet is minimally extended from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\{\pm 1,\pm i\}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\{\pm 1,\pm i, 0\}$ </tex-math></inline-formula> . Equivalently, we show that BSSCs are stabilizer states, and we characterize them as columns of a well-controlled collection of Clifford matrices. By construction, the BSSCs inherit all the properties of BCs, which in turn makes them good candidates for a variety of applications. For applications in wireless communication, we use the rich algebraic structure of BSSCs to construct a low complexity decoding algorithm that is reliable against Gaussian noise. In simulations, BSSCs exhibit an error probability comparable or slightly lower than BCs, both for single-user and multi-user transmissions.
Online Change Point Detection in Application With Transition-Aware Activity Recognition

Transition-aware activity recognition is an inherent component of online health monitoring and ambient assisted living. An explosion of technology breakthroughs in wireless sensor networks, wearable computing, and mobile computing has facilitated this. However, real time, dynamic activity recognition is still challenging in practice. As reported in the existing literature, machine learning techniques are successfully used on the presegmented data to deliver transition-aware activity recognition systems. However, these strategies are frequently ineffective when used in a near-real-time context. This article presents an online change point detection (OCPD) strategy to segment the continuous multivariate time-series smartphone sensor data and its application in a transition-aware activity recognition framework. The proposed OCPD strategy is based on the hypothesis-and-verification principle. After the online data stream segmentation using the proposed OCPD strategy, feature engineering is performed to retain the essential features. Then, synthetic minority oversampling technique (SMOTE) is applied to balance the dataset. Finally, practical experiments are carried out to verify the suggested frameworks’ efficiency and reliability. The results reveal that the proposed OCPD strategy with ensemble classifier achieves a greater recognition rate (F-Measure: 99.80%) compared to methods stated in the literature.
From mining to meaning: How B2B marketers can leverage text to inform strategy

Today's marketers are increasingly faced with the need to collect and interpret data to aid firm strategic decision making. At the same time, there has been an explosion of text-based data and numerous advances in big data that enable marketers to mine the collection and aggregation of text. However, for many marketers there is a need to better understand how textual data can go beyond mere descriptive metrics to instead help solve real marketing problems. With this paper, we take a step in this direction. We first review key concepts and terms that are relevant to understanding how text analysis operates, as well as a new development in custom dictionary creation that expands the topics possible with text analysis. Next, we develop the FTTA grid, a new framework that enables text-derived metrics to inform actionable strategies for marketers. We present two real cases demonstrating how the FTTA grid can be employed in action. Finally, we discuss implications for both academics and marketing practitioners.
Constrained and Unconstrained Inverse Potts Modelling for Joint Image Super-Resolution and Segmentation

In this work we consider two methods for joint single-image super-resolution and image partitioning.The proposed approaches rely on a constrained and on an unconstrained version of the inverse Potts model where an 0 regularization prior on the image gradient is used for promoting piecewise constant solutions.For the numerical solution of both models, we provide a unified implementation based on the Alternating Direction Method of Multipliers (ADMM).Upon suitable assumptions on both model operators and on the algorithmic parameters involved, we show that all the ADMM subproblems admit closed-form solutions, thus making the resulting algorithms computationally very cheap even when high-dimensional data are considered.Numerical details of the implementation of both models are given and several experiments are carried out on both synthetic and natural images to underline the accuracy and the computational efficiency of the models.
Handle it in-house?

Abstract Vocabulary lists of high-frequency lexical items are an important resource in language education and a key product of corpus research. However, no single vocabulary list will be useful for every learning context, with the appropriateness of such lists affected by the corpora on which they are based. This paper investigates the impact of corpus selection on one measure of lexical sophistication, Advanced Guiraud, focusing on two frequency lists originating from an in-house learner corpus (PELIC) and a global learner corpus (Cambridge Learner Corpus). This analysis shows that frequency lists derived from both types of learner corpus can effectively serve as the basis for measuring the development of lexical sophistication, regardless of the specific program of the learners. Therefore, publicly available learner corpus frequency lists can be a reliable resource for stakeholders interested in the lexical gains of language learners.
Spatial-temporal measurement of waves in laboratory based on binocular stereo vision and image processing

Acquisition of wave field data is essential for hydrodynamics studies. Current studies show deficiencies in the spatial measurement of wave fields in the laboratory, and a more efficient method is urgently needed. A stereo imaging method was proposed for the spatial-temporal measurement of waves in the laboratory based on binocular stereo vision and digital image processing. Then, a series of regular and focused waves were simulated in a wave flume to examine the feasibility and reliability of the method by comparison with the measurements by wave probes. Floating particles were evenly sprinkled on the water surface as the representation of the water-air interface, providing texture features for wave surface images and allowing the realization of dense reconstruction. Synchronized cameras provided the synchronized stereo pairs sequence for each wave, and temporal reconstruction was achieved by processing frame by frame. The 4D spatial-temporal volume for every wave could be constructed. Subsequently, spatial slices of the volume obtained the instantaneous profiles of the wave field in different directions. Temporal slices obtained the wave time series at a fixed point, which were compared with the measurements of the standard resistive probe to verify the accuracy. The results showed that the proposed method was capable of measuring the spatial-temporal field of waves efficiently and precisely and offered the potential to measure more complex wave fields in nonlinear wave-structure interactions.
Adaptive search space pruning in complex strategic problems

People have limited computational resources, yet they make complex strategic decisions over enormous spaces of possibilities. How do people efficiently search spaces with combinatorially branching paths? Here, we study players' search strategies for a winning move in a "k-in-a-row" game. We find that players use scoring strategies to prune the search space and augment this pruning by a "shutter" heuristic that focuses the search on the paths emanating from their previous move. This strong pruning has its costs-both computational simulations and behavioral data indicate that the shutter size is correlated with players' blindness to their opponent's winning moves. However, simulations of the search while varying the shutter size, complexity levels, noise levels, branching factor, and computational limitations indicate that despite its costs, a narrow shutter strategy is the dominant strategy for most of the parameter space. Finally, we show that in the presence of computational limitations, the shutter heuristic enhances the performance of deep learning networks in these end-game scenarios. Together, our findings suggest a novel adaptive heuristic that benefits search in a vast space of possibilities of a strategic game.
StackVAE-G: An efficient and interpretable model for time series anomaly detection

Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner. In this work, we propose a novel autoencoder-based model, named StackVAE-G that can significantly bring the efficiency and interpretability to multivariate time series anomaly detection. Specifically, we utilize the similarities across the time series channels by the stacking block-wise reconstruction with a weight-sharing scheme to reduce the size of learned models and also relieve the overfitting to unknown noises in the training data. We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels. Combining these two modules, we introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph neural network) model for multivariate time series anomaly detection. We conduct extensive experiments on three commonly used public datasets, showing that our model achieves comparable (even better) performance with the state-of-the-art models and meanwhile requires much less computation and memory cost. Furthermore, we demonstrate that the adjacency matrix learned by our model accurately captures the interrelation among multiple channels, and can provide valuable information for failure diagnosis applications.
Modelly: An open source all in one python package for developing machine learning models

Various machine learning algorithms are developed for classification and prediction purposes. These models have been developed to provide solutions and ease our everyday lives in many fields. Neural networks are used extensively in all fields, yet developing them is a difficult and time-consuming process. In this paper, we discuss our package Modelly which provides interactive no-code as well as low code options for developing, testing, and tuning neural networks and their variants like XBNet. Further, we also provide tree-based models in our package that can also be built interactively. Our package aims to facilitate the entire process of developing machine learning and deep learning models to ease the process of developing real-world applications.
Class-Oriented Self-Learning Graph Embedding for Image Compact Representation

As one of the learning ways for inducing efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are designed to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within classes, but exclude the affinity of different classes, resulting in inadequate capture of marginal structures between different class distributions. To overcome these issues, we propose a learning framework that implements Class-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can be easily extended to semi-supervised scenarios. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method in comparison to some state-of-the-art approaches.
Trust challenges in reusing open source software

Open source projects play a significant role in software production. Most of the software projects reuse and build upon the existing open source projects and libraries. While reusing is a time and cost saving strategy, some of the key factors are often neglected that create vulnerability in the software system. We look beyond the static code analysis and dependency chain tracing to prevent vulnerabilities at the human factors level. Literature lacks a comprehensive study of the human factors perspective to the issue of trust in reusing open source projects. We performed an interview-based initial study with software developers to get an understanding of the trust issue and limitations among the practitioners. We outline some of the key trust issues in this paper and layout the first steps towards a trustworthy reuse of software.
Derivation of subset product lines in FeatureIDE

The development and configuration of software product lines can be challenging tasks. During development, engineers often need to focus on a particular subset of features that is relevant for them. In such cases, it would be beneficial to hide other features and their implementation. During product configuration, requirements of potentially multiple stakeholders need to be considered. Therefore, configuration often happens in stages, in which different people contribute configuration decisions for different features. Moreover, in some cases, stakeholders want to share a set of products rather than a specific one. In all these cases, the necessary operation is the same: some features from the product line are assigned a value (e.g., via a partial configuration) while other features remain configurable. In this work, we propose a subset operation that takes a product line and a partial configuration to derive a subset product line comprising only the desired subset of features and implementation artifacts. Furthermore, we present, evaluate, and publish our implementation of the proposed subset operation within the FeatureIDE framework.
Consistency-based integration of multi-stakeholder recommender systems with feature model configuration

Feature models are used to represent variability properties of complex items. In most of the cases, the assumption in feature model configuration is that single users/stakeholders are interacting with the underlying configurator. However, there are many scenarios where multiple stakeholders need to jointly complete a configuration task, for example, when selecting the features to be included in a company-wide software service or when deciding about the software features to be included in upcoming releases. In such cases, decisions have to be taken jointly where the constraints and preferences of individual stakeholders have to be taken into account. In this paper, we show how multi-stakeholder recommender systems can be integrated in feature model configuration scenarios.