Reviewerly/exHarmony huggingface dataset:
Nonequilibrium effects on one-norm geometric correlations and the emergence of a pointer-state basis in the weak- and strong-coupling regimes

Correlations in the open quantum systems manifest a rich phenomenology under the influence of various physical sources of noise. We theoretically study the dynamics of quantum correlations and their classical counterparts in a two-qubit system, measured by one-norm geometric quantifiers. We consider both qubits are initially prepared in the arbitrary $X$-type states and locally subjected to nonequilibrium dephasing environments with non-Markovian and nonstationary statistical features. We then explore the effects of nonequilibrium on the abrupt changes in the evolution of geometric correlations in the weak- and strong-coupling regimes. Particularly, in the weak-coupling regime, we show that the environmental nonequilibrium feature can efficiently control the appearance of double-sudden transitions and the decay rate in geometric quantum correlation dynamics. However, in the strong-coupling case, we show that the nonequilibrium nature can prolong the interval for the frozen quantum correlation and suppresses the number of multiple sudden changes in its decay rate. Moreover, the abrupt change from decay to a constant regime in classical correlation evolution reveals the emergence of the pointer-state basis. We show that the pointer-state basis appearance can be retarded in the weak-coupling case when the environment deviates from equilibrium. However, in a strong-coupling regime, we observe the metastable pointer-states basis characterized by multiple, successive sudden transitions in classical correlation dynamics. Remarkably, we unveil that the nonequilibrium feature may suppress the apparition of the metastable pointer-states basis.
A Fast Vehicle Counting and Traffic Volume Estimation Method Based on Convolutional Neural Network

Vehicle counting and traffic volume estimation on traffic videos has gained extensive attention from multimedia and computer vision communities. Recent vehicle counting and volume estimation methods, including detection based and time-spatial image (TSI) based methods have achieved significant improvements. However, how to balance the accuracy and speed is still a challenge to this task. In this paper, we design a fast and accurate vehicle counting and traffic volume estimation method. Firstly, traffic videos are converted to TSIs and we annotate the vehicle locations in TSIs manually. Then, we design a simple TSI density map estimation network which utilizes attention mechanism to strengthen the features in the traffic locations for vehicle counting. Finally, we use the parameters obtained from the vehicle counting network to further estimate the traffic volume. Experiments on UA-DETRAC dataset demonstrate that the vehicle counting network not only takes a balance between counting accuracy and speed, but also well estimates the traffic volume when the video data is insufficient.
Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services

Hospital organizations have adopted telehealth systems to expand their services to a portion of the Brazilian population with limited access to healthcare, mainly due to the geographical distance between their communities and hospitals. The importance and usage of those services have recently increased due to the COVID-19 state-level mobility interventions. These services work with sensitive and confidential data that contain medical records, medication prescriptions, and results of diagnostic processes. Understanding how cybersecurity impacts the development of telehealth strategies is crucial for creating secure systems for daily operations. In the application reported in this article, the Fuzzy Cognitive Maps (FCMs) translated the complexity of cybersecurity in telehealth services into intelligible and objective results in an expert-based cognitive map. The tool also allowed the construction of scenarios simulating the possible implications caused by common factors that affect telehealth systems. FCMs provide a better understanding of cybersecurity strategies using expert knowledge and scenario analysis, enabling the maturation of cybersecurity in telehealth services.
Hyperparameter Power Impact in Transformer Language Model Training

Training large language models can consume a large amount of energy.We hypothesize that the language model's configuration impacts its energy consumption, and that there is room for power consumption optimisation in modern large language models.To investigate these claims, we introduce a power consumption factor to the objective function, and explore the range of models and hyperparameter configurations that affect power.We identify multiple configuration factors that can reduce power consumption during language model training while retaining model quality.: These authors contributed to the paper equally.to Strubell et al. ( 2019), we must cut CO 2 emissions by half to slow natural disasters.However, much research in the field ignores the perspective of energy efficiency.When looking at papers from three top AI conferences, namely ACL, NeurIPS, and CVPR, work tends to focus on accuracy rather than efficiency, or a mixture (Schwartz et al., 2019).An added benefit to developing more energyefficient models is a reduced barrier of entry to NLP research.Researchers with good ideas may not be able to execute those ideas, given that state-of-theart results are locked behind large-scale compute (Strubell et al., 2019;Bender et al., 2021).This study investigates how to reduce power consumption in training transformer language models.We seek to address the issue of high-power models by analysing the resulting models' hyperparameters, energy consumption, and perplexity and providing initial parameter guidelines for low-power, highperformance transformers, and an opening into the research of low-power transformers.Our research question is: How can we reduce the energy consumption of models to both lower the barrier of entry and reduce CO2 emissions, while still keeping an effective model?Following Strubell et al. ( 2019), a possible approach to this problem could be the use of Bayesian hyperparameter search.Throughout our work, we managed to identify hyperparameter configurations that provide strong entries for both perplexity and energy consumption.These configurations were found through our methodology utilising Bayesian optimisation, by combining the libraries Hyperopt and PyTorch.The optimal configurations collectively spanned the identified Pareto frontier.
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems

Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP).The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult.Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge.For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pretrained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5.We observed the performance improvement in the opendomain Korean dialogue model even only English knowledge was given.The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.
Robust Structured Convex Nonnegative Matrix Factorization for Data Representation

Nonnegative Matrix Factorization (NMF) is a popular technique for machine learning.Its power is that it can decompose a nonnegative matrix into two nonnegative factors whose product well approximates the nonnegative matrix.However, the nonnegative constraint of the data matrix limits its application.Additionally, the representations learned by NMF fail to respect the intrinsic geometric structure of the data.In this paper, we propose a novel unsupervised matrix factorization method, called Robust Structured Convex Nonnegative Matrix Factorization (RSCNMF).RSCNMF not only achieves meaningful factorizations of the mixed-sign data, but also learns a discriminative representation by leveraging local and global structures of the data.Moreover, it introduces the L 2,1 -norm loss function to deal with noise and outliers, and exploits the L 2,1 -norm feature regularizer to select discriminative features across all the samples.We develop an alternate iterative scheme to solve such a new model.The convergence of RSCNMF is proven theoretically and verified empirically.The experimental results on eight real-world data sets show that our RSCNMF algorithm matches or outperforms the state-of-the-art methods.
ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries

Word embeddings learn implicit biases from linguistic regularities captured by word cooccurrence statistics.By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a novel intrinsic evaluation task and method to quantify the valence dimension of affect in human-rated word sets from social psychology.We apply Val-Norm on static word embeddings from seven languages (Chinese, English, German, Polish, Portuguese, Spanish, and Turkish) and from historical English text spanning 200 years.Val-Norm achieves consistently high accuracy in quantifying the valence of non-discriminatory, non-social group word sets.Specifically, Val-Norm achieves a Pearson correlation of ρ = 0.88 for human judgment scores of valence for 399 words collected to establish pleasantness norms in English.In contrast, we measure gender stereotypes using the same set of word embeddings and find that social biases vary across languages.Our results indicate that valence associations of non-discriminatory, non-social group words represent widely-shared associations, in seven languages and over 200 years.
Vision-and-Language Pretrained Models: A Survey

Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP). This progress leads to learning joint representations of vision and language pretraining by feeding visual and linguistic contents into a multi-layer transformer, Visual-Language Pretrained Models (VLPMs). In this paper, we present an overview of the major advances achieved in VLPMs for producing joint representations of vision and language. As the preliminaries, we briefly describe the general task definition and genetic architecture of VLPMs. We first discuss the language and vision data encoding methods and then present the mainstream VLPM structure as the core content. We further summarise several essential pretraining and fine-tuning strategies. Finally, we highlight three future directions for both CV and NLP researchers to provide insightful guidance.
Causes, prevention, and interventions regarding classroom disruptions in digital teaching: A systematic review

Abstract Digitization and the Sars-CoV-2 pandemic are accelerating the use of digital tools in teaching. Therefore, this systematic literature review offers an overview of international studies with a particular focus on classroom disruptions and their causes, as well as on prevention and intervention strategies in digital settings. Selecting out of over 700 published articles from the last 20 years, the results show that, although the research on classroom management in general is numerous, the connection between digitization and classroom disruptions has received little attention so far. Studies of different methodological orientations have been conducted, but strongly teacher-focused. Also, there are conceptual inaccuracies leading to a variety of different findings and interpretations. Thus, this article provides a definition of the term digital teaching and critically discusses the classification of new findings, their emplacement in existing research, as well as their potential to expand existing models. Furthermore, the results summarize causes of disruptions in digital teaching, their possible prevention and intervention strategies.
DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named DeepEva, a Deep Learning based system capable of classifying both Italian and English sentences on the basis of their complexity. The system exploits the Treetagger annotation tool, two Long Short Term Memory (LSTM) neural unit layers, and a fully connected one. The last layer outputs the probability of a sentence belonging to the easy or complex class. The experimental results show the effectiveness of the approach for both languages, compared with several baselines such as Support Vector Machine, Gradient Boosting, and Random Forest.
Implementation of Augmented Reality in a Mechanical Engineering Training Context

Global industry is at the heart of its fourth industrial revolution, being driven by the emergence of new digital solutions: Augmented reality allows us to consider the evolution towards the “the augmented operator”. This technology is currently little used in higher education, especially for mechanical engineers. We believe that it can facilitate learning and develop autonomy. The objective of this work is to assess the relevance of augmented reality in this context, as well as its impact on learning. The difficulties for a student approaching a technical system are related to reading and understanding 2D and even 3D representations, lack of knowledge on components functions, and the analysis of the chain of power transmission and transformation of movement. The research is intended to see if AR technologies are relevant to answer these issues and help beginners get started. To that end, several AR scenarios have been developed on different mechanical systems, using the relevant features of the AR interfaces that we have identified. Otherwise, these experiences have enabled us to identify specific issues linked to the implementation of AR. Our choice of AR devices and software allows us to have an integrated digital chain with digital tools and files used by mechanical engineers. Finally, we sought to assess how this technology made it possible to overcome the difficulties of learners, in different learning situations.
Superpixel-based active contour model via a local similarity factor and saliency

The region-based active contour models could present difficulties because of undesired initial contour, noise distribution and image weak edges. In order to overcome the above problems, this paper proposes a superpixel-based via a local similarity factor and saliency (SLSFS). Firstly, the initial contour is generated by combining super-pixel and fuzzy c-means clustering. Secondly, the difference between local space and local intensity is used to improve the segmentation accuracy under noise. Finally, the weak edge information is protected by improved saliency detection. In addition, a gradient similarity constraint is used to remove the redundant regions. SLSFS model can generate adaptive initial contour around the target, and protect the weak edge information of the target on the premise of ensuring certain noise robustness. Experiments show that the average dice of SLSFS is 14% higher than that of the optimal comparison model and 19% on gray images.
Lattice Enumeration for Tower NFS: A 521-Bit Discrete Logarithm Computation

The Tower variant of the Number Field Sieve (TNFS) is known to be asymptotically the most efficient algorithm to solve the discrete logarithm problem in finite fields of medium characteristics, when the extension degree is composite. A major obstacle to an efficient implementation of TNFS is the collection of algebraic relations, as it happens in dimension greater than 2. This requires the construction of new sieving algorithms which remain efficient as the dimension grows. In this article, we overcome this difficulty by considering a lattice enumeration algorithm which we adapt to this specific context. We also consider a new sieving area, a high-dimensional sphere, whereas previous sieving algorithms for the classical NFS considered an orthotope. Our new sieving technique leads to a much smaller running time, despite the larger dimension of the search space, and even when considering a larger target, as demonstrated by a record computation we performed in a 521-bit finite field \({\mathbb F}_{p^6}\). The target finite field is of the same form than finite fields used in recent zero-knowledge proofs in some blockchains. This is the first reported implementation of TNFS.
Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.
Big Data Architecture for Building Energy Management Systems

The enormous quantity of data handled by building management systems are key to develop more efficient energy operational systems. However, the inability of current systems to take benefit from the generated data may waste good opportunities of improving building performance. Big Data appears as a suitable framework to sustain the management system and conduct future prospective analysis. In this article, we present a Big Data-based architecture for the efficient management of buildings. The different Big Data components are involved not only in the data acquisition phase, but also in the implementation of algorithms capable of analyzing massive data collected from very heterogeneous sources. They also enable fast computations that can help the generation of optimal operational plan generations to improve the building functioning. The proposed architecture has been effectively introduced in four different-purpose buildings, demonstrating that Big Data can help during the energy cycle of the building.
Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

According to historical information, more than 300 metal smelting enterprises have been in the southwest of Xiongan for 300 years; however, these polluting enterprises have been gradually closed with the increased intensity of environmental protection. In the paper, 264 soil samples were collected and analyzed in the range of 400 nm–2500 nm by the spectra vista corporation (SVC), and the spectral noise was smoothed by the Savitzky–Golay filter. In order to enhance the spectral differences and curve shapes, mathematical transformations, such as the standard normal variate (SNV), first-order differential (FD), second-order differential (SD), multiple scattering correction (MSC), and continuum removal (CR), were performed on the data, and the correlation between spectral transformation and contents of REEs was analyzed. Moreover, three machine learning models—partial least-squares (PLS), random forest (RF), back propagation neural network (BPNN)—were used to predict the contents of REEs. Experimental results prove that REEs are combined with spectral active substances, such as organic compounds, clay minerals, and iron oxide, and it is possible to determine the contents of REEs using the reflection spectrum. The R2 between the predicted values and measured contents reached 0.986 by using BPNN after FD transformation. More importantly, the predicted values basically agree with the actual situation for CASI/SASI airborne hyperspectral images, and this is an effective technique to obtain the contents of REEs in soil at the study area.
A study on the impact of the users’ characteristics on the performance of wearable fall detection systems

Wearable Fall Detection Systems (FDSs) have gained much research interest during last decade. In this regard, Machine Learning (ML) classifiers have shown great efficiency in discriminating falls and conventional movements or Activities of Daily Living (ADLs) based on the analysis of the signals captured by transportable inertial sensors. Due to the intrinsic difficulties of training and testing this type of detectors in realistic scenarios and with their target audience (older adults), FDSs are normally benchmarked against a predefined set of ADLs and emulated falls executed by volunteers in a controlled environment. In most studies, however, samples from the same experimental subjects are used to both train and evaluate the FDSs. In this work, we investigate the performance of ML-based FDS systems when the test subjects have physical characteristics (weight, height, body mass index, age, gender) different from those of the users considered for the test phase. The results seem to point out that certain divergences (weight, height) of the users of both subsets (training ad test) may hamper the effectiveness of the classifiers (a reduction of up 20% in sensitivity and of up to 5% in specificity is reported). However, it is shown that the typology of the activities included in these subgroups has much greater relevance for the discrimination capability of the classifiers (with specificity losses of up to 95% if the activity types for training and testing strongly diverge).