Reviewerly/exHarmony huggingface dataset:
Qibolab: an open-source hybrid quantum operating system

We present <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibolab</mml:mtext></mml:mrow></mml:math>, an open-source software library for quantum hardware control integrated with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibo</mml:mtext></mml:mrow></mml:math> quantum computing middleware framework. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibolab</mml:mtext></mml:mrow></mml:math> provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibolab</mml:mtext></mml:mrow></mml:math> enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources

Abstract Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high quality, closely mimicking human speech. Nevertheless, given the wide array of applications now employing TTS models, mere high-quality speech generation is no longer sufficient. Present-day TTS models must also excel at producing expressive speech that can convey various speaking styles and emotions, akin to human speech. Consequently, researchers have concentrated their efforts on developing more efficient models for expressive speech synthesis in recent years. This paper presents a systematic review of the literature on expressive speech synthesis models published within the last 5 years, with a particular emphasis on approaches based on deep learning. We offer a comprehensive classification scheme for these models and provide concise descriptions of models falling into each category. Additionally, we summarize the principal challenges encountered in this research domain and outline the strategies employed to tackle these challenges as documented in the literature. In the Section 8, we pinpoint some research gaps in this field that necessitate further exploration. Our objective with this work is to give an all-encompassing overview of this hot research area to offer guidance to interested researchers and future endeavors in this field.
CDYL for infrared and visible light image dense small object detection

To address the phenomenon of many small and hard-to-detect objects in infrared and visible light images, we propose an object detection algorithm CDYL (Convolution to Fully Connect-ed-Deformable Convolution You only Look once) based on the CFC-DC (Convolution to Fully Connected-Deformable Convolution) module. The core operator of CDYL is CFC-DC, making our model not only have a large effective receptive field in infrared and visible light images, but also have adaptive spatial aggregation conditioned by input and task information. As a result, the CDYL reduces the strict inductive bias of traditional CNNs and has long-range dependence for large kernel convolution as well as adaptive spatial aggregation, deeply mining of edge and correlation information in images to enhance sensitivity to small objects, thereby improving performance in dense small object detection tasks. In order to improve the ability of the CFC-DC module to perceive the detailed information of the image, we use the Mish activation function, which has a wider minima which improves the generalization. The effectiveness as well as the generalization of CDYL is evaluated on an infrared image dataset and an UAV image dataset, and it is compared with other state-of-the-art object detection algorithms. Compared to the baseline network YOLOv8l, our model achieved a 3.0% improvement in mAP0.5 in infrared image detection tasks and a 1.1% improvement in mAP0.5 in visible light image detection tasks. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight. Code is publicly available at https://github.com/yangzhu1/CDYL .
MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified.
High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.
Beyond the breach: Bitcoin’s response to exchange-related cyberattacks and closures

We investigate the influence of digital exchange-related cyberattacks and closures on Bitcoin's returns and volatility. We observe a negative impact on Bitcoin's return of 1.368% on the days when the cyberattacks occur, but no significant effect linked to exchange closures. Conversely, exchange closures increase Bitcoin's volatility, while cyberattacks on digital exchanges do not generate a significant impact. Subsample analysis over the 2012–2018 and 2019–2023 time intervals yields results that are largely consistent with those obtained from the entire sample period.
A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.
PSMOT: Online Occlusion-Aware Multi-Object Tracking Exploiting Position Sensitivity

Models based on joint detection and re-identification (ReID), which significantly increase the efficiency of online multi-object tracking (MOT) systems, are an evolution from separate detection and ReID models in the tracking-by-detection (TBD) paradigm. It is observed that these joint models are typically one-stage, while the two-stage models become obsolete because of their slow speed and low efficiency. However, the two-stage models have naive advantages over the one-stage anchor-based and anchor-free models in handling feature misalignment and occlusion, which suggests that the two-stage models, via meticulous design, could be on par with the state-of-the-art one-stage models. Following this intuition, we propose a robust and efficient two-stage joint model based on R-FCN, whose backbone and neck are fully convolutional, and the RoI-wise process only involves simple calculations. In the first stage, an adaptive sparse anchoring scheme is utilized to produce adequate, high-quality proposals to improve efficiency. To boost both detection and ReID, two key elements-feature aggregation and feature disentanglement-are taken into account. To improve robustness against occlusion, the position-sensitivity is exploited, first to estimate occlusion and then to direct the post-process for anti-occlusion. Finally, we link the model to a hierarchical association algorithm to form a complete MOT system called PSMOT. Compared to other cutting-edge systems, PSMOT achieves competitive performance while maintaining time efficiency.
Exploratory search in information systems: a systematic review

Purpose Exploratory search activities are ubiquitous in various information systems. Much potentially useful or even serendipitous information is discovered during the exploratory search process. Given its irreplaceable role in information systems, exploratory search has attracted growing attention from the information system community. Since few studies have methodically reviewed current publications, researchers and practitioners are unable to take full advantage of existing achievements, which, in turn, limits their progress in this field. Through a literature review, this study aims to recapitulate important research topics of exploratory search in information systems, providing a research landscape of exploratory search. Design/methodology/approach Automatic and manual searches were performed on seven reputable databases to collect relevant literature published between January 2005 and July 2023. The literature pool contains 146 primary studies on exploratory search in information system research. Findings This study recapitulated five important topics of exploratory search, namely, conceptual frameworks, theoretical frameworks, influencing factors, design features and evaluation metrics. Moreover, this review revealed research gaps in current studies and proposed a knowledge framework and a research agenda for future studies. Originality/value This study has important implications for beginners to quickly get a snapshot of exploratory search studies, for researchers to re-align current research or discover new interesting issues, and for practitioners to design information systems that support exploratory search.
Manufacturing cloud service recommendation model based on deep feature learning and user preference perception

The manufacturing cloud service recommendation model is a key technology for users to quickly discover and obtain the personalized services they need from the increasingly expanding manufacturing cloud; however, the manufacturing cloud service recommendation model faces problems, such as difficulty in representing deep features of services and too sparse user preference models. To address the above problems, this paper proposes a manufacturing cloud service recommendation model framework based on deep feature learning and user preference perception and investigates the key technologies involved. On the one hand, a Convolutional Neural Network (CNN)-based deep feature extraction method for manufacturing cloud services is proposed by combining the Latent Dirichlet Allocation (LDA) topic model and word2vec. On the other hand, the concept of a category is introduced through the method of label clustering to perceive user behavior, efficiently build user preference models, and solve the sparse problem of the user-service matrix. Based on this, the execution process of the recommendation model for manufacturing cloud services is analyzed by fusing the deep features of services and user preference models. The experimental results on the actual dataset prove that the method greatly improves the recommendation effect.
Human Activity Recognition through Smartphone Inertial Sensors with ML Approach

Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms’ performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period.
Algorithms in selection decisions: Effective, but unappreciated

Abstract Selection decisions are often affected by irrelevant variables such as gender or race. People can discount this irrelevant information by adjusting their predictions accordingly, yet they fail to do so intuitively. In five online studies ( N = 1077), participants were asked to make selection decisions in which the selection test was affected by irrelevant attributes. We examined whether in such decisions people are willing to be advised by algorithms, human advisors or prefer to decide without advice. We found that people fail to adjust for irrelevant information by themselves, and those who received advice from an algorithm or human advisor made better decisions. Interestingly, although most participants stated they prefer advice from human advisors, they tend to rely equally on algorithms in actual selection tasks. The sole exception is when they are forced to choose between an algorithm and a human advisor. In that case, they pick human advisors. We conclude that while algorithms may not be people's preferred source of advice in selection decisions, they are equally useful and can be implemented.
An interdisciplinary framework to optimize the anticipation skills of high-level athletes using virtual reality

The ambition of our contribution is to show how an interdisciplinary framework can pave the way for the deployment of innovative virtual reality training sessions to improve anticipation skills in top-level athletes. This improvement is so challenging that some authors say it is like "training for the impossible". This framework, currently being implemented as part of a project to prepare athletes for the 2024 Olympic Games in Paris, based on the ecological-dynamics approach to expertise, is innovative in its interdisciplinary nature, but also and above all because it overcomes the limitations of more traditional training methods in the field designed to optimize anticipation skills in top-level athletes. The ambition is to tackle successive challenges ranging from the design of virtual partners and opponents to the deployment of training programs in virtual reality, while ensuring the acceptability and acceptance of such innovative virtual reality training protocols and measuring associated workloads.
A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

Abstract Recent growth in data dimensions presents challenges to data mining and machine learning. A high-dimensional dataset consists of several features. Data may include irrelevant or additional features. By removing these redundant and unwanted features, the dimensions of the data can be reduced. The feature selection process eliminates a small set of relevant and important features from a large data set, reducing the size of the dataset. Multiple optimization problems can be solved using metaheuristic algorithms. Recently, the Grasshopper Optimization Algorithm (GOA) has attracted the attention of researchers as a swarm intelligence algorithm based on metaheuristics. An extensive review of papers on GOA-based feature selection algorithms in the years 2018–2023 is presented based on extensive research in the area of feature selection and GOA. A comparison of GOA-based feature selection methods is presented, along with evaluation strategies and simulation environments in this paper. Furthermore, this study summarizes and classifies GOA in several areas. Although many researchers have introduced their novelty in the feature selection problem, many open challenges and enhancements remain. The survey concludes with a discussion about some open research challenges and problems that require further attention.
A lightweight and personalized edge federated learning model

Abstract As a new distributed machine learning paradigm, federated learning has gained increasing attention in the industry and research community. However, federated learning is challenging to implement on edge devices with limited resources and heterogeneous data. This study aims to realize a lightweight and personalized model through pruning and masking with insufficient resources and heterogeneous data. Particularly, the server first downloads the subnetwork to the client according to the mask, and client prunes the subnetwork with the alternating direction method of multipliers (ADMM), so as to remove the unimportant parameters and reduce the cost of training and communication. At the same time, mask is used to mark the pruning condition of the model. Then, the unpruned parts and masks of local models are transmitted to the server for aggregation. The experimental results showed that the accuracy of the proposed model was improved by 9.36%, and the communication cost was reduced by 1.45 times compared with state-of-the-art models. Last but not least, we deploy flower identification models in Android Studio to illustrate the practicality of the proposed method.
Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis

Cognitive impairments present challenges for patients, impacting memory, attention, and problem-solving abilities. Virtual reality (VR) offers innovative ways to enhance cognitive function and well-being. This study explores the effects of VR-based training programs and games on improving cognitive disorders.PubMed, Scopus, and Web of Science were systematically searched until May 20, 2023. Two researchers selected and extracted data based on inclusion and exclusion criteria, resolving disagreements through consultation with two other authors. Inclusion criteria required studies of individuals with any cognitive disorder engaged in at least one VR-based training session, reporting cognitive impairment data via scales like the MMSE. Only English-published RCTs were considered, while exclusion criteria included materials not primarily focused on the intersection of VR and cognitive disorders. The risk of bias in the included studies was assessed using the MMAT tool. Publication bias was assessed using funnel plots and Egger's test. The collected data were utilized to calculate the standardized mean differences (Hedges's g) between the treatment and control groups. The heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was conducted using Stata version 17.0.Ten studies were included in the analysis out of a total of 3,157 retrieved articles. VR had a statistically significant improvement in cognitive impairments among patients (Hedges's g = 0.42, 95% CI: 0.15, 0.68; p_value = 0.05). games (Hedges's g = 0.61, 95% CI: 0.30, 0.39; p_value = 0.20) had a more significant impact on cognitive impairment improvement compared to cognitive training programs (Hedges's g = 0.29, 95% CI: -0.11, 0.69; p_value = 0.24). The type of VR intervention was a significant moderator of the heterogeneity between studies.VR-based interventions have demonstrated promise in enhancing cognitive function and addressing cognitive impairment, highlighting their potential as valuable tools in improving care for individuals with cognitive disorders. The findings underscore the relevance of incorporating virtual reality into therapeutic approaches for cognitive disorders.