Reviewerly/exHarmony huggingface dataset:
Towards a post-democratic era? Moral education against new forms of authoritarianism

Educating in a convulsed political context demands a detailed analysis of the new circumstances of our times, especially the current democracy crisis. According to the latest reports issued by international evaluation organisations, one of the greatest challenges for democratic citizenship is the emergence and rise of authoritarianism within the framework of the so-called post-democracy, and also in the manifestations known as illiberal democracy. Moral and civic education has to respond to this challenge. With this in mind, we propose revitalising a participative and deliberative democracy model, rethinking the basic values in citizens' education, redefining the relation between what is public and what is private, and empowering sensitivity and free creation in the new maker culture.
About the social turn in foreign library science

The article presents an analysis of the bibliographic database of the Google Academy as a source for studying the most cited articles of leading foreign journals in library and information sciences. The most rated journals on library science are introduced. The crisis of foreign library science as a social discipline in the subjects of the journal Scientometrics is ascertained. The place of foreign library science in the interdisciplinary discourse for November 2022 has been estimated. The leader of the turn towards new library science with greater focus on knowledge management and socio-ethical component in researches is designated R. David Lankes Professor of Library Science at the University of Texas in Austin. Based on the analysis of the most cited articles in the leading foreign journals on library science, the ways of modern library studies within the framework of the so-called “social turn”, as well as the directions of interdisciplinary research in the field of social sciences with the participation of specialists in library and information sciences, are shown. When searching for a new library methodology, the so-called “chronotopy” and “altmetrics” are considered as a compromise option for combining quantitative and qualitative research methods, which also involve the author’s immersion in the content under study – the basis for scientific introspection, self-reflection and self-control.
A cross-scale framework for low-light image enhancement using spatial–spectral information

This paper presents a spatial–spectral low-light enhancement approach based on the neural network. Considering that the Image Signal Processor (ISP) which non-linearly maps RAW data to RGB images may introduce additional noise and artifacts, we propose to conduct the enhancement task directly on RAW data. To this end, we propose a cross-scale framework to map the input low-light RAW data to visually pleasing RGB images. The cross-scale framework consists of three branches for low-level, mid-level, and high-level representations of input images. We embed paired Fast Fourier Convolution (FFC) and Transformer in each level for global and local feature analysis and aggregation. Specifically, the FFC enlarges the receptive field of our neural network, exploring the pixel correlations in the spectral space; the following Transformer uses self-attention for feature selection and aggregation in the spatial space. As a result, spatial–spectral information within an image is jointly utilized for the final fusion. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art low-light enhancement methods in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Moreover, the proposed method produces more aesthetically pleasing RGB images than other methods.
Multifocus object detector for vehicle tracking in smart cities using spatiotemporal attention map

In recent days, an unrivaled proliferation of autonomous driving becomes an active area of research in the field of smart city applications. Millions of autonomous cars emerge as ubiquitous in smart cities in the foreseeable future. Vehicle detection and tracking is one of the important cases in smart city applications. It clarifies that the efficient scene parsing methods will be a huge potential. There were several traditional methods employed for the detection of object for the purpose of vehicle detection and road segmentation. However, there were some drawbacks such as reduced rate of detection accuracy, improper road segmentations, and missing detections of occluded objects exist. To overcome the existing issues, we propose an effective multi focus object detector (MFOD) that retrieves spatiotemporal data and considers multiple cues of an object for detection and tracking. The proposed MFOD performs lane detection, vehicle detection, and tracking. To accomplish this task, modified Hough transform is proposed for lane detection and road segmentation. Residual attention unit (RAU) with shared convolutional neural network layers is deployed for vehicle detection and tracking. It makes use of motion model to update the network and creates visualization map to explore the visibility of occluded objects and spatial attention map for accurate prediction of object location. The proposed MFOD is also extended for scene parsing task. We performed extensive analysis of the proposed MFOD with standard evaluation metrics on two different datasets, such as KITTI for object detection and tracking task and cityscapes for scene parsing task. The experimental results demonstrate that the proposed MFOD achieves 3% to 4% improvement compared with the existing methods.
P and S wave detection for the Nevados de Chillan Volcanic Complex (Chile), based on spectro-temporal features

The localization of seismic events is a relevant step for volcano monitoring as it provides information of physical parameters that support the forecasting of volcanic activity. Automating this step is necessary to process the enormous amount of signals received by the observatories, improving relevant and opportune information retrieval at the early stages of monitoring. Localisation of seismic events is based on identifying the arrival time of the P and S waves in the raw signals, in a procedure called P and S picking. This is mainly performed manually or semi-automatically, usually applying the STA/LTA algorithm. The literature shows that the automation research on this process has been focused mainly on tectonic seismicity since the P and S waves are more difficult to detect in volcanic seismicity. This work aims to automate this process, proposing a novel and simple technique for volcano seismic events, using spectro-temporal energy features extracted from the spectrograms. The energy accumulated by an event over time, its second derivative, as well as a modification of the STA/LTA method were applied to detect the P and S waves for volcano tectonic (VT) events and only the P wave for long period (LP) events. The proposed method was adjusted and tested with the multi-station events recorded at the active Nevados de Chillan Volcanic Complex (Chile) and the results were compared with the reference manual picking provided by the observatory and with the traditional STA/LTA approach. The spectro-temporal approach presented lower errors and standard deviations than STA/LTA, in particular for the P detection for LP and the S detection, which are more difficult to detect. The median time error for the P wave detection was 0.0430 ± 0.21s for VT events and 0.0870 ± 0.35s for LP. For the S wave detection the error was 0.05 ± 0.49s. 80% of the P wave detection absolute errors were below 0.13[s] for VT and below 0.48[s] for LP. In turn, for the S wave detection, 80% of the errors were below 0.47[s]. In addition to the time errors, the distance errors were also evaluated, thus the localization was calculated for each picking method. The results presented a longitudinal error of 0.3° for both methods and a latitude error of 0.01° for the spectro-temporal method and 0.03° for STA/LTA method, once again demonstrating the good performance of the proposal. These results are promising despite the complexity of the P and S wave detection in volcanic events and the simplicity of the method compared to the methods proposed in the literature.
Learning Double-Level Relationship Networks for image captioning

Image captioning aims to generate descriptive sentences to describe image main contents. Existing attention-based approaches mainly focus on the salient visual features in the image. However, ignoring the learning relationship between local features and global features may cause local features to lose the interaction with global concepts, generating impropriate or inaccurate relationship words/phrases in the sentences. To alleviate the above issue, in this work we propose the Double-Level Relationship Networks (DLRN) that novelly exploits the complementary local features and global features in the image, and enhances the relationship between features. Technically, DLRN builds two types of networks, separate relationship network and unified relationship embedding network. The former learns different hierarchies of visual relationship by performing graph attention for local-level relationship enhancement and pixel-level relationship enhancement respectively. The latter takes the global features as the guide to learn the local–global relationship between local regions and global concepts, and obtains the feature representation containing rich relationship information. Further, we devise an attention-based feature fusion module to fully utilize the contribution of different modalities. It effectively fuses the previously obtained relationship features and original region features. Extensive experiments on three typical datasets verify that our DLRN significantly outperforms several state-of-the-art baselines. More remarkably, DLRN achieves the competitive performance while maintaining notable model efficiency. The source code is available at the GitHub https://github.com/RunCode90/ImageCaptioning.
Knowledge reconstruction assisted evolutionary algorithm for neural network architecture search

Neural architecture search (NAS) aims to provide a manual-free search method for obtaining robust and high-performance neural network structures. However, limited search space, weak empirical reusability, and low search efficiency limit the performance of NAS. This study proposes an evolutionary knowledge-reconstruction-assisted method for neural network architecture searches. First, a search space construction method based on network blocks with a-priori knowledge of the network morphism is proposed. This can reduce the computational burden and the time required for the search process while increasing the diversity of the search space. Next, a hierarchical variable-length coding strategy is designed for application to the complete evolutionary algorithm; this strategy divides the neural network into two layers for coding, satisfies the need for decoding with neural network weights, and achieves coding of neural network structures with different depths. Furthermore, the complete differential evolution algorithm is used as the search strategy, thus providing a new possibility of using the search space based on network morphism for applications related to evolutionary algorithms. In addition, the results of comparison experiments conducted on CIFAR10 and CIFAR100 indicate that the neural networks obtained using this method achieve similar or better classification accuracy compared with other neural network structure search algorithms and manually designed networks, while effectively reducing computational time and resource requirements.
Testing of highly configurable cyber–physical systems — Results from a two-phase multiple case study

Cyber–physical systems are commonly highly configurable. Testing such systems is particularly challenging because they comprise numerous heterogeneous components that can be configured and combined in different ways. Despite a plethora of work investigating software testing in general and software product line testing in particular, variability in tests and how it is applied in industry with cyber–physical systems is not well understood. In this paper, we report on a multiple case study with four companies maintaining highly configurable cyber–physical systems focusing on their testing practices, with a particular focus on variability. Based on the results of the multiple case study, we conducted an interactive survey with experienced engineers from eight companies, including the initial four. We reflect on the lessons learned from the multiple case study. We conclude that experience-based selection of configurations for testing is currently predominant. We learned that variability modeling techniques and tools are not utilized and the dependencies between configuration options are only partially modeled at best using custom artifacts such as spreadsheets or configuration files. Another finding is that companies have the need and desire to cover more configuration combinations by automated tests. Our findings raise many questions interesting to the scientific community and motivating future research.
On the cost-effectiveness of composite metamorphic relations for testing deep learning systems

Deep Learning (DL) components are increasing their presence in mission and safety-critical systems, such as autonomous vehicles. The verification process of such systems needs to be rigorous, for which automated solutions are paramount. To allow test automation, test oracles are necessary. In the context of DL systems, metamorphic test oracles have found to be effective. However, such oracles require the execution of multiple tests, which makes testing more expensive. Metamorphic relation composition can reduce the cost of metamorphic testing. However, its effectiveness has found mixed answers. This paper reports the preliminary results of our study on measuring the cost-effectiveness of composite metamorphic relations for testing DL systems. To this end, we empirically evaluate the cost-effectiveness of composite metamorphic relations within a DL model for object classification. Our results suggest that composite metamorphic relations reduce the failure revealing capability when compared to their component metamorphic relations.
T-GCN

As many real-world applications are streaming and attached with time instances, a few works have been proposed to learn streaming graph neural networks (GNNs). Unfortunately, current streaming GNNs are observed to have a large training overhead and suffer from bad parallel scalability on multiple GPUs. These drawbacks pose severe challenges to online learning of streaming GNNs and their application to real-time scenarios. To improve training efficiency, one promising solution is to use sampling, a technique widely used in static GNNs. However, to the best of our knowledge, sampling has not been investigated in learning streaming GNNs. Based on these observations, in this paper, we propose T-GCN, the first sampling-based streaming GNN system, which targets temporal-aware streaming graphs and takes advantage of a hybrid CPU-GPU co-processing architecture to achieve high throughput and low latency. T-GCN proposes an efficient sampling method, namely Segment Its Search, to offer high sampling speed with respect to three typical types of general graph sampling methods (i.e., node-wise, layer-wise, and subgraph sampling). We propose a locality-aware data partitioning method to reduce CPU-GPU communication latency and data transfer overhead, and an NVLink-specific task schedule to fully exploit NVLink's fast speed and improve GPU-GPU communication efficiency. Besides, we further pipeline the computation and the communication by introducing an efficient memory management mechanism, to improve scalability while hiding data communication. Overall, with respect to end-to-end performance, for single-GPU training, T-GCN achieves up to 7.9× speedup than state-of-the-art works. In terms of scalability, T-GCN runs 5.2× faster on average with 8 GPUs than one GPU. Additionally, in terms of sampling, T-GCN also yields a maximum of 38.8× speedup with our Segment Its Search sampling method.
Research on the Application of Blockchain in Agricultural Logistics Park

China is a large agricultural country. With the rapid development of e-commerce logistics, agricultural logistics parks have emerged. The characteristics of blockchain technology make it feasible to be applied in agricultural logistics park which as a comprehensive park integrating the government, enterprises, information platform, financial institutions and agricultural scientific research units, and gathering various social service organizations. This paper combines the formation characteristics and functions of agricultural logistics park and the unique functions of blockchain technology to discuss the feasibility and expected effects of blockchain in the new format of agricultural logistics park. Based on the existing research foundation, this paper discusses the application prospects of blockchain technology from four aspects which are agricultural finance, agricultural product supply chain, agricultural product quality traceability, and rural precision poverty alleviation. The spillover effect of technology alliance on logistics park is discussed through the model, which provides technical support for the further development of agricultural logistics park in China.
Analyzing Test Smells Refactoring from a Developers Perspective

Test smells represent a set of poorly designed tests, which can harm a test code's maintenance and quality criteria. Although fundamental steps to understand test smells have been investigated, there is still an evident lack of studies evaluating the impact of test smell refactoring from internal quality attributes, such as size, cohesion, coupling, and complexity. In addition, the literature still lacks research that addresses the difficulties developers encounter during test smell refactoring. The paper investigates the impact of test smell refactoring from developers' perspectives and internal quality attributes. We investigated the perceptions and difficulties encountered by 20 developers while removing 5 types of test smells in 4 open-source projects over two months. Through this experiment, we analyzed: (i) the impact that test smell refactoring has on internal quality attributes; (ii) developers' perception of test smells as actual problems within a software system; and (iii) main difficulties encountered by developers during test smell refactoring. Our findings can help developers design a prioritization scheme for test smell refactoring and make them aware of the real benefits of test smell refactoring.
Deep Learning Image Age Approximation - What is More Relevant: Image Content or Age Information?

The field of temporal image forensics is the science of approximating the age of a digital image relative to images from the same device. For this purpose, classical methods exist that exploit handcrafted features based on hidden age traces (i.e., in-field sensor defects). In contrast to these classical methods, a Convolutional Neural Network (CNN) learns the features used independently. This has the benefit that other (unknown) age traces can be exploited. However, this also carries the risk of learning non-age-related features to predict the age class. In this work, we analyze the features learned by a standard CNN trained in the context of image age approximation on regular scene images. To analyze whether the model learned to exploit hidden age traces or just other general, non-age-related intra-class properties (e.g., common scene properties or lighting conditions), we applied methods from the field of Explainable Artificial Intelligence (XAI). This analysis is performed with 14 models trained with images from 14 different devices from two datasets.
Robust and Imperceptible Watermarking Scheme for GWAS Data Traceability

This paper proposes the first robust watermarking method of outsourced or shared genomic data in the context of genome-wide association studies (GWAS) with the primary purpose of identifying the individual or entity at the origin of an illegal information redistribution or disclosure. Our scheme’s first unique feature is that it employs a database watermarking strategy to take advantage of the fact that GWAS data are stored in variant call format (VCF) files, which have a database-like structure. Second, it proposes a quantization index modulation based on watermarking modulation for GWAS data under the constraint of not interfering with identifying candidate variants or genes involved in the pathology. We evaluate the theoretical performance of our method in terms of watermarking insertion capacity, distortion, and robustness against different attacks. Experimental results conducted on real data and the weighted-sum statistic (WSS) GWAS study demonstrate the efficiency of the proposed scheme and that it can be used for identifying the cloud service providers (geneticists) at the origin of an information disclosure even if the genotype data has been modified.
High Capacity Reversible Data Hiding for Encrypted 3D Mesh Models Based on Topology

Reversible data hiding in encrypted domain(RDH-ED) can not only protect the privacy of 3D mesh models and embed additional data, but also recover original models and extract additional data losslessly. However, due to the insufficient use of model topology, the existing methods have not achieved satisfactory results in terms of embedding capacity. To further improve the capacity, a RDH-ED method is proposed based on the topology of the 3D mesh models, which divides the vertices into two parts: embedding set and prediction set. And after integer mapping, the embedding ability of the embedding set is calculated by the prediction set. It is then passed to the data hider for embedding additional data. Finally, the additional data and the original models can be extracted and recovered respectively by the receiver with the correct keys. Experiments declare that compared with the existing methods, this method can obtain the highest embedding capacity.
Visual Explanations for Exposing Potential Inconsistency of Deepfakes

In recent years, the rapid development of Deepfake has aroused public concerns. Existing Deepfake detection methods mainly focus on improving the accuracy. However, when real-world victims require additional interpretable results to refute, the accuracy of these methods is certainly insufficient. To mitigate this issue, we delve into forgery traces and propose a novel framework, named Find-X, that presents additional visual information as an explanation of the results. Specifically, we design a new module named Separation Potential Inconsistency (SPI) which aims to visually explain the forgery traces of fake videos. Find-x detection of Deepfake consists of three stages: (1) A frequency-aware module and a spatial-aware module to enhance the features. (2) A multi-scale feature extraction module to extract richer features. (3) A classification module and a SPI module to output the visual explanations. Our method outperforms state-of-the-art competitors on three popular benchmark datasets: FaceForensics++, Celeb-DF, and DeepFakeDetection. In addition, extensive visualization experiments on FaceForensics++ demonstrate that SPI can effectively separate the potentially inconsistent features of videos generated by five different Deepfake methods.