Reviewerly/exHarmony huggingface dataset:
B!SON: A Tool for Open Access Journal Recommendation

Abstract Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project.
Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement

Deep convolutional neural networks have recently been applied to improve the quality of low-light images and have achieved promising results. However, most existing methods cannot suppress noise during the enhancement process effectively, resulting in unknown artifacts and color distortions. In addition, these methods do not fully utilize illumination information and perform poorly under extremely low-light condition. To alleviate these problems, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">illumination guided attentive wavelet network</i> (IGAWN) for low-light image enhancement (LLIE). Considering that the wavelet transform can separate high-frequency noise and desired low-frequency content effectively, we enhance low-light images in the frequency domain. By integrating attention mechanisms with wavelet transform, we develop the attentive wavelet transform to capture more important wavelet features, which enables the desired content to be enhanced and the redundant noise to be suppressed. To improve the image enhancement performance under extremely low-light environment, we extract illumination information from the input images and exploit it as the guidance for image enhancement through the frequency feature transform (FFT) layer. The proposed FFT layer generates frequency-aware affine transformation from the estimated illumination information, which can adaptively modulate the image features of different frequencies. Extensive experiments on synthetic and real-world datasets demonstrate that our IGAWN performs favorably against state-of-the-art LLIE methods.
Robust Multimodal Sentiment Analysis via Tag Encoding of Uncertain Missing Modalities

Multimodal sentiment analysis aims to extract emotions with multiple data sources, usually under the assumption that all modalities are available. In practice, such a strong assumption does not always hold, and most of multimodal sentiment analysis methods may fail when partial modalities are missing. Some existing works have started to address the missing modality problem; but only considered the single modality missing case, while ignoring the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities. Besides, a new space projection pattern is adopted to align common vectors, taking into account the different importance of each modality. Afterwards, a Transformer encoder-decoder network is utilized to learn the missing modality features, and the outputs of the Transformer encoder are extracted for the final sentiment classification. Extensive experiments and analyses are conducted on CMU-MOSI, IEMOCAP, and MELD datasets, which show that the proposed method can achieve significant improvements compared with several baselines.
Efficient Machine-Learning Based 3D Face Identification System Under Large Pose Variation

Large pose variation is considered a major research concern due to its significant impact on the performance of face recognition systems. In this paper, we present a new efficient Machine-Learning Based System that identifies a person’s face in 3D under Large pose variation. The proposed system can automatically detect and recognize people under translations and rotation by leveraging the anthropometric methodology and geometric descriptors. We evaluated our system on three well-known 3D face databases: 3DFPE, GAVAB, and 3DFRAV. These databases have a considerable change in the position of the face, even for the same individual. Additionally, we use the ICP algorithm to align the face to the front view. Our experimental simulation results show that the proposed system posed great robustness for significant changes in the pose and proved its advantages over state-of-the-art systems in terms of identification accuracy, specificity, and sensitivity.
Realistic Game Avatars Auto-Creation from Single Images via Three-pathway Network

We propose a novel single image 3D face reconstruction method for realistic in-game avatar auto-creation. Although some existing 3D face reconstruction methods have been able to generate good geometry, there are still some shortages in texture generation, especially diffuse prediction, which limits its application in games or other scenarios. The main problems of these methods include: the details in the photo are not accurately restored, the produced diffuse is over smoothed, or the occlusion and lighting are not correctly removed, and so on. Although some methods collect high-quality 3D face data for neural networks to learn to generate realistic 3D faces, collecting 3D face data is known expensive. To address the above problems, we propose to utilize data from three sources, including single face images, manually inpainted diffuse maps paired with face portraits, and multiple photos of single IDs generated by a pretrained network. To make full use of these data, we propose a three-pathway network architecture that takes face images as input, produces diffuse maps, normal maps, as well as pose and light coefficients. The network parameters are optimized by comparing the rendered results with the input images, along with some other objective functions.
Performance Enhancement of Adaptive Neural Networks Based on燣earning燫ate

Deep learning is the process of determining parameters that reduce the cost function derived from the dataset.The optimization in neural networks at the time is known as the optimal parameters.To solve optimization, it initialize the parameters during the optimization process.There should be no variation in the cost function parameters at the global minimum.The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem).Moreover, existing approaches use techniques; the learning rate is reduced during the iteration period.These techniques are monotonically reducing at a steady rate over time; our goal is to make the learning rate parameters.We present a method for determining the best parameters that adjust the learning rate in response to the cost function value.As a result, after the cost function has been optimized, the process of the rate Schedule is complete.This approach is shown to ensure convergence to the optimal parameters.This indicates that our strategy minimizes the cost function (or effective learning).The momentum approach is used in the proposed method.To solve the Momentum approach non-stop problem, we use the cost function of the parameter in our proposed method.As a result, this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter.To verify that the learning works to test the strategy, we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.
An integrative model of trust toward crypto-tokens applications: A customer perspective approach

Trust is a key driver of consumers' acceptance and adoption of innovations. The purpose of the paper is to explore and understand the factors that facilitate the creation of trust in crypto-tokens decentralized applications (DApp) from the perspective of the consumer. A qualitative research design has been used and forty-five participants were interviewed for the study. Based on a theory-driven approach of qualitative data analysis, a multidimensional, holistic conceptual framework of trust has been proposed. The model captures the core categories that affect consumer's trust toward crypto-tokens applications. It highlights the need to understand the individual, technological, environmental and use-case features which shape the way blockchain-based applications can be trusted. The framework can be used by policymakers, educators, exchange platforms, DApp developers, and entrepreneurs to develop strategies that cultivate on the drivers of trust, and create value with crypto-tokens and Web3.0 economies. Higher trust ultimately promotes acceptance and adoption of blockchain applications.
A forensic analysis of rclone and rclone's prospects for digital forensic investigations of cloud storage

Organizations and end users are moving their data into the cloud and trust Cloud Storage Providers (CSP) such as pCloud, Dropbox, or Backblaze. Given their popularity, it is likely that forensic examiners encounter one or more online storage types that they will have to acquire and analyze during an investigation. To access cloud storage, CSPs provide web-interfaces, proprietary software solutions (e.g., Dropbox client for Windows) as well as APIs allowing third-party access. One of these third-party applications is rclone which is an open-source tool to access many common CSPs through a command line interface. In this article, we look at rclone from two perspectives: First, we perform a forensic analysis on rclone and discuss aspects such as password recovery of the configuration file, encryption, and JA3 fingerprints. Second, we discuss rclone as a prospect to be a forensic tool which includes its read-only mount feature and sample cases. Under the circumstances tested, rclone is suitable for forensic practitioners as it is open-source, documented, and includes some essential functionality frequently needed but practitioners need to be aware of the caveats.
Markovian and non-Markovian dynamics of quantum coherence in the extended <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>X</mml:mi></mml:mrow></mml:math> chain

The Markovian and non-Markovian dynamics of quantum coherence, including single-spin coherence, two-spin coherence, and its distribution (collective and localized coherence), are investigated in the $XX$ spin chain with three-spin interaction. We find that the single-spin and localized coherence do not decrease but increase with time, which makes the dynamic evolution of the two-spin coherence more robust than entanglement and quantum discord. The three-spin interaction can lower the values of quantum coherence in Markovian evolution and heighten the oscillation frequency of quantum coherence in non-Markovian evolution because it can speed up the flow of coherence information. Finally, the trade-off relation between the collective and the localized coherence is clearly shown in their dynamic evolution. Thanks to the coherence trade-off, we find the substitution of localized coherence for collective coherence in dominating the dynamics of the two-spin coherence.
Joint Disentanglement of Labels and Their Features with VAE

Most of previous semi-supervised methods that seek to obtain disentangled representations using variational autoencoders divide the latent representation into two components: the non-interpretable part and the disentangled part that explicitly models the factors of interest. With such models, features associated with high-level factors are not explicitly modeled, and they can either be lost, or at best entangled in the other latent variables, thus leading to bad disentanglement properties. To address this problem, we propose a novel conditional dependency structure where both the labels and their features belong to the latent space. We show using the CelebA dataset that the proposed model can learn meaningful representations, and we provide quantitative and qualitative comparisons with other approaches that show the effectiveness of the proposed method.
Improved interpolation with sub-pixel relocation method for strong barrel distortion

• The uneven distortion of the fisheye image will cause a sub-pixel bias phenomenon of the projected pixels on the perspective image in the backward projection interpolation. • The sub-pixel relocation method corrects the sub-pixel bias using the center of the projected four vertices of the pixel other than the projected center of the pixel • The corrected pixels are meshed by the Voronoi diagram to help the to-beinterpolated points found natural neighbours, and the look-up-table structure is used for real-time performance. In this paper, we propose an improved interpolation method with sub-pixel relocation method to correct fisheye images with strong barrel distortion. The proposed method takes as inputs the fisheye image and the projection model, and outputs the corrected perspective image. In the method, we project the fisheye image pixels to perspective image plane using the backward projection. The projected pixels are treated as scatter data, and their location is refined by the sub-pixel relocation method. The method is designed to aim at the sub-pixel bias phenomenon of the projected pixels. The sub-pixel bias phenomenon is that there is a sub-pixel bias between the projected pixel center point and the center point of the projected pixel. This phenomenon is caused by the uneven distortion of the fisheye image plane, and it will affect the accuracy of the interpolation method. The sub-pixel relocation method relocates the projected pixel centers to the centers of the projected pixels. Then the relocated points are meshed by the Voronoi diagram method. The mesh is used to select the neighbors of the interpolation points as natural neighbor selection method. The weights of the neighbors are calculated using the inverse distance method. The indexes of the neighbors and their weights are stored in a look-up-table structure for reuse. For each fisheye image, the proposed method can quickly find the neighbors of the interpolation points and the weight of each neighbor through the look-up-table, and the perspective image can be calculated in real-time. Test results illustrate that the proposed method achieves the best accuracy among the tested methods for fisheye images with strong barrel distortion.
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI

The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often require large amounts of data available during training time and updates w.r.t. new data are difficult after the training process has been completed. In fact, when new data or tasks arise, previous progress may be lost as neural networks are prone to catastrophic forgetting. Catastrophic forgetting describes the phenomenon when a neural network completely forgets previous knowledge when given new information. We propose a novel training algorithm called Relevance-based Neural Freezing in which we leverage Layer-wise Relevance Propagation in order to retain the information a neural network has already learned in previous tasks when training on new data. The method is evaluated on a range of benchmark datasets as well as more complex data. Our method not only successfully retains the knowledge of old tasks within the neural networks but does so more resource-efficiently than other state-of-the-art solutions.
Quantum Renewable Scenario Generation

This paper underpins the potential of quantum generative adversarial networks (QGANs) for renewable scenario generation in power grids. A single QGAN with either amplitude or angle encoding is hard to construct. To bridge the gaps, this paper devises a Multi-QGAN framework utilizing multiple QGANs. A correlation-based Multi-QGAN (CMulti-QGAN) approach is further established to improve the Multi-QGAN performance. Data from real solar systems in Connecticut are collected for numerical studies. Results demonstrate the effectiveness and robustness of Multi-QGAN and CMulti-QGAN, and also validate the superiority of CMulti-QGAN over Multi-QGAN.
HR based Chatbot using Deep Neural Network

Originally, human resource management was re- ferred to as personal or people management. Human resource managers are responsible for a variety of tasks, including managing people within a firm . Employee management decisions, practises, strategies, philosophies, operations, functions, activities, and approaches all fall under this category, as well as ensuring that employees are satisfied with their working conditions and focusing on bringing in new employees with new talents for the company and managing employees. HR chatbots can help alleviate this overwork. A chatbot is a form of software that is created by a computer. It is capable of interacting with the user in a simulated manner. In such a way that it does not feel as if you are conversing directly with the machine. The main goal of this project is to implement an HR chatbot that uses natural language processing and deep learning to allow effective communication between HR and employees as well as HR and interview candidates.
Billingual phenomenon usage in presenting make up tutorial

Background: The writer interested to analyze the code switching and code mixing in Suhay Salim’s video because she is a bilingual speaker. She is able to speak both Bahasa and English. In each her video, Suhay Salim always uses code switching and code mixing. Purpose: The objective of this research is to know the types of code switching and code mixing used in the video, and also to know the reasons why the speaker used code switching and code mixing in her YouTube video. Design and methods: The data were collected through library research. The method of this research is descriptive qualitative method. Results: The result of this analysis is indicated that: (1) Types of code switching that found are situational code switching and metaphorical code switching. (2) types of code mixing found are insertion and alternation. (3) while the reasons to use code switching and code mixing are talking about particular topic, real lexical need, interjection, repetition used for clarification, and expressing group identity.
Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability

GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeoff between distortion and editability, i.e. reconstructing the input image accurately and editing the inverted image with a small visual quality drop. The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code. Here, we show that both reconstruction and editability can be improved by a proper design of the pivot code. We present a simple yet effective method, named cycle encoding, for a high-quality pivot code. The key idea of our method is to progressively train an encoder in varying spaces according to a cycle scheme: W->W+->W. This training methodology preserves the properties of both W and W+ spaces, i.e. high editability of W and low distortion of W+. To further decrease the distortion, we also propose to refine the pivot code with an optimization-based method, where a regularization term is introduced to reduce the degradation in editability. Qualitative and quantitative comparisons to several state-of-the-art methods demonstrate the superiority of our approach.
Automatic Outdoor Image Geolocation with Focal Modulation Networks

We address the problem of estimating a photo’s geographical location. Success in this estimation enables many impactful applications, like facilitating Disaster Management circumstances. However, this is also a very challenging task. Due to the complexity of the problem, we restrict the area of geolocation to a single city, treating geolocation as a classification problem where the districts of a city are the classes to be distinguished. In this paper, we exploit the Focal Modulation Network that is proven to perform effectively and efficiently in visual modeling for real-world applications. Experimental results on two diverse datasets, crawled from online sources, show the effectiveness of our approach. We can geolocate correctly more than two-thirds of test images from the larger dataset and about one-third from an experimental training dataset of a ten-times smaller size.
MERGE: A Multi-graph Attentive Representation learning framework integrating Group information from similar patients

It is an important research task in the field of medical big data to predict patient’s future health status according to the historical temporal Electronic Health Records (EHRs). Most of the existing deep learning-based medical prediction methods only focus on the patient’s individual information. However, due to the sparseness and low quality of EHR data, individual clinical records of single patient often cannot provide complete health information, which severely limits the accuracy of the prediction models. In this paper, we propose a Multi-graph attEntive Representation learning framework integrating Group information from similar patiEnts(MERGE) for medical prediction. In this framework, while capturing the individual patient’s temporal characteristics through the individual representation learning module, the group representation leaning module is used to learn group representations of similar patients from different aspects as a supplement, thereby effectively improving the accuracy of patients’ representation. We evaluate our method on the MIMIC-III dataset for the task of in-hospital mortality prediction and Xiangya dataset for cardiovascular diseases (CVDs) prediction. The experimental results show that MERGE outperforms the state-of-the-art methods.
Teachers’ Perceptions of Changes in Their Professional Development as a Result of ICT

The introduction of digital information and communication technologies has influenced many aspects of the teaching profession. In addition to their changing use in the classroom, these technologies have strongly impacted the work and professional development of teachers. In this study, which was framed within the R+D+i project DePrInEd, we analyzed the perceptions, beliefs, opinions, and attitudes of teachers on this subject. We conducted a qualitative study through interviews, with a thematic analysis addressing the duality of technology and change. The results indicated that these produced benefits and created difficulties in the teaching task, with the latter being more demanding. Teachers stated that one of the main difficulties they encountered was related to the evolution of students as a result of technology, both in the school and social contexts. This highlights the risk that its extensive use did not lead to the acquisition of other key competencies, including digital and media competencies, in the school environment. Finally, other dimensions that impacted the corporate intelligence of educational centers included the continuous adaptation and mastery of digital competence required by teachers regarding the changes in their tasks, as well as the excessive bureaucratization that technologies have necessitated.