Reviewerly/exHarmony huggingface dataset:
LIMPID: A Lightweight Image-to-Curve MaPpIng moDel for Extremely Low-Light Image Enhancement

Low-light conditions severely degrade the captured image quality with few details, while deep learning approaches are trending towards low-light image enhancement (LLIE) due to their superior performance. However, few methods face the challenges of lower dynamic range and greater noise from extremely low-light directly. Existing methods for extremely low-light enhancement are end-to-end, requiring RAW data as input. Meanwhile, they often lack the potential for real-time mobile deployment owing to the high model complexity. In this paper, we introduce the image-to-curve transformation to ELLIE for the first time and present a Lightweight Image-to-curve MaPpIng moDel for ELLIE (LIMPID). Compared with existing image-to-curve mapping methods, the proposed module is constructed for a wider dynamic range according to the light scattering model. Furthermore, we propose a new pyramid fusion strategy based on Laplacian and Gaussian. This strategy attempts to achieve dynamic fusion of multi-scale images via learnable fusion weight parameters. Specifically, LIMPID consists of a low-resolution dense CNN network stream and a full-resolution guidance stream. First, the curve generation and refinement are achieved in the low-resolution stream constructed on a light scattering model. Then, the curves are up-sampled to full resolution via bilateral grid cells. Finally, the enhanced result is obtained through dynamically adapted multi-scale pyramid fusion. Experimental results show that our method is competitive with existing state-of-the-art methods in terms of performance.
Forecasting Maximum Mechanism Temperature in Advanced Technology Microwave Sounder (ATMS) Data Using a Long Short-Term Memory (LSTM) Neural Network

Among the monitored telemetry raw data record (RDR) parameters with the STAR Integrated/Validation System (ICVS), the Advanced Technology Microwave Sounder (ATMS) scan motor mechanism temperature is especially important because the instrument might be unavoidably damaged if the mechanism temperature exceeds 50 °C. In the current operational flight processing software, the instrument automatically enters safe mode and stops collecting scientific data whenever the mechanism temperature exceeds 40 °C. This approach inevitably leads to the instrument entering safe mode unnecessarily at a premature time, causing the loss of scientific data before the mechanism temperature reaches 50 °C. This study seeks to leverage the influence the main motor current, compensation motor current, and main motor loop integral error have on mechanism temperature to forecast the maximum mechanism temperature over the upcoming 6 min. A long short-term memory (LSTM) neural network predicts maximum mechanism temperature using ATMS RDR telemetry data as the input. The performance of the LSTM is compared with observed maximum mechanism temperatures by applying the LSTM coefficients to several cases. In all cases studied, the mean average error (MAE) of the forecast remained under 1.1 °C, and the correlation between forecasts and measurements remained above 0.96. These forecasts of maximum mechanism temperature are expected to be able to provide information on when the ATMS instrument should enter safe mode without needlessly losing valuable data for the ATMS flight operational team.
Evaluation of 6 DOF Robotic Arm Using Leap Motion Sensor

Due to their increased functionality, robotic arms provide a well-organized method for developing assistive devices. By 2035, statistics indicate that half of Germany’s population will be over the age of fifty, and every third person will be over the age of sixty. These aging societies face numerous obstacles when it comes to performing basic activities of daily living, or ""ADLs."" A growing body of research is focusing on Ambient Assisted Living, or ""AAL,"" as a novel approach to addressing the needs of elderly people. A critical objective of AAL is to improve the quality of life for the elderly and disabled and to assist them in maintaining an independent lifestyle. Robotics and technology-enabled environments will be critical in enabling elderly and physically disabled people to maintain a self-determined, independent lifestyle in their familiar surroundings. The purpose of this article is to propose the implementation of a novel intuitive and adaptive manipulation scheme by creating a human-machine communication interface between the Leap Motion controller and the 6-DOF Jaco robotic arm. An algorithm is developed to optimize the mapping between the user’s hand movement and the Jaco arm, as tracked by the Leap Motion controller. By constantly adapting to the user’s hand tremor or shake, the system should enable a more natural human-computer interaction and smooth manipulation of the robotic arm. The implementation would significantly improve people’s quality of life, particularly those with upper limb problems, by assisting them in performing several essential Activities of Daily Living ""ADLs."" The applications of this human-robot interaction will be discussed in relation to Ambient Assisted Living, with the introduction of several use case scenarios.
MM-ConvBERT-LMS: Detecting Malicious Web Pages via Multi-Modal Learning and Pre-Trained Model

In recent years, the number of malicious web pages has increased dramatically, posing a great challenge to network security. While current machine learning-based detection methods have emerged as a promising alternative to traditional detection techniques. However, these methods are commonly based on single-modal features or simple stacking of classifiers built on various features. As a result, these techniques are not capable of effectively fusing features from different modalities, ultimately limiting the detection effectiveness. To address this limitation, we propose a malicious web page detection method based on multi-modal learning and pre-trained models. First, in the input stage, the raw URL and HTML tag sequences of web pages are used as input features. To help the subsequent model learn the relationship between the two modalities and avoid information confusion, modal-type encoding, and positional encoding are introduced. Next, a single-stream neural network based on the ConvBERT pre-trained model is used as the backbone classifier, and it learns the representation of multi-modal features through fine-tuning. For the output part of the model, a linear layer based on large margin softmax is applied to the decision-making. This activation function effectively increases the classification boundary and improves the robustness. In addition, a coarse-grained modal matching loss is added to the model optimization objective to assist the models in learning the cross-modal association features. Experimental results on synthetic datasets show that our proposed method outperforms traditional single-modal detection methods in general, and has advantages over baseline models in terms of accuracy and reliability.
Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data

Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the symptoms of diabetes and the medication regimens for machine learning. The eigenvector of the DTBM is the stable distribution of different symptoms and medication regimens. Based on the DTBM, this paper proposes a machine-learning algorithm for completing missing medical data, which provides a theoretical basis for the prediction of a Bayesian matrix with missing medical information. The experimental results show the rationality and applicability of the given algorithms.
GhostNeXt: Rethinking Module Configurations for Efficient Model Design

Despite the continuous development of convolutional neural networks, it remains a challenge to achieve performance improvement with fewer parameters and floating point operations (FLOPs) as a light-weight model. In particular, excessive expressive power on a module is a crucial cause of skyrocketing the computational cost of the entire network. We argue that it is necessary to optimize the entire network by optimizing single modules or blocks of the network. Therefore, we propose GhostNeXt, a promising alternative to GhostNet, by adjusting the module configuration inside the Ghost block. We introduce a controller to select channel operations of the module dynamically. It holds a plug-and-play component that is more useful than the existing approach. Experiments on several classification tasks demonstrate that the proposed method is a better alternative to convolution layers in baseline models. GhostNeXt achieves competitive recognition performance compared to GhostNet and other popular models while reducing computational costs on the benchmark datasets.
Hidden Markov Mixture of Gaussian Process Functional Regression: Utilizing Multi-Scale Structure for Time Series Forecasting

The mixture of Gaussian process functional regressions (GPFRs) assumes that there is a batch of time series or sample curves that are generated by independent random processes with different temporal structures. However, in real situations, these structures are actually transferred in a random manner from a long time scale. Therefore, the assumption of independent curves is not true in practice. In order to get rid of this limitation, we propose the hidden-Markov-based GPFR mixture model (HM-GPFR) by describing these curves with both fine- and coarse-level temporal structures. Specifically, the temporal structure is described by the Gaussian process model at the fine level and the hidden Markov process at the coarse level. The whole model can be regarded as a random process with state switching dynamics. To further enhance the robustness of the model, we also give a priori parameters to the model and develop a Bayesian-hidden-Markov-based GPFR mixture model (BHM-GPFR). The experimental results demonstrated that the proposed methods have both high prediction accuracy and good interpretability.
MTGEA: A Multimodal Two-Stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment

Because of societal changes, human activity recognition, part of home care systems, has become increasingly important. Camera-based recognition is mainstream but has privacy concerns and is less accurate under dim lighting. In contrast, radar sensors do not record sensitive information, avoid the invasion of privacy, and work in poor lighting. However, the collected data are often sparse. To address this issue, we propose a novel Multimodal Two-stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment (MTGEA), which improves recognition accuracy through accurate skeletal features from Kinect models. We first collected two datasets using the mmWave radar and Kinect v4 sensors. Then, we used zero-padding, Gaussian Noise (GN), and Agglomerative Hierarchical Clustering (AHC) to increase the number of collected point clouds to 25 per frame to match the skeleton data. Second, we used Spatial Temporal Graph Convolutional Network (ST-GCN) architecture to acquire multimodal representations in the spatio-temporal domain focusing on skeletal features. Finally, we implemented an attention mechanism aligning the two multimodal features to capture the correlation between point clouds and skeleton data. The resulting model was evaluated empirically on human activity data and shown to improve human activity recognition with radar data only. All datasets and codes are available in our GitHub.
Attention-Oriented Deep Multi-Task Hash Learning

Hashing has wide applications in image retrieval at large scales due to being an efficient approach to approximate nearest neighbor calculation. It can squeeze complex high-dimensional arrays via binarization while maintaining the semantic properties of the original samples. Currently, most existing hashing methods always predetermine the stable length of hash code before training the model. It is inevitable for these methods to increase the computing time, as the code length converts, caused by the task requirements changing. A single hash code fails to reflect the semantic relevance. Toward solving these issues, we put forward an attention-oriented deep multi-task hash learning (ADMTH) method, in which multiple hash codes of varying length can be simultaneously learned. Compared with the existing methods, ADMTH is one of the first attempts to apply multi-task learning theory to the deep hashing framework to generate and explore multi-length hash codes. Meanwhile, it embeds the attention mechanism in the backbone network to further extract discriminative information. We utilize two common available large-scale datasets, proving its effectiveness. The proposed method substantially improves retrieval efficiency and assures the image characterizing quality.
Keyword-Aware Transformers Network for Chinese Open-Domain Conversation Generation

The open-domain conversation generation task aims to generate contextually relevant and informative responses based on a given conversation history. A critical challenge in open-domain dialogs is the tendency of models to generate safe responses. Existing work has often incorporated keyword information in the conversation history for response generation to relieve this problem. However, these approaches interact weakly between responses and keywords or ignore the association between keyword extraction and conversation generation. In this paper, we propose a method based on a Keyword-Aware Transformers Network (KAT) that can fuse contextual keywords. Specifically, the model enables keywords and contexts to fully interact with responses for keyword semantic enhancement. We jointly model the keyword extraction task and the dialog generation task in a multi-task learning fashion. Experimental results of two Chinese open-domain dialogue datasets showed that our proposed model outperformed the methods in both semantic and non-semantic evaluation metrics, improving Coherence, Fluency, and Informativeness in manual evaluation.
Why do we need a sociocognitive-CDA in hate speech studies? A corpus-based systematic review

This article is a systematic review of previous research on hate speech in discourse studies indexed in the Scopus database in the last five years, from 2015 to 2021. This review aims to map the main topics and methods used in hate speech studies and then provide critical remarks related to the methodological issues. The review focused on 70 selected articles and was analyzed using the NVivo 12 combined with the LancsBox 6.0. Based on the analysis, previous studies show a strong relationship between hate speech, political issues, and discrimination. Although many studies of hate speech have applied the CDA approach, and some of them have used sociocognitive CDA, it is surprising and unfortunate that previous researchers did not show much attention and further exploration of the cognitive aspects of the theory itself. It means that the cognitive aspects of hate speech have yet to be appropriately explored.
Social Media Effects on E-Commerce Activities: The Case of Kuwait during and following the COVID-19 Pandemic

This paper discusses the impact of social media on the e-commerce industry in Kuwait. The paper highlights the slow growth of e-commerce in the region due to traditional shopping practices and lack of knowledge about e-commerce instruments. The paper explores the potential of e-commerce in Kuwait and how social media platforms like Facebook and Instagram are contributing to its growth. The paper also highlights the efforts of the Kuwaiti government in enhancing its e-Government potential and adopting laws in line with international regulations to make e-commerce more efficient and secure. Finally, the paper discusses the growth of regional-wide platforms in Kuwait and the shift towards the integration of digital technologies into the national economy.
Targeted deep learning: Framework, methods, and applications

Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic situations for which they were not specifically trained. In general, the ability to cope with a broad spectrum of unseen test inputs is called generalization. Generalization is definitely important in applications where the possible test inputs are known but plentiful or simply unknown, but there are also cases where the possible inputs are few and unlabeled but known beforehand. For example, medicine is currently interested in targeting treatments to individual patients; the number of patients at any given time is usually small (typically one), their diagnoses/responses are still unknown, but their general characteristics (such as genome information, protein levels in the blood, and so forth) are known before the treatment. We propose to call deep learning in such applications In this paper, we introduce a framework for targeted deep learning, and we devise and test an approach for adapting standard pipelines to the requirements of targeted deep learning. The approach is very general yet easy to use: It can be implemented as a simple data‐preprocessing step. We demonstrate on a variety of real‐world data that our approach can indeed render standard deep learning faster and more accurate when the test inputs are known beforehand.
Repurposed collection marketing and visibility: using LibGuide gallery boxes as virtual library bookshelves

Purpose This paper aims to provide a reflection on innovating and repurposing collection development and marketing strategy, particularly for eBooks in libraries. Design/methodology/approach This paper provides a general review where previous research papers on the phenomenon of the study have been consulted. Findings The findings indicate that there is a lack of awareness of eBooks and marketing, and virtual bookshelves in the form of LibGuide gallery boxes are identified as a solution in promoting these resources. Originality/value This is a valuable contribution that sheds insight on the usage of LibGuide gallery boxes as virtual bookshelves in marketing library resources, particularly eBooks.
Real-world low-light image enhancement via domain-gap aware framework and reverse domain-distance guided strategy

Low-light image enhancement (LLIE) has high practical value and development potential in real scenarios. However, the current LLIE methods reveal inferior generalization competence to real-world low-light (LL) conditions of poor visibility. We can attribute this phenomenon to the severe domain bias between the synthetic LL domain and the real-world LL domain. In this article, we put forward the Domain-Gap Aware Framework, a novel two-stage framework for real-world LLIE, which is the pioneering work to introduce domain adaptation into the LLIE. To be more specific, in the first stage, to eliminate the domain bias lying between the existing synthetic LL domain and the real-world LL domain, this work leverages the source domain images via adversarial training. By doing so, we can align the distribution of the synthetic LL domain to the real-world LL domain. In the second stage, we put forward the Reverse Domain-Distance Guided (RDDG) strategy, which takes full advantage of the domain-distance map obtained in the first stage and guides the network to be more attentive to the regions that are not compliance with the distribution of the real world. This strategy makes the network robust for input LL images, some areas of which may have large relative domain distances to the real world. Numerous experiments have demonstrated the efficacy and generalization capacity of the proposed method. We sincerely hope this analysis can boost the development of low-light domain research in different fields.
RESTful API Analysis, Recommendation, and Client Code Retrieval

Numerous companies create innovative software systems using Web APIs (Application Programming Interfaces). API search engines and API directory services, such as ProgrammableWeb, Rapid API Hub, APIs.guru, and API Harmony, have been developed to facilitate the utilization of various APIs. Unfortunately, most API systems provide only superficial support, with no assistance in obtaining relevant APIs or examples of code usage. To better realize the “FAIR” (Findability, Accessibility, Interoperability, and Reusability) features for the usage of Web APIs, in this study, we developed an API inspection system (referred to as API Prober) to provide a new API directory service with multiple supplemental functionalities. To facilitate the findability and accessibility of APIs, API Prober transforms OAS (OpenAPI Specifications) into a graph structure and automatically annotates the semantic concepts using LDA (Latent Dirichlet Allocation) and WordNet. To enhance interoperability, API Prober also classifies APIs by clustering OAS documents and recommends alternative services to be substituted or merged with the target service. Finally, to support reusability, API Prober makes it possible to retrieve examples of API utilization code in Java by parsing source code in GitHub. The experimental results demonstrate the effectiveness of the API Prober in recommending relevant services and providing usage examples based on real-world client code. This research contributes to providing viable methods to appropriately analyze and cluster Web APIs, and recommend APIs and client code examples.
Using keywords in the automatic classification of language of gender violence

This paper employs lexical analysis tools, quantitative processing methods, and natural language processing procedures to analyze language samples and identify lexical items that support automatic topic detection in natural language processing. This paper discusses how keyword extraction, a technique from corpus linguistics, can be employed in obtaining features that improve automatic classification; in particular, this research is concerned with extracting keywords from a corpus obtained from social networks. The corpus consists of 1,841,385 words and is subdivided into three sub-corpora that have been categorized according to the topic of the comments in each one of them. These three topics are violence against women, violence against the LGBT community, and violence in general. The corpus has been obtained by scraping comments from YouTube videos that address issues such as street harassment, femicide, feminist movements, drug trafficking, forced disappearances, equal marriage, among others. The topic detection tasks performed with the corpus extracted from the social media showed that the keywords rendered a 98% accuracy when classifying the collection of comments from 51 videos, as one of the three categories mentioned above, and 92% when classifying almost 7,500 comments individually. When keywords were removed from the classification task and all words were used to perform the classification task, accuracy dropped by an average of 17%. These results support the argument for keyword relevance in automatic topic detection.
Per studiare il vocabolario del passato

This paper aims to propose a new method for describing the lexicon of a language in a specific period of its history. The first paragraph outlines the two main ideas to be found in the studies concerning both synchronic and diachronic lexicology. In the second paragraph our method for lexical inquiry is presented together with its core concepts, which are textual Corpus Representativeness, Connotation, Connotation Rate (Quoziente Connotativo, QC) and word Position in the Center-Periphery Vocabulary Model. The third paragraph sketches two possible research lines, the first one regarding the lexicon of a given historical period (Old Italian), the second dealing with the comparison between two different linguistic historical phases (Old Italian vs. Contemporary Italian).