Reviewerly/exHarmony huggingface dataset:
Corporate Social Responsibility impacts sustainable organizational growth (firm performance): An empirical analysis of Pakistan stock exchange-listed firms

This study examines of impact on Organizational sustainable growth (firm performance) of Corporate Social responsibility, Leverage on Assets, firm age and firm. This study used sample data of 296 Pakistan stock exchange-listed firms and applied correlation, Ordinary least square regression model for estimate factor impact, and Robustness use for the result is reliable and sustainable. This study used Sustainable Corporate Social responsibility (independent variable), leverage on Assets (moderator variable), firm age and firm size (control variable) and Correlation, Ordinary least square regression model that confirmed their variables, i.e. Corporate Social responsibility, Leverage on Assets, firm age and firm size highly impacting on sustainable organizational growth (firm performance). Robustness test results also confirm the reliability, validity and sustainability of results. That shows results are highly significant reliable, and sustainable. Sustainable Corporate Social responsibility is the leading factor that enhances the firm performance. Firm size and age are significant for sustainable organizational growth (firm performance). This study implication is very significant; policymakers more focus on Sustainable Corporate Social responsibility and corporate commitments. Study recommended to firms; developed a sustainable environmental structure: Enhancing the employee's motivation (self-efficacy), performance per-motion bonuses, employee's need and Corporate Social responsibility leads to sustainable organizational growth (firm performance).
Application of eye-tracking systems integrated into immersive virtual reality and possible transfer to the sports sector - A systematic review

Abstract In recent years, Virtual Reality (VR) has become a valuable tool in rehabilitation and sports training applications. New technologies offer opportunities to combine various systems and use them for sports-related scientific purposes. For instance, examining the visual perception of athletes within a standardized environment could be helpful to understand the differences between novices and experts in their visual behavior and could further reveal possible training applications for enhancing athletes’ visual attention. The current systematic literature review thematizes the importance of eye-tracking (ET) systems’ usage integrated into head-mounted displays (HMDs) in virtual environments for further inclusion in sports-related usage. An overview of possible implementations is given, and additional recommendations for using the combined technic regarding sports are made. Although only one study examined gaze behavior during sports activity within a standardized virtual environment, 38 relevant papers were identified using the ET systems integrated into the HMDs, which ideas can be transferred to the sports sector. The increased usability and fidelity in the virtual environment enabled through the combined technology were illustrated, and different approaches were listed in using and calculating gaze parameters. This literature review examines the possibility of integrating ET in VR, which can be further used to improve usability, interaction methods, image presentation, and visual perception analyses within future physical training scenarios. The compiled studies have shown that the existing methods are feasible due to the performance of the integrated ET systems but still need to be improved for practical use.
DNNV: A Framework for Deep Neural Network Verification

Despite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, verifier developers must contend with the rapidly changing DNN field to support new DNN operations and property types. Second, verifier users have the burden of selecting a verifier input format to specify their problem. Due to the many input formats, this decision can greatly restrict the verifiers that a user may run. Finally, researchers face difficulties in re-using benchmarks to evaluate and compare verifiers, due to the large number of input formats required to run different verifiers. Existing benchmarks are rarely in formats supported by verifiers other than the one for which the benchmark was introduced. In this work we present DNNV, a framework for reducing the burden on DNN verifier researchers, developers, and users. DNNV standardizes input and output formats, includes a simple yet expressive DSL for specifying DNN properties, and provides powerful simplification and reduction operations to facilitate the application, development, and comparison of DNN verifiers. We show how DNNV increases the support of verifiers for existing benchmarks from 30% to 74%.
PLM-ICD: Automatic ICD Coding with Pretrained Language Models

Automatically classifying electronic health records (EHRs) into diagnostic codes has been challenging to the NLP community.State-ofthe-art methods treated this problem as a multilabel classification problem and proposed various architectures to model this problem.However, these systems did not leverage the superb performance of pretrained language models, which achieved superb performance on natural language understanding tasks.Prior work has shown that pretrained language models underperformed on this task with the regular finetuning scheme.Therefore, this paper aims at analyzing the causes of the underperformance and developing a framework for automatic ICD coding with pretrained language models.We spotted three main issues through the experiments: 1) large label space, 2) long input sequences, and 3) domain mismatch between pretraining and fine-tuning.We propose PLM-ICD, a framework that tackles the challenges with various strategies.The experimental results show that our proposed framework can overcome the challenges and achieves state-ofthe-art performance in terms of multiple metrics on the benchmark MIMIC data. 1
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies

Event trigger detection, entity mention recognition, event argument extraction, and relation extraction are the four important tasks in information extraction that have been performed jointly (Joint Information Extraction -JointIE) to avoid error propagation and leverage dependencies between the task instances (i.e., event triggers, entity mentions, relations, and event arguments).However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and meanfield factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance.To overcome these limitations, we propose to induce a dependency graph among task instances from data to boost representation learning.To better capture dependencies between instance labels, we propose to directly estimate their joint distribution via Conditional Random Fields.Noise Contrastive Estimation is introduced to address the maximization of the intractable joint likelihood for model training.Finally, to improve the decoding with greedy or beam search in prior work, we present Simulated Annealing to better find the globally optimal assignment for instance labels at decoding time.Experimental results show that our proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages.
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla

In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature.To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed 'Bangla2B+') by crawling 110 popular Bangla sites.We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction.In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB).BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models.We are making the models, datasets, and a leaderboard publicly available at https://github.com/csebuetnlp/banglabert to advance Bangla NLP.
A Deeper Look into the EU Text and Data Mining Exceptions: Harmonisation, Data Ownership, and the Future of Technology

Abstract This paper focuses on the two exceptions for text and data mining (TDM) introduced in the Directive on Copyright in the Digital Single Market (CDSM). While both are mandatory for Member States, Art. 3 is also imperative and finds application in cases of text and data mining for the purpose of scientific research by research and cultural institutions; Art. 4, on the other hand, permits text and data mining by anyone but with rightholders able to ‘contract-out’ (Art. 4). We trace the context of using the lever of copyright law to enable emerging technologies such as AI and the support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of permanent copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction; we argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for training data that offer the wrong incentives. To avoid licensing, it may be economically attractive for EU-based developers to train their algorithms on older, less accurate, biased data, or import AI models already trained abroad on unverifiable data.
An adaptive Drop method for deep neural networks regularization: Estimation of DropConnect hyperparameter using generalization gap

Deep neural networks (DNNs) have solved numerous challenging reels problems. However, successful DNNs often require a large number of parameters, which may produce some undesirable phenomena, notably overfitting. DropConnect, which is a generalization of Dropout, is one of the successful stochastic regularizers that prevent overfitting in deep neural networks. Indeed, it allows dropping some parameters according to a fixed probability, generating at the end dynamic sparse DNNs. The study of the DropConnect hyperparameter is still not estimated and needs a theoretical understanding. In this context, we propose an estimation of the DropConnect hyperparameter using the gap generalization and the Rademacher complexity. This estimation gives rise to a new DropConnect technique named Adaptive DropConnect (A-DropConnect), in which the studied hyperparameter is updated using the data-dependent during training. Efficiency of A-DropConnect is demonstrated by several experiments. Numerical results demonstrate that the proposed method yields significant improvement in classification performance compared to the state of the art. • Derivation of a generalization gap upper bound using DropConnect Rademacher complexity. • Minimizing the obtained upper bound which is a constraint function of the DropConnect hyperparameters. • Estimation of DropConnect hyperparameter using the gap generalization optimization.
A Review of Extended Reality (XR) Technologies in the Future of Human Education: Current Trend and Future Opportunity

Nowadays technology development in real life will reflect on education inexorably. Today, the concept of Extended Reality (XR) technologies (XRs), such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), as very promising technological tools for education. The purpose of these technologies is to facilitate human life. In this context, educational technologies create new opportunities for students and teachers every day especially in education field. With various virtual environments (VE) and imaging systems, content that needs to be learned can be presented independently of school and classrooms in a low-cost and safe environment. As a result, teachers can create their own content with various applications, providing them with freedom of flexibility, and creativity. In this context, it is seen that XR has started to be used in various fields in education. When these fields and subjects are examined, it is observed that there is a concentration in the fields of education. The purpose of this narrative review is to describe the accelerators of XR (VR, AR, MR) technology in education settings. This literature review was conducted by conducting a systematic search of relevant articles and literature reviews through the university's online library, Google Scholar, Web of Science, IEEE Xplore, and the ACM Digital Library. In this paper, the authors demonstrate the main components of XR technology in the educational process. A comprehensive review of XR technology including education, and learning was provided. The authors identify limitations and conclude with future work of XR in educational uses.
Trusted and Secure Blockchain-Based Architecture for Internet-of-Medical-Things

The Internet of Medical Things (IoMT) global market has grown and developed significantly in recent years, and the number of IoMT devices is increasing every year. IoMT systems are now very popular and have become part of our everyday life. However, such systems should be properly protected to preventing unauthorized access to the devices. One of the most popular security methods that additionally relies on real-time communication is Blockchain. Moreover, such a technique can be supported by the Trusted Third Party (TTP), which guarantees data immutability and transparency. The research and industrial community has predicted the proliferation of Blockchain-based IoMT (BIoMT), for providing security, privacy, and effective insurance processing. A connected environment comprises some of the unique features of the IoMT in the form of sensors and devices that capture and measure, recognize and classify, assess risk, notify, make conclusions, and take action. Distributed communication is also unique due to the combination of the fact that the Blockchain cannot be tampered with and the Peer-to-Peer (P2P) technique, especially compared to the traditional cloud-based techniques where the reliance of IoMT systems on the centralized cloud makes it somewhat vulnerable. This paper proposes a Blockchain-based technique oriented on IoMT applications with a focus on maintaining Confidentiality, Integrity, and Availability (the CIA triad) of data communication in the system. The proposed solution is oriented toward trusted and secure real-time communication. The presented method is illustrated by an example of a cloud-based hospital application. Finally, the security aspects of the proposed approach are studied and analyzed in detail.
A Survey of Cross-lingual Sentiment Analysis: Methodologies, Models and Evaluations

Abstract Cross-lingual sentiment analysis (CLSA) leverages one or several source languages to help the low-resource languages to perform sentiment analysis. Therefore, the problem of lack of annotated corpora in many non-English languages can be alleviated. Along with the development of economic globalization, CLSA has attracted much attention in the field of sentiment analysis and the last decade has seen a surge of researches in this area. Numerous methods, datasets and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art CLSA approaches from 2004 to the present. This paper teases out the research context of cross-lingual sentiment analysis and elaborates the following methods in detail: (1) The early main methods of CLSA, including those based on Machine Translation and its improved variants, parallel corpora or bilingual sentiment lexicon; (2) CLSA based on cross-lingual word embedding; (3) CLSA based on multi-BERT and other pre-trained models. We further analyze their main ideas, methodologies, shortcomings, etc., and attempt to reach a conclusion on the coverage of languages, datasets and their performance. Finally, we look into the future development of CLSA and the challenges facing the research area.
Without appropriate metadata, data-sharing mandates are pointless

Funders and investigators must demand appropriate metadata standards to take data from foul to FAIR. Funders and investigators must demand appropriate metadata standards to take data from foul to FAIR.
Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO

In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability. Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed. After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.19%, which is 4.76% higher than the YOLOv4-tiny algorithm. Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes.
FallNeXt: A Deep Residual Model based on Multi-Branch Aggregation for Sensor-based Fall Detection

Falls are uncommon and pose a substantial health danger to adults and the elderly. These situations are a leading cause of severe injury. More harm could be averted if the faller could be located in time. The rising older population necessitates the rapid development of fall detection and prevention technologies. The burgeoning technology industry is focused on developing such technologies to improve the living conditions for the elderly in particular. A fall detection system monitors falls and provides an assistance notice to support mitigation of falls. This study proposes a sensor-based solution based on a deep learning network named FallNeXt to safeguard individual privacy and increase fall detection performance. This proposed network is a novel deep residual network that utilizes multi-branch aggregation to enhance fall detection capability. The detection effectiveness of this study was evaluated using three benchmark datasets for sensor-based fall detection: UpFall, SisFall, and UMAFall datasets. Compared to benchmark deep learning models on the three datasets, the experimental findings indicate that the proposed FallNeXt network scored the most significant overall accuracy and F1-score, with 96.16% and 99.12%, respectively. The benefit of the FallNeXt model's small but highly effective size for fall detection is its portability.
Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm.
Diffusion Model with Detail Complement for Super-Resolution of Remote Sensing

Remote sensing super-resolution (RSSR) aims to improve remote sensing (RS) image resolution while providing finer spatial details, which is of great significance for high-quality RS image interpretation. The traditional RSSR is based on the optimization method, which pays insufficient attention to small targets and lacks the ability of model understanding and detail supplement. To alleviate the above problems, we propose the generative Diffusion Model with Detail Complement (DMDC) for RS super-resolution. Firstly, unlike traditional optimization models with insufficient image understanding, we introduce the diffusion model as a generation model into RSSR tasks and regard low-resolution images as condition information to guide image generation. Next, considering that generative models may not be able to accurately recover specific small objects and complex scenes, we propose the detail supplement task to improve the recovery ability of DMDC. Finally, the strong diversity of the diffusion model makes it possibly inappropriate in RSSR, for this purpose, we come up with joint pixel constraint loss and denoise loss to optimize the direction of inverse diffusion. The extensive qualitative and quantitative experiments demonstrate the superiority of our method in RSSR with small and dense targets. Moreover, the results from direct transfer to different datasets also prove the superior generalization ability of DMDC.