Reviewerly/exHarmony huggingface dataset:
A multi-scale feature extraction fusion model for human activity recognition

Human Activity Recognition (HAR) is an important research area in human-computer interaction and pervasive computing. In recent years, many deep learning (DL) methods have been widely used for HAR, and due to their powerful automatic feature extraction capabilities, they achieve better recognition performance than traditional methods and are applicable to more general scenarios. However, the problem is that DL methods increase the computational cost of the system and take up more system resources while achieving higher recognition accuracy, which is more challenging for its operation in small memory terminal devices such as smartphones. So, we need to reduce the model size as much as possible while taking into account the recognition accuracy. To address this problem, we propose a multi-scale feature extraction fusion model combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). The model uses different convolutional kernel sizes combined with GRU to accomplish the automatic extraction of different local features and long-term dependencies of the original data to obtain a richer feature representation. In addition, the proposed model uses separable convolution instead of classical convolution to meet the requirement of reducing model parameters while improving recognition accuracy. The accuracy of the proposed model is 97.18%, 96.71%, and 96.28% on the WISDM, UCI-HAR, and PAMAP2 datasets respectively. The experimental results show that the proposed model not only obtains higher recognition accuracy but also costs lower computational resources compared with other methods.
Can digital adherence technologies reduce inequity in tuberculosis treatment success? Evidence from a randomised controlled trial

Tuberculosis (TB) is a global health emergency and low treatment adherence among patients is a major barrier to ending the TB epidemic. The WHO promotes digital adherence technologies (DATs) as facilitators for improving treatment adherence in resource-limited settings. However, limited research has investigated whether DATs improve outcomes for high-risk patients (ie, those with a high probability of an unsuccessful outcome), leading to concerns that DATs may cause intervention-generated inequality.We conducted secondary analyses of data from a completed individual-level randomised controlled trial in Nairobi, Kenya during 2016-2017, which evaluated the average intervention effect of a novel DAT-based behavioural support programme. We trained a causal forest model to answer three research questions: (1) Was the effect of the intervention heterogeneous across individuals? (2) Was the intervention less effective for high-risk patients? nd (3) Can differentiated care improve programme effectiveness and equity in treatment outcomes?We found that individual intervention effects-the percentage point reduction in the likelihood of an unsuccessful treatment outcome-ranged from 4.2 to 12.4, with an average of 8.2. The intervention was beneficial for 76% of patients, and most beneficial for high-risk patients. Differentiated enrolment policies, targeted at high-risk patients, have the potential to (1) increase the average intervention effect of DAT services by up to 28.5% and (2) decrease the population average and standard deviation (across patients) of the probability of an unsuccessful treatment outcome by up to 8.5% and 31.5%, respectively.This DAT-based intervention can improve outcomes among high-risk patients, reducing inequity in the likelihood of an unsuccessful treatment outcome. In resource-limited settings where universal provision of the intervention is infeasible, targeting high-risk patients for DAT enrolment is a worthwhile strategy for programmes that involve human support sponsors, enabling them to achieve the highest possible impact for high-risk patients at a substantially improved cost-effectiveness ratio.
A Web-Based Program About Sustainable Development Goals Focusing on Digital Learning, Digital Health Literacy, and Nutrition for Professional Development in Ethiopia and Rwanda: Development of a Pedagogical Method

East African countries face significant societal challenges related to sustainable development goals but have limited resources to address these problems, including a shortage of nutrition experts and health care workers, limited access to physical and digital infrastructure, and a shortage of advanced educational programs and continuing professional development.This study aimed to develop a web-based program for sustainable development with a focus on digital learning, digital health literacy, and child nutrition, targeting government officials and decision-makers at nongovernmental organizations (NGOs) in Ethiopia and Rwanda.A web-based program-OneLearns (Online Education for Leaders in Nutrition and Sustainability)-uses a question-based learning methodology. This is a research-based pedagogical method developed within the open learning initiative at Carnegie Mellon University, United States. Participants were recruited during the fall of 2020 from ministries of health, education, and agriculture and NGOs that have public health, nutrition, and education in their missions. The program was conducted during the spring of 2021.Of the 70 applicants, 25 (36%) were selected and remained active throughout the entire program and filled out a pre- and postassessment questionnaire. After the program, of the 25 applicants, 20 (80%, 95% CI 64%-96%) participants reported that their capacity to drive change related to the sustainable development goals as well as child nutrition in their organizations had increased to large extent or to a very large extent. Furthermore, 17 (68%, 95% CI 50%-86%) and 18 (72%, 95% CI 54%-90%) participants reported that their capacity to drive change related to digital health literacy and digital learning had increased to a large extent and to a very large extent, respectively.Digital learning based on a question-based learning methodology was perceived as a useful method for increasing the capacity to drive change regarding sustainable development among government officials and decision-makers at NGOs in Ethiopia and Rwanda.
Delivering A Good Speech by Identifying the Interpersonal Meaning Found in Joe Biden’s Victory Speech: A Case on Systemic Functional Linguistics

This research aims to classify the interpersonal meaning in Joe Biden’s victory speech. It is an interesting phenomenon because by revealing the interpersonal meaning, people can be assisted to be successful in communication. The researchers used a qualitative research design to answer the research question and chose content analysis since the results of data analysis were in descriptive phenomena, such as words, clauses, and utterances. The research findings showed that mood elements: found 174 subjects and 176 finites. Meanwhile, residue elements found 120 predicators, 133 complements, 9 mood adjuncts, 71 circumstantial adjuncts, 2 comment adjuncts, and 54 conjunctive adjuncts. In addition, the speech function identified 162 Statements, 4 Questions, and 11 Commands. Thus, it is concluded that Joe Biden tended to use finites, in contrast with comment adjuncts, and Statements, in contrast with questions (only 4 questions were found in his speech). This study also showed that the speaker was greatly assisted by using interpersonal meaning to make a good speech.
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data.
Digital Teacher Competence Frameworks Evolution and Their Use in Ibero-America up to the Year the COVID-19 Pandemic Began: A Systematic Review

The COVID-19 pandemic changed the way education was conducted, not only at the time when the face-to-face model was replaced by virtuality but also in the period of return to normality because the digital skills of teachers are not the same as before. Digital competency frameworks allow for assessment and comparisons between individuals and over time, so they can be used to understand the transformation that may have occurred in teachers’ digital competencies following the pandemic. This systematic literature review analyzes the competency frameworks that have been used in Ibero-America up to the year 2022, with the purpose of defining a concept foundation as an input on which to build a tool to assess digital competencies. The review was done following the pathway proposed by the PRISMA methodology between 2018 and 2022. Results show that there is no consensus or unification of the frameworks, and that there are five purposes in the research being conducted on digital competencies with publications concentrated on two of them. Interest on digital competence frameworks increased substantially in 2020.
The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: Evidence from Jordanian telecommunication firms

This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big Data External and Internal, Innovative Usage, Indexing, and Sources Accuracy. In addition, Artificial intelligence positively affects business performance, including Data Accuracy, Data Transparency, Data Speed, and Creative Thinking and Learning. Moreover, business intelligence has a direct and positive impact on business performance, including Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. In addition, the findings indicate that e-learning which represents system quality, information quality, and self-efficacy has a positive relationship on enhancing business performance. Interestingly, the present findings are inconsistent with those of previous studies showing the variables of interest which have no effect on e-learning and business performance. Taken together, the findings of this study suggest that firms should begin to apply processes related with applying e-learning and developing business performance. The novelty of the present study lies in highlighting the key dimensions of big data, artificial intelligence, and business intelligence when it comes to enhancing e-learning and business performance at Jordanian telecommunications industry.
Reducing Latency in Sensing for Optical Convolutional Neural Network

Effects of sensing latency in optical neural networks on system energy efficiency in an optical convolutional neural network accelerator and a methodology and subsequent results for reducing latency by optimizing the data flow in electro-optical connections.
The Financial availability and Innovation link with Firms &amp; Environmental Performance

Conventionally, organizations tend to rely on financial performance and profit capacity to flow shareholder wealth. Currently, in adding to environmental and financial results, it had also developed a vital effort. Though, all organizations are unable to achieve financial and environmental results, especially medium and small-sized enterprises that need performance-enhancing financial resources. This analysis confirms the part of financial obtainability in financial, and environmental performance with a mediating role in recognizing the opportunity. Pragmatic tests are accumulated through framework questionnaires from 305 SMEs working in Pakistan developing markets. The hypotheses are verified in the AMOS by, Structural Equations Modeling (SEM). The results show that suitable financial resources have contributed to the performance of financial instruments, but they also play a significant part in environmental performance. Our study results indorse CEOs and senior administrators effectively use their financial resources to gain the benefit of a new opportunity, excellent financial and environmental performance. Also, companies with sufficient financial capital identify innovative opportunities significantly via recognition of opportunity mediator where they partially mediate the link among financial performance and financial obtainability.
Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.
Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection

Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to capture multi-scale context information, so as to obtain more feature information and improve detection accuracy. Finally, location information is added to the channel attention using the Coordinated Attention (CA) mechanism, thus enhancing the feature expression. The experimental results show that compared with YOLOv5, the mAP value of this method increased by 2.27%. Our approach can be effectively applied to recognizing traffic signs in complex scenes. At road intersections, traffic planners can better plan traffic and avoid traffic jams.
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From a Replication Study

In the last decade, several studies have explored automated techniques to estimate the effort of agile software development.We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since.Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation.We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study.To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats.The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE.The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed.These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates.
Controlled quantum teleportation between discrete and continuous physical systems

Abstract Quantum teleportation of an unknown state basing on the interaction between discrete-valued states (DV) and continuous-valued states (CV) presented a particular challenge in quantum technologies. Here we consider the problem of controlled quantum teleportation of an amplitude-matched CV qubit, encoded by a coherent state of a varied phase as a superposition of the vacuum- and single-photon optical states among two distant partners Alice and Bob, with the consent of controller, Charlie. To achieve this task, we use an hybrid tripartite entangled state (interaction between the discrete and continuous variables states) as the quantum resource where the coherent part belongs to Alice, while the single-photon belongs to Bob and Charlie and the CV qubit is at the disposal of Alice. The discrete-continuous interaction is realized on highly transmissive beam-splitter. We have shown that the perfectly of teleportation fidelity depends on the phase difference between the phase of the state to teleport and the phase of the sender’s mode, we found that for a difference which approaches 0 or π , near perfect controlled quantum teleportation can be obtained in terms of the fidelity and independently of the amplitude α and the squeezing parameter ζ . Experimentally, this proposed scheme has been implemented using linear optical components such as beam splitter, phase shifters and photon counters.
Immersive environments, Metaverse and the key challenges in programming

Metaverse is a virtual reality (VR) environment with avatars and software agents using the internet and augmented reality (AR). This one is described as a new iteration of the internet, Metaverse uses VR headsets, blockchain technology and avatars in a new fusion of real and virtual worlds. The role of the programmer in the metaverse is to create and maintain programs that control the behavior of virtual objects and entities within the virtual space. This can include characters controlled by the computer, animations, special effects, and other visual and interactive elements. Programmers in the metaverse may work on a variety of different projects, such as developing new virtual worlds, creating games or other interactive experiences, or building tools and applications for use within the metaverse. They may also be responsible for maintaining and updating existing programs, fixing bugs, and optimizing performance. The main challenge facing programmers in the metaverse is creating a seamless and immersive experience for users. This involves designing and implementing a wide range of features, such as realistic and interactive 3D environments, avatars, physics and interactions, network communication and synchronization, and AI-powered non-player characters. The development of the metaverse requires a wide range of skills and technologies, including expertise in computer graphics, networking, AI, and user experience design.
Uniaxial MEMS-based 3D reconstruction using pixel refinement

A uniaxial micro-electro-mechanical systems (MEMS) micro-vibration mirror can be used to construct a new type of fringe projection profilometry (FPP) system. In FPP system calibration, some pixels may be calibrated worse than other pixels due to various error sources, which will affect the final reconstruction accuracy. In addition, there are some difficulties in calibrating the MEMS-based system because a projector using the uniaxial vibration mirror does not have focusing optics and can only project unidirectional fringes. In this paper, we developed an FPP system using a uniaxial MEMS micro-vibration mirror. To solve the calibration problems, we propose a calibration model suitable for the MEMS-based system and a pixel refinement method. These pixels with relatively large calibration errors are called outlier-pixels, which will significantly increase the error of the following 3D mapping. Therefore, the pixel refinement method classifies all pixels based on a frequency distribution histogram of calibration errors during calibration and prevents outlier-pixels from participating in the following 3D mapping. The experimental results show that the proposed method can improve the accuracy of 3D reconstruction, and the feasibility of the self-developed system is verified.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization

Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages. However, previous work on FRS performs similarity search via inner product in continuous embedding space, which causes an efficiency bottleneck when the scale of items is extremely large. We argue that such a scheme in federated settings ignores the limited capacities in resource-constrained user devices ( i.e. , storage space, computational overhead, and communication bandwidth), and makes it harder to be deployed in large-scale recommender systems. Besides, it has been shown that transmitting local gradients in real-valued form between server and clients may leak users’ private information. To this end, we propose a lightweight federated recommendation framework with privacy-preserving matrix factorization, LightFR , that is able to generate high-quality binary codes by exploiting learning to hash technique under federated settings, and thus enjoys both fast online inference and economic memory consumption. Moreover, we devise an efficient federated discrete optimization algorithm to collaboratively train model parameters between the server and clients, which can effectively prevent real-valued gradient attacks from malicious parties. Through extensive experiments on four real-world datasets, we show that our LightFR model outperforms several state-of-the-art FRS methods in terms of recommendation accuracy, inference efficiency and data privacy.