Reviewerly/exHarmony huggingface dataset:
A Study on the Development of Touchless Mouse by Object Tracking through Webcam

In this paper, we study the continuous growth of computer technology and the increasing importance of human-computer interaction.Interactive touch-less is now an undergoing developing technology in real life.Touchless technology introduces a new way of interacting with computers by object tracking method.Nowadays most mobile devices are using touchscreen technology.However, this technology is still not cheap enough to be used in desktop systems.Designing a touchless device such as a mouse or keyboard using a webcam and computer vision techniques can be an alternative way of touch screen technology.Recent trends in technology aim to build highly interactive and easy-to-use applications as a replacement for conventional devices.Such a device is touchless mouse.Its development is completed on the MATLAB platform.The overall objective is to apply image processing techniques from video to track the movement of color which is captured by a webcam and that is converted into mouse movements and operations to control the system.The sub-system which is implemented here would allow a person to control his/her mouse without any input other than the marker movements.We use three fingers (with color) as three color markers (red, green, blue) for completing the activities of a mouse.My first goal was to successfully track the marker color and the second goal was to track the marker position from the acquired image frame for performing the mouse operations.The webcam is used to capture the information on the marker and trigger the associated actions.I use java.awt.Robot file for performing the mouse operations using the acquired data from the image frame.
Hybrid model from cloud motion vector and spatio-temporal autoregressive technics for hourly satellite-derived irradiance in a complex meteorological context

Islands in tropical regions have high potential for solar energy, but the weather conditions in these areas are complex, with high fluctuations in the amount of sunlight received over time and across different locations, making it difficult to predict solar irradiance accurately.In a preliminary study, two spatio-temporal technics STVAR (spatio-temporal autoregressive) and CMV (cloud motion vector) showing a good predictive performance in literature, were assessed in this challenging environment. The strengths and the weaknesses of different models for different conditions/locations were presented. In this paper, we focus on the validation STVAR/CMV blends for the same satellite-derived irradiance dataset. In a first step, the research of the equation defining the blended model is investigated, highlighting a linear combination of irradiance predicted from CMV and STVAR by least-squares fit, as being optimal. A benchmarking illustration as a function of the orographic context exhibits the reduction of their respective gaps forced by their separate application. Then, the analysis of spatial evolution of the linear combination coefficients, led us to propose a model that quantifies coefficients of the blended model as a function of site elevation that represents an effective proxy for the microclimatological/topographical nature of the considered location. The proposed model shows good performance with an averaged relative RMSE of 16.50% in the entire study area. This model can be an appropriate choice for short-term forecasting even under complex orography conditions.
Modeling the predictors of mobile health adoption by Rohingya Refugees in Bangladesh: An extension of UTAUT2 using combined SEM-Neural Network Approach

While the healthcare facilities for the people is questionable in Bangladesh, Rohingya refugees is a burning issue for both Bangladesh and global community. Integrating Rohingya refugees into the framework of mHealth could be beneficial for both Bangladesh and Rohingya refugees in general, and in specific situation like COVID-19 outbreak. However, no research has been found on what motivates Rohingya refugees to accept mHealth in Bangladesh. Drawing on the UTAUT2 model, this study investigates the predictors of acceptance of mHealth services technologies among Rohingya refugees. The study also seeks to clarify the roles of mHealth developers, the Bangladesh government, and non-governmental organizations working with the 1.1 million Rohingya refugees in Bangladesh. Quantitative data were collected from refugee camps with the permission of the Refugee Relief and Repatriation Commissioner (RRRC). The data were analyzed in two stages using a mixed approach that combines PLS-SEM and Artificial Neural Network (ANN). This study revealed that Effort expectancy (EE, with t = 5.629, β = 0.313) and facilitating conditions (FC with t = 4.442, β = 0.269) in PLS-SEM, and FC (with 100 percent importance) and Health consciousness (HC, with 94.88 percent importance) in ANN analysis were found to be the most substantial predictors of mHealth adoption. The study also revealed that EE and FC are more important for low education group, while PE and Situational Constraint (SC) are more important for the high education group of refugees. In addition to providing insights for mHealth developers, this study particularly focuses on the role of government institutions and non-governmental social workers in working with the subjects to increase FC and HC among Rohingya refugees and bring them under mHealth services.
Low Light Image Enhancement Algorithm Based on Improved Multi-Objective Grey Wolf Optimization with Detail Feature Enhancement

This article aims to enhance low-light images by proposing a novel algorithm called MoGDF. It departs from the traditional approach of using deep learning with low-light and normal images as training data. Instead, exposure images at five different exposure scales are generated using a proposed model called EIPM, which consists of an improved Gamma function. Then it introduces a feature extraction network called FMEM, which leverages an improved PFPN structure to enhance the fusion of multi-scale information and strengthen the connection between high-level and low-level semantic information. Finally, it optimizes the fusion weights using an improved multiple-objective grey wolf optimization algorithm with comprehensive improvements in the convergence factor updating strategy, adaptive inertia weight strategy, and individual position strategy. The experiments show that the MoGDF outperforms the current state-of-the-art algorithms in terms of color restoration and detail preservation, and has significant advantages in objective and subjective evaluation metrics, with improvements of 5.69%, 2.60%, 1.11%, and 6.67% in PSNR, SSIM, NIQE, and LPIPS, respectively.
Efficient framework for low-resource abstractive summarization by meta-transfer learning and pointer-generator networks

Recently, large language models have shown great success on various abstractive summarization datasets. These datasets consist of numerous data that are enough to train a large number of parameters. However, for a new domain, there is a lack of labeled data to train those parameters and the model is easily overfitted to a small amount of data. In addition, because annotating document-summary pairs is too expensive and transfer learning using high-resource datasets causes a domain shifting problem, a low-resource abstractive summarization task is becoming necessary. Herein, we propose an efficient framework for low-resource abstractive summarization using a pointer-generator network and a meta-learning technique to address the above problems. Meta-learning using existing high-resource datasets enables our model to rapidly adapt to a new domain using limited data to solve the domain shifting problem. In addition, we explore the copy mechanism using a pointer-generator network that can copy words from a source document when generating a summary. The experimental results on 11 different datasets show that the proposed model outperforms the previous state-of-the-art models in low-resource abstractive summarization on most of the datasets.
An approach to identification and forensic analysis of DNS attacks

Domain name resolution servers (DNS) perform a key function in establishing access to web pages. Because of their importance, they are constant targets for cyber-attacks, which aim to erase or replace some of their records, causing huge losses for users, companies and institutions worldwide. In Brazil, to prevent such attacks, a legal provision is established that criminally typifies the invasion of computer devices connected to the World Wide Web, which includes attacks on the DNS service. Still, cyber-attack identification is difficult as it depends on the correct application of means of protection, monitoring of network services and extraction and interpretation of data that allow the identification of criminal factors. The present work proposes a computational forensics approach to automatically detect the occurrence of a DNS cache poisoning attack, subsuming the elements that constitute the attack to the legal device, thus identifying the occurrence of a crime.
A novel multi-featured decision system for multi-classification tasks

Abstract Feature engineering is a difficult task, and for real signal data, it is difficult to find a certain feature that can easily distinguish all classes. Multiple features can provide more information, which means the fusion of multi-feature learning strategies has potential significant advantages. Based on this premise, this paper proposes a multi-class framework based on the multi-featured decision to distinguish all the different classes, and takes automatic dependent surveillance-broadcast (ADS-B) signal data as an example, first extracts the phase features and wavelet decomposition features of the signal data, then selects the features with high discrimination between classes, then proposes a one-dimensional residual neural network based on 16 convolutional layers to learn the unique features of different features and classes separately, and finally proposes a novel multi-featured decision method based on voting method and a priori probability. Results show that the proposed one-dimensional residual neural network has better performance metrics on the test set compared to some machine learning-based and neural network-based algorithms, with classification accuracies of 86.1%, 84.6% and 83.6% on wavelet decomposition features, raw features and phase features, respectively, on ADS-B preamble signals. The proposed feature decision framework based on the voting method and a priori probability has a recall, precision and F 1 value of 80.24%, 89.89% and 84.79% on ADS-B preamble signals, respectively.
HNPU-V2: An Energy-Efficient DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation

A DNN training processor with a maximum of 332 TOPS/W is proposed for efficient and robust object detection. The proposed processor is able to support both quantization and pruning-based personalization to make a user-optimized lightweight network. In addition to personalization, it supports real-time adaptation to compensate for accuracy degradation caused by environmental changes or unpredictable situations. It maintains conventional input slice skipping architecture and stochastic rounding-based computing for the efficient acceleration of the DNN training. It further improves efficiency by removing pseudo-RNGs during the stochastic rounding and adding blocks to pruning-aware training. Moreover, it suggests an LT-flag-based reconfigurable accumulation network and enables multi-learning task allocation for low-latency DNN training with the backward unlocking solution. Fabricated in 28-nm technology, the proposed processor demonstrates 46.6 FPS object detection with 0.95 mJ/frame energy consumption that is the state-of-the-art performance compared with the previous processors.
Real-Time Face Recognition with Mask using Deep Convolutional Neural Network

The COVID-19 pandemic started in 2019, from this situation people learned that the use of face masks is one of the most effective ways to protect themselves from Coronavirus. A problem has arisen from this situation. Face recognition systems are widely used nowadays but all those systems are trained to detect perfectly exposed faces, not masked or occluded faces. As most people wear masks recently, it has become challenging for the existing face recognition systems to recognise faces. To suppress this problem, a feasible method for masked face recognition is proposed in the paper. For extracting the facial features of the non-occluded part of the face, VGG Face model is used. After extracting the facial features, those would be included in the dataset along with zoomed and rotated facial images for training. After that CS classifier is used for the classification and determines if the masked face is recognised or not. We have created Masked and Non-masked Face Dataset for the experiments.
Kororā: A secure live virtual machine job migration framework for cloud systems integrity

The article introduces an innovative framework called Kororā, which aims to enhance the security and integrity of live virtual machine migration in a public cloud computing environment. The framework incorporates a trusted platform module to ensure the integrity of the migration process. It offers a new approach for virtual machine migration and has been specifically designed and implemented on a public infrastructure-as-a-service cloud platform. The primary research problem identified is the vulnerability of virtual machine instances to attacks during the live migration procedure. The evaluation used involves running the framework simultaneously on the same hardware components (such as I/O, CPU, and memory) and utilizing the same hypervisor's platform (Xen's open-source hypervisor). In addition, the security aspect of live migration is a crucial consideration due to the possibility of security threats across different area: data plane, control plane, and migration plane. Potential attackers may employ both passive and active attack techniques, putting the live migration at risk and resulting in a decline in performance. This poses a significant and alarming risk to the overall platform. To address the research gap, the Kororā framework emerged as a successful approach for achieving control-flow integrity by incorporating the Clark-Wilson security model proved effective in bridging the research gaps while maintaining system integrity. The primary achievement of this research is the introduction of the Kororā framework, which consists of seven agents operating within the Xen-privileged dom0 and establishing communication with the hypervisor. Overall, the finding indicate that the suggested framework offers an effective defence mechanism for moving a virtual machine from one host to another host with minimal disruption to normal operation with enhanced integrity.
The role of CCI in supporting children’s engagement with environmental sustainability at a time of climate crisis

Today’s children will live life navigating the impacts of climate change triggering new questions about their environmental education and how we can prepare them to take active roles that shape our ecological futures. The aim of our paper is to reflect on the role that the Child-Computer Interaction (CCI) community can play to this end. We do this by analysing thirteen years of HCI research concerned with the application of children’s digital technology to environmental sustainability (ES). Content analysis of the 25 papers identified shows that climate change is not a motor theme, with half of the papers using ES as an application area that drives other aims. Our analysis contributes a novel research agenda proposing to expand the domains, theories and user groups researchers have thus far focused on. Examining the distinctive design properties of previous research, we advance new insights into the role technology can play for children’s ES.
Few-shot Question Answering with Entity-Aware Prompt

Providing simple task descriptions or prompts in natural language for large pre-trained language models yields impressive few-shot learning results in different tasks, such as text classification, knowledge probing, machine translation, and named entity recognition. In this paper, we apply this idea to question-answering task to fine-tune pre-trained language models by constructing entity-type prompts. Specifically, we augment the context sequences with semantic labels to enhance the understanding of pre-trained models, and dynamically adjust the prompts via intention recognition of the questions. Our proposition is simple yet powerful over traditional fine-tune training strategies and robust under few-shot conditions. The contributions of our work are as follows: 1. We proposed a few-shot learning method with entity-aware prompts for question-answering tasks to fine-tune the pre-trained language model. 2. Based on the SQuAD dataset, we extract a subset with 1,131 samples containing different categories of answer type, in which the answers to all questions are entities. 3. Experiments on multiple pre-trained language models validate that our method can effectively improve the performance of few-shot learning of question-answering tasks over the promptless ones.
CADCN: A click-through rate prediction model based on feature importance

Recommendation systems are widely used in real-world advertising recommendations. In traditional recommendation system prediction models, click-through rate plays a crucial role. However, traditional recommendation systems cross-combine original features to make the linear model memorable and generalizable while taking into account the importance of features requires a lot of computational cost, and it uses manual cross-combination of features on data, which requires a lot of time and effort. Traditional recommendation systems simply learn the relationship between features without considering the importance of features. We combine deep crossover network and AFM network, dynamically assign weights to different features prior to feature crossover using a synthetic incentive network, and introduce attention mechanism based on feature crossover explicitly. We then propose an advertisement click-through rate prediction based on feature importance model, and the experimental results demonstrate that the algorithm is superior to the deep crossover network in predicting the click-through rate of advertisements.
Ghost-Depth: A Lightweight Encoder-Decoder Network for Monocular Depth Estimation

In recent years, convolutional neural networks especially those based on encoder-decoder architecture have been used in the task of monocular depth estimation, and it has brought great success. However, the parameters and computational cost of networks are increasing constantly so as to improve predicting accuracy. In view of this situation, a network model based on a lightweight encoder-decoder structure is designed to estimate monocular depth in this paper, which has good accuracy and reduces the number of parameters. In order to design a lightweight network, a lightweight network named GhostNet was chose when encoding. In the upsampling process in decoder, Ghost convolution with fewer parameters was used instead of conventional convolutions. And a lightweight attentional feature fusion module was combined with traditional skip connections when upsampling. It compensated for the loss of detail information in feature maps due to downsampling, and further improved the model accuracy. Experiments on dataset NYU-Depth V2 demonstrate that the number of parameters is 2.7M and the accuracy is 79.7%. Our proposed method is lightweight and efficient and outperforms most lightweight methods.
Deep Multifaceted Highlight Network for Multi-objective Ranking in Trigger-Induced Recommendation

Recommender Systems have been proven to be of great business value in e-commerce platforms and recommendation algorithms play an important role in it. E-commerce platforms provide entrances for customers to enter channels that can meet their specific shopping needs. Trigger items displayed on entrance icons are useful to attract more entering. User instant interest can be explicitly induced by the trigger items and follow-up related target items are recommended accordingly, this recommendation scenario is called trigger-induced recommendation. However, existing trigger-induced recommendation algorithms usually optimize a single task (e.g., click-through rate prediction) based on users’ historical click sequences, and do not consider modeling the similarity between the trigger item and the target item. In this paper, we propose a novel recommendation method named Deep Multifaceted Highlight Network (DMHN) that can model users’ multiple types of behavior sequences simultaneously for multi-objective ranking in trigger-induced recommendation. DMHN utilizes the Multi-Interest Module to model user’s various behavior sequences to capture the different latent user interests, the Similarity Module to focus on the trigger item by modeling the similarity between the target item and the trigger item, and the Progressive Layered Extraction to optimize multiple objectives. Experiments on JD.com real production dataset demonstrate the superiority of DMHN over state-of-the-art methods.
Image Diagnosis of Breast Masses Based on Deep Learning

With the change of lifestyle, the incidence rate of breast diseases is also increasing. In some cases, there will also be malignant diseases, that is, breast cancer. It is a malignant tumor occurring in the epithelial tissue of the breast, with a very high morbidity and mortality. At present, there is no medical means to completely eradicate the disease. Therefore, early screening plays a very important role in preventing the disease. There are many types of breast diseases, such as malignant tumors and benign tumors. Different types of lesions have different characteristics, which increases the difficulty and workload of doctors in diagnosis, thereby increasing the diagnostic time and cost of individual patients. CT imaging diagnosis accounts for a large proportion in breast disease screening. Therefore, this article proposes a deep learning model to classify breast CT images based on how to help doctors reduce workload and improve diagnostic accuracy. The model can label the CT images as normal breast, malignant breast tumors, and benign breast tumors based on the characteristics of the patient's breast CT images, in order to achieve early diagnosis of breast diseases and reduce the workload of doctors, Improve the recovery rate of patients. After completing the model construction, this article trained and evaluated the model to verify its effectiveness.
A Reliable Blockchain Computation Offloading Scheme for Internet of Things

At present, the integration of blockchain and Internet of Things (IoT) has become a popular concept. Blockchain can provide IoT with a trusted data-sharing and computing environment. Yet the on-chain computing efficiency of blockchain is still limited by the underlying consensus algorithms, but blockchain can offload computation-intensive tasks to off-chain IoT nodes to improve its performance. However, most of the existing computation offloading schemes are incapable to guarantee the reliability of off-chain computing in untrustworthy IoT environments since participants can collude. Therefore, this work aims at improving the reliability of computation offloading. We introduce a voting-based game into our system workflow to enhance the confidence of computation results. Meanwhile, an anonymous node selection algorithm, which depends on a priority assignment method based on Verifiable Random Function (VRF), is designed to improve the quality of selected nodes and avoid the collusion between participants. The extensive results shows that the proposed scheme can achieve competitive performance in terms of fairness, accuracy, and robustness.