Reviewerly/exHarmony huggingface dataset:
Span-based dual-decoder framework for aspect sentiment triplet extraction

Aspect sentiment triplet extraction (ASTE) aims to extract aspects from review sentences along with their corresponding opinions and sentiments to form opinion triplets. Since each factor within a sentiment triplet could be a single word or a phrase, defining and implementing the span-level features for this span-level task is critical and challenging. However, prior works typically formulate the ASTE task as a sequence tagging problem and address it with token-level models, limiting the extraction performances of long entities and suffering from cascading errors due to sequential decoding. Although some methods have enumerated all possible spans as input, they fail to explicitly build the interaction among the potential triplets and semantic information within the sentence explicitly. To address these problems, we propose a span-based joint training framework, where each potential entity is represented as an independent span and sentiment polarity is classified by using the corresponding independent span representations. Specifically, we design two different transformer-based decoders to extract the aspects and their corresponding opinions, respectively. Those decoders utilize multiple multi-head attention mechanisms to model the associations among the spans and the semantic information between the spans and the sentences. To verify the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets. The experimental results demonstrate that our proposed method significantly outperforms existing state-of-art methods.
Resistance to contact tracing applications: The implementation process in a social context

The success of new technologies such as contact tracing mobile applications depends on large-scale end-user adoption. However, the implementation may encounter resistance, since the uncertainty surrounding novel technology may raise anxiety, and persuasion efforts to promote use can evoke reactance. Thereby, anxiety and reactance are two forms of resistance to new technology. Little is known about the role of resistance over the course of the innovation implementation process, in a social environment where technology functionality depends on adoption by others. Therefore, this four-wave longitudinal study followed adoption of the Dutch COVID-19 contact tracing app during four months (N = 1120), and explored the time dynamics and interplay of reactance to freedom threat, anxiety, and perceived social norms on app use. Mixed-effect analyses showed that anxiety and, subtly, reactance decreased with time; initial freedom threat predicted later reactance. App use related negatively to reactance and anxiety; and positively to positive social norms. Over time, the norm effect was mediated by lower reactance and anxiety. The results imply that resistance is pervasive, suggest that self-perceived app use norms may be key to overcoming resistance to new applications, and demonstrate that theories predicting innovation or technology acceptance benefit from studying predictors over time.
A Blockchain-based Framework for IoT based Secure Identity Management

Blockchain science&#x0027;s distributed system structure makes it an ideal option for addressing device-to-device transmission data breaches. This paper investigates the potential use of blockchain systems to enhance Information security issues in the context of the 5G Mobile communication infrastructure. Relying on blockchains, we offer a multi-layer protection network architecture for IoT networks. By splitting the IOT based platform infrastructure into a multi-layer decentralized system, the suggested approach overcomes the issues connected with the real establishment of distributed ledger technology. We split the infrastructure into K-based clusters in the conceptual scheme by utilizing Evolutionary - based methods such as Evolutionary Computation and Particle-based Swarm intelligence evaluation. Every Cluster Members selects a unique access control for identification and authorization purposes inside every cluster group head (CGH). The distributed ledgers technology&#x0027;s strong security and trustworthiness guarantee offers an authentication system for CGHs communicating with one another and Ground Stations via a localized blockchain application with no need for a centralized controller. In addition, we suggest a worldwide blockchain deployment for base stations communications. Furthermore, to validate the proposed methodology, we use the free software blockchain application Hyper ledger Cloud hosting Network.
Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges

Smartphone devices have become an essential part of our daily activities for performing various essential applications containing very confidential information. For this reason, the security of the device and the transactions is required to ensure that the transactions are performed legally. Most regular mobile users’ authentication methods used are passwords and short messages. However, numerous security vulnerabilities are inherent in various authentication schemes. Fingerprint identification and face recognition technology sparked a massive wave of adoption a few years back. The international mobile equipment identity (IMEI) and identity-based public key cryptography (ID-based PKC) have also become widely used options. More complex methods have been introduced, such as the management flow that combines transaction key creation, encryption, and decryption in processing users’ personal information and biometric features. There is also a combination of multiple user-based authentications, such as user’s trip routes initialization with the coordinates of home and office to set template trajectories and stay points for authentication. Therefore, this research aimed to identify the issues with the available authentication methods and the best authentication solution while overcoming the challenges.
Gradient rectified parameter unit of the fully connected layer in convolutional neural networks

Existing visualization approaches of the convolutional neural network (CNN) present the importance of the positive gradient in backpropagation, and remove the irrelevant negative gradients for improvement. However, the training procedure in CNN pays the same attention to positive and negative gradients’ optimizations. In this work, we present a gradient rectified parameter unit of the fully connected layer (GRU-FC) approach, which rectifies the corresponding parameters generating the negative gradient in the fully connected layer by zero clearing and retrains the networks with the rectified parameters. Besides, a simplified version of GRU-FC is provided to accelerate the training of the network that has a single fully connected layer for classification. Theoretical analysis of the rationalization of GRU-FC presents that GRU-FC-2L is an appropriate approach for networks with more than one fully connected layer. Experiments on the convergence analysis of the network by GRU-FC-2L is conducted. The GRU-FC approach is verified on several datasets (i.e., SV HN, STL10, CIFAR10 and ImageNet) with the recognition accuracy increased effectively. Furthermore, the GRU-FC approach shows a way to dropout unimportant weights regularly instead of randomness.
On the Performance of Federated Learning Algorithms for IoT

Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified. In this study, we thoroughly analyze the impact of heterogeneity in FL and present an overview of the practical problems exerted by the system and statistical heterogeneity. We have extensively investigated state-of-the-art algorithms focusing on their practical use over IoT networks. We have also conducted a comparative analysis of the top available federated algorithms over a heterogeneous dynamic IoT network. Our analysis shows that the existing solutions fail to effectively mitigate the problem, thus highlighting the significance of incorporating both system and statistical heterogeneity in FL system design.
Attention Guided Global Enhancement and Local Refinement Network for Semantic Segmentation

The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated-FCN model for two major problems. First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts. Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. To tackle these challenges, a Global Enhancement Method is proposed to aggregate global information from high-level feature maps and adaptively distribute them to different decoder layers, alleviating the shortage of global contexts in the upsampling process. Besides, aLocal Refinement Module is developed by utilizing the decoder features as the semantic guidance to refine the noisy encoder features before the fusion of these two (the decoder features and the encoder features). Then, the two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed. Extensive experiments on PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the effectiveness of the proposed approach. In particular, with a vanilla ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean IOU) on the PASCAL Context dataset. The code is available at https://github.com/zhasen1996/AGLN.
Refined U-net: A new semantic technique on hand segmentation

Hand segmentation aims to segment the hand profile however the biggest challenge is the segmentation hand over the face or skin-related environments. To solve these problems, many previous papers rely on a very deep neural network or collect new large-scale datasets on real-life scenes to increase the diversity and complexity. To perform it on a standard GPU, the training and inference time is still long, and always requires a large amount of GPU memory. In this paper, we propose a new hand segmentation technique, Refined U-Net, based on the original U-Net [1]. The main objective of Refined U-Net is to perform with few parameters and increasing the inference speed while achieving high accuracy during the hand segmentation process. We substantially improve its performance by refining the segmentation results and reducing the gap of feature vectors. In inference time, our Refined U-Net can prune the refinement block to increase the speed and reduce the number of parameters. We also eliminate the disadvantages of the popular dataset and propose a reliable way to create the virtual dataset. Our Refined U-Net can achieve 200 frames per second (FPS) on the GPU (RTX 2080Ti) and outperforms in accuracy for the state-of-the-art designs in Egohands [2] and GTEA [3] datasets.
Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communication in the industries. The researchers are aimed at gathering both primary data and secondary data for the study. The respondents are chosen in the industry so that they can provide better inputs and insights related to the area of research. The key determinants considered for the study are machine learning-influenced management of hotspots, identification of critical congestion points, spectrum availability, and management. The analysis is made using SPSS data analysis package based on which it is noted that all the factors make major influences towards the intelligent communication, and hence machine learning supports critically in enhancing the user experience effectively.
Environmental, User, and Social Context-Aware Augmented Reality for Supporting Personal Development and Change

Robust pervasive context-aware augmented reality (AR) has the potential to enable a range of applications that support users in reaching their personal and professional goals. In such applications, AR can be used to deliver richer, more immersive, and more timely just in time adaptive interventions (JITAI) than conventional mo-bile solutions, leading to more effective support of the user. This position paper defines a research agenda centered on improving AR applications&#x0027; environmental, user, and social context awareness. Specifically, we argue for two key architectural approaches that will allow pushing AR context awareness to the next level: use of wearable and Internet of Things (IoT) devices as additional data streams that complement the data captured by the AR devices, and the development of edge computing-based mechanisms for enriching existing scene understanding and simultaneous localization and mapping (SLAM) algorithms. The paper outlines a collection of specific research directions in the development of such architectures and in the design of next-generation environmental, user, and social context awareness algorithms.
REU-Net: Region-enhanced nuclei segmentation network

Nuclei segmentation is a key technique for automatic pathological screening. Although many methods have been proposed, it remains a challenge because of numerous nuclei clusters, high variability of object appearances and complex backgrounds. To address these issues, we propose a novel multi-task region-enhanced nuclei segmentation network (REU-Net). It stacks three U-shaped structures by combining serial and parallel approaches to construct a multi-task architecture. The model employs two auxiliary tasks, i.e., contour extraction and rough segmentation to help the main task of fine segmentation. The saliency regions are enhanced by the prediction results of the auxiliary tasks, and the enhanced images are further segmented through the main task. In addition, the spatial and texture features in auxiliary tasks are aggregated by attention gates, helping the main task to refine the details of nuclei and contours. Extensive experiments are conducted to evaluate the proposed method qualitatively and quantitatively. Experimental results show that REU-Net outperforms the state-of-the-art methods on HUSTS, MoNuSeg, CoNSep and CPM-17 datasets.
Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting.
Interval type-2 generalized fuzzy hyperbolic modelling and control of nonlinear systems

Generalized Fuzzy Hyperbolic Models (GFHMs) offer a simpler structure and less computational complexity than the typical fuzzy systems. Type-2 fuzzy systems, in contrast, have better handling of uncertainty but at the cost of higher computational complexity. Here, we propose a synergistic hybrid framework of interval type-2 fuzzy systems and GFHMs for a better uncertainty handling and simpler computational structure in the modelling and control of nonlinear systems. For this purpose, we first extend the GFHM to a computational model with various width and subsequently propose interval type-2 generalized fuzzy hyperbolic systems (IT2-GFHS) as a computational framework for nonlinear systems modelling. We then employ this IT2-GFHS in a general sliding-based robust nonlinear controller. Theoretical Lyapunov analysis reveals the overall asymptotic stability of the resulting closed-loop system. The numerical simulations for system modelling and identification on two nonlinear benchmark problems also reveal higher accuracy, lower computation time, and fewer adjustable parameters for the proposed IT2-GFHS models. Furthermore, applications to two nonlinear benchmark control problems show similar performance in terms of robustness to noise and disturbances compared with type-2 fuzzy systems, with the IT2-GFHS-based nonlinear controller having considerably fewer computations and floating-point operations. Finally, the proposed approach is experimentally implemented to control a 3-Prismatic-Series-Prismatic (3-PSP) parallel robot. Experimental results also confirm the improved tracking performance of the proposed method compared with interval type-2 and type-1 fuzzy systems, while also requiring fewer adjustable parameters.
High resolution elemental characterization of prehistoric flint sources in southern Israel: Implications for archaeological provenance studies

Identifying the geological sources of archaeological flint is crucial to various studies in prehistoric research. Here we present a study of flint sources in southern Israel using elemental analysis. Six Neolithic quarries and three geological outcrops of several geological formations were sampled and analyzed by ICP-MS in order to investigate whether differences in trace element concentrations can be used to distinguish between flint sources. Using (1) patterns of chondrite-normalized rare earth elements (REE), and (2) new advanced statistical methods utilizing the concentrations of REE and 25 additional elements, we were able to clearly differentiate between Upper Cretaceous and Eocene flints, and in certain cases also between flint sources of different ages within the Upper Cretaceous across the southern Levantine landscape. In addition, we used the newly established elemental database and the methods developed in this research as a pilot study towards establishing a regional dataset and locating target sites for flint exploited in those sources. This concept is evaluated by investigating the elemental analyses of flint artifacts from the Neolithic settlement which is nearest to the quarries and outcrops in the eastern part of our research area – the late Pre-Pottery Neolithic settlement in the Wadi Faynan region (Jordan). The current results indicate that the flint from the studied quarrying sites in the eastern Negev was probably not exploited by the inhabitants of Wadi Faynan during the Neolithic, but suggest that multiple sources were used. This highlights the need to expand the database by analyzing additional flint from other potential sources.
Methodological Guidelines for Systematic Assessments of Health Care Websites Using Web Analytics: Tutorial

With the growing importance of communicating with the public via the web, many industries have used web analytics to provide information that organizations can use to better achieve their goals. Although the importance of health care websites has also grown, the health care industry has been slower to adopt the use of web analytics. Web analytics are the measurement, collection, analysis, and reporting of internet data used to measure direct user interaction. Our objective is to provide generalized methods for using web analytics as key performance metrics to evaluate websites and outline actionable recommendations for improvement. By deconstructing web analytic categories such as engagement, users, acquisition, content, and platform, we describe how web analytics are used to evaluate websites and how improvements can be made using this information. Engagement is how a user interacts with a website. It can be evaluated using the daily active users to monthly active users (DAU/MAU) ratio, bounce rate, pages viewed, and time on site. Poor engagement indicates potential problems with website usability. Users pertains to demographic information regarding the users interacting with a website. This data can help administrators understand who is engaging with their website. Acquisition refers to the overall website traffic and the method of traffic, which allows administrators to see how people are accessing their website. This information helps websites expand their methods of attracting users. Content refers to the overall relevancy, accuracy, and trustworthiness of a website's content. If a website has poor content, it will likely experience difficulty with user engagement. Finally, platform refers to the technical aspects of how people access a website. It includes both the internet browsers and devices used. By providing detailed descriptions of these categories, we have identified how web administrators can use web analytics to systematically assess their websites. We have also provided generalized recommendations for actionable improvements. By introducing the potential of web analytics to augment usability and the conversion rate, we hope to assist health care organizations in better communicating with the public and therefore accomplishing the goals of their websites.