Reviewerly/exHarmony huggingface dataset:
Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model

Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of authors and journals. However, for a fair evaluation, the qualitative aspect should be considered along with the quantitative measures. The sentiments expressed in citation play an important role in evaluating the quality of the research because the citation may be used to indicate appreciation, criticism, or a basis for carrying on research. In-text citation analysis is a challenging task, despite the use of machine learning models and automatic sentiment annotation. Additionally, the use of deep learning models and word embedding is not studied very well. This study performs several experiments with machine learning and deep learning models using fastText, fastText subword, global vectors, and their blending for word representation to perform in-text sentiment analysis. A dimensionality reduction technique called principal component analysis (PCA) is utilized to reduce the feature vectors before passing them to the classifier. Additionally, a customized convolutional neural network (CNN) is presented to obtain higher classification accuracy. Results suggest that the deep learning CNN coupled with fastText word embedding produces the best results in terms of accuracy, precision, recall, and F1 measure.
Posthuman Creativities: Pluralist Ecologies and the Question of <i>How</i>

This editorial lays the groundwork and context for this special issue addressing a range of posthuman ecological approaches to the study and theorization of creativity, and its potential to transform understandings of 21st-century learning events and environments, including cities, schools, museums, parklands, digital environments, wild places, and more. Importantly, this collection establishes an ethics and politics of posthumanism as it intersects with creativity, including attention to the necessity and ethics of the ways in which Indigenous knowing and knowledge creation are changing and expanding traditional academic framings of arts-based research, creativity, and posthuman scholarship.
Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim of this research work. Initially IoT, Edge devices capture the job and process to help cloud infrastructure. Data transferring process is a Non-deterministic Polynomial (NP) hard problem which can be easily supported by technologies like IoT and Edge computing. Main issue is, tremendous increase of data over the cloud causes difficult to manage consumption of energy. In this research work server clouds with edge devices and centralized cloud servers are combined to work for providing efficient energy consumption. Here, in this paper we implement novel dynamic speed scaling (NDS) algorithm. The CPU workload is first computed using NDS algorithm for incoming application. The less energy computation is achieved using energy internet of things (EIoT). Fine methodology called speed processor scaling, helps to consume less energy and less computation price. High energy consumption of processor is due to high computation speed. likewise, less energy is consumed during less processor computation speed. This technique is building in NDS algorithm and computed in Edge devices for less energy consumption. The result evaluation proves that proposed technique consumes only 10 s for data computation when compared to other existing techniques.
Geospatial Assessment of Groundwater Quality with the Distinctive Portrayal of Heavy Metals in the United Arab Emirates

Groundwater is a valuable resource, and its quality is critical to human survival. Optimal farming and urbanization degraded groundwater reserves. This research investigates and reports the spatial variability of selected heavy metals developed in the Liwa area of the United Arab Emirates. Forty water samples were collected from existing wells and analyzed for different elements. Principal components analysis was applied to a subgroup of the data set in terms of their usefulness for determining the variability of groundwater quality variables. Geographic information systems were used to produce contour maps to analyze the distribution of heavy metals. Ordinary kriging was used with Circular, Spherical, Tetraspherical, Pentaspherical-Bessel, K-Bessel, Hole effect, and Stable models for better representation. The water quality index was constructed using heavy metal concentrations and other variables. This yielded a value of 900 beyond the limit stated by WHO and US EPA. Nugget analysis showed that Cd (0), K (7.38%), and SO4 (1.81%) variables exhibited strong spatial dependence. Al (27%), Ba (40.87%), Cr (63%), Cu (34%), EC (27%), HCO3 (56%), NO3(36%), Pb (64%), and TDS (53%) represented moderate spatial dependence. As (76%), Mn (79%), Ni (100%), pH (100%), Temp (93%), and Zn (100%) exhibited weak spatial dependence.
Harnessing service robots to increase frontline service employees’ safety and health: The critical role of CSR

Service organizations are increasingly introducing service robots to frontline service deliveries. The majority of existing research focuses on how customers perceive and experience these robots, whereas limited attention has been paid to understanding how they might impact employee health and safety. Building on sociotechnical systems theory, we propose a conceptual framework for understanding the mixed effect of service robots on frontline service employees' health and safety. We also examine what organizations can do to maximize the benefits and minimize adverse health and safety implications through their corporate social responsibility (CSR) initiatives. The proposed framework can not only guide future research, but can also be used to help organizations ensure that the introduction and implementation of service robots to frontline encounters can promote a physically, financially, emotionally, and identity safe work environment.
Active contour model based on local absolute difference energy and fractional-order penalty term

The active contour model (ACM) is one of the popular image segmentation methods. In practice, however, images are often corrupted by severe noise and intensity inhomogeneity, making traditional ACMs unavailable. In this paper, a novel ACM is presented to effectively cope with noise and intensity inhomogeneity. To better discriminate the object and background regions in inhomogeneous images, a new saliency fitting image (SFI), defined as the difference between the original image and the local-weighted image (LWI), is first constructed. In addition, the local mean absolute deviation (LMAD) based weight factor can reflect the degree of the intensity variation in a local region, and the LWI represents the bias component accounting for intensity inhomogeneity. Then, a local fitting energy term is defined as the absolute difference between two local fitting functions, which represent the local averages of image intensity inside/outside the contour in two windows with different sizes. And a threshold strategy is utilized to improve the robustness of the proposed model to noise. Finally, a fractional-order penalty term is defined to penalize the deviation between the level set function (LSF) and the signed distance function (SDF), which makes the proposed model avoid the side effect of the penalty energy term in traditional ACMs. Experimental results show the efficiency and accuracy of the proposed ACM.
Security Threats and Cryptographic Protocols for Medical Wearables

In the past few years, the use of several medical devices is increasing. This paper will pay attention to a device developed to get measures of the temperature of diabetic foot. These wearables usually do not have cryptographic protocols to guarantee data security. This study analyzes the existing security in these devices, and simulate malware propagation taking into account the vulnerabilities and lack of security in these highly-constrained interconnected devices. A simulation of malware spreading in a network made by 10 and 15 individuals with 6 and 34 sensors each one, respectively, is included in this study. To avoid such attacks, a lightweight cryptographic protocol could be a satisfactory solution. Considering the quick development of quantum computers, several current cryptographic protocols have been compromised.
Medical Image Captioning Using Optimized Deep Learning Model

Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.
I<sup>2</sup>Transformer: Intra- and Inter-Relation Embedding Transformer for TV Show Captioning

TV show captioning aims to generate a linguistic sentence based on the video and its associated subtitle. Compared to purely video-based captioning, the subtitle can provide the captioning model with useful semantic clues such as actors' sentiments and intentions. However, the effective use of subtitle is also very challenging, because it is the pieces of scrappy information and has semantic gap with visual modality. To organize the scrappy information together and yield a powerful omni-representation for all the modalities, an efficient captioning model requires understanding video contents, subtitle semantics, and the relations in between. In this paper, we propose an Intra- and Inter-relation Embedding Transformer (I2Transformer), consisting of an Intra-relation Embedding Block (IAE) and an Inter-relation Embedding Block (IEE) under the framework of Transformer. First, the IAE captures the intra-relation in each modality via constructing the learnable graphs. Then, IEE learns the cross attention gates, and selects useful information from each modality based on their inter-relations, so as to derive the omni-representation as the input to the Transformer. Experimental results on the public dataset show that the I2Transformer achieves the state-of-the-art performance. We also evaluate the effectiveness of the IAE and IEE on two other relevant tasks of video with text inputs, i.e., TV show retrieval and video-guided machine translation. The encouraging performance further validates that the IAE and IEE blocks have a good generalization ability. The code is available at https://github.com/tuyunbin/I2Transformer.
Blockchain technologies for museum management. The case of the loan of cultural objects

The paper investigates the application of blockchain technologies to cultural heritage organizations, with a focus on museums, and outlines the advantages and limits of such technologies applied to the loan of cultural objects. To this aim, the paper employs the qualitative methodology of case study and presents two cases concerning the development of blockchain-based tracking systems and blockchain-based smart contracts, and their application to museums for the management of the loan of cultural objects. The application of blockchain technologies facilitates the circulation of cultural items, with benefits for the overall cultural offer, the attractiveness of museums, and, possibly, the consequent tourist flows. Moreover, it is argued that the use of blockchain-based tracking systems and smart contracts for the management of loans of cultural objects between organizations allows to overcome some organizational issues and to reduce transaction costs. Limits of the blockchain technologies for museums and managerial implications are also discussed.
Secure Data Communication Using Information Hiding and Encryption Algorithms

In today’s innovation, for the information trade, the Internet is the best known and most important medium. With the advancement of the web and data innovation, computerized media has perhaps become the most famous and remarkable data transfer tools. This advanced information includes text, images, sound, video, etc. In this paper presented the secure data communication technique using the information hiding and encryption algorithms. For information hiding least significant bits (LSB) technique or algorithm is used and for encryption purpose asymmetric RSA algorithm is used. Using proposed technique enhanced the security of the digital data or information over communication channel that is undeniably challenging to break by the unapproved person.
Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion

Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers' hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer's hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.
Difference in gaze control ability between low and high skill players of a real-time strategy game in esports

This research investigated the difference in aspects of gaze control between esports experts (Expert) and players with lower skills (Low Skill) while playing the real-time strategy game called StarCraft. Three versions of this game at different difficulty levels were made with the StarCraft Editor, and the gaze movements of seven Expert and nine Low Skill players were analyzed while they played the games. The gaze of Expert players covered a significantly larger area in the horizontal direction than the gaze of Low Skill players. Furthermore, the magnitude and number of saccadic eye movements were greater, and saccade velocity was faster in the Expert than in the Low Skill players. In conclusion, StarCraft experts have a specific gaze control ability that enables them to quickly and widely take visual information from all over the monitor. This could be one of the factors enabling StarCraft experts to perform better than players with lower skills when playing games that require task-switching ability.
Reduced annotation based on deep active learning for arabic text detection in natural scene images

Providing labeled Arabic text images dataset for scene text detection is inherently difficult and costly at the same time. Consequently, only few small datasets are available for this task. Previous work has only focused on the data augmentation technique of small datasets; however, the images generated with these techniques cannot reproduce the complexity and variability of natural images. In this paper, we propose a new Arabic text images dataset using the Google Street View service named Tunisia Street View Dataset (TSVD). The dataset contains 7k images collected from different Tunisian cities. It is much more diverse and complex than current image datasets. Taking advantage of this dataset to train Convolutional Neural Network (CNN) models, annotation is required for building high performance models. The annotation task consumes a lot of time and effort for researchers due to its repetitiveness. The development time of text detection systems in natural images is valuable with an effective use. We believe that we have developed a Deep Active Learning algorithm for the annotation phase. A Deep Active Learning algorithm for the annotation phase has been developed by approaching the annotation suggestion task using a deep learning text detector. CNN are used to perform the text detection in natural scene images. Our deep active learning framework combines CNN and active learning approach. This reduces annotation effort by making pertinent suggestions on the most effective annotation areas. We utilize uncertainty provided by CNN models to determine the maximum uncertain areas for annotation. Deep active learning is shown in order to reduce significantly the number of training samples required and also to minimize the annotation work of our dataset up to 1/5. Our dataset is publicly available in IEEE DataPort https://dx.doi.org/10.21227/extw-0k60.
IoE based private multi-data center cloud architecture framework

This paper proposes an Internet of Everything (IoE) based private multi-data center cloud architecture framework to utilize the merits of multi-data centers. The inclusive security structure for the entire cloud data center presented here is to minimize the potential vulnerabilities from the adoption of the cloud. This work would leverage the novelty in allocating the distribution of data smartly and in a reliable manner to have the network for private cloud data centers. On the other hand, the architecture must have the ability to make out an enterprise's data and association applications in the cloud using the facility of contributing the opportunity. To overcome these shortcomings, a "Facility Overhead-aware multi-objective Dynamic Private Cloud Data Center Algorithm" (FOMDPC) is proposed with the merits of migration payload awareness, dynamic load balancing methodology, and facility infrastructure costs. The factors to be considered to determine the efficiency are time complexity and memory overhead. In addition, aggregated migration energy consumption and aggregated SLA violations are also taken into account.
The witching week of herding on bitcoin exchanges

This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange (CME). The database extends from December 2017 to October 2020, taking as a reference the main exchanges that trade bitcoin (Binance, Bitfinex, Bitstamp, Coinbase, itBit, Kraken, and Gemini) and using hourly closing prices and trading volumes in bitcoin and US dollars. Adapting the proposal of Chang, Cheng and Khorana (2000) (CCK) to test conditional herding, we obtain results that indicate that the herding effect is significant during the week before expiration. After expiration, the herding effect lasts for a few hours and disappears. Information overload originating, among other causes, from sophisticated investors' strategies may generate this mimetic behaviour. The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows, in general, anti-herding behaviour throughout the period of study.
Research on Automatic Error Correction Method in English Writing Based on Deep Neural Network

As one of the most widely used languages in the world, English plays a vital role in the communication between China and the world. However, grammar learning in English is a difficult and long process for English learners. Especially in English writing, English learners will inevitably make various grammatical writing errors. Therefore, it is extremely important to develop a model for correcting various writing errors in English writing. This can not only be used for automatic inspection and proofreading of English texts but also enable students to achieve the purpose of autonomous practice. This paper constructs an English writing error correction model and applies it to the actual system to realize automatic checking and correction of writing errors in English composition. This paper uses the deep learning model of Seq2Seq_Attention model and transformer model to eliminate deep-level errors. Statistical learning is combined with deep learning and adopted a model integration method. The output of each model is sent to the n-gram language model for scoring, and the highest score is selected as output.
ReCDroid+: Automated End-to-End Crash Reproduction from Bug Reports for Android Apps

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers heavily rely on bug reports in issue tracking systems to reproduce failures (e.g., crashes). However, the process of crash reproduction is often manually done by developers, making the resolution of bugs inefficient, especially given that bug reports are often written in natural language. To improve the productivity of developers in resolving bug reports, in this paper, we introduce a novel approach, called ReCDroid+, that can automatically reproduce crashes from bug reports for Android apps. ReCDroid+ uses a combination of natural language processing (NLP) , deep learning, and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid+ on 66 original bug reports from 37 Android apps. The results show that ReCDroid+ successfully reproduced 42 crashes (63.6% success rate) directly from the textual description of the manually reproduced bug reports. A user study involving 12 participants demonstrates that ReCDroid+ can improve the productivity of developers when resolving crash bug reports.