Reviewerly/exHarmony huggingface dataset:
A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic

Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this severe situation. Accordingly, there has also been an increase in research related to a decision support system based on simulation approaches used as a basis for PHSMs. However, previous studies showed limitations impeding utilization as a decision support system for policy establishment and implementation, such as the failure to reflect changes in the effectiveness of PHSMs and the restriction to short-term forecasts. Therefore, this study proposes an LSTM-Autoencoder-based decision support system for establishing and implementing PHSMs. To overcome the limitations of existing studies, the proposed decision support system used a methodology for predicting the number of daily confirmed cases over multiple periods based on multiple output strategies and a methodology for rapidly identifying varies in policy effects based on anomaly detection. It was confirmed that the proposed decision support system demonstrated excellent performance compared to models used for time series analysis such as statistical models and deep learning models. In addition, we endeavored to increase the usability of the proposed decision support system by suggesting a transfer learning-based methodology that can efficiently reflect variations in policy effects. Finally, the decision support system proposed in this study provides a methodology that provides multi-period forecasts, identifying variations in policy effects, and efficiently reflects the effects of variation policies. It was intended to provide reasonable and realistic information for the establishment and implementation of PHSMs and, through this, to yield information expected to be highly useful, which had not been provided in the decision support systems presented in previous studies.
Humans in (Digital) Space

Technology continues to pervade social and organizational life (e.g., immersive, and artificial intelligence) and our environments become increasingly virtual. In this context we examine the challenges of creating believable virtual human experiences— photo-realistic digital imitations of ourselves that can act as proxies capable of navigating complex virtual environments while demonstrating autonomous behavior. We first develop a framework for discussion, then use that to explore the state-of-the-art in the context of human-like experience, autonomous behavior, and expansive environments. Last, we consider the key research challenges that emerge from review as a call to action.
The Quality Evaluation Method of Sci-Tech English Translation for Intercultural Communication

English for Science and Technology (EST), as a special language style, is widely used in the field of Science and Technology. For this kind of articles, the requirements of translation quality are relatively high. Therefore, this paper studies a quality evaluation method of Sci-Tech English translation for cross-cultural communication. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This paper also describes the evaluation of machine translation quality with and automatic evaluation process with machine learning technology. The evaluation index of EST translation quality is selected according to the selection principle and expert consultation method. Then, the weight of the index is calculated by using the analytic hierarchy process. Finally, the translation quality evaluation is given by using the fuzzy comprehensive evaluation, glass-box and black-box evaluation with machine learning method. The results show that under the application of the research method, the evaluation results are completely corresponding to the actual competition results of four competitors, which proves the effectiveness of the research method.
Cross‐domain sequence labelling using language modelling and parameter generating

Sequence labelling (SL) tasks are currently widely studied in the field of natural language processing. Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning, which is time-consuming and expensive. As an alternative, domain adaptation is proposed to train a deep-learning model for sequence labelling in a target domain by exploiting existing labelled training data in related source domains. To this end, the authors propose a Bi-LSTM model to extract more-related knowledge from multi-source domains and learn specific context from the target domain. Further, the language modelling training is also applied to cross-domain adaptability facilitating. The proposed model is extensively evaluated with the named entity recognition and part-of-speech tagging tasks. The empirical results demonstrate the effectiveness of the cross-domain adaption. Our model outperforms the state-of-the-art methods used in both cross-domain tasks and crowd annotation tasks.
Underwater Image Enhancement with Feature Preservation using Generative Adversarial Networks (UIEFP GAN)

Exploration of the ocean’s resources has recently been of significant importance on the world stage as it facilitates functioning in various domains including marine biology, marine archaeology, AUV navigation etc. However, a major problem is that underwater images are usually of sub-optimal quality, having traits such as low brightness, low contrast, high noise and the image is usually blurry with an uneven color composition. This paper presents a GAN based model to perform enhancement on underwater images. The proposed UIEFP GAN model has been inspired by the pix2pix GAN architecture, wherein a patchGAN based discriminator and a encoder-decoder network based generator are used. An enhanced loss function is also proposed to train the network, that focusses on both, local and global feature preservation during image enhancement. The EUVP dataset has been used for training the model.
Multi-level context features extraction for named entity recognition

Bidirectional long short-term memory (Bi-LSTM), as one of the effective networks for sequence labeling tasks, is widely used in named entity recognition (NER). However, the sequential nature of Bi-LSTM and the inability to recognize multiple sentences at the same time make it impossible to obtain overall information. In this paper, to make up for the shortcomings of Bi-LSTM in extracting global information, we propose a hierarchical context model embedded with sentence-level and document-level feature extraction. In sentence-level feature extraction, we use the self-attention mechanism to extract sentence-level representations considering the different contribution of each word to the sentence. For document-level feature extraction, 3D convolutional neural network (CNN), which not only can extract features within sentences, but also pays attention to the sequential relationship between sentences, is used to extract document-level representations. Furthermore, we investigate a layer-by-layer residual (LBL Residual) structure to optimize each Bi-LSTM block of our model, which can solve the degradation problem that appears as the number of model layers increases. Experiments show that our model achieves results competitive with the state-of-the-art records on the CONLL-2003 and Ontonotes5.0 English datasets respectively.
Spectrum Anomaly Detection Based on Spatio-Temporal Network Prediction

With the miniaturization of communication devices, the number of distributed electromagnetic devices is increasing. In order to achieve effective management of the electromagnetic spectrum, prediction and anomaly detection of the spectrum has become increasingly critical. This paper proposes an algorithmic framework for detecting spectrum anomalies using deep learning techniques. More specifically, the framework includes spectrum prediction and anomaly detection. We use the sliding window method to divide the time series, construct multi-timescale historical data, and train the model with normal data to have high accuracy spectrum prediction capability. We analyze and determine the discriminant function to distinguish the spectral anomalies by calculating the differences between the predicted and real data. The experimental results show that the proposed method outperforms existing baseline algorithms based on real-world spectrum measurement data and simulated anomaly data.
Spooky Technology

Our everyday technologies could have appeared terrifying to our ancestors: instantaneous disembodied communication, access to knowledge, objects with 'intelligence' that talk to us (and each other). Black boxes and intangible entities are omnipresent in our homes and lives without our necessarily understanding the hidden flows of data, unknown agendas, imaginary clouds, and mysterious rules that govern them. Have humanity's ways of relating to the unknown throughout history gone away, or have they perhaps transmuted into new forms? In an ongoing project, we have inventoried examples, encounters and reflections on contemporary technology, framed through the perspective of the haunted, spectral and otherworldly. In this paper, we excerpt this collection to illustrate the value and opportunity of an unfamiliar, disquieting perspective in helping to frame the frictions, beliefs and myths that are emerging around interactions with everyday technologies. We posit and demonstrate 'spooky technology' as an accessible framework to reflect and respond to our increasingly entangled relationships with technology.
Evaluation of Weighted Nuclear Norm Minimization Algorithm for Ultrasound Image Denoising

Anatomical structures manifested in ultrasound (US) images are crucial in efficient disease diagnosis. This modality has been used to analyze different tissue properties, such as blood flow, in-depth tissue motion, and elasticity. Analysis of these US images is posing a critical challenge, as these images are corrupted with noise primarily induced during acquisition. The biological structures intended to be investigated need to be detected, enhanced, and preserved during image processing-based diagnosis. US-based common carotid artery (CCA) images were considered in this study, and five denoising techniques were explored for noise removal after converting the images to grayscale to identify efficient preprocessing for effective diagnosis. Furthermore, filtered images were subjected to different entropy-inspired segmentation for qualitative validation and to segment the CCA. The objective of this paper is a deliberate attempt to investigate the possible use of edge- and structure-preserving filtering techniques to segment tissues of interest. The weighted nuclear norm minimization (WNNM) approach appears to be effective in removing noise and simultaneously preserving the sensitive structures. Quantitative validation with peak signal to noise ratio (PSNR), structural symmetry index measure (SSIM), and feature similarity index measure (FSIM) found to be <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>27.84</mn> <mo>±</mo> <mn>1.04</mn> </math> dB; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>0.76</mn> <mo>±</mo> <mn>0.01</mn> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>0.87</mn> <mo>±</mo> <mn>0.01</mn> </math> were observed to be superiorly high with WNNM filtering. The input image and the filtered image histograms are also compared for qualitative validation. The key finding in this study can be attributed to the ability to remove noise from US images corrupted with noise while preserving the anatomical details. Furthermore, it can be hypothesized that the anatomical structures under the influence of noise can be efficiently preprocessed and can be fed as a viable image towards segmentation followed by recognition and morphological inference.
Short Sequence Chinese-English Machine Translation Based on Generative Adversarial Networks of Emotion

With the steady growth of the global economy, the communication between countries in the world has become increasingly close. Due to its translation efficiency and other problems, the traditional manual translation has gradually failed to meet the current people's translation requirements. With the rapid development of machine-learning and deep-learning related technologies, artificial intelligence-related technologies have affected various industries, including the field of machine translation. Compared with traditional methods, neural network-based machine translation has high efficiency, so this field has attracted many scholars' intensive research. How to improve the accuracy of neural machine translation through deep learning technology is the core problem that researchers study. In this paper, the neural machine translation model based on generative adversarial network is studied to make the translation result of neural network more accurate and three-dimensional. The model uses adversarial thinking to consider the sequence of emotion direction so that the translation results are more humanized. We set up several experiments to verify the efficiency of the model, and the experimental results prove that the proposed model is suitable for Chinese-English machine translation.
Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme

In order to enhance the anti-submarine capability of multi-unmanned aerial vehicles (multi-UAVs) in the unknown sea environment and improve the search efficiency, in this paper, we propose a rule-inspired-multi-ant colony (RI-MAC)-based UAV cooperative search algorithm. First, a special sea area anti-submarine search model is established, including an association rule-driven target probability map (TPM) model, a UAV kinematics model, and a sensor model. The novel model has the characteristics of rule linkage, which effectively improves the accuracy of target detection probability in unknown environments. Secondly, according to the established search model, a multi-objective utility function based on association rules is derived. In order to solve the problem of multi-objective optimization, an RI-MAC algorithm based on association rules is proposed, and a pheromone update method using threat avoidance is designed to optimize the search path of multi-UAVs. Finally, a simulation experiment is conducted to verify the effectiveness and superiority of the proposed search algorithm.
Extended Chaotic-Map-Based User Authentication and Key Agreement for HIPAA Privacy/Security Regulations

Background: The US government has enacted the Health Insurance Portability and Accountability Act (HIPAA), in which patient control over electronic protected health information is a major issue of concern. The two main goals of the Act are the privacy and security regulations in the HIPAA and the availability and confidentiality of electronic protected health information. The most recent authenticated key-agreement schemes for HIPAA privacy/security have been developed using time-consuming modular exponential computations or scalar multiplications on elliptic curves to provide higher security. However, these authenticated key-agreement schemes either have a heavy computational cost or suffer from authorization problems. Methods: Recent studies have demonstrated that cryptosystems using chaotic-map operations are more efficient than those that use modular exponential computations and scalar multiplications on elliptic curves. Additionally, enhanced Chebyshev polynomials exhibit the semigroup property and the commutative property. Hence, this paper develops a secure and efficient certificate-based authenticated key-agreement scheme for HIPAA privacy/security regulations by using extended chaotic maps. Results and Conclusions: This work develops a user-authentication and key-agreement scheme that solves security problems that afflict related schemes. This proposed key-agreement scheme depends on a certificate-management center to enable doctors, patients and authentication servers to realize mutual authentication through certificates and thereby reduce the number of rounds of communications that are required. The proposed scheme not only provides more security functions, but also has a lower computational cost than related schemes.
Toward enhancing the information base on costs of cyber incidents: implications from literature and a large-scale survey conducted in Germany

Purpose Literature repeatedly complains about the lack of empirical data on the costs of cyber incidents within organizations. Simultaneously, managers urgently require transparent and reliable data in order to make well-informed and cost-benefit optimized decisions. The purpose of this paper is to (1) provide managers with differentiated empirical data on costs, and (2) derive an activity plan for organizations, the government and academia to improve the information base on the costs of cyber incidents. Design/methodology/approach The authors analyze the benchmark potential of costs within existing literature and conduct a large-scale interview survey with 5,000 German organizations. These costs are directly assignable to the most severe incident within the last 12 months, further categorized into attack types, cost items, employee classes and industry types. Based on previous literature, expert interviews and the empirical results, the authors draft an activity plan containing further research questions and action items. Findings The findings indicate that the majority of organizations suffer little to no costs, whereas only a small proportion suffers high costs. However, organizations are not affected equally since prevalence rates and costs according to attack types, employee classes, and other variables tend to vary. Moreover, the findings indicate that board members and IS/IT-managers show partly different response behaviors. Originality/value The authors present differentiated insights into the direct costs of cyber incidents, based on the authors' knowledge, this is the largest empirical survey in continental Europe and one of the first surveys providing in-depth cost information on German organizations.
Creating workflow for mediated archiving in CoRSAL

Purpose Language archiving is achieved through the detailed assessment and assemblage of various types of language data. The Computational Resource for South Asian Languages (CoRSAL) is an emerging language archive that prioritizes the accommodation of depositors who have a variety of needs with respect to both research and infrastructure. As such, the CoRSAL team uses a workflow approach that caters to this diversity. The purpose of this paper is to detail the mediated workflow for collection ingest and promotion, citing two specific examples from recently published language collections, as well as discuss specific feedback the team has received from individual depositors and language community members on the effectiveness and usefulness of these efforts thus far. Design/methodology/approach This paper provides an exploration of the author’s approaches to a mediated archiving workflow. Findings The author discusses the encouraging and constructive feedback the team has received so far and includes instances of specific communication with individuals who have recently deposited and published collections with them. Originality/value This is the first research paper published by anyone on our team describing our workflow. It is an expansion of a shorter conference paper presented at the LangArc 2021: 1st International Workshop on Digital Language Archives on September 30, 2021 and published in the conference proceedings
“My Brain Does Not Function That Way”: Comparing Quilters’ Perceptions and Motivations Towards Computing and Quilting

The systemic, mathematical, and procedural underpinnings of quilting make the domain a useful metaphor for introductory Computer Science (CS) education, although it is currently used primarily in K-16 educational settings. Considering informal CS education for adult women, we examine the potential depth of this metaphor by exploring how skilled craftspeople engage with and understand quilting-as-metaphor in the context of CS education. In this paper we report the findings of our first focus group with quilters to compare their perceptions and experiences related to quilting and CS. We identified six common themes in how quilters relate the two domains: innate versus learned skills, computing skills as an aid to personal expression, avoiding computing, time investment and tangible rewards, community influence on motivation and learning, and systematic prejudice and its effects. We elaborate upon our findings and discuss potential applications to the design of educational technologies that integrate craft and computation.
Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization

Internet users are benefiting from technologies of abstractive summarization enabling them to view articles on the internet by reading article summaries only instead of an entire article. However, there are disadvantages to technologies for analyzing articles with texts and images due to the semantic gap between vision and language. These technologies focus more on aggregating features and neglect the heterogeneity of each modality. At the same time, the lack of consideration of intrinsic data properties within each modality and semantic information from cross-modal correlations result in the poor quality of learned representations. Therefore, we propose a novel Inter- and Intra-modal Contrastive Hybrid learning framework which learns to automatically align the multimodal information and maintains the semantic consistency of input/output flows. Moreover, ITCH can be taken as a component to make the model suitable for both supervised and unsupervised learning approaches. Experiments on two public datasets, MMS and MSMO, show that the ITCH performances are better than the current baselines.
Interactive Digital Narratives as Complex Expressive Means

Is our way of expressing meanings through digital interactive artifacts simple? How does our sensemaking work when we try to understand Interactive Digital Narratives? To answer these and other questions, the present article discusses a complex-systemic understanding of the expressive mechanisms of Interactive Digital Narratives, to argue the expressive complexity of these artifacts. Interactors of Interactive Digital Narratives necessarily base their hermeneutic processes mainly on what is conveyed in the artifact itself; yet the question of how meaning is expressed in (and sense-making is guided by) Interactive Digital Narratives remains significantly open. I contend that sense-making in such artifacts works by synthetizing the knowledge coming from a number of layers of information, which are intercurrent, interdependent and interoperating, and which concurrently participate in the creation of an overall meaning of a higher order. According to complex systems theory, these layers are therefore elements of a complex system: this justifies the understanding of Interactive Digital Narratives as complex expressive means. Even though largely unexplored, this understanding may help advance our knowledge of the representational capabilities and affordances of Interactive Digital Narrarives, not least in representing multifaceted worlds and complex phenomena. A complex-systemic view can also improve our comprehension of the interpretative processes involved in the sense-making of Interactive Digital Narratives. Furthermore, the awareness gained through this understanding could be useful to get a better sense of the impact of the narratives featured in these artifacts, and ultimately to create more engaging and more powerful experiences that can help foster the societal impact of Interactive Digital Narratives.