Reviewerly/exHarmony huggingface dataset:
Analysis of Situation Map User Cognitive Characteristics Based on Eye Movement Data

A situation map shows the dynamic changes of the environment within a specific space and time in the form of graphics, which can help users effectively understand the current situation and predict the future situation from multi-dimensional and massive information. It is an essential tool for users to make decisions in a complex information system. However, the current situation map interface has some problems, such as poor visibility, difficulty in interaction, etc., which has a particular impact on the user's performance of situation awareness and the efficiency in completing tasks. In order to improve the current problems of the situation map interface, this study hopes to optimize the design of the situation map interface based on the cognitive characteristics of users. A large number of eye movement data of users in the situation map were collected and analyzed to. After analyzing the time series data of eye movement coordinates, the duration of fixation point entering the target area of interest (AOI) for the first time, eye movement speed and other eye movement characteristic indicators, as well as the scanning path chart, it is concluded that the differences of users' eye movement behaviors were mainly manifested in three aspects. They are the differences in visual search strategy, target positioning speed and accuracy, and decision-making speed and accuracy. Then, according to the differences in cognitive characteristics such as attention distribution mechanism and information processing efficiency reflected by the differences in users' eye movement behaviors, the cognitive types of users are summarized as blind cognition, purpose cognition, learning cognition, and memory cognition. Based on the characteristics of the above four cognitive types, the situation map design elements are extracted and sorted out as the criteria for subsequent interface optimization. The optimization design scheme of the interface elements of the situation map is proposed to help users improve their situation awareness level, information identification efficiency, and task operation efficiency.
Never Mind the Semantic Gap: Modular, Lazy and Safe Loading of RDF Data

Any attempt at a tight integration between semantic technologies and object oriented programming will invariably stumble over the gap between the two underlying object models. We illustrate how this semantic gap manifests from the point of view of data retrieval with SPARQL. We present a novel mechanism to load data from RDF knowledge graphs into object-oriented languages that gives static guarantees about the data access and modularly integrates the mapping between the program and the RDF view with the class definition in the program. This allows us to preserve the separation of concerns between the class system of RDF (geared towards domain modeling and data), and that of the program (geared towards typing and code reuse). Loading of RDF can be performed lazily, when required by the program, based on query-futures – subqueries that are only evaluated if and when the data is accessed. We formulate a Liskov principle for the mapping queries to characterize when they respect the subclass relation. Moreover, we provide tool support to detect when the user-provided mapping would cause the loading mechanisms to result in data structures that manifest the semantic gap.
A Physical Cognitive Assistant for Monitoring Hand Gestures Exercises

The use of cognitive assistants has grown significantly in recent years. The recent appearance of advanced devices for the detection and monitoring of a wide range of physical tasks has allowed a great advancement in the development of environments that facilitate the well-being of people, particularly the elderly. In this sense, this paper proposes the development of a physical cognitive assistant that allows the monitoring of hand gestures exercises for elderly people or people with some kind of hand-related disease. In addition, the cognitive assistant incorporates the detection of the patient's emotional state during the exercise.
Security of Big Data: Threats and Different Approaches Towards Big Data Security

In theYashi Chaudhary Dr. Heman Pathak present era, the use of the Internet has extended abruptly. With this abrupt increase, massive data is being created, resulting in big data. Big data means more diverse, more impetus, and more complex data streams. Data is being produced in abundance in exabytes and zettabytes by electronic devices, power grids, and modern software. This big data brings different challenges such as incompleteness, inconsistency, heterogeneity, and security with itself. The presented paper targets the security challenge as it is a very significant feature overseen by various data analysts; thus, data must be secured from dwindling in the wrong hands. This paper discusses the approaches and mechanisms mainly based on anonymization, access control, and encryption.
Smart Pedagogy: An Extended Reality (XR) Based Approach

Prior research has indicated that smart pedagogy will be utilized in leading academic institutions in the near future. Based on a review of the literature on both smart pedagogy and Extended Reality (XR) in the fields of education, a research methodology was devised, and a comprehensive survey was distributed to Interactive Media students at Bradley University in Peoria, IL who responded to prompts based on their experiences and several XR prototypes that were programmed by one of the co-authors. Analysis of the responses demonstrated that students are extremely interested in utilizing XR within their classes and feel that XR-based smart pedagogy would provide an excellent complement to traditional lectures and out-of-class experiences rating higher than any of the other components of Smart Pedagogy by themselves.
A study on automatic correction of English grammar errors based on deep learning

Abstract Grammatical error correction (GEC) is an important element in language learning. In this article, based on deep learning, the application of the Transformer model in GEC was briefly introduced. Then, in order to improve the performance of the model on GEC, it was optimized by a generative adversarial network (GAN). Experiments were conducted on two data sets. It was found that the performance of the GAN-combined Transformer model was significantly improved compared to the Transformer model. The F 0.5 value of the optimized model was 53.87 on CoNIL-2014, which was 2.69 larger than the Transformer model; the generalized language evaluation understanding (GLEU) value of the optimized model was 61.77 on JFLEG, which was 8.81 larger than that of the Transformer model. The optimized model also had a favorable correction performance in an actual English essay. The experimental results verify the reliability of the GAN-combined Transformer model on automatic English GEC, suggesting that the model can be further promoted and applied in practice.
Zero and Few-Shot Learning for Author Profiling

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment and evaluate our systems on several profiling tasks in Spanish and English. In addition, we study the effect of both the entailment hypothesis and the size of the few-shot training sample. We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50% of the training data on average.
Requirements and Motivations of Low-Resource Speech Synthesis for Language Revitalization

This paper describes the motivation and development of speech synthesis systems for the purposes of language revitalization. By building speech synthesis systems for three Indigenous languages spoken in Canada, Kanien’kéha, Gitksan & SENĆOŦEN, we re-evaluate the question of how much data is required to build low-resource speech synthesis systems featuring state-of-the-art neural models. For example, preliminary results with English data show that a FastSpeech2 model trained with 1 hour of training data can produce speech with comparable naturalness to a Tacotron2 model trained with 10 hours of data. Finally, we motivate future research in evaluation and classroom integration in the field of speech synthesis for language revitalization.
Neural Network Visualization in Virtual Reality: A Use Case Analysis and Implementation

Software systems and components increasingly rely on machine learning methods such as Artificial Neural Networks (ANNs) to provide intelligent functionality. Therefore, software developers and machine learning users should have a basic understanding of such methods. However, since ANNs are complex and thus challenging to grasp, novel visualization approaches can contribute to a better comprehension. We conducted an online survey to identify use cases and requirements for visualizing ANNs. Based on our results we designed and implemented an ANN visualization in virtual reality (VR) specifically targeted at machine learning users. Our approach is particularly suitable for teaching purposes and machine learning novices or non-experts who want to get an impression of the general functionality of neural networks.
Robust Zero-Watermarking Algorithm for Medical Images Based on Residual Network and Depthwise Separable Convolutions

Using traditional watermarking technology to embed watermark in the carrier image will destroy the pixel information and is not suitable for the privacy protection of medical images. Based on this, we proposed a zero-watermarking algorithm based on residual network and depthwise separable convolutions for medical images. First, a convolutional neural network was designed, which was trained on a medical image data set to extract advanced features of medical image, and then the binary feature sequences of medical images are obtained by combining Discrete Cosine Transform (DCT) and perceptual hashing algorithm. This binary feature sequence has good stability and greatly improves the algorithm's ability to resist geometric attacks. Utilizing the chaotic state of Logistic Map and its high sensitivity to initial value, it is applied to the watermark encryption to improve the security of the watermark algorithm. The simulation experiment results show that the proposed zero-watermarking algorithm has good resistance to geometric attacks and high computing speed, and has certain practicability.
Economic Growth With Improvement of People's Lives in Vietnam

In order to improve people's living standards (PLS), in addition to the economic growth, the regulation on distribution of economic growth results by the government plays a very important role. In recent years, the achievements of economic growth have contributed to improving the quality of people's lives; however, there have been many challenges that require the Government of Vietnam to take specific and appropriate measures to ensure both promotion of economic growth and improvement of people's living standards.
Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes

Driver attention cues contribute to the following intended maneuver prediction and provide a risk indicator for the advanced driver-assistance systems in complex driving scenarios. The diverse traffic scenes result in a challenging task to predict human visual attention with high generalization capability. The data heterogeneity caused by multiple sources with different data characteristics, such as video sources, was investigated in the current research and mitigated using the domain adaption modules (i.e., domain-specific batch normalization, Gaussian priors, and smoothing filter) and the domain-specific focal loss. Inspired by human attention mechanism, generic coders and task-driven attention modules were incorporated into a lightweight network to replicate human-like perceptual patterns, such as the perception of latent risk. Integrating these adaptive modules, we proposed the adaptive driver attention (ADA) model to predict salient regions in different traffic scenes. Consequently, the ADA model trained jointly on four driver attention datasets achieves the best performance against the state-of-the-art methods across seven metrics. Retrospective visualizations of the network and cross-validation results further explain the merit of the proposed method. Since these approaches are generic, adaptive modules are universally applicable for standard deep neural network architectures to alleviate the heterogeneity across datasets and easily extended for arbitrary traffic scenes in the real world.
Approach to Anomaly Detection in Cyber-Physical Object Behavior

Though the problem of anomaly detection in a cyber-physical object (CPO) behaviour is highly researched topic, there are still open challenges in this field that mainly relate to the lack of annotated real world data sets. Manual analysis of the anomalies and incidents in such data sets poses a certain difficulties as a typical cyber physical object is described by hundreds of heterogeneous parameters. This paper proposes an approach targeted to simplify the process of anomaly detection and investigation in CPO behaviour represented by multivariate time series. It is based on a novel metric that reflects a measure of the changes in CPO’s behaviour. This metric incorporates data from a set of parameters defined by an analyst, and outputs a high level integral value that could be easily visualized using a standard timeline. The efficiency of the approach is demonstrated on real world data set describing the behaviour of aerospace object and consisting of almost 500 attributes.
Embodying Metaverse as artificial life: At the intersection of media and 4E cognition theories

In the last decades of the 20th century we have seen media theories and cognitive sciences grow, mature and reach their pinnacles by analysing, each from their own disciplinary perspective, two of the same core phenomena: that of media as the environment, transmitter and creator of stimuli, and that of embodied human mind as the stimuli receiver, interpreter, experiencer, and also how both are affected by each other. Even though treating a range of very similar problems and coming to similar conclusions, this still has not brought these two disciplines closer together or resulted in their interdisciplinary approach. They did coalesce in regards to traditional media such as film, but more points of connection are needed for untangling interactive and immersive media environments and their effects on human cognition, action, and perception. With the rise of VR and VR-like systems, especially as they start to evolve into the Metaverse as their main platform of interconnectivity, the tissue of the body becomes almost physically intertwined with that of the virtual surrounding it inhabits through immersion. Simultaneously, the interest in these disciplines arises anew, and especially the need to use their concepts in an interdisciplinary way. This paper?s main interest is to bring these disciplines together in problematising the position of a physical body and its sensory-motor capabilities and their development within synthetic surroundings as Metaverse and anticipate potential downsides of Metaverse?s uncontrolled growth. We will do so also by looking into Metaverse as an artificial-life-like phenomenon, following artificial-life rules and evolving a completely new ?corporeality?, a body which is completely adapted to virtual spaces. We call this body the Dry Body, an entity sharing cognitive resources with the physical body it is not a physical part of, but has to extend to.
Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations

Abstract To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly, the invariances they capture turn neural networks into black-box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant to. We present an approach based on invertible neural networks (INNs) that (i) recovers the task-specific, learned invariances by disentangling the remaining factor of variation in the data and that (ii) invertibly transforms these recovered invariances combined with the model representation into an equally expressive one with accessible semantic concepts. As a consequence, neural network representations become understandable by providing the means to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black-box models by enabling post hoc interpretations of state-of-the-art networks without compromising their performance. Our implementation is available at https://compvis.github.io/invariances/ .
A Hybrid Recommender System for Pedagogical Resources

The present paper proposes an e-learning system that combines popularity and collaborative filtering techniques to recommend pedagogical resources. A recommender system helps users get a correct and personalized decision by applying several recommendation methods such as content-based, collaborative filtering, and other hybrid approaches. However, predicting a relevant resource with a specific context, like pedagogical content, becomes a challenge. In our work, we propose a model to ameliorate the traditional collaborative filtering technique by (i) using the Singular Value Decomposition (SVD) to tackle the problem of scalability and data sparsity; (ii) extracting the most popular resources that the user does not interact with before to resolve the cold start problem; and (iii) combining the results of popularity and SVD factorization methods to improve the recommendation accuracy that evaluated by applying the recall, precision and f1-score of each approach. The comparison shows that the obtained results exhibit an encouraging performance of our model.
Robust Optimization Over Time by Estimating Robustness of Promising Regions

Many real-world optimization problems are dynamic. The field of robust optimization over time (ROOT) deals with dynamic optimization problems in which frequent changes of the deployed solution are undesirable. This can be due to the high cost of switching the deployed solutions, the limitation of the needed resources to deploy such new solutions, and/or the system being intolerant towards frequent changes of the deployed solution. In the considered ROOT problems in this article, the main goal is to find solutions that maximize the average number of environments where they remain acceptable. In the state-of-the-art methods developed to tackle these problems, the decision makers/metrics used to select solutions for deployment mostly make simplifying assumptions about the problem instances. Besides, the current methods all use the population control components which have been originally designed for tracking the global optimum over time without taking any robustness considerations into account. In this paper, a multi-population ROOT method is proposed with two novel components: a robustness estimation component that estimates robustness of the promising regions, and a dual-mode computational resource allocation component to manage sub-populations by taking several factors, including robustness, into account. Our experimental results demonstrate the superiority of the proposed method over other state-of-the-art approaches.
Extractive Text Summarization of Kannada Text Documents Using Page Ranking Technique

Text summarization is one of the core concepts of natural language processing techniques. Text summarization is a process of producing a quality summary statements from a document which has a set of raw information, without losing or altering any valid information. We have developed a Kannada text summarization model using page ranking algorithm. The sentences are represented as vectors; similarity between the sentences is calculated using cosine similarity and a similarity graph is generated. The graph is fed as an input to the text ranking algorithm. The text ranking algorithm will then prioritize the sentences, and this output is considered as the summary of the Kannada document. The proposed model is evaluated by Rouge Scores; ROUGE 1 and L average F1 values are 65% and 64% respectively. ROUGE-2 average F1 score is near to 60%. This indicates the performance of the proposed text summarization model for the Kannada language is better and can further be improved with different Kannada datasets.
Secure and Scalable Attribute Based Access Control Scheme for Healthcare Data on Blockchain Platform

With the emergence of blockchain technology, the process of healthcare data storage has shifted from traditional storage mode to online mode. This has become possible due to the various benefits associated with the blockchain technology like immutability which means data can’t be changed or erased once it’s recorded on the blockchain. Next one is availability which means data is available in real time and verifiability which means the legitimacy of data being kept may be verified by all nodes on the blockchain. In case of healthcare where sensitive data is being stored, it is not acceptable that this data is visible to all as it raises the privacy concerns. All data is visible to other nodes in the network. Therefore, there is an alarming need of a secure access control mechanism that will ensure that only authorized and selective parties are able to see the personal medical data. In this paper, we address this issue by proposing hierarchical ABE (Attribute Based Encryption) based access control mechanism on blockchain. We explain why data encryption alone cannot solve this issue and then propose a framework that utilizes hierarchical CP-ABE (Ciphertext Policy Attribute Based Encryption) access control mechanism to set permission rights for the different parties involved. We show that our framework provides scalability and fine grained access control, i.e., every data element is given its own access policy. We present a thorough security analysis of our proposed framework and show that it is secure and resistant to collusion and cheating attacks. We prove the accuracy of the functionality of the proposed scheme using BAN logic.
Using the Electronically Activated Recorder (EAR) to Capture the Day-to-Day Linguistic Experiences of Young Adults

Both recently and historically, naturalistic datasets and corpus analyses have played an important role in the formulation and testing of key theories and hypotheses in language development and use. The present work details ways in which an existing tool, the Electronically Activated Recorder (EAR), can be used in the cognitive and language science domains to better understand the content of day-to-day speech. From our sample of 75 young adult college students – a population with diverse linguistic experiences – we found enormous variability in the total amount of speech produced and the number of unique words spoken. Further, we discovered that individuals who speak frequently may not be the same individuals that produce long utterances, and we quantified the contexts in which individuals tend to speak. We argue that studies examining naturalistic speech in adults are rare, and through our data, we aim to demonstrate how the EAR can be used in novel ways to create both individual and group-level corpora of adults’ spoken language use.