Reviewerly/exHarmony huggingface dataset:
Face Reconstruction Based on Multiscale Feature Fusion and 3D Animation Design

The development of the Internet of Things and 3D technology promotes the wide application of face models in 3D animation. However, because the expression is inconsistent with the facial muscle movement, the reconstruction results may be far from the real appearance in the process of reconstructing the face appearance. Therefore, this paper proposes a character expression simulation model under the framework of 3DS Max. According to the relationship between head bones and muscles, a facial muscle motion model was established. Then, the expression simulation design of the original three-dimensional animation character “yaya” was carried out under the framework of 3DS Max technology. The experimental results of “yaya” facial expression test showed that the face simulation model using this method not only has vivid and natural expression but also conforms to the law of facial muscle movement, which provides an important reference for the construction and application of 3D face model.
Improving Multi-Label Learning by Correlation Embedding

In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such as high time and memory costs. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. Specifically, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we construct a multi-label classifier from the low-dimensional latent feature space to the label space, where the embedding matrix is utilized as the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE. The comparison experiment with the state-of-the-art approaches shows that the proposed method MLLCE has a competitive performance in multi-label learning.
A Personalized Recommendation Algorithm Based on Weighted Information Entropy and Particle Swarm Optimization

Similarity calculation is the most important basic algorithm in collaborative filtering recommendation. It plays an important role in calculating the similarity between users (items), finding nearest neighbors, and predicting scores. However, the existing similarity calculation is affected by over reliance on item scores and data sparsity, resulting in low accuracy of recommendation results. This paper proposes a personalized recommendation algorithm based on information entropy and particle swarm optimization, which takes into account the similarity of users’ score and preference characteristics. It uses random particle swarm optimization to optimize their weights to obtain the comprehensive similarity value. Experimental results on public data sets show that the proposed method can effectively improve the accuracy of recommendation results on the premise of ensuring recommendation coverage.
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data

Industrial internet of things has grown quite popular in recent years and involves a large number of intelligent devices linked together to build a system that can investigate, communicate, gather and observe information. Due to this requirement, there is more demand for compression techniques which compresses data, leading to less usage of resources and low complexity. This is where Convolutional Neural Networks (CNN) play a large role in the field of computer vision, especially in places where high applications such as interpretation coupled with detection is required. Similarly, low-level applications such as image compression cannot be resolved using this methodology. In this paper, a compression technique for remote sensing images using CNN is proposed. This methodology incorporates CNN in a compact learning environment wherein the actual image that consists of structural data is coded using Lempel Ziv Markov chain algorithm. This process is followed by image reconstruction in order to obtain the actual image in high quality. Other methodologies such as optimized trunctiona, JPEG2000, JPEC and binary tree were compared using a large number of experiments in terms of space saving, reconstructed image quality and efficiency. The output obtained indicates that the proposed methodology shows effective improvement, attaining a 50 dB signal to noise ratio and space saving of 90%.
An Improved Simulated Annealing Particle Swarm Optimization Algorithm for Path Planning of Mobile Robots Using Mutation Particles

Artificial intelligence technology has brought tremendous changes to human life and production methods. Mobile robots, UAVs, and autonomous driving technology have gradually entered people’s daily life. As a typical issue for a mobile robot, the planning of an optimal mobile path is very important, especially in the military and emergency rescue. In order to ensure the efficiency of operation and the accuracy of the path, it is crucial for the robot to find the optimal path quickly and accurately. This paper discusses a new method and MP-SAPSO algorithm for addressing the issue of path planning based on the PSO algorithm by combining particle swarm optimization (PSO) algorithm with the simulated annealing (SA) algorithm and mutation particle and adjusting the parameters. The MP-SAPSO algorithm improves the accuracy of path planning and the efficiency of robot operation. The experiment also demonstrates that the MP-SAPSO algorithm can be used to effectively address path planning issue of mobile robots.
Effects of interacting with facial expressions and controllers in different virtual environments on presence, usability, affect, and neurophysiological signals

Virtual Reality (VR) interfaces provide an immersive medium to interact with the digital world. Most VR interfaces require physical interactions using handheld controllers, but there are other alternative interaction methods that can support different use cases and users. Interaction methods in VR are primarily evaluated based on their usability, however, their differences in neurological and physiological effects remains less investigated. In this paper—along with other traditional qualitative matrices such as presence, affect, and system usability—we explore the neurophysiological effects—brain signals and electrodermal activity—of using an alternative facial expression interaction method to interact with VR interfaces. This form of interaction was also compared with traditional handheld controllers. Three different environments, with different experiences to interact with were used—happy (butterfly catching), neutral (object picking), and scary (zombie shooting). Overall, we noticed an effect of interaction methods on the gamma activities in the brain and on skin conductance. For some aspects of presence, facial expression outperformed controllers but controllers were found to be better than facial expressions in terms of usability.
A Computational Look at Oral History Archives

Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.
Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies (Xiao Kang et al., 2018). The result further showed that using FSVM with Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PDF
Remote Teaching in Professor Training: Three Latin American Experiences in Times of COVID-19 Pandemic

The subject matter has been based on three systematizations of experiences in teacher training through remote teaching, during the COVID-19 pandemic of 2020–2021. They were developed in IES in Brazil, Ecuador, and Peru. The objective has been to show the experiences implemented to build the teachers’ pedagogical knowledge, from online teaching practices. The theoretical framework has been the theory of collaborative and developmental learning, according to L. S. Vigotski and his followers. The work methodology has been the systematization of experiences, according to Jara and others. The results of the Brazilian experience show how it is possible for students of the degree in pedagogy to acquire didactic knowledge and develop skills, through collaborative work in virtual rooms. The Ecuadorian experience shows the effectiveness of teaching focused on autonomous work, which is based on asynchronous activities. The results of the experience in Peru show how asynchronous accompaniment is an effective means for guidance and sustained feedback by the trainer, with an impact on the process of construction of a scientific text and on the productivity of the doctoral student in Education.
Hackathons and Libraries

Libraries foster a thriving campus culture and function as “third space,” not directly tied to a discipline.[i] Libraries support both formal and informal learning, have multipurpose spaces, and serve as a connection point for their communities. For these reasons, they are an ideal location for events, such as hackathons, that align with library priorities of outreach, data and information literacy, and engagement focused on social good. Hackathon planners could find likely partners in either academic or public libraries as their physical spaces accommodate public outreach events and many are already providing similar services, such as makerspaces. Libraries can act solely as a host for events or they can embed in the planning process by building community partnerships, developing themes for the event, or harnessing the expertise already present in the library staff. This article, focusing on years from 2014 to 2020, will highlight the history and evolution of hackathons in libraries as outreach events and as a focus for using library materials, data, workflows, and content.&#x0D; [i] James K. Elmborg, “Libraries as the Spaces Between Us: Recognizing and Valuing the Third Space,” Reference and User Services Quarterly 50, no. 4 (2011): 338–50.
A Study of English Informative Teaching Strategies Based on Deep Learning

The rapid development of artificial intelligence brings new development opportunities and challenges to English teaching university. This paper explores the concept of “smart education” and the path of building an ecological information-based teaching model of English college by interpreting the concepts of artificial intelligence, deep learning, ecological linguistics, and language education. Artificial intelligence, especially deep learning, will be promising in many aspects, such as the analysis of individual differences of language learners, customized learning content, diversified and three-dimensional teaching media, the role of teachers as smart classroom designers, and multidimensional and dynamic formative assessments. By relying on the data mining technology of deep learning to analyze learners’ characteristics, the smart classroom design, the promotion of language learners’ independent learning, and the establishment of dynamic and complete learner profiles, the language learning process is no longer a linear process but an evolving open loop, ultimately forming a harmonious development of various ecological niches in the language learning process. In this paper, we study and design a deep learning-based English informatics teaching system to develop a deep learning-based scoring prediction model. The model incorporates deep learning models based on word embedding and text convolutional networks, which can uncover the hidden interest features of academics for English. The experimental research results prove that the online e-learning service platform cannot only effectively meet the diverse and personalized English learning needs of university students, but also improve the learning efficiency of teachers and students.
Towards Zero Trust: The Design and Implementation of a Secure End-Point Device for Remote Working

COVID-19 pandemic and lockdowns forced employees across the world to work from home. Remote working has become a necessity rather than a choice. However, in order to meet this increasing demand, the most pressing security concerns of organizations should be addressed. In this paper, we present the design and implementation of ProGun, an end-point device (a USB dongle) for remote working. We present the hardware/software co-design of ProGun, by which most security risks due to lack of physical protection could be mitigated. We also discuss choices we made among many alternatives for user authentication and their security and usability implications in a remote working environment.
Lightweight and Secure Elliptical Curve Cryptography (ECC) Key Exchange for Mobile Phones

Open networks enable data communication between different types of mobile devices that showcase the need to enforce elevated security measures. Securing sensitive or confidential data in mobile phones is accomplished by implementing a diverse range of cryptographic techniques. While encryption algorithms, such as Rivest–Shamir–Adleman (RSA) may offer secure solutions that are often difficult to compromise, these in turn prerequisite high speed computational resources for effective operation. Elliptical curve cryptography (ECC) is well thought-out standard that offers a workable and feasible methods of encryption/decryption, whilst being applicable to resource constraint devices. This paper implements a novel key exchange mechanism that helps to secure exchange of data between the communicating mobile devices. The study aims to address the limitation of Elliptic Curve Deffie Hellman, which is susceptible to Man-in-the-Middle attack and proposes an enhanced Elliptic Curve Deffie Hellman (ECDH) technique for secure data communication in open networks. The study results reveal, how the implementation of ECDH allows exchange of keys between the two communicating devices with limited resources.
Multi-Cloud Resource Management Techniques for Cyber-Physical Systems

Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described.
Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.
Twitter stance detection towards Job Creation Bill

The formation of Job Creation Bill has raised the polemics in Indonesia. This study aims to identify the public’s stance on the Job Creation Bill on Twitter social media. We collect tweets using keywords related to the Job Creation Bill and annotate nearly 10K tweets with a class label describing stance position. The experiments were conducted using the Naïve Bayes, Support Vector Machine, and Logistic Regression, with unigram and bigram features. The best performance in our experiment achieved by the Logistic Regression classifier using the unigram feature obtains a micro F-1 score of 71.8%.
Harm Imbrication and Virtualised Violence: Reconceptualising the Harms of Doxxing

This article develops a framework for analysing the harms of doxxing: the practice of publishing personal identifying information about someone on the internet, usually with malicious intent. Doxxing is not just a breach of privacy, nor are its effects limited to first‑order harms to an individual’s bodily integrity. Rather, doxxing increases the spectre of second-order harms to an individual’s security interests. To better understand these harms—and the relationships between them—we draw together the theories of Bhaskar, Deleuze and Levi to develop two concepts: the virtualisation of violence and harm imbrication. The virtualisation of violence captures how, when concretised into structures, the potential for harm can be virtualised through language, writing and digitisation. We show that doxxed information virtualises violence through constituting harm-generating structures and we analyse how the virtual harm-generating potential of these structures is actualised through first- and second-order harms against a doxxing victim. The concept of harm imbrication, by contrast, helps us to analyse the often-imbricated and supervenient relationship between harms. In doing so, it helps us explain the emergent – and supervenient – relationship between doxxing’s first- and second-order harms.
Foreseeing the Benefits of Incidental Supervision

Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.These could include partial labels, noisy labels, knowledgebased constraints, and cross-domain or crosstask annotations -all having statistical associations with gold annotations but not exactly the same.However, we currently lack a principled way to measure the benefits of these signals to a given target task, and the common practice of evaluating these benefits is through exhaustive experiments with various models and hyperparameters.This paper studies whether we can, in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through combinatorial experiments.We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals.We demonstrate PABI's effectiveness by quantifying the value added by various types of incidental signals to sequence tagging tasks.Experiments on named entity recognition (NER) and question answering (QA) show that PABI's predictions correlate well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
Data integrity issues and challenges in next generation non-relational document-oriented database outsourced in public cloud

Database as a Service provides high availability and scalability in cloud but users do not physically control over their data, therefore, data integrity is serious concerned. Authenticity of data and its verification is one of major risk in next generation document-oriented databases. It is possible that malicious insider and outsider can change and compromise the data. A system is proposed in which cloud environment secure storage and access semi-structured data for non-relational document-oriented database. The proposed system effectively provides data integrity for sensitive and confidential fields and verification of data whether it has been altered or not in outsource database in the public domain.