Reviewerly/exHarmony huggingface dataset:
Impact of Facial Landmark Localization on Facial Expression Recognition

Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks? In this paper, the focus is put on facial expression recognition (FER), where facial landmarks are used for face registration, which is a common usage. Since the most used datasets for facial landmark localization do not allow for a proper measurement of performance according to the different difficulties (e.g., pose, expression, illumination, occlusion, motion blur), we also quantify the performance of recent approaches in the presence of head pose variations and facial expressions. Finally, a study of the impact of these approaches on FER is conducted. We show that the landmark accuracy achieved so far optimizing the conventional Euclidean distance does not necessarily guarantee a gain in performance for FER. To deal with this issue, we propose a new evaluation metric for FLL adapted to FER.
Taxonomy of Cybersecurity Awareness Delivery Methods: A Countermeasure for Phishing Threats

Phishing is a serious threat to the Internet users and has become a vehicle for cybercriminals to perpetrate largescale crimes worldwide.A wide range of technical and educational measures have been developed and used to address phishing threats.However, the technical anti-phishing measures have been widely studied in the current literature whereas comprehensive analysis of the non-technical anti-phishing techniques has generally been ignored.To close this gap, we develop a new taxonomy of the most common cybersecurity training delivery methods and compare them along various factors.The work reported in this paper is useful for various stakeholders.For organizations conducting or considering phishing training, it helps them understand the various awareness training and phishing campaigns capabilities and design an appropriate program with a meaningful return.For researchers, it offers a clearer understanding of the main challenges, the existing solution space, and the potential scope of future research to be addressed.
BnVec: Towards the Development of Word Embedding for Bangla Language Processing

Progression in machine learning and statistical inference are facilitating the advancement of domains like computer vision, natural language processing (NLP), automation &amp; robotics, and so on. Among the different persuasive improvements in NLP, word embedding is one of the most used and revolutionary techniques. In this paper, we manifest an open-source library for Bangla word extraction systems named BnVec which expects to furnish the Bangla NLP research community by the utilization of some incredible word embedding techniques. The BnVec is splitted up into two parts, the first one is the Bangla suitable defined class to embed words with access to the six most popular word embedding schemes (CountVectorizer, TF-IDF, Hash Vectorizer, Word2vec, fastText, and Glove). The other one is based on the pre-trained distributed word embedding system of Word2vec, fastText, and GloVe. The pre-trained models have been built by collecting content from the newspaper, social media, and Bangla wiki articles. The total number of tokens used to build the models exceeds 395,289,960. The paper additionally depicts the performance of these models by various hyper-parameter tuning and then analyzes the results.
Adversarial Robustness in Multi-Task Learning: Promises and Illusions

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research has considered complex multi-task models that are common in real applications. In this paper, we evaluate the design choices that impact the robustness of multi-task deep learning networks. We provide evidence that blindly adding auxiliary tasks, or weighing the tasks provides a false sense of robustness. Thereby, we tone down the claim made by previous research and study the different factors which may affect robustness. In particular, we show that the choice of the task to incorporate in the loss function are important factors that can be leveraged to yield more robust models. We provide the appendix, all our algorithms, models, and open source-code at https://github.com/yamizi/taskaugment
Deep4D: A Compact Generative Representation for Volumetric Video

This paper introduces Deep4D a compact generative representation of shape and appearance from captured 4D volumetric video sequences of people. 4D volumetric video achieves highly realistic reproduction, replay and free-viewpoint rendering of actor performance from multiple view video acquisition systems. A deep generative network is trained on 4D video sequences of an actor performing multiple motions to learn a generative model of the dynamic shape and appearance. We demonstrate the proposed generative model can provide a compact encoded representation capable of high-quality synthesis of 4D volumetric video with two orders of magnitude compression. A variational encoder-decoder network is employed to learn an encoded latent space that maps from 3D skeletal pose to 4D shape and appearance. This enables high-quality 4D volumetric video synthesis to be driven by skeletal motion, including skeletal motion capture data. This encoded latent space supports the representation of multiple sequences with dynamic interpolation to transition between motions. Therefore we introduce Deep4D motion graphs, a direct application of the proposed generative representation. Deep4D motion graphs allow real-tiome interactive character animation whilst preserving the plausible realism of movement and appearance from the captured volumetric video. Deep4D motion graphs implicitly combine multiple captured motions from a unified representation for character animation from volumetric video, allowing novel character movements to be generated with dynamic shape and appearance detail.
The Intention and Expectations of Modern Robotic Technologies in the Hotel Industry

The purpose of this paper was to study the feasibility and expectations of use and quality of service robots in the hotel industry. For this purpose, the authors have relied on a research tool, also used in the pilot research conducted in 2020, regarding the intention to the use and expectations of service robots. The research was based on a sample of 413 hotel employees in hotels in Greece. An important outcome of this research is that overall the intention and expectations from the use of robots affect quality of service of robots in the hotel industry. Also, it appears that demographics can be a mediator in the examined relationships. This research presents a significant originality and importance for two reasons; it examines the perceptions of hotel personnel to use robots – since most of the existing empirical evidence derives mostly from consumers – and secondly it creates the necessary research framework and tools for future research.
A Comparative and Comprehensive Analysis of Smart Contract Enabled Blockchain Applications

Blockchain is a disruptive innovation that is already reshaping corporate, social, and political connections, as well as any other form of value exchange. Again, this isn't simply a shift; it's a fast-moving phenomenon that has already begun. Top financial institutions and a large number of businesses have begun to investigate blockchain in order to cut transaction costs, speed up transaction times, reduce fraud risk, and eliminate the need for middlemen or intermediate services. Blockchain is believed to be the component that completes the Internet puzzle and makes it more open, more accessible, and more reliable. In this article, we first introduced the blockchain technology and smart contracts and their merits and demerits. Second, we present a comparative and comprehensive analysis of smart contract-enabled blockchain applications. Toward the end, we discussed the future development trends of smart contract enabled blockchain applications. This document is intended to serve as a guide and resource for future research initiatives.
Effective Video Summarization Approach Based on Visual Attention

Video summarization is applied to reduce redundancy and develop a concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling.In these schemes, the frames that stand out visually are extracted as key frames based on human attention modeling theories.The schemes for modeling visual attention have proven to be effective for video summaries.Nevertheless, the high cost of computing in such techniques restricts their usability in everyday situations.In this context, we propose a method based on KFE (key frame extraction) technique, which is recommended based on an efficient and accurate visual attention model.The calculation effort is minimized by utilizing dynamic visual highlighting based on the temporal gradient instead of the traditional optical flow techniques.In addition, an efficient technique using a discrete cosine transformation is utilized for the static visual salience.The dynamic and static visual attention metrics are merged by means of a non-linear weighted fusion technique.Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy.The experimental results of our proposed model indicate the efficiency and high standard in terms of the key frames extraction as output.
GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery

Energy system information for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers&#x2014;termed the power grid&#x2014;is often incomplete, outdated, or altogether unavailable. Furthermore, conventional means for collecting this information is costly and limited. We propose to automatically map the grid in overhead remotely sensed imagery using an deep learning approach. Toward this goal, we develop and publicly release a large dataset (263 km<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>) of overhead imagery with ground-truth for the power grid&#x2014;to our knowledge, this is the first dataset of its kind in the public domain. Additionally, we propose scoring metrics and baseline algorithms for two grid-mapping tasks: 1) tower recognition and 2) power line interconnection (i.e., estimating a graph representation of the grid). We hope the availability of the training data, scoring metrics, and baselines will facilitate rapid progress on this important problem to help decision-makers address the energy needs of societies around the world.
Short term Predication of Risk Management Integrating Artificial Neural Network ANN

The IT industry has boomed in the past few years with an ever increasing number of risk management applications being developed. There are inherent risks in software development projects and failure to deliver software projects within deadline or failure to develop software according to specifications can be costly. The software risks may occur during the project process. The management process of software risks consists the risk refinement, risk identification, risk monitoring, risk maintenance, risk estimation and risk mitigation. Neural Network has ability to stimulate hidden pattern recognition skill. The primary study of this paper is to focus on various risk management models and how risk tools may help in mitigating software risks during the project development. With the application of Neural Network, We propose short term risk management model which can predict the risk involvement with the upcoming project risks, analyzing from the previous projects causing serious loss in the IT project in terms of values on certain risk factors. Neural Network model can also ability to evaluate the assessment of risks in software development and acts as an effective instrument in analysis and minimizing risks that enable continuous improvement in software processes and products.
Identify of Factors Affecting Information Security Awareness and Weight Analysis Process

Information exchange is a key aspect of using technology in everyday life. Crimes associated with the lack of information security awareness (ISA), misuse and carelessness are on the increase and often result in heavy losses and serious consequences. In order for ISA campaigns and programmes to be effective, the most successful and influential factors must be employed in the human component of the security awareness process. The purpose of this study is to investigate the causes of human breaches to information security and undertake a weight analysis of the models’ predictors’ relationships utilised in ISA literature from the current decade. Usable data were collected from twenty-one empirical studies related to ISA research in order to obtain the correlations required to perform a weight analysis process for a predictor's relationships. The relationships examined in all studies used in this research (significant–nonsignificant) are presented in a diagram. Findings show that six independent variables were found to be classified as ‘wellutilised’ variables, and the rest of the independent variables converge at the ‘Promising’ classification level. Contributions, limitations and directions of future work are presented.
Use of Technological Equipment for E-learning in Peruvian University Students in Times of Covid-19

The advance of technology grew on a large scale due to the presence of Covid-19 together with education, this growth accompanied the teaching and learning process. The transfer from face-to-face to virtual suggested choosing a teaching process, having e-learning as the most friendly. The method applied to the research is descriptive, correlational, transversal and predictive. The sample was applied randomly and voluntarily to 3532 university students from all over Peru, the sample was collected through an online survey. The results show that there are still problems with the materials they share due to the lack of reading comprehension, but by making use of communication technology and the Internet, students are able to take advantage of it and improve their teaching and learning through self-learning.
S2A: Scale-Attention-Aware Networks for Video Super-Resolution

Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features, thus limiting the feature expression of the models. To address this problem, we propose a novel SAA network, that is, Scale-and-Attention-Aware Networks, to apply different attention to different temporal-length streams, while further exploring both spatial and channel attention on separate streams with a newly proposed Criss-Cross Channel Attention Module (C3AM). Experiments on public VSR datasets demonstrate the superiority of our method over other state-of-the-art methods in terms of both quantitative and qualitative metrics.
Development of New Rotating Ring Topology of PSO-Algorithm

A rotating ring topology of particles' connections in the particle swarm optimization (PSO) is developed in the article. It provides better communication between different particles and increases the exploration ability of the proposed PSO modification. Its simplicity does not need big computational recourses. Comparative analysis has been carried out in the work. It shows good performance of the developed modification. In addition, some perspectives of its improvement are given.
Digital Literacy As A Solution To Improve The Quality Of Indonesia's Human Resources

&lt;p&gt;The Indonesian literacy index in 2020 is still low, to be precise at 0.54. Even though literacy skills, especially in the digital field, are an important component in facing challenges in the future. Therefore, various efforts are needed to increase the national digital literacy index. This study aims to provide an overview of the importance of digital literacy education to improve the quality of human resources to face the demographic bonus in 2030-2040. This study used a qualitative descriptive method, while the data were taken from various literature sources, both print and online. The result of this research is that digital literacy education is very important for improving the quality of Indonesia's human resources. Individuals who have digital literacy will have certain characteristics, such as 1) the ability to use digital potential maximally, 2) digital resilience, 3) become positive and productive individuals, and 4) become part of the world community&lt;/p&gt;
Resource theory of quantum uncomplexity

Quantum complexity is emerging as a key property of many-body systems, including black holes, topological materials, and early quantum computers. A state's complexity quantifies the number of computational gates required to prepare the state from a simple tensor product. The greater a state's distance from maximal complexity, or ``uncomplexity,'' the more useful the state is as input to a quantum computation. Separately, resource theories---simple models for agents subject to constraints---are burgeoning in quantum information theory. We unite the two domains, confirming Brown and Susskind's conjecture that a resource theory of uncomplexity can be defined. The allowed operations, fuzzy operations, are slightly random implementations of two-qubit gates chosen by an agent. We formalize two operational tasks, uncomplexity extraction and expenditure. Their optimal efficiencies depend on an entropy that we engineer to reflect complexity. We also present two monotones, uncomplexity measures that decline monotonically under fuzzy operations, in certain regimes. This work unleashes on many-body complexity the resource-theory toolkit from quantum information theory.
Spectral CG Algorithm for Solving Fuzzy Nonlinear Equations

The nonlinear conjugate gradient method is an effective technique for solving large-scale minimizations problems, and has a wide range of applications in various fields, such as mathematics, chemistry, physics, engineering and medicine. This study presents a novel spectral conjugate gradient algorithm (non-linear conjugate gradient algorithm), which is derived based on the Hisham–Khalil (KH) and Newton algorithms. Based on pure conjugacy condition The importance of this research lies in finding an appropriate method to solve all types of linear and non-linear fuzzy equations because the Buckley and Qu method is ineffective in solving fuzzy equations. Moreover, the conjugate gradient method does not need a Hessian matrix (second partial derivatives of functions) in the solution. The descent property of the proposed method is shown provided that the step size at meets the strong Wolfe conditions. In numerous circumstances, numerical results demonstrate that the proposed technique is more efficient than the Fletcher–Reeves and KH algorithms in solving fuzzy nonlinear equations.
A Provably Secure IBE Transformation Model for PKC Using Conformable Chebyshev Chaotic Maps under Human-Centered IoT Environments

The place of public key cryptography (PKC) in guaranteeing the security of wireless networks under human-centered IoT environments cannot be overemphasized. PKC uses the idea of paired keys that are mathematically dependent but independent in practice. In PKC, each communicating party needs the public key and the authorized digital certificate of the other party to achieve encryption and decryption. In this circumstance, a directory is required to store the public keys of the participating parties. However, the design of such a directory can be cost-prohibitive and time-consuming. Recently, identity-based encryption (IBE) schemes have been introduced to address the vast limitations of PKC schemes. In a typical IBE system, a third-party server can distribute the public credentials to all parties involved in the system. Thus, the private key can be harvested from the arbitrary public key. As a result, the sender could use the public key of the receiver to encrypt the message, and the receiver could use the extracted private key to decrypt the message. In order to improve systems security, new IBE schemes are solely desired. However, the complexity and cost of designing an entirely new IBE technique remain. In order to address this problem, this paper presents a provably secure IBE transformation model for PKC using conformable Chebyshev chaotic maps under the human-centered IoT environment. In particular, we offer a robust and secure IBE transformation model and provide extensive performance analysis and security proofs of the model. Finally, we demonstrate the superiority of the proposed IBE transformation model over the existing IBE schemes. Overall, results indicate that the proposed scheme posed excellent security capabilities compared to the preliminary IBE-based schemes.
Decentralized Data Management Privacy-Aware Framework for Positive Energy Districts

Energy Transition (ET) needs actors to perform independent actions on multiple levels of governance. These actors may need to write and read their data, and at the same time they want to protect their data from unauthorized access. This is particularly the case for positive energy districts (PED), a growing trend in the EU that requires actors to perform, write and read operations on a neighborhood scale where governance competences are typically absent. This paper presents a decentralized privacy-aware data management framework that enables actors to store, read, and modify data in PEDs. Our framework design integrates blockchain with a Distributed Hash Table (DHT), role-based access control, ring signature, and different encryption techniques. The proposed framework stores encrypted data on the DHT, and metadata and hash key are sent to the blockchain, which allows the data owner to keep track of their data. The proposed framework components handle multi-level data access in PEDs and enable data security at run-time. Moreover, we show security and privacy analysis and performance evaluation in time overhead. The results show that the proposed solution is effective, secure, and scalable.
Fine-Grained Algorithm for Improving KNN Computational Performance on Clinical Trials Text Classification

Text classification is an important component in many applications. Text classification has attracted the attention of researchers to continue to develop innovations and build new classification models that are sourced from clinical trial texts. In building classification models, many methods are used, including supervised learning. The purpose of this study is to improve the computational performance of one of the supervised learning methods, namely KNN, in building a clinical trial document text classification model by combining KNN and the fine-grained algorithm. This research contributed to increasing the computational performance of KNN from 388,274 s to 260,641 s in clinical trial texts on a clinical trial text dataset with a total of 1,000,000 data.