Reviewerly/exHarmony huggingface dataset:
Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model

Summary Recently, encryption technology plays a vital role in providing secure information. In the multimedia sector, several images and posts are broadcasted via the internet on a daily basis; in which the images are transmitted on social media and are subjected to several security attacks. As a result, it is essential to secure the images from illegal or forbidden accessing. This paper aims to develop a novel Supervision Kernel Entropy Component Analysis‐enhanced moth flame optimization (SKECA‐EMFO)‐based encrypted transmission system. Here, an effective encryption transmission system is designed using a Bayes minimum risk classifier to secure sensitive information during the transmission processes. Furthermore, the SM4 encryption algorithm performs high‐speed encrypted transmission speed and achieves intelligent recognition performance. The novel SKECA‐EMFO approach is employed in demonstrating facial expression recognition thereby obtaining an optimal feature set. Finally, seven benchmark functions are utilized to examine and calculate the efficiency of the newly established proposed approach. The comparative analysis is carried out with a few approaches, and the results reveal that the proposed approach provides superior performances than other existing techniques.
Sequential Attack Detection in Recommender Systems

Recommender systems are widely used in electronic commerce, social media and online streaming services to provide personalized recommendations to the users by exploiting past ratings and interactions. This paper considers the security aspect with quick and accurate detection of attacks by observing the newly created profiles sequentially to prevent the damage which may be incurred by the injection of new profiles with dishonest ratings. The proposed framework consists of a latent variable model, which is trained by a variational EM algorithm, followed by a sequential detection algorithm. The latent variable model generates homogeneous representations of the users given their rating history and mixed data-type attributes such as age and gender. The representations are then exploited to generate univariate statistics to be efficiently used in a CUSUM-like sequential detection algorithm that can quickly detect persistent attacks while maintaining low false alarm rates. We apply our proposed framework to three different real-world datasets and exhibit superior performance in comparison to the existing baseline algorithms for both attack profile and sequential detection. Furthermore, we demonstrate robustness to different attack strategies and configurations.
Fuzzy membership function design: An adaptive neuro-fuzzy inference system (ANFIS) based approach

In recent times, neuro-fuzzy approaches have been widely used for solving real-world complex problems. Adaptive neuro-fuzzy inference system (ANFIS) is one of the prominent models in this field of soft computing. This model combines both the fuzzy and neural approaches to represent non-linear and ill-defined problems more preciously. However, membership function design is an important factor of fuzzy-based system design. The number and shape of membership functions affect the performance as well as the computational cost of the fuzzy system. The main objective of this research is to find the suitable type of membership function for fuzzy model using ANFIS approach. In this experiment, several types of major membership functions- triangular, trapezoidal, generalized bell, and gaussian are analyzed with an effective dataset. Backpropagation and hybrid optimization techniques are used for training purposes with different numbers of epochs. In addition to that, RMSE is measured for both linear and constant type membership functions to evaluate the performance. Our experimental result shows that the trapezoidal membership function outperforms other types of membership function in all aspects for designing a fuzzy-based model which can be used in real-life applications.
Legion: Massively Composing Rankers for Improved Bug Localization at Adobe

Studies have estimated that, in industrial settings, developers spend between 30 and 90 percent of their time fixing bugs. As such, tools that assist in identifying the location of bugs provide value by reducing debugging costs. One such tool is BugLocator. This study initially aimed to determine if developers working on the Adobe Analytics product could use BugLocator. The initial results show that BugLocator achieves a similar accuracy on five of seven Adobe Analytics repositories and on open-source projects. However, these results do not meet the minimum applicability requirement deemed necessary by Adobe Analytics developers prior to possible adoption. Thus, we consequently examine how BugLocator can achieve the targeted accuracy with two extensions: (1) adding more data corpora, and (2) massively composing individual rankers consisting of augmented BugLocator instances trained on various combinations of corpora and parameter configurations with a Random Forest model. We refer to our final extension as Legion. On average, applying Legion to Adobe Analytics repositories results in at least one buggy file ranked in the top-ten recommendations 76.8 percent of the time for customer-reported bugs across all 7 repositories. This represents a substantial improvement over BugLocator of 36.4 percent, and satisfies the minimum applicability requirement. Additionally, our extensions boost Mean Average Precision by 107.7 percent, Mean Reciprocal Rank by 86.1 percent, Top 1 by 143.4 percent and Top 5 by 58.1 percent.
Use of a Persona to Support the Interdisciplinary Design of an Assistive Technology for Meal Preparation in Traumatic Brain Injury

Abstract User-centered design (UCD) facilitates the creation of technologies that are specifically designed to answer users’ needs. This paper presents the first step of a UCD using a persona, a fictitious character representing the targeted population, which in this case is people having sustained a traumatic brain injury (TBI). The persona is used to better understand the possible interactions of a TBI population with a prototype of a technology that we wish to develop, namely the Cognitive Orthosis for coOKing (COOK). COOK is meant to be an assistive technology that will be designed to promote independence for cooking within a supported-living residence. More specifically, this paper presents the persona’s creation methodology based on the first four phases of the persona’s lifecycle. It also describes how the persona methodology served as a facilitator to initiate an interdisciplinary collaboration between a clinical team and a computer science team. Creation of personas relied on a clinical model (Disability Creation Process) that contextualized the needs of this population and an evaluation tool [Instrumental Activities of Daily Living (IADL) Profile] that presented a wide range of cognitive assistance needs found in this same population. This paper provides an in-depth description of some of the most frequent everyday difficulties experienced by individuals with TBI as well as the persona’s abilities, limitations and social participation during the realization of IADL, and an evaluation of the manifestations of these difficulties during IADL performance as represented through scenarios. The interdisciplinary team used the persona to complete a first description of the interactions of a persona with TBI with COOK. This work is an attempt at offering a communication tool, the persona, to facilitate interdisciplinary research among diverse disciplines who wish to learn to develop a common language, models and methodologies at the beginning of the design process.
How Secure is Home: Assessing Human Susceptibility to IoT Threats

The use of Internet of Things (IoT) devices within the home has become more popular in recent years and with the COVID-19 pandemic more employees are working from home. Risk management has become decentralised, which is problematic for organisations since potential risks towards the company can not be controlled in a standardised and formal way. On the other side, users are suffering from smart home attacks due to the nature of IoT such as its heterogeneity and non-standardised architecture. However, the behaviour and attitudes of the user can dictate the increase or decrease of risk and possible losses due to the end user’s responsibility within the IoT life cycle. In this paper, we suggest that a user’s behaviour and attitude towards IoT devices within the smart home is imperative when designing a risk model for the home. We then consider the human element in the risk assessment process in IoT. We present a Smart Home Behaviour and Attitude Risk Model (SH-BARM) to discuss the importance of human behaviour and attitudes within the home and propose a solution to that will aid smart home inhabitants and organisations.
Incremental Computation in Dynamic Argumentation Frameworks

Dealing with controversial information is a challenging and important task for intelligent systems. Formal argumentation enables reasoning on arguments for and against a claim to decide on an outcome. An argumentation framework often models a dynamic situation where arguments as well as the way they interact frequently change over the time. As a consequence, the sets of accepted arguments (i.e., extensions under a given semantics) often need to be computed again after performing an update. In this article, we address the problem of efficiently recomputing extensions of dynamic argumentation frameworks. We present an incremental algorithmic solution whose main idea is that of using an initial extension and the update to identify a (potentially small) portion of the argumentation framework, which is sufficient to compute an extension of the whole updated framework.
Solving Linear Systems of Equations by Using the Concept of Grover’s Search Algorithm: an IBM Quantum Experience

Quantum algorithm, as compared to classical algorithm, plays a notable role in solving linear systems of equations with an exponential speedup. Here, we demonstrate a method for solving a particular system of equations by using the concept of well-known Grover's quantum search algorithm. The algorithm finds the solution by rotating the initial state vector in the Hilbert space to get the target solution state. It mainly involves finding particular matrices that solve the set of equations and constructing corresponding quantum circuits using the basic quantum gates. We explicitly illustrate the whole process by taking 48 different set of equations and solving them by using the concept of Grover's algorithm. We propose new quantum circuits for each set of equations and design those on the IBM quantum simulator. We run the quantum circuit for one set of equations and obtain the desired results, and hence verify the working of the algorithm.
Design of English online teaching assistant system based on deep learning

In order to improve the resource training efficiency and login success rate of English online teaching assistant system, this paper proposes and designs an English online teaching assistant system based on deep learning. The overall structure of the system is designed in the web environment. The hardware of the system mainly includes the system login module, the homework function module and the examination information management module, so as to enhance the role of the system in English online teaching. In the software part of the system, convolution neural network in deep learning is used to train English online teaching resources to improve the quality of English online teaching resources. The experimental results show that, compared with the traditional system, the training efficiency and login success rate of the proposed method are improved, so the application performance of the designed system is strong.
Augmented Reality in Esthetic Dentistry: a Case Report

Abstract Purpose of Review The aim of this case report was to illustrate the clinical procedure integrating augmented reality (AR) for complex patient cases requiring full mouth rehabilitation. Recent Findings The introduction of AR technology to the fields of medicine and dentistry has led to numerous applications in education, surgery, and esthetics. Recently, a new AR software was introduced in esthetic dentistry which allows for real-time smile projection and thus improves communication with patients and the dental laboratory. Summary The presented case shows a patient with multiple missing teeth, diastemata, and an impaired masticatory and phonetic ability. After reconstruction of the posterior zone, the AR software was used for the conception of the esthetic zone, integrating the patient into the decision-making process. The result was an esthetic rehabilitation applying palatal and buccal veneers which corresponded to the chosen AR design. The patient appreciated the opportunity to pre-visualize a possible final outcome in an interactive way which increased his confidence in the chosen treatment. Further studies are needed to assess the precision and reproducibility of the described protocol.
Time is On The Side of Grammatical Evolution

The computational complexity of Evolutionary Algorithms (EAs) is a well-known concern. This paper is concerned with the resource consumption of GELAB, a novel Grammatical Evolution (GE) system implemented in Matlab. GE is an evolutionary technique for program search that manipulates large populations of computer programs over multiple generations. In this paper, we present our reflections on some recently conducted experiments with GELAB. GELAB leverages the power of Matlab to perform hybrid optimization on its individuals. The results show that we can substantially decrease computation time without compromising the accuracy of the system.
Research and Application of Multi-Round Dialogue Intent Recognition Method

In the existing dialogue system, there are numerous sentences in non-standardized verbal expression form, which usually is brief and vague. It is a challenging task to identify the intentions through the analysis of these sentences. Considering that the supervised learning approach is the mainstream on multi-intention recognition, an amount of public labeled multi-intention dialogue data is necessary. However, labeling work is costly and time-consuming. In this paper, we put forward a multi-label classification method based on existing mainstream classification algorithms and used for dialogue-level multi-intention recognition to reduce the cost of labeling work. We publish the Chinese Multi-Intention Dialogue (CMID-Transportation) dataset of transportation customer service, which is collected by us in an actual production project. We conduct a series of experiments on the CMID-Transportation corpus by using the mainstream classification algorithms and then produce the basic benchmark performance. We find that BERT achieves the best results. We hope that the CMID-Transportation dataset can promote the research and development of intent recognition tasks in multiple rounds of dialogue.
Model for the Generation of Scenarios in Virtual Reality Applied in Health

This work presents a model as a proposal for the development of virtual reality scenarios, these scenarios are designed to be a support problem presented at Education and Health. The proposed model consists of various components such as the design of virtual reality scenarios under a specific context of health and education, the design for the generation of virtual reality scenarios in which user interaction strategies are included to achieve their specific objectives. And finally, the strategies to evaluate the user experience are considered in order to have feedback on these generated scenarios. This work proposes the use of Natural User Interfaces (NUI) as a tool that allows offering a better experience in user interaction within virtual reality scenarios. Also, some examples of existing platforms in the literature and how they can be oriented to health and education scenarios are presented. As a result of this proposal, the design elements and components are presented under the proposed model for the generation of these virtual reality scenarios under the proposed model. As a case study, a series of virtual reality applications is proposed for the generation of scenarios aimed at children with learning and language problems who come to a health institution in Mexico to receive language therapy.
Document Level Emotion Detection from Bangla Text Using Machine Learning Techniques

Understanding emotion from documents automatically is an interesting research topic in the machine learning field. Nowadays, many applications like email, blog, etc have the ability to suggest joyful or angry expressions from written documents. In spite of being a popular language, Bangla lacks a rich corpus with annotated emotion labels, so recognizing emotion from documents is still not developed as other languages. In this work, we have proposed a new dataset containing Bangla documents with annotation of three emotions- Happy, Sad and Angry. Two major feature extraction techniques - Bag of Words(BoW) and Word Embedding is used to extract features from the documents. BoW is used by Logistic Regression and Multinomial Naive Bayes classifiers. Word Embedding is used by Artificial Neural Network(ANN) and Convolutional Neural Network(CNN) classifiers. Among all, Multinomial Naive Bayes classifier has given the best performance on the test set and the accuracy is 68.27%. We have made our dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Dataset: https://doi.org/10.6084/m9.figshare.13052789.v1 available for all to be used in further research purposes.
Perception in HDR and Displays

Abstract Although the world has been high dynamic range (HDR) all along, commercial HDR displays’ burgeoning availability provides new and interesting challenges for colorimetry and perceptual modeling. The biggest challenge stems from the way that large luminance changes spatial and temporal scales often compressed relative to the natural world. This results in veridical luminance/chromaticity reproduction not representing appearance reproduction. Here, the authors review some of the perceptual and colorimetric issues that create challenges in HDR image reproduction.
Optimization of English Learning Platform Based on a Collaborative Filtering Algorithm

This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.
Implementation of Neural Network Control in a Nonlinear Plant Using MATLAB

In modern control theory, there are several variations to different controller designs. The same can be said for Neural Network (NN) Controllers. The goal of this paper is to implement a variant of NN controllers called the Predictive Neural Network controller for a nonlinear plant using MATLAB. The controller will not only be used to determine the performance of the plant but also model future inputs of the system by using the data it has collected. The data will undergo training to create a predictive model of the system. The predicted inputs can then be used to optimize the performance of the system. The NN controller was implemented on a nonlinear plant model and simulated using Simulink which is available in MATLAB using the Deep Learning Toolbox. Another motivation for this paper is to gain a better understanding of the applications of predictive neural networks in control systems.
Development of a Design Strategy for Playful Products of Older Adults

To support developing new playful product for older adults, this paper briefly reviewed a range of existing playful products, and describes how they could be strategically deconstructed and refactored from the perspective of the five interaction elements (person, purpose, means, contexts, and actions). A design framework combining case analogies and persona methods was proposed. The formalized design strategy of persona and case-based interaction elements analysis system that mainly considers two parts in the design process which are called 'inspiration cases' and 'typical users' in this paper. The strategy can be applied to the process of re-designing a specific class of playful products, such as tangible toys or digital games, for particular older adults.
Compact CNN Structure Learning by Knowledge Distillation

The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices. However, existing methods achieve this objective at the cost of a drop in inference accuracy in computer vision tasks. To address such a drawback, we propose a framework that leverages knowledge distillation along with customizable block-wise optimization to learn a lightweight CNN structure while preserving better control over the compression-performance tradeoff. Considering specific resource constraints, e.g., floating-point operations per inference (FLOPs) or model-parameters, our method results in a state of the art network compression while being capable of achieving better inference accuracy. In a comprehensive evaluation, we demonstrate that our method is effective, robust, and consistent with results over a variety of network architectures and datasets, at negligible training overhead. In particular, for the already compact network MobileNet_v2, our method offers up to 2× and 5.2× better model compression in terms of FLOPs and model-parameters, respectively, while getting 1.05% better model performance than the baseline network.