Reviewerly/exHarmony huggingface dataset:
The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.
Home Management System: Artificial Intelligence

Artificial Intelligence is changing the way we work and the way we live. Today, we are witnessing the first moments of a long and profound revolution that will affect the future of industries, jobs and lives. Even though electronic devices have become smarter thanks to IoT, they cannot provide as much service as desired. Therefore, to experience smart homes in theory and daily life, there is a need to develop an automatic system capable of being self-sustaining and responding to modern life needs. This research aims to study the possibilities of Artificial Intelligence applications for domestic use, particularly in establishing a Home Management System (HMS) that reduces consumption, wastes and optimises resources to impact on a small and large scale positively. For this purpose, this study provides a comprehensive analysis of machine learning techniques such as deep learning and reinforcement learning on sustainable smart homes considering several application fields. We present how this system can be used at homes, such as energy management, food & agriculture, water consumption & generation, waste management, healthcare, customisation & entertainment and security.
Mr. Hulot’s Invisible Gorilla

This article examines the work of mid-century French filmmaker Jacques Tati. Tati suggested that his films allow more visual freedom to audiences and that audiences discover new material upon multiple viewings of his films. We review the scholarship on Tati, especially in relation to critic André Bazin’s theories of realism, and then propose another model for understanding Tati’s films: the psychological concept of inattentional blindness. The article then discusses our experiment using eye tracking technology to study how subjects watch Tati’s films versus other types of cinema and also how they re-watch films. Finally, we applied several statistical and mathematical tests to the eye tracking data to understand key differences between Tati’s films and other filmmaking practices.
Mobilizing the Learning Management Systems in a Smartphone Device Amidst the Current Pandemic

Technology transcends today by mobilizing the Educational Technology tool that can be done in an internetbased system smartphone device especially on the current pandemic crisis we are currently experiencing. The Current pandemic brought real-life situation because of the fast increasing of COVID-19 infected. At present we already have 190 infected persons in the province of Zambales. The Researcher will be employing a Learning Management System (LMS) in SMARTPHONE Device, the reason behind this study is its convenience and flexibility wherever you are you can still access the LMS and then receive notifications like the accessible of the students in this type of learning platform as means of flexible learning The features of the Learning Management Systems has a capability to do automated settings of topics, quizzes, and exercises for his class even the assessment methods on the system, even Students were no longer able to start discussions in the target language in online forums nor were they able to use their smartphone devices to access and share resources directly from the classroom (Gabarre, Gabarre, Din, Shah, & Karim, 2013). The Researcher will be conducting a mixed-method research design. Mixed method is a combination of qualitative and quantitative research design to come-up with a multidisciplinary study, (Aramo-Immonen, 2011). A Stratified Questionnaire will be used during the conduct of a study using Google Form, in this approach data retrieval will be easy for the respondents to answer back because answers can be drawn from the online database immediately. After collecting all the data and will be statistically treated using graphical chart and frequency based data analysis approach. Based on the level of profiles of the users mobilizing the Learning Management System using SmartPhone there are 22 respondents coming from different institutions namely: (1) College of Subic Bay Montessori, (2) Central Luzon College of Science and Technology, and (3) Department of Education-Olongapo City with different levels of age ranging from 18 to 46 years old. 95.5 percentages agreed that the Learning Management System fit the requirements of standards and norms of traditional learning in their corresponding institutions and 4.5 percentage do not agree. There are 40.9 degrees of percentage saying that there is a very satisfactory significance on the LMS that responds as a new educational platform amidst the new normal. Based on the above results gathered the Learning Management Systems really imply as effective educational platform on their own institutions.
Android-Based Nutrition Education Application

Abstract Nutrition is a constituent of foodstuff having an important function to body health. Many people ignore as to how the food can influence the body. Due to their negligence, it is often that the calorie or energy entering the body is excessive resulting in a negative impact on the body health. The lack of nutrition knowledge along with the negligence of food intake arrangement entering the body can have a negative impact on the body. Therefore, the needs to find out the number of nutritional need required by the body can be manually calculated; however, it is a little bit complex. This study aimed to design application to provide nutrition education easiness in finding out the nutrition needs and arranging the proper food pattern. The application was made by using studio android with database firebase and operated by using Android Smartphone, Harris Benedict method in calculating energy, System Development Life Cycle (SDLC) method approach with the Waterfall model. This study result was Android-based nutrition education application. In this application, there is a user menu to find out the nutrition needs and administrator in managing data. System testing was performed by Black box, expert validation test, and System Usability Scale (SUS) method, with 30 respondents resulting in a very good SUS score of 84.5 and thus the application can be accepted.
Linked Open Data Supporting Semantic Integration and Collaboration in Disaster Management Cycle

An increasing number of multiple types of disasters challenge the current role of Disaster Management (DM) support systems regarding the interventions at the different phases of the DM cycle. Disaster Management is known for the heterogeneity of domain’s concepts, the kind of resources deployed for disaster response, and the complexity of the information shared among the several organizations participating in a catastrophe scenario. The goal of this work is to envision what seems to be the next evolution of DM support systems towards a better decision-making support along the disasters’ management cycle, specifically, concerning the integration of the several available distributed data sources using ontologies and Linked Open Data as tools.
Algorithm selection using transfer learning

Per-instance algorithm selection has been shown to achieve state of the art performance in solving Travelling Salesman Problems (TSP). By selecting optimization algorithms for each TSP instance, significant time savings have been achieved. In this work, we highlight how recent algorithm selection techniques apply to service composition; which is posed as a TSP problem. However, the service composition environment is highly dynamic, which poses unique challenges for algorithm selection. Chief amongst those is the availability of training data for all algorithms on unseen tasks, which is infeasible to obtain. To address this problem, we propose the use of transfer learning techniques to improve classification accuracy in dynamic settings such as service composition.
Novel activation function with pixelwise modeling capacity for lightweight neural network design

Summary The development of lightweight networks makes neural networks more efficient to be widely applied to various tasks. Considering the deployment of hardware like edge devices and mobile phones, we prioritize lightweight networks. However, their accuracy has always lagged far behind SOTA networks. In this article, we present a simple yet effective activation function, called WReLU, to improve the performance of lightweight networks significantly by adding a residual spatial condition. Moreover, we use a strategy to switch activation functions after determining which convolutional layer to use. We perform experiments on ImageNet 2012 classification dataset in CPU, GPU, and edge devices. Experiments demonstrate that WReLU improves the accuracy of classification significantly. Meanwhile, our strategy balances the effect of additional parameters and multiply accumulate. Our method improves the accuracy of SqueezeNet and SqueezeNext by more than 5% without increasing extensive parameters and computation. For the lightweight network with a large number of parameters, such as MobileNet and ShuffleNet, there is also a significant improvement. Additionally, the inference speed of most lightweight networks using our WReLU strategy is almost the same as the baseline model on different platforms. Our approach not only ensures the practicability of the lightweight network but also improves its performance.
THE LAW OF INCREASING THE ROLE OF SUBJECTIVE FACTORS IN THE FORMATION OF AESTHETIC ATTITUDE TO NATURE

The functional role and significance of ecological consciousness and culture as the main subjective factor of aesthetic perception of nature is determined by the general intellectual potential of the individual, the ability to express emotional experiences through ideal images, the need to synthesize universal ecological spiritual values. At the individual level. At the same time, a certain commonality of different directions and levels of development of ecoaesthetics ensures the integrity of the subjective factor.
Quantum random access memory via quantum walk

A novel concept of quantum random access memory (qRAM) employing a quantum walk is provided. Our qRAM relies on a bucket brigade scheme to access the memory cells. Introducing a bucket with chirality left and right as a quantum walker, and considering its quantum motion on a full binary tree, we can efficiently deliver the bucket to the designated memory cells, and fill the bucket with the desired information in the form of quantum superposition states. Our procedure has several advantages. First, we do not need to place any quantum devices at the nodes of the binary tree, and hence in our qRAM architecture, the cost to maintain the coherence can be significantly reduced. Second, our scheme is fully parallelized. Consequently, only O(n) steps are required to access and retrieve O(2n) data in the form of quantum superposition states. Finally, the simplicity of our procedure may allow the design of qRAM with simpler structures.
Revisiting Human Relativism – Guidelines for Precision in Information Systems Modelling

Human Relativism (HR) was presented a decade ago as a new philosophical stance for thinking and modelling Information Systems. The Normative Approach for Modelling Information Systems (NOMIS) adopted HR by using a human-observable-action centred perspective of information systems. By using HR, NOMIS claims to have reduced unpredictability, attributed to human behaviour, and increased precision, required by formal methods. Still, there are other approaches, some of them using formal methods, supported by different ontologies, such as the well-known Bunge–Wand–Weber ontology, that lack the necessary precision. In this paper, we explore different ontologies, their relationship to language and why they fail to deliver precision. Precision, a concept introduced in HR, as a basis for engineering, is analysed, and the way to achieve it is proposed through suggesting guidelines for further discussion and research.
Vulnerability Assessment and Penetration Testing Approach Towards Cloud-Based Application and Related Services

Cloud solutions provide a powerful computing platform that enables individuals and Cloud users to perform a wide range of tasks, such as using an online storage system, implementing business applications, developing customized computer software, and establishing a realistic network environment. The number of people using cloud services has increased dramatically in recent years, and a massive amount of data has been stored in cloud computing environments. As a result, data breaches in cloud services are increasing year after year as a result of hackers who are constantly attempting to exploit cloud architecture's security vulnerabilities. In this paper, we investigate and analyse real-world cloud attacks in order to demonstrate the techniques used by hackers against cloud computing systems and how to prevent such malicious activities.
Feature Impact on Sentiment Extraction of TEnglish Code-Mixed Movie Tweets

Sentiment extraction isPadmaja, S. a natural language processing task dealing with the detection and classification of sentiments in various monolingual and bilingual texts.Nikitha, M. In this context,Bandu, Sasidhar the automation of extracting sentiments from social media text is one of the pertinent areas of research as there is an enormous noisy multilingual content.Sameen Fatima, S. This work focuses on extracting sentiments for code-mixed Telugu–English (TEnglish) bilingual Roman script movie tweets extracted using Twitter API. Initially, every tweet in the dataset was annotated with the source language of all the words present and also the sentiment expressed in the code-mixed tweet. The annotated data was automated for sentiment extraction through machine learning-based approach. Sentiment classification was accomplished with features like character N-grams, emoticons, repetitive characters, intensifiers, and negation words using support vector machine classifier with radial basis function as it performs efficiently in high-dimensional feature vectors. The study was to focus on identifying the type of feature which has more impact in capturing sentiments. The results show that character N-grams, emoticons, and negation words are the features that affect the accuracy most.
Effects of Gender Matching on Performance in Human-Robot Teams and Acceptance of Robots

Robots provide important help in relieving work pressure and reducing workload. This study aimed to explore the impact of robots' gender on individuals' performance and their acceptance of robots. This study explored the effects of human-robot gender matching on the performance and the mediating role of acceptance of robots. A questionnaire survey was conducted and 212 valid questionnaires were collected. The results indicated that for the male respondents, their acceptance of opposite-sex robots was higher than the acceptance of same-sex robots and their performance was higher while working with opposite-sex robots. For the female respondents, their acceptance of same-sex robots was higher and their performance wasn't influenced by robots' gender. Moreover, the acceptance of robots mediated in the influence of gender matching on performance. Implications were discussed for future work.
Protocol for the Interaction of Agents Solving the Problem of Distributed Data Analysis

When analyzing data located on distributed sources, multi-agent technologies are used. The fundamental component of the interaction of agents in is their coordination. There are different strategies for performing distributed data analysis. For example, federated learning can be done sequentially, centrally, or decentralized. The coordination of the agents and the communication protocol used for the interaction must be carried out taking into account the learning strategy. This article describes: communication protocols, interactions between agents, and the types of agents used in each of the federated learning strategies.
MIDAS-OWL: An Ontology for Interoperability between Data and Service Cloud Layers

As different cloud computing services have emerged over the years, the diversity of technologies and the lack of standardization has given rise to an interoperability problem in cloud computing. Cloud computing services include those such as Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Data as a Service (DaaS). In this context, interoperability enables a service to communicate with another service transparently. Among the solutions proposed in the literature, a middleware can be used to intermediate such communication and to mitigate the lack of interoperability in cloud computing. For instance, the middleware MIDAS (Middleware for DaaS and SaaS) provides transparent interoperability between SaaS and DaaS. Although MIDAS current version promotes syntactic interoperability, semantic interoperability is only superficially addressed. In collaboration with this project, we develop an OWL-based ontology to formally represent the communication between SaaS and DaaS, and discuss its strengths in providing semantic interoperability on MIDAS. We conduct a set of experiments to validate our ontology. We evaluate intrinsic (consistency, correctness, acceptance) and extrinsic (integration between ontology and MIDAS) issues. Results provide evidence that a semantic MIDAS interoperability can be enhanced by our ontology.
Environmental Assessment Based on Health Information Using Artificial Intelligence

A holistic care system which enables extensive medical care even outside the hospital brings significant benefits for health care. The application of novel communication and computation technologies is essential in order to accomplish such a system. In the presented chapter, a conceptual system is described which links environmental parameters measured by building automation and control systems with data from electronic health records. The system’s purpose is to provide medical personnel with interpreted data about possible adverse health effects of the indoor environment with respect to the patient’s health condition. Additionally, the patient receives real-time feedback about the environmental parameters and their potential health effects. The purpose of this feedback is to inspire behavior changes in the patient, which results in a more health-friendly environment. A special focus of the chapter lies on the analysis of possibly applicable artificial intelligenceArtificial Intelligence (AI) approaches for the estimation of the individual environmental risk factor. These are necessary because the system combines knowledge about the adverse health effect of environmental parameters and knowledge about health parameters for the environmental assessment. This knowledge is often incomplete, ambiguous, and is linked to uncertainty, which makes the interpretation of the raw data non-trivial and would overstrain the occupant as well as the medical personnel.
Individual Difference Assessment Method Based on Cluster Scale Using a Data Reduction Method

This paper proposes a new method for assessing individual differences in time series data from multiple subjects, based on the cluster scale using a data reduction method as a measure for quantitatively detecting differences over subjects. This evaluation method is based on a previously proposed method of measuring individual difference among subjects by a common cluster scale over the subjects. However, it is unique in that we can reduce significant amounts of data by extracting data that affect the obtained common clusters by defining a new distance between the target data and the common clusters using the idea of distance based on maximum likelihood estimation using fuzzy covariance. Also, we demonstrate the effectiveness of the proposed method by showing that measuring the data between different subjects on only the selected data on the common cluster scale yields similar results to those obtained when using all data before selection.
Improved Point Transformation Methods For Self-Supervised Depth Prediction

Given stereo or egomotion image pairs, a popular and successful method for unsupervised learning of monocular depth estimation is to measure the quality of image reconstructions resulting from the learned depth predictions. Continued research has improved the overall approach in recent years, yet the common framework still suffers from several important limitations, particularly when dealing with points occluded after transformation to a novel viewpoint. While prior work has addressed the problem heuristically, this paper introduces a z-buffering algorithm that correctly and efficiently handles occluded points. Because our algorithm is implemented with operators typical of machine learning libraries, it can be incorporated into any existing unsupervised depth learning framework with automatic support for differentiation. Additionally, because points having negative depth after transformation often signify erroneously shallow depth predictions, we introduce a loss function to explicitly penalize this undesirable behavior. Experimental results on the KITTI data set show that the z-buffer and negative depth loss both improve the performance of a state of the art depth-prediction network. The code is available at https://github.com/arthurhero/ZbuffDepth and archived at https://hdl.handle.net/11084/10450.
Signal set expansion based on GAN

Generating confrontation network is a hot research direction of artificial intelligence. With the rise of deep learning, generative adversarial networks have become a breakthrough development. Big data plays a very important role in life, in order to increase the diversity of signal data, artificial intelligence method is used to generate a variety of modulation signals through GAN to achieve the purpose of expanding the signal data set. By providing the original square wave signal as input to the generator, the modulated signal is used as the input of the discriminator, a new signal is generated by the GAN network, and the task of expanding the data set is finally completed.