Reviewerly/exHarmony huggingface dataset:
Automating Cognitive Modelling Considering Non-Formalisable Semantics

The paper addresses the acceleration of cognitive modelling by automating the creation of cognitive semantics of Artificial Intelligence (AI) models considering such human abilities as free will, consciousness, unconsciousness, feelings, thoughts and experience. The problem consists of the impossibility of formalising these abilities during modelling, especially considering their uncaused character. Traditional AI tools such as knowledge management, logical ontologies and neuron networks cannot totally embrace these abilities, to understand and explain events. The paper suggests using the Hybrid AI (HAI) approach, which integrates formalisable AI and non-formalisable human abilities. This paper’s main idea consists of using a non-local approach to enrich the cognitive semantics of AI models that consider the subatomic structure of the human mind’s biological tissue. In this case, quantum operators map an AI model on relevant big data, thereby enriching the cognitive semantics. The quantum non-local approach helps to increase the quality of cognitive models and synthesise their drafts automatically. The inverse problem-solving method on topological spaces, which ensures the purposefulness and sustainability (convergence) of decision-making processes, is also used. The approach is applied in real practice during collective strategic planning and is now developing for decision-making in an emergency.
Constrained prediction time random forests using equivalent trees and genetic programming: application to fall detection model embedding

Budgeted learning is a research field of growing interest that aims at including real world resource constraints into the design of machine learning models, for instance to reduce real environment prediction time. One family of method to do so consists in simplifying a pre-trained machine learning model in order to fit the prediction time constraints, while keeping model’s prediction accuracy as best as possible.However we show in this work that the performance of these kinds of methods strongly depends on the pre-trained model structure. To overcome this dependence, we propose to tackle the budgeted prediction time optimization problem, by using equivalent classifiers with different structures and therefore different computation costs. The main contribution of this work is to propose an innovative evolutionary computing approach to decrease the prediction time of random forest classifiers, by using the notion of equivalence between decision trees. This method is applied for a real-time fall detection system embedding.Our genetic algorithm relies on two core operations : classifier equivalence and decision tree pruning. The first step of our method consists in building, from a pre-trained random forest, an initial population of random forests, that share the same decision function but have different structures using a randomized equivalent trees generation procedure. Then a genome reduction operation is iteratively applied on the individuals via random pruning based mutations.Our experiments show good reduction of random forests prediction time, as well as an efficient impact of using equivalent decision trees to reach better budgeted prediction time solutions. Results obtained both on a synthetic data made of gaussian-shaped clusters and on a real industrial fall detection dataset, advocate for the efficiency of our genetic random pruning approach in reducing random forests prediction time and for the use of equivalent decision trees in budgeted learning.
Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network

Chinese language is also an important way to understand Chinese culture and an important carrier to inherit and carry forward Chinese traditional culture. Chinese language teaching is an important way to inherit and develop Chinese language. Therefore, in the era of big data, data mining and analysis of Chinese language teaching can effectively sum up experience and draw lessons, so as to improve the quality of Chinese language teaching and promote Chinese language culture. Text clustering technology can analyze and process the text information data and divide the text information data with the same characteristics into the same category. Based on big data, combined with convolutional neural network and K-means algorithm, this paper proposes a text clustering method based on convolutional neural network (CNN), constructs a Chinese language teaching data mining analysis system, and optimizes it so that the system can better mine Chinese character data in Chinese language teaching data in depth and comprehensively. The results show that the optimized k-means algorithm needs 683 iterations to achieve the target accuracy. The average K-measure value of the optimized system is 0.770, which is higher than that of the original system. The results also show that K-means algorithm can significantly improve the clustering effect, optimize the data mining analysis system of Chinese language teaching, and deeply mine the Chinese data in Chinese language teaching, so as to improve the quality of Chinese language teaching.
The Use of Non-Verbal Communication in Supporting the Realization of Brown &amp; Levinson Politeness Strategies

One of the prominent issues in pragmatics is politeness. Politeness can manifest in two actions, verbal and non-verbal communication. This study aimed to analyze the lecturer's and the students' non-verbal communication (NVC) in supporting the realization of Brown &amp; Levinson's politeness strategies in English classroom interaction at Universitas Muhammadiyah Purworejo. This study explains the lecturer’s and students’ NVC in supporting the realization of bald on record strategy, positive politeness strategy, negative politeness strategy, off-record strategy, and don’t do the face-threatening act (FTA) strategy in English classroom interaction. In this study, the researchers applied qualitative research. The subjects were one English lecturer and students. The objects were the lecturer's and the students' utterances during the English learning process. The methods the researchers used to analyze the data were Brown &amp; Levinson's (1987) politeness strategies theory and Novinger's (2001) NVC theory. The findings showed that politeness strategies were used by the lecturer and students with the total frequency of 559 times, namely bald on record, positive politeness (dominantly used), negative politeness, off-record, and don’t do the FTA (the least used). Besides, in supporting the realization of politeness strategy, the dominantly used methods of NVC were chronemics, kinesics, and vocalic. In conclusion, politeness strategies are crucial in maintaining the relationship and creating a comfortable environment in the EFL classroom. NVC can support performance that cannot be expressed in words.
Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection

This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns.
Gaussian Random Number Generator With Reconfigurable Mean and Variance Using Stochastic Magnetic Tunnel Junctions

Generating high-quality random numbers with a Gaussian probability distribution function is an important and resource-consuming computational task for many applications in the fields of machine learning and Monte Carlo algorithms. Recently, complementary metal&#x2013;oxide&#x2013;semiconductor (CMOS)-based digital hardware architectures have been explored as specialized Gaussian random-number generators (GRNGs). These CMOS-based GRNGs have a large area and require entropy sources at their input that increase the computing cost. In this letter we present a GRNG that works on the principle of the Boltzmann law in a physical system made from an interconnected network of thermally unstable magnetic tunnel junctions. The presented hardware can produce multibit Gaussian random numbers at gigahertz speed and can be configured to generate distributions with a desired mean and variance. An analytical derivation of the required interconnection and bias strengths is provided, followed by numerical simulations to demonstrate the functionalities of the GRNG.
Correction: Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

[This corrects the article DOI: 10.2196/26256.].
SIMONIC: IoT Based Quarantine Monitoring System for Covid-19

COVID-19, which has become a global pandemic since March 2020, has tremendously affected human life globally. The negative impact of COVID-19 affects societies in almost all aspects. Implementing quarantine monitoring, also social distancing, and contact tracing are a series of processes that can suppress the new infected COVID-19 cases in various countries. Prior works have proposed different monitoring systems to assist the monitoring of individuals in quarantines, as well as many methods are offered for social distancing and contact tracing. These methods focus on one function to provide a reliable system. In this paper, we propose IoT-based quarantine monitoring by implementing a geofence equipped with social distancing features to offer an integrated system that provides more benefits than one system carrying one particular function. We propose a system consisting of a low cost, low complexity, and reusable wristband design and mobile apps to support the quarantine monitoring system. For the geofencing, we propose a GPS-based geofence system that was developed by taking advantage of the convenience offered by the Traccar application. Meanwhile, we add the notification for social distancing feature with adaptive distance measurement RSSI-based set up in the android application. Based on the experiment we did to validate the system, in terms of wristband-to-smartphone communication, scanning interval in smartphone and advertising interval in wristband is best to set in 7 s for both. For social distancing notification and geofence, we measure the system performance through precision, recall, accuracy, and F-measure.
Toxic Comments Classification using Neural Network

Humans have built broad models of expressing their thoughts via several appliances. The internet has not only become a credible method for expressing one's thoughts, but is also rapidly becoming the single largest means of doing so. In this context, one area of focus is the study of negative online behaviors of users like, toxic comments that are threat, obscenity, insults and abuse. The task of identifying and removing toxic communication from public forums is critical. The undertaking of analyzing a large corpus of comments is infeasible for human moderators. Our approach is to use Natural Language Processing (NLP) techniques to provide an efficient and accurate tool to detect online toxicity. We apply TF-IDF feature extraction technique, Neural Network models to tackle a toxic comment classification problem with a labeled dataset from Wikipedia Talk Page.
Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.
Video semantics exploration for indexing and retrieval

Video semantic concepts exploration is a fundamental problem in a video indexing and retrieval.It has a long history of investigation since early days till recent achievements.The challenges lie in bridging the gap between low level features and semantic level ones.To stand on the thoroughly situation, video semantic concepts exploration for indexing and retrieval purposes evolution from conventional methods to the state-ofthe-art ones will be reviewed.The main contribution is to unify concepts involved and evolution in this interesting topic.
ASSEMBLY AND INSTALLATION GUIDANCE BY AUGMENTED REALITY

The electrical technicians use several assembly guides and work instructions to assemble any electrical product. This instruction carries information that is essential for the safety, working, and performance of the product. Such instructions are passed to the technician either in the form of a paper printout or a software document. Due to packaging constraints, size restrictions, safety precautions, or any other design constraints, some products could not be dispatched to the customer in fully assembled conditions. In the conventional process, the manufacturer ships the assembly instructions in the paperback document. The document may differ from one to hundreds of pages depending on the assembly process and complexity. Often instructions are provided in complicated 2D schematic representations. So, there could always be a chance of difficulty in understanding or mistake during the assembly. Another common possibility is missing or skipping a few instructions in the manual. There is a probability that a customer may lose the assembly step in the middle or overlook some instructions during the assembly procedure. Since there is no control over the instructions, it may lead to re-assembling or any other functionality failure. This paper explains how we can mitigate all these issues with application of Augmented Reality technology.
PENDIDIKAN ETIKA REKA BENTUK DALAM PROGRAM INDUSTRI KREATIF DI INSTITUSI PENGAJIAN TINGGI MALAYSIA [DESIGN ETHICS EDUCATION FOR CREATIVE INDUSTRIES PROGRAMMES IN MALAYSIAN HIGHER EDUCATION INSTITUTIONS]

Nowadays, the creative industry is rapidly developing with various technologies and creativity moving forward. However, problems and issues involving ethical aspects are still often seen on social media, television and print media, it clearly shows the lack of values and ethical aspects of the individual in the production of designs. Therefore, this study is conducted to propose the guidelines on ethical education design for creative industry programs in the institutions of higher learning. The objective of this study is to evaluate students' behavior towards design ethics among students of higher education institutions, evaluate teaching practices implemented by educators of higher education institutions as well as to assess challenges related to design ethics faced by creative industry professionals in submitting proposal regarding ethical education guidelines for creative industry programs. This study uses quantitative method which is the survey method in which questionnaires are distributed to respondents. For the sampling of studies, which is stratified sampling that refers to individual specific characteristic features in terms of his/her involvement in the creative industry. The numbers of respondents were 164 for the students, 108 respondents for the teaching staff and 89 respondents were from creative industry professionals. The findings of this study that are based on the mean value and standard deviation from the data collected through the survey study, show that students' behavior on ethical aspects is an important aspect of design ethics. Meanwhile, for the lecturers, the teaching practices implemented show that ethical aspects are a crucial element in the teaching sessions. Next, for the creative industry professionals, ethical aspects are considered as an important element in facing the challenges regarding design ethics. This is explained through the findings of students, lecturers and creative industry professionals through high recorded mean value. Therefore, it is clear that ethical aspects are a vital element to these three groups. Thus, a guideline on ethical education design for creative industry programs in institutions of higher learning is produced through the findings. Hence, this study provides a module on design ethics education that can be practiced in creative industry programs in Institutions of Higher Learning in Malaysia.
GSTARI-ARCH MODEL AND APPLICATION ON POSITIVE CONFIRMED DATA FOR COVID-19 IN WEST JAVA

Time series model that is commonly used is the Box-Jenkins based time series model. Time series data phenomena based on Box-Jenkins can be combined with spatial data, it is called the space time model One model based on Box-Jenkins model with heterogeneous location characteristics is the Generalized Space Time Autoregressive Integrated (GSTARI) model for a model that assumes data is not stationary or has a trend. This paper discusses the development of the GSTARI model with the assumption that the error variance is not constant which is applied to positive data confirmed by Covid-19 in West Java Province, especially in 4 regencies/cities that have cases in the high category from 6 March 2020 until 31 December 2020. Four regencies/cities are Depok City, Bekasi City, Bekasi Regency, and Karawang Regency. Parameter estimation method for the assumption of non-constant error variance can use Autoregressive Conditional Heteroscedasticity (ARCH) method. GSTARI-ARCH modeling procedure followed three Box-Jenkins stages, namely the identification process, parameter estimation and checking diagnostic. Application of the GSTARI-ARCH Model to Covid-19 positive confirmed data in 4 regencies/cities has a minimum value of RMSE in Bekasi City. The plot of forecast results for the four regencies/cities has a similar pattern to the actual data only applicable for a short time for 1-2 days.
Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation

In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting only a meaningful subset from source data for training. Our key assumption is that the entire source domain data may contain samples that are unhelpful for the adaptation. Therefore, the domain adaptation can benefit from a subset of source data composed solely of helpful and relevant samples. The proposed method effectively subsamples full source data to generate a small-scale meaningful subset. Therefore, training time is reduced, and performance is improved with our subsampled source data. To further verify the scalability of our method, we construct a new dataset called Ocean Ship, which comprises 500 real and 200K synthetic sample images with ground-truth labels. The SDSS achieved a state-of-the-art performance when applied on GTA5 to Cityscapes and SYNTHIA to Cityscapes public benchmark datasets and a 9.13 mIoU improvement on our Ocean Ship dataset over a baseline model.
Dynamic Channel Pruning for Real-Time Object Detection Networks

Real-time object detection is a highly practical technical means which has been the focus of research in recent years. While the high requirement of running memory and computing resources hinder the deployment on resource-limited devices. In this paper, we propose an effective method to dynamically enhance the sparsity on channel-level. To this end, we introduce dynamic sparsity coefficient (DSC) to balance model training and sparse training as well as adaptable sparsity regularization (\(TL_{p}\)) to reinforce sparsity. We monitor the saliency of channels to evaluate their significance for model performance during the sparse training process, according to which, we implement different sparsity strategy on channels. Aiming to maintain the representation ability of important parameters on a fine-grained level, we automatically discern insignificant channels and remove with channel pruning. We demonstrate our method on latest object detector YOLOv4 and lightweight model YOLOv4-Tiny. Compare with uncompressed model, our method can obtain 85.8% decrease of FLOPs, 88.9% declines of parameters with a moderate accuracy loss. Compared with other model compression methods, we achieves comparable results with fewer trainable parameters but better detection performance.
Challenges and Opportunities in a Local College in Time of COVID-19 Pandemic

The COVID-19 pandemic has presented challenges and opportunities to higher education institutions all over the world. This paper adds to the growing literature on new perspectives in education amidst the global pandemic. It discusses challenges and opportunities in the context of a local higher education institution. Inputs from teaching and non-teaching personnel were considered. Documentary analysis of proposed transition plans and actions plans from the different departments of the college was made. Recommendations to address these challenges were presented, which served as a basis in the formulation of a transition program in upgrading/sustaining quality education in a local college attuned to the needs of the times.
Chatbot as a Trading Tool in the Cryptocurrency Market

Today, cryptocurrencies and topics related to information technology are attracting more attention not only on the part of traders, but also scientists. More research is being carried out aimed at the thorough study of cryptocurrencies, as well as the search for ways to facilitate interaction with blockchain. The topic of data analysis for cryptocurrencies is becoming increasingly important as the number of companies dependent on cryptocurrencies is growing rapidly. There are problems related to the cryptocurrency trading process, such as forecasting prices and trends, forecasting volatility, building a portfolio, detecting fraud, analyzing indicators for various cryptocurrencies. To solve these problems, trading bots are used. Trading bots are software products or websites that offer so-called «algorithmic trading», as they automatically analyze the actions and indicators of the market, offer strategies to maximize the trader’s profits and increase his satisfaction. They can aggregate historical market data, calculate indicators, model the order fulfillment and can even be set up to execute strategies while the customer is asleep. When analyzing the needs of the market, it turned out that there was a lack of a chat bot that would help traders or simply persons interested in the topic of cryptocurrencies to receive fresh information about the latest changes in the market. The article considers the functions and examples of performance of the chat bot CryptoAlert, created by one of the authors, which helps users to always be aware of the latest changes in the cryptocurrency market. The main function of the bot is to receive notifications about significant changes in the price of the selected coin. The use of CryptoAlert facilitates the trader’s work and significantly increases the likelihood of successful trading in the market.