Reviewerly/exHarmony huggingface dataset:
A Local Path Planning Method Based on Q-Learning

Q-learning belongs to reinforcement learning and artificial intelligence learning algorithm. Reinforcement learning does not need external guidance; it interacts with the external environment through its own sensors. It maps the state of the external input environment to output action through continuous learning, and makes the corresponding reward value of this action the maxi-mum. In order to make the submersible have the ability to adapt to the environment independently, it can adjust the path automatically through its own learning. This paper proposes to introduce Q-learning mechanism in reinforcement learning to complete the adjustment of fuzzy rule strategy in un-known environment.
Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law

Abstract This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where ‘intelligent’ systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited.
HML-RF: Hybrid Multi-Label Random Forest

Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels.In this, labels are correlated, and hence label dependency information plays a vital role.Its always been a question of research to decide the order of labels to exploit their inter-dependency.Hence, to this end, many research works are done that, in general, can be categorized as problem transformation and algorithm adaptation techniques.The problem transformation reconstructs the multi-label problem as a multiple single class problem.The algorithm transformation modifies the existing well-known machine learning approaches to solve the multi-label classification problem.However, these two techniques have their pros and cons.In this paper, we propose a novel approach to consider the merits of both techniques, hence named Hybrid Multi-Label Random Forest (HML-RF).The multi-label decision trees are used as base classifiers in the proposed approach to construct the HML-RF model.Each base classifier is constructed over a randomly selected subset of labels to exploit the label dependency.We also formulate a way to compute the tree strength of a multi-label decision tree, which is used to construct the HML-RF with strength (HML-RFws).The efficacy of the proposed approach is tested over the ten well-known and publicly available datasets.Experimental results show the HML-RF is performing better for at-least six datasets, and the HML-RFws is performing better for at-least nine datasets in comparison to state-of-the-art approaches in terms of accuracy, hamming loss, and zero-one loss.Finally, the statistical test is also validating all the experimental results.
A Novel Traffic Surveillance System Using an Uncalibrated Camera

The objective of this paper is to present an effective and reliable method for the traffic surveillance using the concepts of digital image processing. The paper proposes a system that can detect, track, and estimate velocity of vehicles using an uncalibrated camera and also detect and recognize their registration number plate. This approach provides a cost-effective alternative for traffic flow monitoring and surveillance. This robust system finds its applications in urban traffic management systems, military installation, and research facility security systems. This approach is a computationally efficient approach for detecting and tracking moving cars on the road utilizing uncalibrated cameras mounted on the road. It is also helpful for military installation because all the security issues have been detected by using this approach.
Students’ Perception in Learning English through Blended Learning

Mobile technology plays an equal role with face-to-face interaction in the learning process in this current era. One of the most commonly utilized technologies is the use of social media integrated with the face-to-face teaching and learning as called a blended-learning. This recent study aims: (1) to investigate how the blended learning process is operated, (2) to determine students’ perceptions in learning English through blended learning. This study was an explanatory mixed-method research design using observation, questionnaire, and interview as instruments to collect data from the purposively selected 26 students. The results of this study indicated that the process of learning English through blended learning both offline (face-to-face) and online (WhatsApp application) was well-implemented. Besides, students also showed a lot of positive perceptions when learning English through blended learning utilizing social media of WhatsApp and through the face-to-face form of language learning interaction. Thus, the researchers concluded that blended learning maintains essential behavioral, cognitive, and emotional aspects in English learning.
Learning Human Strategies for Tuning Cavity Filters with Continuous Reinforcement Learning

Learning to master human intentions and behave more humanlike is an ultimate goal for autonomous agents. To achieve that, higher requirements for intelligence are imposed. In this work, we make an effort to study the autonomous learning mechanism to solve complicated human tasks. The tuning task of cavity filters is studied, which is a common task in the communication industry. It is not only time-consuming, but also depends on the knowledge of tuning technicians. We propose an automatic tuning framework for cavity filters based on Deep Deterministic Policy Gradient and design appropriate reward functions to accelerate training. Simulation experiments are carried out to verify the applicability of the algorithm. This method can not only automatically tune the detuned filter from random starting position to meet the design requirements under certain circumstances, but also realize the transfer of learning skills to new situations, to a certain extent.
Differentiating between Good Readers and Poor Readers: The Role of Linguistic and Cognitive Factors

&#x0D; &#x0D; &#x0D; &#x0D; Attainment of reading ability is one of the most important goals of early elementary education. The goal of the present study was to compare groups of good readers and poor readers on several cognitive and linguistic skills and to determine the influence of these variables in differentiating these two groups of readers. An additional goal was to determine whether there are differences in proportion of boys and girls in groups of good and poor readers. The sample for this study comprised 60 third-grade children divided in two categories, 30 children in the category of good readers and 30 children in the category of poor readers. The results of this study revealed the largest differences between good readers and poor readers were on the phoneme deletion task and processing speed task. Another finding is that more boys than girls were in the group of good readers and more girls than boys in the group of poor readers. It is very important to know which underlying skills best differentiate good and poor readers at certain age. This in turn will lead to creating more effective reading interventions.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Education and COVID-19: Learning Arabic Language and Perspectives

The study discusses the online teaching and learning of Arabic in order to evaluate students’ difficulties and the way in which to approach this language in an interactive way, overcoming the barriers of computer screens. Nevertheless, e-learning is the future of education and it is crucial to offer a quality education, implementing emotional learning consciousness within the academic community. The COVID-19 pandemic impacted practically every country in the world. It created an extremely difficult global situation, and humanity found itself faced with an emergency state of working online and swiftly adapting its routine to a new reality. This was certainly the case in Spain when, in mid-March 2020, students were forced to study from home and teachers to teach at a distance. COVID-19 - and its related restrictions arrived suddenly, and there was little to no time to acclimatise to the new way of studying. The objective of this study is to analyse the difficulties faced by students of Arabic as a foreign language, and to offer some recommendations to improve online teaching, applying the communicative framework, combined with the importance of introducing the psychology theories and emotional education which is key to obtaining positive results. First, the topic is introduced, then the way to teach and learn the Arabic language online is discussed. Afterwards, the methodology is defined, highlighting the relation between emotional education and the Communicative Approach. A hypothetical-deductive method is applied to this analysis. A questionnaire is responded to by students of the Arabic language in Spanish universities in order to get an insight into their difficulties when learning online. A qualitative study is carried out in order to analyse the problems faced by the students and offer some possible recommendations to improve the quality teaching of Arabic as a foreign language, based on the perceptions of the students, taking into account the social context experienced during the 2020-2021 academic year.
Measurement and Analysis of the Distribution of Pb-214 Lead Isotope in Baghdad Soil using Remote Sensing Techniques

The present research aims to measure concentration of lead Pb214 in soil using remote sensing and GIS, associated radiological hazards in Baghdad, Iraq. Concentration of specific radioactivity of radioactive elements was measured and analyzed naturally and artificially in 48 soil samples for separate sites from Baghdad, Iraq using crystalline spectroscopy to detect germanium. The average radioactivity concentrations of lead were found, as it was found to have varying values ​​from one site to another, as most of them exceeded the international permissible limit, as the highest concentration was recorded at 180 Bq in the sample H28 in Waziriyah district. Battery Lab (1), and the lowest concentration value is 40Bq in (H4, H15, H45) which represents (Dora Expressway, Mansour Street, Fuel Express, and Mahmoudiya Road 2).&#x0D; Remote sensing data techniques, and Global Positioning System (GPS) have been used to analyze the effect of the isotopic distribution of heavy bullets for the city of Baghdad. Using Geographic Information Systems (GIS), concentrations were measured and observed. Results of interpolation maps showed isotope of lead is concentrated in the center and west of Baghdad, and then graded with fewer values in other sites.
Semi-automation of gesture annotation by machine learning and human collaboration

Abstract Gesture and multimodal communication researchers typically annotate video data manually, even though this can be a very time-consuming task. In the present work, a method to detect gestures is proposed as a fundamental step towards a semi-automatic gesture annotation tool. The proposed method can be applied to RGB videos and requires annotations of part of a video as input. The technique deploys a pose estimation method and active learning. In the experiment, it is shown that if about 27% of the video is annotated, the remaining parts of the video can be annotated automatically with an F-score of at least 0.85. Users can run this tool with a small number of annotations first. If the predicted annotations for the remainder of the video are not satisfactory, users can add further annotations and run the tool again. The code has been released so that other researchers and practitioners can use the results of this research. This tool has been confirmed to work in conjunction with ELAN.
Graph Embedding With Data Uncertainty

Spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty. Thus, learning directly from raw data can be misleading and can negatively impact the accuracy. In this paper, we propose to model artifacts in training data using probability distributions; each data point is represented by a Gaussian distribution centered at the original data point and having a variance modeling its uncertainty. We reformulate the Graph Embedding framework to make it suitable for learning from distributions and we study as special cases the Linear Discriminant Analysis and the Marginal Fisher Analysis techniques. Furthermore, we propose two schemes for modeling data uncertainty based on pair-wise distances in an unsupervised and a supervised contexts.
Multiple Scene Sentiment Analysis Based on Chinese Speech and Text

&lt;p&gt;This paper proposes a multi-scene sentiment analysis model for Chinese speech and text based on CNN-BiGRU-CTC + ERNIE-BiLSTM. The model is applied to the intelligent customer service scenario. While conducting voice interaction, intelligent customer service can obtain the user’s current emotion, to give a more humane answer and improve the user experience. All the training data sets in this paper adopted public data sets such as Aishell-1 and NLPCC 2014, etc.We have been able to achieve a testing accuracy of about 94.5%. The accuracy is improved by 5.24% compared to the latest speech sentiment analysis model that uses audio as a feature. The advantage of this paper is that it adopts the ERNIE language pre-training model to conduct sentiment analysis on speech signals, which still has a good classification accuracy in the case of individual wrong words in speech recognition. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Optimization of Accounting Information System for Enterprises in Smart City by Intelligent Sensor under the Internet of Things

In order to realize the continuous optimization of the enterprise financial accounting information system of the smart city, from the perspective of intelligent sensor technology, firstly, this paper understands the use feelings of the enterprise financial accounting-related personnel about the information system in the smart city based on the relevant theories of intelligent sensor, smart city, and accounting information. Secondly, the Internet of things technology and intelligent sensing technology are applied to the optimization of enterprise financial accounting information system. Finally, the corresponding opinions and suggestions are put forward from the perspectives of government, employees, and enterprises. The results show that about 40%∼60% of enterprise financial accountants believe that data integration and sharing, data governance, and data operation management systems need to be continuously optimized and upgraded. Applying the Internet of things technology and intelligent sensing technology to the optimization of enterprise financial accounting information system, it is found that it can not only improve the accuracy of financial data processing, but also receive the required results efficiently and in real time. It is more important to apply intelligent sensing technology to enterprise financial information system. After comparison, it is found that the net profit of the enterprise increased from 112.69 million yuan to 3329.33 million yuan. The owner’s equity of the enterprise increased from 2045.68 million yuan to 11177.34 million yuan. The cash flow of the enterprise increased from 1056.91 million yuan to 6892.56 million yuan. Therefore, this paper studies how to optimize the enterprise financial accounting information system based on the intelligent sensor of the Internet of things in the smart city, which has great reference significance for the management of enterprise financial operation.
Accelerating COVID-19 Contact Tracing Capacity through Multi-Sectoral Collaboration Training in Indonesia

easily implement sample triage, which reduced turnaround time to less than 24h for most samples.Starting May 2021, public health authorities add monitoring of the positive predictive value of rapid antigen test used at the community level to support evidencebased public health decisions about the best possible use of those assays.Conclusion: This simple scenario-based coding system allowed timely PH and management of both sampling and processing priorities which proved most useful during surge periods.Quebec Public Health Authorities were better able to target preventive actions and to plan outreach screening activities in subpopulations, neighbourhoods, and communities, while modulating clinical criteria to get access to testing and allowing laboratories to better triage samples.
Conversational AI over Military Scenarios Using Intent Detection and Response Generation

With the rise of artificial intelligence, conversational agents (CA) have found use in various applications in the commerce and service industries. In recent years, many conversational datasets have becomes publicly available, most relating to open-domain social conversations. However, it is difficult to obtain domain-specific or language-specific conversational datasets. This work focused on developing conversational systems based on the Chinese corpus over military scenarios. The soldier will need information regarding their surroundings and orders to carry out their mission in an unfamiliar environment. Additionally, using a conversational military agent will help soldiers obtain immediate and relevant responses while reducing labor and cost requirements when performing repetitive tasks. This paper proposes a system architecture for conversational military agents based on natural language understanding (NLU) and natural language generation (NLG). The NLU phase comprises two tasks: intent detection and slot filling. Detecting intent and filling slots involves predicting the user’s intent and extracting related entities. The goal of the NLG phase, in contrast, is to provide answers or ask questions to clarify the user’s needs. In this study, the military training task was when soldiers sought information via a conversational agent during the mission. In summary, we provide a practical approach to enabling conversational agents over military scenarios. Additionally, the proposed conversational system can be trained by other datasets for future application domains.
Detecting the steerability bounds of generalized Werner states via a backpropagation neural network

We use an error backpropagation (BP) neural network to determine whether an arbitrary two-qubit quantum state is steerable and optimize the steerability bounds of the generalized Werner state. Results show that, regardless of how we select the features for the quantum states, we can use the BP neural network to construct several models to obtain high-performance quantum steering classifiers compared with the support vector machine. Moreover, we predict the steerability bounds of the generalized Werner states using the classifiers that are newly constructed by the BP neural network; that is, the predicted steerability bounds are closer to the theoretical bounds. In particular, high-performance quantum steering classifiers with partial information about the quantum states that we need to measure in only three fixed measurement directions are obtained.
Analysis of factors affecting lecturer performance at a university during the COVID-19 pandemic using logistic regression and genetic algorithms

The Covid-19 pandemic has forced us to change all aspects of our lives, including higher education. As a result, lecturers get an impact in terms of technology literacy obligations. This situation certainly affects their performance in implementing the Tri Dharma of Higher Education. The purpose of this study is to analyze the factors that affect the performance of lecturers during the Covid-19 pandemic. These factors include age, education, motivation, satisfaction, perception of appreciation, supervision, learning facilities, and technological literacy. The method for collecting data was questionnaires and open interviews with 150 lecturer respondents at a university. Furthermore, the data obtained were analyzed using a logistic regression model, where the parameter estimation was conducted using a genetic algorithm. The estimation process is assisted by Matlab 7.0 software. The results of the analysis show that the factors of age, education, motivation, satisfaction, perception of supervision, learning facilities, and technological literacy have a significant effect on lecturer performance. This study implies that the University needs to consider significant factors for improving lecturers' performance so that teaching and learning activities can run effectively.&#x0D; Keywords: Covid-19, · teaching and learning, lecturer performance, Logistic regression, Genetic algorithm.
Evaluasi Usability Website Shopee Menggunakan System Usability Scale (SUS)

The progress of information technology is currently growing rapidly. Technology related to the internet is often a solution to most of the problems in existing needs, especially those related to the effectiveness and efficiency of activities and procedures. In this final project, the author discusses websites in the business field, namely e-commerce websites. In this study, the authors chose one of the most popular e-commerce websites in Indonesia this year, namely the shopee website, a website that not only offers products but also puts forward the appearance of the Shopee website which must always be considered because it is the main factor to increase customer purchases. In this study, the quality of the web that will be measured by users, especially for consumers, is based on measuring the quality of the website using the System Usability Scale (SUS). Evaluation of the shopee website is the first step to measure the level of usability on the website. Usability evaluation on the website is carried out to collect opinions from various respondents regarding the functionality of the website. In this study, the results obtained from the calculation of the average usability of the shopee website of 67.08 so that it can be said that the usability of the shopee website on product purchases has entered the OK category.