Reviewerly/exHarmony huggingface dataset:
GENERAL PROOF OF CONVERGENCE OF THE NASH-Q-LEARNING ALGORITHM

In this paper, the convergence of the Nash-Q-Learning algorithm will be studied mainly. In the previous proof of convergence, each stage of the game must have a global optimal point or a saddle point. Obviously, the assumption is so strict that there are not many application scenarios for the algorithm. At the same time, the algorithm can also get a convergent result in the two Grid-World Games, which do not meet the above assumptions. Thus, previous researchers proposed that the assumptions may be appropriately relaxed. However, a rigorous theoretical proof is not given. The convergence point is a fractal attractor from the view of Fractals, general proof of convergence of the Nash-Q-Learning algorithm will be shown by the mathematical method. Meanwhile, some discussions on the efficiency and scalability of the algorithm are also described in detail.
An Effective and Robust Framework by Modeling Correlations of Multiplex Network Embedding

The dependencies across different layers are an important property in multiplex networks and a few methods have been proposed to learn the dependencies in various ways. When capturing the dependencies across different layers, some of them assumed the structure among layers following consistent connectivity to force two nodes with a link in one layer tend to have links in other layers, some introduced a common vector to model the shared information across all layers. However, the correlations among layers in multiplex networks are diverse, which go beyond the connectivity consistency. In this paper, we propose a novel Modeling Correlations for Multiplex network Embedding (MCME) framework to learn the robust node representations for each layer. It can deal with complex correlations with a common structure, layer similarity and node heterogeneity through a unified framework in multiplex networks. To evaluate our proposed model, we conduct extensive experiments on several real-world datasets and the results demonstrate that our proposed model consistently outperforms state-of-the-art methods.
Introduction to Actionable Knowledge

This chapter presents an introduction to actionable knowledge, its related notions, and to what general context actions are going to take effect? What is actionable knowledge? From what angle, this book is approaching it? Where and how do we position relational calculus with respect to actionable knowledge? The context of Cyber-Physical and Social Systems is briefly described. Important related notions of knowledge, dynamic decision-making, situations and situation awareness, and analytics and information fusion are being introduced. These notions are necessary to position relational calculus in the processes of creating actionable knowledge.
AMobileQoE: An SPL Framework to Measure the QoE of Android Applications

Mobile applications are increasingly present in most aspects of our daily life, satisfying the needs of information, communication, and entertainment. Different factors can degrade the performance of these applications or services, and users can undergo unsatisfactory experiences. To achieve acceptance of mobile applications by the user, it is necessary to consider the Quality of Experience, which allows to measure the quality of an application based on the user’s perception. This work presents AMobileQoE, an SPL framework to automatically generate the code necessary to analyse the QoE of mobile applications following the Design Science Research approach. It enables developers to generate more than a thousand different product configurations to measure QoE from a set of network, device, and user metrics. The CovidInfo application was developed to test the SPL framework in a real context with real users, showing the ease of integration and the lightness of the framework. Furthermore, a quality assessment was performed on AMobileQoE showing a high degree of modularity and variability.
Teacher-Student Based Domain Adaptation for Person Re-identification

Abstract In traditional deep Convolutional Neural Network (CNN) based person re-identification (Re-ID) methods, there must be thousands of training samples with annotated information under the same data distribution, in other words, the data used to train the CNN model is collected from the same scene. However, if we need to deploy a trained Re-ID model at the airport, but the data for training the model is collected on campus, the traditional methods will suffer considerable performance degradation because the data come from different distributions. It is a non-trivial task to straightforwardly re-collect sufficient annotations from the new scene, due to the high cost of time and human labor. Therefore, the domain adaptation paradigm is used to resolve the data distribution problem. Specifically, an efficacious method for domain adaptive person Re-ID based on the teacher-student framework is proposed in our research. Our method has been evaluated by numerous experiments and proved its effectiveness.
Opinion mining-based secured collaborative recommender system

Recommender systems have impressively hit the e-commerce industry in the last few years. With the increased use of recommender systems, the risk of preventing the authentic and true data has also been increased. Many algorithms have been proposed by various researchers to detect and prevent an attack, in recent years. But presently, no algorithm exists as a complete solution to this issue because of one or other constraints. The research in the paper intends to provide a solution to this issue and proposes a framework of more secured collaborative recommender system as compared to conventional recommender systems. The proposed framework uses the approach of Turing test to identify any robotic involvement and does not allow insertion of ratings from such non-human users. Ratings from real users are allowed, collected and examined. Textual opinions are also collected while collecting the ratings. Opinion mining is performed on given textual opinion to identify and discard push and nuke ratings. Valid ratings are stored in the database for generating recommendations for items. The paper also examines the effect of push and nuke ratings on performance by evaluating the accuracy of a recommender system using various measure metrics.
Industry 4.0 Technologies as Decision Aid in Performance Measurement System

Performance measurement systems nowadays often involve the use of technologies in measuring, storing, and providing meaningful presentation of the data collected to improve its efficacy. To maximize the potential of a performance measurement system of banks in this era of rapid technological development, Industry 4.0 technologies are essential to be taken into consideration by managers. Given the lack of study on the relationship between technologies and performance measurement systems in banks, the purpose of this paper is to discuss and assess the impacts of Industry 4.0 technologies on performance measurement system of banks. This study conceptually explored the linkage between the performance measurement system of banks and Industry 4.0 technologies as well as the challenges concerning the adoption of the technologies in the performance measurement system of banks through examination of previous studies on the area. In summary, both the design and management of performance measurement systems can be influenced by Industry 4.0 technologies as it is urgently imperative for the banks to revamp their performance measurement systems to cope with the changes in the banks' operations and management that are prompted by the evolving technologies in Industry 4.0, to ensure the success of their performance measurement systems.
A Method for Object Extraction from Crop Image Based on Visual Saliency

Abstract In order to solve the problem of accurate extraction of crop targets in natural background, an image target extraction method based on visual saliency is proposed. Firstly, an image is divided into multiple super pixels. Secondly, the significance detection method of ‘background first’ is used to calculate the significance of the whole image. Thirdly, the optimal data source is generated according to the characteristics of the high saliency region. Finally, the optimal data source is divided into target region and background region, and the crop target is extracted from the original image. Experimental results on a large number of crop images show that this method can extract targets accurately and completely.
Performance Assessment of Random Forest Induction Methods

In this paper, we propose a performance assessment of a forest induction method called IDTNIM-RF which uses the IDT_NIM “Induction of Decision Tree New Information Measure” for the trees induction. The specific techniques applied to forests such as bagging and random feature selection are used for generating multiple IDT _ NIM trees. We compare the IDTNIM-RF method with a RF random forest that uses CART as a basic rule. The success rate criterion of the single CART and IDT_NIM trees used respectively to generate URF and IDTNIM - RF forest sets is used as a performance assessment basis. To achieve this evaluation, data sets are carried from the UCI Repository and some learning bases we have already developed such MONITDIAB and COVID_EHU.
Development of a low-resource wearable continuous gesture-to-speech conversion system

Mild to profound hearing impairment places limits on effective communication and day-to-day interaction. Sign language, being the primary mode of communication for people with hearing loss, lacks communicative efficacy. A wearable assistive device that aims to convert sign language into speech is proposed to facilitate communication between the unimpaired population (untrained in sign language) and the hearing impaired population. However, the wide use of geo-centric sign languages in India has resulted in the lack of standardised sign-language datasets. In the proposed work, a compact, low-resource, motion sensor-based, wireless, single and double hand-gesture recognition module is designed to address this issue.The proposed module is designed to perform a two-step process with a Hidden Markov Model (HMM) based gesture-to-text conversion and a bilingual text-to-speech synthesis. Multi-threading based parallel processing is implemented to enable simultaneous working of the two systems to reduce the delay. In the proposed continuous-gesture recognition system, non-gesture hand motions are modelled using ergodic HMMs that are trained by concatenating all the states of gesture models, allowing equiprobable transitions. The proposed system is modelled and tested for American Sign Language (ASL) and user-defined gestures.The maximum performance of the proposed system in recognising single-handed and double-handed gestures in terms of F1-score is 98.17% and 84.85%, respectively. Further, the proposed system achieves a maximum F1-score of 98% and 83% in recognising isolated and continuous gestures, respectively.The gesture-to-speech conversion system is ported on Raspberry Pi making the proposed system wireless, and highly mobile.Implications for rehabilitationThe research work proposes to develop a gesture-to-speech conversion system to enable the deaf-mute population in communication. The major implications of the proposed work are:•A light-weight raspberry pi 3B + module hosts the entire hardware andsoftware, and is sufficient to train and test the gesture-to-speech conversion system, thereby ensuring greater mobility.•The proposed system can be customised to recognise user-defined gestures with just 5 examples of the new gesture.•The proposed system can be expanded to control home appliances (IoT applications) by combining the output of the proposed gesture recognition system with appropriate control interfaces.
Faster Parallel Training of Word Embeddings

Word embeddings have gained increasing popularity in the recent years due to the Word2vec library and its extension fastText that uses subword information. In this paper, we aim at improving the execution speed of fastText training on homo-geneous multi- and manycore CPUs while maintaining accuracy. We present a novel open-source implementation that flexibly incorporates various algorithmic variants including negative sample sharing, batched updates, and a byte-pair encoding-based alternative for subword units. We build these novel variants over a fastText implementation that we carefully optimized for the architecture, memory hierarchy, and parallelism of current manycore CPUs. Our experiments on three languages demon-strate 3–20 x speed-up in training time at competitive semantic and syntactic accuracy.
Population based Reinforcement Learning

Genetic algorithms have recently seen an increase in application due to their highly scalable nature. Enabling more efficient utilization of processing power that has become readily available. This study introduces Population based reinforcement learning (PBRL), a method that hybridizes a GA with a policy gradient reinforcement learning algorithm. This combination not only enables more scalable policy optimization, but also helps mitigate some of the common weaknesses of policy gradient algorithms. Furthermore, PBRL is also extended to include automatic hyper-parameter tuning, which is used to evaluate the impact that such tuning can have on the performance of the policy gradient algorithm being used. Experiments comparing these methods are conducted on a number of continuous control problems simulated by MuJoCo. Results show that PBRL is capable of outperforming a commonly used policy gradient algorithm, while also producing results in nearly one fifth the time. It is also observed that the addition of automatic hyperparameter tuning can be greatly beneficial for environments where well tuned hyper-parameters are not known.
Stacking Ensemble Learning-based Gender Identification for User Profiling in Smart Education

To improve company profit, user profiling has become an important research topic in understanding customers and improving products or services, in which identifying users' gender is a fundamental problem. However, little research work was carried out on user profiling for improving learning in the domain of Smart Education. By analyzing Chinese microblog data, this paper proposes a stacking ensemble learning-based gender identification method for user profiling. It extracts gender features according to user published microblog messages and uses the base learner to extract stacking features, which are used as input to the meta-learner to obtain gender identification results. Its effectiveness is verified by comparison experiments with existing algorithms such as Logistic Regression and Random Forest for social media user gender identification.
Early Stopping for Two-Stream Fusion Applied to Action Recognition

Various information streams, such as scene appearance and estimated movement of objects involved, can help in characterizing actions in videos. These information modalities perform better in different scenarios and complementary features can be combined to achieve superior results compared to the individual ones. As important as the definition of representative and complementary feature streams is the choice of proper combination strategies that explore the strengths of each aspect. In this work, we analyze different fusion approaches to combine complementary modalities. In order to define the best parameters of our fusion methods using the training set, we have to reduce overfitting in individual modalities, otherwise, the 100%-accurate outputs would not offer a realistic and relevant representation for the fusion method. Thus, we analyze an early stopping technique for training individual networks. In addition to reducing overfitting, this method also reduces the training cost, since it usually requires fewer epochs to complete the classification process. Experiments are conducted on UCF101 and HMDB51 datasets, which are two challenging benchmarks in the context of action recognition.
SimpAI: Evolutionary Heuristics for the ColorShapeLinks Board Game Competition

We present SimpAI, an AI agent created for the ColorShapeLinks competition, based on an arbitrarily sized version of the Simplexity board game. The agent uses a highly efficient parallelized Minimax-type search, with an heuristic function composed of several partial heuristics, the balance of which was optimized with an evolutionary algorithm. SimpAI was the runner-up in the competition’s most challenging session, which required an AI agent with good adaptation capabilities.
Statistical Moderation: A Case Study in Grading on a Curve

There is a negative perception about “grading on a curve,” because of the feeling that the cohort strength may skew the final grades one way or another. However, given the difficulties in ensuring absolute uniformity in assessment across the years, especially when taught and assessed by different instructors under different settings, grading on a curve may be a necessary evil. Once we accept this type of statistical moderation as the last line of defense in standardizing the final scores so that student cohorts from different terms or sections or schools may be compared, we have to implement it well. In this paper, we present the practice of grading on a curve as form of statistical moderation, and a moderation tool to achieve the objective behind the standardization of grades, the strategy and principles behind its implementation and our experiences with it.
Jigsaw Cooperative Learning to Improve Critical Reading Skills

This research aims to analyze (1) the design of learning planning for critical reading comprehension of English through jigsaw-type cooperative learning in Critical reading courses; (2) the implementation of learning the understanding of critical reading of English through jigsaw type cooperative learning in critical reading courses; (3) the evaluation system of learning critical reading comprehension of English through jigsaw type cooperative learning; and (4) improved students' critical English reading comprehension skills through jigsaw-type cooperative learning on Critical reading courses. The research method used is Classroom Action Research which is carried out in three cycles. The subject of this study is students who take Critical Reading courses in the even semester of 2020. The data collection techniques used in this study are assessment sheets, observations, and tests. The data analysis used is the analysis of description. The conclusions of this study are (1) the learning design used in preparing the lesson plan is ASSURE. The preparation of SAP in critical reading learning is based on competency standards, basic competencies, indicators, learning objectives, subject matter, methods, and sources, as well as a cooperative learning approach consisting of four activities, namely critical reading, expert group discussions, home group discussions, and quizzes; (2) the implementation of learning is an activity carried out by students and lecturers in learning critical reading comprehension using jigsaw type cooperative learning. The observed student activities include activeness, attention, cooperation, and responsibility. Lecturer activities observed include lecturer activities from preliminary activities, presentation and closing of learning; (3) the evaluation system is the result of analysis of critical reading comprehension questions in the form of multiple choices with four choices using an interest which includes validity, reliability, discriminatory power, and level of difficulty of the questions, and; (4) Jigsaw cooperative learning can improve critical reading comprehension skills, namely the ability of students to understand and answer questions about the basics of critical reading comprehension and the ability to conclude reading or text content.
Towards Explainable Person Re- Identification

Visually recognizing an individual in a crowded area using a distributed camera network is essential for a range of biometric and security applications. We propose a shift in perspective of the ongoing re-identification studies, towards creating more explainable and coherent models that are applicable in real-world engineering problems, even if this comes with a slight decrease in performance. The proposed explainable model uses attribute classification to perform the task of re-identification. This method steps away from intrusive and controversial techniques such as facial recognition to improve public acceptance of re-identification models. Current methods of person re-identification do not explain the importance of each attribute in determining the results, and often use complicated and esoteric algorithms to improve the performance on closed-world datasets which may not represent more realistic open-world scenarios. We applied our approach to the Market-1501 dataset and examined the impact of careful selection of backbone outputs for each individual attribute in our experiment. Our simple model is capable of performing attribute classification for 0-shot re-identification that is explainable and less intrusive when compared to state-of-the-art models focused on re-identification.
BusTap: A Real-Time Bus Tracking Android Application

Commuters and tourists in the Philippines heavily rely on buses as transportation links in cities and provinces, yet an issue that remains unsolved is the lack of real-time bus information readily available for bus passengers and operators. Victory Liner, Inc. is one of the country's largest provincial bus companies. However, their bus schedules are still drastically affected by unpredictable conditions, resulting to passengers cluelessly waiting for their buses' arrival. Hence, this paper explores the development of a real-time bus- tracking Android application called “BusTap” to address the gap in knowledge in the public bus transport service in Victory Liner Baguio using Global Positioning System (GPS) technology. With the use of Scrum methodology, the researchers were able to identify the necessary information requirements needed to create BusTap, determine the application's architectural framework, design and develop its features, as well as measure the app's extent of usability by floating Usefulness, Satisfaction, and Ease of use (USE) questionnaires to Baguio City bus passengers and Victory Liner Baguio drivers. It was concluded that: a) the identified information requirements were vital in developing the application's features, b)using a 4+1 architectural view model was helpful for project cohesion and organization, c) the features identified were crucial in eliminating the gap in knowledge between the bus operator and bus passengers, and d) based from the computed overall mean levelof agreement of 6.46 and 6.48 from the evaluation of passenger respondents and driver respondents respectively, BusTap users strongly agree that BusTap is a useful, easy to use, and easy to learn mobile application that satisfies its users' needs.