Reviewerly/exHarmony huggingface dataset:
A Study on Control Parameter and Output Sequence of the Logistic Map over Integers for Pseudorandom Number Generation

In the presented paper, we discuss the domain of a control parameter that can give suitable output sequences for pseudorandom number generation using the logistic map over integers. We defined the control parameter in the closed interval [3],[4] because of investing characteristic properties of the logistic map over integers. However, the interval includes a range where it is difficult to obtain outputs with sufficient randomness. After we execute some numerical experiments, we will show a suitable definition of the control parameter.
GAMEOP: Software Quality Management Approach for Gaming Industry

The Software Development Lifecycle has been a crucial and vital part of Software Engineering, and with the advancements in technology is has become an essential task to decide on a suitable methodology; hence it is crucial to weigh the pros and cons of each model. With the digitalization of every aspect of life, even games are developed with utmost features and budget and time constraints. This paper cites the detailed study of Software Development Methodology along with a proposed theoretical and hybrid model GAMEOP for Game Development Lifecycle to scale up the development procedure that incorporates each phase of the development and rapidly builds modules that ensure the software quality management process and user needs at the same time.
Research on Big Data Encryption Algorithm Based on Vector Evaluation Genetic Algorithm

In recent years, with the rapid development of cloud computing, Internet of Things and social networks, the amount of data carried by the network has increased dramatically. Traditional encrypted storage technology and management methods have been difficult to meet the requirements of big data in terms of speed, capacity, storage efficiency and security. Under the data environment, users' data security and privacy protection are facing great challenges. A vector evaluation genetic algorithm is proposed. The algorithm uses population diversity information to adjust inertia weight nonlinearly, and introduces velocity mutation operator and position crossover operator in the later stage of the algorithm, so that the algorithm can get rid of the constraint that it is easy to fall into local optimum in the later stage, so as to solve the multi-objective reactive power optimization problem and solve the Pareto optimal solution set of the problem. Simulation results show that the proposed method can improve the privacy protection performance of users' information, and the encryption and hiding overhead of the algorithm is small, and the real-time performance of information coding and hiding is high.
Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features

In this paper, we present the solution of team JKU-AIWarriors, submitted to the ACM Recommender Systems Challenge 2021 (http://www.recsyschallenge.com/2021). Our proposed model relies on features that we compute from user engagement counts. These counts are used to create compact user-specific features, which enables our model to make predictions swiftly. We adopt a simple XGB classifier, trained on a subset of the training data. To regularize during training, adding Gauss-distributed noise and randomly masking users helps to avoid overfitting.
Effective Human–Robot Collaboration: Case Study of a Thai Hospital

This study explores the state of human-robot collaboration (HRC) in a Thai hospital at multiple levels, using qualitative research methods. The case study was undertaken at the only hospital in Thailand to have deployed three robots to deliver patient documents between units. The objectives of this study were to investigate stakeholders’ opinions on using service robots in the hospital, explore organizational context related to robot deployment, and identify the success factors of robot deployment in the hospital. The participants in this study were a hospital executive, human resources staff, nurses, IT officer, and hospital clients. Findings revealed that the hospital stakeholders accepted the robots; the main advantages were that they were attractive and reduced the work burden on the staff; however, their lack of human sense was the main disadvantage. The organizational context in both human and system issues is significant. The key success factors of HRC were found in human, robot, and organizational dimensions. In terms of human dimension, user acceptance and user ability were the key success factors. Robot capability and robot company were very crucial in the robot dimension, while leadership, competent IT officer, and well-planned robot deployment were success factors in the organizational dimension.
A Study on the Application of Collaborative Learning by Using Moment Cam to Improve Mathematics Acceptability

In the workplace, mathematics is a fundamental skill. Mathematics is very important to business management students, and it is used as a tool in almost all four-year college courses. However, the number of students who can truly enjoy mathematics should be very small among business management students. The integration of information software into teaching has become an important trend, and the charm of anime for young people should not be underestimated. Therefore, in this study, the university students were given the opportunity to create a digital textbook for Grade 12 mathematics in which they were transformed into anime characters. In addition, the students were able to learn how to use the new software to create digital teaching materials by using group work and peer-to-peer collaboration. The study was conducted with a valid population of 36 students, and a focus group and questionnaire were used to collect information. It was found that by searching, developing the content, designing the questions and solutions, and explaining the origin of the formulae, the participating students were given the opportunity to learn the mathematics of Grade 12 again, gaining positive thoughts about mathematics and increasing their enthusiasm and willingness to learn mathematics. In addition, the study used the app “Moment Cam”, which none of the study participants had experience with, for peer-to-peer collaborative learning. It was found that collaborative learning is also a good way to learn to use new tools.
Research on Evaluation System of Recommendation System

In view of the problem that the rapid growth of data makes it difficult for users to obtain information and the recommendation system lacks a perfect evaluation system, this paper puts forward an evaluation system of the recommendation system, through which the recommendation algorithm which is used more and more frequently is evaluated.Firstly, the evaluation index is set from online evaluation and offline evaluation separately; Secondly, the calculation method of each index is analyzed and designed; Finally, the two evaluation ideas are combined to form a complete recommendation system evaluation system. The establishment of recommendation system evaluation system is conducive to the comprehensive evaluation of the recommendation system and the improvement of the shortcomings, so as to enhance the user experience. Experimental results show that the proposed evaluation system can measure the advantages and disadvantages of each recommendation algorithm well.
Multi-Robot Learning Dynamic Obstacle Avoidance in Formation With Information-Directed Exploration

This paper presents an algorithm that generates distributed collision-free velocities for multi-robot while maintain formation as much as possible. The adaptive formation problem is cast as a sequential decision-making problem, which is solved using reinforcement learning that trains several distributed policies to avoid dynamic obstacles on the top of consensus velocities. We construct the policy with Bayesian Linear Regression based on a neural network (called BNL) to compute the state-action value uncertainty efficiently for sequential decision making. The information-directed sampling is applied in our BNL policy to achieve efficient exploration. By further combining the distributional reinforcement learning, we can estimate the intrinsic uncertainty of the state-action value globally and more accurately. For continuous control tasks, efficient exploration can be achieved by optimizing a policy with the sampled action value function from a BNL model. Through our experiments in some contextual Bandit and sequential decision-making tasks, we show that exploration with the BNL model has improved efficiency in both computation and training samples. By augmenting the consensus velocities with our BNL policy, experiments on Multi-Robot navigation demonstrate that adaptive formation is achieved.
Outlier mining method for high dimensional massive data based on RBF neural network

The density of high-dimensional massive data is relatively high, and it is difficult to mine outlier information. The traditional mining method is too slow to mine outliers in highdimensional massive data because the divided mining density is too balanced. Therefore, the research on the high-dimensional massive data outlier mining method based on RBF neural network is studied. In this method, RBF neural network structure and hidden layer parameters are set, and the mining density of highdimensional massive data is set to realize the outlier mining of high-dimensional massive data. The experimental results show that: compared with the traditional mining methods, the outlier mining method in this study can first complete the task of outlier mining in high-dimensional massive data. It can be seen that the mining method of this study improves the speed of data mining.
Reframing Difficult Deep Learning Problems

To speak very roughly, the deep learning process can be represented with three central sequentially connected components: the creation of the architecture, the definition of training procedures and methods, and the training of the model itself. This is the general deep learning modeling workflow. Each of the chapters this book has covered up to this point can be categorized approximately into at least one of these categories. Chapter 2 , on transfer learning and pretraining, discussed methods of training models to transfer and develop knowledge from sources other than the original dataset. Chapter 3 , on autoencoders, discussed various usages of the versatile autoencoder concept and architecture. Chapter 4 , on model compression, discussed various modifications to the neural network architecture, in addition to alterations in the training procedure. Chapter 5 , on meta-optimization, discussed the automation of parameters in neural network architecture and training procedures. Chapter 6 , on successful neural network design, discussed successful design patterns and techniques in neural network architectures and model implementation.
Employee Engagement as a Mediator in the Relationship Between Motivational Factors and Job Performance

The purpose of this study is to evaluate the link between motivational variables, work performance, and employee engagement, as well as the influence of motivational factors on job performance via the use of employee engagement as a mediator in the Sultanate of Oman's government sector. The research used a quantitative technique and examined six hypotheses using 111 representative government workers from the Ministry of Education. This is pilot research. This research used quota sampling and analyzed data using the Statistical Package for the Social Sciences (SPSS) and path analysis approaches. The study's results suggest that these elements are associated positively, that motivational factors are associated positively with employee engagement and work performance, and that employee engagement functions as a partial mediator in the link between motivational factors and job performance. This report is crucial for public sector executives and regulators interested in improving job performance in Oman. This is one of the few research that examines employee engagement's mediating role in the relationship between motivational characteristics and work performance. This is one of the first studies of its sort for the public sector in Oman. In future studies, other motivating elements might be studied and assessed in the private sector.
Geochemical Studies in India: CSIR-NGRI Contributions

The Indian Shield, composed of diversified rock types, formed at different ages, emplaced in varied tectonic environments, is a natural laboratory for the geoscientific community. Multi-disciplinary studies involving tools like field geology, petrography, geophysics and geochemistry can better probe the Earth's interior at a higher resolution. The Geochemistry Division, equipped with state-of-the-art analytical facilities such as XRF, SEM-EDS, MC-TIMS and LA-HR-ICP-MS, has made pioneering contributions to better understand the evolutionary history of the Indian Shield. This excerpt provides a glimpse of the research activities carried out by the Geochemistry Group during the last 55 years. Significant contributions include (1) geodynamics and crustal evolution of the Indian Plate in terms of nucleation, accretion and amalgamation of the three Protocontinents: Dharwar, Aravalli and Singhbhum; (2) geological and geochemical studies on the evidence for early life; and (3) deciphering the depositional environment and provenance of clastic sediments of the Dharwar Craton. Besides southern India, focused research studies in Bastar, Singhbhum and Bundelkhand cratons, Eastern Ghat Mobile Belt, Central Indian Tectonic Zone, Andaman Islands and Antarctica have yielded significant results that were published in recognised peer-reviewed National and International Journals. In addition to fundamental research, the Geochemistry Group has actively collaborated with industries like Hutti Gold Mines Limited (Hutti), Hindustan Zinc Limited (Udaipur) and National Mineral Development Corporation (Hyderabad) for the exploration of base metals and gold in different parts of the country. The analytical facilities created and developed by the Group are currently helping scores of researchers and students from a host of R&D organisations/universities across the country and even a few organisations abroad in pursuing world-class geochemical and environmental research.
Movement in Virtual Reality

This chapter presents concepts pertaining to movement in virtual reality (VR), with an emphasis on its prospective applications in psychological studies. Behavioral measurements, a key tool in such studies, are almost invariably conducted in unnatural and experimental settings, and entail considerable difficulty. These issues may be addressed by the application of VR. The chapter also illustrates examples of the technology’s use in studies of personality and differences between the sexes in psychological and psychiatric practice—for example, as a supporting tool in addiction treatment; and in more general applications that utilize VR in user movement tracking, such as the learning of dance movement patterns. The final section presents negative sensations experienced while using VR systems and discusses the mechanisms and causes of cybersickness.
A Lightweight Encryption Scheme for IoT Devices in the Fog

The Internet of Things (IoT) is the collection of everyday smart devices which connect to the Cloud, often through Fog nodes, to transmit and receive information. These everyday devices are distinct from traditional computers because they typically have notable constraints on their RAM, flash memory, and computational power. Due to these constraints, we believe that many of the proposed encryption schemes are too heavyweight to be employed in the IoT. In this paper we present a lightweight, flexible encryption scheme that relies on the one-way information loss property of a secure hash function. Our scheme imposes minimal computational and storage requirements, and imposes no non-negligible burdens on the encrypting device, except for the hash itself. We find that the encryption algorithm is particularly lightweight, and holds up strongly in terms of its speed and memory efficiency.
An Improved Capsule Network-based Embedding Model for Knowledge Graph Completion

Knowledge graphs are structured representations of information in the world. However, the current knowledge graphs suffer from incompleteness, which can be inferred from existing information. A popular approach of knowledge graphs (KGs) completion involves learning a low-dimensional representation for the semantics of entities and relations and using them to predict new facts. Recently, several proposed models suggest that convolutional neural network (CNN) based models gain more expressive feature embedding and thus achieve state-of-the-art performance on KGs completion, such as ConvE. Nevertheless, some feature of the embedding of entities and relations are easily lost in these models. In this paper, we introduce an embedding model, named HCapsE, utilizing an improved capsule network(CapsNet) to predict missing links knowledge graphs. Experiments demonstrate that HCapsE achieves better prediction performance than previous embedding models on three commonly used datasets WN18RR, FB15k-237, and YAGO3-10.
Knowledge Gaps: A Challenge for Agent‐Based Automatic Task Completion

The study of human cognition and the study of artificial intelligence (AI) have a symbiotic relationship, with advancements in one field often informing or creating new work in the other. Human cognition has many capabilities modern AI systems cannot compete with. One such capability is the detection, identification, and resolution of knowledge gaps (KGs). Using these capabilities as inspiration, we examine how to incorporate detection, identification, and resolution of KGs in artificial agents. We present a paradigm that enables research on the understanding of KGs for visual-linguistic communication. We leverage and enhance and existing KG taxonomy to identify possible KGs that can occur for visual question answer (VQA) tasks and use these findings to develop a classifier to identify questions that could be engineered to contain specific KG types for other VQA datasets. Additionally, we examine the performance of different VQA models through the lens of KGs.
A peek into one’s emotion through pen: analyzing individual’s emotion through their thoughts expressed on various social media platforms using ensemble classifier

Maximum countries of the world are considered as the youth or young nation. But unfortunately, these young ones are the ones suffering the most from emotional distress and suicide ideation. In India alone, every hour one student commits suicide and nearly 28 per day. (NRCB). The NCRB data shows that 10,159 students died by suicide in 2018, an increase from 9,905 in 2017, and 9,478 in 2016 [1]. According to the survey conducted by CSDS in 2017 between the age group of 15-34 approximately one out of every four youth moderately suffered from depression, loneliness, worthlessness, and suicidal thoughts. 6% of them got suicidal thoughts at least once [1]. Despite increasing knowledge, most affected young people do not receive mental health care because the professionals can not easily identify such groups of people [2]. With most people expressing their thoughts and emotions on social media whether positive or negative, examining their posting on the internet has become an essential part of identifying a person’s emotion. Manual identification and examining of this data can be very time-consuming so this paper uses a much better and technical aspect to solve this problem. A dataset is formed by the collection of a few individual’s daily blogs and texts and a combination of machine learning classifiers is used to identify an individual’s emotion. Then the accuracy of these classifiers is tested and the best classifier is used in the system to predict people’s emotions. The result of the analysis shows that the classifier in the system performs better than the other individual classifiers, professionals can understand more people’s emotions and see early signs of emotional distress and provide them with assistance.
A Fast Head Reconstruction Algorithm based on Single Photo

With the development of Virtual Reality and Augment Reality technology in recent years, the requirement for reconstruction of people based on photos increases gradually. At present, the practical 3D face reconstruction technology is mainly based on the 3D Morphable Model (3DMM) method. However, the collection and production of the 3DMM model are complicated, and the 3DMM model only has a facial part model. As a result, it cannot reconstruct a complete 3D human head and is difficult to apply to actual engineering. To solve those problems, we propose a manual 3D human head reconstruction method, which based on 3DMM. Compared with the traditional 3DMM model production method, the method proposed in this paper reduces the difficulty of production. It adopts the common 3D format so that it can be developed and used by mainstream 3D editing software. In addition, it simplifies the size of the model to make it easy to deploy on mobile devices. Traditional face reconstruction can achieve face reconstruction by directly using the face of the photo as the texture. For head reconstruction, we first made a fixed full-head map, than design algorithm to match the color of the head map with the skin color of the face. Finally, we calculate the affine transformation to transform the face of the photo into the head map and achieve smooth fusion. Aiming at the actual application scenarios, we design the illumination normalization algorithm to deal with the uneven illumination of the face photos, and improves the reconstruction efficiency of the head under the condition of uneven illumination. Experiments show that the above algorithm can reconstruct a realistic 3D human head. The average reconstruction speed on the i7-4790 CPU is only 700ms.
Articulatory Copy Synthesis using Long-Short Term Memory Networks

Investigating speech imitation, in particular articulatory copy synthesis,benefits the understanding of speech production and can improve speech recognitionand synthesis. We proposed a framework for copy synthesis with an artificialneural network (LSTM regression model) and an articulatory speech synthesizer(VocalTractLab), which were responsible for the acoustic-to-articulatory mappingand the inversion, respectively. We used rule-based method to create gestural scoresfrom texts, which were converted to articulatory trajectories and subsequently simulatedto produce the corresponding acoustic signals. To make the subsequentmapping more robust, we expanded the acoustic and articulatory space by manipulatingspeaking effort, voice quality, pitch level, and vocal tract length of thecreated gestural scores or acoustic signals, producing 81 variants for each utterance.With acoustic features as input and articulatory trajectories as output, wetrained the LSTM models to build the acoustic-to-articulatory inversion. For testing,we estimated the articulatory trajectories from acoustic features, thus obtainingthe underlying articulatory process. The experiments showed that the correlationcoefficients (between estimated articulatory trajectories from acoustic features andthe real ones converted from gestural scores) ranged from 0.18 to 0.973 and the rootmean square error (RMSE) ranged from 0.043 to 0.255 for the concerned 30 articulatoryparameters of VocalTractLab. The estimated articulatory parameters werefurther fed into VocalTractLab, whose output speech achieved a word recognitionaccuracy of 17.24%.