Reviewerly/exHarmony huggingface dataset:
Towards smarter cities: Learning from Internet of Multimedia Things-generated big data

In today’s technological era, smart devices connected through the IoT and giant IoT infrastructures are playing a vital role in making daily life easier and simpler than it ever was. Numerous sensors including IoT-interconnected multimedia sensors communicating with each other generate a huge amount of data. In particular, IoT multimedia sensors play a vital role for green cities, providing secure and efficient analytics to monitor routine activities. Big data generated by these sensors contain dense information that needs to be processed for various applications such as summarization, security, and privacy. The heterogeneity and complexity of video data is the biggest hurdle and a pretty number of techniques are already developed for the efficient processing of big video data. IoT big data processing is an emerging field and many researchers are enthusiastic to contribute in making the cities smarter. Among all these methods, deep learning-based techniques are dominant over existing traditional multimedia data processing algorithms with convincing results emerged recently. This special issue targets the current problems in smart cities development and provides future challenges in this domain and invite researchers working in IoT domain to make cities smarter. It also focuses on some related technologies comprising Internet of Multimedia Things (IoMTs) and machine learning for big data. Furthermore, it covers deep learning-based solutions for real-time data processing, learning from big data, distributed learning paradigms with embedded processing, and efficient inference.
Corona COVID-19 Analysis: Switzerland and Europe

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VoxAR: 3D Modelling Editor Using Real Hands Gesture for Augmented Reality

Augmented Reality (AR) is a technology where it supports both real and virtual content in a single view which merges virtual objects and real world in real-time in a single display view. Interaction in AR allows the user to interact with the virtual images using real objects. The remaining issues need to be improved are the occlusion problems when interacting with virtual object using both hands for AR environment. However, it is crucial to bring Three-Dimensional (3D) modelling approach in AR where user can modify the virtual objects using their real hands. Therefore, this study aims to develop a prototype for 3D modelling editor in AR by using real hands gestures. The 3D modelling editor using real hands gesture running in real-time which capture user's hands to modify 3D object and model an object using voxel-based modelling technique. Voxel-based is a 2D pixel-like 3-dimensional element. An object is represented in volumetric modelling as a collection of 3D arrangement voxels that may be regular or irregular. The evaluation was conducted to evaluate users experience when they interact with the virtual objects using hands gesture in AR 3D modelling editor prototype called VoxAR which provide the basic features such as colour picker, draw voxel, erase voxel and select voxel. This project is significant to produce intuitive 3D hands gesture interaction in AR environment.
Language Distance using Common N-Grams Approach

Within the framework of historical linguistics, the prevailing view of the linguist experts is that language similarity cannot be measured. However, there has been significant research to attempt to quantify this phenomenon. We extend experiments conducted by Gamallo et al. from 2017 to compare forty-one European languages. We use a distance measure that was proven to work well in authorship attribution tasks. We build a distance network where the nodes represent the languages, and the weighted edges represent distances. We additionally provide statistical analysis that confirms the stability of our method across different experimental setups.
Testing a hybrid hardware quantum multi-agent system architecture that utilizes the quantum speed advantage for interactive computer music

This paper introduces MIq (Multi-Agent Interactive qgMuse), which builds on the single agent quantum system qgMuse using teleportation. MIq is the first attempt at a real-time interactive quantum computer music algorithm that utilises the quantum advantage. Previous interactive or real-time quantum music algorithms running on quantum computers have been mappings of classical computing algorithms, with no quantum advantage obtained. MIq provides a quadratic speed-up over classical methods. It is a Quantum Hybrid Multi-agent System architecture implemented on 5 and 14 qubit quantum hardware. Classical agents and quantum agents connect via a classical/quantum hybrid agent that enables communication using quantum teleportation.
Designing a Rapid Optical Response Trigger(RORT) for self-navigating and path explorer robot

Human beings are capable of making complex decisions with a limited number of sensors due to their rich logical reasoning capability. The decision-making process derives in combination with perceiving the environment and referring to previously known concepts. Similarly, machines can interact with the environment with large support of sensors in a number of ways and each of them has their advantage as well as limitations. In this work, systematic Optical Marker(OM) are used as control triggers as an efficient alternative of sensing the physical environment and rapid movement-control of Remotely Operating Vehicle(ROV). The quick response of OM is much more efficient in different lighting condition, cost-effective and less resource hungry. Sequential placement of OM as control triggers contain information about the physical environment which in turn provides a guided path to ROV for a kind of supervised path exploration. The device scans the optical marker and perceives that information about its physical surroundings. For self-navigation, the robot uses a mobile device camera as an optical precept and parse the OM code to process state-space representation. The path of navigation is explored by applying graph theoretic algorithmic approach for finding an optimal path. The episodic interaction design is experimented under different device and lighting variation. The proposed RORT algorithm for path exploration and compensating missing information proved to be robust enough to hop forward or retract to the point of origin without any external support of complex computations.
Smart Word Aplication Design for Learning Mandarin- Indonesia Vocabulary

Abstract The need for human resources who have Mandarin skills in Indonesia has increased along with the growth of Chinese investors in Indonesia. For this reason, the growth of Chinese language learners, especially in universities, also increases from time to time. Given that Chinese is a language that is difficult to learn, it is not enough to only face-to-face lessons in class, so that a tool is needed that can help students learn independently in a comfortable place and time. This research begins with a survey of what students need to face obstacles in learning Mandarin vocabulary and the applications needed to support their learning. From the results of the survey on 37 students who studied Mandarin for 4 months, the researchers designed learning applications based on the needs of students. The material from the application is taken from the subject matter in the textbook. Listening lessons are considered difficult by 83.49% of students because of vocabulary. As many as 73.38% of students consider the need for an application that helps vocabulary learning and as many as 83.78% of students need an application on a smartphone that can be used offline. From the student input, an application called “Smart Word” was designed. For the next research, the researcher suggestion is that the “Smart Word” application can be used to measure the results of student studies.
Deriving Big Data insights using Data Visualization Techniques

Exponential growth rate in the data generation in diverse fields revolutionized the way the analytics tools and machine learning algorithms applied in the Big Data. Considering the pace at which data is generated and the variety of data, identifying the right data at the right time and the relationship is crucial to make decisions. Identifying the data type and the relationship between the parameters is challenging as the size of the real time data is massive and dynamic in nature. Data Visualization as part of Big Data Exploratory analysis helps to identify the relationship and to understand the characteristics of big data in an effective way. In other words, data visualization is the graphical representation of data, it links the data availability and data analysis, organizes and presents important findings from the data. As plenty of visualization packages and tools are available, choosing the right tool is very important. gglot2 is one among the statistical graphics tool for data visualization as the working model is entirely based on grammar of graphics. The unique feature of ggplot is the layered approach, as the each component is highly interdependent on the other and so it helps in the step by step analysis of the data. With this feature available ggplot2 was used in the Real time Public Distribution System data to identify the relationship between various parameters and to understand the behavior pattern.
Coresets for PCB Character Recognition based on Deep Learning

The PCB (Printed Circuit Board) character image is acquired in a limited environment. Characters in the form of printed text are considered to be an easier task than handwriting. However, because the different factories use different parts, the shape of the font used on the parts may also change. In addition, characters may be damaged due to the heat treatment occurring during the character printing process. In this study, we analyze the data collected at the production plant and build coresets to create a flexible deep learning model that can be applied to multiple plant sites. Also, This experiment reveals which factors are important for coresets configuration. At this time, the generated coresets are used to analyze performance through various ResNet models.
Designing a humanoid robot integrated Exer-Learning-Interaction(ELI)

This is a design of transitive gesture-based human-robot interaction (HRI) for enhancing elementary learning outcome of special need children. It is an experimental Precept Action Sequence (PAS) interaction module, incorporating Leap Motion and Soft bank humanoid robot NAO as an implementation platform. In the primary phase of this work restricted recognition and recall task designed, where natural hand movements of the children captured to train and repeat certain task synchronous with robot movements. Traditional learning contents are transformed with tailored action-reaction mapping for improving the learning experience by the children. It is observed that the playful interaction with a robot on context-dependent evaluation of learning contents make the children engaged and natural learning attention grows. Based on empirical experience, viable suggestions are mentioned for the systematic use of Closed Loop HRI(CLHRI) as an effective medium of addressing children with learning trouble. The work may also be extended as exer-learning, therapeutic exercise feedback etc.
FPGA Implementation of a Pseudorandom Number Generator Based on k – Logistic Map

Data encryption is a way to increase security of data by using cryptographic keys generated from random numbers. Random Number generator (RNG), which produces such numbers, is classified as True Random Number Generator (TRNG) or Pseudorandom Number generator (PRNG). The first one uses physical phenomena to generate random numbers whereas the second one uses deterministic systems. A commonly used deterministic system in PRNG applications is the logistic map, a nonlinear system that under certain operating ranges becomes chaotic. However, the logistic map alone does not generate satisfactory random sequence because the sequence generated is not uniformly distributed, and it does not have good results in statistical tests, such as the NIST test suite. To solve this problem, this paper proposes an implementation in Field Programmable Gate Array (FPGA) of a PRNG, which uses the k - logistic map concept, discarding the most significant k decimal digits of an underlying orbit generated from the traditional logistic map equation. The results from synthesis and simulations show that the designed circuit is a low-cost and a high efficiency RNG solution.
Window-Based Topic Model for HDP

Hierarchical Dirichlet process (HDP) is a non-parametric Bayesian model, and has been widely applied in the application of topic models. However, the model is based on the "bag of words" hypothesis, ignoring the order of words in the document, resulting in a lack of word context semantics. In this regard, this paper proposes a window-based hierarchical Dirichlet process model (WHDP). The model uses windows to divide documents into smaller fragments, and keeps the order between words while moving windows, so as to reduce the semantic confusion of the text. We applied our method in real dataset and compared with other existing methods, such as sampling belief propagation algorithm for HDP, LDA model, and slidingwindow based topic model. The results show that the proposed method performs the superiority in convergence rate, perplexity and generalization ability.
Area and memory efficient tunable VLSI implementation of DWT filters for image decomposition using distributed arithmetic

Dedicated hardware realisation of 'Discrete Wavelet Transform' (DWT) is highly demanded for real-time multimedia operations in any handheld gadgets, as DWT is a popular transform-domain mathematical tool widely employed for many applications. Several DWT techniques exist in the literature assisting the software-based DWT realizations, inappropriate for real-time operations. There exist several hardware-based solutions for DWT. But most of the VLSI architectures of DWT fail to offer balanced performance, i.e. such architectures show good performance with respect to certain metrics while sacrificing other performance metrics. In this article, a convolutional DWT-based pipelined and tunable VLSI architecture of Daubechies 9/7 and 5/3 DWT filter is presented. The proposed architecture is designed with an ardent focus of maintaining balanced performance. The presented design, which blends the merits of convolutional and lifting DWT while dumping their demerits, is prepared area and memory efficient by employing 'Distributed Arithmetic' (DA) in our own ingenious method. Comparative experimental results justify the superiority of the proposed design regarding 30% area reduction, memory saving, remarkable clock frequency attainment and acceptable computation time. The proposed 2D DWT architecture is applied for real-time image decomposition. The outcomes of such real-time on-board testing prove the practicability of the presented work.
Visible to Band Gender Classification: An Extensive Experimental Evaluation Based on Multi-spectral Imaging

The ability of multi-spectral imaging to acquire spatial and spectral details across electromagnetic spectrum, has gained significant importance to perform under varying illumination conditions. Due to which a maximum preference is shown in the recent times to employ multi-spectral imaging in biometric systems. Although there are substantial studies carried out independently using visible image face database and multi-spectral face database operated in several bands, the visible to band gender classification is still an open problem. In this paper, we present gender classification, thereby training the Support Vector Machine (SVM) classifier model using visible face images and testing independently using individual band images for analysis. We evaluate the proposed approach on 79750 sample images which comprises of multi-spectral face database and newly introduced visible face database to present the importance of this study. The extensive evaluation results in the form of average classification accuracy is presented by using 10 fold cross-validation method. The highest average classification accuracy of 95.96±1.68% demonstrates the applicability of visible to band comparison approach for gender classification.
Augmented Reality and its Implementation in Health Care Sector

The monstrous mechanical headways around the world have made huge testing rivalry among organizations where every one of the organizations attempts to pull in the clients utilizing distinctive procedures. One of such ongoing technology is AR (Augmented Reality). The Augmented Reality (AR) is another innovation that is fit for introducing potential outcomes that are troublesome for different advances to be meet. These days various AR applications are being utilized in that business of various types or scattered everywhere throughout the world. Augmented Reality will surely change the manner in which people view the environment . The Augmented-Reality is till now in its underlying periods of explore and advancement at various schools and cutting edge organizations. All devices through the most recent years, AR applications wound up transportable and by and large accessible on various gadgets. In addition, Augmented-Reality initialize to possess its position in our broad medium to be used in diverse domains throughout our lifespan in substantial or energizing ways, for example, Television, games and is utilized in numerous areas throughout human's lifespan..This investigation presents AR applications in medical practices and instruction, and plans to help health experts find out about these applications, getting to be intrigued to enhance the nature of medical by means of the innovation.
Improving Mandarin Chinese Learning in Tibetan Second-Language Learning by Artificial Intelligent Speech Technology

Mandarin plays an important role in the world and it is also the national common language in China that means mandarin need to be also popularized in ethnic minority areas. Tibetan is located in remote areas and their language is mainly Tibetan, which makes it difficult for Tibetan students to learn Mandarin and it has seriously affected the popularization of Mandarin in minority areas. Therefore, this paper proposed an effective method to assist Tibetan students' second language learning by using intelligent speech technology. Firstly, speech recognition algorithm is adopted to detect pronunciation errors. Secondly, speech synthesis method is utilized to correct pronunciation errors. And thirdly, the STRAIGHT algorithm is used to adjust speaking rate to further help Tibetan students understand the correct pronunciation. We carry out the experiment with 20 Tibetan students and the results shows that the method can effectively reduce the error rate of Mandarin pronunciation and improve the accuracy of learning Mandarin. The result also shows that as the speaking rate becomes slower, the comprehension accuracy increases.
Ontology-Driven Generation of Interactive 3D Worlds

In this paper, the ontology-driven approach to automated generation of interactive 3D applications is presented. The ontologies describing both static and dynamic application properties combined with domain-specific knowledge are leveraged. The approach is evaluated in two case studies: smart city simulator and virtual robotics testbed. The generated output is JavaScript code using Three.js library. According to the achieved results, the adoption of ontologies dramatically speeds up the development time compared to manual process.
Verification of Virtual Machine Architecture in a Hypervisor through Model Checking

Hypervisors are used to virtualize the architecture on which it runs. These are small computer programs that are typically safety-critical, and also hard to debug. As the size of hypervisors are small that is why the formal verification at code level is feasible for these. In this paper, the authors present the formal verification of architecture of the virtual machines created by the hypervisors. Authors have verified the target by model checking with the help of mCRL2 tool. The verification is carried out on the design level. The formal verification of architecture of virtual machines along with its code written in the mCRL2 modelling language is presented.
Knowledge-Aware Graph Collaborative Filtering for Recommender Systems

To solve the data sparseness and cold start problems in collaborative filtering (CF) based recommender systems (RS), various complex algorithms are proposed to extract and integrate explicit or implicit information of data for the recommendation. In this paper, we propose to aggregate and transmit the rich semantic information with the help of knowledge graph (KG) that is regarded as one of the main sources of auxiliary information. Specifically, we first propose a Neural Graph Collaborative Filtering to construct and aggregate information. And then we build a scalable and end-to-end knowledge-aware graph collaborative filtering model named KGCF. In KGCF, neighbourhood information in KG is encoded to construct information in a complex new way. And the information from neighbours are merged with a personalized bias calculated by attention mechanism based on KG. In order to extend the interacted items and capture the high-level semantic information of KG, multiple KGCF layers stacked is used in KGCF. Experimental results on three real data sets indicate that the KGCF model proposed in this paper is superior to the existing models in terms of accuracy and can also effectively solve the data sparsity problem of RS.
Performance comparison in simulation of Mandelbrot set fractals using Numba

This paper aims to describe numerical simulation of the Mandelbrot Set Fractals by comparing performance of Python function which generate the Mandelbrot set fractals in three cases, using pure Python, Numba and Numba CUDA which run Python function in parallel. The fractal is simulated using iterated function system (IFS) based on number of iteration and size of generated fractals. The result shows that the Python function run in parallel using Numba CUDA is 1856 times faster than pure Python function on CPU and 14 times faster than Python function using Numba JIT compilation. The comparison measure used is the execution time of the Python function using %timeit Python magic command. Fractals have many application which needs faster computation like video games creation, flight simulation, and Cryptography for security purpose.
Identifying Notable News Stories

The volume of news content has increased significantly in recent years and systems to process and deliver this information in an automated fashion at scale are becoming increasingly prevalent. One critical component that is required in such systems is a method to automatically determine how notable a certain news story is, in order to prioritize these stories during delivery. One way to do so is to compare each story in a stream of news stories to a notable event. In other words, the problem of detecting notable news can be defined as a ranking task; given a trusted source of notable events and a stream of candidate news stories, we aim to answer the question: "Which of the candidate news stories is most similar to the notable one?". We employ different combinations of features and learning to rank (LTR) models and gather relevance labels using crowdsourcing. In our approach, we use structured representations of candidate news stories (triples) and we link them to corresponding entities. Our evaluation shows that the features in our proposed method outperform standard ranking methods, and that the trained model generalizes well to unseen news stories.