Reviewerly/exHarmony huggingface dataset:
Digital Storytelling on a Virtual Heritage Museum with Believable Agents

The preservation of cultural heritage plays a very important role in terms of the sustainability of societies since culture is one of the most important phenomena that hold individuals together. However, although the protection of cultural heritage is a very important issue for societies, there are serious problems in the accuracy of information or access to information due to the verbal or written communication of the subjects that need to be conveyed. In particular, there is a serious decrease in the sense of belonging in individuals due to the inability to transfer cultural heritage to the younger generations correctly. At this point, the aim of the study is to teach individuals the Turkish horror culture by experiencing them in a realistic environment with various different stories in a virtual reality-based gamified system instead of teaching them in writing or verbally. For this purpose, a 3D virtual museum was developed within the scope of the study, inspired by real characters and areas, and it was aimed for individuals to learn Turkish horror culture elements through tasks in different scenarios. The developed system was tested by 5 experts in the field as a preliminary study and the realism level of the created system was measured with the comments of the experts. According to the findings, it has been determined that the level of realism offered by the designed virtual environment is sufficient to explain the Turkish horror culture to individuals.
Machine Learning and Imbalanced Learning Approaches in Condition-Based Monitoring and Predictive Maintenance: A Systematic Literature Review

Maintaining machine health remains a significant challenge in general industrial production because of the complexities involved in diagnostics or prognostics. Current methods rely heavily on the human touch, which makes them expensive and unsustainable, especially for busy industrial complexes such as fulfillment centers. Specifically, in fulfillment centers, the stoppage duration of failures is particularly relevant as they lead to significantly different operational costs. In this context, failure events and fault data exhibit distributional asymmetry especially when looking at it from a failure duration lens. This is because in practical applications, machines operate normally and failure events occur seldomly, with extreme failure events being very rare. What is more, data scarcity under machinery faulty conditions causes data amounts acquired in faulty conditions to be far less than those of normal conditions resulting in an Imbalanced scenario. As such, imbalanced learning is a critical step in modeling failure of equipment. In this paper, we implement a systematic review approach PRISMA, also known as Preferred Reporting Items for Systematic Reviews and Meta-Analysis to find relevant past publications that can help us understand the state-of-the-art machine learning methods, imbalanced learning techniques applying condition-based monitoring data for equipment failure detection and analysis. We find that in the recent past, machine learning applications in this context have mostly shifted from traditional approaches to more modern sophisticated approaches mostly leveraging artificial neural networks learning. SMOTE and its variants still feature frequently as go to class rebalancing approach. Other common non-sampling approaches for class asymmetry resolution found in literature include various forms of cost-sensitive methods and generative adversarial networks. Finally, the most commonly researched areas in condition-based monitoring and predictive maintenance are vibration and acoustic monitoring. While there are a handful of articles on smart-manufacturing, we do not find publications specific to fulfillment sortation equipment.
EmoMix+: An Approach of Depression Detection Based on Emotion Lexicon for Mobile Application

Emotion lexicon is an important auxiliary resource for text emotion analysis. Previous works mainly focused on positive and negative classification and less on fine-grained emotion classification. Researchers use lexicon-based methods to find that patients with depression express more negative emotions on social media. Emotional characteristics are an effective feature in detecting depression, but the traditional emotion lexicon has limitations in detecting depression and ignores many depression words. Therefore, we build an emotion lexicon for depression to further study the differences between healthy users and patients with depression. The experimental results show that the depression lexicon constructed in this paper is effective and has a better effect of classifying users with depression.
AutoCAD fundamentals—Part II

This chapter concludes the introduction of AutoCAD basics by discussing the concept of grips, units, scale, snap, and grid. The Cartesian coordinate system is reviewed, followed by a discussion of various geometric data entry techniques including dynamic and manual input. Then, the chapter covers Inquiry commands (quick, area, distance, list, ID, radius, and angle) followed by additional drafting commands and concepts (explode, polygon, ellipse, chamfer, templates, limits, and save). This chapter concludes with a discussion of the Help files, the Tangent OSNAP, and a drawing challenge.
Resume Designing using Speech Recognition System

There is abundance of resume builder tools used to design the resume of the job seeker in the simplest way but still their resume cannot get shortlisted. By keeping this in mind so many professionals are still working on it to build the resume in the smartest way. In our proposed system, we have designed a prototype which uses the voice of the jobseeker. We have proposed a desktop application in which user is not responsible for resume format rather the application interacts with user like an assistant and collects the data from the user using voice. Our tool is based on Speech-To-Text (STT) and Text-To-Speech (TTS) and other APIs which we will discuss in the paper.
Countering Audiovisual Content Piracy: A Hybrid Watermarking and Fingerprinting Technology

Protecting the copyright of multimedia content is required to discourage illegal distribution, broadcasting and sharing audiovisual content over the spider web. In this paper a hybrid algorithm is propose which combines the benefits of both fingerprinting and watermarking technologies with the objective to authenticate audio-visual Content copyright in spite of transcoding occurrences including compression and geometrics attacks during sharing and distribution on social media; precisely YouTube, Facebook. In the proposed approach a method to enhance security process of the mentioned social media against the most common attacks is proposed, such as geometric distortion embracing cropping resizing rotation. Moreover, the Proposed algorithm contributes to the limitation of H.264 compression attacks flows. In this paper, the host video is operated following the next steps; first, scene change detection is practiced in order to ensure a preeminent computational complexity. Then a hybrid frequency double transform incorporating Discrete Wavelet and Discreet Cosines is processed alternatively to the selected frames in the aim to perform a peak-signal-to- noise-ratio values with a higher compression ratio. Afterwards video fingerprinting is executed heading for tracing the behavior of the video after being published. Experiment results shows new scores of high robustness and versatility in performance for transcoding and sharing multimedia content legally under YouTube.
Particle Rider Mutual Information and Dendritic-Squirrel Search Algorithm With Artificial Immune Classifier for Brain Tumor Classification

Abstract Magnetic Resonance Images (MRI) is an imperative imaging modality employed in the medical diagnosis tool for detecting brain tumors. However, the major obstacle in MR images classification is the semantic gap between low-level visual information obtained by MRI machines and high-level information alleged by the clinician. Hence, this research article introduces a novel technique, namely Dendritic-Squirrel Search Algorithm-based Artificial immune classifier (Dendritic-SSA-AIC) using MRI for brain tumor classification. Initially the pre-processing is performed followed by segmentation is devised using sparse fuzzy-c-means (Sparse FCM) is employed for segmentation to extract statistical and texture features. Furthermore, the Particle Rider mutual information (PRMI) is employed for feature selection, which is devised by integrating Particle swarm optimization, Rider optimization algorithm and mutual information. AIC is employed to classify the brain tumor, in which the Dendritic-SSA algorithm designed by combining dendritic cell algorithm and Squirrel search algorithm (SSA). The proposed PRMI-Dendritic-SSA-AIC provides superior performance with maximal accuracy of 97.789%, sensitivity of 97.577% and specificity of 98%.
Assessing the significance of co-occurring terms in Goods and Services Tax in India using co-occurrence graphs and Attention based Deep Learning Models

Implementation of Goods and Services Tax (GST) in India in 2017 resulted in major tax reforms and the tax paying users tweeted their experience of the new system commenting on different aspects. Tweets of users commenting on the GST information system were extracted for the period June 2017 to May 2020 using pre-identified key terms. The paper presents a unique approach of combining co-occurrence graphs and Attention based deep learning technique for analyzing the importance of co-occurring terms in the tweets by taxpayers. Co-occurrence graphs between the terms in the tweets helped in identifying important terms related to GST, based on the frequency of their connections with the key terms. In order to assess how much the pair of key terms and the adjoining important term contribute, attention weights were found for this pair using Attention based bi-directional LSTM (Long Short Term Memory) model. A measure called "Attention factor" has been introduced to find the relevance of the pair in the tweets which will help in making improvements in the GST system.
Simulation of wave propagation using graph-theoretical algorithm

Abstract We simulate the wave propagation through various mediums using a graph-theoretical path-finding algorithm. The mediums are discretized to the square lattices, where each node is connected up to its 4th nearest neighbours. The edge connecting any 2 nodes is weighted by the time of flight of the wave between the nodes, which is calculated from the Euclidean distance between the nodes divided by the average velocity at the positions of those nodes. According to Fermat’s principle of least time, wave propagation between 2 nodes will follow the path with minimal weight. We thus use the path-finding algorithm to find such a path. We apply our method to simulate wave propagation from a point source through a homogeneous medium. By defining a wavefront as a contour of nodes with the same time of flight, we obtain a spherical wave as expected. We next investigate the wave propagation through a boundary of 2 mediums with different wave velocities. The result shows wave refraction that exactly follows Snell’s law. Finally, we apply the algorithm to determine the velocity model in a wood sample, where the wave velocity is determined by the angle between the propagation direction and the radial direction from its pith. By comparing the time of flight from our simulation with the measurements, the parameters in the velocity model can be obtained. The advantage of our method is its simplicity and straightforwardness. In all the above simulations, the same simple path-finding code is used, regardless of the complexity of the wave velocity model of the mediums. We expect that our method can be useful in practice when an investigation of wave propagation in a complex medium is needed.
Preventing Against Virus Transmission by Detecting Facemask for Gateway Operation System

The COVID-19 epidemic compelled régimes around the globe to enact quarantine in order to protect the virus from transmitting. According to the documentation, wearing a face mask at work minimizes the chance of spreading. Use of AI to provide an innocuous milieu in a production setup that is both efficient and cost-effective. Face mask detection will be enabled utilizing a mixed model combining machine learning and deep learning. We will utilize Open CV to do real-time face detection from a live feed through our camera using a face mask detection library that comprises of photos with and without a mask. In this work, this dataset is utilized to create a COVID-19 face mask detector with CV utilizing Open CV, Python, Tensor Flow, and other tools. The mail aim of the work is to find whether the being on the image/cinematic rivulet is exhausting a face mask or not through the aid of deep learning and computer revelation. By using this face mask detection, we are going to make a gateway system. This system allows people in only if they wear a face mask. We use Raspberry pi to make this system and an a4899 driver module to control the stepper motor. The gateway is controlled by the motor which is connected to the driver module.
Big Data Visualization Tools and Techniques

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.
Terms and Appositions: What Unstructured Texts Tell Us

Terminological resources, invaluable tools for language experts, translators, learners, among others, are widely employed in many applicative scenarios from Machine Translation (MT) to Natural Language Processing (NLP). Automatic terminology extraction from unstructured texts represents a useful, yet non-trivial task, in order to create terminological resources. In this study, we propose a methodology to extract domain terminology from unstructured texts, based on the recognition of appositive structures, as they may mark the presence of terms. Our hypothesis is that, due to their semi-fixed, easily recognizable structure and the semantic richness they convey, appositions are suitable linguistic constructions that can be exploited as markers within texts in order to identify terminology and from which to derive additional and valuable information about terms. Once several syntactic patterns identifying different types of appositional structures have been defined, we aim at automatically extracting them from a collection of unstructured texts in Italian, i.e., a domain corpus, by means of NooJ syntactic Grammars.
Road Vehicle Tracking Using Moving Horizon Estimation

In this era of advanced driver assistance systems (ADAS) and autonomous vehicles, tracking road users has gained a significant role among various fields of research. Although similar techniques are being used in aerospace as well as military operations, but tracking road users still remains an unexplored field. This paper proposes a vehicle tracking modeling approach considering the noisy control distortions which come into effect during the process. An optimal moving horizon observer has been designed and implemented for both linear and nonlinear dynamic problem of tracking ground vehicles in a two dimensional plane.
No rain in the world: A novel residual deep attention network for single image rain removal

Clear images are generally desirable in high-level computer vision algorithms which are mostly deployed outdoors. However, affected by the changeable weather in the real world, images are inevitably contaminated by rain streaks. Deep convolutional neural networks (CNNs) have shown significant potential in rain streaks removal. The performance of most existing CNN-based deraining methods is often enhanced by stacking vanilla convolutional layers and some other methods use dilated convolution which can only model local pixel relations to provide the necessary but limited receptive field. Therefore, long-range contextual information is rarely considered for this specific task, thus, deraining a single image remains challenging problem. To address the above problem, an effective residual deep attention network (RDANet) for single image rain removal is proposed. Specifically, we design a strong basic unit that contains dilated convolution, spatial and channel attention module (SCAM) simultaneously. As contextual information is very important for rain removal, the proposed basic unit can capture global long-distance dependencies among pixels in feature maps and model feature relations across channels. Compared with a single dilated convolution, the spatial and channel attention enhance the feature expression ability of the network. Moreover, some previous works have proven that the no-rain information in a rain image will be missing during deraining. To enrich the detailed information in the clean images, we present a residual feature processing group (RFPG) that contains several source skip connections to inject rainy shallow source information into each basic unit. In summary, our model can effectively handle complicated long rain streaks in spatial and the outputs of the network can retain most of the details of the original rain images. Experiments demonstrate the superiority of our RDANet over state-of-the-art methods in terms of both quantitative metrics and visual quality on both synthetic and real rainy images.
Real-World Virtual Reality With Head-Motion Parallax

Most of the real-world virtual reality (VR) content available today is captured and rendered from a fixed vantage point. The visual-vestibular conflict arising from the lack of head-motion parallax degrades the feeling of presence in the virtual environment and has been shown to induce nausea and visual discomfort. We present an end-to-end framework for VR with head-motion parallax for real-world scenes. To capture both horizontally and vertically separated perspectives, we use a camera rig with two vertically stacked rings of outward-facing cameras. The data from the rig are processed offline and stored into a compact intermediate representation, which is used to render novel views for a head-mounted display, in accordance with the viewer's head movements. We compare two promising intermediate representations-Stacked OmniStereo and Layered Depth Panoramas-and evaluate them in terms of objective image quality metrics and the occurrence of disocclusion holes in synthesized novel views.
Existential Threat or Digital Yawn: Evaluating China's Central Bank Digital Currency

China was one of the first governments to rein in private digital currencies while also working to consider how a sovereign digital currency could advance its policy goals. Policy makers elsewhere have been troubled by the speed with which China has rolled out plans for its central bank digital currency (CBDC), and are especially concerned by the possibilities such a technology could have in the hands of an increasingly authoritarian regime. This Paper aims to evaluate the impact of China’s CBDC (the e-CNY) on the U.S. and the international status quo. It contributes to the literature on the e-CNY by situating the e-CNY within China’s broader domestic policy initiatives. Specifically, the history of China’s prohibition on private cryptocurrencies and its increasingly punitive regulation of private payment platforms like Ant Group’s Alipay indicate that while China certainly hopes to reduce reliance on U.S.-dominated international financial infrastructure in the decades ahead, its more immediate motivations are inward-facing. Instead, these policy moves evince a desire to crack down on financial crime and illegal gambling, reassert control over outbound capital flows, and address an increasingly concentrated market of private sector payments platforms. Implications follow for the U.S. sanctions regime, as well as U.S. dollar dominance, the global balance of economic power, and the efficacy of an increasingly authoritarian regime. However, the introduction of a new financial technology is but one factor among many others, and any gains from its adoption may be marginal.
Crypto-Asset Services under the Draft MiCA Regulation

The draft Markets in Crypto-assets Regulation (MiCA) seeks to regulate crypto-asset services and crypto-asset service providers within the European Union as an area, which was mainly built on the idea of independence from the current “traditional” financial system and its regulatory framework. MiCA, inspired by the Markets in Financial Instruments Directive (MiFID II) as a cornerstone of the EU financial services regulation, now aims to change the paradigm.The paper deploys cross-sectoral approach, focuses on examining the scope and categorising the relevant crypto-asset services compared with the investment services catalogue under MiFID II. By analysing the definitions and content of individual services, it seeks to identify possible differences and frictions. It also examines the impacts MiCA might have on how to interpret the scope of individual corresponding investment services under MiFID II, and vice versa. Further, it categorises and evaluates prospective requirements put on the crypto-asset service providers (CASPs). As a result, the paper introduces three crypto-asset service categories, emphasizes the relevant differences of MiCA and MiFID II lists, as well as provides an overview of the new requirements put on crypto-asset service providers.
Association Rule Hiding in Privacy Preserving Data Mining

This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.