Reviewerly/exHarmony huggingface dataset:
Virtual Learning Object Based on a Virtual Laboratory for Microcontroller Practices

The Virtual Learning Objects (OVA) have generated positive changes in teaching, availability and accessibility to knowledge, with the purpose of offering more flexible and multiple consultation mechanisms to consolidate a broader content of knowledge. The OVAs are digital tools that are used in virtual education, but they do not exist for specific subjects in the area of electronics, so it has been seen the need to create a virtual laboratory for teaching of microcontrollers, through a web service and the use of Google’s Fire-base database. The virtual laboratory will run on as a tool for teacher and as information and access mechanisms for the development of practices for students. Through the web application, the students will have access to the information of the practices and when connecting an Arduino card remotely, it will be linked to the database, thus achieving two-way communication, with the virtual laboratory the teacher will be able to demonstrate the operation of microcontroller practices, in real time. For the development of the virtual laboratory, a mixed methodology based on AODDEI (Analysis, Obtaining, Design, Development, Evaluation, Implementation) and component-based software engineering was used. As a result of the evidence of the operation of the virtual laboratory, a practice with 28 students connected at the same time was carried out.
Zero-shot domain paraphrase with unaligned pre-trained language models

Abstract Automatic paraphrase generation is an essential task of natural language processing. However, due to the scarcity of paraphrase corpus in many languages, Chinese, for example, generating high-quality paraphrases in these languages is still challenging. Especially in domain paraphrasing, it is even more difficult to obtain in-domain paraphrase sentence pairs. In this paper, we propose a novel approach for domain-specific paraphrase generation in a zero-shot fashion. Our approach is based on a sequence-to-sequence architecture. The encoder uses a pre-trained multilingual autoencoder model, and the decoder uses a pre-trained monolingual autoregressive model. Because these two models are pre-trained separately, they have different representations for the same token. Thus, we call them unaligned pre-trained language models. We train the sequence-to-sequence model with an English-to-Chinese machine translation corpus. Then, by inputting a Chinese sentence into this model, it could surprisingly generate fluent and diverse Chinese paraphrases. Since the unaligned pre-trained language models have inconsistent understandings of the Chinese language, we believe that the Chinese paraphrasing is actually performed in a Chinese-to-Chinese translation manner. In addition, we collect a small-scale English-to-Chinese machine translation corpus in the domain of computer science. By fine-tuning with this domain-specific corpus, our model shows an excellent capability of domain-paraphrasing. Experiment results show that our approach significantly outperforms previous baselines regarding Relevance, Fluency, and Diversity.
A Medical Image Enhancement to Denoise Poisson Noises Using Neural Network and Autoencoders

Poisson noises are common in underexposed radiographs due to a lack of photons reaching the detector. Scatter radiations damage radiographs, and the severity of picture quality degradations is determined by the amount of scatters reaching the detectors. To forecast scatters and reduce Poisson noises, a convolutional neural network (CNN) method and autoencoders are employed in this study. Autoencoders (AEs) are neural networks with the goal of copying their inputs to their outputs. They function by compressing the input into a latent-space representation and reconstructing the output from this representation. Radiation exposures of 60% underexposed the radiograph. Poisson noises are successfully minimised, and image contrast and details are increased, thereby enhancing the image. After applying the CNN algorithm, the contrast and details in the radiograph were considerably improved and are now adequate for establishing a diagnosis, resulting in a 60% reduction in radiation exposure. The quality of radiographs can be enhanced by minimising scatters and Poisson disturbances, as demonstrated in this study.
Anonymized Questionnaire Analysis with Differential Privacy for Large-Scale Crowdsourcing

Crowdsourcing, wherein requesters post tasks and workers conduct selected tasks on the Web system, can be used for various purposes. Questionnaires are one of the most popular tasks in crowdsourcing. Because some questionnaires require workers’ sensitive information, such as their disease names and salary, many workers hesitate to answer them. Recent studies have proposed anonymized data collection schemes, which can statistically analyze the attribute values of each person without knowing the exact information of each person. However, existing studies require a large sample for proper estimation. Furthermore, these studies assume that all people have the same level of privacy protection. In this paper, we propose a new scheme using differential privacy, one of the most well-founded privacy models, which can estimate the value distribution more accurately and can be used in situations where the privacy level varies according to the requirements of each worker. Simulations on real datasets prove that the proposed scheme can estimate more accurately than existing schemes.
Ontology for Enhanced Industrial Process Control

The cyber-physical integration in the sense of industry 4.0, applied to industrial process control, needs to develop new methodologies. Both top-down hierarchies of commands for Enterprise Resource Planning, Optimization, Advanced Control, Local Control, and bottom-up flux of information from the plant floor to control system, optimization level, and planning strategy are dissolved, aiming to enhance the vertical and horizontal integration and flexible operability. Even though this is a requirement for Cyber-Physical Systems, it would be merely a coexistence of advanced optimization IoT technologies states of art. Therefore, there is a need for a new architecture of a functional system. Since it works essentially as an event-based system, ontology plays a key concept in the practical working of the new process control. First, an overview of the general ontologies framework applied in Industry 4.0, and on the other hand, some ontologies published for chemical process design are discussed extensively here. Then the practical ontology and taxonomy needed for industrial process control are discussed. Here is presented how to recover the functional layers to each specific application of former traditional hierarchy top-down and bottom-up, now named as semantic layers. A description’s language development for process control using this ontology is another challenging task, also proposed. A case based on the transition of the traditional batch reactor process to modern industry 4.0 application illustrates the change of the operation mode. Finally, the potential gains and technology limitations are analyzed as a critique as the enabling technologies (or pillars) of Industry 4.0 to show what concepts apply to this enhanced process control. It also compares and analyzes this modern approach in the manufacturing process and the difference with the industrial process control. The construction of a complete system based on ontology and description language ready for application is an impressive task to be developed further. The main objective of the work, for now, is to clarify the concepts and show the methodology with its practical application, discussing limitations.
Optical Character Recognition-Based Signboard Detection

In the world of artificial intelligence and advancement in technologies, big companies like Tesla, Uber, Google, etc. are operating on independent vehicles and self-riding cars. The content or image identification and acknowledgment from traffic boards is a difficult issue. The quantity of significant application zones is reliant upon text discovery and acknowledgment, including progressed driver help frameworks, street looking over, and self-sufficient vehicles. In this proposed work, a framework for programmed discovery and acknowledgment of text and image in rush hour traffic signs is proposed. The present work uses a German traffic sign recognition dataset that contains diverse traffic signs composed of arbitrary roadsides. This dataset contains more than 50,000 images of 43 different classes of traffic signs. The work can be divided into two stages; the first stage is the observation of the area and the second is character identification. For textual content observation in signboard and recollection reason, the optical individual identification technique is used. The experimental results show that the proposed system achieved a better accuracy for text detection and text extraction.
Enhancing Data Security for Sharing Personalized Data in Mobile Cloud Environments

Due to the evolving technologies in mobile computing, smart devices play a pivotal role in sharing data among peers. In the present scenario, the users share their personalized information in real time anywhere and anytime to their family and friends through smart phones, tablets, and smart watches. The mobile cloud computing is a promising technology that has grown with the advancements in cloud computing. As the mobile devices suffer from limited battery life, storage capacity, and network bandwidth, the data to be shared or processed are moved to cloud through offloading process. Today, the data shared among multiple cloud applications like Google, Facebook, Twitter, and Amazon poses serious security threats on the shared information. The data is vulnerable to security threats due to the user mobility and data storage in cloud. The existing cloud security methods cannot be directly applied to mobile cloud due to the limited storage space and computational power of mobile devices. Hence, in this work it is proposed to develop an improved AES algorithm for encrypting and decrypting data files that are stored in cloud. The work presented here also provides improved security features through additional functionalities like one-time download, share time expire, and insider threat security. The experimental results show that there is 20% reduction in the computational time for encryption and decryption and the computational overhead is also reduced by 23%.
Visual Question Answering System Using Co-attention Model

The areas of computer vision, natural language processing, and object detection have been achieving great progress in various applications these days. One notable application of NLP is Question answering system which helps computer systems to answer the questions based on the information provided as input. The next level to this system is Visual Question Answering. Scenarios like helping visually challenged people in understanding visual information to improve their daily life, implementation of intelligence systems that can process and understand visual information, etc., has been the key for advent of VQA systems. Hence, an image question answering system is proposed that makes use of CNN and NLP for image and question processing. When an input image along with a question regarding the image is given, the system provides an accurate response for the question in the form of text by understanding the image, question, and relationship between them.
A Meta-Analysis on the Algorithms for Virtual Machine Consolidation

Data centers play a major role in providing versatile service over the cloud, and so the efficient energy consumption of data centers is the popularly sought-after area of research in cloud computing. This paper highlights the prominent existing works in the area of energy-efficient virtual machine consolidation. The key contribution of this paper is a thorough study on the significant works in VM Consolidation for the past ten years and a listing of the effective unique approaches. A meta-analysis on the existing algorithms for detecting overloaded hosts, selecting suitable VM for migration, and the placement of VMs is performed with PlanetLab workload. Results are compared using ten benchmark parameters, and the best existing algorithmic combination for each parameter is identified and listed.
An Automatic Indian Traffic Signs Detection and Recognition System Using OpenCV

The main objective of this research is to develop an automatic Indian traffic signs detection and recognition (TSDR) system which is fast and efficient for real-time implementation in autonomous vehicles. A TSDR system is an essential component of autonomous vehicles and a fast algorithm is necessary for its implementation in a real-time environment. This paper presents a TSDR system developed using Open Source Computer Vision Library (OpenCV) which includes two working stages: traffic sign detection and traffic sign recognition. A robust shape and colour-based approach is adapted for the detection of traffic signs. Image processing techniques such as image thresholding, Gaussian filter, contour detection and RGB color segmentation have been applied for traffic sign detection. The detected traffic signs are classified based on the shape and color properties of the Indian traffic signs and recognized by region of interest (ROI) segmentation based on feature matching. The developed technique is efficient in detecting and recognizing traffic signs with complex natural backgrounds under various lighting conditions and requires a low processing time of 0.30 s, allowing for real-time applications.
AR Cloud-Based Indoor Navigation

Indoor navigation is more common nowadays which enables the location of people or objects inside large buildings such as colleges, hospitals, and malls. The location information is stored in the database so that the users can access the data at any time. This indoor navigation system guides the users to reach the required destination by showing the accurate pathway. The instructions regarding the navigation like turning toward left, turning toward right, and straight directions will be appeared on the screen when the user reaches the particular location toward the destination. When the user uses 2D navigation map, there is a chance of getting confused, while they are present between the 2D navigation map and the real-time environment. The augmented reality (AR) is a cloud-based 3D indoor navigation system which has been developed for guiding the users to reach the destination without any delay. The images are captured and stored in the cloud for scalability and flexibility in rendering services.
Application of multiplicative homomorphic encryption in process industries

Flexibility and mobility of modern value chains have created a need for continuous information exchange between involved chain parties. In such process industries, many companies share their manufacturing assets with each other and thereby allow partners to have control over them. In this way, some sensitive essential information about operations and control methods could be leaked from the asset supplier to the user and vice versa. Therefore, such information sharing raises confidentiality concerns between the service provider and its operator. The goal of this work is to apply and evaluate a confidentiality-preserving information sharing model for a time series use case in process industries. There are various ways to maintain privacy of the sensitive information, such as anonymization and encryption. To preserve control for the user and to allow data gathering by the vendor about asset operation, homomorphic encryption methods could be implemented. Homomorphic encryption allows for the preservation of confidentiality of the data while enabling computations on the encrypted data. The main focus of this study is an investigation of homomorphic encryption schemes with multiplicative properties, such as RSA and ElGamal, which can be applied to process data within information exchanges. This research investigates the probabilistic and the deterministic homomorphic algorithms with respective differences in encryption and decryption speeds. This approach is based on the simulation of the use case between asset vendor and asset operator. The confidentiality model of the information exchange sustains the zero-knowledge proof between involved value chain partners. The result implies the adaptability of both methods within the privacy-preserving sharing model. This study is limited to a use case with the application of partial homomorphic cryptosystems in process industries. The outcome highlights the statistical justification of the application of multiplicative homomorphic encryption (MHE) for confidentiality preservation.
Classification of Indian Monument Architecture Styles Using Bi-Level Hybrid Learning Techniques

India is known for its rich architectural and cultural inheritance. In this research work, classification of the Indian historical monuments has been attempted in two levels, primarily based on the time period of its construction and secondly based on its architectural style, using machine learning (ML) and deep learning (DL) techniques. A rich corpus of monument images of varying historical periods and architectural styles has been collected from the web, blogs, and tourism websites. Feature extraction methods such as speeded-up robust feature (SURF), scale-invariant feature transform (SIFT), features from accelerated segment test (FAST), oriented FAST and rotated brief (ORB), Hu moments, image moments, and Zernike moments are used to extract the features from the input images. ML techniques such as decision tree algorithm (ID3) were used for time period classification in the first level and support vector machine (SVM) for classification of architectural style in the second level. Convolutional neural network (CNN) is designed and validated for training and testing images for both the levels of classification to observe the performance of DL technique in monument classification. Results of this research show that features extracted using Hu moments and Zernike moments resulted in improved accuracy compared to other feature sets. CNN outperforms ML approaches in overall accuracy in both levels of monument classification and also for few specific architectural styles.
Analysis of Prediction Accuracies for Memory Based and Model-Based Collaborative Filtering Models

Due to extensive growth of e-commerce industries, recommender systems are widely being used by many sites to provide better services to their customers by assisting them to finding their items of interest. However, there is still scope of improvement in finding the best technique to implement recommender systems. Many techniques have been developed to implement the recommender systems. Both in academic research and commercial applications, collaborative filtering algorithms are very popular and widely used techniques. Neighborhood-based or memory based methods are classic approaches to collaborative filtering methods, while model-based methods, notably matrix factorization techniques, are more modern approaches. In this work we perform the comparative analysis of various approaches of memory and model-based approaches with different similarity measures using MovieLens dataset with three variations 100 k, 1 M and ML small. The results indicate that the item-based approach with adjusted cosine similarity performs better in memory based and in model-based SVD performs better than other algorithms.
FDLS: A Deep Learning Approach to Production Quality, Controllable, and Retargetable Facial Performances.

Visual effects commonly requires both the creation of realistic synthetic humans as well as retargeting actors’ performances to humanoid characters such as aliens and monsters. Achieving the expressive performances demanded in entertainment requires manipulating complex models with hundreds of parameters. Full creative control requires the freedom to make edits at any stage of the production, which prohibits the use of a fully automatic “black box” solution with uninterpretable parameters. On the other hand, producing realistic animation with these sophisticated models is difficult and laborious. This paper describes FDLS (Facial Deep Learning Solver), which is Weta Digital’s solution to these challenges. FDLS adopts a coarse-to-fine and human-in-the-loop strategy, allowing a solved performance to be verified and (if needed) edited at several stages in the solving process. To train FDLS, we first transform the raw motion-captured data into robust graph features. The feature extraction algorithms were devised after carefully observing the artists’ interpretation of the 3d facial landmarks. Secondly, based on the observation that the artists typically finalize the jaw pass animation before proceeding to finer detail, we solve for the jaw motion first and predict fine expressions with region-based networks conditioned on the jaw position. Finally, artists can optionally invoke a non-linear finetuning process on top of the FDLS solution to follow the motion-captured virtual markers as closely as possible. FDLS supports editing if needed to improve the results of the deep learning solution and it can handle small daily changes in the actor’s face shape. FDLS permits reliable and production-quality performance solving with minimal training and little or no manual effort in many cases, while also allowing the solve to be guided and edited in unusual and difficult cases. The system has been under development for several years and has been used in major movies.
Systems of Discrete Walsh-Like Sequential Functions

In this paper the algorithm for constructing the discrete (0,1)-sequent functions constituting the whole symmetric systems of orthogonal equidistant functions on the example of the eighth-order systems developed. Discrete sequential functions form by replacing their piecewise constant values +1 or -1 in the time domain (from the original space) with numerical values 0 and 1 in the image space. We refer to Walsh-like functions as (0,1)-sequent functions in which the number of zeros and ones in each half of the definition interval is not necessarily the same as in classical Walsh functions. By the directed search method, each of the 30 formed whole groups of equidistant sequent functions unfolds, like the group of classical Walsh functions of the eighth order, into 28 symmetric systems of sequent functions. The main result achieved in this work should consider an expansion of the set of Walsh-like systems of the eighth order by more than an order of magnitude. The algorithm's simplicity for synthesizing such systems of sequential functions and the high speed of spectral processing of discrete signals provided by the proposed bases open the Walsh-like systems for broad prospects of application in various fields of science and technology.
Eye-Gaze-Controlled Wheelchair System with Virtual Keyboard for Disabled Person Using Raspberry Pi

Eye-gaze technology enables people to operate a system by gazing at instructions on a screen. It can assist paralyzed people who are unable to communicate with others due to their inability to speak or use their muscles well enough to write on paper or type on a keyboard. So, this paper presented an eye-gaze-driven wheelchair with a virtual keyboard for paralyzed people. The main aim was to make it easier for individuals with impairments to use wheelchairs and type on a keyboard, as well as to eliminate the need for assistance for the impaired person. The system integrates an eye-controlled wheelchair with a virtual keyboard using a low-cost system. In this system, the wheelchair was controlled by eye movement and the virtual keyboard was controlled by eye blinking. The camera was positioned in front of the disabled person to take real-time images and track eye movement. The wheelchair travels left, right, and forward according to eye movement. The whole system was controlled by a Raspberry Pi.