Reviewerly/exHarmony huggingface dataset:
Decentralized Distributed Ledger Based Marketplace for Trading Agricultural Goods

Modern information and communication technologies (ICT) are reshaping the agriculture sector. As a result, the marketing domain is widened by facilitating online agricultural commodities trading. The settlement of transactions is under centralized management in the marketing of agricultural products. The trading platform can be decentralized using blockchain technology, which also increases transaction speed and the ability to verify the origin of the product. Agricultural marketing could benefit from using blockchain technology to eliminate centralized control, improve transparency, and reduce transaction costs. This paper proposes a decentralized trading system for agricultural commodities. The cost of the transaction has been computed at 36gwei, 30gwei, and 27gwei gas prices for smart contracts designed for exchanging agricultural products. The ERC721 standards are used for product representation, enabling asset tracking and ownership transfer.
Enhancement of Diagnostic Information in T1-weighted Contrast Enhanced MRI using Deep Back-Projection based Super-Resolution

Deep learning based magnetic resonance (MR) image super-resolution (SR) using a convolutional neural network (CNN) is gaining popularity very rapidly. Since MR images generally suffer from low resolution, SR reconstruction is very useful for clinical analysis. Most of the existing SR network architectures are based on absolute error minimization. However, in medical images, preservation or enhancement of diagnostic quality of SR reconstructed images is an essential requirement. In this paper, we propose a deep back-projection network architecture to improve diagnostic information in SR reconstruction. We have also introduced a novel diagnostic information distortion measurement parameter based on the normalized spectral energy difference. For performance evaluation, we consider a T1-weighted contrast enhanced MRI pathological dataset. From the experimental results, we observed that the proposed technique outperforms well-known existing in terms of preservation of fine details, edges, and diagnostic information, compared to some of the recent SR methods.
Video Synthesis Strategies for IoRT Environments: Research Challenges and Opportunities

The Internet of Remote Things (IoRT) is the linking of material things, automobiles, residences, and many other entities that are integrated with circuitry, programming, sensing, actuation, and networking capabilities that enable them to gather and transfer files. IoRT enables items to be monitored or remotely controlled using existing network infrastructure, enabling for much more seamless integration of the external reality into computer-based applications, but also minimal user involvement. This study focuses on the many ways for mixing video frames and producing High Dynamic Range Videos for improved quality and analysis.
Stacked Attention based Textbook Visual Question Answering with BERT

In the process of learning (especially in online learning), students may come across various complex images/diagrams which they might find difficult to understand. They may have doubts and ambiguities regarding its structure, components and usage etc. In such situations, it is difficult to map these questions to the actual facts. In such a scenario students may tend to refer to various websites and textbooks to understand it. This method is quite laborious, time consuming and is found to be less efficient. To overcome these problems, introducing a system that intends to respond to multi-model questions given a textual context, diagrams or images. Students can ask any queries regarding the content of the image and as an outcome, the system will provide a descriptive explanation by combining relevant points from the textbook, as the answer. The proposed system takes an image/diagram and a natural language question from the student as input. The question is analyzed and understood by the system using Bidirectional Encoder Representations from Transformers (BERT). The image is analyzed with computer vision technique and deep learning algorithm like VGG16 using the training dataset built upon specific curriculum-based data. To provide more precise answer these two extracted features are given to Stacked attention network (SAN). The correct answer for the visual question is generated from the system. Since there isn’t any dataset available for the proposed problem, a dataset with textbook diagrams, associated questions and the corresponding answers is created from scratch and used for training. It helps students to develop a curious mind and helps to have a thorough understanding of the concepts.
Fast Classification for Identification of Vehicles on the Road from Audio Data of Pedestrian’s Mobile Phone

Vehicle classification using machine learning is introduced in numerous application areas such as traffic management in foggy and rainy weather, vehicle recognition in driver-free vehicles, for pedestrians to give alarms, also sent a notifications to phone handling persons while walking, etc. Numerous research papers are available with ≥ 90% accuracy, but only a few of them are available for real time classification using 1-D audio signals. The main idea of this work is to classify various vehicles within a short span of time with acceptable accuracy. The real-time data collection is done at three different locations using various mobile phones. The Mel frequency cpectrum coefficient (MFCC) is used to extract features from audio samples to train and test classifiers. Machine learning classifiers such as K-nearest neighbor (KNN), Support Vector Machine (SVM), and LightGBM are used to classify a number of vehicles. The experimental results we presented for comparison of prediction timing and accuracy aimed for real-time implementation.
Small Text Extraction from Documents and Chart Images

Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, Our aim is to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.
Sequential Memory Modelling for Video Captioning

In recent years, the automatic generation of natural language descriptions of video has focused on deep learning research and natural voice processing. Video understanding has multiple applications such as video search and indexing, but video subtitles are a correct sophisticated topic for complex and diverse types of video content. However, the understanding between video and natural language sets remains an open issue to better understand the video and create multiple methods to create a set automatically. The deep learning method has a major focus on the direction of video processing with performance and high-speed computing capabilities. This polling discusses an encoder-decoder network end-in-frame based on a deep learning approach to generate caption. In this paper we will describe the model, dataset and parameters used to evaluate the model.
Identifying Similar Questions in the Medical Domain Using a Fine-tuned Siamese-BERT Model

A large number of people search about their health related problems on the web. However, the number of sites with qualified and verified people answering their queries is quite low in comparison to the number of questions being put up. The rate of queries being searched on such sites has further increased due to the COVID-19 pandemic. The main reason people find it difficult to find solutions to their queries is due to ineffective identification of semantically similar questions in the medical domain. For most cases, answers to the queries people ask would be present, the only caveat being the question may be present in a different form than the one asked by the particular user. In this research, we propose a Siamese-based BERT model to detect similar questions using a fine-tuning approach. The network is fine-tuned with medical question-answer pairs and then with question-question pairs to get a better question similarity prediction.
Multi-Person Fall Detection in Complex IoT-Assisted Living Environments

Fall is one of the major concerns of the elderly population. Falls can cause fractures that lead to immobility in the elderly. Internet of Things (IoT) based solutions can help detect falls, thereby, ensuring health care services are delivered in a timely manner. Automating fall detection is a challenging process in retirement homes, nursing homes, and other senior care centers. Although, there exist several studies on the detection of single-person falls, only limited research has focused on detecting multi-person falls. Single-person fall detection uses ambient as well as body-based sensors along with a computer vision approach for capturing and detecting falls. This paper proposes a new approach for the detection of multi-person falls in a complex and challenging environment. The proposed approach involves four steps: (i) Object Detection and Image Segmentation using Region-Based Convolutional Neural Networks (RCNN) and Human Class Bounding Box Extraction, (ii) Pose estimation of individual segments and extraction of coordinates of important key points, (iii) Features extraction, and (iv) Classification to determine the occurrence of fall. We created a new data set as part of this work, including the frames created from videos of complex environments and collecting various YouTube video clips where fall incidents take place while several persons are present in a frame. Our approach for detecting falls in a complex environment and with multiple people in a frame, is much more robust and can detect and classify fall events accurately. Our method demonstrates the high accuracy of multi-person fall detection.
An Approach to Effectively Manage Access Control, Privacy, and Information Transparency in Hybrid Blockchain for Decentralized IoT Applications

The industry is transitioning to a decentralized, self-managed supply chain network based on use case development. Finance, manufacturing, shipping, auto, food, and real estate supply chains are decentralizing information exchange. IoT data goes to a decentralized network, where transactions are executed without a central authority. This architecture faces difficulties including centralized trust management, interoperability, reusability, privacy, and access control for IoT data sharing. Decentralized group formation and information visibility limits might be mission-critical for the banking, finance, and insurance industries. In past work, we advocated decentralized trust management, reusability, and interoperability. This work addresses privacy, access control, permission, and information exchange transparency without compromising decentralization. In a decentralized context, our technique and proof of concept build trust.
Ontology-based communication service for multi-stage production processes

In production processes, huge amounts of heterogeneous data have to be modelled and made interpretable in order to create value of this data. The problem is particularly relevant for multi-stage and possibly distributed production processes in which an overview of the individual process steps must be created. Modelling of data with semantic technologies provides a solution basis for this, but has not yet been established in industry due to a lack of knowledge about the technologies and advantages of semantic modelling. This paper describes a possible concept for information modelling of production process data with ontologies of different granularity levels. Subsequently, a concept for communication between developed ontologies is presented, with which data of different levels of manufacturing can be networked.
English-to-Malayalam Machine Translation Framework using Transformers

Machine Translation (MT) is the task of automatically transforming one language into another while keeping the meaning of the input text and creating fluent text in the output language. Machine translation is in high demand in today’s world. Even while this field has risen in popularity, it still falls short of meeting the needs of people who speak various languages. This work investigates how multiple models can be utilised to translate English into Malayalam. Models such as Seq2Seq and pre-trained MarianMT model for encoder and decoder, as well as several attention models such as Bahdanau attention, multihead attention, and scaled dot product attention, have been examined, and both subjective and objective analysis have been performed to check the validity of translation. Fine-tuning of the MarianMT model has been done to improve the translation task. The findings show how these models can improve low-resource language translation.
Optimized Two-Axis Inertial Stabilization Platform Design for Unmanned Aerial Vehicle Integration

The onset of the fourth industrial revolution, Industry 4.0, is characterized by the Internet of Things (IoT), Artificial Intelligence (AI), cyber-physical systems, and the automation of processes to produce smart factories. Included in Industry 4.0, falling under the “intelligent devices” category, are Unmanned Aerial Vehicles (UAVs) or drones. Industry 4.0 has highlighted the potential of UAVs to perform tasks previously performed by humans or other, more cumbersome production systems, in a more economical and efficient way. This potential function of UAVs can be extrapolated into the disaster management scenario, where the UAV will be used for the transportation of medical supplies, and essential items such as food, water, vaccines, and blood samples This paper details the research and design of an Inertial Stabilization Platform (ISP) which is compatible with the DJI Matrice 300 UAV for disaster management applications. The design was required to be lightweight but durable, and capable of repetitively stabilizing the load being transported by the UAV. Key features of the design included the use of standard off-the-shelf batteries, actuators, a microcontroller, and an Inertial Measurement Unit (IMU). The final design assembly features an Arduino Nano microcontroller, an Adafruit BNO055 Inertial Measurement Unit, two DSSERVO 35kg servo motors, and two 7.4V lithiumion batteries to power the device, one with a 1200mAh capacity, and one with a 2600mAh capacity. The final payload capacity, subject to increase due to further mass reductions, is 1658.94 grams.
Collaborative Hand-eye Virtual Interaction Visualization Method and Technologies

Human computer interaction is one of the core technologies in advanced scenes such as virtual reality. It plays an important role in promoting the wide application of such scenes and improving the operation efficiency and experience. At present, due to the progress of sensor and other hardware technologies, human-computer interaction ways are constantly enriched and refined, providing opportunities and laying a technical foundation for the improvement of human-computer cooperation in related fields. This paper summarizes the research progress in the three fields of virtual multi-channel interaction, virtual interactive visualization in recent years and their summary, and forms an review of hand-eye collaboration and other virtual interaction methods.
Research on User Transparency Perception Based on Mixed Reality

The user's visual perception in mixed reality (MR) includes the combination of the real physical environment and virtual objects rendered in an optical see-through head mounted display (OST-HMD), so the presentation of MR rendering objects will be affected by the light from the real world; thus, rendering effect deviates from the original design intent. This paper focuses on the user's perception of transparency in mixed reality and the impact of ambient illumination on the perception of transparency in mixed reality. We conducted a psychophysical experimental study and recruited 20 participants to recognize the transparency of virtual objects under two kinds of ambient illumination via Microsoft HoloLens and select the corresponding value in the slider. Our analysis shows that the transparency of virtual objects will be underestimated when the ambient illumination is high. Additionally, the experiments also found that as the magnitude of transparency increases, the perception of transparency will be underestimated. This study can provide a reference for the design of MR interface transparency coding.
Deep Learning-based Border Surveillance System using Thermal Imaging

Border surveillance plays an essential role in border security and the safety of citizens. Borders are monitored 24/7 to maintain law and order within the nation. To control terrorist infiltration and illegal movement of people and goods, it becomes vital to protect the border, even in harsh conditions strictly. Maintaining constant surveillance of border forces uses a lot of manpower and assets; this sometimes even leads to the loss of precision lives of armed forces. Hence, this is the need of the hour for an automated border surveillance framework with high accuracy and low resources requirement. So, this paper proposes a framework to help create a smart border surveillance system based on Machine learning which can be used in any IoT-based device. A study has also been conducted on freezing various layers of the YOLOv5 model. Also, a comparison of the proposed framework is made with existing methods for pedestrian detection. The study shows that lower variants of YOLOv5 have comparable performance as compared to more complex variants for the object detection task. In contrast, much lower GPU and CPU utilization are observed, which enables it to be used in Io devices. Also, the proposed method surpasses the existing methods in terms of mean Average Precision(mAP), Precision, and Recall.
Detection and State Classification of Bolts Based on Faster R-CNN

With the increase of the training frequency of aircraft, the frequency of daily maintenance work also increases dramatically. As key connectors in aircraft, the demand for bolts also significantly increases. Hardness is an important performance index of bolts. At present, the manual inspection method cannot meet the requirement for the hardness test of the large number of the bolts. Therefore, there is an urgent need to develop an automated hardness detection system. In this paper, a bolt state detection method based on Faster R-CNN is proposed, which is an important part of an automated hardness detection system and can assist the robot grasping the bolt and placing it on the hardness tester. This method can detect bolts on the pallet and classify their states. The mean average precision (mAP) of this model used in the bolt dataset is 96.44%.
To Increase Security and Privacy, the QAES Encryption Algorithm is used for Storage of Data for Cloud Computing

In this paper an asymmetric key algorithm is used towards provide effective safety measures. At first new files encrypted with the QAES algorithm at the end of the data holder and then stored in the cloud. Then the encrypted data request delivered towards data owner. The user and the sender analyses data then direct encoded documents towards cloud. Later data will be cleared of encryption using the key. The data user receives data that has already been encrypted. If a data user fails to retrieve data, they should re-request. The effect of this method is to provide improved security. Similarly, Period it takes on the way to compile files faster than it even now is. Brute force attacks are not possible in this way, as we use a QAES algorithm that provides additional security.
Research on the Display Area of Chinese Text Boxes in the Virtual Reality Field of View

Reading is a fundamental activity for acquiring information, whether in the real world or virtual environment. The virtual environment provides users with a text display that is different from reality, and its reading experience has always been problematic. This paper aims to explore the effect of text presentation depth on the maximum display area of the Chinese text box that users can read comfortably in a virtual environment. The experiment adopts the parameter adjustment method. The text will be randomly presented in the virtual environment at four depths (0.5 m, 2.5 m, 6 m, and 10 m). Experiment participants needed to adjust the maximum display area of the Chinese text box under the conditions of ensuring reading comfort. The research results show that the width and height of the largest text box display area will decrease with increasing text rendering depth. This research suggests that the width of the display area of the Chinese text box in the virtual environment should not exceed 20°, and the highest point of the display area in the vertical direction should preferably not be higher than eye level 5°.
Design of real-time image processing system of medical high-definition electronic endoscope based on FPGA

Medical high-definition electronic endoscopes have high requirements on real-time performance and video quality. Compared with software-based image algorithms, the algorithms based on field programmable gate array (FPGA) have higher real-time performance, but are more difficult to implement. Referring to the working requirements of the medical high-definition electronic endoscope image processing system, an experimental platform for the real-time image processing system based on FPGA is designed in this paper. And the FPGA implementation of lens shading correction, white balance and gamma correction is studied in this system. The hardware consumption is effectively reduced by improving the relevant algorithm equations. After these three kinds of processing, the output image quality is obviously improved, and high-definition output is realized. At the same time, the total time delay of three kinds of image signal processing based on FPGA is within 16 working clock cycles, which can well meet the real-time working requirements of endoscope. The design of this system has certain reference for the research of medical high-definition electronic real-time image processing system. The realization of the three algorithms also enriches the research of FPGA-based image processing algorithms.
Forensic Imaging Integrity Guarantor using Raspberry Pi and RFID Tag

Digital Evidence used in criminal investigation could help reveal the unlawful act committed by the perpetrators. Digital evidence is more revealing, but it is easier to tamper. Tampering the digital evidence can lead to misinterpretation, deletion of evidence, or evidence privacy breach. Digital evidence protection, including maintaining digital evidence integrity while doing forensic imaging, is needed to avoid these things. Common digital forensic tools use hash function to maintain digital evidence integrity, such as MD-5 and SHA-1. These common hashing algorithms has been found to have weaknesses. This research presents a prototype of digital evidence integrity guarantor which provide forensic imaging process used in digital forensic and hash value as integrity data for the evidence using a more secure SHA-256 hash function built on a single board computer Raspberry Pi. Hash value will be stored and secured automatically in a RFID tag. The prototype also provides entity authentication to avoid hash value forging. All of these processes are carried out automatically by the system so as to reduce human interaction in it, which also reduces the possibility of human error.