Reviewerly/exHarmony huggingface dataset:
Improved Zero-DCE for pig face image enhancement with low-light and high-noise

To solve the problem that individual visual features could not be accurately extracted from low-light and high-noise pig face images in intensive farming, the optimal fitting curve parameters of image brightness enhancement were defined, and the Zero-DCE model was improved and Denoise-Net was introduced to achieve brightness enhancement and high-noise suppression of a single low-light pig face image. The experimental results show that, compared with EnlightGAN, Zero-DCE, Retinex, and SSE, the algorithm in this paper (DCE-Denoise-Net) has good results on image quality metrics such as information entropy, Brisque, NIQE, and PIQE in the absence of reference images. The image quality is improved. On the basis of improving the low visibility of low-light images, denoising was achieved. It is more suitable for low-light pig face image enhancement in a real breeding environment.
A detailed library perspective on nearly unsupervised information extraction workflows in digital libraries

Abstract Information extraction can support novel and effective access paths for digital libraries. Nevertheless, designing reliable extraction workflows can be cost-intensive in practice. On the one hand, suitable extraction methods rely on domain-specific training data. On the other hand, unsupervised and open extraction methods usually produce not-canonicalized extraction results. This paper is an extension of our original work and tackles the question of how digital libraries can handle such extractions and whether their quality is sufficient in practice. We focus on unsupervised extraction workflows by analyzing them in case studies in the domains of encyclopedias (Wikipedia), Pharmacy, and Political Sciences. As an extension, we analyze the extractions in more detail, verify our findings on a second extraction method, discuss another canonicalizing method, and give an outlook on how non-English texts can be handled. Therefore, we report on opportunities and limitations. Finally, we discuss best practices for unsupervised extraction workflows.
High-Capacity Reversible Data Hiding Based on Two-Layer Embedding Scheme for Encrypted Image Using Blockchain

In today’s digital age, ensuring the secure transmission of confidential data through various means of communication is crucial. Protecting the data from malicious attacks during transmission poses a significant challenge. To achieve this, reversible data hiding (RDH) and encryption methods are often used in combination to safeguard confidential data from intruders. However, existing secure reversible hybrid hiding techniques are facing challenges related to low data embedding capacity. To address these challenges, the proposed research presents a solution that utilizes block-wise encryption and a two-layer embedding scheme to enhance the embedding capacity of the cover image. Additionally, this technique incorporates a blockchain-enabled RDH method to ensure traceability and integrity by storing confidential data alongside the hash value of the stego image. The proposed work is divided into three phases. First, the cover image is encrypted. Second, the data are embedded in the encrypted cover image using a two-layer embedding scheme. Finally, the stego image along with the hash value are deployed through blockchain technology. The proposed method reduces challenges associated with traceability and integrity while increasing the embedding capacity of images compared to traditional methods.
An Evaluation-based Analysis of Video Summarising Methods for Diverse Domains

As technology progresses, a gigantic amount of video data is generated day-by-day. Processing of such a huge video requires time, increased storage, and a computational power. Sometimes it is convenient for the user to watch a summary or highlight rather than watching a complete video, which is a time-consuming task. So, a fully automated solution is required to extract important segments from video. Researchers have proposed multiple approaches / techniques for summarizing the videos which resolve the problem of long videos and summarize them according to the video type. This survey and comparative evaluation of video summarizing techniques based on several domains are presented in this study. Primarily, these methods are classified into different categories based on their methods or techniques used. Furthermore, an overview of some of the latest literature is presented with the dataset and the evaluation approaches used. The review is also made related to the domain direction, and is concluded by presenting the benefits and difficulties associated with the current video summarization techniques.
Daten vor Ort erleben: Eine Fallstudie zur Visualisierung der Luftqualität für Bürgerinnen und Bürger

Abstract Visualizing urban data has different purposes. Besides more traditional goals such as supporting experts to decide on smart city policies, supporting citizens to understand aspects of the city relevant to one’s everyday life needs different visualization approaches. In contrast to urban dashboards, which typically show various data sources in a more or less unified way, visualizations tailored for one specific task may be more appropriate for citizens. This paper introduces data experience points where people can experience data on location on their own device, and explore and understand urban phenomena through visualizations. Based on an expert workshop, we propose a set of design goals for these data experience points, and demonstrate the concept through a case study of visualizing air quality data. We designed and implemented multiple prototypes in this case study, and gathered feedback at a public outreach event.
A Narrative Inquiry of Student’s Public Speaking Ability by Bilingual (English &amp; Arabic) at Boarding School of the Al-Mashduqiyah Kraksaan

A narrative inquiry of students' public speaking, ability using bilingual, English, and Arabic. This research aims to explore the story of the experience of public speaking, students during students studying at Al-Mashduqiyah. More specifically, the study aimed to understand the difficulties and coping strategies that students face when speaking in public using a foreign language. The method used by the qualitative approach is narrative inquiry. A Junior High student participated in the study. To collect data valid information researcher use questionnaires, interviews, dairy, and documentation obtained from participants and a teacher. As a result, this narrative investigation shows the problems of public speaking in two languages, individual factors, and implementing them into public speaking. To overcome difficulties in public speaking, participants apply strategies with repetition and habituation. This study shows that the mastery of language, accent, and manner of delivery is important because it affects the audience's understanding of the communication.
A joint event extraction model based on RoBERTa-wwm-ext and gating mechanism

Event extraction, as one of the difficult tasks of information extraction, can quickly obtain valuable information from the massive information on the Internet. This paper proposes a joint event extraction model based on RoBERTa-wwm-ext and gating mechanism for document-level long text data, which not only uses the prior knowledge from event types and pre-trained language models, but also uses gated fusion module to aggregate information in the event argument extraction tasks to enhance entity representation and splices entity type embedding, thereby enhancing the correlation among events, arguments and argument roles in the text, and improving the recognition accuracy of the arguments of each event in the document. Finally, the effectiveness of the model is verified on the public dataset.
Classification of Traffic Signal Images Using Deep Neural Networks

Traffic sign recognition system plays a crucial role in vehicles that assures the safety of humankind. The system provides feedbacks on-road information to the drivers on time which helps them in driving the car without accidents. Deep Neural Networks (DNN) are gaining more attention in the field of image analysis since it produces a high accuracy rate. Recently many researchers are focusing on the recognition of traffic signs using DNN to get better results. So, in this paper, we have applied the transfer learning technique to classify traffic signs. We choose to work with 2 architectures to classify road signs, namely, fine-tuned Convolutional Neural Network (CNN) and Resnet50. The two architectures were trained using the GTSRB—German Traffic Sign Recognition Benchmark downloaded from Kaggle. With these models, we have obtained good results of 98% and 75% respectively.
Implementation of Real Time Face Mask Recognition Technique Using Keras, TensorFlow

Each area of the continent is currently experiencing a global health disaster caused by Corona Virus (COVID-19). Due to this, there is a critical need to adopt preventive measures to effectively combat this infection. Using a face mask is an effective way to protect everyone in public. When there are large crowds at public places, it is very difficult to monitor a person who is with a mask or without a mask using physical checking. Hence developing a solution for detecting face masks is a challenging task. This paper deals with the implementation of a real time face mask recognition methodology. Using Keras TensorFlow, MobileNetV2 and OpenCv. In this proposed approach, a two-phase system is used to train our model on different datasets from KAGGLE, GITHUB and our own real-world database consisting of people with and without mask. By comparing our approach with other existing approaches, we find that it has the highest validation accuracy of 99.35%. The performance analysis of the presented results also shows that MobileNetV2 is the best approach to detect a face mask with high accuracy.
A Qualitative Study on Supply Chain Risk Management Adopting Blockchain Technology

A blockchain refers to a digital ledger of transactions that is duplicated and distributed across the network of computers in blocks. This study attempts to present Blockchain Technology (BCT) as an effective tool for managing risks inherent in supply chain. Based on extant literature and experts’ opinion collected through in-depth interviews, a general impression is drawn on the current state of BCT as an application in supply chains of organizations. A qualitative study based on Interpretivism theory was conducted where experts from related domain were interviewed on various themes related to BCT and its role in Supply Chain Risk Management (SCRM). It was discovered that the concept of Blockchain is relatively new and is mostly unexplored in the Supply Chain Management (SCM). Moreover, there exists confusion among people regarding the distinction between blockchain and bitcoin. This leads to unwillingness to adopt BCT in managing risks in supply chain. All the experts collectively agreed that traceability, immutability, and transparency features of blockchain can improve the risk management of supply chains.
Automatic detection method of small target in tennis game video based on deep learning

Due to the large number of frames and low video resolution, tennis match videos cannot accurately identify and extract effective data, which reduces the level of fine analysis of tennis matches. In order to solve the problem of poor detection effect of small targets in tennis video, an automatic detection method of small targets in tennis video based on deep learning is proposed. Non-maximum suppression algorithm is used to determine the position of the target between different adjacent video image sequences, and SVM classifier is used to train a large number of target behaviors. According to the hierarchical structure of dataset annotation, the hierarchical structure of tennis video for deep learning is optimized. The reconstruction algorithm is used to enhance the video image in the input VOC data set and improve the fine segmentation effect of the image. The difference video image is binarized to complete the automatic detection of small targets in tennis video. The experimental results show that the proposed method has high integrity of tennis video information collection, high recognition accuracy and short detection time.
Anomaly Detection in Medical IoT Devices Using Federated Learning

IoT as a technology has gained a lot of prominence, especially in the healthcare sector which is more commonly known as IoMT. The Internet of Things (IoT) is a network of physical items that are provided with software, sensors, processing power, and other technologies and may communicate with other systems and devices over communication systems like the Internet. This work is mainly focused on the Internet of Medical Things (IoMT) that specifically focuses on health care and medical applications that consist of medical IoT devices. In these devices, as data travels through the Internet and is vulnerable to various security threats and shared among many systems, it can result in multiple anomalies. In cases where a patient’s physiological parameters like heart rate and temperature need to be constantly monitored, any anomaly in the readings from the sensors may lead to erroneous diagnosis and treatment. Hence, timely detection of such anomalies is important. Anomaly detection involves discovering patterns in data that contradict normal behavior. This work uses Federated Learning to detect anomalies in medical IoT devices like heart rate and temperature sensors. The data from the sensors of different patients is collected and trained locally on the edge devices. The results from these local trainings are aggregated in the central Federated Learning Model and the local model is enhanced. The Federated Learning method preserves data privacy and security as only the training results are shared and not the actual data.
The Role of Blockchain in Developing Supply Chain Resilience against Disruptions

Blockchain technology is still being widely adopted in real-world supply chains. The chapter describes the most recent trends and fills knowledge gaps in blockchain technology. It will help both researchers and practitioners better understand the landscape and trends of blockchain technology. It also emphasizes the value of blockchain technology for enhancing trust, time, and cost-effectiveness within the supply chain, and enhancing the overall supply chain’s resilience during disruptions such as the COVID-19 pandemic. It then investigates the role of blockchain technology in the resilience key components such as visibility, digitization, and provenance, and then investigates the role of blockchain technology in minimizing pandemic-induced supply chain risks, as well as the advantages and managerial challenges of blockchain technology implementation.
RealDApp—Decentralized Real Estate Asset Transfer Protocol Based on Blockchain

The RealDApp is made to give the government an effective means of managing and storing the land asset. It enables the government to move all of the data from the centralized registration and keep the land assets over the blockchain network, where they can be managed appropriately. The user is given the ability to buy or sell land assets via a web platform thanks to buying/selling protocols. It is essential for the government, which is in charge of keeping the land registration, to keep all the records safe and shield them from foreign cyberattacks and fraud. Blockchain technology can be used to do this. The user upload all of his documents which are relevant land assets on the blockchain network after registering and confirming his identity, protecting them by transferring submitting them to a decentralized registry. The system now includes buying/selling protocols in addition to merely being a decentralized database. Users can use the buying/selling protocols on the blockchain network to buy new land or sell their existing land without the use of a third-party system. It is impossible for fraud to occur because all network transactions are permanently stored over the chain and cannot be changed. This entire process helps the government manage the land registry in a secure manner while enabling users to acquire or sell land assets in a transparent and risk-free manner.
Multiclass Discriminator for Blind Steganalysis Using Statistical Features of Digital Images

Today’s world is becoming computer fanatic. Thousands of images are uploaded to World Wide Web in every second. A survey of various security and privacy threats that focus on use of social networking sites is conducted and it's ascertained that multimedia system content thread is one in all these security issues. Most of covert communication can be done through data hiding techniques like steganography in which secret data will be covered under carrier images. It can bear out by different Steganographic techniques of frequency domain or spatial domain. During data embedding process in digital images statistical properties of images get modified. By tracking those changes the analyzer can reveal the secret communication. In this paper, the study of the different steganography techniques is observed along with their artifacts that left after data hiding and proposed a multiclass discriminator for blind steganalysis along with novel features determination approach that adds advantage for discrimination of stego and cover images with 98.2% accuracy.
RtTSLC: A Framework for Real-Time Two-Handed Sign Language Translation

Indian Sign Language is one of the most prevalent sign languages used in South Asia. Being a two-handed language, Indian Sign Language faces various problems when it comes to recognition and classification. The research on the interpretation of such two-handed sign languages is still in its early stages of growth. To explore the real-time translation of sign languages, the RtTSLC framework is proposed, which translates 26 finger-spelled English alphabet gestures in Indian Sign Language to their corresponding letters. The framework introduces a new technique that creates bounding boxes for two-handed sign languages, and as for classification, state-of-the-art deep learning architectures like VGG16, EfficientNet, and AlexNet have been used. The RtTSLC framework provided promising results, with a recognition accuracy of 92% with the VGG16 architecture, 99% with EfficientNetB0, and 89% with AlexNet in real time. The results provide evidence that the real-time classification framework proposed is viable for recognition and will help the community of people who are hard of hearing.
Meta-Learned Word Embeddings for Few-Shot Sentiment Classification

For each new domain, researchers and technologists developing sentiment classification algorithms must gather and curate data. In particular for domains where the features change over time, the work required to annotate these corpora is prohibitive. The idea of training classifiers on a limited set of domains before using them on further domains is explored in this study. One issue is that classifiers frequently perform poorly when the distributions of the training and testing sets of data are different. Another issue is that it is unclear how to choose domains that are suitable for training and testing other domains based on similarities. To train classifiers that generalize and still perform well on test sets that come from various distributions, the authors approach this challenge by making use of numerous, smaller training datasets. The authors suggest using current developments in meta-learning to learn stronger cross-domain embeddings that can generalize for the few-shot learning context to achieve this. This work investigates sentiment classification using few-shot learning. There are a variety of existing approaches to this problem, but recent advances in meta-learning have proven to be quite effective in the few-shot context. For few-shot learning, the authors suggest using MAML (Model Agnostic Meta-Learning) to quickly train better cross-domain embeddings and networks. Our final model performs at a cutting-edge level.
Chess Board: Performance of Alpha–Beta Pruning in Reducing Node Count of Minimax Tree

As the processing power of modern-day computers is increasing at an exponential rate, computers are able to process data at increasingly high speeds. Computers are able to surpass the limits of even the human mind by combining speed with ingenious algorithms. One example of this is the game of Chess where computers have proven to be a worthy foe to even the smartest of people. This fact is not so surprising to most people because they see the potential of artificial intelligence and machine learning algorithms. However, machines have been defeating humans for quite some time now, and not high-tech supercomputers but regular personal computers. How are computers performing better than humans in the most complex game on this planet while all our lives we have learned that computers are ‘dumb machines’ which do exactly what a programmer says. Well, a programmer cannot code all 10^120 possible games of chess so computers have to rely on two things: math and computation power. The computer uses brute force to analyse all the outcomes of the game by creating a search tree. It evaluates the search tree to find the best possible moves that would enhance the chances of winning through the minimax algorithm. However, the search tree constructed is huge, and to analyse every node in the minimax algorithm is not feasible. This is where alpha–beta pruning comes to picture. It is a search algorithm that decreases the number of nodes to be evaluated by the minimax algorithm in its search tree. As alpha–beta pruning became more popular, there were extensions made to it to increase the potency of it even further. The two mentioned in the thesis are the Transposition Tables and History Heuristic. This paper explains in detail about how they function and how effective they are at achieving the expected results.
Online Label Refinement for Weakly Semi-supervised Semantic Vehicle Parsing Using CNN and Transformer

Pixel-level semantic segmentation, or part segmentation, is currently one of the widely researched and implemented techniques pertaining to segmentation tasks. While there are a considerable number of works in the literature involving humans, semantic part segmentation tasks involving only particular classes of rigid bodies like vehicles is an area of research that can be explored more. This area of vehicle part segmentation can support the problem of automated vehicle damage assessment to identify the damages or missing parts of a vehicle. In this work, a weakly semi-supervised part segmentation technique using a strategical online label refinement process is adopted for the task of segmenting car parts. A novel architecture is proposed which captures both the boundary information and the parsing information (semantic features) of car parts. This technique is experimented on a car parts dataset (DSMLR) and the model is evaluated using mean Intersection-over-Union (mIoU), Pixel Accuracy (PA), and mean Accuracy (mA) metrics. Various CNN Transformer architectures are used for the training and evaluation processes, and an extensive comparison is made across the same. Overall, an mIoU of 73.85 is achieved using CNN architectures, and 73.4 using Transformer architectures for this task, where the existing works in literature reported the mAP metric for the same.