Reviewerly/exHarmony huggingface dataset:
Sensitivity of Quantum-Enhanced Interferometers

We review various schemes of quantum-enhanced optical interferometers, both linear (SU(2)) and non-linear (SU(1,1)) ones, as well as hybrid SU(2)/SU(1,1) options, using the unified modular approach based on the Quantum Cramèr–Rao bound (QCRB), and taking into account the practical limitations pertinent to all real-world highly-sensitive interferometers. We focus on three important cases defined by the interferometer symmetry: (i) the asymmetric single-arm interferometer; (ii) the symmetric two-arm interferometer with the antisymmetric phase shifts in the arms; and (iii) the symmetric two-arm interferometer with the symmetric phase shifts in the arms. We show that while the optimal regimes for these cases differ significantly, their QCRBs asymptotically correspond to the same squeezing-enhanced shot noise limit (2), which first appeared in the pioneering work by C. Caves in 1981.We show also that in all considered cases the QCRB can be asymptotically saturated by the standard (direct or homodyne) detection schemes.
Asset Ownership Transfer and Inventory Using RFID UHF TAGS and Ethereum Blockchain NFTs

In the present, many organizations grow on a daily basis, using many assets to perform their activities and generate profit. In large organizations, all of these assets must be managed, occasionally leading to challenges depending on the organization’s size. For this reason, the role of asset custodian is needed. This role entails assigning the fixed assets to one person for their care, maintenance, and safekeeping. In this process, it is necessary to update information in the central system, leading to further administrative processes, which, in the majority of cases, are carried out through traditional methods. This involves time to obtain wet signatures, a great deal of paperwork, and time for the person or people in charge to update the information. Due to these reasons, the process can be updated partially or entirely to use digital means in order to solve the mentioned inconveniences. This paper presents a proof-of-concept system to offer a modernized and practical solution to this problem using the advantages of blockchain technology, and speeding up the process by using assets identified with UHF RFID technology to permit the reading of many tags that can be embedded and hidden with no need for line-of-sight, allowing fast ownership transfer, using smart contracts in the Ethereum private blockchain.
Memory-Enhanced Transformer for Representation Learning on Temporal Heterogeneous Graphs

Abstract Temporal heterogeneous graphs can model lots of complex systems in the real world, such as social networks and e-commerce applications, which are naturally time-varying and heterogeneous. As most existing graph representation learning methods cannot efficiently handle both of these characteristics, we propose a Transformer-like representation learning model, named THAN, to learn low-dimensional node embeddings preserving the topological structure features, heterogeneous semantics, and dynamic patterns of temporal heterogeneous graphs, simultaneously. Specifically, THAN first samples heterogeneous neighbors with temporal constraints and projects node features into the same vector space, then encodes time information and aggregates the neighborhood influence in different weights via type-aware self-attention. To capture long-term dependencies and evolutionary patterns, we design an optional memory module for storing and evolving dynamic node representations. Experiments on three real-world datasets demonstrate that THAN outperforms the state-of-the-arts in terms of effectiveness with respect to the temporal link prediction task.
The use of a multi-sensor core scanner workflow as the backbone of a digital core repository

Abstract A core repository is a physical record of a country's or commercial organization's subsurface wealth. Some of the largest core repositories hold thousands of kilometres of core material and it is a challenge to turn this physical archive into an accessible digital resource for all. Non-destructive multi-sensor core logger, hyperspectral and X-ray imaging techniques offer a unique chance to rescue valuable data trapped within core samples, improving the way that a core repository delivers data to academic or industrial end users. Here we present a case study of an archived petroleum core acquired in 1985 at the Osprey Field, UK Continental Shelf. Data from the UK National Data Repository are augmented by a multi-sensor core logger, hyperspectral and X-ray dataset that is uploaded into a cloud-based digital repository. The data were analysed using a multi-variant analysis to reclassify the original lithological interpretations, uncovering a greater proportion of clay and cemented horizons than was previously interpreted. A workflow is established to optimize the use of legacy cores and exploit the abundance of data trapped within the core repository using continuous multi-sensor core scanning and imaging data, which are stored within the virtual environment for visualization and access to all.
A survey on multi-objective recommender systems

Recommender systems can be characterized as software solutions that provide users with convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives, e.g., long-term vs. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including (i) competing recommendation quality objectives at the individual and aggregate level, (ii) competing objectives of different involved stakeholders, (iii) long-term vs. short-term objectives, (iv) objectives at the user interface level, and (v) engineering related objectives. In this paper, we review these types of multi-objective recommendation settings and outline open challenges in this area.
THE USE OF PARALINGUISTICS IN JIM BUTCHER'S WORK

In this article, we explore the use of paralinguistics in Jim Butcher's work, specifically in his popular series, The Dresden Files. Through a comprehensive analysis of various paralinguistic cues used in his writing, we delve into how Butcher creates a rich and immersive reading experience for his fans.
Real-World Underwater Image Enhancement Based on Attention U-Net

In recent years, with the increasingly serious problems of resource shortage and environmental pollution, the exploration and development of underwater clean energy were particularly important. At the same time, abundant underwater resources and species have attracted a large number of scientists to carry out research on underwater-related tasks. Due to the diversity and complexity of underwater environments, it is difficult to perform related vision tasks, such as underwater target detection and capture. The development of digital image technology has been relatively mature, and it has been applied in many fields and achieved remarkable results, but the research on underwater image processing technology is rarely effective. The underwater environment is much more complicated than that on land, and there is no light source underwater. Underwater imaging systems must rely on artificial light sources for illumination. When light travels through water, it is severely attenuated by water absorption, reflection, and scattering. The collected underwater images inevitably have problems such as limited visible range, blur, low contrast, uneven illumination, incoherent colors, and noise. The purpose of image enhancement is to improve or solve one or more of the above problems in a targeted manner. Therefore, underwater image enhancement technology has become one of the key contents of underwater image processing technology research. In this paper, we proposed a conditional generative adversarial network model based on attention U-Net which contains an attention gate mechanism that could filter invalid feature information and capture contour, local texture, and style information effectively. Furthermore, we formulate an objective function through three different loss functions, which can evaluate image quality from global content, color, and structural information. Finally, we performed end-to-end training on the UIEB real-world underwater image dataset. The comparison experiments show that our method outperforms all comparative methods, the ablation experiments show that the loss function proposed in this paper outperforms a single loss function, and finally, the generalizability of our method is verified by executing on two different datasets, UIEB and EUVP.
Automated segmentation and morphological characterization of placental intervillous space based on a single labeled image

In this study, a novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. As a result, a diversified artificial dataset of images is generated that can be used for training deep learning segmentation models. We have observed that on average the presented method of data augmentation led to a 42% decrease in the binary cross-entropy loss of the validation dataset compared to the common approach in the literature. Additionally, the morphology of the intervillous space is studied under the effect of the proposed image reconstruction technique, and the diversity of the artificially generated population is quantified. We have demonstrated that the proposed method results in a more accurate morphological characterization of the placental intervillous space with an average feature relative error of 6.5%, which is significantly lower than the 11.5% error observed with conventional augmentation techniques. Due to the high resemblance of the generated images to the real ones, applications of the proposed method may not be limited to placental histological images, and it is recommended that other types of tissue be investigated in future studies.
Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment

For a sustainable operation of multiple Self-Guided Vehicles (SGVs) in a dynamic manufacturing environment, it is essential to guarantee collision-free and efficient navigation to the autonomous mobile platforms and safety to the surrounding subjects. To prevent from navigation failures, an SGV must avoid conflicts that constrain itself to abruptly brake or stop to avoid collisions. These inefficient conflicts result from unexpected changes in the configuration space or due to nearby unforeseen obstacle. In this paper, a navigation approach is proposed to adapt the global trajectory in order to reduce conflict occurrence while limiting energy consumption of the mobile platform. To generate such trajectory, first the collision risks are characterized using an objective risk perception parameter, the Time-To-Collision TTC, that rely on the kinematics of the egoSGV and the neighboring obstacles. Next, weighted Kernel Density Estimation (wKDE) defines the spatial distribution of conflict severity in configuration space. The defined zones are incorporated as a conflict layer in the global map. Then, a global trajectory planner algorithm is used to weigh between the length cost and conflict cost. Finally, to test the proposed solution, a simulation is performed in a factory-like environment, then an experiment is conducted with a real SGV. In comparison with the state-of-the-art geometrical path planning method, the results show that the proposed approach reduces navigation failures by up to 52%, while reducing the trajectory execution time by around up to 10 %. Also, the smoothness of the executed motion allowed to reduce energy consumption by over 12%.
CarDD: A New Dataset for Vision-Based Car Damage Detection

Automatic car damage detection has attracted significant attention in the car insurance business.However, due to the lack of high-quality and publicly available datasets, we can hardly learn a feasible model for car damage detection.To this end, we contribute with Car Damage Detection (CarDD), the first public large-scale dataset designed for vision-based car damage detection and segmentation.Our CarDD contains 4,000 high-resolution car damage images with over 9,000 wellannotated instances of six damage categories (examples are shown in Figure 1).We detail the image collection, selection, and annotation processes, and present a statistical dataset analysis.Furthermore, we conduct extensive experiments on CarDD with state-of-the-art deep methods for different tasks and provide comprehensive analyses to highlight the specialty of car damage detection.CarDD dataset and the source code are available at https://cardd-ustc.github.io.
Object Detection using YOLO: A Survey

In recent years, object detection is becoming very popular field in computer vision developments. Object detection has many applications viz. vehicle detection, pedestrian detection, blood cell counting etc. Various studies have been conducted in order to improve object detecting accuracy and speed. The latest technique is You Only Look Once object detection. It is state-of-the-art detection technique and considered as a regression problem. YOLO has the ability to predict various objects present in an image in a single run. This paper presents a survey of various detections based on YOLO which aims to improve the accuracy of existing system. This paper presents various modifications done on basic YOLO method and shows their analysis.
Gender and Age Classification Enabled Blockchain Security Mechanism for assisting Mobile Application

As the blockchain has numerous benefits like decentralization, anonymity, auditability, and lastly persistency. The blockchain is used in a wide spectrum of applications like cryptocurrency, risk management, financial server, IoT, and also in various public and social services. This study is focused on the various application of the blockchain in gender and age classifications that might occur in mobile application development. As there is a huge gap in the study of blockchain in the development of mobile applications. To fill the gap we have done a deep and thorough analysis here in this research paper so that this study might help various researchers with the data that they are looking for as well as this research paper will help various businesses that are interested in implementing the blockchain in their various business approaches.
The Block Chain Technology to protect Data Access using Intelligent Contracts Mechanism Security Framework for 5G Networks

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security operations.Our literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants.
Implementation and Execution of Blockchain Technology in the Field of Education

Sharing student educational credentials is a crucial and essential part of the educational system, which includes a variety of stakeholders including students, educational institutions, firms, teachers, and the governing body. Currently, all of these parties must exert a great deal of effort to guarantee the validity and confidentiality of students’ educational credentials. Despite these initiatives, the process of transmitting student educational credentials is difficult, susceptible to mistakes, and not entirely safe. Our goal is to employ blockchain technology to lessen the security concerns that now exist with regard to the sharing of student educational credentials. Thus, this article employs blockchain technology to address security concerns about the sharing of student educational credentials. It suggests a revolutionary blockchain-based architecture, then goes on to describe how to implement it as a decentralized application (DApp). On the basis of the trials run, cost and performance analyses are also done.
Diagnose Colon and Lung Cancer Histopathological Images Using Pre-Trained Machine Learning Model

Lung cancers and colon cancers are two of the leading causes of morbidity and mortality in human being. One of the essential elements to determining the type of cancer is the histopathological diagnosis. One of the most hazardous and severe diseases that people experience worldwide is colon and lung cancer, which has spread to become a common medical issue. It is very important to make a reliable and early discovery in order to reduce the danger of death. The difficulty of the task ultimately depends on the histopathologists’ experience. Recent times have seen a rise in the popularity of deep learning, which is now appreciated in the interpretation of medical imaging. As a result, artificial intelligence will soon become a useful technology. In order to identify lung cancers and colon cancer using histopathological pictures and more effective augmentation strategies, this research aims to utilize and modify the current pre-trained Convolutional Neural Network (CNN) based model. From the LC25000 dataset, the results were obtained. Precision, recall, f1score, and accuracy are all used to estimate the model performances. The findings show that the pre-trained and improved pre-trained models produced impressive outcomes ranging from 93% to 97% accuracy.
Database Management Difficulties in the Internet of Things

The “Internet of Things (IoT)” is an internet protocol for which real-world, virtual as well as digital objects are given recognition, detecting, connectivity, and process technology so they can interact with one another and other Internet-connected devices and services to carry out users’ tasks. There are many IoT solutions available to improve and comfort civilian lives. Additionally, the use of IoT technology in the automotive sector gave rise to the concept of the “Industrial Internet of Things (IIoT),” which has simplified the usage of Cyber Physic Systems, which enable machine and human communication. In general, the variety, heterogeneity, and vast volume of data produced by these businesses make the use of traditional database management systems inappropriate. While constructing IoT data management systems, a number of special issues should be taken into account. These varied guiding notions have led to the proposal of range Of iot data management strategies. The Internet of Things will undoubtedly become a realization as more gadgets are linked to the Internet. Massive amounts of information will be instantiated by items in environment. A high rate and numerous increments will be made to its quantity. This research paper has highlighted database management and its challenges in IoT technology through secondary qualitative analysis.
Some Blockchain Design Patterns for Overcoming Immutability, Chain-Boundedness, and Gas Fees

Ethereum network has introduced an important innovation to blockchain technology that has deeply changed the world of blockchains and cryptocurrencies: Turing-complete smart contracts. Thanks to smart contracts it is possible to mint fungible and non-fungible tokens, manage decentralized organizations and offer services such as digital identity, advertisement, time commitments, decentralized exchanges, etc. However, smart contract development is not free from challenges. This paper analyses a set of design patterns that have been documented and groups them according to related smart contracts' weaknesses. Moreover, two novel solutions are presented in the form of design patterns. The first is a micro architecture that deals with a treasure reserve and aims at reducing gas consumption when having to update the code of a smart contract. The second presents a solution for packing bits each representing a Boolean value.
Finetuning BERT on Partially Annotated NER Corpora

Most Named Entity Recognition (NER) models operate under the assumption that training datasets are fully labelled. While it is valid for established datasets like CoNLL 2003 and OntoNotes, sometimes it is not feasible to obtain the complete dataset annotation. These situations may occur, for instance, after selective annotation of entities for cost reduction. This work presents an approach to finetuning BERT on such partially labelled datasets using self-supervision and label preprocessing. Our approach outperforms the previous LSTM-based label preprocessing baseline, significantly improving the performance on poorly labelled datasets. We demonstrate that following our approach while finetuning RoBERTa on CoNLL 2003 dataset with only 10% of total entities labelled is enough to reach the performance of the baseline trained on the same dataset with 50% of the entities labelled.
Medical Educators’ Response to Changes in Medical Education due to COVID‐19

To analyze medical professors’ evaluation of their online education experiences in an attempt to support faculty members and indicate the future direction of medical education. Faculty members who taught online in the first semester of 2020 at Yonsei University College of Medicine in South Korea were surveyed. The results of the survey were analyzed in June 2020. There were 88 respondents (35.1% of 251): 59 professors (67.0%), 16 associate professors (18.2%), and 13 assistant professors (14.8%). Their specialties lay in basic medicine (25.0%), clinical medicine (65.9%), and research and special purposes (9.1%). Sixteen participants (18.2%) had previous experience in online lectures; 23 (26.1%) reported that preparation time for online lectures was the same as before; 65 (73.9%) reported that the preparation time had increased; 38 (43.2%) faced difficulties in preparation and lecturing online, and among them 16 (42.1%) indicated inadequate interaction with students; 11 (28.9%) needed extra preparation time; and 11 (28.9%) attributed their difficulties to technical issues with the online platform. Participants’ satisfaction with online lectures was low (p