Reviewerly/exHarmony huggingface dataset:
MC-OCR Challenge 2021: An end-to-end recognition framework for Vietnamese Receipts

Recognizing text from receipts is a significant step in automating office processes for many fields such as finance and accounting. MC-OCR Challenge has formed this problem into two tasks (1) evaluating the quality, and (2) recognizing required fields of the captured receipt. Our proposed framework is based on three key components: preprocessing with receipt detection using Faster R-CNN, alignment based on the angle and direction of rotation; estimate the receipt image quality score in task 1 using EfficientNet-B4 which has been retrained using transfer learning; while PAN is for text detection and VietOCR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for text recognition. In the final round, our systems have achieved the best result in task 1 (0.1 RMSE) and a comparable result with other teams (0.3 CER) in task 2 which demonstrated the effectiveness of the proposed method.
No-reference image quality assessment for dehazed images

Traditional image quality prediction methods require the pristine image as a reference, such as structural similarity. However, it is difficult to provide a haze-free image as a reference when predicting the quality of the dehazed image. Therefore, it is necessary to use a no-reference image quality assessment (NR-IQA) method. In addition, most NR-IQA methods are based on known distortion type, using a large number of subjective opinion scores and images with the same distortion to train the model. We developed an innovative NR-IQA specifically for dehazed images without such prior knowledge. Since most images will undergo color distortion and blur after dehazing, it is proposed to combine color and sharpness for evaluation. The quality of the image is evaluated on the HSI color space, where the H and S channels are utilized to evaluate color, and the I channel to sharpness. Experimental results show that the performance of the proposed metric is better than other existing evaluation methods for dehazed images.
Real-Time Motion Analysis System Using Low-Cost Web Cameras and Wearable Skin Markers

COVID-19 has restricted outdoor exercise and hospital visits for rehabilitation therapy. Home-based training and rehabilitation coaching systems have emerged as a way to overcome these circumstances. Conventional optical motion-capture systems, such as VICON, have been used for measuring precise movement and providing posture feedback during exercise or rehabilitation; however, its application is limited to professional facilities because of its high cost and space requirement. To extend the applicability to home-based use, we designed wearable skin markers (WSMs) with body segment-specific patterns that can be detected by low-cost web cameras. WSMs are band-shaped and stretchable and thus can be worn like cloth, with minimal effort for placement. The body segment-specific patterns enable real-time data processing, which reduces the marker data post-processing time. A 6-degree-of-freedom (DOF) pose for each WSM is obtained by recognizing the segment-specific patterns; the 3D configuration of the contoured corners of the patterns found by triangulation is then utilized to construct the coordinates of each WSM. The WSM system was validated via three experiments. The robustness of marker recognition was evaluated by measuring the false-positive and false-negative rates of WSM. For accuracy validation, the angle estimation results were obtained for the mechanical joint of a 3-DOF gimbal and lower-limb joints of a walking human subject and compared to the reference systems. The gimbal experiment was included to evaluate the accuracy of our system in the condition with no skin movement artifact. The maximum standard deviation of the difference between WSM and the encoder was 0.9 ° for the gimbal experiment, and that between WSM and VICON was 5.0 ° for the human experiment. The accuracy was comparable to the reference systems, making it suitable for home environment application.
THE TYPES OF BIOLOGICAL DATABASES (BIOBANKS)

In the world, it is customary to create biological databases of different species. And initially, the databases for the investigation of crimes were widespread. However, later, when their potential and benefits, including for medicine, were assessed, the databases for other areas appeared. Russia was no exception in this regard. Although, in our country, unlike foreign states, the activities of biological databases based on purposes other than the disclosure of crimes are practically not regulated in any way. This article deals with the analysis of legal regulation of biobanks in the Russian Federation and abroad. Special attention is paid to the classification of biobanks. The purpose of the study is to determine the feasibility in the legislative regulation of their activities, as well as the patterns in such a regulation. To achieve this goal, the authors studied extensive regulatory material, which included EU directives and national regulations of the EU member states. The methodological basis of the study was the general scientific and private scientific meth-ods of research. Of course, such private scientific research methods as the comparative-legal method and the formal legal method have been widely used. Due to the comparative legal analysis, it is established that the EU countries have a high level of legislative activity in terms of determining the legal regime of biological databases. All countries recognize the specifics of such a legal regime, which can largely be explained by a special legal nature of biological samples and biological data. In this regard, the following issues related to the activities of biological databases are reflected everywhere in the EU countries at the level of law: the procedure for their creation; the procedure for receiving, processing, storing and transmitting biological samples and the data obtained on their basis; the rights and obligations of database creators and persons who have provided their biological samples and biological data about themselves; a set of measures aimed at protecting the rights and interests of donors and third parties, etc. As it seems, a similar approach to the regulation of the activities of biological bases estab-lished not for the investigation of crimes should be implemented by Russia. At the same time, special attention should be paid to the research of biological databases. In the Russian Federa-tion, they are created, as a rule, at the local level. Their main drawback is that they are sepa-rate sources of limited biological information, functioning independently of each other while comprehensive (concentrated in one place) information can bring invaluable benefits and advantages for Russian science and medicine as a whole. However, this requires the estab-lishment of an appropriate legal framework.
Detection of GPS Spoofing Attacks Based on Isolation Forest

As Global Positioning System(GPS) use non-encryption signals,GPS receivers are vulnerable to spoofing attacks,most of applications of defense base on detect signal power or spatial position,which need expensive hardware.In the recent years,machine learning algorithm have been used to detect the spoofing as a low-cost method. This paper proposes a detection method based on isolation forest algorithm to detect the GPS spoofing.We use observation files from satellite as data source.We use the detection score acquired by the polymerization degree of each data to detect data,which may be normal or abnormal,to determine if the GPS receiver is suffering the spoofing attack.The simulation compares different combinations of input features derived from satellite signals.The results show that the most efficient combination can reach at least 95% accuracy.
QED Coherence and Super-Coherence of Water in Brain Microtubules and Quantum Hypercomputation

The possibility to explain higher human brain functions as the result of quantum computation is a very fascinating hypothesis considered in a certain number of theoretical studies. Nevertheless, none of these has so far proposed a convincing model able to explain the ultimate origin and the features of such quantum computational schemes. According to our model, water trapped inside brain microtubules can undergo a spontaneous quantum phase transition toward a macroscopic coherent quantum state in which water molecules oscillate in phase with an e. m. field, condensed from quantum vacuum, corresponding to a suitable electronic transition. This implies the arising of macroscopic quantum “Coherent Domains”, in which the oscillating e.m. field is trapped and which shows, at their boundaries, an evanescent-wave-like behavior. We also discuss how the special dynamics of water quantum domains and the hypothesis that microtubule’s walls could show some features similar to those characterizing artificial metamaterials allow a superfast interaction between brain microtubules mediated by a virtual photon tunneling, also determining a sort of super-coherence between microtubules. The resulting interaction network, involving a very high number of quantum objects, could give a novel theoretical basis for explaining the actual origin and features of quantum hyper-computational processes in human brain.
A Stable and Secure One-Time-Password Generation Mechanism using Fingerprint Features

The growth of online application used for financial transactions and transferring personal information is increasingly common on internet and in mobile communication. These applications require authenticating legitimate users by assigning digital identities. Static passwords are perhaps most common type of credentials used today to authenticate the users. To avoid tedious task of remembering passwords, users often behave less securely by using low entropy and weak passwords, thus presenting security threats to online services. Various solutions have been provided to eliminate the need of users to create and manage passwords’ typical solution is based on generating one time password (OTP) for a single session or transaction. Unfortunately in most of the general mechanisms used for generating one time password (OTP) randomness of OTP system breaks after certain period of time and hence passwords become predictable. To solve this problem, in this paper a novel OTP generation method has been proposed, which generates OTP from fingerprint features of the user. The OTP produced from the system is secure as it uses fingerprint features in the seed and RIPEMD160 hash function in OTP generation procedure.
The Impact of Global Structural Information in Graph Neural Networks Applications

Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours. A known limitation of GNNs is that, as the number of layers increases, information gets smoothed and squashed and node embeddings become indistinguishable, negatively affecting performance. Therefore, practical GNN models employ few layers and only leverage the graph structure in terms of limited, small neighbourhoods around each node. Inevitably, practical GNNs do not capture information depending on the global structure of the graph. While there have been several works studying the limitations and expressivity of GNNs, the question of whether practical applications on graph structured data require global structural knowledge or not remains unanswered. In this work, we empirically address this question by giving access to global information to several GNN models, and observing the impact it has on downstream performance. Our results show that global information can in fact provide significant benefits for common graph-related tasks. We further identify a novel regularization strategy that leads to an average accuracy improvement of more than 5% on all considered tasks.
A comprehensive survey on aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is the sub-field of natural language processing that deals with breaking or splitting texts into aspects and then classification into sentiments like happy, sad, angry, etc. towards the aspect. Aspect-based sentiment analysis provides more information about the context than general sentiment analysis. In this survey paper, we compared a variety of papers and the methods that were introduced through them on achieving better results for the problem at hand. As ABSA is currently being used for customer satisfaction and product feedback most of the papers have datasets containing reviews. This survey paper discusses various solutions in-depth and gives a comparison between them. Firstly, we discuss rule-based approaches and then move to complex approaches that involve both rule-based approaches and neural networks. Also, major emphasis is shed on the principles and the workflow of the method discussed.
Asymptotic Spectral Representation of Linear Convolutional Layers

By stacking a number of convolutional layers, convolutional neural networks (CNNs) have made remarkable performance boosts in many artificial intelligence applications. While the convolution operation is well-understood, it is still a mystery why repeated convolutions yield so good expressive power and generalization performance. Noting that the linear convolution operation can be represented as a matrix-vector product with the matrix being of a Toeplitz structure, we propose to inspect the individual convolutional layer through its asymptotic spectral representation - the spectral density matrix - by leveraging Toeplitz matrix theory. Thanks to such spectral representation, we are able to develop a simple singular value approximation method with improved accuracy, and spectral norm upper bounds with reduced computational complexity, compared with the state-of-the-art methods. Both the improved approximation and upper bounds can be employed as regularization techniques to further enhance the generalization performance of CNNs. By extensive experiments on well-deployed CNN models (e.g., ResNets), we also demonstrate that the approximation approach achieves higher accuracy and the upper bounds are effective spectral regularizers for generalization.
A location privacy protection algorithm based on differential privacy in sensor network

The continuous development and innovation in sensor networks has simplified the processes of data collection and transmission and has led to their widespread use. However, these processes often involve sensitive location information. Therefore, the location of a sensor should be protected for privacy reasons during data collection and transmission. In this paper, we propose a privacy protection scheme for location in a sensor network, based on differential privacy. Although the algorithm satisfies the privacy protection requirements, the accumulation of noise in the privacy protection process may decrease the utility of the data. To solve this problem, we propose a square sum error-based location privacy protection algorithm. Our algorithm can reduce the accumulation of noise, while also protecting the structure of the sensor network.
Detection of scene-irrelevant head movements via eye-head coordination information

Accurate motion tracking in head-mounted displays (HMDs) has been widely used in immersive VR interaction technologies. However, tracking the head motion of users at all times is not always desirable. During a session of HMD usage, users may make scene-irrelevant head rotations, such as adjusting the head position to avoid neck pain or responding to distractions from the physical world. To the best of our knowledge, this is the first study that addresses the problem of scene-irrelevant head movements. We trained a classifier to detect scene-irrelevant motions using temporal eyehead-coordinated information sequences. To investigate the usefulness of the detection results, we propose a technique to suspend motion tracking in HMDs where scene-irrelevant motions are detected. /Conclusions Experimental results demonstrate that the scene-relevancy of movements can be detected using eye-head coordination information, and that ignoring scene-irrelevant head motions in HMDs improves user continuity without increasing sickness or breaking immersion.
Dynamic Hand Segmentation

Real-time segmentation of moving areas in picture sequences is required for many vision systems, including automated visual surveillance, human-computer interactions, and very low-bandwidth telecommunications. Hand gesture segmentation faces numerous obstacles, including fluctuating illumination, shadows, and varied backgrounds. Despite the fact that there are a various techniques for detecting moving hand areas, they have a tendency to produce false positives. This paper proposes a robust hand gesture segmentation approach based on adaptive background removal with a skin colour based threshold. Under a variety of lighting conditions and complicated backgrounds, the suggested approach attempts to automatically extract hand gestures from a video. The proposed approach gives better accuracy when compared with the traditional techniques as per the findings of the experiments. Python and OpenCV are used to program the system.
Toward Artificial Intelligence–Driven Pathology Assessment for Hematologic Malignancies

In this issue of Blood Cancer Discovery, Brück et al applied unsupervised and supervised machine learning to bone marrow histopathology images from Myelodysplastic Syndrome (MDS) patients. Their study provides new insights into the pathobiology of MDS and paves the way for increased use of artificial intelligence for the assessment and diagnosis of hematological malignancies.
Multi-Site Best Practice Discovery: From Free Text to Standardized Concepts to Clinical Decisions

This study establishes interoperability among electronic medical records from 800 clinical sites and uses machine learning for best practice discovery. A novel extraction-mapping algorithm is designed that accurately extracts, summarizes, and maps free text and content to concise structured medical concepts. Multiple concepts are mapped, including patient diagnoses, laboratory results, medications, and procedures, which allow shared characterization and hierarchical comparison. In addition, clinical and decision processes are established. These integrated data can be accessed through a secure web-based portal. A classification machine learning model (DAMIP) is then leveraged to establish predictive rules by uncovering relatively small subsets of discriminatory features that can predict the quality of treatment outcomes. We demonstrate system usability by analyzing treatment outcome for cardiovascular diseases, diabetes, hypertension, and chronic kidney disease. DAMIP yields good blind prediction accuracies of 89% – 97%. Moreover, for each disease the best practice was only used at fewer than 5% of the clinical sites, offering an excellent opportunity for knowledge sharing and rapid learning. Our findings also led to implementation of a new treatment policy for chronic kidney disease management. This resulting policy offers a better outcome for patients, saves lives, improves the quality of life for patients, and reduces 35% of treatment costs. Thus, this work has critical health practice implications. Through data-driven interoperability and predictive analytics, we have established and identified practice characteristics that result in good outcomes. The system is scalable and generalizable to different hospital settings and health conditions.
Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.
The uberisation of higher education

Data capitalism builds on the expansion of markets through the datafication of evermore social domains via social media, search engines and interconnected online communication technologies. Education is one such domain in which datafication has entered, and with the COVID-19 pandemic, the introduction of large-scale online teaching has become a necessity, which has further speeded up the transfer from campus-based to online education. With the move of teachers and students from on-site to on-line mode, universities are now increasingly acting like platform companies. Classrooms, lecture halls, offices and meeting rooms are abandoned, and the sites of learning are now delegated to the private sphere of the home of lecturers and students. First, this means transferring the responsibilities for creating a functional educational environment to teachers and students. Second, this would transform large parts of educational practices, thus affecting the very essence of knowledge production and dissemination. This aim of the chapter is to discuss the economic and administrative dynamics behind this transformation on the basis of the different value regimes underlying data capitalism. It is argued that the drive towards the platformisation – or uberisation as one could provocatively label it – of education is partly driven by an economic, and partly by administrative rationality.
Argue to Learn: Accelerated Argumentation-Based Learning

Human agents can acquire knowledge and learn through argumentation. Inspired by this fact, we propose a novel argumentation-based machine learning technique that can be used for online incremental learning scenarios. Existing methods for online incremental learning problems typically do not generalize well from just a few learning instances. Our previous argumentation-based online incremental learning method outperformed state-of-the-art methods in terms of accuracy and learning speed. However, it was neither memory-efficient nor computationally efficient since the algorithm used the power set of the feature values for updating the model. In this paper, we propose an accelerated version of the algorithm, with polynomial instead of exponential complexity, while achieving higher learning accuracy. The proposed method is at least $200\times$ faster than the original argumentation-based learning method and is more memory-efficient.