Reviewerly/exHarmony huggingface dataset:
Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology

Several advances in recent decades in digital imaging, artificial intelligence, and multiplex modalities have improved our ability to automatically analyze and interpret imaging data. Imaging technologies such as optical coherence tomography, optical projection tomography, and quantitative phase microscopy allow analysis of tissues and cells in 3-dimensions and with subcellular granularity. Improvements in computer vision and machine learning have made algorithms more successful in automatically identifying important features to diagnose disease. Many new automated multiplex modalities such as antibody barcoding with cleavable DNA (ABCD), single cell analysis for tumor phenotyping (SCANT), fast analytical screening technique fine needle aspiration (FAST-FNA), and portable fluorescence-based image cytometry analyzer (CytoPAN) are under investigation. These have shown great promise in their ability to automatically analyze several biomarkers concurrently with high sensitivity, even in paucicellular samples, lending themselves well as tools in FNA. Not yet widely adopted for clinical use, many have successfully been applied to human samples. Once clinically validated, some of these technologies are poised to change the routine practice of cytopathology.
Your Censor is My Censor: Weaponizing Censorship Infrastructure for Availability Attacks

Nationwide Internet censorship threatens free and open access to communication and information for millions of users living inside of censoring regimes. In this paper, we show that this poses an even greater threat to the Internet than previously understood. We demonstrate an off-path attack that exploits a little-studied but widespread feature of many censoring infrastructures: residual censorship, in which a censor continues blocking traffic between two end-hosts for some time after a censorship event. Our attack sends spoofed packets with censored content, keeping two victim end-hosts separated by a censor from being able to communicate with one another. Although conceptually simple, this attack has several challenges, which we address. We demonstrate the feasibility of the attack through two studies: one to capture the current state of residual censorship, and another to actually launch the attack (against machines we control). We show that the attack can be launched despite stateful TCP tracking used by many censors, and that it also works against those who censor by null-routing. We will be making our code publicly available.
Library space and COVID-19: Re-thinking of place and re-designing of digital space

Libraries for years now have a distinct and constant reference for collection development and information services offered—their building. Even digital libraries accommodate physical space, work offices, and premises for their staff. In the era of health pandemic, physical distancing and contactless communication are promoted as necessary measures to ensure public health, whereas teleworking and home office are widely considered as the new "normal." Physical space and library buildings of gathering, socializing, interacting, collaborating, learning, community building, co-creating, and co-working become obsolete, or maybe not? This paper focuses on the challenges the closing down of the building is posing for the libraries (of every type) and thus the need to develop and promote a digital/virtual space that would address the same and additional needs of the physical space.
Optical Character Recognition and Neural Machine Translation Using Deep Learning Techniques

Over the years, the applications of text detection and text translation have expanded across various fields. Many researchers have used several deep learning algorithms for text detection and text translation separately. We propose a hybrid methodology to use NMT with OCR to develop a better result to perform text detection and translation from an image. In this paper, we present techniques to detect and recognize texts in Hindi from a given image and translate them into English and vice versa. To achieve this, we are combining two concepts: optical character recognition (OCR) and neural machine translation (NMT). The output from this hybrid scheme gives the optimized feature.
Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.
Bringing Human-Centredness to Technologies for Buildings

The politics around data and power relations related to technologies for buildings is a new area for HCI. This paper proposes an agenda for linking new types of data to the challenge of sustainability, bringing human-centredness to a particular tool for design and engineering professionals, Building Information Modeling (BIM). BIM is the preferred technology platform for coordination and collaboration in architectural design and construction. BIM contains different types of data and information about a building including 3D (geometry), 4D (time), 5D (cost), 6D (facility management) and 7D (sustainability). Once constructed, this ‘digital twin’ of the building allows for adding new services and for stakeholders interacting with the building design through through sensors, immersive experiences and virtual, augmented and mixed realities. As a socio-technical software process, BIM also accommodates diverging agendas on design and construction for sustainability, and these diverging concepts about ‘sustainability’ “live” in different places with implications for the resulting BIM models. Based on our findings, we suggest a better integration and coherent representation of such issues of interest not only to new services but also stakeholders into the different forms of data (e.g. facilities management and sustainability). We argue for a stronger shared understanding of BIM as a platform for engaging with technologies designed for interacting with buildings and push agendas of sustainable construction.
Research on the Application of Artificial Intelligence Technology in the Field of Sports Refereeing

Abstract This article uses artificial intelligence machine vision technology to design a method for judging violations of passing passes. First, use Adaptive Enhanced Variant Algorithm (GAB) to train the classification model to complete the detection of sports (badminton and racket); combine the detection area to extract the sphere center coordinates are used as the target point, and the detected racket part is constructed as the target area. Then the position relationship between the two is defined, and the judgment key frame is extracted according to the relative position change relationship between the target point and the target area; finally, the image angle representation method is used to establish the state judgment model takes the state of the racket in the judgment key frame as an input, and quantizes the output result as an angle value, so as to realize the judgment on whether to serve the ball.
Investigating gaze interaction to support children’s gameplay

Gaze interaction has become an affordable option in the development of innovative interaction methods for user input.Gaze holds great promise as an input modality, offering increased immersion and opportunities for combined interactions (e.g., gaze and mouse, touch).However, the use of gaze as an input modality to support children's gameplay has not been examined to unveil those opportunities.To investigate the potential of gaze interaction to support children's gameplay, we designed and developed a game that enables children to utilize gaze interaction as an input modality.Then, we performed a between subjects research design study with 28 children using mouse as an input mechanism and 29 children using their gaze (8-14 years old).During the study, we collected children's attitudes (via self-reported questionnaire) and actual usage behavior (using facial video, physiological data and computer logs).The results show no significant difference on children's attitudes regarding the ease of use and enjoyment of the two conditions, as well as on the scores achieved and number of sessions played.Usage data from children's facial video and physiological data show that sadness and stress are significantly higher in the mouse condition, while joy, surprise, physiological arousal and emotional arousal are significantly higher in the gaze condition.In addition, our findings highlight the benefits of using multimodal data to reveal children's behavior while playing the game, by complementing self-reported measures.As well, we uncover a need for more studies to examine gaze as an input mechanism.
Hybrid encodings for neuroevolution of convolutional neural networks

The Neuroevolution of Convolutional Neural Networks have yield into highly competitive results in the field of visual recognition in contemporary years. Some of the most recent advances in this field have been related to the design of neural encodings to represent these highly complex Deep Learning structures. Hybrid encodings have shown potential at distributing the representation of these networks into different sub-structures and thus improving the search. In this paper, we propose a compact hybrid encoding, which is used in an evolutionary framework called Deep Genetic Algorithm (DeepGA). We assess the performance of our simple representation against a hybrid encoding based on DenseBlocks, to evaluate how certain encodings might bias the search towards larger CNNs, in both single- and multi-objective scenarios. Our case study is the classification of lung conditions in chest X-ray images.
Feature Engineering for Machine Learning and Deep Learning Assisted Wireless Communication

Feature engineering involves extracting information from raw-data to use in machine learning or deep learning algorithms through feature transformation, feature generation or feature extraction, feature construction, feature selection, etc. Feature engineering optimizes the feature space dimensions, thereby reducing complexity. Hence, makes the data suitable for machine learning or deep learning applications: process prediction, detection accuracy, estimation accuracy, and quality of clustering and classi cation. Traditionally machine learning algorithms have been used in various wireless communication appli cations viz spectrum access and sharing, resource allocation, spectrum coverage, capacity optimization, signal intelligence, etc. This chapter presents the need for feature engineering and its application to machine learning and deep learning in wireless communication. The chapter also gives the details of the feature engineering assisted machine-learning algorithm for automatic modulation classi cation and path-loss prediction in wireless communication.
On the Legal Attributes of Digital Currency

Digital currency has been widely used ever since the concept of Bitcoin was formulated. It uses blockchains as its underlying technical support and is characterized by decentralization, programmability, and security verification based on the principles of cryptology. Generally speaking, theories on the legal attributes of digital currency regard it either as non- monetary property or as currency. The former theory can be subdivided into theories on digital currency as commodity, as securities or as data. All of these subdivisions present insurmountable theoretical difficulties and practical obstacles. If we return to the substantive nature of currency as a generally accepted accounting symbol, it can be seen that currency issuance by the state or a private bank is simply a means of building currency credit rather than a necessary condition. Unlike traditional currency, digital currency relies on blockchain technology to complete the construction of decentralized currency credit. This has resulted in a new theory of currency based on the theory of digital currency. On the one hand, it offers a jurisprudential foundation for the construction of the quasi-currency attributes of digital currency; on the other, it provides a theoretical basis for the gradual improvement of digital currency legislation. We can first determine the legal attributes of digital currency as a quasi-currency and then confirm its legal position when the time is ripe. We need to confirm the legal status of digital currency as a quasi-currency through legislation, and use this as a logical starting point for the construction of a series of legal systems for digital currency as a quasi-currency. Digital currency can easily be used as a tool for crime or for evading financial controls; therefore, we need to determine a regulatory body and formulate regulatory rules.
Promize - Blockchain and Self Sovereign Identity Empowered Mobile ATM Platform

Banks provide interactive money withdrawal/payment facilities, such as ATM, debit and credit card systems. With these systems, customers could withdraw money and make payments without visiting a bank. However, traditional ATM, debit and credit card systems inherit several weaknesses such as limited ATM facilities in rural areas, the high initial cost of ATM deployment, potential security issues in ATM systems, high inter-bank transaction fees etc. Through this research, we propose a blockchain-based peer-to-peer money transfer system “Promize” to address these limitations. The Promize platform provides a blockchain-based, low cost, peer-to-peer money transfer system as an alternative for traditional ATM system and debit/credit card system. Promize provides a self-sovereign identity empowered mobile wallet for its end users. With this, users can withdraw money from registered banking authorities (e.g. shops, outlets etc.) or their friends without going to an ATM. Any user in the Promize platform can act as an ATM, which is introduced as a mobile ATM. The Promize platform provides blockchain-based low-cost inter-bank transaction processing, thereby reducing the high inter-bank transaction fee. The Promize platform guarantees data privacy, confidentiality, non-repudiation, integrity, authenticity and availability when conducting electronic transactions using the blockchain.
2LSPE: 2D Learnable Sinusoidal Positional Encoding using Transformer for Scene Text Recognition

Positional Encoding (PE) plays a vital role in a Transformer's ability to capture the order of sequential information, allowing it to overcome the permutation equivarience property. Recent state-of-the-art Transformer-based scene text recognition methods have leveraged the advantages of the 2D form of PE with fixed sinusoidal frequencies, also known as 2SPE, to better encode the 2D spatial dependencies of characters in a scene text image. These 2SPE-based Transformer frameworks have outperformed Recurrent Neural Networks (RNNs) based methods, mostly on recognizing text of arbitrary shapes; However, they are not tailored to the type of data and classification task at hand. In this paper, we extend a recent Learnable Sinusoidal frequencies PE (LSPE) from 1D to 2D, which we hereafter refer to as 2LSPE, and study how to adaptively choose the sinusoidal frequencies from the input training data. Moreover, we show how to apply the proposed Transformer architecture for scene text recognition. We compare our method against 11 state-of-the-art methods and show that it outperforms them in over 50% of the standard tests and are no worse than the second best performer, whereas we outperform all other methods on irregular text datasets (i.e., non horizontal or vertical layouts). Experimental results demonstrate that the proposed method offers higher word recognition accuracy (WRA) than two recent Transformer-based methods, and eleven state-of-theart RNN-based techniques on four challenging irregular-text recognition datasets, all while maintaining the highest WRA values on the regular-text datasets.
Analysis, Evaluation and Benchmark The Ethereum Incentive Mechanism

Ethereum is a permissionless blockchain with a Turing complete machine for smart contracts. Ethereum's Gas mechanism, which charges the execution of each operation code, is proposed to ensure the termination of smart contracts that run in the EVM (Ethereum Virtual Machine) and to compensate the computational usage. Thus, the Gas awarded should be rational to the required computational, to ensure the alignment and to avoid the denial of services attack. Currently, in Ethereum, Gas awarded is set statically for each operational code in the smart contract, but it is unknown if whether these values are correct or not for various computer architectures. Therefore, in this paper, we show the most common used operational codes and the rationality of the Gas to the CPU usage for a selected set of the EVM's operational codes.
Action Recognition in Handball Scenes

Action recognition in sports, especially in handball, is a challenging task due to a lot of players being on the sports field performing different actions simultaneously. Training or match recordings and analysis can help an athlete, or his coach gain a better overview of statistics related to player activity, but more importantly, action recognition and analysis of action performance can indicate key elements of technique that need to be improved. In this paper the focus is on recognition of 11 actions that might occur during a handball match or practice. We compare the performance of a baseline CNN-model that classifies each frame into an action class with LSTM and MLP based models built on top of the baseline model, that additionally use the temporal information in the input video. The models were trained and tested with different lengths of input sequences ranging from 20 to 80, since the action duration varies roughly in the same range. Also, different strategies for reduction of the number of frames were tested. We found that increasing the number of frames in the input sequence improved the results for the MLP based model, while it didn't affect the performance of the LSTM model in the same way.
Human Activity Recognition based on Acceleration sensor and Neural Network

Human activity recognition is widely used in medical treatment, safety, entertainment and so on. To obtain the behavior information of human activity efficiently and accurately, a personal activity behavior recognition method based on acceleration sensor and neural network is proposed. This method records sensor data and labels on STM32 data acquisition device with the help of smartphone/tablet APP. The NN model is trained by using the common data set and the log created by ourselves. With the help of STM32 CubeMx.AI development tool, the model trained by neural network is imported into STM32 firmware to realize low-power MCU classification and recognition of individual jogging, stationary, walking, standing, stairs behavior characteristics. The experimental results show that this method can effectively detect the characteristic parameters of individual activities and can accurately identify the five typical human activities.
Low-loss belief propagation decoder with Tanner graph in quantum error-correction codes

Abstract Quantum error-correction codes are immeasurable resources for quantum computing and quantum communication. However, the existing decoders are generally incapable of checking node duplication of belief propagation (BP) on quantum low-density parity check (QLDPC) codes. Based on the probability theory in the machine learning, mathematical statistics and topological structure, a GF(4) (the Galois field is abbreviated as GF) augmented model BP decoder with Tanner graph is designed. The problem of repeated check nodes can be solved by this decoder. In simulation, when the random perturbation strength p =0.0115–0.0116 and number of attempts N = 60–70, the highest decoding efficiency of the augmented model BP decoder is obtained, and the low-loss frame error rate (FER) decreases to 7.1975 ×10 −5 . Hence, we design a novel augmented model decoder to compare the relationship between GF(2) and GF(4) for quantum code [[450,200]] on the depolarization channel. It can be verified that the proposed decoder provides the widely application range, and the decoding performance is better in QLDPC codes.
Philipp Koehn: Neural Machine Translation

Abstract Neural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning based on deep artificial neural networks (NNs). The book Neural Machine Translation by Philipp Koehn targets a broad range of readers including researchers, scientists, academics, advanced undergraduate or postgraduate students, and users of MT, covering wider topics including fundamental and advanced neural network-based learning techniques and methodologies used to develop NMT systems. The book demonstrates different linguistic and computational aspects in terms of NMT with the latest practices and standards and investigates problems relating to NMT. Having read this book, the reader should be able to formulate, design, implement, critically assess and evaluate some of the fundamental and advanced deep learning techniques and methods used for MT. Koehn himself notes that he was somewhat overtaken by events, as originally this book was envisaged only as a chapter in a revised, extended version of his 2009 book Statistical Machine Translation . However, in the interim, NMT completely overtook this previously dominant paradigm, and this new book is likely to serve as the reference of note for the field for some time to come, despite the fact that new techniques are coming onstream all the time.