Reviewerly/exHarmony huggingface dataset:
Knowledge-aware Few-shot Learning Framework for Biomedical Event Trigger Identification

Biomedical event extraction aims to detect fine-grained interactions between biomedical entities in biomedical texts, and has become a research hotspot for researchers in the biomedical field. As the first step in biomedical event extraction, biomedical event trigger identification plays an important role in the whole process. Although researchers have proposed many methods, the performance of existing methods is not desirable due to the reliance on a large number of labeled training samples and the need for expert knowledge in the biomedical field. To treat this issue, we model the biomedical event trigger identification as a few-shot learning problem. Specifically, we utilize a knowledge-aware attention layer to obtain a rich informative representation for entities, and improve the derived prototypes accordingly by prototypical network. In addition, the module of relation network is introduced to train a more reasonable distance function for trigger type prediction. The results demonstrate the effectiveness of the proposed framework according to F1-score.
A Novel Approach to Address External Validity Issues in Fault Prediction Using Bandit Algorithms

Various software fault prediction models have been proposed in the past twenty years. Many studies have compared and evaluated existing prediction approaches in order to identify the most effective ones. However, in most cases, such models and techniques provide varying results, and their outcomes do not result in best possible performance across different datasets. This is mainly due to the diverse nature of software development projects, and therefore, there is a risk that the selected models lead to inconsistent results across multiple datasets. In this work, we propose the use of bandit algorithms in cases where the accuracy of the models are inconsistent across multiple datasets. In the experiment discussed in this work, we used four conventional prediction models, tested on three different dataset, and then selected the best possible model dynamically by applying bandit algorithms. We then compared our results with those obtained using majority voting. As a result, Epsilon-greedy with ϵ=0.3 showed the best or second-best prediction performance compared with using only one prediction model and majority voting. Our results showed that bandit algorithms can provide promising outcomes when used in fault prediction.
Clustering of Handwritten Mathematical Expressions for Computer-Assisted Marking

Many approaches enable teachers to digitalize students' answers and mark them on the computer. However, they are still limited for supporting marking descriptive mathematical answers that can best evaluate learners' understanding. This paper presents clustering of offline handwritten mathematical expressions (HMEs) to help teachers efficiently mark answers in the form of HMEs. In this work, we investigate a method of combining feature types from low-level directional features and multiple levels of recognition: bag-of-symbols, bag-of-relations, and bag-of-positions. Moreover, we propose a marking cost function to measure the marking effort. To show the effectiveness of our method, we used two datasets and another sampled from CROHME 2016 with synthesized patterns to prepare correct answers and incorrect answers for each question. In experiments, we employed the k-means++ algorithm for each level of features and considered their combination to produce better performance. The experiments show that the best combination of all the feature types can reduce the marking cost to about 0.6 by setting the number of answer clusters appropriately compared with the manual one-by-one marking.
Procedural Reading Comprehension with Attribute-Aware Context Flow.

Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that represents processes as a sequence of transitions over entity attributes (e.g., location, temperature). Leveraging pre-trained language models, our model obtains entity-aware and attribute-aware representations of the text by joint prediction of entity attributes and their transitions. Our model dynamically obtains contextual encodings of the procedural text exploiting information that is encoded about previous and current states to predict the transition of a certain attribute which can be identified as a span of text or from a pre-defined set of classes. Moreover, our model achieves state of the art results on two procedural reading comprehension datasets, namely ProPara and npn-cooking
Cancer Cell Segmentation Based on Unsupervised Clustering and Deep Learning

This paper proposed a methodology that can segment, recognize, classify and detect different types of cancer cell from RGB, CT and MRI images. In this study, partial contrast stretching is used on the preprocessed images to increase the cells' visual aspect. Otsu method is applied to enhance the stretched image and the K-means clustering method to segment the desired cancer cell. Median filtering is applied to improve the appearance of the segmented cancer cell and relevant features are extracted from the filtered cell and multi-Support Vector Machine (M-SVM) is applied to classify the test image and identified different types of cancer cell. This paper also proposed the Convolutional Neural Network (CNN) classifier to extract features from the filtered cell and to classify the image. Then two classified results are compared to identify either equal or not. If the result is same, the extracted cancer region shown using region growing method and finally calculated percentage of detected areas of the different types of cancer cell. The Experimentation has been done on MATLAB environment on RGB, CT and MRI images.
A VR Interface for Browsing Visual Spaces at VBS2021

The Video Browser Showdown (VBS) is an annual competition in which each participant prepares an interactive video retrieval system and partakes in a live comparative evaluation at the annual MMM Conference. In this paper, we introduce Eolas, which is a prototype video/image retrieval system incorporating a novel virtual reality (VR) interface. For VBS’21, Eolas represented each keyframe of the collection by an embedded feature in a latent vector space, into which a query would also be projected to facilitate retrieval within a VR environment. A user could then explore the space and perform one of a number of filter operations to traverse the space and locate the correct result.
A Protocol for Preventing Transaction Commitment Without Recipient’s Authorization on Blockchain and It’s Implementation

In recent years, blockchain is utilized practically as a distributed secure digital ledger of some sorts of transactions. Blockchain is regarded as one of the most important next generation infrastructure technologies of the financial industry, as well as artificial intelligence and big data. In 2020, cryptocurrencies based on blockchain, such as Bitcoin, Ethereum, or XRP, have a value of more than $450 billion in the market capitalization. Furthermore, on blockchains such as Ethereum, transactions can also represent automatic executions of programs, which are called smart contracts. Thus, many institutes in various categories show their positive attitude toward processing financial transactions or non-financial contracts on blockchain. Although many researchers have studied for various types of issues on blockchain, there always exist security and privacy concerns for blockchain. In this paper, we point out a new concern for abusing the publicity of blockchain and also show the possibility of suspicions aroused by the concern. Then we propose a selective mechanism for self-protecting against the approach from crimes or computer viruses on blockchain, whether the disclosure of user's privacy occurs or not. Next, we also propose a concrete implementation of our proposed selective mechanism with two new address types. We aim to incorporate the mechanism in Bitcoin Core, which is the official Bitcoin client software, and using libbitcoin library functions for Bitcoin software development. We show experimental results to estimate overhead costs for processing our proposed address types toward processing the current standard address type in nodes on the peer-to-peer network.
The Advent of Deep Learning-Based Image Forgery Detection Techniques

Technological advancement has been playing a crucial role in the generation of digital images and processing them using various image manipulation software that are available in the market today. It has thus become essential to come up with an efficient image forgery detection technique to classify these images as either authentic or forged. The application of image forensics can be seen in national intelligence agencies, scientific publications, social networks, etc. In the past few years, deep learning-based techniques have achieved commendable results in the field of computer vision by involving applications such as object recognition, facial recognition, images classification, and many more, by making their use more evident day by day. The paper gives a comparison between various forgery detection techniques that are based on deep learning with techniques that do not use a neural network architecture for feature extraction.
Deep Learning Using Isotroping, Laplacing, Eigenvalues Interpolative Binding, and Convolved Determinants with Normed Mapping for Large-Scale Image Retrieval

Convolutional neural networks (CNN) are relational with grid-structures and spatial dependencies for two-dimensional images to exploit location adjacencies, color values, and hidden patterns. Convolutional neural networks use sparse connections at high-level sensitivity with layered connection complying indiscriminative disciplines with local spatial mapping footprints. This fact varies with architectural dependencies, insight inputs, number and types of layers and its fusion with derived signatures. This research focuses this gap by incorporating GoogLeNet, VGG-19, and ResNet-50 architectures with maximum response based Eigenvalues textured and convolutional Laplacian scaled object features with mapped colored channels to obtain the highest image retrieval rates over millions of images from versatile semantic groups and benchmarks. Time and computation efficient formulation of the presented model is a step forward in deep learning fusion and smart signature capsulation for innovative descriptor creation. Remarkable results on challenging benchmarks are presented with a thorough contextualization to provide insight CNN effects with anchor bindings. The presented method is tested on well-known datasets including ALOT (250), Corel-1000, Cifar-10, Corel-10000, Cifar-100, Oxford Buildings, FTVL Tropical Fruits, 17-Flowers, Fashion (15), Caltech-256, and reported outstanding performance. The presented work is compared with state-of-the-art methods and experimented over tiny, large, complex, overlay, texture, color, object, shape, mimicked, plain and occupied background, multiple objected foreground images, and marked significant accuracies.
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outperform other machine learning methods when faced with heterogeneous tabular data. But what approach should be used for graphs with tabular node features? Previous GNN models have mostly focused on networks with homogeneous sparse features and, as we show, are suboptimal in the heterogeneous setting. In this work, we propose a novel architecture that trains GBDT and GNN jointly to get the best of both worlds: the GBDT model deals with heterogeneous features, while GNN accounts for the graph structure. Our model benefits from end-to-end optimization by allowing new trees to fit the gradient updates of GNN. With an extensive experimental comparison to the leading GBDT and GNN models, we demonstrate a significant increase in performance on a variety of graphs with tabular features. The code is available: https://github.com/nd7141/bgnn.
Spam SMS Filtering Using Support Vector Machines

In recent years, SMS spam messages are increasing exponentially due to the increase in mobile phone users. Also, there is a yearly increment in the volume of mobile phone spam. Filtering the spam message has become a key aspect. On the other side, machine learning has become an attractive research area and shown the capacity in data analysis. So, in this paper, two popular algorithms named Naive Bayes and support vector machine are applied to SMS data. The SMS dataset is considered from Kaggle resource. The detailed result analysis is presented. Accuracy of 96.19% and 98.79% is noticed for the chosen algorithms, respectively, for spam SMS detection.
Software Testing: Issues and Challenges of Artificial Intelligence &amp; Machine Learning

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.
Efficient Quantum Circuit Decompositions via Intermediate Qudits

Many quantum algorithms make use of ancilla, additional qubits used to store temporary information during computation, to reduce the total execution time. Quantum computers will be resource-constrained for years to come so reducing ancilla requirements is crucial. In this work, we give a method to generate ancilia out of idle qubits by placing some in higher-value states, called qudits. We show how to take a circuit with many O(n) ancilla and design an ancilla-free circuit with the same asymptotic depth. Using this, we give a circuit construction for an in-place adder and a constant adder both with O(log n) depth using temporary qudits and no ancilla.
Deep Reinforcement Learning for Path Planning by Cooperative Robots: Existing Approaches and Challenges

Cooperative robots became a very important research topic due to their capability to accomplish tasks fast and more efficiently. In order for cooperative robots to accomplish their tasks, they need to be able to plan their paths in the environment without any collision. Path planning for cooperative robots with deep reinforcement learning is a new research topic in robotics and artificial intelligence. Path planning via deep reinforcement learning is an end to end method. The robot directly receives the data from the sensor (usually high dimensional images) and generates an optimal policy that plans a safe path to the target. In this paper, we first present an overview of reinforcement learning, deep learning, and deep reinforcement learning. Then, we introduce methods used for path planning. Finally, we discuss the challenges of deep reinforcement learning path planning for cooperative robots.
Generation, characterization, and manipulation of quantum correlations in electron beams

Abstract Entanglement engineering plays a central role in quantum-enhanced technologies, with potential physical platforms that outperform their classical counterparts. However, free electrons remain largely unexplored despite their great capacity to encode and manipulate quantum information, due in part to the lack of a suitable theoretical framework. Here we link theoretical concepts from quantum information to available free-electron sources. Specifically, we consider the interactions among electrons propagating near the surface of a polariton-supporting medium and study the entanglement induced by pair-wise coupling. These correlations depend on the controlled interaction interval and the initial electron bandwidth. We show that long interaction times of broadband electrons extend their temporal coherence. This in turn is revealed through a widened Hong–Ou–Mandel peak and is associated with an increased entanglement entropy. We then introduce a discrete basis of electronic temporal modes and discriminate between them via coincidence detection with a shaped probe. This paves the way for ultrafast quantum information transfer by means of free electrons, rendering the large alphabet that they span in the time domain accessible.
Compact Fuzzy Systems Based on Boolean Relations

This document presents some considerations and procedures to design a compact fuzzy system based on Boolean relations. In the design process, a Boolean codification of two elements is extended to a Kleene’s of three elements to perform simplifications for obtaining a compact fuzzy system. The design methodology employed a set of considerations producing equivalent expressions when using Boole and Kleene algebras establishing cases where simplification can be carried out, thus obtaining compact forms. In addition, the development of two compact fuzzy systems based on Boolean relations is shown, presenting its application for the identification of a nonlinear plant and the control of a hydraulic system where it can be seen that compact structures describes satisfactory performance for both identification and control when using algorithms for optimizing the parameters of the compact fuzzy systems. Finally, the applications where compact fuzzy systems are based on Boolean relationships are discussed allowing the observation of other scenarios where these structures can be used.
High-efficiency single-photon router in a network with multiple outports based on chiral waveguide–emitter couplings

Abstract We propose a potentially practical scheme for constructing a quantum network based on a system where three waveguides are coupled to two two-level atoms. In the case of chiral couplings between the waveguides and atoms, we realize a directed single-photon router and the efficiency of single-photon routing reaches 100%. In addition, in this network, the photon input from one port of the channel can be fully routed to any outport of the other two channels by modulating the coupling strength of the waveguides and atoms and the frequency of the incident photon. Thus, a quantum network can be constructed based on our scheme. The influence of the ratio of chiral coupling strength on the properties of the single-photon router is also shown. We also show how the efficiency of routing is affected by system dissipation.
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and} may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics. In this paper, we challenge this conditional independence assumption and propose a family of expressive autoregressive dynamics models that generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We demonstrate that autoregressive dynamics models indeed outperform standard feedforward models in log-likelihood on heldout transitions. Furthermore, we compare different model-based and model-free off-policy evaluation (OPE) methods on RL Unplugged, a suite of offline MuJoCo datasets, and find that autoregressive dynamics models consistently outperform all baselines, achieving a new state-of-the-art. Finally, we show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer through data augmentation and improving performance using model-based planning.
ICPR 2020 Competition on Text Block Segmentation on a NewsEye Dataset

We present a competition on text block segmentation within the framework of the International Conference on Pattern Recognition (ICPR) 2020. The main goal of this competition is to automatically analyse the structure of historical newspaper pages with a subsequent evaluation of the participants’ algorithms performance. In contrast to many existing segmentation methods, instead of working on pixels, the present study has a focus on clustering baselines/text lines into text blocks. Therefore, we introduce a new measure based on a baseline detection evaluation scheme. But also common pixel-based approaches could participate without restrictions. Working on baseline level addresses directly the application scenario where for a given image the contained text should be extracted in blocks for further investigations. We present the results of three submissions. The experiments have shown that text blocks can be reliably detected both on pages with a simple layout and on pages with a complex layout.
DIGITAL EDUCATIONAL SPACE: PROBLEMS AND PRACTICES OF APPLICATION OF INFORMATION EDUCATIONAL RESOURCES

Information educational resources in the conditions of formation and development of digital educational space become an integral element of educational technologies and provide the increase of education availability and quality, overcoming the inequality of access to quality education, represent interests of various educational subjects, and also offer the opportunity to choose an educational trajectory. In this situation, the problems and practices of information resources, their thematic content, placement and design become a significant issue. The methodological basis of the research is the concept of digital society, developed by modern sociology, in which the changes occurring in society are explained by the influence of digital technologies on social space and the level of inclusion of people in digitalization, the qualitative characteristic of which is the connection of knowledge and people. The purpose of the study is to identify the problems and practices of the use of information educational resources, based on the identification of factors affecting the digitalization of the educational process in an educational institution. The main hypothesis was that the digital index of a student (namely, the possibility of access to the Internet, the practice of using the Internet, the level of human capital development) determines the use of information educational resources and readiness to use them and build educational trajectories of lifelong education on their basis. Students» positions on the choice of educational resources, ideas about the structure of resources, evaluation and satisfaction with the resources they use in educational practices were revealed on the basis of the questionnaire survey of students conducted by the authors. The methods of description, comparison, classification and data interpretation were used in the interpretation of the results. Results: the study reveals the range of problems caused by the educational environment and the digital index of the consumer of educational services/student, as well as practice of application of information educational resources. The practical significance of the results is that the study of factors that identify the digital index of the student, allows you to determine the directions and forms of influence on the readiness of the student to choose the strategy of education through life on the basis of digital technologies.