Reviewerly/exHarmony huggingface dataset:
Bangla Handwritten Character Recognition Using Extended Convolutional Neural Network

The necessity of recognizing handwritten characters is increasing day by day because of its various applications.The objective of this paper is to provide a sophisticated, effective and efficient way to recognize and classify Bangla handwritten characters.Here an extended convolutional neural network (CNN) model has been proposed to recognize Bangla handwritten characters.Our CNN model is tested on "BanglalLekha-Isolated" dataset where there are 10 classes for digits, 11 classes for vowels and 39 classes for consonants.Our model shows accuracy of recognition as: 99.50% for Bangla digits, 93.18% for vowels, 90.00% for consonants and 92.25% for combined classes.
Temporal network embedding using graph attention network

Abstract Graph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.
Compound-eye imaging imitation-based whole-field flow measurement

Large-scale particle image velocimetry (LSPIV) has been increasingly applied in whole-field flow measurement for its non-contact operation mode. However, due to the complicated flow conditions and optical disturbances on the water-air interface, commonly-used imaging technologies are degenerated by the low visibility and image distortion. There are also increased challenges for control point calibration. This paper proposes an imaging measurement method that imitates the imaging computation mechanism in compound eyes. The method is independent of ground control points and can achieve flow measurement with natural patterns, which can solve the problem caused by the lack of visible tracers. Field evaluation results demonstrate that this is the first method to achieve real-world whole-field flow measurement in challenging conditions. Specifically, the velocimetry error is less than 5%, while ranging from 5% to 15% for other LSPIV systems. The method meets current hydrological standards and acquires flow data in wild rivers, performance that is realized with difficulty by other methods.
Evolutionary Neural Architecture Search Supporting Approximate Multipliers

There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer’s effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to minimize the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural networks (CNN). The method allows approximate operations to be used in CNNs to reduce power consumption of a target hardware implementation. During the NAS process, a suitable CNN architecture is evolved together with approximate multipliers to deliver the best trade-offs between the accuracy, network size and power consumption. The most suitable approximate multipliers are automatically selected from a library of approximate multipliers. Evolved CNNs are compared with common human-created CNNs of a similar complexity on the CIFAR-10 benchmark problem.
Automatic detection of procedural knowledge in robotic-assisted surgical texts

The automatic extraction of knowledge about intervention execution from surgical manuals would be of the utmost importance to develop expert surgical systems and assistants. In this work we assess the feasibility of automatically identifying the sentences of a surgical intervention text containing procedural information, a subtask of the broader goal of extracting intervention workflows from surgical manuals.We frame the problem as a binary classification task. We first introduce a new public dataset of 1958 sentences from robotic surgery texts, manually annotated as procedural or non-procedural. We then apply different classification methods, from classical machine learning algorithms, to more recent neural-network approaches and classification methods exploiting transformers (e.g., BERT, ClinicalBERT). We also analyze the benefits of applying balancing techniques to the dataset.The architectures based on neural-networks fed with FastText's embeddings and the one based on ClinicalBERT outperform all the tested methods, empirically confirming the feasibility of the task. Adopting balancing techniques does not lead to substantial improvements in classification.This is the first work experimenting with machine / deep learning algorithms for automatically identifying procedural sentences in surgical texts. It also introduces the first public dataset that can be used for benchmarking different classification methods for the task.
XNLP: A Living Survey for XAI Research in Natural Language Processing

We present XNLP: an interactive browser-based system embodying a living survey of recent state-of-the-art research in the field of Explainable AI (XAI) within the domain of Natural Language Processing (NLP). The system visually organizes and illustrates XAI-NLP publications and distills their content to allow users to gain insights, generate ideas, and explore the field. We hope that XNLP can become a leading demonstrative example of a living survey, balancing the depth and quality of a traditional well-constructed survey paper with the collaborative dynamism of a widely available interactive tool. XNLP can be accessed at: https://xainlp2020.github.io/xainlp.
Development of a Remote-Controlled Drone System by Using Only Eye Movements: Design of a Control Screen Considering Operability and Microsaccades

In recent years, the number of bedridden patients, including amyotrophic lateral sclerosis (ALS) patients, has been increasing with the aging of the population, owing to advances in medical and long-term care technology. Eye movements are physical functions that are relatively difficult to be affected, even if the symptoms of ALS progress. Focusing on this point, in this paper, in order to improve the quality of life (QOL) of bedridden patients, including ALS patients, we propose a drone system connected to the Internet that can be remotely controlled using only their eyes. In order to control the drone by using only their eyes, a control screen and an eye-tracking device were used in this system. By using this system, for example, the patients in New York can operate the drone in Kyoto using only their eyes, enjoy the scenery, and talk with people in Kyoto. In this drone system, since a time delay could occur depending on the Internet usage environment, agile operation is required for remotely controlling the drone. Therefore, we introduce the design of the control screen focused on remote control operability and human eye movements (microsaccades). Furthermore, considering the widespread future use of this system, it is desirable to use a commercial drone. Accordingly, we describe the design of a joystick control device to physically control the joysticks of various drone controllers. Finally, we present experimental results to verify the effectiveness of this system, including the control screen and the joystick control device.
Avoiding the Geometric Boundary Effect in Shear Measurement

Abstract In image processing, source detections are inevitably affected by the presence of the geometric boundaries in the images, including the physical boundaries of the CCD, and the boundaries of masked regions due to column defects, bright diffraction spikes, etc. These boundary conditions make the source detection process not statistically isotropic and can lead to additive shear bias near the boundaries. We build a phenomenological model to understand the bias, and propose a simple method to effectively eliminate the influence of geometric boundaries on shear measurement. We demonstrate the accuracy and efficiency of this method using both simulations and the z -band imaging data from the third data release of the DECam Legacy Survey.
On universal algorithms for classifying and predicting stationary processes

This is a survey of results on universal algorithms for classification and prediction of stationary processes. The classification problems include discovering the order of a k-step Markov chain, determining memory words in finitarily Markovian processes and estimating the entropy of an unknown process. The prediction problems cover both discrete and real valued processes in a variety of situations. Both the forward and the backward prediction problems are discussed with the emphasis being on pointwise results. This survey is just a teaser. The purpose is merely to call attention to results on classification and prediction. We will refer the interested reader to the sources. Throughout the paper we will give illuminating examples.
Blockchain-based root of trust management in security credential management system for vehicular communications

Security Credential Management System (SCMS) provides the Public Key Infrastructure (PKI) for vehicular networking. SCMS builds the state-of-the-art distributed PKI to protect the vehicular networking privacy against an honest-but-curious authority (by the use of multiple PKI authorities) and to decentralize the PKI root of trust (by the Elector-Based Root Management or EBRM, having the distributed electors manage the Root Certificate Authority or RCA). We build on the EBRM architecture and construct a Blockchain-Based Root Management (BBRM) to provide even greater decentralization and security. More specifically, BBRM uses blockchain to i) replace the existing RCA and have the electors directly involved in the root certificate generation, ii) control the elector network membership including elector addition and revocation, and iii) provide greater accountability and transparency on the aforementioned functionalities. We implement BBRM on Hyperledger Fabric using smart contract for system experimentation and analyses. Our experiments show that BBRM is lightweight in processing, efficient in ledger size, and supports a bandwidth of multiple transactions per second. Our results show that the BBRM blockchain is appropriate for the root certificate generation and the elector membership control for EBRM within SCMS, which are significantly smaller in number and occurrences than the SCMS outputs of vehicle certificates. We also experiment to analyze how the BBRM distributed consensus protocol parameters, such as the number of electors and the number of required votes, affect the overall scheme's performances.
The Concept of the FSES HE of the Fourth Generation for Engineering Education in the Context of Implementing the Assignments of the President of Russia

Within the framework of the activities of the Coordinating Council of the Ministry of Science and Higher Education of the Russian Federation in the field of education “Engineering, Technology and Technical Sciences”, proposals have been developed for the implementation of a set of tasks for the development of higher education set by the President of Russia. The necessity of implementing different methodological and normative approaches to different areas of higher education is argued. The authors substantiate the concept of the fourth generation Federal State Educational Standard for the engineering field of education. A new approach to the organization of admission to universities is proposed, which allows students to choose the direction of training after completing the second year of study. An innovative structure of lists of specialties and areas of preparation of higher education is presented, which provides the possibility of implementing the concept of the fourth generation Federal State Educational Standard, as well as greater flexibility and enlargement of the lists.
A novel hybrid permutation substitution base colored image encryption scheme for multimedia data

A novel hybrid permutation substitution-based color image encryption scheme is presented. The permutation is desirable property to have which make any security system diffusing. In present article, permutation is applied through p-box. The substitution process is also vital because it create confusion. In presented work substitution is applied through S-p-box. 3D mixed chaotic map is utilized to make our algorithm more diffusing and confusing. The permutation box, permutation substitution box and initial conditions of 3D mixed chaotic map are also utilized as a key. The experimental outcomes of proposed technique have been critically analyzed and a comprehensive comparison with existing schemes is presented.
Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution

Medication combination prediction can be applied to the clinical treatment for critical patients with multi-morbidity. The suitable medication combination can help cure patients and keep the treatment medication safe. However, the complexity and uncertainty of clinical circumstances limit the predictive accuracy of medication combination. Thus, this paper proposes a new medication combination prediction model based on the temporal attention mechanism (TAM) and the simple graph convolution (SGC), named as TAMSGC. More specifically, the TAM can capture the temporal sequence information in the medical records, and the SGC is implemented to acquire the medication knowledge from the complicated medication combination. Experiments in a real dataset show that TAMSGC surpasses the baseline models on the predictive accuracy of medication combination.
Research on the Application of Artificial Intelligence in Computer Network Technology

All articles must contain an abstract. The abstract text should be formatted using 10 point Times or Times New Roman and indented 25 mm from the left margin. Leave 10 mm space after the abstract before you begin the main text of your article, starting on the same page as the abstract. The abstract should give readers concise information about the content of the article and indicate the main results obtained and conclusions drawn. The abstract is not part of the text and should be complete in itself; no table numbers, figure numbers, references or displayed mathematical expressions should be included. It should be suitable for direct inclusion in abstracting services and should not normally exceed 200 words in a single paragraph. Since contemporary information-retrieval systems rely heavily on the content of titles and abstracts to identify relevant articles in literature searches, great care should be taken in constructing both. Abstract: With the rapid development of science and technology, people have higher and higher requirements for network information technology. In order to meet the diverse needs of people, artificial intelligence has emerged and has been applied in various fields. The application of artificial intelligence in computer network technology is to give full play to the advantages of the two technologies. In this way, the computer network can be used to provide people with convenience in life and production, and the role of artificial intelligence can be personalized and humanized. At the same time, it can also play a role in protecting information security and strengthening network management. Based on this, this article will summarize the meaning, characteristics and functions of artificial intelligence, combined with the current security and management problems of computer network technology, and talk about how to use the advantages of artificial intelligence in computer network technology.
Quantum operations with indefinite time direction

The fundamental dynamics of quantum particles is neutral with respect to the arrow of time. And yet, our experiments are not: we observe quantum systems evolving from the past to the future, but not the other way round. A fundamental question is whether it is possible to conceive a broader set of operations that probe quantum processes in the backward direction, from the future to the past, or more generally, in a combination of the forward and backward directions. Here we introduce a mathematical framework for these operations, showing that some of them cannot be interpreted as random mixtures of operations that probe processes in a definite direction. As a concrete example, we construct an operation, called the quantum time flip, that probes an unknown dynamics in a quantum superposition of the forward and backward directions. This operation exhibits an information-theoretic advantage over all operations with definite direction. It can be realised probabilistically using quantum teleportation, and can be reproduced experimentally with photonic systems. More generally, we introduce a set of multipartite operations that include indefinite time direction as well as indefinite causal order, providing a framework for potential extensions of quantum theory.
Correlations between Information Science research groups in Brazil: an approach based on keywords

Analyzing correlations between research groups has been increasingly appealing in recent years. The identification of proximity between different research projects can not only contribute to triggering new partnerships, but also optimize resources and share results. In Brazil, the Lattes Curriculum System of the Brazilian National Council for Scientific and Technological Development is a rich source of information about the academic and professional life of professors, researchers, and students. Lattes curricula present information, much of it up-to-date, in a semi-structured text format. This paper intends to identify correlations between Brazilian research groups in Information Science through the analysis of keywords contained in the informative summaries and in the descriptions of the research projects found in the Lattes curricula of the participants of these groups. The analysis presented below was made with the application of text mining techniques to the Lattes curricula of researchers linked to 27 graduate programs in Information Science from 24 Brazilian institutions of higher education, totaling 399 curricula analyzed. Among the results obtained, it was possible to identify some existing research trends between the groups and link them to the areas of Information Science, Archivology, Library Science, and Museology. It was also possible to identify the most used research terms at the moment. In addition, the analysis of the occurrence of the terms allowed to identify the areas that concentrate most of the research in Information Science in Brazil, as well as to realize that there is a propensity of researchers to use certain terms to describe their research and their informative summaries.
Constrained Multi-Task Learning for Event Coreference Resolution

We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction.To guide the learning of this complex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions.In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution.The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.
Event Time Extraction and Propagation via Graph Attention Networks

Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.This problem is challenging due to the inherent ambiguity of language and the requirement for information propagation over inter-related events.This paper first formulates this problem based on a 4-tuple temporal representation used in entity slot filling, which allows us to represent fuzzy time spans more conveniently.We then propose a graph attention networkbased approach to propagate temporal information over document-level event graphs constructed by shared entity arguments and temporal relations.To better evaluate our approach, we present a challenging new benchmark on the ACE2005 corpus, where more than 78% of events do not have time spans mentioned explicitly in their local contexts.The proposed approach yields an absolute gain of 7.0% in match rate over contextualized embedding approaches, and 16.3% higher match rate compared to sentence-level manual event time argument annotation.
The power of thinning in balanced allocation

Balls are sequentially allocated into n bins as follows: for each ball, an independent, uniformly random bin is generated. An overseer may then choose to either allocate the ball to this bin, or else the ball is allocated to a new independent uniformly random bin. The goal of the overseer is to reduce the load of the most heavily loaded bin after Θ(n) balls have been allocated. We provide an asymptotically optimal strategy yielding a maximum load of (1+o(1)) 8logn loglogn balls.
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reasonable compromise between fairness and accuracy is desired. Another common approach is to limit the ability of a particular adversary who seeks to maximize parity. Unfortunately, representations produced by adversarial approaches may still retain biases as their efficacy is tied to the complexity of the adversary used during training. To this end, we theoretically establish that by limiting the mutual information between representations and protected attributes, we can assuredly control the parity of any downstream classifier. We demonstrate an effective method for controlling parity through mutual information based on contrastive information estimators and show that they outperform approaches that rely on variational bounds based on complex generative models. We test our approach on UCI Adult and Heritage Health datasets and demonstrate that our approach provides more informative representations across a range of desired parity thresholds while providing strong theoretical guarantees on the parity of any downstream algorithm.