Reviewerly/exHarmony huggingface dataset:
Query-focused Abstractive Summarization via Question-answering Model

Text summarization is a task that creates a short version of a document while preserving the main content. In the age of information explosion, how to obtain the content that users care about from a large amount of information becomes par-ticularly significant. Under these circumstances, query-focused abstractive summarization (QFS) becomes more dominant since it is able to focus on user needs while generating fluent, con-cise, succinct paraphrased summaries. However, different from generic summarization that has achieved remarkable results driven by a large scale of parallel data, the QFS is suffering from lacking enough parallel corpus. To address the above issues, in this paper, we migrate the large-scale generic summarization datasets into query-focused datasets while preserving the informative summaries. Based on the synthetic queries and data, we proposed a new model, called SQAS, which is capable of extracting fine-grained factual information with respect to a specific question, and take into account the reasoning information by understanding the source document leveraged by the question-answering model. Receiving the extracted content, the summary generator can not only generate semantically relevant content but also assure fluent and readable sentences thanks to the language generation capability of a pre-trained language model. Experimental results on both generic datasets and query-focused summary datasets demonstrate the effectiveness of our proposed model in terms of automatic ROUGE metrics and investigating real cases.
MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction

Relation extraction (RE), an important information extraction task, faced the great challenge brought by limited annotation data. To this end, distant supervision was proposed to automatically label RE data, and thus largely increased the number of annotated instances. Unfortunately, lots of noise relation annotations brought by automatic labeling become a new obstacle. Some recent studies have shown that the teacher-student framework of knowledge distillation can alleviate the interference of noise relation annotations via label softening. Nevertheless, we find that they still suffer from two problems: propagation of inaccurate dark knowledge and constraint of a unified distillation temperature . In this article, we propose a simple and effective Multi-instance Dynamic Temperature Distillation (MiDTD) framework, which is model-agnostic and mainly involves two modules: multi-instance target fusion (MiTF) and dynamic temperature regulation (DTR). MiTF combines the teacher’s predictions for multiple sentences with the same entity pair to amend the inaccurate dark knowledge in each student’s target. DTR allocates alterable distillation temperatures to different training instances to enable the softness of most student’s targets to be regulated to a moderate range. In experiments, we construct three concrete MiDTD instantiations with BERT, PCNN, and BiLSTM-based RE models, and the distilled students significantly outperform their teachers and the state-of-the-art (SOTA) methods.
Modified Reinhard Algorithm for Color Normalization of Colorectal Cancer Histopathology Images

In recent trends, Computer Assisted Diagnosis (CAD) enables the pathologists to diagnose cancer disease from histopathology images very efficiently. Color normalization is a pre-processing step prior to cancer classification task which can reduce the computational complexity of the classifier. However, existing color normalization methods are fraught with the problems of data loss and huge computational complexity. The purpose of employing this color normalization method is to reduce the color variation among a set of histopathology images so that in the next step, the classifier can efficiently extract the prominent features for cancer grading. This color variation is generally occurred due to using different scanners, stain concentration variability and poor tissue sectioning, while preparing the histopathology slides. In this paper, a modified Reinhard algorithm is proposed for color normalization of Hematoxylin and Eosin (H&E) stained colorectal cancer histopathology images. The limitations of Reinhard algorithm are alleviated by the proposed algorithm. Moreover, a statistical analysis is provided to prove that proposed algorithm does not cause any data loss and subsequently, it satisfies all four hypotheses of color normalization. Furthermore, the performance of the proposed algorithm is compared with other existing color normalization methods both qualitatively and quantitatively.
Development and application of computer graphics training software in information technology

The article discusses the didactic potential for the use of a modern multimedia textbook as software, as an auxiliary visual means of teaching computer graphics. The article gives instructions and suggestions for teaching and popularization of the subject of computer graphics on the basis of modern electronic textbooks and their use.
Examining children’s design processes, perspective-taking, and collaboration when using VR head-mounted displays

We explore how children leverage virtual reality (VR) to reshape their design sensibilities and to expand their perspective-taking, which may lead to the children showing more empathic concerns for future users of their designs. We worked with middle school children, ages 13 to 14, who collaboratively designed sandbox maze models using our VR SandScape system, a spatial augmented reality (SAR) physical sandbox hybridized with a VR head-mounted display (HMD) that allowed them to experience the sandbox mazes in a full scale virtual environment. We compared this group with a second group of middle school children who worked in the same SAR sandbox without access to virtual immersion from the VR HMD. We found that the virtual immersion and multiple perspectives afforded by the HMD influenced the children’s design analysis methods by motivating them to evaluate their maze models from a designer’s view, a user’s view, or sometimes both views simultaneously, which involved consideration of allocentric perspectives (i.e., seeing their design from others’ perspectives compared to from only egocentric perspectives). The VR HMD group empathized with future users of their maze designs and approached design as an iterative problem-solving process, continually discovering and addressing new user needs. They gradually incorporated more user-accessible features such as wider paths and gentler slopes. In contrast, the Non-HMD group was less concerned about users’ needs but designed more playful and creative mazes. Overall, they were more quickly satisfied with their early designs without feeling the need to iterate. We discuss the role of virtual immersion in supporting children’s perspective-taking, which influences their critical or complimentary attitudes towards their design processes and design outcomes.
Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject's hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation.
Lifelong Machine Learning-Based Quality Analysis for Product Review

Reading product reviews is the best way to know the product quality in online shopping. Due to the huge review number, customers and merchants need product analysis algorithms to help with quality analysis. Current researches use sentiment analysis to replace quality analysis. However, it has a significant drawback. This paper proves that the sentiment-based analysis algorithms are insufficient for online product quality analysis. They ignore the relationship between aspect and its description and cannot detect noise (unrelated description). So this paper raises a Lifelong Product Quality Analysis algorithm LPQA to learn the relationship between aspects. It can detect the noise and improve the opinion classification performance. It improves the classification F1 score to 77.3% on the Amazon iPhone dataset and 69.99% on Semeval Laptop dataset.
Determining Action Reversibility in STRIPS Using Answer Set Programming with Quantifiers

In the field of automated planning, an action is called reversible when other actions can be applied in order to revert the effects of this action and return to the original state. In recent years, there has been renewed interest in this topic, which led to novel results in the widely known STRIPS formalism and the PDDL planning language. In this paper, we aim to solve the computational problem of deciding action reversibility in a practical setting, applying recent advances in the field of logic programming. In particular, a quantified extension of Answer Set Programming (ASP) named ASP with Quantifiers (ASP(Q)) has been proposed by Amendola, Ricca, and Truszczynski, which allows for stacking logic programs by quantifying over answer sets of the previous layer. This language is well-suited to express encodings for the action reversibility problem, since this problem naturally contains a quantifier alternation. In addition, a prototype solver for ASP(Q) is currently developed. We make use of the ASP(Q) language to offer an encoding for action reversibility, and then report on preliminary benchmark results on how well this encoding performs compared to classical ASP.
Dual-Pathway Deep CNN for Offline Writer Identification

Purohit, Naresh Panwar, SubhashWriter Identification is a challenging problem based on small amount of handwritten text. It is also a foremost research topic in forensic analysis of documents. For this, a convolution neural network (CNN) is proposed to address the writer recognition task. In this work, we propose a dual-path CNN to extract local features maps from given input handwritten document image patches and then classify writer by Softmax loss function. The experiments are done on IAM English language dataset and obtained accuracy of 92.7%.
A Semantic Approach to Identifier Management in Engineering Systems

Mapping identifiers across the systems is an essential part of the work towards automatic system integration. It will help us move from the traditional processes that are often manual, error-prone, and time and resource-consuming. In this paper, we propose a solution that will allow us to approach the problem from a data mesh perspective, where organisations and domains serve and host their own data for consumption, combined with ontology-based data access to move towards machine-readable data, more automatic data integration, and further digitalisation in engineering systems. This is an important step as structured and enhanced system-to-system data interchange has enormous efficiency gains and consequential savings in the oil and gas industry, as well as in other sectors of engineering with similar challenges.
Identification of time series models using sparse Takagi–Sugeno fuzzy systems with reduced structure

Abstract Simplifying fuzzy models, including those for predicting time series, is an important issue in terms of their interpretation and implementation. This simplification can involve both the number of inference rules (i.e., structure) and the number of parameters. This paper proposes novel hybrid methods for time series prediction that utilize Takagi–Sugeno fuzzy systems with reduced structure. The fuzzy sets are obtained using a global optimization algorithm (particle swarm optimization, simulated annealing, genetic algorithm, or pattern search). The polynomials are determined by elastic net regression, which is a sparse regression. The simplification is based on reducing the number of polynomial parameters in the then-part by using sparse regression and removing unnecessary rules by using labels. A new quality criterion is proposed to express a compromise between the model accuracy and its simplification. The experimental results show that the proposed methods can improve a fuzzy model while simplifying its structure.
A Secure and Privacy Preserving Federated Learning Approach for IoT Intrusion Detection System

Recently, machine learning (ML) has been shown as a powerful method for outstanding capability of resolving intelligent tasks across many fields. Nevertheless, such ML-based systems require to centralize a large amount of data in the training phase that causes privacy leaks from user data. This is also true with the ML-based intrusion detection system (IDS) due to containing sensitive user and network data, especially in the context of Internet of Things (IoT) intrusion detection. To promote the collaboration between multiple parties in building an efficient IDS model to detect more attack types and cope with the privacy preservation issues, federated learning (FL) is considered as a potential approach for localized training scheme without sharing any data collection between organizations or data silos. In this paper, we investigate the feasibility of adopting FL for anomaly behavior detection in the context of large-scale IoT networks while facilitating the secure and privacy preserving aggregation using homomorphic encryption and differential privacy.
A Prediction Model for Software Requirements Change Impact

Software requirements Change Impact Analysis (CIA) is a pivotal process in requirements engineering (RE) since changes to requirements are inevitable. When a requirement change is requested, its impact on all software artefacts has to be investigated to accept or reject the request. Manually performed CIA in large-scale software development is time-consuming and error-prone so, automating this analysis can improve the process of requirements change management. The main goal of this research is to apply a combination of Machine Learning (ML) and Natural Language Processing (NLP) based approaches to develop a prediction model for forecasting the requirement change impact on other requirements in the specification document. The proposed prediction model will be evaluated using appropriate datasets for accuracy and performance. The resulting tool will support project managers to perform automated change impact analysis and make informed decisions on the acceptance or rejection of requirement change requests.
Chinese Medical Named Entity Recognition Based on Chinese Character Radical Features and Pre-trained Language Models

With the continuous innovation of medical diagnosis forms, the communication and interaction between patients and medical staff have become more frequent and informationalized. How to extract valuable information from a large amount of clinical medical text has attracted more and more attention of researchers. Medical named entity recognition is the basis for structuring the information in clinical medical text, and has great technical significance. This paper aims at the task of medical named entity recognition, using Bidirectional Long Short-Term Memory, conditional random field, adapting Transformer encoder for named entity recognition, incorporating Chinese character radical information, and converting it into machine reading comprehension task, to build medical named entity recognition models based on different pre-trained language models. Finally, the model ensemble is carried out on the results of the above multiple different models to obtain the final recognition result. In the final test data set of medical term recognition track of “Chinese Clinical Medical Text Word Segmentation and Named Entity Challenge”, the method in this paper obtains the F1 score of 82.72%.
Fault Analysis of the Beam Acceleration Control System at the European XFEL using Data Mining

The European X-Ray Free-Electron Laser (EuXFEL) relies like other high integrity systems on several sub systems. The Low Level Radio Frequency (LLRF) sub system of the EuXFEL is responsible for the correct acceleration of electron bunches. The LLRF system comprises several embedded components that are directly connected to the accelerator hardware. Due to the high complexity of the LLRF system, unforeseen machine trips occur regularly.In this work we built the basis for a mechanism that automatically identifies faulty behavior of the embedded components. To achieve that, we performed two different experiments, where a faulty behavior was artificially injected to the system. We analyzed the experiment data, performed a feature extraction and applied different machine learning methods. We used basic anomaly detection and basic clustering methods for identifying the faulty data elements. Additionally, we used a support vector machine for modelling the systems behavior. The selected algorithms are compared with respect to their ability to classify LLRF data correctly.
Selection of a Best Humanoid Robot Using “TOPSIS” for Rescue Operation

A rescue robot is introduced and built to reduce people’s work and to rescue people. The rescue robots are needed in cases of urban disasters, mining accidents, research endeavors, high-powered explosions, hostage situations, and military operations involving victim location and explosives detection. Rescue robots prove worthy since they reduce people’s work, reduce corresponding fatigue, and withstand high temperature since robots are made up of high temperature withstanding materials like aluminum. A rescue robot can access areas with ease, where humans find it difficult to reach. The advances in the field of robotic technologies are quite high these days. Recent developments are oriented on producing machines that have the capability of traversing along debris in building collapses, rescuing people from marine disasters, military reconnaissance and mapping, aiding in surgeries, and meticulous evacuations. This research paper proposes a selection method of an ideal humanoid robot by utilizing the multiple criterion decision model: “technique for order of preference by similarity to ideal solution” (TOPSIS) for rescue operation. Five important attributes of the robot were shortlisted to function as the criterion for selection. The simulated study of the robotic structure’s capabilities pertaining to rescue missions is considered.
GAR-Net: A Graph Attention Reasoning Network for conversation understanding

Conversation understanding, as a necessary step for many applications, including social media, education, and argumentation mining, has been gaining increasing attention from the research community. Reasoning over long-term dependent contextual information is the key to utterance-level conversation understanding. Aiming to emphasize the importance of contextual reasoning, an end-to-end graph attention reasoning network which takes both word-level and utterance-level context into concern is proposed. To be specific, a word-level encoder with a novel convolution gate is first built to filter out irrelevant contextual information. Based on the representation extracted by word-level encoder, a graph reasoning network is designed to utilize the context among utterance-level, where the entire conversation is treated as a fully connected graph, utterances as nodes, and attention scores between utterances as edges. The proposed model is a general framework for conversation understanding tasks, which can be flexibly applied on most conversation datasets without changing the network architecture. Furthermore, we revise the tensor fusion network by adding a residual connection to explore cross-modal conversation understanding. For uni-modal scene (text modality), experiments show that the proposed method surpasses current state-of-the-art methods on emotion recognition, intent classification, and dialogue act identification tasks. For cross-modal scenes (text modality and audio modality), experiments on IEMOCAP and MELD datasets show that the proposed method can use cross-modal information to improve model performance.2
Research data policies of journals in the Chinese Science Citation Database based on the language, publisher, discipline, access model and metrics

Abstract This article investigates the research data policies of China's scholarly journals to provide a regional perspective. In total, 1243 science, technology, and medicine (STM) journals in the Chinese Science Citation Database (CSCD) were examined in June and July of 2021. The findings show that the existence and attitudinal strength of the research data policies of China's scholarly journals are weak overall, but differences exist across journals with different languages, publishers, disciplines, access models and metrics. A large imbalance exists between English‐language journals and Chinese‐language journals in setting their research data policies. The existence and attitudinal strength of the research data policies of journals co‐published by Western publishers and Chinese publishers are much stronger than those of journals published solely by Chinese publishers. In terms of discipline, journals belonging to the life sciences have stronger attitudes towards research data sharing. Open access model journals and hybrid model journals are better than subscription model journals in opening and sharing research data. The higher the impact factor (IF) and CiteScore of a journal, the more likely the journal is to have data policies.
Genetic programming

tutorial Open Access Share on Genetic programming: a tutorial introduction Authors: Una-May O'Reilly The ALFA Group: AnyScale Learning for All, CSAIL, MIT The ALFA Group: AnyScale Learning for All, CSAIL, MITView Profile , Erik Hemberg The ALFA Group: AnyScale Learning for All, CSAIL, MIT The ALFA Group: AnyScale Learning for All, CSAIL, MITView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 443–453https://doi.org/10.1145/3449726.3461394Published:08 July 2021Publication History 0citation103DownloadsMetricsTotal Citations0Total Downloads103Last 12 Months48Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF