Reviewerly/exHarmony huggingface dataset:
Graph-based abstractive biomedical text summarization

Summarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first challenge of these methods is to detect the concepts that well convey the main topic of the text and extract sentences that better describe these essential concepts. The second challenge is the correct interpretation of the essential concepts to generate new paraphrased sentences such that they are not exactly the same as the sentences in the main text. The first challenge has been addressed by many researchers. However, the second one is still in progress. In this study, we focus on the abstractive summarization of biomedical documents. In this regard, for the first challenge, a new method is presented based on the graph generation and frequent itemset mining for generating extractive summaries by considering the concepts within the biomedical documents. Then, to address the second challenge, a transfer learning-based method is used to generate abstractive summarizations from extractive summaries. The efficiency of the proposed solution has been evaluated by conducting several experiments over BioMed Central and NLM's PubMed datasets. The obtained results show that the proposed approach admits a better interpretation of the main concepts and sentences of biomedical documents for the abstractive summarization by obtaining the overall ROUGE of 59.60%, which, on average, is 17% better than state-of-the-art summarization techniques. The source code, datasets, and results are available in GitHub1.
Segmentation of Images Using Deep Learning: A Survey

In this present era of technology, segmentation of image is a fundamental and significant component of computer vision and image processing applications. These applications include medical image analysis, compression of images, surveillance, security footage, and many others. Researchers have proposed and developed various models, theories, and algorithms in this advancing field of image segmentation. Due to the success of a wide range of computer vision applications, work whose goal was to use deep learning algorithms for the development and usability of image segmentation was completed not long ago. In this study, a detailed review of the literature on image segmentation is being written, which covers a vast and extensive amount of work done by researchers at the time of writing this review. This review investigates the similarity and differences between the proposed models by researchers in the past. Along with similarities, we have also discussed the strength and challenges of the model under the umbrella of deep learning algorithms. In the end, future work has been discussed along with keeping in view the past work of researchers.
A gentle introduction and survey on Computing with Words (CWW) methodologies

Human beings have an inherent capability to use linguistic information (LI) seamlessly even though it is vague and imprecise. Computing with Words (CWW) was proposed to impart computing systems with this capability of human beings. The interest in the field of CWW is evident from a number of publications on various CWW methodologies. These methodologies use different ways to model the semantics of the LI. However, to the best of our knowledge, the literature on these methodologies is mostly scattered and does not give an interested researcher a comprehensive but gentle guide about the notion and utility of these methodologies. Hence, to introduce the foundations and state-of-the-art CWW methodologies, we provide a concise but a wide-ranging coverage of them in a simple and easy to understand manner. We feel that the simplicity with which we give a high-quality review and introduction to the CWW methodologies is very useful for investigators, especially those embarking on the use of CWW for the first time. We also provide future research directions to build upon for the interested and motivated researchers.
Breast Cancer: Using Deep Transfer Learning Techniques AlexNet Convolutional Neural Network For Breast Tumor Detection in Mammography Images

Breast cancer is the most common form of cancer among women worldwide. Mammography has become a valuable tool for detecting breast cancer. We were able to achieve the purpose of our study, which was to create an accurate Convolutional Neural Network (CNN) model that classifies mammography images into normal and abnormal using deep transfer learning and data augmentation approaches, to avoid overfitting issues with images. The Mammographic Image Analysis Society MiniMammographic Database (MiniMIAS) was used to train and test the CNN model. The shortage of abnormal images in the MiniMIAS caused a low accuracy. Because of that, 92 abnormal images were added from the Digital Database for Screening Mammography (DDSM), which leads us to great accuracy. The proposed method starts with a pre-processing step which includes several filters that eliminate any noises, background, enhances the images, and data augmentation for better training. AlexNet was modified and trained after splitting the dataset into 75%, 5%, and 20% as training, validation, and testing sets respectively. The evaluation results were not very satisfying with the MiniMIAS database with 96.87%. On the other hand with balanced data, the obtained results were very satisfying with 99.99%, and better results according to the existing models, which prove that the chosen filters in the pre-processing phase, as well as the chosen pre-trained model AlexNet, is very useful and suitable for breast tumor detection.
Intelligent Phishing Website Detection Using Deep Learning

Artificial Intelligence and Machine Learning technologies have become a crucial tool for the identification and detection of phishing URLs from samples with a reduced inspection time while avoiding human errors due to inconsistency and fatigue. The “blacklisting” approach, which involves the recording of IP address of servers as well as blacklisted URLs to an antivirus database, is a standard and well-known method for detecting phishing sites. To bypass the blacklisting defense layer, attackers employ a variety of sophisticated techniques to deceive users, including altering the URLs to make them look genuine through obfuscation and other methods. However, using such techniques have its own drawbacks such as false-positive results. To beat the downsides of the blacklisting method, security researchers are looking for ways to adapt and work on several Deep Learning technologies. Therefore, it is very important to develop a model that intelligently detects phishing URLs using small training data with higher accuracy. In addition, it is also crucial to increase our efficiency of detection to ensure that our system is free from phishing and malicious URLs. In this paper, we have proposed a neural network named Autoencoder which employs outlier analysis to discriminate and classify websites as genuine or phishing websites. The algorithm analyzes several fake and genuine URLs and study their features to precisely identify the phishing sites including those that are made in real-time also known as zero-hour phishing websites utilizing this approach.
The journey to technical excellence in agile software development

Technical excellence is a nebulous term in agile software development. This vagueness is risky because it may lead to misunderstandings and to agile implementations that may overlook a key principle of agile development. This study investigates how agile practitioners interpret the concept of technical excellence brought up in Principle 9 of the Agile manifesto. Moreover, we investigate how agile practitioners put the concept into practice and what conditions facilitate putting technical excellence into practice. We conducted semi-structured interviews with twenty agile practitioners, coded the data inductively, and performed two sessions to validate the emerging findings. We find that technical excellence is first and foremost a mindset that is underpinned by continuous attention to sustainable code, continuous learning, and teamwork. Fostering technical excellence requires the adoption of design and development practices, such as continuous architecting, and is supported by continuous learning. We also identify three enabling conditions for technical excellence: Leadership support, customer buy-in, and psychological safety. These enablers provide teams with leeway to nurture their pursuit of technical excellence. Our findings highlight the key role of people-based strategies in promoting technical excellence in agile software development. They show that the attainment of technical excellence does not only involve technical practices. On the contrary, it relies on social and organizational support and, most importantly, a mindset.
Tile-based Lightweight Integer Compression in GPU

GPUs are increasingly used for high-performance and interactive data analytics workloads due to their capability to accelerate computation using massive parallelism. A key constraint of GPU-based data analytics today is the limited memory capacity in GPU devices.
Making space for material entanglements: A diffractive analysis of woodwork and the practice of making an interactive system

A shift in perspective is underway in design research and human-computer interaction (HCI) from humans as the centre of attention to considering complex assemblages of human and non-human stakeholders. While this shift is often approached from a broad ecological level, there is opportunity for a more local shift in understanding our day to day meeting with the material world. Drawing on the posthuman theories of Karen Barad, we explore the creation of a digital interactive system as a material-discursive practice in which matter and culture are inseparably entangled. We seek a fresh look at the process rather than the outcome of interactive system design through a diffractive reading of four traditional woodworking practices and an auto-ethnographic account of the development of a digital sensor and actuator apparatus as a way to find alternative ways of attending to materials in HCI.
Towards building a pragmatic cross-project defect prediction model combining non-effort based and effort-based performance measures for a balanced evaluation

• We propose a two-phase transfer boosting-based cross-project prediction model. • The proposed model is assessed using non-effort based and effort-based measures. • Training data weights are based on feature importance and inter-project similarity. • We validate the effectiveness of the proposed model on a large corpus of data. Recent years have witnessed the growing trend in cross-project defect prediction (CPDP), where the training and the testing data come from different projects having different data distributions. Several CPDP methods have been presented in the literature to overcome differences in their distributions, but the majority of the existing approaches have been evaluated considering the availability of unlimited inspection effort, which is practically impossible, thus leading to fallacious conclusions. Further, they focused more on improving Recall over Precision leading to a high probability of false alarm (PF), causing significant wastage of developer's efforts and time. Addressing these issues, we propose a Two-Phase Transfer Boosting (TPTB) model, which aims at improving the performance not only in terms of non-effort based measures (NEBMs) (making a balance between Recall and PF) but also in terms of effort based measures (EBMs), considering the availability of limited inspection effort. To mitigate the distribution differences, the first phase assigns initial weights to the training modules based on the feature distribution and feature importance. The second phase applies the Dynamic Transfer AdaBoost algorithm to build an ensemble classifier to lessen the impact of contradictory training modules. In addition, a sorting strategy is designed to prioritize the modules for further inspection. Statistical results on 62 datasets revealed a better-balanced performance of our TPTB model holistically over NN-filter, ManualDown, EASC, and Cruz model with performance comparable to WPDP (Within-project defect prediction) considering NEBMs. Besides, when considering EBMs together, TPTB showed statistically and practically more balanced performance as compared to ManualUP and Cruz with overall performance comparable to EASC. Our results demonstrate the efficacy of the TPTB model in a practical setting empowering the quality assurance team to predict and prioritize the defective modules allocating limited inspection effort by optimally focusing on highly defective modules.
DRGCNN: Dynamic region graph convolutional neural network for point clouds

Convolutional Neural Network (CNN) is good at processing regular data, but point clouds are regular and discretely distributed in space. In order to handle point cloud, we use graph to relate points to points and use graph convolution to process point cloud. In neurology, it is well known that the receptive field size of visual cortical neurons is regulated by stimulation. Different sizes of receptive fields have different information. So, we construct different dynamic graph with point’s neighbors to expand each point’s receptive field and adaptively select the information in the different size of receptive field. So in this paper, we propose a new convolution method, Dynamic Region Graph Convolution. It consists of three parts: graph construction, region selection, feature fusion. And it is easily integrated into other popular networks. In experiment, to proof the effectiveness and robustness, we choose different k to construct graph and different number of points for training. We show the encouraging performance on point cloud data sets ModelNet40, ShapeNetPart with 93.3% and 86.2%.
Perception-Aware Planning for Active SLAM in Dynamic Environments

This paper presents a perception-aware path planner for active SLAM in dynamic environments using micro-aerial vehicles (MAV). The “Next-Best-View” planner (NBVP planner) is combined with an active loop closing, which is called the Active Loop Closing Planner (ALCP planner). The planner is proposed to avoid both static and dynamic obstacles in unknown environments while reducing the uncertainty of the SLAM system and further improving the accuracy of localization. First, the receding horizon strategy is adopted to find the next waypoint. The cost function that combines the exploration gain and the loop closing gain is designed. The former can reduce the mapping uncertainty, while the latter takes the loop closing possibility into consideration. Second, a key waypoint selection strategy is designed. The selected key waypoints, instead of all waypoints, are treated as potential loop-closing points to make the algorithm more efficient. Moreover, a fuzzy RRT-based dynamic obstacle avoidance algorithm is adopted to realize obstacle avoidance in dynamic environments. Simulations in different challenging scenarios are conducted to verify the effectiveness of the proposed algorithm.
Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach

Massive online text reviews can be a powerful market research tool for understanding consumer experiences and helping firms improve and innovate. This research exploits the rich semantic properties of text reviews and proposes a novel machine learning modeling framework that can reliably and efficiently extract consumer opinions and uncover potential interaction effects across these opinions, thereby identifying hidden and nuanced areas for product and service improvement beyond existing modeling approaches in this domain. In particular, we develop an opinion extraction and effect estimation framework that allows for uncovering customer opinions’ average effects and their interaction effects. Interactions among opinions can be synergistic when the co-occurrence of two opinions yields an effect greater than the sum of two parts, or as what we call dysergistic, when the co-occurrence of two opinions results in dampened effect. We apply the model in the context of large-scale customer ratings and text reviews for hotels and demonstrate our framework’s ability to screen synergy and dysergy effects among opinions. Our model also flexibly and efficiently accommodates a large number of opinions, which provides insights into rare yet potentially important opinions. The model can guide managers to prioritize joint areas of product and service improvement and innovation by uncovering the most prominent synergistic pairs. Model comparison with extant machine learning approaches demonstrates our improved predictive ability and managerial insights. This paper was accepted by Gui Liberali, marketing. Funding: The authors acknowledge the support of research funding from the National Natural Science Foundation of China [Grant 72072173]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4443 .
Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean

The vanishing gradient problem (i.e., gradients prematurely becoming extremely small during training, thereby effectively preventing a network from learning) is a long-standing obstacle to the training of deep neural networks using sigmoid activation functions when using the standard back-propagation algorithm. In this paper, we found that an important contributor to the problem is weight initialization. We started by developing a simple theoretical model showing how the expected value of gradients is affected by the mean of the initial weights. We then developed a second theoretical model that allowed us to identify a sufficient condition for the vanishing gradient problem to occur. Using these theories we found that initial back-propagation gradients do not vanish if the mean of the initial weights is negative and inversely proportional to the number of neurons in a layer. Numerous experiments with networks with 10 and 15 hidden layers corroborated the theoretical predictions: If we initialized weights as indicated by the theory, the standard back-propagation algorithm was both highly successful and efficient at training deep neural networks using sigmoid activation functions.
Anomaly Data Detection of Rolling Element Bearings Vibration Signal Based on Parameter Optimization Isolation Forest

Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signals of a rolling element bearing, this paper proposes an abnormal data detection model based on comprehensive features and parameter optimization isolation forest (CF-POIF), which can adaptively identify abnormal data segments. First, in order to extract the mutation feature of vibration signals more accurately, the concept of comprehensive feature is proposed, which integrates the time domain and wavelet packet energy features. Then, the particle swarm optimization (PSO) algorithm is used to optimize the rectangular window length and sub sample set capacity in the isolation forest for anomaly detection. Finally, three real cases concerning abnormal data are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method is able to detect missing data, drift data and external interference data effectively, and it has a higher F1 score and accuracy compared to other methods.
Research on Small Sample Data-Driven Inspection Technology of UAV for Transmission Line Insulator Defect Detection

Insulators are important safety devices on high-voltage transmission lines. An insulator inspection system based on UAVs is widely used. Insulator defect detection is performed against two main engineering problems: 1. The scarcity of defect images, which leads to a network overfitting problem. 2. The small object detection, which is caused by the long aerial photography distance, and the low resolution of the insulator defect area pictures. In this study, firstly, the super-resolution reconstruction method is used to augment the dataset, which can not only solve the overfitting problem but also enrich the image texture features and pixel values of defect areas. Secondly, in the process of insulator defect detection, a two-stage cascading method is used. In the first stage, the rotated object detection algorithm is used to realize the object location of insulator strings, and then images of the identified insulators are cropped to reduce the proportion of the background area in defect images. In the second stage, YOLO v5 is used for the detection of insulator caps that are missing defects. The method proposed shows good detection effect on the self-built training set which contains only 85 images captured from real inspection environments. The method has practical industrial application value.
Deep Learning: Convolutional Neural Networks for Medical Image Analysis - A Quick Review

Deep learning has quickly evolved over the past several years from a promising to a feasible solution for medical image analysis. This is a great topic for research because more and more people are using medical imaging to treat and diagnose. A significant benefit of deep learning is the ability to use enormous volumes of data to eliminate the painstaking hand-crafting of features, which needs strong domain expertise. This study discusses how convolutional neural networks (CNNs) are utilized in the field, including detection, classification, registration, segmentation, and picture enhancement. It also gives some important information about how CNNs can be used to analyze medical images of the brain, eye, breast, chest, and skin.
Optimizing Implementation of Artificial‐Intelligence‐Based Automated Scoring: An Evidence Centered Design Approach for Designing Assessments for AI‐based Scoring

Abstract Artificial‐intelligence‐based automated scoring is often an afterthought and is considered after assessments have been developed, resulting in nonoptimal possibility of implementing automated scoring solutions. In this article, we provide a review of Artificial intelligence (AI)‐based methodologies for scoring in educational assessments. We then propose an evidence‐centered design framework for developing assessments to align conceptualization, scoring, and ultimate assessment interpretation and use with the advantages and limitations of AI‐based scoring in mind. We provide recommendations for defining construct, task, and evidence models to guide task and assessment design that optimize the development and implementation of AI‐based automated scoring of constructed response items and support the validity of inferences from and uses of scores.
Learned Cardinality Estimation: An In-depth Study

Learned cardinality estimation (CE) has recently gained significant attention for replacing long-studied traditional CE with machine learning, especially for deep learning. However, these estimators were developed independently and have not been fairly or comprehensively compared in common settings. Most studies use a subset of IMDB data which is too simple to measure their limits and determine whether they are ready for real, complex data. Furthermore, they are regarded as black boxes, without a deep understanding of why large errors occur.