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The purpose of this paper is to examine the use of a new feature reduction technique with novelty detection on vibration and acoustic‐emission sensors monitoring bearings mounted in the test benches of automotive manufacturers.

Signals from standard accelerometers and acoustic‐emission sensors were gathered from bearings operating under steady conditions on an accessory‐drive test bench. The bearings under test were subject to a variety of faults including fretting. These signals were processed and reduced to standard feature vectors, the dimensionality of which was reduced using a new principal‐component‐like technique optimized for novelty detection. The reduced data were analyzed with a novelty detection technique called the Support Vector Data Descriptor.

The classification results from these sensors, after being reduced with the proposed feature reduction technique, are substantially improved over those achievable with only standard novelty detection; nearly zero‐percent classification error was achieved.

The feature reduction technique depends, in part, on the availability of the fault type in question – potentially violating the normal novelty detection assumption of limited abnormal data. This may require the manufacturer to gather real or simulated fault data prior to running tests.

Incipient faults may be detectable at a much earlier stage in a manufacturer's component failure analysis. Test engineers may use this technique to reliably automate the fault detection process and enable improved root‐cause analysis through the earlier identification of faults.

The application of the feature reduction technique will provide manufacturers and researchers with a new means of improving fault detection in machinery components.

The purpose of this paper is to complement an available system of qualitative analysis of efficiency of scientific activities with assessment of novelty of a subject of research that gives a more complete pattern for evaluating the efficiency of efforts of both scientists and research teams.

The approach is based on detection of specified linguistic patterns with further evaluation of similarity and novelty scores of obtained definitions at the sentence level.

This work presents an algorithm of automatic search for a new subject of research in scientific papers on the basis of statistical and linguistic analyses of description of new terms. Application of patterns specified in a given manuscript with further utilization of well-known methods of similarity and novelty detection scores makes it possible to evaluate the degree of novelty of a subject of research.

As a practical application of the proposed algorithm, the algorithm of determination of authority of a scientist will facilitate assessment of personal contributions of certain authors made in a certain field of study.

The main contribution of a given manuscript is in application of linguistic patterns recognition and calculation of similarity and novelty scores to the area of scientific results with further proposition of the method of automatic search for a new subject of research in scientific manuscripts.

The object of this study is to develop methods for automatically annotating the argumentative role of sentences in scientific abstracts. Working from Medline abstracts, sentences were classified into four major argumentative roles: objective, method, result, and conclusion. The idea is that, if the role of each sentence can be marked up, then these metadata can be used during information retrieval to seek particular types of information such as novelty, conclusions, methodologies, aims/goals of a scientific piece of work.

Two approaches were tested: linguistic cues and positional heuristics. Linguistic cues are lexico‐syntactic patterns modelled as regular expressions implemented in a linguistic parser. Positional heuristics make use of the relative position of a sentence in the abstract to deduce its argumentative class.

The experiments showed that positional heuristics attained a much higher degree of accuracy on Medline abstracts with an F‐score of 64 per cent, whereas the linguistic cues only attained an F‐score of 12 per cent. This is mostly because sentences from different argumentative roles are not always announced by surface linguistic cues.

A limitation to the study was the inability to test other methods to perform this task such as machine learning techniques which have been reported to perform better on Medline abstracts. Also, to compare the results of the study with earlier studies using Medline abstracts, the different argumentative roles present in Medline had to be mapped on to four major argumentative roles. This may have favourably biased the performance of the sentence classification by positional heuristics.

To the best of one's knowledge, this study presents the first instance of evaluating linguistic cues and positional heuristics on the same corpus.

Automated sentence‐level relevance and novelty detection would be of direct benefit to many information retrieval systems. However, the low level of agreement between human judges performing the task is an issue of concern. In previous approaches, annotators were asked to identify sentences in a document set that are relevant to a given topic, and then to eliminate sentences that do not provide novel information. This paper aims to explore a new approach in which relevance and novelty judgments are made within the context of specific, factual information needs, rather than with respect to a broad topic.

An experiment is conducted in which annotators perform the novelty detection task in both the topic‐focused and fact‐focused settings.

Higher levels of agreement between judges are found on the task of identifying relevant sentences in the fact‐focused approach. However, the new approach does not improve agreement on novelty judgments.

The analysis confirms the intuition that making sentence‐level relevance judgments is likely to be the more difficult of the two tasks in the novelty detection framework.

In novelty detection, the autoencoder based image reconstruction strategy is one of the mainstream solutions. The basic idea is that once the autoencoder is trained on normal data, it has a low reconstruction error on normal data. However, when faced with complex natural images, the conventional pixel-level reconstruction becomes poor and does not show the promising results. This paper aims to provide a new method for improving the performance of novelty detection based autoencoder.

To solve the problem that conventional pixel-level reconstruction cannot effectively extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global semantic feature information. Then, the channel attention mechanism is introduced to perform adaptive feature refinement on the intermediate feature map to optimize the correspondingly passed feature map.

Experimental results on three public data sets show that the proposed method has potential performance for novelty detection.

This study explores the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images. In this way, the performance of novelty detection can be improved.

The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities. There have been many intrusion detection schemes proposed, most of which apply both normal and intrusion data to construct classifiers. However, normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect. Internet intrusion detection can be considered as a novelty detection problem, which is the identification of new or unknown data, to which a learning system has not been exposed during training. This paper aims to address this issue.

In this paper, a novelty detection‐based intrusion detection system is proposed by combining the self‐organizing map (SOM) and the kernel auto‐associator (KAA) model proposed earlier by the first author. The KAA model is a generalization of auto‐associative networks by training to recall the inputs through kernel subspace. For anomaly detection, the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns. The hybrid SOM/KAA model can also be applied to classify different types of attacks.

Using the KDD CUP, 1999 dataset, the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state‐of‐art novelty detection methods, showing marked improvements in terms of the high intrusion detection accuracy and low false positives. Simulations on the classification of attack categories also demonstrate favorable results of the accuracy, which are comparable to the entries from the KDD CUP, 1999 data mining competition.

The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.

The journey of novelty – from the moment it arises to the time it takes hold – is as fascinating as it is problematic. A new entity, to be recognized as such, needs to be differentiated from what existed before. However, novelty poses cognitive challenges that hamper its appreciation since it is difficult to form expectations about and make sense of something genuinely new. And since novel ideas, products, technologies, or organizational forms often violate existing practices and social structures, they are usually met with skepticism and resistance. In this introductory piece, we take stock of research into the challenges of generating, recognizing, and legitimating novelty. We review each paper in this volume and highlight the new perspectives and insights they offer about how individuals, teams, and organizations search for novelty, see novelty, and sustain novelty. Finally, we outline several research themes that, we believe, are worthy of further scholarly attention.

The purpose of this paper is to provide a robust method for automatic detection of seam lines based only on digital images of the garments.

A local standard deviation pre‐processing filter is applied to enhance the contrast between the seam line and the texture and the Prewitt operator extracts the edges of the enhanced image. The seam line is detected by a maximum at the Radon transform. The proposed method is invariant to the illumination intensity and it has been also tested with moving average and fast Fourier transform low‐pass filters used in the pre‐processing module. Extensive experiments are carried out in the presence of additive Gaussian and uniform noise.

The proposed method detects 109 out of 118 seams when the local standard deviation is used at the pre‐processing stage, giving a mean distance error between the real and the estimated line of 2 mm when the image is digitised at 97 dpi. However, in case the images are distorted by additive Gaussian noise at 20 dB signal‐to‐noise ratio, the moving average low‐pass filtering method gives the best results, detecting 104 noisy images.

The proposed method detects seam lines that can be approximated by a continuation of straight lines. The current work can be extended in the detection of the curved parts of seam lines.

Since the method addresses garments instead of seam specimens, the proposed approach can be imported in automatic systems for online quality control of seams.

Local standard deviation belongs to first‐order statistics, which makes it suitable for texture analysis and that is why it is mostly used in web defect detection. The novelty in the approach, however, is that by considering the seam as an abnormality of the texture, the authors applied that method at the pre‐processing stage to enhance the seam before the detection. Moreover, the presented method is illumination invariant, a property that has not been addressed in similar methods.

Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames.

Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues.

An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s.

The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge.

The concept of novelty is central to questions of creativity, innovation, and discovery. Despite the prominence in scientific inquiry and everyday discourse, there is a chronic ambiguity over its meaning and a surprising variety of empirical measures, which muddle the interpretation of prior findings and frustrate the consolidation of knowledge. To help dispel some of the unclarity, this paper presents a survey and synthesis of conceptualizations and operationalizations of novelty scattered across social, cognitive, and organizational studies. From this analysis, I advance the argument that novelty is generally regarded as a function of frequency or proximity, and in these two complementary perspectives, it is commonly bounded its empirical study and theoretical understanding. I further argue that contextual and temporal aspects are integral to the specification of novelty and primary contributors to its multifaceted nature.