1,721,038 research outputs found
Report on the 4th Workshop on Human-Centric Software Engineering & Cyber Security (HCSE&CS 2023)
Full text linkIn the domain of software design and development, humans play crucial roles as creators, designers, coders, testers, users, and, on occasion, even abusers of software systems, especially in cyber security. The International Workshop on Human-Centric Software Engineering & Cyber Security (HCSE&CS) focuses on human-centric concerns in software engineering and cyber security, addressing the various roles of individuals in these domains. The fourth edition of the HCSE&CS Workshop was conducted in a hybrid format and took place in Kirchberg, Luxembourg, on September 11, 2023, in conjunction with the 38th IEEE/ACM International Conference on Automated Software Engineering. This post-workshop report serves to outline the workshop's goals and motivations, while offering a concise summary of the presentations and discussions that took place during this event.</p
New Objects on the Road? No Problem, We’ll Learn Them Too
Full text linkObject detection plays an essential role in providing localization, path planning, and decision making capabilities in autonomous navigation systems. However, existing object detection models are trained and tested on a fixed number of known classes. This setting makes the object detection model difficult to generalize well in real-world road scenarios while encountering an unknown object. We address this problem by introducing our framework that handles the issue of unknown object detection and updates the model when unknown object labels are available. Next, our solution includes three major components that address the inherent problems present in the road scene datasets. The novel components are a) Feature-Mix that improves the unknown object detection by widening the gap between known and unknown classes in latent feature space, b) Focal regression loss handling the problem of improving small object detection and intra-class scale variation, and c) Curriculum learning further enhances the detection of small objects. We use Indian Driving Dataset (IDD) and Berkeley Deep Drive (BDD) dataset for evaluation. Our solution provides state-of-the-art performance on open-world evaluation metrics. We hope this work will create new directions for open-world object detection for road scenes, making it more reliable and robust autonomous systems
Supervised Hashing for Retrieval of Multimodal Biometric Data
Full text linkBiometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. The main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. Through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopted for most unimodal and multimodal biometric identification tasks.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
An EEG-Based Image Annotation System
Full text linkThe success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20–200 ms, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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