From Gabor Magnitude to Gabor Phase Features: Tackling the Problem of Face Recognition under Severe Illumination Changes

Among the numerous biometric systems presented in the literature, face recognition systems have received a great deal of attention in recent years. The main driving force in the development of these systems can be found in the enormous potential face recognition technology has in various application domains ranging from access control, human-machin

UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition

Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should yield improvements by de-emphasizing noise and increasing signal in an input image. But the historically divergent goals of the computational photography and visual recognition communities have created a significant need for more work in this direction. To facilitate new research, we introduce a new benchmark dataset called UG^2, which contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches. Further, current image restoration and enhancement techniques are evaluated by determining whether or not theyimprove baseline classification performance. Results showthat there is plenty of room for algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset: https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or

A Corpus-based Investigation of Syntactic Complexity, Fluency, Sentence Variety, and Sentence Development in L2 Genre Writing

Measures of syntactic complexity have been used to evaluate task-related variation and pedagogic interventions in L2 writing production, and to assess differences across proficiency levels and over time. This paper reports on a corpus-based investigation of syntactic complexity and fluency in narrative and argumentative writing, comparing texts produced by 170 L2 learners at the starts of their first and second years at university. Conventional metrics were used to compare syntactic complexity and fluency in the two sets of samples; novel methods were also devised to examine possible changes in sentence variety and the development of sentence construction. Significant differences within the first and second year corpora reflected responses to the genre-specific demands on text production, but length of instruction only significantly impacted on narrative writing. Argumentative texts presented significantly greater syntactic complexity but, in sharp contrast to the findings of previous studies, also evidenced significantly greater fluency. The Sentence Variety Index and sentence reconstruction both offered insights into writing production, suggesting the value of the suite of metrics used in this research to the longitudinal study of L2 genre writing

A Corpus-based, Longitudinal Study of Syntactic Complexity, Fluency, Sentence Variety, and Sentence Development in L2 Genre Writing

The findings and expectations of four broadly-drawn approaches to the investigation of syntactic complexity frame and inform this corpus-based examination of narrative and argumentative texts written by 22 L2 learners at a university in Japan. A suite of conventional and novel metrics is used to explore complexity, fluency, sentence variety, and sentence development over a two-year period and to compare texts in the two genres. Longitudinal gains in fluency and decreases in fragment use were largely as anticipated for both genres, but significant gains in complexity and increases in sentence variety were unexpectedly limited to narrative texts. In comparing genres, the expected higher value MLTU, the greater complexity of argumentative texts, the differences in clause usage, and the significant divergence in sentence variety values contrast with the surprisingly similar values for MLT. It is suggested that longitudinal changes in the syntactic construction of text are strongly influenced by the constraints and affordances of usage-derived genre, the form, function, and exponents of narrative writing being relatively easier at this level.A Corpus-based, Longitudinal Study of Syntactic Complexity, Fluency, Sentence Variety, and Sentence Development in L2 Genre Writing（Nicholas Wood and Nicolai Struc）The difficulties posed by argumentation may be compensated for by the use of formulaic constructions and templates. We conclude that understandings of cognition, structure, function, and patterns of acquisition and usage need to be incorporated into a coherent paradigm in order to fully appreciate L2 writing and its development

Using a Learner Corpus to Develop Learner Language Profiles

This paper describes the development and subsequent analysis of across-sectional learner corpus comprising written samples from students in the first, second and third years of the English language writing program at Reitaku University, Japan. Students completed two writing tasks, a narrative and an argumentative essay, which were then analyzed to determine whether gains were made in the areas of fluency, lexical richness, grammatical accuracy and use of rhetorical/cohesive devices.Gains were observed in all of these four areas, the process of annotating the raw data revealed areas which were problematic for student writers but were unelaborated by the analysis employed in this research. These observations illuminate the limitations of this study and highlight directions for further analysis

Synthetic Data for Face Recognition: Current State and Future Prospects

Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition.Comment: Accepted at Image and Vision Computing 2023 (IVC 2023

Do Suicide Attempters Have a Right Not to Be Stabilized in an Emergency?

The standard of care in the United States favors stabilizing any adult who arrives in an emergency department after a failed suicide attempt, even if he appears decisionally capacitated and refuses life-sustaining treatment. I challenge this ubiquitous practice. Emergency clinicians generally have a moral obligation to err on the side of stabilizing even suicide attempters who refuse such interventions. This obligation reflects the fact that it is typically infeasible to determine these patients’ level of decisional capacitation—among other relevant information—in this unique setting. Nevertheless, I argue, stabilizing suicide attempters over their objection sometimes violates a basic yet insufficiently appreciated right of theirs—the right against bodily invasion. In such cases, it is at least prima facie wrong to stabilize a patient who wants to die even if they lack a contrary advance directive or medical order and suffer from no terminal physical illness

Animalization

Although the concept of objectification is seen as a valuable tool in feminist theorizing, far less attention has been paid to animalization: treating or regarding a person as a nonhuman animal. I argue that animalization is a distinctive category of wrongdoing, modeling a theory of the phenomenon on Kantian theories of objectification in feminist philosophy. Actions are animalizing, I claim, when they embody a kind of disregard for a person’s characteristically human capacities that is analogous to the fitting treatment of animals. I contend that my view overcomes standard objections to the use of the concept of animalization and show how, despite surface similarities, animalization is different from both objectification and infantilization

A corpus-based analysis of Japanese university-level leaners\u27 L2 writing development over a one-year period

This paper describes the development and analysis of a longitudinal learner corpus comprised of Japanese university students’ English writing over the period of one year. Students completed two writing tasks, a narrative and an argumentative essay, in response to the same prompts at two points in time one year apart. The resulting subcorpora are analyzed and compared with respect to fluency, lexical diversity, grammatical accuracy and use of rhetorical/cohesive devices. Gains were observed in these areas, most notably in fluency and lexical diversity. Methodological issues in analyzing grammatical accuracy and use of rhetorical/cohesive devices render interpretation of these results less conclusive. Patterns of observed developments in these four areas are discussed, followed by an acknowledgement of the limitations of the study and considerations for directions in further research

Humanism: A Reconsideration

Humanism is the view that people treat others inhumanely when we fail to see them as human beings, so that our treatment of them will tend to be more humane when we (fully) see their humanity. Recently, humanist views have been criticized on the grounds that the perpetrators of inhumanity regard their victims as human and treat them inhumanely partly for this reason. I argue that the two most common objections to humanist views (and their relatives) are unpersuasive: not only does the evidence marshalled against these views fail to actually disprove them, it could threaten them only if some questionable assumptions were granted. By providing necessary conceptual ground-clearing and routing common lines of attack, I hope to determine what it would take for a humanist project to succeed, paving the way for a full defense of humanism that fulfills its explanatory ambitions