Dr Sara Ahmed

Relative attributes indicate the strength of a particular attribute between image pairs. We introduce a deep Siamese network with rank SVM loss function, called Deep-RankSVM, that can decide which one of a pair of images has a stronger presence of a specific attribute. The network is trained in an end-to-end fashion to jointly learn the visual features and the ranking function. The trained network for an attribute can predict the relative strength of that attribute in novel images. We demonstrate the effectiveness of our approach against the state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig, UTZap50K-2 and UTZap50K-lexi datasets. Deep-RankSVM surpasses state-of-art in terms of the average accuracy across attributes, on three of the four image benchmark datasets.

The rapid growth of technology has massively increased the amount of text data. The data can be mined and utilized for numerous natural language processing (NLP) tasks, particularly text classification. The core part of text classification is collecting the data for predicting a good model. This paper collects Kurdish News Dataset Headlines (KNDH) for text classification. The dataset consists of 50000 news headlines which are equally distributed among five classes, with 10000 headlines for each class (Social, Sport, Health, Economic, and Technology). The percentage ratio of getting the channels of headlines is distinct, while the numbers of samples are equal for each category. There are 34 distinct channels that are used to collect the different headlines for each class, such as 8 channels for economics, 14 channels for health, 18 channels for science, 15 channels for social, and 5 channels for sport. The dataset is preprocessed using the Kurdish Language Processing Toolkit (KLPT) for tokenizing, spell-checking, stemming, and preprocessing.

Early detection of skin cancer is vital when treatment is most likely to be successful. However, diagnosis of skin lesions is a very challenging task due to the similarities between lesions in terms of appearance, location, color, and size. We present a deep learning method for skin lesion classification by fusing and fine-tuning three pre-trained deep learning architectures (Xception, Inception-ResNet-V2, and NasNetLarge) using training images provided by ISIC2019 organizers. Additionally, the outliers and the heavy class imbalance are addressed to further enhance the classification of the lesion. The experimental results show that the proposed framework obtained promising results that are comparable with the ISIC2019 challenge leader board.

The scales of the data accessible through internet search engines can reach hundreds of millions, or even billions. The existence of such large weak-labeled databases has gained importance in the training of face recognition algorithms. Starting with the publicly available YFCC100M, we propose a weaklylabeled subset for multi-label face recognition for self-supervised methods. A 392K image subset of YFCC100M of 128x128 images was obtained by querying for the 40 facial attributes. We made this dataset publicly available for other face recognition studies, by sharing the IDs, the links and the bounding boxes1. To reduce outliers with respect to CelebA, we apply the Elliptic Envelope algorithm, in the the latent feature space learned over CelebA, obtaining 353K face images. MixMatch algorithm is applied to this last set, to obtain pseudo labels. Pretraining with these pseudo-labels and final fine-tuning with CelebA brings an improvement of 0.4% points in the Area Under the ROC Curve (AUC) score over the system trained only with CelebA.

This paper reports findings from a survey into applicant reactions of working adults in Saudi Arabia. A sample of 193 participants from four job functions was obtained, with measures of organizational attractiveness, core-self evaluation, and applicant reactions to four popular selection methods in the country – interviews, résumés, work sample tests, and references – being included. Findings indicate a notably similar pattern of preference reactions to previous studies in other (Western) countries, affirming arguments for so-called reaction generalizability. Work sample tests were rated the most favorably followed by interviews, résumés, and references. For specific procedural dimensions, résumés were perceived as the most favorable, followed by work sample tests, interviews, and references. Several significant differences were found across job functions, mostly for interviews and résumés. Significant effects were found between reactions and organizational attractiveness, and between reactions and core-self evaluation, including some interaction effects. Implications for future research and for practice in employee selection are considered in the conclusion.

We provide a comprehensive but critical review of research on applicant reactions to selection procedures published since 2000 (n = 145), when the last major review article on applicant reactions appeared in the Journal of Management. We start by addressing the main criticisms levied against the field to determine whether applicant reactions matter to individuals and employers (“So what?”). This is followed by a consideration of “What’s new?” by conducting a comprehensive and detailed review of applicant reaction research centered upon four areas of growth: expansion of the theoretical lens, incorporation of new technology in the selection arena, internationalization of applicant reactions research, and emerging boundary conditions. Our final section focuses on “Where to next?” and offers an updated and integrated conceptual model of applicant reactions, four key challenges, and eight specific future research questions. Our conclusion is that the field demonstrates stronger research designs, with studies incorporating greater control, broader constructs, and multiple time points. There is also solid evidence that applicant reactions have significant and meaningful effects on attitudes, intentions, and behaviors. At the same time, we identify some remaining gaps in the literature and a number of critical questions that remain to be explored, particularly in light of technological and societal changes.

We provide a comprehensive but critical review of research on applicant reactions to selection procedures published since 2000 (n = 145), when the last major review article on applicant reactions appeared in the Journal of Management. We start by addressing the main criticisms levied against the field to determine whether applicant reactions matter to individuals and employers (“So what?”). This is followed by a consideration of “What’s new?” by conducting a comprehensive and detailed review of applicant reaction research centered upon four areas of growth: expansion of the theoretical lens, incorporation of new technology in the selection arena, internationalization of applicant reactions research, and emerging boundary conditions. Our final section focuses on “Where to next?” and offers an updated and integrated conceptual model of applicant reactions, four key challenges, and eight specific future research questions. Our conclusion is that the field demonstrates stronger research designs, with studies incorporating greater control, broader constructs, and multiple time points. There is also solid evidence that applicant reactions have significant and meaningful effects on attitudes, intentions, and behaviors. At the same time, we identify some remaining gaps in the literature and a number of critical questions that remain to be explored, particularly in light of technological and societal changes.

We present a targeted review of recent developments and advances in digital selection procedures (DSPs) with particular attention to advances in internet-based techniques. By reviewing the emergence of DSPs in selection research and practice, we highlight five main categories of methods (online applications, online psychometric testing, digital interviews, gamified assessment and social media). We discuss the evidence base for each of these DSP groups, focusing on construct and criterion validity, and applicant reactions to their use in organizations. Based on the findings of our review, we present a critique of the evidence base for DSPs in industrial, work and organizational psychology and set out an agenda for advancing research. We identify pressing gaps in our understanding of DSPs, and ten key questions to be answered. Given that DSPs are likely to depart further from traditional non-digital selection procedures in the future, a theme in this agenda is the need to establish a distinct and specific literature on DSPs, and to do so at a pace that reflects the speed of the underlying technological advancement. In concluding, we, therefore, issue a call to action for selection researchers in work and organizational psychology to commence a new and rigorous multidisciplinary programme of scientific study of DSPs.

Rapid changes in selection technologies have impacted inexorably the science and practice of personnel selection in recent years. Technological advances are allowing organizations to use new internet-based selection procedures (IBSPs) as well as big data and analytics to make empirical-based employment decisions by collecting and analyzing the digital footprints that job applicants leave behind them in social networks, social media and other internet platforms. The aim of the paper is to make a contribution to these important and emerging issues by reviewing the literature in this area and designing a study to examine applicant privacy and fairness reactions to 10 types of new selection procedures. It also aims to examine the application of these new selection and assessment practices in organizations across 10 countries.