Reviewerly/exHarmony huggingface dataset:
Using Gender- and Polarity-Informed Models to Investigate Bias

In this work we explore the effect of incorporating demographic metadata in a text classifier trained on top of a pre-trained transformer language model.More specifically, we add information about the gender of critics and book authors when classifying the polarity of book reviews, and the polarity of the reviews when classifying the genders of authors and critics.We use an existing data set of Norwegian book reviews with ratings by professional critics, which has also been augmented with gender information, and train a document-level sentiment classifier on top of a recently released Norwegian BERT-model.We show that gender-informed models obtain substantially higher accuracy, and that polarity-informed models obtain higher accuracy when classifying the genders of book authors.For this particular data set, we take this result as a confirmation of the gender bias in the underlying label distribution, but in other settings we believe a similar approach can be used for mitigating bias in the model.
Distorted pinhole camera model for tangential distortion

Different camera models are usually employed to address specific imaging processes such as telecentric, pinhole, and radial distortion. Recently, the distorted pinhole camera model was developed, and several imaging processes were analyzed as particular cases. This paper shows that the tangential distortion is also a particular case of the distorted pinhole camera model. The mathematical principles of this generalized approach are presented, and its usefulness is illustrated by distorting test images. The results show that the distorted pinhole camera model provides an advanced mathematical framework for computer vision and optical metrology applications
Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces

Deep clustering techniques combine representation learning with clustering objectives to improve their performance. Among existing deep clustering techniques, autoencoder-based methods are the most prevalent ones. While they achieve promising clustering results, they suffer from an inherent conflict between preserving details, as expressed by the reconstruction loss, and finding similar groups by ignoring details, as expressed by the clustering loss. This conflict leads to brittle training procedures, dependence on trade-off hyperparameters and less interpretable results. We propose our framework, ACe/DeC, that is compatible with Autoencoder Centroid based Deep Clustering methods and automatically learns a latent representation consisting of two separate spaces. The clustering space captures all cluster-specific information and the shared space explains general variation in the data. This separation resolves the above mentioned conflict and allows our method to learn both detailed reconstructions and cluster specific abstractions. We evaluate our framework with extensive experiments to show several benefits: (1) cluster performance – on various data sets we outperform relevant baselines; (2) no hyperparameter tuning – this improved performance is achieved without introducing new clustering specific hyperparameters; (3) interpretability – isolating the cluster specific information in a separate space is advantageous for data exploration and interpreting the clustering results; and (4) dimensionality of the embedded space – we automatically learn a low dimensional space for clustering. Our ACe/DeC framework isolates cluster information, increases stability and interpretability, while improving cluster performance.
Refocusing Zuboff’s ‘division of learning’ on Education

This paper examines the concept of the ‘division of learning’, and the broader thesis of ‘surveillance capitalism’ within which it is situated, in terms of its relevance to education. It begins with defining the term, before suggesting two key ways in which aligning the ‘division of learning’ with perspectives from educational research might provide productive insights for both domains. The first considers the impact of increasing ‘datafication’ in education, where platform technologies are proliferating as powerful actors that both mediate and shape educational activity. Here the ‘division of learning’ offers useful insights concerning the disparities resulting from learning in and learning from educational platforms. The second explores the extent to which education theory might offer ways to develop the concept of the ‘division of learning’, through critique of the term ‘learning’ itself, as well as the foregrounding of questions of educational ‘purpose’. Here the ‘division of learning’ is suggested to maintain, rather than challenge, the dominant practices of data exploitation, for which further engagement with a purposive, political, and emancipatory form of ‘data science’ is suggested.
Overview of mobile learning

Since the end of the last century, the research on learning activities based on mobile communication devices has begun to rise. In just a few decades, mobile learning has become a research hotspot in the field of distance education and digital learning. This paper first discusses the concept and definition of M-learning, analyzes the theoretical basis of M-learning, and then points out that m-learning is an important new learning paradigm in the process of socialization and lifelong learning in the knowledge-based economic society. Finally, it points out several key issues in the research of M-learning.
Image forgery detection based on fusion of lightweight deep learning models

Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate the forgery of the images. Secondly, the fine-tuned weights are used to compare the results of the forgery of the images with the pre-trained models. The experimental results suggest that the fusion based decision approach achieves better accuracy as compared to the state-of-the-art approaches.
Historical Information-based Differential Evolution for Dynamic Optimization Problem

Dynamic optimization problems (DOP) widely exist in many application fields and remain a challenge. The multi-population evolutionary computation approach is an efficient framework for solving the DOPs. The key issue of the multi-population approach is how to efficiently generate subpopulations in new environment by well reusing historical information in past environments. However, most existing works try to divide the population into small ones merely considering the individuals' distribution, which may have the limitation that the partition leads to the unbalanced computational resources allocation among subpopulations. To address this limitation, we propose a historical information-based differential evolution (HIDE) to effectively solve the DOP. Firstly, a region-based subpopulation initialization (RSI) strategy is proposed to generate multiple subpopulations in the new environment in a balanced way. By initializing multiple subpopulations in different regions of the search space, the diversity of the population is enhanced, which is helpful to solve the DOP with multiple peaks. Secondly, to fully make use of the found peaks of the previous environments, an archive-based historical information reuse (AHIR) strategy is put forward to manage and reuse the historical information to guide the search in the new environment. Experiments have been carried out on the moving peaks benchmark (MPB), which is a commonly used DOP test suite. Experimental results show that the proposed HIDE algorithm generally outperforms the classic DE algorithms with different parameter settings and some existing competitive state-of-the-art DOP approaches.
Analysing Spatio-Temporal Voting Patterns in Brazilian Elections Through a Simple Data Science Pipeline

Since 1989, the first year of the democratic presidential election after a long period of a dictatorship regime, Brazil conducted eight presidential elections. Short and long-term shifts of power and two impeachment processes marked such a period. This instability is a research case in electoral studies, mainly regarding the understanding of citizens' voting behavior. Comprehending patterns in the population behavior can give us insight into phenomena and processes that affect democratic political decisions. In light of this, our paper analyses Brazilian electoral data at the municipal level from 1998 to 2018 using a simple data science pipeline, which consists of five steps: (i) data selection; (ii) data preprocessing; (iii) identification of spatial patterns, in which we seek to understand the role of space in the election results employing spatial auto-correlation techniques; (iv) identification of temporal patterns, where we investigate similar trends of votes over the years applying a hierarchical clustering method; and (v) evaluation of results. We study the presidential elections focusing on the right and left-wing parties most relevant for the period: the Brazilian Social Democracy Party~(PSDB) and the Workers' Party~(PT). We also analyse the congressman election data regarding parties ideologically to the right and left in the political spectrum. Through the obtained results, we found the existence of spatial dependence in every electoral year investigated. Moreover, despite the changes in the political-economic context over the years, neighboring cities seem to present similar voting behavior trends.
Rethink the Connections among Generalization, Memorization, and the Spectral Bias of DNNs

Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance trade-off in classical learning theory. Recent studies claimed that DNNs first learn simple patterns and then memorize noise; some other works showed a phenomenon that DNNs have a spectral bias to learn target functions from low to high frequencies during training. However, we show that the monotonicity of the learning bias does not always hold: under the experimental setup of deep double descent, the high-frequency components of DNNs diminish in the late stage of training, leading to the second descent of the test error. Besides, we find that the spectrum of DNNs can be applied to indicating the second descent of the test error, even though it is calculated from the training set only.
The Career Choice Motivation in View of Maslow's Hierarchy in Innovative Economy: The Case of Russia

The challenge of human resource management is to align with these expectations in order to keep students motivated to choose a complying career in the innovation economy. The research gap concerns a problem of stimulating the employees without-material means if they are not fond of their profession because they cannot achieve their potential. The goals of the study consider analyzing opinions of students of schools, colleges and university about their future profession and determine the problem of professional orientation among students. The article aims to confirm the challenge of choosing a future occupation among students, to determine the factors that influence on their career choice, to assess changes in their attitude towards the chosen occupation in the learning process. The methodology consisted of preparing questionnaires, conducting a survey, grouping responses by category and correlation detection among students of grades 9-11 in 7 schools, one college and university students of years 3-6. It was found that there is a high likelihood of complete coincidence between the sympathy to the profession and its choice. We have found that only two-thirds of students admitted that their expectations about their line of work coincided with the real situation. According to the results, only half of school students choose their specialty because of personal interest, half of college and university students are interested in their specialty and enthusiastic about it, and less than half of the students intend to work in their field. Further research is needed to classify career guidance tools, evaluate their effectiveness, and develop virtual tools.
On the Intrinsic Differential Privacy of Bagging

Differentially private machine learning trains models while protecting privacy of the sensitive training data. The key to obtain differentially private models is to introduce noise/randomness to the training process. In particular, existing differentially private machine learning methods add noise to the training data, the gradients, the loss function, and/or the model itself. Bagging, a popular ensemble learning framework, randomly creates some subsamples of the training data, trains a base model for each subsample using a base learner, and takes majority vote among the base models when making predictions. Bagging has intrinsic randomness in the training process as it randomly creates subsamples. Our major theoretical results show that such intrinsic randomness already makes Bagging differentially private without the needs of additional noise. Moreover, we prove that if no assumptions about the base learner are made, our derived privacy guarantees are tight. We empirically evaluate Bagging on MNIST and CIFAR10. Our experimental results demonstrate that Bagging achieves significantly higher accuracies than state-of-the-art differentially private machine learning methods with the same privacy budgets.
The latest trends in cybercrime

The article deals with current trends in cyber crime, including its organized forms, and proposes to strengthen the counteraction to this phenomenon.
Locally checkable labelings with small messages

A rich line of work has been addressing the computational complexity of locally checkable labelings (LCLs), illustrating the landscape of possible complexities. In this paper, we study the landscape of LCL complexities under bandwidth restrictions. Our main results are twofold. First, we show that on trees, the CONGEST complexity of an LCL problem is asymptotically equal to its complexity in the LOCAL model. An analog statement for general (non-LCL) problems is known to be false. Second, we show that for general graphs this equivalence does not hold, by providing an LCL problem for which we show that it can be solved in $O(\log n)$ rounds in the LOCAL model, but requires $\tilde{\Omega}(n^{1/2})$ rounds in the CONGEST model.
Partial Multi-Label Optimal Margin Distribution Machine

Partial multi-label learning deals with the circumstance in which the ground-truth labels are not directly available but hidden in a candidate label set. Due to the presence of other irrelevant labels, vanilla multi-label learning methods are prone to be misled and fail to generalize well on unseen data, thus how to enable them to get rid of the noisy labels turns to be the core problem of partial multi-label learning. In this paper, we propose the Partial Multi-Label Optimal margin Distribution Machine (PML-ODM), which distinguishs the noisy labels through explicitly optimizing the distribution of ranking margin, and exhibits better generalization performance than minimum margin based counterparts. In addition, we propose a novel feature prototype representation to further enhance the disambiguation ability, and the non-linear kernels can also be applied to promote the generalization performance for linearly inseparable data. Extensive experiments on real-world data sets validates the superiority of our proposed method.
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy

Recently, convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most widely used technique for improving adversarial robustness to strong white-box attacks. Prior works have been evaluating and improving the model average robustness without per-class evaluation. The average evaluation alone might provide a false sense of robustness. For example, the attacker can focus on attacking the vulnerable class, which can be dangerous, especially, when the vulnerable class is a critical one, such as human in autonomous driving. In this preregistration submission, we propose an empirical study on the class-wise accuracy and robustness of adversarially trained models. Given that the CIFAR10 training dataset has an equal number of samples for each class, interestingly, preliminary results on it with Resnet18 show that there exists inter-class discrepancy for accuracy and robustness on standard models, for instance, cat is more vulnerable than other classes. Moreover, adversarial training increases inter-class discrepancy. Our work aims to investigate the following questions: (a) is the phenomenon of inter-class discrepancy universal for other classification benchmark datasets on other seminal model architectures with various optimization hyper-parameters? (b) If so, what can be possible explanations for the inter-class discrepancy? (c) Can the techniques proposed in the long tail classification be readily extended to adversarial training for addressing the inter-class discrepancy?
Self-identification of electronically scanned signatures (ESS) and digitally constructed signatures (DCS)

The use of electronic signatures as a form of identification is increasingly common, yet they have been shown to lack the dynamic features found in online signatures. In this study, handwritten signatures were scanned to produce electronically scanned signatures (ESS) which were then digitally altered to produce digitally constructed signatures (DCS). The ESS and DCS were presented back to participants to identify which were genuine. Only 1% of participants correctly identified all signatures, with a mean score of 57.6% identifications. The lack of self-recognition of ESS raises questions on their reliability and usefulness as means of personal identification.
Ranking Extensions in Abstract Argumentation

Extension-based semantics in abstract argumentation provide a criterion to determine whether a set of arguments is acceptable or not. In this paper, we present the notion of extension-ranking semantics, which determines a preordering over sets of arguments, where one set is deemed more plausible than another if it is somehow more acceptable. We obtain extension-based semantics as a special case of this new approach, but it also allows us to make more fine-grained distinctions, such as one set being "more complete'' or "more admissible'' than another. We define a number of general principles to classify extension-ranking semantics and develop concrete approaches. We also study the relation between extension-ranking semantics and argument-ranking based semantics, which rank individual arguments instead of sets of arguments.
CoSEP: A compound spring embedder layout algorithm with support for ports

This paper describes a new automatic layout algorithm named CoSEP for compound graphs with port constraints. The algorithm works by extending the physical model of a previous algorithm named CoSE by defining additional force types and heuristics for constraining edges to connect to certain user-defined locations on end nodes. Similar to its predecessor, CoSEP also accounts for non-uniform node dimensions and arbitrary levels of nesting via compound nodes. Our experiments show that CoSEP significantly improves the quality of the layouts for compound graphs with port constraints with respect to commonly accepted graph drawing criteria while running reasonably fast, suitable for use in interactive applications for small to medium-sized (up to 500 nodes) graphs. A complete JavaScript implementation of CoSEP as a Cytoscape.js extension along with a demo page is freely available at https://github.com/iVis-at-Bilkent/cytoscape.js-cosep .
La dimensión educativa de la robótica : del desarrollo del pensamiento al pensamiento computacional en el aula

espanolEn los ultimos anos, uno de los motores principales que ha incentivado la innovacion educativa ha sido la introduccion y el uso de las tecnologias de la informacion y la comunicacion en el aula. El uso de ordenadores, pizarras digitales y tabletas como dispositivos que facilitan el aprendizaje, ya sea a traves de la gamificacion, la realidad virtual, la impresion 3D, etc., son algunos de ellos. En este articulo, el interes se centra sobre la Robotica Educativa y programacion en el aula, es decir, como a traves de la robotica se puede desarrollar el pensamiento y la logica (transformada en pensamiento computacional). A traves de la presente revision documental, pretendemos desentramar cual es la dimension educativa, si es que la hay, del uso de estos dispositivos en la etapa escolar, pasando por la evolucion del pensamiento y la robotica educativa y acabando con varias investigaciones realizadas en el area. EnglishIn recent years, one of the main drivers of educational innovation has been the introduction and use of information and communication technologies in the classroom. The use of computers, digital whiteboards and tablets as devices that facilitate learning, whether through gamification, virtual reality, 3D printing, etc., are some of them. In this article, the interest focuses on Educational Robotics and classroom programming, that is, how through robotics, thinking and logic can be developed (transformed into computational thinking). Through this documentary review, we intend to unravel what is the educational dimension, if any, of the use of these devices in the school stage, going through the evolution of thought and educational robotics and ending with several investigations carried out in the area.
An Active Learning Method Based on Variational Autoencoder and DBSCAN Clustering

Active learning is aimed to sample the most informative data from the unlabeled pool, and diverse clustering methods have been applied to it. However, the distance-based clustering methods usually cannot perform well in high dimensions and even begin to fail. In this paper, we propose a new active learning method combined with variational autoencoder (VAE) and density-based spatial clustering of applications with noise (DBSCAN). It overcomes the difficulty of distance representation in high dimensions and prevents the distance concentration phenomenon from occurring in the computational learning literature with respect to high-dimensional p-norms. Finally, we compare our method with four common active learning methods and two other clustering algorithms combined with VAE on three datasets. The results demonstrate that our approach achieves competitive performance, and it is a new batch mode active learning algorithm designed for neural networks with a relatively small query batch size.