Reviewerly/exHarmony huggingface dataset:
Questioning the Austrian Universities’ Distance Learning Landscape during the COVID-19 crisis

The outbreak of the Coronavirus disease 2019 (COVID-19) pandemic quickly escalated into a global health emergency with substantial implications for the higher education sector.The COVID-19 situation at Austrian universities has led to significant challenges for the higher education community.Starting from spring 2020, COVID-19 caused universities to close the campuses and forced them to adopt online mode immediately with manifold implications: (1) lockdown time has significantly disrupted the usual information and communication ways previously used between colleagues, working groups, lecturers and students as well as between students; (2) to appropriately communicate with students and to meet their needs, instructors had to adapt new didactical concepts and had to deal with new web conference-, learning platform-and online examination-tools; (3) university staff had to manage some kind of uncertainty, especially related to information lacks due to the communication policy of responsible university rectors based on given and mainly politically-driven changing overall conditions.-Academic staff of 18 universities has been surveyed to find their perspectives about online education and surrounding aspects.Collected contemporary data was exposed to descriptive and inductive statistical analysis using appropriate econometrical techniques, reflecting respondents' experiences concerning distance learning (adjustment and implementation) as well as surrounding issues like reasons for working restrictions and home office aspects, technically-related restrictions, issues of workers' protection, data protection, and certain additional aspects.As an example, a lack of face-to-face interaction with students and inadequate infrastructure and tools were, among some other issues, highlighted by the respondents.
A Novel Hybrid Chaotic Map–Based Proactive RSA Cryptosystem in Blockchain

Blockchain is an up-and-coming technology, and it is exploring its existence in all our day-to-day lives, particularly for business applications. Due to this, security has become a major and a higher priority concern during the transaction process of data, cryptocurrency, etc. Achieving integrity, authentication, and non-repudiation with blockchain technology through digital signatures are the factors for its success. Hence, we imply a novel hybrid chaotic map–based proactive RSA/digital signature scheme for a better securable transaction of blocks compared to earlier works. Faster encryption and decryption could be expected by this novel approach. This could surely be resistant to brute force attacks, chosen plain text attacks and chosen cipher text attacks. OPENMP and OPENSSL libraries could be employed for good performance and security in a multicore platform. Chaotic map theory has its unique characteristics in the design of highly secure asymmetric encryption and decryption processes, with lesser computational cost in lesser time. The strength of RSA lies in the prime factorization of larger prime numbers in the generation of public and private keys. RSA has a severe backlog in computational time; while the length of the prime number is longer, to ensure higher security, the longer one is preferable. Hence to speedup the factorization process, chaotic mapping theory has been synchronized with the RSA cryptosystem. Second, chaotic mapping theory provides an enhanced avalanche effect, which leads to a two-factor RSA encryption process. Chaotic maps are highly sensitive to initial values and control parameters. The unique feature of inclusion of chaotic maps is that any slight change in the initial conditions of the plain text would cause a remarkable deviation in the message digest. This nature would make the hacker limit their prediction ability. Computational time, cost, and level of resistance to attacks are the factors to be considered to measure the efficiency of our new approach when compared to other RSA approaches. The work was simulated using MATLAB and it is proved that that the proposed RSA with hybrid GLT chaotic maps is 1.53 times and 1.27 times faster than the existing ones.
Person Entity Profiling Framework: Identifying, Integrating and Visualizing Online Freely Available Entity-Related Information

When we consider our CV, it is full of entities that we are or were associated with and that define us in some way(s). Such entities include where we studied, where we worked, who we collaborated with on a project or on a paper etc. Entities we are linked to are part of who we are and may reveal about what we are interested in. Hence, we can view any CV as a graph of interlinked entities, where nodes are entities and edges are relations between them. This study proposes a novel entity search framework that in response to a real-time query about an entity, searches, crawls, analyzes and consolidates relevant information that is freely available on the Web about the entity of interest, culminating in the generation a profile of the searched entity. Unlike typical entity search settings, in which a ranked list of entities related to the target entity over a pre-specified relation is processed, we present and visualize rich information about the entity of interest as a typed entity-relation graph without an apriori definition of the types of related entities and relations. This view is structured and compact, making it easy to understand as well as interpret. It enables the user to learn not only about the entity in question, but also about related entities, thereby obtaining a better understanding of the entity in question. We evaluated each of the frameworks components separately and then performed an overall evaluation of the framework, its visualization and the interest of users in the results. The results show that the proposed framework performs entity searches, related entity identification and relation identification very well and that it satisfies users needs.
FORMATION AND CONTENT OF RESEARCH ACTIVITIES IN BACHELORS TRAINING

This article deals with the formation of research activities of future bachelors in vocational training. Four components of research competence are considered. The development of research competence is confirmed by the use of analytical, critical, communication and other skills by students in research
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective

Deep neural networks (DNNs) often rely on easy-to-learn discriminatory features, or cues, that are not necessarily essential to the problem at hand. For example, ducks in an image may be recognized based on their typical background scenery, such as lakes or streams. This phenomenon, also known as shortcut learning, is emerging as a key limitation of the current generation of machine learning models. In this work, we introduce a set of experiments to deepen our understanding of shortcut learning and its implications. We design a training setup with several shortcut cues, named WCST-ML, where each cue is equally conducive to the visual recognition problem at hand. Even under equal opportunities, we observe that (1) certain cues are preferred to others, (2) solutions biased to the easy-to-learn cues tend to converge to relatively flat minima on the loss surface, and (3) the solutions focusing on those preferred cues are far more abundant in the parameter space. We explain the abundance of certain cues via their Kolmogorov (descriptional) complexity: solutions corresponding to Kolmogorov-simple cues are abundant in the parameter space and are thus preferred by DNNs. Our studies are based on the synthetic dataset DSprites and the face dataset UTKFace. In our WCST-ML, we observe that the inborn bias of models leans toward simple cues, such as color and ethnicity. Our findings emphasize the importance of active human intervention to remove the inborn model biases that may cause negative societal impacts.
USING A MODULAR APPROACH IN THE CREDIT SYSTEM OF EDUCATION

The article describes the credit system of education, which is one of the main tasks in the university today is the selection of new forms and methods of teaching, allowing not only to give the necessary knowledge, but to include the student in the system of continuous professional education and self-education.
OmniPrint: A Configurable Printed Character Synthesizer

We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research. It draws inspiration from famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of generating a wide variety of printed characters from various languages, fonts and styles, with customized distortions. We include 935 fonts from 27 scripts and many types of distortions. As a proof of concept, we show various use cases, including an example of meta-learning dataset designed for the upcoming MetaDL NeurIPS 2021 competition. OmniPrint is available at https://github.com/SunHaozhe/OmniPrint.
Voxel-based Morphometry Analysis for Healthy Aging and Cognitive Function

20～70 歳代の健常者 120 名を対象に, 年齢と高次脳機能の関係を明らかにするため, voxelbased morphometry (VBM) を用いて検討した。高次脳機能評価として ACE-R を施行し下位項目得点を算出した。3T MRI で撮像した解剖画像を使用した。加齢に有意に相関する因子は, 脳形態では灰白質容積 (負相関) と髄液容積 (正相関) , 高次脳機能では遅延再生得点 (負相関) であった。加齢に対する近似直線/ 曲線では 20 歳以降から灰白質の減少がはじまる一方, 遅延再生は緩やかな低下を示した。VBM 解析では, 遅延再生は両側の前頭葉, 側頭葉, 島回, 海馬付近, 楔部など広い領域で灰白質の減少と相関したが, 年齢を共変量とした条件では有意な相関を示す領域はなかった。最も加齢の影響を受けるのは遅延再生であり, 緩やかに低下し, 脳の広範な領域が関与していたが, 年齢の影響を除いた特異領域は本研究では同定できなかった。
CTRL-C: Camera calibration TRansformer with Line-Classification

Single image camera calibration is the task of estimating the camera parameters from a single input image, such as the vanishing points, focal length, and horizon line. In this work, we propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration, which directly estimates the camera parameters from an image and a set of line segments. Our network adopts the transformer architecture to capture the global structure of an image with multi-modal inputs in an end-to-end manner. We also propose an auxiliary task of line classification to train the network to extract the global geometric information from lines effectively. Our experiments demonstrate that CTRL-C outperforms the previous state-of-the-art methods on the Google Street View and SUN360 benchmark datasets.
On Architectures and Training for Raw Waveform Feature Extraction in ASR

With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined standard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained supervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an existing acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance.
Are Training Resources Insufficient? Predict First Then Explain!

Natural language free-text explanation generation is an efficient approach to train explainable language processing models for commonsense-knowledge-requiring tasks. The most predominant form of these models is the explain-then-predict (EtP) structure, which first generates explanations and uses them for making decisions. The performance of EtP models is highly dependent on that of the explainer by the nature of their structure. Therefore, large-sized explanation data are required to train a good explainer model. However, annotating explanations is expensive. Also, recent works reveal that free-text explanations might not convey sufficient information for decision making. These facts cast doubts on the effectiveness of EtP models. In this paper, we argue that the predict-then-explain (PtE) architecture is a more efficient approach in terms of the modelling perspective. Our main contribution is twofold. First, we show that the PtE structure is the most data-efficient approach when explanation data are lacking. Second, we reveal that the PtE structure is always more training-efficient than the EtP structure. We also provide experimental results that confirm the theoretical advantages.
Rank Aggregation for Non-stationary Data Streams

The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner. The bottleneck of this process is a rank aggregation problem.
LibGuides: Omeka: Add an Item

A guide to Omeka, a web publishing platform for online archives, collections, and exhibits. A walkthrough of the process to add an item to an Omeka Classic site
Feature Level Combination for Face Recognition Based on Convolutional Neural Networks

Face detection and recognition systems have recently achieved encouraging results using deep learning, especially Convolutional Neural Network (CNN). Face Recognition (FR) systems have many challenges in unconstrained environments that decrease the accuracy; for overcoming these challenges, a deep learning-based features combination has been proposed. The scheme performs feature-level combination by applying two pre-trained GoogLeNet and VggNet-16 models as deep feature extractors. First, faces are detected and aligned using the Multi-Task Convolutional Neural Networks (MTCNN) face detector. The deep features are extracted from a face image using each individually pre-trained CNN. Second, features obtained from GoogLeNet and VggNet-16 models are combined using the serial-feature combination method. Finally, a classification task is performed using a multiclass Support Vector Machine (SVM) classifier. Experiments on the following datasets: VggFace2, LFW, Essex, and ORL, indicate the efficacy of the proposed system as the combination of the two pre-trained CNN models improves performance. The combination strategy, in particular, yields an accuracy of 95.33% to 99.29% on all datasets. The proposed system was compared to existing models in terms of the LFW, and ORL datasets, the findings showed that the proposed system outperformed most current models in terms of accuracy.
Electoral Programs of German Parties 2021: A Computational Analysis Of Their Comprehensibility and Likeability Based On SentiArt

The electoral programs of six German parties issued before the parliamentary elections of 2021 are analyzed using state-of-the-art computational tools for quantitative narrative, topic and sentiment analysis. We compare different methods for computing the textual similarity of the programs, Jaccard Bag similarity, Latent Semantic Analysis, doc2vec, and sBERT, the representational and computational complexity increasing from the 1st to the 4th method. A new similarity measure for entire documents derived from the Fowlkes Mallows Score is applied to kmeans clustering of sBERT transformed sentences. Using novel indices of the readability and emotion potential of texts computed via SentiArt (Jacobs, 2019), our data shed light on the similarities and differences of the programs regarding their length, main ideas, comprehensibility, likeability, and semantic complexity. Among others, they reveal that the programs of the SPD and CDU have the best chances to be comprehensible and likeable -all other things being equal-, and they raise the important issue of which similarity measure is optimal for comparing texts such as electoral programs which necessarily share a lot of words. While such analyses can not replace qualitative analyses or a deep reading of the texts, they offer predictions that can be verified in empirical studies and may serve as a motivation for changing aspects of future electoral programs potentially making them more comprehensible and/or likeable.
Scientific-Methodological Aspects Of Students’ Concepts Formation At A Technical University

The current stage of the society's development is characterized by a sharp increase in the volume of scientific and technical information, an acceleration of information exchange among its producers and consumers, a reduction in the time for introducing the results of scientific research into production, and an increasingly close cooperation of all participants in research and production processes. Apparently, these trends are long-term, so they will have a significant impact on all spheres of life related to scientific and technical activities of a person, including the system of higher technical education, which must respond in a timely and adequate manner to the changed demands of society. It is becoming increasingly obvious that there is a need to solve a complex of didactic problems related to the specification of goals, the selection of content, the search for methods, forms and means of training that would prepare young professionals for professional activity in the conditions of continuous updating of professionally relevant information. The formation of a scientific concept is considered as a process of development of the level of generalization of the studied concept from the primary to the generalization of the corresponding scientific concept. The technology and methods of formation of scientific concepts in students of a technical university are presented, the corresponding methodological recommendations, sample questions and tasks at different stages of concept formation are given. The materials of the article can be useful for teachers of professional educational institutions working with students of technical training areas.
Еnactivist Criticism of the Neurobiological Theory of Consciousness

The article considers the main arguments against the neurobiological theory of consciousness from the point of view of the enactivist approach within the philosophy of mind. The neurobiological theory of consciousness, which reduces consciousness to neural activity, is currently the dominant approach to the mind-body problem. The neurobiological theory emerged as a result of advances in research on the phenomena of consciousness and through the development of technologies for visualizing the internal processes of mind. However, at the very heart of this theory, there is a number of logical contradictions. The non-reductive enactivist approach to consciousness, introduced in this article, contributes to the existing argumentation against the reduction of consciousness to neural processes with remonstrations that take into account the modern neuroscientific data. The article analyzes the argumentation of the sensorimotor enactivism developed by A. Noe and offers the account of the teleosemantic approach to the concept of information provided by R. Cao. The key problems of the neurobiological theory of consciousness are highlighted, and the objections emerging within the framework of the enactivist approach are analyzed. Since the main concepts on which the neural theory is based are the concepts of neural substrate, cognition as representation, and information as a unit of cognition, the author of the article presents three key enactivist ideas that oppose them. First, the enactivist concept of cognition as action allows us to consider the first-person experience as a mode of action, and not as a state of the brain substrate. Second, the article deals with the “explanatory externalism” argument proposed by Noe, who refutes the image of cognition as a representation in the brain. Finally, in order to critically revise the concept of information as a unit of cognition, the author analyzes Cao’s idea, which represents a teleosemantic approach, but is in line with the general enactivist argumentation. Cao shows that the application of the concept “information” to neural processes is problematic: no naturalized information is found in the brain as a physical substrate. A critical revision of beliefs associated with the neural theory of consciousness leads us to recognize that there are not enough grounds for reducing consciousness to processes that take place in the brain. That is why Noe calls expectations that the visualization of processes taking place in the brain with the help of the modern equipment will be able to depict the experience of consciousness the “new phrenology”, thus indicating the naive character of neural reduction. The article concludes that natural science methods are insufficient for the study of consciousness.
Automated Testing of AI Models

The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we took the first step of creating a testing framework called AITEST for metamorphic properties such as fairness, robustness properties for tabular, time-series, and text classification models. In this paper, we extend the capability of the AITEST tool to include the testing techniques for Image and Speech-to-text models along with interpretability testing for tabular models. These novel extensions make AITEST a comprehensive framework for testing AI models.
Manifold Matching via Deep Metric Learning for Generative Modeling

We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator. In our framework, we view the real data set as some manifold embedded in a high-dimensional Euclidean space. The distribution generator aims at generating samples that follow some distribution condensed around the real data manifold. It is achieved by matching two sets of points using their geometric shape descriptors, such as centroid and $p$-diameter, with learned distance metric; the metric generator utilizes both real data and generated samples to learn a distance metric which is close to some intrinsic geodesic distance on the real data manifold. The produced distance metric is further used for manifold matching. The two networks are learned simultaneously during the training process. We apply the approach on both unsupervised and supervised learning tasks: in unconditional image generation task, the proposed method obtains competitive results compared with existing generative models; in super-resolution task, we incorporate the framework in perception-based models and improve visual qualities by producing samples with more natural textures. Experiments and analysis demonstrate the feasibility and effectiveness of the proposed framework.