Reviewerly/exHarmony huggingface dataset:
Analyzing structural similarity of user interface layouts of Android apps using deep learning

The layout of graphical user interfaces (GUIs) takes into consideration the distribution of buttons, images, texts, and other components that are important factors for a positive experience of interactivity and usability. Therefore, it may be helpful to consult similar layouts during the design of GUIs for inspiration as well as to direct the development of original interfaces. This may be even more important in the context of the design of mobile applications in an educational context or when being developed by end-users with low-code environments, such as App Inventor. Therefore, this article presents an approach for analyzing the similarity of GUI layout using deep learning. First, GUI components and their positions are detected using the YOLO model. Then, the similarity of layouts is analyzed based on the GUI components detected. Results of the evaluation of the developed approach indicated a good accuracy of the detection of GUI components and the analysis of similarity close to human predictions. The results of this research can be used for the automated analysis of the similarity of GUI layouts of mobile applications, which can be used to analyze the design of mobile applications (consistency), search for similar designs, or assess the originality of a GUI design. It can also be applied in an educational context to assess the development of creativity as part of the development of mobile applications in order to teach computing.
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning

Delta tuning (DET, also known as parameter-efficient tuning) is deemed as the new paradigm for using pre-trained language models (PLMs). Up to now, various DETs with distinct design elements have been proposed, achieving performance on par with fine-tuning. However, the mechanisms behind the above success are still under-explored, especially the connections among various DETs. To fathom the mystery, we hypothesize that the adaptations of different DETs could all be reparameterized as low-dimensional optimizations in a unified optimization subspace, which could be found by jointly decomposing independent solutions of different DETs. Then we explore the connections among different DETs by conducting optimization within the subspace. In experiments, we find that, for a certain DET, conducting optimization simply in the subspace could achieve comparable performance to its original space, and the found solution in the subspace could be transferred to another DET and achieve non-trivial performance. We also visualize the performance landscape of the subspace and find that there exists a substantial region where different DETs all perform well. Finally, we extend our analysis and show the strong connections between fine-tuning and DETs.
FEATURES OF A FREE MAMMOGRAPHY DATABASE TO MANAGE A RANDOM BREAST PHANTOM IMAGES DATASET INTENDED FOR TESTING CAD SCHEMES

A previous structured images database intended to be used as a tool to researchers testing developed image processing techniques in mammography is being upgraded with new tools and images sets.Its original management system allows to choose and download high quality mammographic images, as from old digitized films as well as from direct digital files acquired more recently.Its search engine is of freely access online, it has incorporated new features, and new types of images intended to aid performance tests of processing schemes.In this sense, we discuss here new tools implemented in the management system to be able to handle images of a new breast phantom with random distributions, exposed to different DR mammography equipment.We described some of the new database management system features to archive, search and download phantom images, together with an evaluation which compares these kind of images with those from actual breasts in order to validate this new set in terms of visual analysis.Previous results not only have indicated a reasonable level of similarity between phantom (ie., simulated) images and actual ones, but the utility of implement this dataset as a section of our mammography database in aiding performance tests of CADx schemes techniques addressed to detect and/or classify signals of interest in digital mammography.
Assessment of reading performance of non-Native English learners

English being an international language is the need of the time. Reading is one of the essential skills of language learning for non-native English learners. In recent years the concern of reading decline among students has become a point of discussion among educators. In this article, we probe how the quality of English reading is going down gradually among non-native English learning students. For this purpose, we measure the reading abilities of grade 5 students with multiple languages as their mother tongue, having probably not very different socio-economic backgrounds. The data of students (N=589) were collected through convenience sampling. Students were assessed in three domains of reading (fluency, comprehension, and reading vocabulary) through the self-developed English Reading Achievement Scale (ERAS). The results demonstrated unsatisfactory performance of sampled students' reading in English. Only 56 percent of students remained successful in the assessment of English reading. The performance of girls' school students in reading was comparatively better than the boys' students. Students' performance was probably better in reading fluency and worse in reading vocabulary.
Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models

Numerical optimization has become a popular approach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efficiency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modifiers to a differentiable human predictive model based on a dedicated recurrent neural network. These modifiers allow to change the human prediction within motion optimization. We empirically evaluate our method using the publicly available MoGaze dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other.
Augmenting Task-Oriented Dialogue Systems with Relation Extraction

The standard task-oriented dialogue pipeline uses intent classification and slot-filling to interpret user utterances. While this approach can handle a wide range of queries, it does not extract the information needed to handle more complex queries that contain relationships between slots. We propose integration of relation extraction into this pipeline as an effective way to expand the capabilities of dialogue systems. We evaluate our approach by using an internal dataset with slot and relation annotations spanning three domains. Finally, we show how slot-filling annotation schemes can be simplified once the expressive power of relation annotations is available, reducing the number of slots while still capturing the user's intended meaning.
Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation

For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation. For online spoken language translation, where models need to start translating before the full utterance is spoken, most previous work has ignored the segmentation problem. In this paper, we compare various methods for improving models' robustness towards segmentation errors and different segmentation strategies in both offline and online settings and report results on translation quality, flicker and delay. Our findings on five different language pairs show that a simple fixed-window audio segmentation can perform surprisingly well given the right conditions.
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds

Despite impressive successes, deep reinforcement learning (RL) systems still fall short of human performance on generalization to new tasks and environments that differ from their training. As a benchmark tailored for studying RL generalization, we introduce Avalon, a set of tasks in which embodied agents in highly diverse procedural 3D worlds must survive by navigating terrain, hunting or gathering food, and avoiding hazards. Avalon is unique among existing RL benchmarks in that the reward function, world dynamics, and action space are the same for every task, with tasks differentiated solely by altering the environment; its 20 tasks, ranging in complexity from eat and throw to hunt and navigate, each create worlds in which the agent must perform specific skills in order to survive. This setup enables investigations of generalization within tasks, between tasks, and to compositional tasks that require combining skills learned from previous tasks. Avalon includes a highly efficient simulator, a library of baselines, and a benchmark with scoring metrics evaluated against hundreds of hours of human performance, all of which are open-source and publicly available. We find that standard RL baselines make progress on most tasks but are still far from human performance, suggesting Avalon is challenging enough to advance the quest for generalizable RL.
Bridging Machine Learning and Sciences: Opportunities and Challenges

The application of machine learning in sciences has seen exciting advances in recent years. As a widely applicable technique, anomaly detection has been long studied in the machine learning community. Especially, deep neural nets-based out-of-distribution detection has made great progress for high-dimensional data. Recently, these techniques have been showing their potential in scientific disciplines. We take a critical look at their applicative prospects including data universality, experimental protocols, model robustness, etc. We discuss examples that display transferable practices and domain-specific challenges simultaneously, providing a starting point for establishing a novel interdisciplinary research paradigm in the near future.
EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain

Existing summarization datasets come with two main drawbacks: (1) They tend to focus on overly exposed domains, such as news articles or wiki-like texts, and (2) are primarily monolingual, with few multilingual datasets. In this work, we propose a novel dataset, called EUR-Lex-Sum, based on manually curated document summaries of legal acts from the European Union law platform (EUR-Lex). Documents and their respective summaries exist as cross-lingual paragraph-aligned data in several of the 24 official European languages, enabling access to various cross-lingual and lower-resourced summarization setups. We obtain up to 1,500 document/summary pairs per language, including a subset of 375 cross-lingually aligned legal acts with texts available in all 24 languages. In this work, the data acquisition process is detailed and key characteristics of the resource are compared to existing summarization resources. In particular, we illustrate challenging sub-problems and open questions on the dataset that could help the facilitation of future research in the direction of domain-specific cross-lingual summarization. Limited by the extreme length and language diversity of samples, we further conduct experiments with suitable extractive monolingual and cross-lingual baselines for future work. Code for the extraction as well as access to our data and baselines is available online at: https://github.com/achouhan93/eur-lex-sum.
Twitter Users' Behavioral Response to Toxic Replies

Online toxic attacks, such as harassment, trolling, and hate speech have been linked to an increase in offline violence and negative psychological effects on victims. In this paper, we studied the impact of toxicity on users' online behavior. We collected a sample of 79.8k Twitter conversations. Then, through a longitudinal study, for nine weeks, we tracked and compared the behavioral reactions of authors, who were toxicity victims, with those who were not. We found that toxicity victims show a combination of the following behavioral reactions: avoidance, revenge, countermeasures, and negotiation. We performed statistical tests to understand the significance of the contribution of toxic replies toward user behaviors while considering confounding factors, such as the structure of conversations and the user accounts' visibility, identifiability, and activity level. Interestingly, we found that compared to other random authors, victims are more likely to engage in conversations, reply in a toxic way, and unfollow toxicity instigators. Even if the toxicity is directed at other participants, the root authors are more likely to engage in the conversations and reply in a toxic way. However, victims who have verified accounts are less likely to participate in conversations or respond by posting toxic comments. In addition, replies are more likely to be removed in conversations with a larger percentage of toxic nested replies and toxic replies directed at other users. Our results can assist further studies in developing more effective detection and intervention methods for reducing the negative consequences of toxicity on social media.
Enlightenment of User’s Thinking on Technical Writing from the Perspective of Technical Communication

Technical communication started relatively late in China, but the market demand for talent in this industry is increasing. At present, the training system for domestic technical communication talents is not perfect, and the level of technical writing document delivery in the market is uneven, which affects the related development of enterprises to a certain extent. This paper analyzes technical writing from the perspective of users, answers three questions: who is the target user of technical writing, what is the demand of the target user and how to meet the demand of the target user, and concludes with three inspirations, aiming at providing some reference for the development of technical communication industry. These three inspirations are: technical writing must accurately depict the user groups, plan the content according to the needs of the users, and polish the expression according to the preferences of people from different countries.
Analisis Teks Deskriptif pada Buku Pembelajaran Prigel Bahasa Jawa Kelas X Terbitan Erlangga

The Feasibility of a Descriptive Text in the Class X Javanese Prigel Learning Book Published by Erlangga AbstractThe purpose of this paper is to analyze the adequacy of a language in the Javanese descriptive text in the Javanese Prigel book whether it is good and worthy to be used as a learning text in the classroom later. This writing discusses the use of a text scope including: (1) Describing the purpose/social function in a descriptive text (2) Describing the stages in a descriptive text (3) Describing a linguistic feature in a descriptive text entitled sego megono . Data collection techniques used are reading and writing techniques and data analysis techniques using qualitative methods and descriptive analysis techniques which include description, classification and analysis. The results of this study are to find out in the descriptive text entitled sego megono whether it is feasible to be included in a study and whether the text already has a social function, stages, and linguistic characteristics in the text. Keywords: language, description, social function text, social stages, linguistic features
Teks Pawarta dalam Buku Pendamping Materi Pengayaan Asah Basa Jawa untuk SMP/MTs Kelas VIII sebagai Analisis Kajian Teks Eksposisi

Exposition text is a text that contains information in it with the aim of providing information or knowledge for the reader. Exposition text is non-fiction so that the text must contain things such as factual and verifiable statements as well as real events. useful for readers. In learning activities, for example language subjects, students are introduced to various types of texts with the aim that students are able to distinguish and understand each type of text. As written in this study, in learning Javanese, students are presented with a Javanese news exposition text that contains information about an event in an area. The news text provides detailed information about the occurrence of these events based on the results of interviews conducted by the news writers. In order to be called an exposition text, every text that is included as an expository text must have a certain structure so that the information conveyed is really short, concise, clear and can be understood by the reader. The news text must include 5W+1H questions so that the news can be considered complete and clear. The news text which is the object of this research will be analyzed its structure according to the structure of the exposition text and described through a qualitative descriptive method that describes the analysis of the structure of the text as a whole. The news text is analyzed for the completeness of the structure of the text whether it is in accordance with the structure of the exposition text by understanding the content of the news text that is presented. This research was made as a final project for the Text Study course which aims to make students understand what has been taught during the lecture so that they can apply it in this research.
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models

Model selection, via penalized likelihood type criteria, is a standard task in many statistical inference and machine learning problems. Progress has led to deriving criteria with asymptotic consistency results and an increasing emphasis on introducing non-asymptotic criteria. We focus on the problem of modeling non-linear relationships in regression data with potential hidden graph-structured interactions between the high-dimensional predictors, within the mixture of experts modeling framework. In order to deal with such a complex situation, we investigate a block-diagonal localized mixture of polynomial experts (BLoMPE) regression model, which is constructed upon an inverse regression and block-diagonal structures of the Gaussian expert covariance matrices. We introduce a penalized maximum likelihood selection criterion to estimate the unknown conditional density of the regression model. This model selection criterion allows us to handle the challenging problem of inferring the number of mixture components, the degree of polynomial mean functions, and the hidden block-diagonal structures of the covariance matrices, which reduces the number of parameters to be estimated and leads to a trade-off between complexity and sparsity in the model. In particular, we provide a strong theoretical guarantee: a finite-sample oracle inequality satisfied by the penalized maximum likelihood estimator with a Jensen-Kullback-Leibler type loss, to support the introduced non-asymptotic model selection criterion. The penalty shape of this criterion depends on the complexity of the considered random subcollection of BLoMPE models, including the relevant graph structures, the degree of polynomial mean functions, and the number of mixture components.
Weakly-Supervised Action Detection Guided by Audio Narration

Videos are more well-organized curated data sources for visual concept learning than images. Unlike the 2-dimensional images which only involve the spatial information, the additional temporal dimension bridges and synchronizes multiple modalities. However, in most video detection benchmarks, these additional modalities are not fully utilized. For example, EPIC Kitchens is the largest dataset in first-person (egocentric) vision, yet it still relies on crowdsourced information to refine the action boundaries to provide instance-level action annotations. We explored how to eliminate the expensive annotations in video detection data which provide refined boundaries. We propose a model to learn from the narration supervision and utilize multimodal features, including RGB, motion flow, and ambient sound. Our model learns to attend to the frames related to the narration label while suppressing the irrelevant frames from being used. Our experiments show that noisy audio narration suffices to learn a good action detection model, thus reducing annotation expenses.
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks

Deep Neural Networks (DNNs) have been shown vulnerable to Test-Time Evasion attacks (TTEs, or adversarial examples), which, by making small changes to the input, alter the DNN's decision. We propose an unsupervised attack detector on DNN classifiers based on class-conditional Generative Adversarial Networks (GANs). We model the distribution of clean data conditioned on the predicted class label by an Auxiliary Classifier GAN (AC-GAN). Given a test sample and its predicted class, three detection statistics are calculated based on the AC-GAN Generator and Discriminator. Experiments on image classification datasets under various TTE attacks show that our method outperforms previous detection methods. We also investigate the effectiveness of anomaly detection using different DNN layers (input features or internal-layer features) and demonstrate, as one might expect, that anomalies are harder to detect using features closer to the DNN's output layer.
LEARNING ECOSYSTEM IN THE CONTEXT OF THE CONCEPT OF DIGITAL TRANSFORMATION OF EDUCATION AND SCIENCE OF UKRAINE

The global crisis in many areas of human life caused by the COVID-19 pandemic has accelerated the digitalization of many processes. Many people tried to continue their activities online, such as work, study, trade, etc [1].
Natural Computation of Cognition, from Single Cells Up

At the time when the first models of cognitive architectures have been proposed, some forty years ago, understanding of cognition, embodiment and evolution was substantially different from today's.So was the state of the art of information physics, information chemistry, bioinformatics, neuroinformatics, computational neuroscience, complexity theory, self-organization, theory of evolution, as well as the basic concepts of information and computation.Novel developments support a constructive interdisciplinary framework for cognitive architectures based on natural morphological computing, where interactions between constituents at different levels of organization of matter-energy and their corresponding time-dependent dynamics, lead to complexification of agency and increased cognitive capacities of living organisms that unfold through evolution.Proposed info-computational framework for naturalizing cognition considers present updates (generalizations) of the concepts of information, computation, cognition, and evolution in order to attain an alignment with the current state of the art in corresponding research fields.Some important open questions are suggested for future research with implications for further development of cognitive and intelligent technologies.