Reviewerly/exHarmony huggingface dataset:
Personalized Federated Learning With Gaussian Processes

Federated learning aims to learn a global model that performs well on client devices with limited cross-client communication. Personalized federated learning (PFL) further extends this setup to handle data heterogeneity between clients by learning personalized models. A key challenge in this setting is to learn effectively across clients even though each client has unique data that is often limited in size. Here we present pFedGP, a solution to PFL that is based on Gaussian processes (GPs) with deep kernel learning. GPs are highly expressive models that work well in the low data regime due to their Bayesian nature. However, applying GPs to PFL raises multiple challenges. Mainly, GPs performance depends heavily on access to a good kernel function, and learning a kernel requires a large training set. Therefore, we propose learning a shared kernel function across all clients, parameterized by a neural network, with a personal GP classifier for each client. We further extend pFedGP to include inducing points using two novel methods, the first helps to improve generalization in the low data regime and the second reduces the computational cost. We derive a PAC-Bayes generalization bound on novel clients and empirically show that it gives non-vacuous guarantees. Extensive experiments on standard PFL benchmarks with CIFAR-10, CIFAR-100, and CINIC-10, and on a new setup of learning under input noise show that pFedGP achieves well-calibrated predictions while significantly outperforming baseline methods, reaching up to 21% in accuracy gain.
Internet of Things Software and Hardware Architectures and Their Impacts on Forensic Investigations: Current Approaches and Challenges

The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in the types of devices that need to be examined, few widely agreed-upon standards exist. In this paper, we look at some of the architectures, current frameworks, and methods available to perform forensic analysis of IoT devices to provide a roadmap for investigators and researchers to form the basis of an investigation.
Sistem Informasi Pendaftaran Paket Belajar Bahasa Inggris di Englishvit Yogyakarta

The registration is one of the important processes in recruiting new students in educational institutions. Registration data management should be carried out regularly so that the results can be useful. The manual registration process carried out by Englishvit Yogyakarta raises several problems in the management process. The problems that arise include; unstructured storage and grouping of data, data duplication, and limited access. In addition, the registration process which is carried out directly is no longer the main choice, given the Covid-19 pandemic which has not yet subsided. The purpose of this research is to develop a learning package registration information system at Englishvit Yogyakarta. This information system was developed to solve problems in data management. Access to this information system is made online so that administrators and registrants can take advantage of this system using their respective devices. The development method used in this development is water fall which in the process is carried out coherently, starting from the analysis, design, coding and testing processes. Based on black box testing, this information system can function properly as expected.
О задачном подходе в искусственном интеллекте и когнитивных науках

This work continues a series of publications on the task approach in artificial intelligence. As noted earlier, the agent-based approach described in the monograph by Stuart Russell and Pieter Norwig “Artificial Intelligence. The Modern Approach, may be more argumentatively presented within the framework of the task approach. This paper will show that not only the problems of the bases of mathematics and artificial intelligence, but also many cognitive functions performed by humans and analyzed in cognitive sciences, can also be described and studied within the framework of the task approach. In particular, this paper shows that the analogue of the concept of task in cognitive sciences is the concept of goal and that the Functional Systems Theory (FST), which describes purposeful behavior, can be presented as the brain's solution of tasks to achieve goals and satisfaction of needs. It gives the chance to compare directly the tasks of artificial intellect with cognitive processes and, thereby, to reveal the list of those tasks of natural intellect and schemes of their solution which can be successfully used for the solution of artificial intelligence tasks.
QuestEval: Summarization Asks for Fact-based Evaluation

Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this issue, recent work has proposed evaluation metrics which rely on question answering models to assess whether a summary contains all the relevant information in its source document. Though promising, the proposed approaches have so far failed to correlate better than ROUGE with human judgments. In this paper, we extend previous approaches and propose a unified framework, named QuestEval. In contrast to established metrics such as ROUGE or BERTScore, QuestEval does not require any ground-truth reference. Nonetheless, QuestEval substantially improves the correlation with human judgments over four evaluation dimensions (consistency, coherence, fluency, and relevance), as shown in extensive experiments.
IMPORTANT ISSUES OF THE CONTENT OF THE PHYSICS COURSE IN THE TRAINING OF FUTURE TECHNICAL SPECIALISTS IN HIGHER EDUCATIONAL INSTITUTIONS

The article presents new content areas in the traditional work program for the course of physics in the preparation of technical specialties in universities. The majority of physical education in the system of technical education and the systemic role of the physics course in the structure of technical training are noted. A number of problems and vulnerabilities in the practice of teaching physics were analyzed. The necessity of preserving the integrity of the logical structure of the physics course in the system of technical education, regardless of the direction of training, is noted. The article substantiates the inclusion of a complex of methodological knowledge in the traditional work program in physics, which forms ideas about the complex nature of the physical picture of the world. The structure of this complex is proposed. It is shown that the structure of the physical picture of the world can be represented in the form of two interrelated blocks: the scientific paradigm and scientific knowledge. The multi-level content of each block is revealed, corresponding to the logic of the development of scientific knowledge. A comparative analysis of the proposed approach to the formation of the course content is carried out. The article develops the structure and defines the content of the methodological educational complex of physical science, proposed for inclusion in the content of the physics course for the specialties of the technical bachelor's degree.
Morpheus: Bringing The (PKCS) One To Meet the Oracle

This paper focuses on developing an automatic, black-box testing approach called Morpheus to check the non-compliance of libraries implementing PKCS#1-v1.5 signature verification with the PKCS#1-v1.5 standard. Non-compliance can not only make implementations vulnerable to Bleichenbacher-style RSA signature forgery attacks but also can induce interoperability issues. For checking non-compliance, Morpheus adaptively generates interesting test cases and then takes advantage of an oracle, a formally proven correct implementation of PKCS#1-v1.5 signature standard, to detect non-compliance in an implementation under test. We have used Morpheus to test 45 implementations of PKCS#1-v1.5 signature verification and discovered that 6 of them are susceptible to variants of the Bleichenbacher-style low public exponent RSA signature forgery attack, 1 implementation has a buffer overflow, 33 implementations have incompatibility issues, and 8 implementations have minor leniencies. Our findings have been responsibly disclosed and positively acknowledged by the developers.
Una unidad didáctica transversal sobre Topología básica y sus relaciones con los nudos marineros

espanolEn este articulo se presenta un estudio teorico-practico en forma de unidad didactica dirigida a los alumnos de Matematicas de Secundaria y Bachillerato, referida a la Topologia y sus relaciones con los nudos marineros, programada para ser llevada a la practica en tres anos, si bien unicamente se ha podido experimentar en el primero de ellos debido a la pandemia causada por la Covid-19. El principal objetivo es facilitarle al profesorado de Matematicas de estos niveles nuevos recursos metodologicos que permitan motivar e interesar a los alumnos por esa disciplina EnglishThis article shows a Didactic Unit aimed at Secondary and Baccalaureate Mathematics students and referred to Topology and its applications to sea knots, programmed to be put into practice in three years, which could only be developed in the first of them due to the pandemic caused by Covid-19. The main objective is to provide teachers at these levels with new methodological resources that allow students to be motivated and interested in Mathematics
Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech

Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization. We employ pretrained language models (LMs) to alleviate this data bottleneck. We utilize the GPT LM for generating large amounts of synthetic hate speech sequences from available labeled examples, and leverage the generated data in fine-tuning large pretrained LMs on hate detection. An empirical study using the models of BERT, RoBERTa and ALBERT, shows that this approach improves generalization significantly and consistently within and across data distributions. In fact, we find that generating relevant labeled hate speech sequences is preferable to using out-of-domain, and sometimes also within-domain, human-labeled examples.
STUDY OF THE PRAGMATICS OF CHINESE INTERNET LANGUAGE AND SLANG IN SOCIAL NETWORKS

The Internet provides a space for network users to communicate freely. The desire of network users to express their thoughts, imagination and self-knowledge is reflected in the slang on the Internet and social networks. The language of the Internet appeared as a product of the network, formed its own characteristics in the process of use and acquired its own pragmatic function. These qualities have made it an important tool of modern communication and a cultural carrier.The pragmatic approach to the recipient of information is an important function of any language communication. Although the root of Chinese Internet slang comes from Chinese, it gives a completely different meaning and character as a network language than literal, which requires a pragmatic study to understand. From the point of view of the speaker and the listener, people's speech actions are considered as social behavior governed by various social conditions. It is aimed at explaining that the use of special words in a particular environment can determine the meaning of the text, affect all aspects, and thus determine the pragmatic rules. Before using a slang word, any network user should pay attention to its meaning, as it can also have a negative connotation.
End-to-end Multi-modal Video Temporal Grounding

We address the problem of text-guided video temporal grounding, which aims to identify the time interval of certain event based on a natural language description. Different from most existing methods that only consider RGB images as visual features, we propose a multi-modal framework to extract complementary information from videos. Specifically, we adopt RGB images for appearance, optical flow for motion, and depth maps for image structure. While RGB images provide abundant visual cues of certain event, the performance may be affected by background clutters. Therefore, we use optical flow to focus on large motion and depth maps to infer the scene configuration when the action is related to objects recognizable with their shapes. To integrate the three modalities more effectively and enable inter-modal learning, we design a dynamic fusion scheme with transformers to model the interactions between modalities. Furthermore, we apply intra-modal self-supervised learning to enhance feature representations across videos for each modality, which also facilitates multi-modal learning. We conduct extensive experiments on the Charades-STA and ActivityNet Captions datasets, and show that the proposed method performs favorably against state-of-the-art approaches.
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay

Despite the conventional wisdom that using batch normalization with weight decay may improve neural network training, some recent works show their joint usage may cause instabilities at the late stages of training. Other works, in contrast, show convergence to the equilibrium, i.e., the stabilization of training metrics. In this paper, we study this contradiction and show that instead of converging to a stable equilibrium, the training dynamics converge to consistent periodic behavior. That is, the training process regularly exhibits instabilities which, however, do not lead to complete training failure, but cause a new period of training. We rigorously investigate the mechanism underlying this discovered periodic behavior both from an empirical and theoretical point of view and show that this periodic behavior is indeed caused by the interaction between batch normalization and weight decay.
Improving Synonym Recommendation Using Sentence Context

Traditional synonym recommendations often include ill-suited suggestions for writer’s specific contexts. We propose a simple approach for contextual synonym recommendation by combining existing human-curated thesauri, e.g. WordNet, with pre-trained language models. We evaluate our technique by curating a set of word-sentence pairs balanced across corpora and parts of speech, then annotating each word-sentence pair with the contextually appropriate set of synonyms. We found that basic language model approaches have higher precision. Approaches leveraging sentence context have higher recall. Overall, the latter contextual approach had the highest F-score.
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation

Domain adaptation (DA) aims to alleviate the domain shift between sourcedomain and target domain. Most DA methods require access to the source data,but often that is not possible (e.g. due to data privacy or intellectualproperty). In this paper, we address the challenging source-free domainadaptation (SFDA) problem, where the source pretrained model is adapted to thetarget domain in the absence of source data. Our method is based on theobservation that target data, which might no longer align with the sourcedomain classifier, still forms clear clusters. We capture this intrinsicstructure by defining local affinity of the target data, and encourage labelconsistency among data with high local affinity. We observe that higheraffinity should be assigned to reciprocal neighbors, and propose a selfregularization loss to decrease the negative impact of noisy neighbors.Furthermore, to aggregate information with more context, we consider expandedneighborhoods with small affinity values. In the experimental results we verifythat the inherent structure of the target features is an important source ofinformation for domain adaptation. We demonstrate that this local structure canbe efficiently captured by considering the local neighbors, the reciprocalneighbors, and the expanded neighborhood. Finally, we achieve state-of-the-artperformance on several 2D image and 3D point cloud recognition datasets. Codeis available in https://github.com/Albert0147/SFDA_neighbors.
Reengineering Process To Improve Operations Management In The Sasirangan Fabric Industry

The traditional sasirangan fabric industry has become a mainstay product of South Kalimantan Province, both as part of compulsory clothing for students, government employees, various official events, souvenirs and exports to various countries. However, the cost of the production process which is rather expensive and takes a long time makes the selling price also more expensive, making the fabric industry from China start producing cheaper which is a threat to sasirangan fabric craftsmen. Through this community service activity, the team tested the use of technology to improve the production process faster and cheaper that was affordable for sasirangan fabric craftsmen. The trial of this activity involved 2 partners of saasirangan cloth craftsmen who were also involved in part of the cost of the activity. In the first stage, changing the way of designing which has been directly making motifs by writing using a pencil on cloth into a design using a computer with the Photoshop program eliminating the binding of the cloth, then changing the coloring process so far by dyeing the cloth to the dyeing place replaced with a digital printer, changing from disposal used waste to the river into a special place of waste, replacing the mold drying process from drying in the hot sun which is very dependent on weather conditions by drying using a blower, finally replacing packaging using plastic bags with more attractive packaging bags and boxes. As a result, rival fabric products are processed faster, safer, healthier, neater, and more attractive.
Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization

This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data. We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty metrics based on specificity and abstractiveness. We conduct experiments to show that these three techniques can help improve abstractive summarization across two summarization models and two different small datasets. Furthermore, we show that these techniques can improve performance when applied in isolation and when combined.
A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification

Relation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English. Despite recent interest in deep generative models for Indian languages, relation classification is still not well-served by public data sets. In response, we present IndoRE, a dataset with 39K entity- and relation-tagged gold sentences in three Indian languages, plus English. We start with a multilingual BERT (mBERT) based system that captures entity span positions and type information and provides competitive monolingual relation classification. Using this system, we explore and compare transfer mechanisms between languages. In particular, we study the accuracy-efficiency tradeoff between expensive gold instances vs. translated and aligned ‘silver’ instances.
Accounting for Gaussian Process Imprecision in Bayesian Optimization

Bayesian optimization (BO) with Gaussian processes (GP) as surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we propose Prior-mean-RObust Bayesian Optimization (PROBO) that outperforms classical BO on specific problems. First, we study the effect of the Gaussian processes’ prior specifications on classical BO’s convergence. We find the prior’s mean parameters to have the highest influence on convergence among all prior components. In response to this result, we introduce PROBO as a generalization of BO that aims at rendering the method more robust towards prior mean parameter misspecification. This is achieved by explicitly accounting for GP imprecision via a prior near-ignorance model. At the heart of this is a novel acquisition function, the generalized lower confidence bound (GLCB). We test our approach against classical BO on a real-world problem from material science and observe PROBO to converge faster. Further experiments on multimodal and wiggly target functions confirm the superiority of our method.
Digital competencies â€“ state and future

To be equally included in todayâ€™s digital society, every citizen should pose at least some digital competences.
GENRE APPROACH TO TEACHING MUSIC AS A METHODOLOGY OF PEDAGOGY OF ADDITIONAL EDUCATION

In the article, the theoretical substantiation and experimental testing of a complex of methods of teaching music, ensuring the implementation of the genre approach in conditions of additional education. A complex of methods of teaching music has been developed, which ensures the effectiveness of the implementation of the model for the implementation of the genre approach in the conditions of additional education, which contributes to the theory of building a holistic educational process
Negation-Instance Based Evaluation of End-to-End Negation Resolution

In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g. “not”, “never”) and scope resolution. In the context of previous shared tasks, a variety of evaluation metrics have been proposed. Subsequent works usually use different subsets of these, including variations and custom implementations, rendering meaningful comparisons between systems difficult. Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution. Our proposed metrics correspond to expectations over per-instance scores and hence are intuitively interpretable. To render research comparable and to foster future work, we provide results for a set of current state-of-the-art systems for negation resolution on three English corpora, and make our implementation of the evaluation scripts publicly available.