Reviewerly/exHarmony huggingface dataset:
MULTI-REFERENCED TRAINING FOR DIALOGUE RESPONSE GENERATION

In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this work, we first analyze the training objective of dialogue models from the view of Kullback-Leibler divergence (KLD) and show that the gap between the real world probability distribution and the single-referenced data's probability distribution prevents the model from learning the one-to-many relations efficiently. Then we explore approaches to multi-referenced training in two aspects. Data-wise, we generate diverse pseudo references from a powerful pretrained model to build multi-referenced data that provides a better approximation of the real-world distribution. Model-wise, we propose to equip variational models with an expressive prior, named linear Gaussian model (LGM). Experimental results of automated evaluation and human evaluation show that the methods yield significant improvements over baselines. We will release our code and data in this https URL.
APPLICATION OF BLOCKCHAIN TECHNOLOGIES IN ENGINEERING EDUCATION IN THE CONDITIONS OF COVID-19 TO INCREASE ITS QUALITY TO SMART-LEARNING

APPLICATION OF BLOCKCHAIN TECHNOLOGIES IN ENGINEERING EDUCATION IN THE CONDITIONS OF COVID-19 TO INCREASE ITS QUALITY TO SMART-LEARNING
MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection

This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection.The task concerns evaluating systems that detect the spans that make a text toxic when detecting such spans are possible.To address the possibly multi-span detection problem, we develop a start-to-end tagging framework on the top of RoBERTa based language model.Besides, we design a custom loss function which take distance into account.In comparison to other participating teams, our system has achieved 69.03% F1 score, which is slight lower (-1.8 and -1.73) than the top 1 (70.83%) and top 2 (70.77%), respectively.
Probing neural networks with t-SNE, class-specific projections and a guided tour.

We use graphical methods to probe neural nets that classify images. Plots of t-SNE outputs at successive layers in a network reveal increasingly organized arrangement of the data points. They can also reveal how a network can diminish or even forget about within-class structure as the data proceeds through layers. We use class-specific analogues of principal components to visualize how succeeding layers separate the classes. These allow us to sort images from a given class from most typical to least typical (in the data) and they also serve as very useful projection coordinates for data visualization. We find them especially useful when defining versions guided tours for animated data visualization.
FREE: Feature Refinement for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. Such a bias inevitably results in poor-quality visual features for GZSL tasks, which potentially limits the recognition performance on both seen and unseen classes. In this paper, we propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE), to tackle the above problem. FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples. Furthermore, we propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations, and concatenate the features in FR to extract the fully refined features. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of FREE over its baseline and current state-of-the-art methods. Our codes are available at https://github.com/shiming-chen/FREE .
A survey on NIST PQ signatures

Shor's shockingly fast quantum algorithm for solving the period-finding problem is a threat for the most common public-key primitives, as it can be efficiently applied to solve both the Integer Factorisation Problem and the Discrete Logarithm Problem. In other words, many once-secure protocols have to be replaced by still-secure alternatives. Instead of relying, for example, on the RSA protocol, the Diffie-Hellman key-exchange or the (Elliptic Curve) Digital Signature Algorithm, many researchers moved their attention to the design and analysis of primitives which are yet to be broken by quantum algorithms. The urgency of the threat imposed by quantum computers led the U.S. National Institute of Standards and Technology (NIST) to open calls for both Post-Quantum Public-Keys Exchange Algorithms and Post-Quantum Digital Signature Algorithms. In this brief survey we focus on the round 3 finalists and alternate candidates for Digital Signatures: CRYSTALS-DILITHIUM, FALCON, Rainbow, SPHINCS+, GeMSS, Picnic.
An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

One of the key challenges that the computer vision is facing is the age prediction. A well efficient CNN is selected for age prediction by performing various CNN operations by taking the categories as age 40 and above age 40. The selected CNN method obtained a training accuracy of 100% at more than 100 epochs. Hence, 100 epochs is considered for training. At this, the validation accuracy achieved is 84.9%. Three kinds of age phases with an age gap of 20,10 and 5 are used to predict the age. The normal method results in very less accuracy. Hence a hierarchical method is formulated. Under the hierarchical method, CNN is trained to estimate the age gaps in decreasing order. Hence not a single classifier, a group of classifiers are used for testing the image. From traditional method to hierarchical method, the 20 age gap accuracy increased from 27% to above 60%, ten age gap increased from 12% to above 35%, and five age gap increased from 5.5% to above 21%. To improve further, the features of the face parts are derived and combined which improves the efficiency compared to normal method, but not good accuracy as Hierarchical method. The combination of hierarchical method along with the face feature extraction method results in a considerable improvement in accuracy.
Application of neural network autoencoder algorithm in the cancer informatics research

Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.
Rethinking and Improving Relative Position Encoding for Vision Transformer

Relative position encoding (RPE) is important for transformer to capture sequence ordering of input tokens. General efficacy has been proven in natural language processing. However, in computer vision, its efficacy is not well studied and even remains controversial, e.g., whether relative position encoding can work equally well as absolute position? In order to clarify this, we first review existing relative position encoding methods and analyze their pros and cons when applied in vision transformers. We then propose new relative position encoding methods dedicated to 2D images, called image RPE (iRPE). Our methods consider directional relative distance modeling as well as the interactions between queries and relative position embeddings in self-attention mechanism. The proposed iRPE methods are simple and lightweight. They can be easily plugged into transformer blocks. Experiments demonstrate that solely due to the proposed encoding methods, DeiT and DETR obtain up to 1.5% (top-1 Acc) and 1.3% (mAP) stable improvements over their original versions on ImageNet and COCO respectively, without tuning any extra hyperparameters such as learning rate and weight decay. Our ablation and analysis also yield interesting findings, some of which run counter to previous understanding. Code and models are open-sourced at https://github.com/microsoft/Cream/tree/main/iRPE.
Challenges in teaching-learning higher mathematics remotely at Riga technical university in COVID-19 pandemic

This article overviews difficulties and challenges faced by mathematics professors and students at the Riga Technical University due to the necessity of enforced and immediate transition from the classroom to distance learning.It also contains research focused on finding an optimal way of teaching mathematics when only distance education is available.The research is based on surveys completed by students and mathematics professors about the experience gained during the spring and autumn semesters of 2020.The methods found through the research can be applied in teaching related subjects such as natural sciences and engineering.
Exploring the Dependency Network of Docker Containers: Structure, Diversity, and Relationship

Container technologies are being widely used in large scale production cloud environments, of which Docker has become the de-facto industry standard. As a key step, containers need to define their dependent base image, which makes complex dependencies exist in a large number of containers. Prior studies have shown that references between software packages could form technical dependencies, thus forming a dependency network. However, little is known about the details of docker container dependency networks. In this paper, we perform an empirical study on the dependency network of docker containers from more than 120,000 dockerfiles. We construct the container dependency network and analyze its network structure. Further, we focus on the Top-100 dominant containers and investigate their subnetworks, including diversity and relationships. Our findings help to characterize and understand the container dependencies in the docker community and motivate the need for developing container dependency management tools.
Triangulation clay bodies formulation for lapohan traditional pottery

This study investigated traditional local clay in Kampung Selakan, Selakan Island. Traditional lapohan pottery uses the triangulation formula to define the body of the clay. The purpose of this research was to study the effect of gosong (local black sand) on the lapohan's properties as a solution to protect the market from decline as traditional Lapohan pottery is important for the preservation of heirloom heritage requires. In the experiment, 18 formulations used varied the composition of Selakan clay (SC) by introducing gosong (black local sand) from 2% to 18% of the weight percentages studied. The durability, water absorption and shrinkage of the clay body is tested for each sample. The higher gosong content has resulted in higher water absorption, low shrinkage, and stability. Experiment on the production of the firing temperature at 900°C. The effects of heat on the composition of clay were measured quantitatively and the interaction between the mechanical resistance and the components of the lapohan pottery clay body was tested. The result comparison shows that the best composition is obtained by adding gosong which generated pottery products with improved aesthetic workability of clay. A descriptive approach of qualitative analysis, including observation and in-depth interviews for written or visual data collection, will gather the necessary details for the report. The study's findings and significance indicate that traditional development of lapohan pottery in Selakan Island primarily involves awareness and technological usage of clay as a primary material, preparation of clay bodies, and adaptation for potters utilising local natural source.
Hierarchical Adaptive Information Filtering Algorithm Considering System Noise and Observation Noise

High-precision carrier dynamic navigation and positioning requires not only good control of abnormal disturbance and observation abnormality, but also accurate recognition and processing of time-varying characteristics of noise and observation noise in equation of state system. Aiming at the system noise dynamics model and the time-varying observation noise navigation system, and based on information filtering, a hierarchical adaptive filtering algorithm is proposed. The unbiasedness and effectiveness of the new algorithm are proved. Considering the gradual and fast changes of system noise, the new algorithm adds forgetting factor or two-stage adaptive factor to improve the stability of noise estimation for catastrophic systems. In addition, considering the time-varying of the observation noise, two different difference observation data are used and the covariance of the observation noise is effectively estimated by using the high-precision equation of state. The simulation and experimental results show that the new filtering algorithm can not only estimate the system noise simply and effectively, but also estimate the covariance matrix of the observation noise effectively, which improves the accuracy of parameter estimation of dynamic system.
Pelatihan Penulisan Artikel Ilmiah: Menuju Guru Berkualitas

Writing scientific articles is one of teacher’ efforts to improve their quality and develop their professionalism. Unfortunately, their reading interest and wriitng habit are still low. This makes the teachers have difficulties when they have to submit a promotion because they do not have enough background knowledge and understanding in writing scientific articles, while one of the requirements for submitting a promotion is to publish a scientific article. Therefore, a training on scientific article writing was held for teachers in Situbondo to provide knowledge and skills for them to write scientific articles. After participating in this activity, teachers are expected to be able to publish scientific articles and be able to improve the quality and professionalism of their work so that the quality of education becomes better.KEYWORDS:1Situbondo, Scientific Articles, Teachers, Training, Workshop.
Shallow Feature Matters for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize objects by only utilizing image-level labels. Class activation maps (CAMs) are the commonly used features to achieve WSOL. However, previous CAM-based methods did not take full advantage of the shallow features, despite their importance for WSOL. Because shallow features are easily buried in background noise through conventional fusion. In this paper, we propose a simple but effective Shallow feature-aware Pseudo supervised Object Localization (SPOL) model for accurate WSOL, which makes the utmost of low-level features embedded in shallow layers. In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly. Besides, we further propose a general class-agnostic segmentation model to achieve the accurate object mask, by only using the initial CAMs as the pseudo label without any extra annotation. Eventually, a bounding box extractor is applied to the object mask to locate the target. Experiments verify that our SPOL outperforms the state-of-the-art on both CUB-200 and ImageNet-1K benchmarks, achieving 93.44% and 67.15% (i.e., 3.93% and 2.13% improvement) Top-5 localization accuracy, respectively.
A TEACHER IN THE AUGMENTED REALITY ENVIRONMENT

The future teaching process will most certainly be determined by changes relating to digital technology. Still, these changes should not be comprehended solely as new gadgets which provide a more sophisticated application of multimedia teaching instruments. Main challenges, which a teacher will have to adequately cope with in the future, will be novel forms of reality that will massively shape the educational process. The paper addresses main properties of augmented reality, which represents one of these novelties, and new opportunities which may arise once it is included in the teaching process. This type of reality is the environment or a medium in which digital information overlaps with physical reality, but with an evident complementary space-time coordination and interaction. Digitalized data and physical environment are functionally integrated in a joint coordinate system, in which virtual objects must appear at an exact location with a regular overlap of virtual and physical segments. Otherwise, a user will fail to perceive adequate spatial relations and interaction between these two types of reality. Positioned between so-called physical and virtual realities, augmented reality may simultaneously use the potential of the virtual one in order to create digital objects and environment and the physical one which is inevitably omnipresent in our daily lives. Hence, a teacher may be able to free the educational process from specific time-material-space limitations brought by physical reality and use virtual reality to augment the educational communicative potential of the teaching process as never before by simply using different digital interfaces. In augmented reality, the class becomes an assisting learning (micro) cosmos and the teacher becomes a didactic demiurge. Despite the fact that augmented reality is being diffidently used in the teaching process, there are some educational applications already developed (SMART, Magnet App, AR Sandbox) and briefly outlined in the paper. Studies on the efficiency of the existing teaching materials based on augmented reality suggest that their highest value lies in the establishment of so-called conceptual understanding with pupils. Processes of mental rotation and virtual object manipulation, encouragement of spatial thinking and higher cognitive capabilities also become more efficient if the teaching materials are taught in the augmented reality environment. The future usage of augmented reality in the teaching process brings challenges related to the design and usage of telepresence, simulation of visual and bodily contacts and other non-visual parts of experience (temperature, tactile perception, odors). Finally, the teaching experience is an individual matter of a pupil so a teacher in the future should attempt to adjust and personalize the usage of augmented reality in the teaching process.
Modeling Hyperhemispherical Points and Calibrating a Dual-Fish-Eye System for Close-Range Applications

Omnidirectional systems composed of two hyperhemispherical lenses (dual-fish-eye systems) are gaining popularity, but only a few works have studied suitable models for hyperhemispherical lenses and dual-fish-eye calibration. In addition, the effects of using points in the hyperhemispherical field of view in photogrammetric procedures have not been addressed. This article presents a comparative analysis of the fish-eye models (equidistant, equisolid-angle, stereographic, and orthogonal) for hyperhemispherical-lens and dual-fish-eye calibration techniques. The effects of adding points beyond 180° field of view in dual-fish-eye calibration using stability constraints of relative orientation parameters are also assessed. The experiments were performed with the Ricoh Theta dual-fish-eye system, which is composed of fish-eye lenses with a field of view of approximately 190° each. The equisolid-angle model presented the best results in the simultaneous calibration experiments. An accuracy of approximately one pixel in the object space units was achieved, showing the potential of the proposed approach for close-range applications.
Appraisal of documentary heritage objects value in scientific library, archives, museum: topical problems of theory and practice

The article is devoted to the topical problems of theory and practice of appraisal of documentary heritage objects value in scientific libraries, archives, museums taking into consideration functional and stock specifics of these institutions. Its goals and tasks, principles and axiological criteria are described. It is shown that the main goal of such appraisal is determining the place of documentary heritage objects as the objects of the appraisal in the system of cultural values of modern society, establishing the belonging of these objects to cultural heritage objects of world, national, regional, local significance. Appraisal of documentary heritage objects is analyzed as one of important areas of scientific and administrative activities which to a large extent ensures the functioning, improvement of information potential and accordingly the image of the library, archives, museum. The Ukrainian experience of the recent decades is highlighted in many aspects, namely: regulation, organization and appraising documentary heritage objects at the state, interdepartmental and departmental levels, at the level of the profile institution fund-holder. The features of the appraisal, the objects of which are archival heritage objects and book heritage objects, are revealed. It is emphasized that the appraisal of value of the existing funds and collections of documentary heritage objects is the basis for the development and implementation of modern individual concepts and integrative projects for libraries, archives and museums, which provides for the coexistence and interrelation of material expositions and various forms of virtual information.
A Comprehensive Survey of Incentive Mechanism for Federated Learning

Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.