Reviewerly/exHarmony huggingface dataset:
Professional communities of young scientists in the face of the uncertainty of the modern world. Case of Russian research centers (according to a qualitative study)

The article discusses the key characteristics of the integration of young scientists into scientific communities in the context of &amp;amp;quot;fluid&amp;amp;quot; modernity. The authors consider the professional communities of young scientists as mobile networks in the society of plasma, fluid modernity, that are endowed with both autonomy and a constant mechanism for adapting to new realities, processes and transformations that exist in modern societies. Fluid modernity in the professional sphere is understood by the authors as a time of opportunity, including opportunities for young scientists to take initiatives, as well as a world of total uncertainty, where swiftness and changeability, constant search and change of various strategies become prerequisites for successful career building and career growth. The empirical basis of the analysis is the result of a qualitative study conducted with the participation of the authors. In the course of the study, 30 biographical interviews with young scientists in a city of regional significance, a young science city, and a large city of federal significance were recorded and analysed. The authors put forward a hypothesis about the importance of horizontal integration of young scientists into scientific communities. The role of the city as an actor that forms scientific communities and strategies for the interaction and integration of young scientists with scientific communities is also assessed. Particular attention is paid to the meaning of a scientific career and the individual role of scientific communities in the career of young scientists, as well as strategies for their integration into the scientific environment. As new opportunities that arise for scientists in the world of fluid modernity, there are considered changes in the configuration of strategies for both building a scientific career and integrating into scientific communities at the local, all-Russian and global levels. The authors conclude that both the city itself, its conditions and the orientation of the local scientific community, that is determined by the presence of specialised disciplines, influence the integration and construction of strategies for advancement in a scientific career. According to the authors, a successful strategy that allows using the potential of the world of fluid modernity is the horizontal integration of young scientists, that gives them the opportunity to maximise their resources.
22‐3: A New Near Infrared Polarizer with High Visible Transparency and its Sensor Applications

We have successfully developed a novel near‐infrared (NIR) polarizer fabricated from dichroic dyes. Unlike conventional wire‐grid polarizers, it exhibits transparency in the visible region and polarization wavelength tunability. Improvement of iris pattern recognition by eliminating the reflected light from the cornea surface has been demonstrated.
Improving stability of detected topics in learning analytics

Due to exponential increase in the number of published research papers, it is becoming more and more difficult to review current research in any discipline. It is especially true for disciplines undergoing fast development like learning analytics and educational data mining. Natural language processing offers opportunity to facilitate extraction of information/knowledge from a large volume of documents. In an attempt to organize papers on application of learning analytics and educational data mining for supporting self-regulated learning, we have used Latent Dirichlet Allocation to identify topics in a set of 107 research papers. Results of Latent Dirichlet Allocation depend on the initial choice of a random number generator seed, and replicated runs do not result in the same topics. Stability of allocation of documents to topics was analyzed using Jaccard similarity coefficient. The proposed method enables identifying stabile clusters of research papers sharing the same topics.
Attention-Based Dynamic Subspace Learners for Medical Image Analysis

Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image sets using a single metric learner. Images, however, have a variety of object attributes such as color, shape, or artifacts. Encoding such attributes using a single metric learner is inadequate and may fail to generalize. Instead, multiple learners could focus on separate aspects of these attributes in subspaces of an overarching embedding. This, however, implies the number of learners to be found empirically for each new dataset. This work, Dynamic Subspace Learners, proposes to dynamically exploit multiple learners by removing the need of knowing apriori the number of learners and aggregating new subspace learners during training. Furthermore, the visual interpretability of such subspace learning is enforced by integrating an attention module into our method. This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features. The benefits of our attention-based dynamic subspace learners are evaluated in the application of image clustering, image retrieval, and weakly supervised segmentation. Our method achieves competitive results with the performances of multiple learners baselines and significantly outperforms the classification network in terms of clustering and retrieval scores on three different public benchmark datasets. Moreover, our method also provides an attention map generated directly during inference to illustrate the visual interpretability of the embedding features. These attention maps offer a proxy-labels, which improves the segmentation accuracy up to 15% in Dice scores when compared to state-of-the-art interpretation techniques.
High-performance Distributed Feature Storage and Consumption Services in Power Grid Scenarios

As more and more services in the power grid introduce machine learning and deep learning technologies, feature engineering has become more and more complex. In order to improve the efficiency of feature engineering, this paper proposes a high-performance distributed feature storage and consumption service based on grid scenarios, referred to as HFSA (High-performance feature storage and access). HFSA designed a feature data storage and consumption platform with the help of the idea of message middleware. HFSA services include HFSA Cluster, Producer and Consumer. This article verifies whether the HFSA service meets the design requirements from three aspects: functional testing, accuracy testing and performance testing. The experimental results show that the HFSA service has complete functions and good performance, which basically solves the problem of characteristic storage and consumption of the power grid.
Online Education Classroom Intelligent Management System Based on Tensor CS Reconstruction Model

To study a high-efficiency online classroom intelligent management system, this article builds an artificial intelligence classroom management system based on the tensor CS reconstruction model. Moreover, this study uses the cosine function to represent the data energy fitting of the traditional active contour model and proposes a local cosine fitting energy active contour model based on partial image restoration, which is used for image and composite image segmentation. Simultaneously, this study proposes a new type of super-resolution algorithm. This algorithm performs Fourier transform of a low-resolution image into a frequency range and then performs an inverse Fourier transform on the image expanded in the frequency range to obtain the initial high-resolution image and finally reconstructs a new super-resolution image using the frequency-domain compressed data of the high-resolution image. Finally, this study verifies and analyzes the performance of the model through experiments. The research results are basically consistent with the expectations of the model.
Application of Cognitive Technologies in Solving the Recognition Task

The article describes the currently relevant task of identifying features of objects for recognition and decision-making with the allocation of classes of artificial and natural origin. Methods of extracting useful data from reflected signals using a convolutional neural network are defined. Two variants of the neural network construction are considered, in which the input data is presented as a graphical representation of the spectrum of the reflected signal in grayscale and in the form of arrays of numbers.
A Two-Stage Decoding Based on Iterative Bounded Distance Approach

The paper considers approaches to multistage decoding of linear codes. Cascade and iterative decoding techniques are well-known directions in coding theory. The idea of creating long codes from a combination of numerous short codes was presented in the 1950s. The paper proposes hybrid decoding algorithm that presents an approach to combine different decoding algorithms to use the same code redundancy. The proposed scheme combines the bounded distance decoding with an iterative decoding in two-stage manner. The main attention is paid to the analysis of the possibility of using such a decoder for decoding a BCH code in an AWGN channel. The expediency of using the second set of decoders is assessed. The analysis of influence of cycles on error probability is investigated.
Committable: A Decentralised and Trustless Open-Source Protocol

Collaborative development in open-source software (OSS) has long been limited by the lack of participation, i.e., A project is often maintained by an insufficient number of developers, especially for small- and medium-size projects. To establish a sustainable ecosystem for global developers and projects, we propose a decentralised and trustless open-source protocol Committable for all OSS software. The key insight behind Committable is an accountable and trusted tokenisation technology on blockchain that creates the CMT software assets for a variety of artefacts (e.g., document, code, testcase, makefile etc..) across the whole development lifecycle. A CMT token defines an abstraction of commits to OSS and systematically models the contribution from developers, therefore is far more comprehensive than a commit hash that are commonly used to identify software versions. In further, Committable introduces the Problem-Solution-Risk (PSR) framework to evaluate and reward a given set of CMT in an unbiased manner based on their contributions to a project. Owners of CMT are allowed to trade their tokens in the marketplace on blockchain established by Committable. The trading of CMT leads to transfer of token rights (e.g., sell, earn PSR rewards etc.), and more importantly, royalty to the developer for his or her original contribution. This demonstration proposal will introduce Committable on the test net of Ethereum and describe a preliminary case study with the OpenZeppelin project.
Building Interactions Through Participative Methodologies: Co-creation Between Crafts and Design in an Academic Context

In the present article we intend to describe the development of a methodology and approach adopted in an implementation strategy, in which we propose to contribute to the promotion of innovation and safeguarding of a craftsmanship technique, which constitutes as intangible cultural heritage, Nisa’s quartz inlaid pottery. The purpose of the research was to achieve a significant. impact at local and community level, privileging the interaction between artisans, local community, and academia, through a methodological action, specifically through practice-led research, by running local workshops, between design students, designers and craftsmen, guided by the practice of design. The expected fruits of the methodology approached, aim above all, to find a model of action and collaboration that is not only possible to be replicated, but can create its own autonomy and rhythm, that is, that will give rise to the creation of collaborative spaces that nurture the safeguarding of knowledge, through its transmission, as well as enable the production of new products, services or experiences for the contemporary market.
Characterization of mitigation schemes against timing-based side-channel attacks on PCIe hardware

PCI-e connected peripheral devices are prevalent in distributed embedded systems. Peripheral devices, such as a GPU connected to a host CPU via PCIe hardware brings massive performance gains for artificial intelligence applications. However, sharing the network hardware resources bring security challenges. The literature shows that timing side-channel attacks on shared PCI-e hardware leak security critical information and covert communication. These attacks are mitigated with performance implications at the algorithm, system, and hardware levels. This paper uses information theory concept of differential signaling and demonstrates that noise injection (a system-level mitigation scheme) is inadequate for practical purposes, and software or hardware level mitigation is required. Oblivious algorithm (at software level) and time-division multiplexing (TDM at hardware level) mitigations strategies are evaluated using a machine learning workload. Our evaluation shows that at varying level of load at the PCI-e, the oblivious algorithms always perform worse than hardware-based TDM.
DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

This article describes an approach for multiagent search planning for a team of agents. A team of UAVs tasked to conduct a forest fire search was selected as the use case, although solutions are applicable to other domains. Fixed-path (e.g., parallel track) methods for multiagent search can produce predictable and structured paths, with the main limitation being poor management of agents' resources and limited adaptability (i.e., based on predefined geometric paths, e.g., parallel track, expanding square, etc.). On the other hand, pseudorandom methods allow agents to generate well-separated paths; but methods can be computationally expensive and can result in a lack of coordination of agents' activities. We present a hybrid solution that exploits the complementary strengths of fixed-pattern and pseudorandom methods, i.e., an approach that is resource-efficient, predictable, adaptable, and scalable. Our approach evolved from the Delaunay triangulation of systematically selected waypoints to allocate agents to explore a specific region while optimizing a given set of mission constraints. We implement our approach in a simulation environment, comparing the performance of the proposed algorithm with fixed-path and pseudorandom baselines. Results proved agents' resource utilization, predictability, scalability, and adaptability of the developed path. We also demonstrate the proposed algorithm's application on real UAVs.
Intelligent Document Processing with Small and Relevant Training Dataset

Nowadays, companies deploy complex mechanisms to automate data collection, storage, and processing. Some of this data is unstructured and contained in pdf or scanned documents. Supervised object detection models exist to address the problem such as Faster-RCNN, but require retraining for each new use case. Annotation is a tedious and repetitive task done regularly when new document templates arrive. We present in this paper a method based on a Triplet-loss architecture to select a small and relevant subset of unstructured documents to annotate. To evaluate the method, we trained a model with many datasets. We compared the performance with different choices of document templates and dataset sizes. We show that a relevant and automated choice of document examples can avoid a huge annotation effort.
A data variability index: Quantifying complexity of models and analyzing adversarial data

In system modeling arises a fundamental question about the level of difficulty one may encounter when designing a model on a basis of some training data. In this study, we advocate that such level of difficulty inherently depends upon the variability of the available function (data). If for a pair of input data which exhibits small differences, the differences of the corresponding outputs are substantial then building a model in the presence of such data becomes more challenging than in cases of data where the differences in the output data are far more limited. Dwelling on this observation, we introduce a variability index quantifying the nature of data in terms of variability observed in input and output data, respectively. The proposed index is model-neutral (model agnostic), namely describes and quantifies the modeling challenge implied by the data irrespectively of the specific model to be constructed. In case of functions, we show that the Lipschitz constant plays a similar role as the variability index computed for experimental data. An original way of reducing values of the variability index through a nonlinear transformation of original data completed by a fuzzy rule-based model is introduced. It is shown that such rule-based architecture gives rise to a piecewise linear transformation (multipoint linear approximation) exhibiting required contraction-dilation characteristics. The optimization of this transformation is carried out with the use of a Particle Swarm Optimization algorithm. We also demonstrate that the index can be used to quantify a concept of adversarial data. Along this line, we introduce a granular characterization of adversarial feature of individual data points. A series of experiments is provided to offer a thorough illustration and detailed insight into the nature and a thorough characterization of publicly available data.
JMetricGrader: A software for evaluating student projects using design object-oriented metrics and neural networks

Object-oriented metrics have become a very important part of software development. They are used to measure software quality, estimate its costs, and improve reliability and sustainability of software development projects. However, the application of design metrics in the evaluation of existing projects is not straightforward, since a detailed analysis of obtained values is typically required.This paper will present a solution for automated evaluation of design quality of Java projects in an educational setting. A set of object-oriented metrics is chosen for extraction from a single project using well known tools. These metrics represent inputs for a machine learning model, while the output is a design quality score.
Deep learning for pedestrian behavior understanding

Visual perception and road scene understanding are critical components of autonomous driving (AD). Detecting objects and predicting surrounding road user behavior are important tasks for robust and safe driving systems. With the rise of Machine Learning (ML) and Deep Learning (DL) methods using convolutional neural networks (CNN) , research on road scene understanding and motion recognition have made significant progress. In this paper, a real-time recognition of pedestrian&#x2019;s intention to cross is presented as a step towards autonomous driving. Our approach is constructed using a real-Time visual pedestrian tracker based on a one-stage object detector that gave a fast and robust learning produces dynamic and static features as inputs of a linear classifier for predicting crossing behavior. We apply our approach to public real-life benchmark - JAAD dataset "Joint Attention in Autonomous Driving." The experimental results have shown that our proposed method is efficient and competitive with other approaches. Overall, our system achieves an accuracy of 92.88%.
Motion estimation for large displacements and deformations

Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour, gradient and smoothness, making them sensitive to noise in the sparse matches, deformations, and arbitrarily large displacements. This paper addresses this problem and presents HybridFlow, a variational motion estimation framework for large displacements and deformations. A multi-scale hybrid matching approach is performed on the image pairs. Coarse-scale clusters formed by classifying pixels according to their feature descriptors are matched using the clusters' context descriptors. We apply a multi-scale graph matching on the finer-scale superpixels contained within each matched pair of coarse-scale clusters. Small clusters that cannot be further subdivided are matched using localized feature matching. Together, these initial matches form the flow, which is propagated by an edge-preserving interpolation and variational refinement. Our approach does not require training and is robust to substantial displacements and rigid and non-rigid transformations due to motion in the scene, making it ideal for large-scale imagery such as aerial imagery. More notably, HybridFlow works on directed graphs of arbitrary topology representing perceptual groups, which improves motion estimation in the presence of significant deformations. We demonstrate HybridFlow's superior performance to state-of-the-art variational techniques on two benchmark datasets and report comparable results with state-of-the-art deep-learning-based techniques.
Investigating the robustness of multi-view detection to current adversarial patch threats

As deep neural networks are increasingly integrated in our daily lives, the safety and reliability of their results has become of paramount importance. However, the vulnerability of these networks to adversarial attacks are an obstacle to wider adoption, especially in safety-critical applications: A malicious actor can manipulate the results of a deep neural network by adding a nearly imperceptible noise to the input. And adversarial patch attacks make real-life implementations of these threats easier. Therefore, studying these attacks has become a rapidly growing field of artificial intelligence research. One aspect of this research is studying the behavior of patch attacks in various scenarios to understand their inner workings and find novel method to secure deep neural networks. In this paper, we examine the effectiveness of existing adversarial patch attacks against a multi-view detector. To this aim, we propose an evaluation framework where an adversarial patch is trained against a single view of a multi-view dataset and transfer the patch to the other views of the dataset with the use of perspective geometric transforms. Our results confirm that current single-view adversarial patches struggle against multi-view detectors, especially when only few views are attacked. These observations suggest that multi-view detection methods may be a step forward towards reliable and safe AI.