Reviewerly/exHarmony huggingface dataset:
Weakly supervised classification model for zero‐shot semantic segmentation

As one of the most fundamental tasks in computer vision, semantic segmentation assigns per pixel prediction of object categories. Training a robust model for semantic segmentation is challenging since pixel-level annotations are expensive to obtain. To alleviate the burden of annotations, the authors propose a weakly-supervised framework for zero-shot semantic segmentation, which can segment images having target classes without any pixel-level labelled instances. Under the assumption that the accessibility to image-level annotations of target classes does not violate the principle of zero pixel-level label in zero-shot semantic segmentation, we utilised image-level annotations to improve the proposed model's ability to extract pixel-level features. Furthermore, unlike existing zero-shot semantic segmentation methods, which use semantic embeddings as class embeddings to transfer knowledge from source classes to target classes, we use image-level features as their class embeddings to transfer knowledge since the distribution of pixel-level features is more similar to the distribution of image-level features rather than the distribution of semantic embeddings. Experimental results on the PASCAL-VOC data set under different data splits demonstrate that the proposed model achieves promising results.
Cross-Informed Domain Adversarial Training for Noise-Robust Wake-Up Word Detection

A proper representation that can well express the characteristics of a word plays an important role in wake-up word detection (WWD). However, it may be easily corrupted due to various types of environmental noise occurred in the place where WWD typically works, causing unreliable performance. To deal with this practical issue, we propose a novel strategy called cross-informed domain adversarial training (CiDAT) for noise-robust WWD. In the method, additional paths were introduced to conventional domain adversarial training (DAT) to encourage its ability to generate domain-invariant representation. Experiments on the Aurora4 corpus verified that CiDAT significantly outperformed the baselines as well as conventional DAT.
Visually Grounded Compound PCFGs

Exploiting visual groundings for language understanding has recently been drawing much attention. In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings. Existing work on this task (Shi et al., 2019) optimizes a parser via Reinforce and derives the learning signal only from the alignment of images and sentences. While their model is relatively accurate overall, its error distribution is very uneven, with low performance on certain constituents types (e.g., 26.2% recall on verb phrases, VPs) and high on others (e.g., 79.6% recall on noun phrases, NPs). This is not surprising as the learning signal is likely insufficient for deriving all aspects of phrase-structure syntax and gradient estimates are noisy. We show that using an extension of probabilistic context-free grammar model we can do fully-differentiable end-to-end visually grounded learning. Additionally, this enables us to complement the image-text alignment loss with a language modeling objective. On the MSCOCO test captions, our model establishes a new state of the art, outperforming its non-grounded version and, thus, confirming the effectiveness of visual groundings in constituency grammar induction. It also substantially outperforms the previous grounded model, with largest improvements on more `abstract' categories (e.g., +55.1% recall on VPs).
Revisiting Design Choices in Proximal Policy Optimization.

Proximal Policy Optimization (PPO) is a popular deep policy gradient algorithm. In standard implementations, PPO regularizes policy updates with clipped probability ratios, and parameterizes policies with either continuous Gaussian distributions or discrete Softmax distributions. These design choices are widely accepted, and motivated by empirical performance comparisons on MuJoCo and Atari benchmarks. We revisit these practices outside the regime of current benchmarks, and expose three failure modes of standard PPO. We explain why standard design choices are problematic in these cases, and show that alternative choices of surrogate objectives and policy parameterizations can prevent the failure modes. We hope that our work serves as a reminder that many algorithmic design choices in reinforcement learning are tied to specific simulation environments. We should not implicitly accept these choices as a standard part of a more general algorithm.
Application research and improvement of particle swarm optimization algorithm

Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future.
Cultural and leisure activities at research libraries in the digitalization era: What to offer users

The author characterizes the society’s dependence on digital technologies and the direct demand for these technologies. The libraries are examined as the system of several functions, and among them — the cultural leisure activities. Modern technologies are seen as a harmonic way for the libraries to enter into the new digital environment. The specificity of research (academic) library resources is emphasized. The author argues that contemporary user demands in these libraries are viewed narrowly which is insufficient. The examples of digital technologies application in educational projects and cultural and leisure activities at research and academic libraries are provided. The author points specifically to three forms of these activities, i. e. teaching foreign languages, virtual reality technologies, and film screening. The specific character of these activities of public and research (including academic) libraries is discussed. The Google’s Expeditions platform is described; the efficiency of this technology in organizing cultural and leisure activities in libraries is proved. Science documentaries are seen as a lost user service offered by research libraries. The author also examines the financial aspect, i. e. high costs of hi-tech devices and equipment and possible funding resources. She concludes that digital technologies have engrained in our life. The trends in research (academic) libraries’ assimilating cultural activities specific to public libraries are described.
Distributional Semantics for Neo-Latin

We address the problem of creating and evaluating quality Neo-Latin word embeddings for the purpose of philosophical research, adapting the Nonce2Vec tool to learn embeddings from Neo-Latin sentences. This distributional semantic modeling tool can learn from tiny data incrementally, using a larger background corpus for initialization. We conduct two evaluation tasks: definitional learning of Latin Wikipedia terms, and learning consistent embeddings from 18th century Neo-Latin sentences pertaining to the concept of mathematical method. Our results show that consistent Neo-Latin word embeddings can be learned from this type of data. While our evaluation results are promising, they do not reveal to what extent the learned models match domain expert knowledge of our Neo-Latin texts. Therefore, we propose an additional evaluation method, grounded in expert-annotated data, that would assess whether learned representations are conceptually sound in relation to the domain of study.
OPAC functionality: Russian and foreign users’ requirements

The article considers the results of studying the Online Public Access Catalogue (OPAC) functionality, revealed by specialists of domestic and foreign libraries. The user survey results showed that OPAC of foreign libraries provides a more developed search system than the OPAC of domestic libraries. While a foreign user is able to interact with a catalogue in a natural language, domestic library OPAC operate mostly in a query-response mode. The problems were revealed which the user encounters during information search, connected with information perception, granting incomplete information on library collections, and absence of stability in work of servers. Often, there is no tooltip system. Users cannot understand the content of classification indices. It is difficult to find information about the library and access to OPAC on university websites, as this information is not always easily accessible. On the other hand, there is often a low level of user information culture. Most of them do not have the skills to write search queries and use logical operators. Maintaining OPAC in different automated library and information systems (ALIS), involving different options for combining, shortening, clarification of terms in information search, exacerbates these problems. After all, even advanced users may get incomplete search results assuming that library ALIS works similarly to other systems they are familiar with. It negatively affects the OPAC use efficiency. Ways of solving the revealed problems are suggested. Conducting regular training activities for OPAC users, development and improvement of tooltip system, ensuring integrated search in all library databases, providing information on classification information retrieval languages, conducting retrospective conversion of card catalogues, ensuring completeness and accuracy of provided bibliographic data, conducting systematic research to identify user requirements for the functionality of the electronic part of the reference and search apparatus will facilitate the process of searching and obtaining data about library information resources, improve the quality of OPAC information.
Gravsearch: Transforming SPARQL to query humanities data

RDF triplestores have become an appealing option for storing and publishing humanities data, but available technologies for querying this data have drawbacks that make them unsuitable for many applications. Gravsearch (Virtual Graph Search), a SPARQL transformer developed as part of a web-based API, is designed to support complex searches that are desirable in humanities research, while avoiding these disadvantages. It does this by introducing server software that mediates between the client and the triplestore, transforming an input SPARQL query into one or more queries executed by the triplestore. This design suggests a practical way to go beyond some limitations of the ways that RDF data has generally been made available.
Secure data outsourcing in presence of the inference problem: issues and directions

With the emergence of cloud computing paradigms, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. Data owners place their data among cloud service providers in order to increase flexibility, optimize storage, enhance data manipulation and decrease processing time. Nevertheless, from a security point of view, access control is a major challenge in this situation seeing that the security policy of the data owner must be preserved when data is moved to the cloud. Nonetheless, the lack of a comprehensive and systematic review motivated us to construct this reviewing paper on this research problem. Here, we discuss current and emerging research on privacy and confidentiality concerns in data outsourcing and pinpoint potential issues that are still unresolved.
Research on the Technology of Searching for Fashion Trend Image Based on ResNet50 Model

With the further development of technology, providing technical support for the forecast and development of fashion design trends through artificial intelligence has gradually become a reality. In order to enable users to obtain more accurate design trend materials in a shorter time, this paper presents a 'searching by image' model of clothing trends based on the ResNet50 model. Through the targeted collection of data and the rational construction of algorithms, the system can retrieve and output more image materials with relevant design features for the existing image materials for the development of design trends as well as the development and design of styles. By comparing the system with Google's and Taobao's 'searching by image' system, it is concluded that this system has high efficiency, high accuracy and high correlation.
What happens in a control room during a cybersecurity attack?

Cyberattacks on the critical infrastructure is a growing concern for businesses, national authorities and public in general. The increasing complexity and connectivity of the critical infrastructure systems have made them susceptible to cyberattacks. The traditional notion of safety systems being isolated is no longer applicable, as we have seen ample examples on how these systems can be exploited through gaps in e.g. supply chain, physical security, insiders. This places greater importance on how the staff belonging to owners and operators of these critical infrastructure, e.g. operators, IT/security personnel, system engineers, management, are prepared to handle cyberattacks. This paper presents our ongoing research on investigating the preparedness of organisations to handle cybersecurity incidents and providing holistic solutions to improve cybersecurity posture. We present one experiment that has been conducted using our cybersecurity centre and man-machine laboratory to study how operators and security team of a power plant will handle a cyberattack. We highlight the main observations made through this experiment.
Research on Embedded Intelligent Robot and Its Motion Control System

The realization of an intelligent robot and its motion control system requires a combination of various hardware and software modules such as processors, infrared sensors, motion control, and geomagnetic navigation to accurately control the robot's basic movements and walking lines to avoid movement Robot collision or abnormal state. This paper uses the Windows software operating system, Visual C++, etc. to develop and design mobile robot embedded control programs, application processing modules, and multi-task mechanisms to achieve algorithmic tracking and tracking of mobile robot motion paths control.
A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder-decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.
Unsupervised Analysis of Encrypted Video Traffic Based on Levenshtein Distance

It is effective for supervisors to monitor the network by analyzing traffic from devices.In this way, illegal video can be detected when it is played on the network. Most Internet traffic is encrypted, which brings difficulties to traffic analysis. However, many researches suggest that even if the video traffic is encrypted, the information of video segmentation leaked by DASH (Dynamic Adaptive Streaming over HTTP) can also be used to identify the content of encrypted video traffic without decryption. Moreover, each encrypted video stream can be represented by a fragment sequence. This paper presents two methods based on Levenshtein distance for encrypted video traffic analysis. Using the distance distribution fitted by gamma distribution functions, we calculated a threshold to determine whether two encrypted video traffic belonging to the same video. The accuracy of the judgment using the threshold reached 89%, stably. As far as I am concerned, it is the first work to apply unsupervised methods for content analysis of encrypted video traffic.
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations on graphical processing units, generating faster systems. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.
f-Divergence Variational Inference

This paper introduces the $f$-divergence variational inference ($f$-VI) that generalizes variational inference to all $f$-divergences. Initiated from minimizing a crafty surrogate $f$-divergence that shares the statistical consistency with the $f$-divergence, the $f$-VI framework not only unifies a number of existing VI methods, e.g. Kullback-Leibler VI, Rényi's $α$-VI, and $χ$-VI, but offers a standardized toolkit for VI subject to arbitrary divergences from $f$-divergence family. A general $f$-variational bound is derived and provides a sandwich estimate of marginal likelihood (or evidence). The development of the $f$-VI unfolds with a stochastic optimization scheme that utilizes the reparameterization trick, importance weighting and Monte Carlo approximation; a mean-field approximation scheme that generalizes the well-known coordinate ascent variational inference (CAVI) is also proposed for $f$-VI. Empirical examples, including variational autoencoders and Bayesian neural networks, are provided to demonstrate the effectiveness and the wide applicability of $f$-VI.
The Didactic Potential of University Electronic Information and Educational Environment in the Context of a Pandemic

The materials of the article can be helpful to teachers of higher educational institutions, further education establishments, to heads of educational institutions and school teachers in resolving the issues of organizing educational activities in the context of a pandemic for successful fulfillment of the basic educational programs.
Exploration and practice of intelligent engineering in Dadu River hydropower construction

Abstract In view of the difficulties in promoting intelligent management in the field of engineering construction, based on the concept of intelligent enterprise and the actual situation of hydropower project-construction management, this paper puts forward the basic concept, main features and general idea of intelligent engineering. Based on a review of the exploration, pilot construction and comprehensive practice of intelligent engineering in the hydropower construction of Dadu River, the practical results of the intelligent engineering of Dagangshan, Houziyan, Shaping-II and Shuangjiangkou hydropower stations are summarized. The technical system and management model of engineering an early-warning decision centre, intelligent dam project, intelligent underground project, intelligent electromechanical project, intelligent security control, intelligent service guarantee, intelligent environmental and water protection, and intelligent resource control in Shuangjiangkou hydropower station are introduced. It is proposed that intelligent engineering is a high integration of information technology, industrial technology and management technology. And it is pointed out that intelligent engineering will lead in the new development of water conservancy and the hydropower industry, and even the engineering-construction field, throughout the world.
Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.
AI and the Humanities

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Operationalizing Agile Methods: Examining Coherence in Large-Scale Agile Transformations

Abstract Following the highly pervasive and effective use of agile methods for software development, attention has now turned to the much more difficult challenge of applying these methods in large scale, organization-wide development. However, identifying to what extent certain factors influence success and failure of sustaining large-scale agile transformations remains unclear and there is a lack of theoretical frameworks to guide such investigations. By adopting Normalization Process Theory and specifically ‘coherence’, we compare two large-scale agile transformation case studies and the different perspectives individuals and teams had when faced with the problem of operationalizing the agile method as part of their large-scale agile transformation. The key contributions of this work are: (i) this is a first attempt to present the results of a comparison between a successful and failed large-scale agile transformations; and (ii) we describe the challenges in understanding the rationale, differences, value, and roles associated with the methods to support the large-scale agile transformation. We also present future research for practitioners and academics on large-scale agile transformation.