bluuebunny/crossref_metadata_2025 huggingface dataset:
Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?
Authors: Peyrard Maxime , Borges Beatriz , Gligorić Kristina , West Robert
Published: August 2021

The automatic detection of humor poses a grand challenge for natural language processing.

Transformer-based systems have recently achieved remarkable results on this task, but they usually

(1) were evaluated in setups where serious vs humorous texts came from entirely different sources, and

(2) focused on benchmarking performance without providing insights into how the models work.

We make progress in both respects by training and analyzing transformer-based humor recognition models on a recently introduced dataset consisting of minimal pairs of aligned sentences, one serious, the other humorous.

We find that, although our aligned dataset is much harder than previous datasets, transformer-based models recognize the humorous sentence in an aligned pair with high accuracy (78\%).

In a careful error analysis, we characterize easy vs hard instances.

Finally, by analyzing attention weights, we obtain important insights into the mechanisms by which transformers recognize humor.

Most remarkably, we find clear evidence that one single attention head learns to recognize the words that make a test sentence humorous, even without access to this information at training time.
Improving Stylized Neural Machine Translation with Iterative Dual Knowledge Transfer
Authors: Wu Xuanxuan , Liu Jian , Li Xinjie , Xu Jinan , Chen Yufeng , Zhang Yujie , Huang Hui
Published: August 2021

Stylized neural machine translation (NMT) aims to translate sentences of one style into sentences of another style, which is essential for the application of machine translation in a real-world scenario. However, a major challenge in this task is the scarcity of high-quality parallel data which is stylized paired. To address this problem, we propose an iterative dual knowledge transfer framework that utilizes informal training data of machine translation and formality style transfer data to create large-scale stylized paired data, for the training of stylized machine translation model. Specifically, we perform bidirectional knowledge transfer between translation model and text style transfer model iteratively through knowledge distillation. Then, we further propose a data-refinement module to process the noisy synthetic parallel data generated during knowledge transfer. Experiment results demonstrate the effectiveness of our method, achieving an improvement over the existing best model by 5 BLEU points on MTFC dataset. Meanwhile, extensive analyses illustrate our method can also improve the accuracy of formality style transfer.
Symbolic Dynamic Programming for Continuous State MDPs with Linear Program Transitions
Authors: Jeong Jihwan , Jaggi Parth , Sanner Scott
Published: August 2021

Recent advances in symbolic dynamic programming (SDP) have significantly broadened the class of MDPs for which exact closed-form value functions can be derived. However, no existing solution methods can solve complex discrete and continuous state MDPs where a linear program determines state transitions --- transitions that are often required in problems with underlying constrained flow dynamics arising in problems ranging from traffic signal control to telecommunications bandwidth planning. In this paper, we present a novel SDP solution method for MDPs with LP transitions and continuous piecewise linear dynamics by introducing a novel, fully symbolic argmax operator. On three diverse domains, we show the first automated exact closed-form SDP solution to these challenging problems and the significant advantages of our SDP approach over discretized approximations.
Anytime Multi-Agent Path Finding via Large Neighborhood Search
Authors: Li Jiaoyang , Chen Zhe , Harabor Daniel , Stuckey Peter J. , Koenig Sven
Published: August 2021

Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for multiple agents. Algorithms for solving MAPF can be categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that can find high-quality solutions for small problems. At the other end are unbounded-suboptimal algorithms that can solve large problems but usually find low-quality solutions. In this paper, we consider a third approach that combines the best of both worlds: anytime algorithms that quickly find an initial solution using efficient MAPF algorithms from the literature, even for large problems, and that subsequently improve the solution quality to near-optimal as time progresses by replanning subgroups of agents using Large Neighborhood Search. We compare our algorithm MAPF-LNS against a range of existing work and report significant gains in scalability, runtime to the initial solution, and speed of improving the solution.
Solving Partially Observable Stochastic Shortest-Path Games
Authors: Tomášek Petr , Horák Karel , Aradhye Aditya , Bošanský Branislav , Chatterjee Krishnendu
Published: August 2021

We study the two-player zero-sum extension of the partially observable stochastic shortest-path problem where one agent has only partial information about the environment.

We formulate this problem as a partially observable stochastic game (POSG): given a set of target states and negative rewards for each transition, the player with imperfect information maximizes the expected undiscounted total reward until a target state is reached. The second player with the perfect information aims for the opposite.

We base our formalism on POSGs with one-sided observability (OS-POSGs) and give the following contributions:

(1) we introduce a novel heuristic search value iteration algorithm that iteratively solves depth-limited variants of the game,

(2) we derive the bound on the depth guaranteeing an arbitrary precision, (3) we propose a novel upper-bound estimation that allows early terminations, and

(4) we experimentally evaluate the algorithm on a pursuit-evasion game.
The Fewer the Merrier: Pruning Preferred Operators with Novelty
Authors: Tuisov Alexander , Katz Michael
Published: August 2021

Heuristic search is among the best performing approaches to classical satisficing planning, with its performance heavily relying on informative and fast heuristics, as well as search-boosting and pruning techniques. While both heuristics and pruning techniques have gained much attention recently, search-boosting techniques in general, and preferred operators in particular have received less attention in the last decade.

Our work aims at bringing the light back to preferred operators research, with the introduction of preferred operators pruning technique, based on the concept of novelty. Continuing the research on novelty with respect to an underlying heuristic, we present the definition of preferred operators for such novelty heuristics. For that, we extend the previously defined concepts to operators, allowing us to reason about the novelty of the preferred operators.

Our experimental evaluation shows the practical benefit of our suggested

approach, compared to the currently used methods.
On the Parameterized Complexity of Polytree Learning
Authors: Grüttemeier Niels , Komusiewicz Christian , Morawietz Nils
Published: August 2021

A Bayesian network is a directed acyclic graph that represents statistical dependencies between variables of a joint probability distribution. A fundamental task in data science is to learn a Bayesian network from observed data. Polytree Learning is the problem of learning an optimal Bayesian network that fulfills the additional property that its underlying undirected graph is a forest. In this work, we revisit the complexity of Polytree Learning. We show that Polytree Learning can be solved in single-exponential FPT time for the number of variables. Moreover, we consider the influence of d, the number of variables that might receive a nonempty parent set in the final DAG on the complexity of Polytree Learning. We show that Polytree Learning is presumably not fixed-parameter tractable for d, unlike Bayesian network learning which is fixed-parameter tractable for d. Finally, we show that if d and the maximum parent set size are bounded, then we can obtain efficient algorithms.
Partition Function Estimation: A Quantitative Study
Authors: Agrawal Durgesh , Pote Yash , Meel Kuldeep S.
Published: August 2021

Probabilistic graphical models have emerged as a powerful modeling tool for several real-world scenarios where one needs to reason under uncertainty. A graphical model's partition function is a central quantity of interest, and its computation is key to several probabilistic reasoning tasks. Given the #P-hardness of computing the partition function, several techniques have been proposed over the years with varying guarantees on the quality of estimates and their runtime behavior. This paper seeks to present a survey of 18 techniques and a rigorous empirical study of their behavior across an extensive set of benchmarks. Our empirical study draws up a surprising observation: exact techniques are as efficient as the approximate ones, and therefore, we conclude with an optimistic view of opportunities for the design of approximate techniques with enhanced scalability. Motivated by the observation of an order of magnitude difference between the Virtual Best Solver and the best performing tool, we envision an exciting line of research focused on the development of portfolio solvers.
Distortion in Social Choice Problems: The First 15 Years and Beyond
Authors: Anshelevich Elliot , Filos-Ratsikas Aris , Shah Nisarg , Voudouris Alexandros A.
Published: August 2021

The notion of distortion in social choice problems has been defined to measure the loss in efficiency---typically measured by the utilitarian social welfare, the sum of utilities of the participating agents---due to having access only to limited information about the preferences of the agents. We survey the most significant results of the literature on distortion from the past 15 years, and highlight important open problems and the most promising avenues of ongoing and future work.
Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning
Authors: Cui Yuchen , Koppol Pallavi , Admoni Henny , Niekum Scott , Simmons Reid , Steinfeld Aaron , Fitzgerald Tesca
Published: August 2021

Human-in-the-loop Machine Learning (HIL-ML) is a widely adopted paradigm for instilling human knowledge in autonomous agents. Many design choices influence the efficiency and effectiveness of such interactive learning processes, particularly the interaction type through which the human teacher may provide feedback. While different interaction types (demonstrations, preferences, etc.) have been proposed and evaluated in the HIL-ML literature, there has been little discussion of how these compare or how they should be selected to best address a particular learning problem. In this survey, we propose an organizing principle for HIL-ML that provides a way to analyze the effects of interaction types on human performance and training data. We also identify open problems in understanding the effects of interaction types.
Bayesian Nonparametric Space Partitions: A Survey
Authors: Fan Xuhui , Li Bin , Luo Ling , Sisson Scott A.
Published: August 2021

Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that

the data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject.

We explore the current progress of BNSP research through three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions and discuss their theoretical foundation, `self-consistency'; (2) Applications, where we detail the current mainstream usages of BNSP models and identify some potential future applications; and (3) Challenges, where we discuss current unsolved problems and possible avenues for future research.
Emergent Prosociality in Multi-Agent Games Through Gifting
Authors: Wang Woodrow Z. , Beliaev Mark , Bıyık Erdem , Lazar Daniel A. , Pedarsani Ramtin , Sadigh Dorsa
Published: August 2021

Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game. However, state of the art reinforcement learning algorithms often suffer from converging to socially less desirable equilibria when multiple equilibria exist. Previous works address this challenge with explicit reward shaping, which requires the strong assumption that agents can be forced to be prosocial. We propose using a less restrictive peer-rewarding mechanism, gifting, that guides the agents toward more socially desirable equilibria while allowing agents to remain selfish and decentralized. Gifting allows each agent to give some of their reward to other agents. We employ a theoretical framework that captures the benefit of gifting in converging to the prosocial equilibrium by characterizing the equilibria's basins of attraction in a dynamical system. With gifting, we demonstrate increased convergence of high risk, general-sum coordination games to the prosocial equilibrium both via numerical analysis and experiments.
Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges
Authors: Sioutis Michael , Wolter Diedrich
Published: August 2021

Qualitative Spatial & Temporal Reasoning (QSTR)

is a major field of study in Symbolic AI that deals

with the representation and reasoning of spatio-

temporal information in an abstract, human-like

manner. We survey the current status of QSTR

from a viewpoint of reasoning approaches, and

identify certain future challenges that we think

that, once overcome, will allow the field to meet

the demands of and adapt to real-world, dynamic,

and time-critical applications of highly active areas

such as machine learning and data mining.
Tournaments in Computational Social Choice: Recent Developments
Authors: Suksompong Warut
Published: August 2021

Tournaments are commonly used to select winning alternatives in scenarios involving pairwise comparisons such as sports competitions and political elections. This survey discusses recent developments in two major lines of work—tournament solutions and single-elimination tournaments—with a focus on how computational social choice has brought new frameworks and perspectives into these decades-old studies.
Hierarchical Graph Traversal for Aggregate k Nearest Neighbors Search in Road Networks (Extended Abstract)
Authors: Abeywickrama Tenindra , Cheema Muhammad Aamir , Storandt Sabine
Published: August 2021

A k nearest neighbors (kNN) query finds k closest points-of-interest (POIs) from an agent's location. In this paper, we study a natural extension of the kNN query for multiple agents, namely, the Aggregate k Nearest Neighbors (AkNN) query. An AkNN query retrieves k POIs with the smallest aggregate distances where the aggregate distance of a POI is obtained by aggregating its distances from the multiple agents (e.g., sum of its distances from each agent). We propose a novel data structure COLT (Compacted Object-Landmark Tree) which enables efficient hierarchical graph traversal and utilize it to efficiently answer AkNN queries. Our experiments on real-world and synthetic data sets show that our techniques outperform existing approaches by more than an order of magnitude in almost all settings.
Robust Domain Adaptation: Representations, Weights and Inductive Bias (Extended Abstract)
Authors: Bouvier Victor , Very Philippe , Chastagnol Clément , Tami Myriam , Hudelot Céline
Published: August 2021

Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new and unlabelled target domain. Unsupervised Domain Adaptation (UDA) in presence of label shift remains an open problem. To this purpose, we present a bound of the target risk which incorporates both weights and invariant representations. Our theoretical analysis highlights the role of inductive bias in aligning distributions across domains. We illustrate it on standard benchmarks by proposing a new learning procedure for UDA. We observed empirically that weak inductive bias makes adaptation robust to label shift. The elaboration of stronger inductive bias is a promising direction for new UDA algorithms.
On Learning Sets of Symmetric Elements (Extended Abstract)
Authors: Maron Haggai , Litany Or , Chechik Gal , Fetaya Ethan
Published: August 2021

Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own symmetries. That case is relevant to numerous applications, from deblurring image bursts to multi-view 3D shape recognition and reconstruction. In this paper, we present a principled approach to learning sets of general symmetric elements. We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images. We further show that networks that are composed of these layers, called Deep Sets for Symmetric Elements layers (DSS), are universal approximators of both invariant and equivariant functions, and that these networks are strictly more expressive than Siamese networks. DSS layers are also straightforward to implement. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point clouds.
Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract)
Authors: Morik Marco , Singh Ashudeep , Hong Jessica , Joachims Thorsten
Published: August 2021

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only do the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users -- as done by virtually all learning-to-rank algorithms -- can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.
Multi-agent Approach to Resource Allocation in Autonomous Vehicle Fleets
Authors: Daoud Alaa
Published: August 2021

The development of autonomous vehicles, capable of peer-to-peer communication, as well as the interest in on-demand solutions, are the primary motivations for this study. In the absence of central control, we are interested in forming a fleet of autonomous vehicles capable of responding to city-scale travel demands.

Typically, this problem is solved centrally; this implies that the vehicles have continuous access to a dispatching portal. However, such access to such a global switching infrastructure (for data collection and order delivery) is costly and represents a critical bottleneck. The idea is to use low-cost vehicle-to-vehicle (V2V) communication technologies to coordinate vehicles without a global communication infrastructure.

We propose to model the different aspects of decision and optimization problems related to this more general problem. After modeling these problems, the question arises as to the choice of centralized and decentralized solution methods. Methodologically, we explore the directions and compare the performance of distributed constraint optimization techniques (DCOP), self-organized multi-agent techniques, market-based approaches, and centralized operations research solutions.
Dominant Resource Fairness with Meta-Types
Authors: Yin Steven , Wang Shatian , Zhang Lingyi , Kroer Christian
Published: August 2021

Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities. Unlike existing settings, we allow each agent to specify requirements to only accept allocations from a subset of the total supply for each resource. These requirements can take form in location constraints (e.g. A hospital can only accept volunteers who live nearby due to commute limitations). This can also model a type of substitution effect where some agents need 1 unit of resource A \emph{or} B, both belonging to the same meta-type. But some agents specifically want A, and others specifically want B. We propose a new mechanism called Dominant Resource Fairness with Meta Types which determines the allocations by solving a small number of linear programs. The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and a notion of sharing incentive for our setting. To the best of our knowledge, we are the first to study this problem formulation, which improved upon existing work by capturing more constraints that often arise in real life situations. Finally, we show numerically that our method scales better to large problems than alternative approaches.
Towards an Explainer-agnostic Conversational XAI
Authors: Nobani Navid , Mercorio Fabio , Mezzanzanica Mario
Published: August 2021

Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overall performance of an XAI system can be augmented by considering the end-user as a human being, we are studying the ways we can improve the explanations by making them more informative and easier to use from one hand, and interactive and customisable from the other hand.
Data Efficient Algorithms and Interpretability Requirements for Personalized Assessment of Taskable AI Systems
Authors: Verma Pulkit
Published: August 2021

The vast diversity of internal designs of black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop algorithms and requirements of interpretability that would enable a user to assess and understand the limits of an AI system's safe operability. We develop an assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system in stationary, fully observable, and deterministic settings.
Adversarial Examples in Physical World
Authors: Wang Jiakai
Published: August 2021

Although deep neural networks (DNNs) have already made fairly high achievements and a very wide range of impact, their vulnerability attracts lots of interest of researchers towards related studies about artificial intelligence (AI) safety and robustness this year. A series of works reveals that the current DNNs are always misled by elaborately designed adversarial examples. And unfortunately, this peculiarity also affects real-world AI applications and places them at potential risk. we are more interested in physical attacks due to their implementability in the real world. The study of physical attacks can effectively promote the application of AI techniques, which is of great significance to the security development of AI.
From Computational Social Choice to Digital Democracy
Authors: Brill Markus
Published: August 2021

Digital Democracy (aka e-democracy or interactive democracy) aims to enhance democratic decision-making processes by utilizing digital technology. A common goal of these approaches is to make collective decision-making more engaging, inclusive, and responsive to participants' opinions. For example, online decision-making platforms often provide much more flexibility and interaction possibilities than traditional democratic systems. It is without doubt that the successful design of digital democracy systems presents a multidisciplinary research challenge. I argue that tools and techniques from computational social choice should be employed to aid the design of online decision-making platforms and other digital democracy systems.
Alleviating Road Traffic Congestion with Artificial Intelligence
Authors: Sharon Guni
Published: August 2021

This paper reviews current AI solutions towards road traffic congestion alleviation. Three specific AI technologies are discussed, (1) intersection management protocols for coordinating vehicles through a roads intersection in a safe and efficient manner, (2) road pricing protocol that induce optimized traffic flow, and (3) partial or full autonomous driving that can stabilize traffic flow and mitigate adverse traffic shock waves. The paper briefly presents the challenges affiliated with each of these applications along with an overview of state-of-the-art solutions. Finally, real-world implementation gaps and challenges are discussed.
Towards Fair and Transparent Algorithmic Systems
Authors: Zick Yair
Published: August 2021

My research in the past few years has focused on fostering trust in algorithmic systems. I often analyze scenarios where a variety of desirable trust-oriented goals must be simultaneously satisfied;

for example, ensuring that an allocation mechanism is both fair and efficient, or that a model explanation framework is both effective and differentially private.

This interdisciplinary approach requires tools from a variety of computer science disciplines, such as game theory, economics, ML and differential privacy.
Data-Efficient Reinforcement Learning for Malaria Control
Authors: Zou Lixin
Published: August 2021

Sequential decision-making under cost-sensitive tasks is prohibitively daunting, especially for the problem that has a significant impact on people's daily lives, such as malaria control, treatment recommendation. The main challenge faced by policymakers is to learn a policy from scratch by interacting with a complex environment in a few trials. This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials. Specifically, the solution is a model-based reinforcement learning method. To avoid model bias, we apply Gaussian Process~(GP) regression to estimate the transitions explicitly. With the GP world model, we propose a variance-bonus reward to measure the uncertainty about the world. Adding the reward to the planning with MCTS can result in more efficient and effective exploration. Furthermore, the derived polynomial sample complexity indicates that VB-MCTS is sample efficient. Finally, outstanding performance on a competitive world-level RL competition and extensive experimental results verify its advantage over the state-of-the-art on the challenging malaria control task.
Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models
Authors: Spliethöver Maximilian , Wachsmuth Henning
Published: August 2021

Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.
Winner Robustness via Swap- and Shift-Bribery: Parameterized Counting Complexity and Experiments
Authors: Boehmer Niclas , Bredereck Robert , Faliszewski Piotr , Niedermeier Rolf
Published: August 2021

We study the parameterized complexity of counting variants of Swap- and Shift-Bribery, focusing on the parameterizations by the number of swaps and the number of voters. Facing several computational hardness results, using sampling we show experimentally that Swap-Bribery offers a new approach to the robustness analysis of elections.
Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition
Authors: Chen Jingye , Li Bin , Xue Xiangyang
Published: August 2021

Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, \textit{e.g.} the zero-shot problem. Previous character-based and radical-based methods have not fundamentally addressed the zero-shot problem since some characters or radicals in test sets may not appear in training sets under a data-hungry condition. Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. However, we observe that there is a one-to-many relationship between stroke sequences and Chinese characters. To tackle this challenge, we employ a matching-based strategy to transform the predicted stroke sequence to a specific character. We evaluate the proposed method on handwritten characters, printed artistic characters, and scene characters. The experimental results validate that the proposed method outperforms existing methods on both character zero-shot and radical zero-shot tasks. Moreover, the proposed method can be easily generalized to other languages whose characters can be decomposed into strokes.
Boundary Knowledge Translation based Reference Semantic Segmentation
Authors: Cheng Lechao , Feng Zunlei , Wang Xinchao , Liu Ya Jie , Lei Jie , Song Mingli
Published: August 2021

Given a reference object of an unknown type in an image, human observers can effortlessly find the objects of the same category in another image and precisely tell their visual boundaries. Such visual cognition capability of humans seems absent from the current research spectrum of computer vision. Existing segmentation networks, for example, rely on a humongous amount of labeled data, which is laborious and costly to collect and annotate; besides, the performance of segmentation networks tend to downgrade as the number of the category increases. In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation. Ref-Net contains a Reference Segmentation Module (RSM) and a Boundary Knowledge Translation Module (BKTM). Inspired by the human recognition mechanism, RSM is devised only to segment the same category objects based on the features of the reference objects. BKTM, on the other hand, introduces two boundary discriminator branches to conduct inner and outer boundary segmentation of the target object in an adversarial manner, and translate the annotated boundary knowledge of open-source datasets into the segmentation network. Exhaustive experiments demonstrate that, with tens of finely-grained annotated samples as guidance, Ref-Net achieves results on par with fully supervised methods on six datasets. Our code can be found in the supplementary material.
Review of medical diagnostics via data mining techniques
Authors: Faisal Suhad , Sameer Sarah , Kamil Mohammed Iyden , S Abd Mustafa
Published: August 2021

Data mining is one of the most popular analysis methods in medical research. It involves finding patterns and correlations in previously unknown datasets. Data mining encompasses various areas of biomedical research, including data collection, clinical decision support, illness or safety monitoring, public health and inquiry research. Health analytics frequently uses computational methods for data mining, such as clustering, classification, and regression. Studies of large numbers of diverse heterogeneous documents, including biological and electronic information, provided medical and health studies.
Multifactor model of the development of digitalization of corporate business processes
Authors: Shirinkina Elena Viktorovna
Published: August 2021

This article provides the results of assessing the factors that impact the cross-sectoral economic and technological effects in the development of digitalization of business processes. The goal of this work is to form a multifactor model for the development of digitalization of corporate business processes and determine the degree of impact of the factors included in this model. The author outlined and tested several hypotheses, the general idea of which is reduced to an assumption that at current stage of economic development in Russia, corporations are actually subject to the emergence of trend in the dynamically changing digital world, which requires quick and effective response to the digital development by increasing investment in digital technologies and intensifying their use in business processes. The empirical framework is comprised on the data provided by the Institute for Statistical Studies and Economics of Knowledge of the Higher School of Economics, as well as Federal State Statistics Service on the current state of digital and business activity. The author conducts a regression analysis for assessing the importance of factors included into the model.; advances and process the hypotheses on the influence of factors upon the index of digitalization and the level of corporate business activity. The acquired results serve as the evidence base for most of the advanced hypotheses, thereby proving the theoretical assumption on the existence of certain consequences of the digital business development. The built multifactor model for the development of digitalization of corporate business processes and assessment of importance of the factors included therein would allow the companies to develop proper solutions aimed at increasing the level of digitalization. The new positions directly depend on the continuous technological changes. As a result, the gap between business needs and competencies of the workforce appears to be the key challenge for the companies.
Points of intersection of space-time planes of the “foreign world” in epistolary prose of the English romanticists
Authors: Dominenko Natal'ya Viktorovna , Kravinskaya Yuliya Yur'evna
Published: August 2021

The subject of this research is the points of intersection of space-time relations of the “foreign world” in such form of authorial self-expression as correspondence of the English romanticists. The goal is to examine the key elements of the chronotope of “foreign world”, and determine the peculiarities of their functionality in the romantic epistolary prose. The object of this research is the 50 letters of W. Wordsworth, 224 letters of G. G. Byron, 67 letters of P. B. Shelley, 51 letters of J. Keats, and 200 letters of R. Southey. The article employs a set of general scientific and special literary criticism methods, such as descriptive, biographical, historical-genetic, historical-functional, structural-semantic, and comparative-typological. It is established that the “foreign world” in the correspondence of English romanticists is represented by the following points of intersection of space-time planes: chronotope of the road / road meetup / traffic accident; contact / meetup / date; cities / countries / villages, with the dominant motifs of the road and contact. Leaning on the analysis of space-time plane of the “foreign world” in the correspondence of English romanticists, the conclusion is made that the chronotope of “foreign world” is a certain access code to the world pattern of English romanticists, the key category that resembles the worldview of a particular epoch, namely romanticism. The scientific novelty lies in the fact that this work is first to analyze space-time relations of “foreign world” in the correspondence of English romanticists. The future research should focus on the types and peculiarities of functionality of the chronotope of “native world” in the correspondence of English romanticists, as well as the interaction of space and time in the correspondence of English realist writers, determining and comparing the integral and variable traits characteristic to epistolary prose as a whole.
Various types of tasks as the condition for successful performance in studying the discipline “Speech Culture and Business Communication” in the technical university
Authors: Krylova Mariia Nikolaevna
Published: August 2021

The object of this research is the organization of educational process in the discipline “Speech Culture and Business Communication” in the technical university, while the subject is the choice of tasks and exercises that make the course more successful. It is noted that in accordance with the latest federal state educational standards of higher education of, all graduates must possess universal linguistic and communicative competencies; this academic discipline is aimed at the formation of such competencies. The article employs the method of analysis of scientific and methodological literature, summary of other researchers, systematization of didactic material, and modeling. The scientific novelty consists in emphasizing the fact that the learning success depends on the choice of tasks and exercises for practical classes by the pedagogue. It established that most effective tasks are of creative and research nature. At the same time, the exercises of a reproductive and partially exploratory nature are also important. The acquired results can be used in organizing educational process in the discipline “Speech Culture and Business Communication” in the technical university, as well as other disciplines. The conclusion is made about the crucial role of the pedagogue and their creative approach towards organization of educational process.
VALUASI ASET TAK BERWUJUD PERUSAHAAN START UP TEKNOLOGI DENGAN MENGGUNAKAN METODE NAVARO LOPEZ UNTUK PENAWARAN SAHAM PERDANA
Authors: Cakranegara Pandu Adi
Published: August 2021

Indonesia has a company whose value has reached more than ten billion US dollars. One of the companies that achieved this valuation was Go Jek. In this rupiah is worth more than one hundred and fifty trillion rupiah. Judging from the fixed assets then Go Jek does not have a car fleet or a motorcycle fleet. While Astra International which is a manufacturer and distributor of car and has subsidiaries engaged in oil valued in total assets has three hundred and fifty trillion rupiah. This huge valuation causes if Go Jek chooses to offer the first major stock in Indonesia at ten percent of its equity then its value is about fifteen trillion rupiah. This is an amount that will be difficult to absorb by the Indonesian market. With such a prospect, Go Jek may never float on Bursa Indonesia. This means that Indonesian investors lose the opportunity to obtain returns from shareholding Go Jack is the work of children abroad. This study examines the valuation method developed by Navado Lopez to be used to assess intangible assets, especially in technology-intensive companies. The Navado Lopez method was chosen because of its relatively simple and straightforward use in generating a value. This study attempts to investigate whether the Navado Lopez method can be applied in the Indonesian context. The hope is that with the method of valuation of intangible assets that can be understood by investors, investors will not hesitate if there is a start-up company (Start Up) that wants to issue prime shares in the Indonesian Stock Exchange.
PANDEMI COVID 19 DAN PENGARUHNYA TERHADAP PASAR TENAGA KERJA DI INDONESIA
Authors: Cakranegara Pandu Adi
Published: August 2021

Pandemic Covid 19 is a crisis that begins with health problems. But the crisis is heading towards an economic crisis. This study examines the impact of the economic crisis using various data from various sources such as Bank Indonesia, Ministry of Finance, World Bank, International Labor Organizations, International Monetary Funds and McKinsey. Based on the data collected, the economic impact on the labor market is analyzed. The results of this study conclude that the high unemployment will create a decrease in demand while the Quantitative Easing policy and private sector debt restructuring from Bank Indonesia is directed to the supply sector. Without the demand for goods and services from consumers, the company will not be able to sell its products and recover as usual. The policy suggested from this study is that the government needs to create manpower and whenever it feels necessary to subsidize the salaries of employees, especially in the national leading sectors.
Development of Telegram-Channels in the Media Space of Russian Regions: Specificity, Typology, Development Prospects (On the Example of Telegram Channels of the Republic of Mordovia)
Authors: Dementieva K. V.
Published: August 2021

The article examines the specifics of the development of Telegram-channels in Russian regions as a whole, which is possible since in small towns they evolve in approximately the same way. However, the main focus of the study is the Republic of Mordovia. The outcomes include the presentation of specificity and characteristics of Telegram-channels, the definition of a news Telegram-channel. Based on the results of analysis, the author not only describes the advantages of these media in the regions but also the typical mistakes of the content creators. Despite the increasing number of channels, there is no reason to predict a massive growth of users due to Telegram’s orientation towards elitism and respectability of the target audience. The author also finds out that public attention is attracted more by texts belonging to the style of gonzo journalism and thrash writing than neutral ones.
Post-Modern Situation in Media Communication: Forecast and Reality
Authors: Semilet T. A. , Fotieva I. V. , Ivanov A. V.
Published: August 2021

In the social transformations that shape the situation of “postmodernity”, media communication plays a leading role. At the first stages of its formation, theorists made a number of predictions about a new sociality: the implementation of the principles of equality and freedom; the ability to participate in significant decision-making; inclusion of all in social integrity and participation in events; consolidation of rationality, ethics, and freedom in reaching a universal consensus; convergence of values and worldviews.

Accordingly,the purpose of this studyis to investigate how and in what forms these forecasts have been carried out; what are the trends of modern media communication.

Research methods: comparative analysis, system-structural and structural-functional analysis.

The main results of the studyare as follows. The forecasts of the leading theorists about the prospects of media communication and the digital society have been reached, at best, only partially. In a real situation, which many authors call “postmodernity”, one can see the following contradictory tendencies: real boundaries, which were expected to become “transparent”, are now reproduced in virtual form; communication processes stimulated consolidation, but at the same time exacerbated ideological, national, socio-cultural divisions. Moreover, the government and business structures that are increasing their influence (“post-democracy”) are competing with new actors for leadership in media communication. The phenomenon of “post-truth” show itself through rejection of the objectivity principle, reliability, rationality. The immersion of communicants in a virtual environment has number of negative consequences for the personality, including “erosion” of basic moral and social norms (“posthumanism”). Furthermore, basic functions of journalism are replaced by dysfunctions (“post-journalism”). Thus, the Internet has served as a kind of catalyst that enhances both positive and negative social trends.
Coverage of Russian-Czech Relations by the Leading Online Media of the Czech Republic
Authors: Sokolova D. V. , Lapuk E. V.
Published: August 2021

Russian science knows the limited number of studies dedicated to the media of the Czech Republic. The article identifies the features of coverage of Russian-Czech relations by the Czech online media iDnes TV and Televize Seznam. This study aims to find out how the Russian-Czech relations are reported by iDnes TV and Televize Seznam. We apply such methods as comparative and quantitative analysis, data systematization, description, and content analysis. The paper examines some theoretical studies and 1,173 video news of iDnes TV and Televize Seznam. Based on the conducted research, we conclude that Czechs consider modern Russia as a threat to world security, and look at the era of communism in the Czech Republic negatively. At the same time, Russia remains an important part of the international agenda of the Czech media: the most popular themes in the coverage of the relations between Russia and the Czech Republic are politics and history. The findings illustrate that online media tend to use simpler forms and genres. To understand the trends of transformation of Russian-Czech relations in recent years, we need to continue our research.
“Struggle” and “Consent” as Media Constructs: The Experience of a Complex Analysis of Siberian Media Reports
Authors: Pustovoyt Yu. A.
Published: August 2021

The article presents the outcomes of narrative, discourse and frames analyses of messages of Internet media of Siberian cities about the results of the vote Internet media in the Regional legislatures of federal subjects. We identify three ways of presenting the material, constructing problems, and interpreting what is happening, which allows us to talk about “a split” and different, rather non-overlapping target audiences and communities. This allows us to put forward the hypothesis of “parallel existence”, the absence of common themes and language between “power” and “society”.