bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond Accuracy: Behavioral Testing of NLP Models with Checklist (Extended Abstract)
Authors: Ribeiro Marco Tulio , Wu Tongshuang , Guestrin Carlos , Singh Sameer
Published: August 2021

Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
Learning with Generated Teammates to Achieve Type-Free Ad-Hoc Teamwork
Authors: Xing Dong , Liu Qianhui , Zheng Qian , Pan Gang
Published: August 2021

In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type-based approach, which pre-trains the agent with a set of pre-prepared teammate types, then associates the unknown teammate with a particular type. Typically, these types are collected manually. This hampers previous works by both the availability and diversity of types they manage to obtain. To eliminate these limitations, this work addresses to achieve ad-hoc teamwork in a type-free approach. Specifically, we propose the model of Entropy-regularized Deep Recurrent Q-Network (EDRQN) to generate teammates automatically, meanwhile utilize them to pre-train our agent. These teammates are obtained from scratch and are designed to perform the task with various behaviors, therefore their availability and diversity are both ensured. We evaluate our model on several benchmark domains of ad-hoc teamwork. The result shows that even if our model has no access to any pre-prepared teammate types, it still achieves significant performance.
Politeness for the Theory of Algebraic Datatypes (Extended Abstract)
Authors: Sheng Ying , Zohar Yoni , Ringeissen Christophe , Lange Jane , Fontaine Pascal , Barrett Clark
Published: August 2021

Algebraic datatypes, and among them lists and trees, have attracted a lot of interest in automated reasoning and Satisfiability Modulo Theories (SMT). Since its latest stable version, the SMT-LIB standard defines a theory of algebraic datatypes, which is currently supported by several mainstream SMT solvers. In this paper, we study this particular theory of datatypes and prove that it is strongly polite, showing also how it can be combined with other arbitrary disjoint theories using polite combination. Our results cover both inductive and finite datatypes, as well as their union. The combination method uses a new, simple, and natural notion of additivity, that enables deducing strong politeness from (weak) politeness.
Imprecise Oracles Impose Limits to Predictability in Supervised Learning (Extended Abstract)
Authors: Sifar Anjali , Srivastava Nisheeth
Published: August 2021

Supervised learning operates on the premise that labels unambiguously represent ground truth. This premise is reasonable in domains wherein a high degree of consensus is easily possible for any given data record, e.g. in agreeing on whether an image contains an elephant or not. However, there are several domains wherein people disagree with each other on the appropriate label to assign to a record, e.g. whether a tweet is toxic. We argue that data labeling must be understood as a process with some degree of domain-dependent noise and that any claims of predictive prowess must be sensitive to the degree of this noise. We present a method for quantifying labeling noise in a particular domain wherein people are seen to disagree with their own past selves on the appropriate label to assign to a record: choices under prospect uncertainty. Our results indicate that `state-of-the-art' choice models of decisions from description, by failing to consider the intrinsic variability of human choice behavior, find themselves in the odd position of predicting humans' choices better than the same humans' own previous choices for the same problem. We conclude with observations on how the predicament we empirically demonstrate in our work could be handled in the practice of supervised learning.
Open Intent Extraction from Natural Language Interactions (Extended Abstract)
Authors: Vedula Nikhita , Lipka Nedim , Maneriker Pranav , Parthasarathy Srinivasan
Published: August 2021

Accurately discovering user intents from their written or spoken language plays a critical role in natural language understanding and automated dialog response. Most existing research models this as a classification task with a single intent label per utterance. Going beyond this formulation, we define and investigate a new problem of open intent discovery. It involves discovering one or more generic intent types from text utterances, that may not have been encountered during training. We propose a novel, domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task in an open-world setting. It employs a CRF on top of a bidirectional LSTM to extract intents in a consistent format, subject to constraints among intent tag labels. We apply multi-headed self-attention and adversarial training to effectively learn dependencies between distant words, and robustly adapt our model across varying domains. We also curate and release an intent-annotated dataset of 25K real-life utterances spanning diverse domains. Extensive experiments show that OPINE outperforms state-of-art baselines by 5-15% F1 score.
TAXOGAN: Hierarchical Network Representation Learning via Taxonomy Guided Generative Adversarial Networks (Extended Abstract)
Authors: Yang Carl , Zhang Jieyu , Han Jiawei
Published: August 2021

Network representation learning aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. Comprehensive experiments and case studies demonstrate the utility of TAXOGAN.
Semantic Linking Maps for Active Visual Object Search (Extended Abstract)
Authors: Zeng Zhen , Röfer Adrian , Jenkins Odest Chadwicke
Published: August 2021

We aim for mobile robots to function in a variety of common human environments, which requires them to efficiently search previously unseen target objects. We can exploit background knowledge about common spatial relations between landmark objects and target objects to narrow down search space. In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations. Based on SLiM, we describe a hybrid search strategy that selects the next best view pose for searching for the target object based on the maintained belief. We demonstrate the efficiency of our SLiM-based search strategy through comparative experiments in simulated environments. We further demonstrate the real-world applicability of SLiM-based search in scenarios with a Fetch mobile manipulation robot.
Distributional Metareasoning for Heuristic Search
Authors: Gu Tianyi
Published: August 2021

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.
Towards Robust Dynamic Network Embedding
Authors: Hou Chengbin , Tang Ke
Published: August 2021

Dynamic Network Embedding (DNE) has recently drawn much attention due to the dynamic nature of many real-world networks. Comparing to a static network, a dynamic network has a unique character called the degree of changes, which can be defined as the average number of the changed edges between consecutive snapshots spanning a dynamic network. The degree of changes could be quite different even for the dynamic networks generated from the same dataset. It is natural to ask whether existing DNE methods are effective and robust w.r.t. the degree of changes. Towards robust DNE, we suggest two important scenarios. One is to investigate the robustness w.r.t. different slicing settings that are used to generate different dynamic networks with different degree of changes, while another focuses more on the robustness w.r.t. different number of changed edges over timesteps.
Robot Manipulation Learning Using Generative Adversarial Imitation Learning
Authors: Jabri Mohamed Khalil
Published: August 2021

Imitation learning allows learning complex behaviors given demonstrations. Early approaches belonging to either Behavior Cloning or Inverse Reinforcement Learning were however of limited scalability to complex environments. A more promising approach termed as Generative Adversarial Imitation Learning tackles the imitation learning problem by drawing a connection with Generative Adversarial Networks. In this work, we advocate the use of this class of methods and investigate possible extensions by endowing them with global temporal consistency, in particular through a contrastive learning based approach.
Planning and Reinforcement Learning for General-Purpose Service Robots
Authors: Jiang Yuqian
Published: August 2021

Despite recent progress in AI and robotics research, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This Ph.D. research aims to combine symbolic planning and reinforcement learning to reason about high-level robot tasks and adapt to the real world. We will introduce task planning algorithms that adapt to the environment and other agents, as well as reinforcement learning methods that are practical for service robot systems. Taken together, this work will make a significant step towards creating general-purpose service robots.
On the Learnability of Knowledge in Multi-Agent Logics
Authors: Mocanu Ionela G
Published: August 2021

Since knowledge engineering is an inherently challenging and somewhat unbounded task, machine learning has been widely proposed as an alternative. In real world scenarios, we often need to explicitly model multiple agents, where intelligent agents act towards achieving goals either by coordinating with the other agents or by overseeing the opponents moves, if in a competitive context. We consider the knowledge acquisition problem where agents have knowledge about the world and other agents and then acquire new knowledge (both about the world as well as other agents) in service of answering queries. We propose a model of implicit learning, or more generally, learning to reason, which bypasses the intractable step of producing an explicit representation of the learned knowledge. We show that polynomial-time learnability results can be obtained when limited to knowledge bases and observations consisting of conjunctions of modal literals.
Inter-Task Similarity for Lifelong Reinforcement Learning in Heterogeneous Tasks
Authors: Serrano Sergio A.
Published: August 2021

Reinforcement learning (RL) is a learning paradigm in which an agent interacts with the environment it inhabits to learn in a trial-and-error way. By letting the agent acquire knowledge from its own experience, RL has been successfully applied to complex domains such as robotics. However, for non-trivial problems, training an RL agent can take very long periods of time. Lifelong machine learning (LML) is a learning setting in which the agent learns to solve tasks sequentially, by leveraging knowledge accumulated from previously solved tasks to learn better/faster in a new one. Most LML works heavily rely on the assumption that tasks are similar to each other. However, this may not be true for some domains with a high degree of task-diversity that could benefit from adopting a lifelong learning approach, e.g., service robotics. Therefore, in this research we will address the problem of learning to solve a sequence of RL heterogeneous tasks (i.e., tasks that differ in their state-action space).
A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text
Authors: Seveso Andrea , Mercorio Fabio , Mezzanzanica Mario
Published: August 2021

Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.
Continual Lifelong Learning for Intelligent Agents
Authors: Sokar Ghada
Published: August 2021

Deep neural networks have achieved outstanding performance in many machine learning tasks. However, this remarkable success is achieved in a closed and static environment where the model is trained using large training data of a single task and deployed for testing on data with a similar distribution. Once the model is deployed, it becomes fixed and inflexible to new knowledge. This contradicts real-world applications, in which agents interact with open and dynamic environments and deal with non-stationary data. This Ph.D. research aims to propose efficient approaches that can develop intelligent agents capable of accumulating new knowledge and adapting to new environments without forgetting the previously learned ones.
Anomaly Mining - Past, Present and Future
Authors: Akoglu Leman
Published: August 2021

Anomaly mining is an important problem that finds numerous applications in various real world do- mains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas, (1) point-cloud and (2) graph-based anomaly mining. I aim to present a broad view of each area, and discuss classes of main research problems, recent trends and future directions. I conclude with key take-aways and overarching open problems.

Disclaimer. I try to provide an overview of past and recent trends in both areas within 4 pages. Undoubtedly, these are my personal view of the trends, which can be organized differently. For brevity, I omit all technical details and refer to corresponding papers. Again, due to space limit, it is not possible to include all (even most relevant) references, but a few representative examples.
Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning
Authors: Lipovetzky Nir
Published: August 2021

Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully applied to model-based and model-free settings where the dynamics of the problem are given through simulation engines. Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption. To facilitate synergies across research communities, this paper summarizes the area of width-based planning, and surveys current and future research directions.
A Neural Network Auction For Group Decision Making Over a Continuous Space
Authors: Bachrach Yoram , Gemp Ian , Garnelo Marta , Kramar Janos , Eccles Tom , Rosenbaum Dan , Graepel Thore
Published: August 2021

We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences.

The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.
Web Interoperability for Ontology Development and Support with crowd 2.0
Authors: Braun German , Marinelli Giuliano , Rios Gavagnin Emiliano , Cecchi Laura , Fillottrani Pablo
Published: August 2021

In this work, we treat web interoperability in terms of interchanging ontologies (as knowledge models) within user-centred ontology engineering environments, involving visual and serialised representations of ontologies.

To do this, we deal with the tool interoperability problem by re-using an enough expressive ontology-driven metamodel, named KF, proposed as a bridge for interchanging both knowledge models.

We provide an extensible web framework, named crowd 2.0, unifying the standard conceptual data modelling languages for generating OWL 2 ontologies from semantic visualisations. Visual models are designed as UML, ER or ORM 2 diagrams, represented as KF instances, and finally, formalised as DL-based models. Reasoning results may be newly incorporated into the shared KF instance to be visualised in any of the provided languages.
Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem
Authors: Giabelli Anna , Malandri Lorenzo , Mercorio Fabio , Mezzanzanica Mario , Seveso Andrea
Published: August 2021

In this paper, we present Skills2Graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics - computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) - and distributional semantics - generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by Skills2Graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by Skills2Graph.
HIVE: Hierarchical Information Visualization for Explainability
Authors: Juan Yi-Ning , Chiang Yi-Shyuan , Liu Shang-Chuan , Tsai Ming-Feng , Wang Chuan-Ju
Published: August 2021

In this demonstration, we develop an interactive tool, HIVE, to demonstrate the ability and versatility of an explainable risk ranking model with a special focus on financial use cases. HIVE is a web-based tool that provides users with automated highlighted financial statements, and HIVE is designed for making comparing statements rather more efficient. Moreover, with the proposed tool, users can find related reports at ease, and we believe that HIVE can benefit both academics and practitioners in finance as they can work around deep learning models with their newly gained insights.
ConvLogMiner: A Real-Time Conversational Lifelog Miner
Authors: Kao Pei-Wei , Yen An-Zi , Huang Hen-Hsen , Chen Hsin-Hsi
Published: August 2021

This paper presents a conversational lifelog mining system, ConvLogMiner, which detects personal life events from the human online conversation in real-time. Given a daily conversation of two speakers, ConvLogMiner identifies the new life events specific to each speaker that occur in the latest utterances. The lifelogs mined by our system are useful to provide complementary information to support lifestyle analysis and memory assistance service.
Connect Multi-Agent Path Finding: Generation and Visualization
Authors: Queffelec Arthur , Sankur Ocan , Schwarzentruber Francois
Published: August 2021

We present a generic tool to visualize missions of the Connected Multi-Agent Path Finding (CMAPF) problem. This problem is a variant of MAPF which requires a group of agents to navigate from an initial configuration to a goal configuration while maintaining connection. The user can create an instance of CMAPF and can play the generated plan. Any algorithm for CMAPF can be plugged into the tool.
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices
Authors: Shen Xuan , Yuan Geng , Niu Wei , Ma Xiaolong , Guan Jiexiong , Li Zhengang , Ren Bin , Wang Yanzhi
Published: August 2021

The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51X faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.
Predictive Analytics for COVID-19 Social Distancing
Authors: Teng Harold Ze Chie , Jiang Hongchao , Ho Xuan Rong Zane , Lim Wei Yang Bryan , Ng Jer Shyuan , Yu Han , Xiong Zehui , Niyato Dusit , Miao Chunyan
Published: August 2021

The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.
Interacting with Explanations through Critiquing
Authors: Antognini Diego , Musat Claudiu , Faltings Boi
Published: August 2021

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques.

Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Empirical results show that our system achieves good performance in adapting to the preferences expressed in multi-step critiquing and generates consistent explanations.
Location Predicts You: Location Prediction via Bi-direction Speculation and Dual-level Association
Authors: Li Xixi , Hu Ruimin , Wang Zheng , Yamasaki Toshihiko
Published: August 2021

Location prediction is of great importance in location-based applications for the construction of the smart city. To our knowledge, existing models for location prediction focus on the users' preference on POIs from the perspective of the human side. However, modeling users' interests from the historical trajectory is still limited by the data sparsity. Additionally, most of existing methods predict the next location according to the individual data independently. But the data sparsity makes it difficult to mine explicit mobility patterns or capture the casual behavior for each user. To address the issues above, we propose a novel Bi-direction Speculation and Dual-level Association method (BSDA), which considers both users' interests in POIs and POIs' appeal to users. Furthermore, we develop the cross-user and cross-POI association to alleviate the data sparsity by similar users and POIs to enrich the candidates. Experimental results on two public datasets demonstrate that BSDA achieves significant improvements over state-of-the-art methods.
Decision Making with Differential Privacy under a Fairness Lens
Authors: Tran Cuong , Fioretto Ferdinando , Van Hentenryck Pascal , Yao Zhiyan
Published: August 2021

Many agencies release datasets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private datasets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.
Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks
Authors: Wang Siyue , Wang Xiao , Chen Pin-Yu , Zhao Pu , Lin Xue
Published: August 2021

This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models. This is the first work taking both robustness and transferability into consideration for generating realistic fingerprints, whereas current methods lack practical assumptions and may incur large false positive rates. To achieve better trade-off between robustness and transferability, we propose three kinds of characteristic examples: vanilla C-examples, RC-examples, and LTRC-example, to derive fingerprints from the original base model. To fairly characterize the trade-off between robustness and transferability, we propose Uniqueness Score, a comprehensive metric that measures the difference between robustness and transferability, which also serves as an indicator to the false alarm problem. Extensive experiments demonstrate that the proposed characteristic examples can achieve superior performance when compared with existing fingerprinting methods. In particular, for VGG ImageNet models, using LTRC-examples gives 4X higher uniqueness score than the baseline method and does not incur any false positives.
Explaining Self-Supervised Image Representations with Visual Probing
Authors: Basaj Dominika , Oleszkiewicz Witold , Sieradzki Igor , Górszczak Michał , Rychalska Barbara , Trzcinski Tomasz , Zieliński Bartosz
Published: August 2021

Recently introduced self-supervised methods for image representation learning provide on par or superior results to their fully supervised competitors, yet the corresponding efforts to explain the self-supervised approaches lag behind. Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing. The probing tasks require knowledge about semantic relationships between image parts. Hence, we propose a systematic approach to obtain analogs of natural language in vision, such as visual words, context, and taxonomy. We show the effectiveness and applicability of those analogs in the context of explaining self-supervised representations. Our key findings emphasize that relations between language and vision can serve as an effective yet intuitive tool for discovering how machine learning models work, independently of data modality. Our work opens a plethora of research pathways towards more explainable and transparent AI.
Novelty Detection via Contrastive Learning with Negative Data Augmentation
Authors: Chen Chengwei , Xie Yuan , Lin Shaohui , Qiao Ruizhi , Zhou Jian , Tan Xin , Zhang Yi , Ma Lizhuang
Published: August 2021

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
Leveraging Human Attention in Novel Object Captioning
Authors: Chen Xianyu , Jiang Ming , Zhao Qi
Published: August 2021

Image captioning models depend on training with paired image-text corpora, which poses various challenges in describing images containing novel objects absent from the training data. While previous novel object captioning methods rely on external image taggers or object detectors to describe novel objects, we present the Attention-based Novel Object Captioner (ANOC) that complements novel object captioners with human attention features that characterize generally important information independent of tasks. It introduces a gating mechanism that adaptively incorporates human attention with self-learned machine attention, with a Constrained Self-Critical Sequence Training method to address the exposure bias while maintaining constraints of novel object descriptions. Extensive experiments conducted on the nocaps and Held-Out COCO datasets demonstrate that our method considerably outperforms the state-of-the-art novel object captioners.

Our source code is available at https://github.com/chenxy99/ANOC.
Phonovisual Biases in Language: is the Lexicon Tied to the Visual World?
Authors: de Varda Andrea Gregor , Strapparava Carlo
Published: August 2021

The present paper addresses the study of cross-linguistic and cross-modal iconicity within a deep learning framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a concrete word, encoded as a sequence of feature vectors, to the visual representation of its referent, expressed as an HCNN-transformed image. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a randomized baseline; we show that such an imaginative network is capable of extracting language-independent generalizations in the mapping from linguistic sounds to visual features, providing empirical support for the hypothesis of a universal sound-symbolic substrate underlying all languages.
Putting a Compass on the Map of Elections
Authors: Boehmer Niclas , Bredereck Robert , Faliszewski Piotr , Niedermeier Rolf , Szufa Stanisław
Published: August 2021

In their AAMAS 2020 paper, Szufa et al. presented a "map of elections" that visualizes a set of 800 elections generated from various statistical cultures. While similar elections are grouped together on this map, there is no obvious interpretation of the elections' positions. We provide such an interpretation by introducing four canonical “extreme” elections, acting as a compass on the map. We use them to analyze both a dataset provided by Szufa et al. and a number of real-life elections. In effect, we find a new parameterization of the Mallows model, based on measuring the expected swap distance from the central preference order, and show that it is useful for capturing real-life scenarios.
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT’s Heads
Authors: Gao Chenyu , Zhu Qi , Wang Peng , Wu Qi
Published: August 2021

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA task.
Feature Space Targeted Attacks by Statistic Alignment
Authors: Gao Lianli , Cheng Yaya , Zhang Qilong , Xu Xing , Song Jingkuan
Published: August 2021

By adding human-imperceptible perturbations to images, DNNs can be easily fooled. As one of the mainstream methods, feature space targeted attacks perturb images by modulating their intermediate feature maps, for the discrepancy between the intermediate source and target features is minimized. However, the current choice of pixel-wise Euclidean Distance to measure the discrepancy is questionable because it unreasonably imposes a spatial-consistency constraint on the source and target features.

Intuitively, an image can be categorized as "cat'' no matter the cat is on the left or right of the image. To address this issue, we propose to measure this discrepancy using statistic alignment. Specifically, we design two novel approaches called Pair-wise Alignment Attack and Global-wise Alignment Attack, which attempt to measure similarities between feature maps by high-order statistics with translation invariance. Furthermore, we systematically analyze the layer-wise transferability with varied difficulties to obtain highly reliable attacks. Extensive experiments verify the effectiveness of our proposed method, and it outperforms the state-of-the-art algorithms by a large margin. Our code is publicly available at https://github.com/yaya-cheng/PAA-GAA.
Learning Spectral Dictionary for Local Representation of Mesh
Authors: Gao Zhongpai , Yan Junchi , Zhai Guangtao , Yang Xiaokang
Published: August 2021

For meshes, sharing the topology of a template is a common and practical setting in face-, hand-, and body-related applications. Meshes are irregular since each vertex's neighbors are unordered and their orientations are inconsistent with other vertices. Previous methods use isotropic filters or predefined local coordinate systems or learning weighting matrices for each vertex of the template to overcome the irregularity. Learning weighting matrices for each vertex to soft-permute the vertex's neighbors into an implicit canonical order is an effective way to capture the local structure of each vertex. However, learning weighting matrices for each vertex increases the parameter size linearly with the number of vertices and large amounts of parameters are required for high-resolution 3D shapes. In this paper, we learn spectral dictionary (i.e., bases) for the weighting matrices such that the parameter size is independent of the resolution of 3D shapes. The coefficients of the weighting matrix bases for each vertex are learned from the spectral features of the template's vertex and its neighbors in a weight-sharing manner. Comprehensive experiments demonstrate that our model produces state-of-the-art results with a much smaller model size.
SIMULASI PENERAPAN TIME-DRIVEN ACTIVITY-BASED COSTING PADA PERHITUNGAN HARGA POKOK JASA BENGKEL KURNIA MOTOR
Authors: Oesef Albert , Faliany L. Jade
Published: August 2021

In a service company, the cost of services is sometimes not calculated accurately by the owner or management. This is due to the difficulty of calculating the cost as indirect costs occur together with the activities of the services performed. One of the methods of calculating the cost of services is the Time-driven activity-based costing (TDABC) method. This study applied the method to calculate the cost of services of Kurnia Motor Workshop, a company that offers antique car restoration services. The analysis showed that the TDABC method was a suitable method to calculate the cost of services accurately in Kurnia Motor Workshop.
Specificity of Representation of Fake Information in Audiovisual Media Content
Authors: Shesterina A. M.
Published: August 2021

The situation of uncertainty, which became a consequence of the pandemic, forced the audience to actively interact with the media. This effect is especially noticeable in the field of video information. Because of this, the ability to check the quality of such information and to detect a video fake is especially in high demand. Meanwhile, the researchers' interest is focused primarily on the verbal component of media content. In our study, we are trying to fill this gap and concretize the models of generating video fakes, their types, and reasons for their appearance.

Based on the analysis of fake audiovisual content distributed in the media sphere in 2020 we identify two models of its appearance - synchronous and asynchronous. In the first case, fake is a result of distorting the video and audio of the work. In the second case, only one of the rows broadcasts a false idea. The latter case is dangerous in that the series of works that do not contain false information inspires confidence in the audience and makes it accept the media message as a whole.

Also, in the study, we single out the most common types of fakes based on such characteristics as the degree of information distortion, the degree of reliability of spatial and temporal characteristics, and the degree of reliability of the source. We determine the most frequent markers of fakes in video works, namely: distortion of the shooting angle, concealment of the staged nature of filming, the use of animation and animation technologies that imitate newsreel footage, placement of inaccurate infographics in video work, fake news announcement, and publication.

Among the most common reasons for the formation of video fakes, we note the desire to increase ratings, discriminate against specific individuals or organizations, draw attention to a real problem, and to entertain the audience.

In the analysis of fake as a global phenomenon using the example of deepfake technology, we show how it can be used in constructively and destructively and emphasize the importance of developing media education to neutralize the negative consequences of the spread of fakes.
Peculiarities of Professional Media Education in 1920s: Methods of Training Military Correspondents (On the Example of the Magazine “Education and Upbringing”, 1924–1926)
Authors: Evdokimova E. V.
Published: August 2021

The studies of the specialized military-political publications of the 1920s usually consider only approaches to organizing the political and educational work of commanders with personnel. Filling the gap, this article focuses on the media educational approach to the analysis of print media that examines the activities of newspapers and magazines as a kind of media platform for the training of regional workers and village correspondents (rabsel'cors), and military correspondents (voencors).

The article reveals the main methods of training military correspondents by the specialized magazine “Education and upbringing”. Voencors were supposed to participate in creating a mass press, perform information functions and be propagandists, agitators, and organizers of the movement of military correspondents.

Based on the analysis of the journal publications the author identifies the main approaches to rabsel'cors and voencors’ training: the introduction of special headings that attracted the Red Army soldiers to read periodicals and create wall newspapers; recommendations for establishing connections between military correspondents and village correspondents; publication of articles by the main authors of the journal on the organization of wall newspapers; analysis of military correspondents’ publications; responses to letters from readers.

As a result, the author comes to the conclusion that the military-political magazine “Education and upbringing” should be considered not only as a means of ideologically educating the serviceman of a new type but as a necessary guide for novice correspondents of specialized and universal media.
The extraction of beautiful sound patterns from Sunthorn Phu’s poem using machine learning technique and internal rhyme rule
Authors: Suksanguan Pattarakorn , Waijanya Sajjaporn , Promrit Nuttachot
Published: August 2021

The melodious poems have been written from the distinctive features of poetry or based on each country's typical style. Especially, Thai poems which composed by the use of specific forming, such as Internal Rhyme to develop melodiousness. The most attractive and well-known poems were composed by a genius Thai poet named Sunthorn Phu. He is a role model for Thai poets. UNESCO honored him as the world’s great poet and the best role model in poetry works. In this article, we proposed extracting 15,796 sentences (Waks) of the beautiful sound patterns of Phra Aphai Mani’s tales by machine learning technology in conjunction with the rules of internal Rhyme Klon-Suphap by using the Apriori Algorithm. The extraction of vowel rhymes separated by a group of Waks including 1) Poem Wak No. 1; 2) Poem Wak No. 2; 3) Poem Wak No. 3; and 4) Poem Wak No. 4. In this article, “Wak” means sentence. The created tool can extract the internal rhyme patterns and the 25 popular pattern vowels. The popular pattern illustrates the melodiousness of the Poem and sets up a standard of how to melodiously compose a poem. Then, the evaluation of the experiments was done by using 144 Waks selected from the extraction of the beautiful patterns and evaluated by the consistency score from 3 experts. The average accuracy score resulted in 95.30%.
ВЕРБАЛІЗАЦІЯ КОНЦЕПТІВ ПОДІЯ, ПРОСТІР ТА ЛЮДИНА В МЕМУАРНИХ ТЕКСТАХ
Authors: Коваленко С. П.
Published: August 2021

Стаття присвячена дослідженню когнітивно-семантичного простору концептів подія, простір та людина в спогадах відомих французьких мемуаристів минулого століття: Симони де Бовуар, Жана Даніеля та Жака Робера. Передбачалося прослідкувати просторову конфігурацію текстових концептів, їх варіативні характеристики та роль у розгортанні текстового змісту мемуарів. Аналіз текстового масиву різних авторів довів, що особливістю концепту подія є поєднання нейтрально-фактуальної інформації і суб’єктивно- авторського ставлення до неї. Це твердження чітко проглядається в концепті подія «Референдум» у мемуарному романі С. де Бовуар «La force des choses II», де загальновідома фактуальна інформація переплетена з авторською добіркою концептів емотивного (головним чином, негативного) порядку: поразка, гіркота, радість, спустошення, пригніченість, песимізм, похмурість, відчай, приголомшеність, розчарування, марні сподівання, невідворотність, мара, туга, жах, бунт, скандал, незгода, тривога, депресія, несконцентрованість. Інший категоріальний концепт простір виконує текстотвірну функцію, локалізуючи описувані мемуаристами події просторово (в поєднанні з концептом час, що локалізує події темпорально). Така локалізація відбувається шляхом інтенсивної стратифікації різнорівневих концептів: макроконцептів, мезоконцептів, катаконцептів. Неодмінною складовою частиною всіх мемуарних текстів є мегаконцепт людина, оскільки будь-яке відтворення минулого пов’язане з людською діяльністю: самого автора-мемуариста чи того/тих, кого він описує. Автори досліджуваних мемуарних текстів – загальновідомі, публічні особистості, що володіють високим рівнем мовленнєвої культури, а отже, потужними можли- востями впливу на читача. Серед таких засобів впливу слід виділити варію- вання текстових концептів темпоральної, просторової чи емотивної семантики. Самі мемуаристи, втім як і численні згадувані ними особистості, асоціативно набули статусу концепту завдяки своїй діяльності, загальновідомості. Проведене дослідження дало змогу зробити важливий висновок: варіювання різнорівневих концептів мемуарних текстів сприяє їх семантико-когнітивному збагаченню та робить суб’єктивно-авторську позицію мемуариста більш помітною в текстовому просторі мемуарів. Втім, це дослідження виводить на цілу низку невирішених питань, пов’язаних з інформативною основою мемуарів, когнітивною периферією, динамікою розгортання текстового змісту і, безумовно, з мовною грою таких текстів. Усі ці питання чекають подальших студій.