2022.01.28.txt

1, TITLE: Inference-optimized AI and High Performance Computing for Gravitational Wave Detection at Scale
AUTHORS: Pranshu Chaturvedi ; Asad Khan ; Minyang Tian ; E. A. Huerta ; Huihuo Zheng
CATEGORY: gr-qc [gr-qc, astro-ph.IM, cs.AI, cs.DC, cs.LG, 68T10, 85-08, 83C35, 83C57, I.2]
HIGHLIGHT: We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours.

2, TITLE: Rational Solutions of First Order Algebraic Ordinary Differential Equations
AUTHORS: Shuang Feng ; Li-Yong Shen
CATEGORY: math.CA [math.CA, cs.SC]
HIGHLIGHT: Rational Solutions of First Order Algebraic Ordinary Differential Equations

3, TITLE: Online Planning in POMDPs with Self-Improving Simulators
AUTHORS: Jinke He ; Miguel Suau ; Hendrik Baier ; Michael Kaisers ; Frans A. Oliehoek
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time.

4, TITLE: Diagnosing AI Explanation Methods with Folk Concepts of Behavior
AUTHORS: Alon Jacovi ; Jasmijn Bastings ; Sebastian Gehrmann ; Yoav Goldberg ; Katja Filippova
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: We aim to provide an answer by leveraging theory of mind literature about the folk concepts that humans use to understand behavior.

5, TITLE: Human-centered Mechanism Design with Democratic AI
AUTHORS: RAPHAEL KOSTER et. al.
CATEGORY: cs.AI [cs.AI, cs.HC, cs.MA, econ.GN, q-fin.EC]
HIGHLIGHT: Here, we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority.

6, TITLE: Epistemic AI Platform Accelerates Innovation By Connecting Biomedical Knowledge
AUTHORS: Emily Koo ; Heather Bowling ; Kenneth Ashworth ; David J. Heeger ; Stefano Pacifico
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: The platform combines state-of-the-art methods for information extraction with machine learning, artificial intelligence and network analysis.

7, TITLE: First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
AUTHORS: Nitesh Kumar ; Ondrej Kuzelka ; Luc De Raedt
CATEGORY: cs.AI [cs.AI, cs.LO]
HIGHLIGHT: This paper introduces a hybrid probabilistic logic programming language, DC#, which integrates distributional clauses' syntax and semantics principles of Bayesian logic programs.

8, TITLE: DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Games
AUTHORS: Qibin Zhou ; Dongdong Bai ; Junge Zhang ; Fuqing Duan ; Kaiqi Huang
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: This article introduces DecisionHoldem, a high-level AI for heads-up no-limit Texas hold'em with safe depth-limited subgame solving by considering possible ranges of opponent's private hands to reduce the exploitability of the strategy.

9, TITLE: Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences
AUTHORS: Dmitri A. Rachkovskij ; Denis Kleyko
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Here, we focus on sequences and propose their transformation to distributed representations that both preserve the similarity of identical sequence elements at nearby positions and are equivariant to the sequence shift.

10, TITLE: Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit
AUTHORS: Ion Stagkos Efstathiadis ; Guilherme Paulino-Passos ; Francesca Toni
CATEGORY: cs.CL [cs.CL, cs.AI, I.2.7; I.2.6]
HIGHLIGHT: Existing analysis of the forum focuses on its comments, and does not make the underlying data publicly available.

11, TITLE: Reasoning Like Program Executors
AUTHORS: XINYU PI et. al.
CATEGORY: cs.CL [cs.CL, cs.AI, cs.SC]
HIGHLIGHT: In this paper, we show three empirically powerful instances, i.e., POET-Math, POET-Logic, and POET-SQL.

12, TITLE: Highly Generalizable Models for Multilingual Hate Speech Detection
AUTHORS: Neha Deshpande ; Nicholas Farris ; Vidhur Kumar
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we introduce a study of multilingual hate speech classification.

13, TITLE: Human Interpretation of Saliency-based Explanation Over Text
AUTHORS: Hendrik Schuff ; Alon Jacovi ; Heike Adel ; Yoav Goldberg ; Ngoc Thang Vu
CATEGORY: cs.CL [cs.CL, cs.AI, cs.HC, cs.LG]
HIGHLIGHT: In this work, we focus on this question through a study of saliency-based explanations over textual data.

14, TITLE: Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
AUTHORS: Alon Albalak ; Sharon Levy ; William Yang Wang
CATEGORY: cs.CL [cs.CL, cs.IR]
HIGHLIGHT: In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19.

15, TITLE: Ontology-enhanced Prompt-tuning for Few-shot Learning
AUTHORS: HONGBIN YE et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this study, we explore knowledge injection for FSL with pre-trained language models and propose ontology-enhanced prompt-tuning (OntoPrompt).

16, TITLE: Yes-Yes-Yes: Donation-based Peer Reviewing Data Collection for ACL Rolling Review and Beyond
AUTHORS: Nils Dycke ; Ilia Kuznetsov ; Iryna Gurevych
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To address this, we present an in-depth discussion of peer reviewing data, outline the ethical and legal desiderata for peer reviewing data collection, and propose the first continuous, donation-based data collection workflow that meets these requirements.

17, TITLE: Learning How to Translate North Korean Through South Korean
AUTHORS: Hwichan Kim ; Sangwhan Moon ; Naoaki Okazaki ; Mamoru Komachi
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this study, we create data for North Korean NMT models using a comparable corpus.

18, TITLE: Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages
AUTHORS: Jivnesh Sandhan ; Ayush Daksh ; Om Adideva Paranjay ; Laxmidhar Behera ; Pawan Goyal
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Thus, we introduce Prabhupadavani, a multilingual code-mixed ST dataset for 25 languages, covering ten language families, containing 94 hours of speech by 130+ speakers, manually aligned with corresponding text in the target language.

19, TITLE: Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
AUTHORS: Jixuan Wang ; Kuan-Chieh Wang ; Frank Rudzicz ; Michael Brudno
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this work, we propose a novel conditional neural process-based approach for few-shot text classification that learns to transfer from other diverse tasks with rich annotation.

20, TITLE: IGLUE: A Benchmark for Transfer Learning Across Modalities, Tasks, and Languages
AUTHORS: EMANUELE BUGLIARELLO et. al.
CATEGORY: cs.CL [cs.CL, cs.CV]
HIGHLIGHT: To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark.

21, TITLE: DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence
AUTHORS: Wei Zhao ; Michael Strube ; Steffen Eger
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we introduce DiscoScore, a discourse metric with multiple variants, which uses BERT to model discourse coherence from different perspectives, driven by Centering theory.

22, TITLE: Tackling Data Scarcity in Speech Translation Using Zero-shot Multilingual Machine Translation Techniques
AUTHORS: Tu Anh Dinh ; Danni Liu ; Jan Niehues
CATEGORY: cs.CL [cs.CL, cs.SD, eess.AS, I.2.7]
HIGHLIGHT: In the related field of multilingual text translation, several techniques have been proposed for zero-shot translation.

23, TITLE: Pan More Gold from The Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation
AUTHORS: YIHE WANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we show existing open-domain dialogue generation methods by memorizing context-response paired data with causal or encode-decode language models underutilize the training data.

24, TITLE: Systematic Investigation of Strategies Tailored for Low-Resource Settings for Sanskrit Dependency Parsing
AUTHORS: Jivnesh Sandhan ; Laxmidhar Behera ; Pawan Goyal
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Thus, in this work, we investigate the following question: How far can we push a purely data-driven approach using recently proposed strategies for low-resource settings?

25, TITLE: A Higher-Order Semantic Dependency Parser
AUTHORS: Bin Li ; Yunlong Fan ; Yikemaiti Sataer ; Zhiqiang Gao
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Inspired by the success of GNNs, we investigate building a higher-order semantic dependency parser by applying GNNs.

26, TITLE: Learning Deep Semantic Model for Code Search Using CodeSearchNet Corpus
AUTHORS: Chen Wu ; Ming Yan
CATEGORY: cs.CL [cs.CL, cs.IR]
HIGHLIGHT: In this paper, we propose a novel deep semantic model which makes use of the utilities of not only the multi-modal sources, but also feature extractors such as self-attention, the aggregated vectors, combination of the intermediate representations.

27, TITLE: Two-Commodity Flow Is Equivalent to Linear Programming Under Nearly-Linear Time Reductions
AUTHORS: Ming Ding ; Rasmus Kyng ; Peng Zhang
CATEGORY: cs.CC [cs.CC, cs.DS, math.OC]
HIGHLIGHT: We give a nearly-linear time reduction that encodes any linear program as a 2-commodity flow problem with only a small blow-up in size.

28, TITLE: FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance
AUTHORS: Prateek Kate ; Vadlamani Ravi ; Akhilesh Gangwar
CATEGORY: cs.CE [cs.CE, cs.LG, cs.NE, 91-04, I.2.6]
HIGHLIGHT: In the first, we propose an oversampling method to generate synthetic samples of minority class using Generative Adversarial Network (GAN).

29, TITLE: Smart City Defense Game: Strategic Resource Management During Socio-Cyber-Physical Attacks
AUTHORS: Dimitrios Sikeridis ; Michael Devetsikiotis
CATEGORY: cs.GT [cs.GT, cs.AI]
HIGHLIGHT: In this paper, we propose an extensive form perfect information game to model interactions and optimal city resource allocations when a Terrorist Organization (TO) performs attacks on multiple targets across two conceptual SC levels, a physical, and a cyber-social.

30, TITLE: In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos
AUTHORS: DAVID BUTLER et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a Kalman filtering tracker that can work together with powerful, but efficient detectors, enabling the implementation of real-time polyp detectors.

31, TITLE: Domain Generalization in Deep Learning-based Mass Detection in Mammography: A Large-scale Multi-center Study
AUTHORS: LIDIA GARRUCHO et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG, 68T07, 68U10, 65D17]
HIGHLIGHT: In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting.

32, TITLE: Constrained Structure Learning for Scene Graph Generation
AUTHORS: Daqi Liu ; Miroslaw Bober ; Josef Kittler
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we present a constrained structure learning method, for which an explicit constrained variational inference objective is proposed.

33, TITLE: Deep Video Prior for Video Consistency and Propagation
AUTHORS: Chenyang Lei ; Yazhou Xing ; Hao Ouyang ; Qifeng Chen
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: To address this issue, we present a novel and general approach for blind video temporal consistency.

34, TITLE: Non-linear Motion Estimation for Video Frame Interpolation Using Space-time Convolutions
AUTHORS: Saikat Dutta ; Arulkumar Subramaniam ; Anurag Mittal
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In our paper, we propose to approximate the per-pixel motion using a space-time convolution network that is able to adaptively select the motion model to be used.

35, TITLE: RelTR: Relation Transformer for Scene Graph Generation
AUTHORS: Yuren Cong ; Michael Ying Yang ; Bodo Rosenhahn
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Inspired by DETR, which excels in object detection, we view scene graph generation as a set prediction problem and propose an end-to-end scene graph generation model RelTR which has an encoder-decoder architecture.

36, TITLE: Continuous Examination By Automatic Quiz Assessment Using Spiral Codes and Image Processing
AUTHORS: Fernando Alonso-Fernandez ; Josef Bigun
CATEGORY: cs.CV [cs.CV, cs.CY]
HIGHLIGHT: We describe a technical solution implemented at Halmstad University to automatise assessment and reporting of results of paper-based quiz exams.

37, TITLE: Revisiting RCAN: Improved Training for Image Super-Resolution
AUTHORS: ZUDI LIN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we revisit the popular RCAN model and examine the effect of different training options in SR.

38, TITLE: Generalised Image Outpainting with U-Transformer
AUTHORS: Penglei Gao ; Xi Yang ; Rui Zhang ; Kaizhu Huang ; Yujie Geng
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To this end, we develop a novel transformer-based generative adversarial network called U-Transformer able to extend image borders with plausible structure and details even for complicated scenery images.

39, TITLE: Interactive 3D Character Modeling from 2D Orthogonal Drawings with Annotations
AUTHORS: Zhengyu Huang ; Haoran Xie ; Tsukasa Fukusato
CATEGORY: cs.CV [cs.CV, cs.GR]
HIGHLIGHT: We propose an interactive 3D character modeling approach from orthographic drawings (e.g., front and side views) based on 2D-space annotations.

40, TITLE: Efficient Divide-and-conquer Registration of UAV and Ground LiDAR Point Clouds Through Canopy Shape Context
AUTHORS: JIE SHAO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Therefore, we use a divide-and-conquer strategy and propose an automated and efficient method to register ULS and ground LiDAR point clouds in forests.

41, TITLE: Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification
AUTHORS: FENG YANG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: This work offers a new strategy for learning powerful representations via a contrastive learning approach that can be embedded into any point cloud classification network.

42, TITLE: Eye-focused Detection of Bell's Palsy in Videos
AUTHORS: Sharik Ali Ansari ; Koteswar Rao Jerripothula ; Pragya Nagpal ; Ankush Mittal
CATEGORY: cs.CV [cs.CV, cs.AI, cs.MM, eess.IV]
HIGHLIGHT: In this paper, we present how Bell's Palsy, a neurological disorder, can be detected just from a subject's eyes in a video.

43, TITLE: Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
AUTHORS: XIN DU et. al.
CATEGORY: cs.CV [cs.CV, cs.LG, 62R07, I.4.0]
HIGHLIGHT: In this paper, we propose Vision Checklist, a framework aimed at interrogating the capabilities of a model in order to produce a report that can be used by a system designer for robustness evaluations.

44, TITLE: DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer
AUTHORS: Sanket Biswas ; Ayan Banerjee ; Josep Lladós ; Umapada Pal
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To advance the research in this direction, we present a transformer-based model for end-to-end segmentation of complex layouts in document images.

45, TITLE: An Analysis on Ensemble Learning Optimized Medical Image Classification with Deep Convolutional Neural Networks
AUTHORS: Dominik Müller ; Iñaki Soto-Rey ; Frank Kramer
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging.

46, TITLE: Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning Via Ranked Positives
AUTHORS: David T. Hoffmann ; Nadine Behrmann ; Juergen Gall ; Thomas Brox ; Mehdi Noroozi
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples.

47, TITLE: A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method
AUTHORS: Pouria Mehrabi
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments.

48, TITLE: Anomaly Detection in Retinal Images Using Multi-Scale Deep Feature Sparse Coding
AUTHORS: Sourya Dipta Das ; Saikat Dutta ; Nisarg A. Shah ; Dwarikanath Mahapatra ; Zongyuan Ge
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We have introduced an unsupervised approach for detecting anomalies in retinal images to overcome this issue.

49, TITLE: Head and Eye Egocentric Gesture Recognition for Human-robot Interaction Using Eyewear Cameras
AUTHORS: Javier Marina-Miranda ; V. Javier Traver
CATEGORY: cs.CV [cs.CV, cs.HC, cs.RO, I.4; I.5]
HIGHLIGHT: A motion-based recognition approach is proposed, which operates at two temporal granularities.

50, TITLE: Exploring Global Diversity and Local Context for Video Summarization
AUTHORS: YINGCHAO PAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In light of this phenomenon, we propose global diverse attention by using the squared Euclidean distance instead to compute the affinities.

51, TITLE: DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras
AUTHORS: Ashar Ali ; Upal Mahbub ; Gokce Dane ; Gerhard Reitmayr
CATEGORY: cs.CV [cs.CV, cs.HC, cs.RO, eess.IV, I.4.8; I.2.10; I.5]
HIGHLIGHT: In this paper, we present DIREG3D, a holistic framework for 3D Hand Tracking.

52, TITLE: Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
AUTHORS: QILONG ZHANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks).

53, TITLE: Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images
AUTHORS: PETER WASHINGTON et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: Here, we discuss the challenges and corresponding opportunities in this space, including handling heterogeneous data, avoiding biased models, labeling massive and repetitive data sets, working with ambiguous or compound class labels, managing privacy concerns, creating appropriate representations, and personalizing models.

54, TITLE: ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
AUTHORS: Yang Zhao ; Han Gao ; Peng Guo ; Zihao Sun
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, ResiDualGAN is proposed for RS images translation, where a resizer module is used for addressing the scale discrepancy of RS datasets, and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks.

55, TITLE: Effective Shortcut Technique for GAN
AUTHORS: Seung Park ; Cheol-Hwan Yoo ; Yong-Goo Shin
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we propose a novel shortcut method, called the gated shortcut, which not only embraces the strength point of the residual block but also further boosts the GAN performance.

56, TITLE: ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
AUTHORS: GYRI REIERSEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present ReforesTree, a benchmark dataset of forest carbon stock in six agro-forestry carbon offsetting sites in Ecuador.

57, TITLE: ASOC: Adaptive Self-aware Object Co-localization
AUTHORS: Koteswar Rao Jerripothula ; Prerana Mukherjee
CATEGORY: cs.CV [cs.CV, cs.MM, eess.IV]
HIGHLIGHT: The primary goal of this paper is to localize objects in a group of semantically similar images jointly, also known as the object co-localization problem.

58, TITLE: Dissecting The Impact of Different Loss Functions with Gradient Surgery
AUTHORS: Hong Xuan ; Robert Pless
CATEGORY: cs.CV [cs.CV, cs.AI, cs.IR, cs.LG]
HIGHLIGHT: The literature reports a large and growing set of variations of the pair-wise loss strategies.

59, TITLE: Transformer Module Networks for Systematic Generalization in Visual Question Answering
AUTHORS: Moyuru Yamada ; Vanessa D'Amario ; Kentaro Takemoto ; Xavier Boix ; Tomotake Sasaki
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: Inspired by Transformers and NMNs, we propose Transformer Module Network (TMN), a novel Transformer-based model for VQA that dynamically composes modules into a question-specific Transformer network.

60, TITLE: Dynamic Rectification Knowledge Distillation
AUTHORS: Fahad Rahman Amik ; Ahnaf Ismat Tasin ; Silvia Ahmed ; M. M. Lutfe Elahi ; Nabeel Mohammed
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: To eliminate the necessity for a cumbersome teacher model completely, we propose a simple yet effective knowledge distillation framework that we termed Dynamic Rectification Knowledge Distillation (DR-KD).

61, TITLE: Deep Confidence Guided Distance for 3D Partial Shape Registration
AUTHORS: Dvir Ginzburg ; Dan Raviv
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present a novel non-iterative learnable method for partial-to-partial 3D shape registration.

62, TITLE: Towards A Secure and Reliable Federated Learning Using Blockchain
AUTHORS: Hajar Moudoud ; Soumaya Cherkaoui ; Lyes Khoukhi
CATEGORY: cs.CR [cs.CR, cs.AI, cs.LG, cs.NI]
HIGHLIGHT: To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner.

63, TITLE: An Algorithmic Framework for Locally Constrained Homomorphisms
AUTHORS: Laurent Bulteau ; Konrad K. Dabrowski ; Noleen Köhler ; Sebastian Ordyniak ; Daniël Paulusma
CATEGORY: cs.DS [cs.DS, cs.CC, cs.DM, math.CO]
HIGHLIGHT: This paper fills this gap: we prove a number of new FPT, W[1]-hard and para-NP-complete results by considering a hierarchy of parameters of the guest graph $G$.

64, TITLE: Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrence
AUTHORS: Thomas Guyet ; Wenbin Zhang ; Albert Bifet
CATEGORY: cs.DB [cs.DB, cs.AI]
HIGHLIGHT: In this paper, we study such a new sequential pattern mining problem and propose a corresponding efficient sequential miner with novel strategies to prune search space efficiently.

65, TITLE: Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
AUTHORS: PENG WEI et. al.
CATEGORY: cs.IT [cs.IT, cs.AI, cs.NI, math.IT]
HIGHLIGHT: This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area.

66, TITLE: $c^3$-Local Testable Codes from Lossless Expanders
AUTHORS: Ting-Chun Lin ; Min-Hsiu Hsieh
CATEGORY: cs.IT [cs.IT, cs.CC, math.IT]
HIGHLIGHT: In this work, we construct a new LTC family using 1-sided lossless expanders and balanced products.

67, TITLE: On The Role of Multi-Objective Optimization to The Transit Network Design Problem
AUTHORS: Vasco D. Silva ; Anna Finamore ; Rui Henriques
CATEGORY: cs.LG [cs.LG, cs.AI, math.OC]
HIGHLIGHT: This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP).

68, TITLE: LiteLSTM Architecture for Deep Recurrent Neural Networks
AUTHORS: Nelly Elsayed ; Zag ElSayed ; Anthony S. Maida
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM using the weights sharing concept to reduce the overall architecture cost and maintain the architecture performance.

69, TITLE: DNNFuser: Generative Pre-Trained Transformer As A Generalized Mapper for Layer Fusion in DNN Accelerators
AUTHORS: Sheng-Chun Kao ; Xiaoyu Huang ; Tushar Krishna
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this work, we propose a mapper, DNNFuser, specifically focusing on this layer-fusion map-space.

70, TITLE: Quantile-Based Policy Optimization for Reinforcement Learning
AUTHORS: Jinyang Jiang ; Jiaqiao Hu ; Yijie Peng
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this work, we consider the RL setting where the goal is to optimize the quantile of the cumulative rewards.

71, TITLE: Synchromesh: Reliable Code Generation from Pre-trained Language Models
AUTHORS: GABRIEL POESIA et. al.
CATEGORY: cs.LG [cs.LG, cs.PL]
HIGHLIGHT: In this paper, we propose Synchromesh: a framework for substantially improving the reliability of pre-trained models for code generation.

72, TITLE: Gap Minimization for Knowledge Sharing and Transfer
AUTHORS: BOYU WANG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks.

73, TITLE: Achieving Personalized Federated Learning with Sparse Local Models
AUTHORS: TIANSHENG HUANG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, cs.DC]
HIGHLIGHT: In this work, we propose federated learning with personalized sparse mask (FedSpa), a novel PFL scheme that employs personalized sparse masks to customize sparse local models on the edge.

74, TITLE: A Systematic Study of Bias Amplification
AUTHORS: Melissa Hall ; Laurens van der Maaten ; Laura Gustafson ; Aaron Adcock
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: To enable this study, we design a simple image-classification problem in which we can tightly control (synthetic) biases.

75, TITLE: Model Agnostic Interpretability for Multiple Instance Learning
AUTHORS: Joseph Early ; Christine Evers ; Sarvapali Ramchurn
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this work, we establish the key requirements for interpreting MIL models.

76, TITLE: DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
AUTHORS: WEIJUN HONG et. al.
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: In this paper, we systematically study these problems and propose a novel grouped operation dropout algorithm named DropNAS to fix the problems with DARTS.

77, TITLE: Unsupervised Change Detection Using DRE-CUSUM
AUTHORS: Sudarshan Adiga ; Ravi Tandon
CATEGORY: cs.LG [cs.LG, cs.CV, cs.IT, math.IT]
HIGHLIGHT: This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available.

78, TITLE: Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
AUTHORS: Noam Razin ; Asaf Maman ; Nadav Cohen
CATEGORY: cs.LG [cs.LG, cs.AI, cs.NE, stat.ML]
HIGHLIGHT: Through a dynamical systems lens, we overcome challenges associated with hierarchy, and establish implicit regularization towards low hierarchical tensor rank.

79, TITLE: Generative Adversarial Exploration for Reinforcement Learning
AUTHORS: WEIJUN HONG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we propose a novel method called generative adversarial exploration (GAEX) to encourage exploration in RL via introducing an intrinsic reward output from a generative adversarial network, where the generator provides fake samples of states that help discriminator identify those less frequently visited states.

80, TITLE: Controlling Directions Orthogonal to A Classifier
AUTHORS: Yilun Xu ; Hao He ; Tianxiao Shen ; Tommi Jaakkola
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.

81, TITLE: To What Extent Should We Trust AI Models When They Extrapolate?
AUTHORS: Roozbeh Yousefzadeh ; Xuenan Cao
CATEGORY: cs.LG [cs.LG, cs.AI, math.OC]
HIGHLIGHT: We study this functional behavior in relation to the data used to create the models.

82, TITLE: Confidence May Cheat: Self-Training on Graph Neural Networks Under Distribution Shift
AUTHORS: HONGRUI LIU et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.

83, TITLE: Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification
AUTHORS: Jianpeng Liao ; Qian Tao ; Jun Yan
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: To develop a flexible and effective model for graph-based semi-supervised node classification, we propose a novel Density-Aware Hyper-Graph Neural Networks (DA-HGNN).

84, TITLE: Hyperparameter Tuning for Deep Reinforcement Learning Applications
AUTHORS: Mariam Kiran ; Melis Ozyildirim
CATEGORY: cs.LG [cs.LG, cs.NE, I.2.6; I.2.11]
HIGHLIGHT: In this paper, we propose a distributed variable-length genetic algorithm framework to systematically tune hyperparameters for various RL applications, improving training time and robustness of the architecture, via evolution.

85, TITLE: Foundations for Entailment Checking in Quantitative Separation Logic (extended Version)
AUTHORS: KEVIN BATZ et. al.
CATEGORY: cs.LO [cs.LO, cs.PL]
HIGHLIGHT: We give a generic reduction from entailment checking in QSL to entailment checking in SL.

86, TITLE: DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
AUTHORS: Sheng-Chun Kao ; Michael Pellauer ; Angshuman Parashar ; Tushar Krishna
CATEGORY: cs.NE [cs.NE, cs.AI]
HIGHLIGHT: To address this, in this paper, we propose a HW-Mapping co-optimization framework, an efficient encoding of the immense design space constructed by HW and Mapping, and a domain-aware genetic algorithm, named DiGamma, with specialized operators for improving search efficiency.

87, TITLE: Fast Moving Natural Evolution Strategy for High-Dimensional Problems
AUTHORS: Masahiro Nomura ; Isao Ono
CATEGORY: cs.NE [cs.NE, cs.LG, stat.ML]
HIGHLIGHT: In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box optimization problems.

88, TITLE: Error-driven Input Modulation: Solving The Credit Assignment Problem Without A Backward Pass
AUTHORS: Giorgia Dellaferrera ; Gabriel Kreiman
CATEGORY: cs.NE [cs.NE]
HIGHLIGHT: Here, we propose to replace the backward pass with a second forward pass in which the input signal is modulated based on the error of the network.

89, TITLE: Search Trajectories Networks of Multiobjective Evolutionary Algorithms
AUTHORS: Yuri Lavinas ; Claus Aranha ; Gabriela Ochoa
CATEGORY: cs.NE [cs.NE, cs.AI]
HIGHLIGHT: We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives.

90, TITLE: On The Mitigation of Read Disturbances in Neuromorphic Inference Hardware
AUTHORS: Ankita Paul ; Shihao Song ; Twisha Titirsha ; Anup Das
CATEGORY: cs.NE [cs.NE, cs.AR]
HIGHLIGHT: We propose a system software framework to incorporate such dependencies in programming model parameters on NVM cells of a neuromorphic hardware.

91, TITLE: Learning Stance Embeddings from Signed Social Graphs
AUTHORS: John Pougué-Biyong ; Akshay Gupta ; Aria Haghighi ; Ahmed El-Kishky
CATEGORY: cs.SI [cs.SI, cs.AI]
HIGHLIGHT: We propose the Stance Embeddings Model(SEM), which jointly learns embeddings for each user and topic in signed social graphs with distinct edge types for each topic.

92, TITLE: The MSXF TTS System for ICASSP 2022 ADD Challenge
AUTHORS: Chunyong Yang ; Pengfei Liu ; Yanli Chen ; Hongbin Wang ; Min Liu
CATEGORY: cs.SD [cs.SD, cs.AI, eess.AS]
HIGHLIGHT: This paper presents our MSXF TTS system for Task 3.1 of the Audio Deep Synthesis Detection (ADD) Challenge 2022.

93, TITLE: Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
AUTHORS: PIOTR ?ELASKO et. al.
CATEGORY: cs.SD [cs.SD, cs.CL, eess.AS]
HIGHLIGHT: The ultimate goal of our work is to build the phone inventory of a language unseen during training in an unsupervised way without any knowledge about the language.

94, TITLE: Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition
AUTHORS: Mohammad Soleymanpour ; Michael T. Johnson ; Rahim Soleymanpour ; Jeffrey Berry
CATEGORY: eess.AS [eess.AS, cs.CL, cs.SD]
HIGHLIGHT: In this paper, we aim to improve multi-speaker end-to-end TTS systems to synthesize dysarthric speech for improved training of a dysarthria-specific DNN-HMM ASR.

95, TITLE: Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks
AUTHORS: Dionne Takudzwa Chasi ; Mkhuseli Ngxande
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: Here, we investigate a way of improving the Multi-Frame Quality Enhancement approach.

96, TITLE: Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging
AUTHORS: MAJA SCHLERETH et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: The NMDs evaluated in this study often manifest in early childhood.

97, TITLE: Matched Illumination
AUTHORS: Yuteng Zhu ; Graham D. Finlayson
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this paper, we provide a novel way of creating the color filtering effect without making a physical filter: we modulate the spectrum of the light source by using a spectrally tunable lighting system to recast the prefiltering effect from a lighting perspective.

98, TITLE: HistoKT: Cross Knowledge Transfer in Computational Pathology
AUTHORS: RYAN ZHANG et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this paper, we take a data-centric approach to the transfer learning problem and examine the existence of generalizable knowledge between histopathological datasets.

99, TITLE: Few-shot Transfer Learning for Holographic Image Reconstruction Using A Recurrent Neural Network
AUTHORS: Luzhe Huang ; Xilin Yang ; Tairan Liu ; Aydogan Ozcan
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets.

100, TITLE: PRNU Based Source Camera Identification for Webcam and Smartphone Videos
AUTHORS: Fernando Martín-Rodríguez ; Fernando Isasi-de-Vicente
CATEGORY: eess.IV [eess.IV, cs.CV, I.4.9]
HIGHLIGHT: In this work we focus on video, first on webcam video and afterwards on smartphone video.

101, TITLE: Pan-Tumor CAnine CuTaneous Cancer Histology (CATCH) Dataset
AUTHORS: FRAUKE WILM et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: We present a publicly available dataset consisting of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes including seven cutaneous tumor subtypes.