OpenRaiser/Intern-Atlas huggingface dataset:
Robust Quantification of Gender Disparity in Pre-Modern English Literature using Natural Language Processing
Venue: arXiv cs.CY 2022
Year: 2022

Research has continued to shed light on the extent and significance of gender disparity in social, cultural and economic spheres. More recently, computational tools from the Natural Language Processing (NLP) literature have been proposed for measuring such disparity using relatively extensive datasets and empirically rigorous methodologies. In this paper, we contribute to this line of research by studying gender disparity, at scale, in copyright-expired literary texts published in the pre-modern period (defined in this work as the period ranging from the mid-nineteenth through the mid-twentieth century). One of the challenges in using such tools is to ensure quality control, and by extension, trustworthy statistical analysis. Another challenge is in using materials and methods that are publicly available and have been established for some time, both to ensure that they can be used and vetted in the future, and also, to add confidence to the methodology itself. We present our solution to addressing these challenges, and using multiple measures, demonstrate the significant discrepancy between the prevalence of female characters and male characters in pre-modern literature. The evidence suggests that the discrepancy declines when the author is female. The discrepancy seems to be relatively stable as we plot data over the decades in this century-long period. Finally, we aim to carefully describe both the limitations and ethical caveats associated with this study, and others like it.
Scratch as Social Network: Topic Modeling and Sentiment Analysis in Scratch Projects
Venue: arXiv cs.SE 2022
Year: 2022

Societal matters like the Black Lives Matter (BLM) movement influence software engineering, as the recent debate on replacing certain discriminatory terms such as whitelist/blacklist has shown. Identifying relevant and trending societal matters is important, and often done using social network analysis for traditional social media channels such as twitter. In this paper we explore whether this type of analysis can also be used for introspection of the software world, by looking at the thriving scene of young Scratch programmers. The educational programming language Scratch is not only used for teaching programming concepts, but offers a platform for young programmers to express and share their creativity on any topics of relevance. By analyzing titles and project comments in a dataset of 106.032 Scratch projects, we explore which topics are common in the Scratch community, whether socially relevant events are reflected and how how the sentiment in the comments is. It turns out that the diversity of topics within the Scratch projects make the analysis process challenging. Our results nevertheless show that topics from pop and net culture in particular are present, and even recent societal events such as the Covid-19 pandemic or BLM are to some extent reflected in Scratch. The tone in the comments is mostly positive with catchy youth language. Hence, despite the challenges, Scratch projects can be studied in the same way as social networks, which opens up new possibilities to improve our understanding of the behavior and motivation of novice programmers.
Do Loyal Users Enjoy Better Recommendations? Understanding Recommender Accuracy from a Time Perspective
Venue: arXiv cs.IR 2022
Year: 2022

In academic research, recommender systems are often evaluated on benchmark datasets, without much consideration about the global timeline. Hence, we are unable to answer questions like: Do loyal users enjoy better recommendations than non-loyal users? Loyalty can be defined by the time period a user has been active in a recommender system, or by the number of historical interactions a user has. In this paper, we offer a comprehensive analysis of recommendation results along global timeline. We conduct experiments with five widely used models, i.e., BPR, NeuMF, LightGCN, SASRec and TiSASRec, on four benchmark datasets, i.e., MovieLens-25M, Yelp, Amazon-music, and Amazon-electronic. Our experiment results give an answer "No" to the above question. Users with many historical interactions suffer from relatively poorer recommendations. Users who stay with the system for a shorter time period enjoy better recommendations. Both findings are counter-intuitive. Interestingly, users who have recently interacted with the system, with respect to the time point of the test instance, enjoy better recommendations. The finding on recency applies to all users, regardless of users' loyalty. Our study offers a different perspective to understand recommender accuracy, and our findings could trigger a revisit of recommender model design. The code is available in \url{https://github.com/putatu/recommenderLoyalty.
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes an automatic image co-segmentation algorithm based on deep reinforcement learning (RL). Existing co-segmentation tasks mainly rely on deep learning methods, and the obtained foreground edges are often rough. In order to obtain more precise foreground edges, we use deep RL to solve this problem and achieve the finer segmentation. To our best knowledge, this is the first work to apply RL methods to co-segmentation. We define the problem as a Markov Decision Process (MDP) and optimize it by RL with asynchronous advantage actor-critic (A3C). The RL image co-segmentation network uses the correlation between images to segment common and salient objects from a set of related images. In order to achieve automatic segmentation, our RL-CoSeg method eliminates user's hints. For the image co-segmentation problem, we propose a collaborative RL algorithm based on the A3C model. We propose a Siamese RL co-segmentation network structure to obtain the co-attention of images for co-segmentation. We improve the self-attention for automatic RL algorithm to obtain long-distance dependence and enlarge the receptive field. The image feature information obtained by self-attention can be used to supplement the deleted user's hints and help to obtain more accurate actions. Experimental results have shown that our method can improve the performance effectively on both coarse and fine initial segmentations, and it achieves the state-of-the-art performance on Internet dataset, iCoseg dataset and MLMR-COS dataset.
S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems
Venue: arXiv cs.SE 2022
Year: 2022

Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study on four open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and Mozilla, shows that taking into account the schedules of the developers decreases the average bug fixing times. We find that S-DABT leads to a high level of developer utilization through a fair distribution of the tasks among the developers and efficient use of the free spots in their schedules. Via the simulation of the issue tracking system, we also show how incorporating the schedule in the model formulation reduces the bug fixing time, improves the assignment accuracy, and utilizes the capability of each developer without much comprising in the model run times. We find that S-DABT decreases the complexity of the bug dependency graph by prioritizing blocking bugs and effectively reduces the infeasible assignment ratio due to bug dependencies. Consequently, we recommend considering developers' schedules while automating bug triage.
CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022
Venue: arXiv cs.CL 2022
Year: 2022

In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022. We explore strategies to utilize an offline model in a simultaneous setting without the need to modify the original model. In our experiments, we show that our onlinization algorithm is almost on par with the offline setting while being $3\times$ faster than offline in terms of latency on the test set. We also show that the onlinized offline model outperforms the best IWSLT2021 simultaneous system in medium and high latency regimes and is almost on par in the low latency regime. We make our system publicly available.
Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability
Venue: arXiv cs.LO 2022
Year: 2022

We describe our implementation of downset-manipulating algorithms used to solve the realizability problem for linear temporal logic (LTL). These algorithms were introduced by Filiot et al.~in the 2010s and implemented in the tools Acacia and Acacia+ in C and Python. We identify degrees of freedom in the original algorithms and provide a complete rewriting of Acacia in C++20 articulated around genericity and leveraging modern techniques for better performances. These techniques include compile-time specialization of the algorithms, the use of SIMD registers to store vectors, and several preprocessing steps, some relying on efficient Binary Decision Diagram (BDD) libraries. We also explore different data structures to store downsets. The resulting tool is competitive against comparable modern tools.
On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance
Venue: arXiv cs.SI 2022
Year: 2022

For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower's historical data is scarce or not available. This study aims to understand the creditworthiness assessment performance dynamics and how it is influenced by the credit history, repayment behavior, and social network features. To accomplish this, we introduced a machine learning classification framework to analyze 97.000 individuals and companies from the moment they obtained their first loan to 12 months afterward. Our novel and massive dataset allow us to characterize each borrower according to their credit behavior, and social and economic relationships. Our research shows that borrowers' history increases performance at a decreasing rate during the first six months and then stabilizes. The most notable effect on perfomance of social networks features occurs at loan application; in personal scoring, this effect prevails a few months, while in business scoring adds value throughout the study period. These findings are of great value to improve credit risk management and optimize the use of traditional information and alternative data sources.
Impossible Triangle: What's Next for Pre-trained Language Models?
Venue: arXiv cs.CL 2022
Year: 2022

Recent development of large-scale pre-trained language models (PLM) have significantly improved the capability of models in various NLP tasks, in terms of performance after task-specific fine-tuning and zero-shot / few-shot learning. However, many of such models come with a dauntingly huge size that few institutions can afford to pre-train, fine-tune or even deploy, while moderate-sized models usually lack strong generalized few-shot learning capabilities. In this paper, we first elaborate the current obstacles of using PLM models in terms of the Impossible Triangle: 1) moderate model size, 2) state-of-the-art few-shot learning capability, and 3) state-of-the-art fine-tuning capability. We argue that all existing PLM models lack one or more properties from the Impossible Triangle. To remedy these missing properties of PLMs, various techniques have been proposed, such as knowledge distillation, data augmentation and prompt learning, which inevitably brings additional work to the application of PLMs in real scenarios. We then offer insights into future research directions of PLMs to achieve the Impossible Triangle, and break down the task into several key phases.
Modular and Didactic Compiler Design with XML Inter-Phases Communication
Venue: arXiv cs.PL 2022
Year: 2022

In Compiler Design courses, students learn how a program written in high level programming language and designed for humans understanding is systematically converted into low level assembly language understood by machines, through different representations. This article presents the design, educative characteristics and possibilities of a modular and didactic compiler for a Pascal-like programming mini-language that is super-set of Niklaus Wirth's PL/0. The main feature is that it implements the compiling phases in such a way that the information delivered to each next one may be reflected as an XML document, which can be studied separately. It is also shown that its design is suitable for being included as learning tool into compiler design courses. It is possible to implement a compiler in a high-level language like Python.
Deep Learning Model with GA based Feature Selection and Context Integration
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that assiminlate or learns independently global and local contextual information alongside visual features. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models.
Rate Splitting Multiple Access Aided Mobile Edge Computing in Cognitive Radio Networks
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we investigate rate splitting multiple access (RSMA) aided mobile edge computing (MEC) in a cognitive radio network. We propose a RSMA scheme that enables the secondary user to offload tasks to the MEC server utilizing dynamic rate splitting without deteriorating the primary user's offloading. The expressions for the optimal rate splitting parameters that maximize the achievable rate for the secondary user and successful computation probability of the proposed RSMA scheme are derived in closed-form. We formulate a problem to maximize successful computation probability by jointly optimizing task offloading ratio and task offloading time and obtain the optimal solutions in closed-form. Simulation results clarify that the proposed RSMA scheme achieves a higher successful computation probability than the existing non-orthogonal multiple access scheme.
Can Question Rewriting Help Conversational Question Answering?
Venue: arXiv cs.CL 2022
Year: 2022

Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA. We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.
Distributed Coalgebraic Partition Refinement
Venue: arXiv cs.DS 2022
Year: 2022

Partition refinement is a method for minimizing automata and transition systems of various types. Recently, a new partition refinement algorithm and associated tool CoPaR were developed that are generic in the transition type of the input system and match the theoretical run time of the best known algorithms for many concrete system types. Genericity is achieved by modelling transition types as functors on sets and systems as coalgebras. Experimentation has shown that memory consumption is a bottleneck for handling systems with a large state space, while running times are fast. We have therefore extended an algorithm due to Blom and Orzan, which is suitable for a distributed implementation to the coalgebraic level of genericity, and implemented it in CoPaR. Experiments show that this allows to handle much larger state spaces. Running times are low in most experiments, but there is a significant penalty for some.
What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data
Venue: arXiv cs.RO 2022
Year: 2022

A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this paper, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language. Codebase and trained models available at http://hulc.cs.uni-freiburg.de
TangoBERT: Reducing Inference Cost by using Cascaded Architecture
Venue: arXiv cs.CL 2022
Year: 2022

The remarkable success of large transformer-based models such as BERT, RoBERTa and XLNet in many NLP tasks comes with a large increase in monetary and environmental cost due to their high computational load and energy consumption. In order to reduce this computational load in inference time, we present TangoBERT, a cascaded model architecture in which instances are first processed by an efficient but less accurate first tier model, and only part of those instances are additionally processed by a less efficient but more accurate second tier model. The decision of whether to apply the second tier model is based on a confidence score produced by the first tier model. Our simple method has several appealing practical advantages compared to standard cascading approaches based on multi-layered transformer models. First, it enables higher speedup gains (average lower latency). Second, it takes advantage of batch size optimization for cascading, which increases the relative inference cost reductions. We report TangoBERT inference CPU speedup on four text classification GLUE tasks and on one reading comprehension task. Experimental results show that TangoBERT outperforms efficient early exit baseline models; on the the SST-2 task, it achieves an accuracy of 93.9% with a CPU speedup of 8.2x.
COCTEAU: an Empathy-Based Tool for Decision-Making
Venue: arXiv cs.HC 2022
Year: 2022

Traditional approaches to data-informed policymaking are often tailored to specific contexts and lack strong citizen involvement and collaboration, which are required to design sustainable policies. We argue the importance of empathy-based methods in the policymaking domain given the successes in diverse settings, such as healthcare and education. In this paper, we introduce COCTEAU (Co-Creating The European Union), a novel framework built on the combination of empathy and gamification to create a tool aimed at strengthening interactions between citizens and policy-makers. We describe our design process and our concrete implementation, which has already undergone preliminary assessments with different stakeholders. Moreover, we briefly report pilot results from the assessment. Finally, we describe the structure and goals of our demonstration regarding the newfound formats and organizational aspects of academic conferences.
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images
Venue: arXiv cs.CV 2022
Year: 2022

Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms. This article introduces the Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active material discrimination. ADVIS selects high-purity, high-density pixels that are far in diffusion distance (a data-dependent metric) from other high-purity, high-density pixels in the hyperspectral image. The ground truth labels of these pixels are queried and propagated to the rest of the image. The ADVIS active learning algorithm is shown to strongly outperform its fully unsupervised clustering algorithm counterpart, suggesting that the incorporation of a very small number of carefully-selected ground truth labels can result in substantially superior material discrimination in hyperspectral images.
Recognition of Freely Selected Keypoints on Human Limbs
Venue: arXiv cs.CV 2022
Year: 2022

Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually annotated and the model needs to be retrained. Our approach leverages the Vision Transformer architecture to extend the capability of the model to detect arbitrary keypoints on the limbs of persons. We propose two different approaches to encode the desired keypoints. (1) Each keypoint is defined by its position along the line between the two enclosing keypoints from the fixed set and its relative distance between this line and the edge of the limb. (2) Keypoints are defined as coordinates on a norm pose. Both approaches are based on the TokenPose architecture, while the keypoint tokens that correspond to the fixed keypoints are replaced with our novel module. Experiments show that our approaches achieve similar results to TokenPose on the fixed keypoints and are capable of detecting arbitrary keypoints on the limbs.