OpenRaiser/Intern-Atlas huggingface dataset:
Online Nash Social Welfare Maximization with Predictions
Venue: arXiv cs.GT 2021
Year: 2021

We consider the problem of allocating a set of divisible goods to $N$ agents in an online manner, aiming to maximize the Nash social welfare, a widely studied objective which provides a balance between fairness and efficiency. The goods arrive in a sequence of $T$ periods and the value of each agent for a good is adversarially chosen when the good arrives. We first observe that no online algorithm can achieve a competitive ratio better than the trivial $O(N)$, unless it is given additional information about the agents' values. Then, in line with the emerging area of "algorithms with predictions", we consider a setting where for each agent, the online algorithm is only given a prediction of her monopolist utility, i.e., her utility if all goods were given to her alone (corresponding to the sum of her values over the $T$ periods). Our main result is an online algorithm whose competitive ratio is parameterized by the multiplicative errors in these predictions. The algorithm achieves a competitive ratio of $O(\log N)$ and $O(\log T)$ if the predictions are perfectly accurate. Moreover, the competitive ratio degrades smoothly with the errors in the predictions, and is surprisingly robust: the logarithmic competitive ratio holds even if the predictions are very inaccurate. We complement this positive result by showing that our bounds are essentially tight: no online algorithm, even if provided with perfectly accurate predictions, can achieve a competitive ratio of $O(\log^{1-ε} N)$ or $O(\log^{1-ε} T)$ for any constant $ε>0$.
Semantic scene synthesis: Application to assistive systems
Venue: arXiv cs.CV 2021
Year: 2021

The aim of this work is to provide a semantic scene synthesis from a single depth image. This is used in assistive aid systems for visually impaired and blind people that allow them to understand their surroundings by the touch sense. The fact that blind people use touch to recognize objects and rely on listening to replace sight, motivated us to propose this work. First, the acquired depth image is segmented and each segment is classified in the context of assistive systems using a deep learning network. Second, inspired by the Braille system and the Japanese writing system Kanji, the obtained classes are coded with semantic labels. The scene is then synthesized using these labels and the extracted geometric features. Our system is able to predict more than 17 classes only by understanding the provided illustrative labels. For the remaining objects, their geometric features are transmitted. The labels and the geometric features are mapped on a synthesis area to be sensed by the touch sense. Experiments are conducted on noisy and incomplete data including acquired depth images of indoor scenes and public datasets. The obtained results are reported and discussed.
Analysing Networks of Networks
Venue: arXiv cs.SI 2021
Year: 2021

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors' social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.
A Large-scale Study of Security Vulnerability Support on Developer Q&A Websites
Venue: arXiv cs.SE 2021
Year: 2021

Context: Security Vulnerabilities (SVs) pose many serious threats to software systems. Developers usually seek solutions to addressing these SVs on developer Question and Answer (Q&A) websites. However, there is still little known about on-going SV-specific discussions on different developer Q&A sites. Objective: We present a large-scale empirical study to understand developers' SV discussions and how these discussions are being supported by Q&A sites. Method: We first curate 71,329 SV posts from two large Q&A sites, namely Stack Overflow (SO) and Security StackExchange (SSE). We then use topic modeling to uncover the topics of SV-related discussions and analyze the popularity, difficulty, and level of expertise for each topic. We also perform a qualitative analysis to identify the types of solutions to SV-related questions. Results: We identify 13 main SV discussion topics on Q&A sites. Many topics do not follow the distributions and trends in expert-based security sources such as Common Weakness Enumeration (CWE) and Open Web Application Security Project (OWASP). We also discover that SV discussions attract more experts to answer than many other domains, but some difficult SV topics (e.g., Vulnerability Scanning Tools) still receive quite limited support from experts. Moreover, we identify seven key types of answers given to SV questions on Q&A sites, in which SO often provides code and instructions, while SSE usually gives experience-based advice and explanations. Conclusion: Our findings provide support for researchers and practitioners to effectively acquire, share and leverage SV knowledge on Q&A sites.
Economical Visual Attention Test for Elderly Drivers
Venue: arXiv cs.HC 2021
Year: 2021

Traffic accidents involving elderly drivers are an issue in a super-aging society. A quick and low-cost aptitude test is required to reduce the number of traffic accidents. This study proposed an oddball-serial visual search task that assesses the individual's performance by his or her responses to the presence of cued stimuli on the screen. Task difficulty varied by changing the number of simultaneous stimuli; Accordingly, low performers were detected. In addition, performance correlated with age. This implies that individual characteristics related to driving performance that decline with age can be detected by the proposed task. Since the task requires low-cost devices (computer and response button), it is feasible for use as a quick and low-cost aptitude test for elderly drivers.
Layout and Image Recognition Driving Cross-Platform Automated Mobile Testing
Venue: arXiv cs.SE 2021
Year: 2021

The fragmentation problem has extended from Android to different platforms, such as iOS, mobile web, and even mini-programs within some applications (app). In such a situation, recording and replaying test scripts is a popular automated mobile app testing approaches. But such approach encounters severe problems when crossing platforms. Different versions of the same app need to be developed to support different platforms relying on different platform supports. Therefore, mobile app developers need to develop and maintain test scripts for multiple platforms aimed at completely the same test requirements, greatly increasing testing costs. However, we discover that developers adopt highly similar user interface layouts for versions of the same app on different platforms. Such a phenomenon inspires us to replay test scripts from the perspective of similar UI layouts. We propose an image-driven mobile app testing framework, utilizing Widget Feature Matching and Layout Characterization Matching. We use computer vision technologies to perform UI feature comparison and layout hierarchy extraction on app screenshots to obtain UI structures with rich contextual information, including coordinates, relative relationship, etc. Based on acquired UI structures, we can form a platform-independent test script, and then locate the target widgets under test. Thus, the proposed framework non-intrusively replays test scripts according to a novel platform-independent test script model. We also design and implement a tool named LIT to devote the proposed framework into practice, based on which, we conduct an empirical study to evaluate the effectiveness and usability of the proposed testing framework. Results show that the overall replay accuracy reaches around 63.39% on Android (14% improvement over state-of-the-art approaches) and 21.83% on iOS (98% improvement over state-of-the-art approaches).
Compression of Deep Learning Models for Text: A Survey
Venue: arXiv cs.CL 2021
Year: 2021

In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanksto deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer [120] based models like Bidirectional Encoder Representations from Transformers (BERT) [24], GenerativePre-training Transformer (GPT-2) [94], Multi-task Deep Neural Network (MT-DNN) [73], Extra-Long Network (XLNet) [134], Text-to-text transfer transformer (T5) [95], T-NLG [98] and GShard [63]. But these models are humongous in size. On the other hand,real world applications demand small model size, low response times and low computational power wattage. In this survey, wediscuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, andSub-quadratic Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects.Given the critical need of building applications with efficient and small models, and the large amount of recently published work inthis area, we believe that this survey organizes the plethora of work done by the 'deep learning for NLP' community in the past fewyears and presents it as a coherent story.
Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting
Venue: arXiv cs.AI 2021
Year: 2021

OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Despite the previous success of object analysis, detecting and segmenting a large number of object categories with a long-tailed data distribution remains a challenging problem and is less investigated. For a large-vocabulary classifier, the chance of obtaining noisy logits is much higher, which can easily lead to a wrong recognition. In this paper, we exploit prior knowledge of the relations among object categories to cluster fine-grained classes into coarser parent classes, and construct a classification tree that is responsible for parsing an object instance into a fine-grained category via its parent class. In the classification tree, as the number of parent class nodes are significantly less, their logits are less noisy and can be utilized to suppress the wrong/noisy logits existed in the fine-grained class nodes. As the way to construct the parent class is not unique, we further build multiple trees to form a classification forest where each tree contributes its vote to the fine-grained classification. To alleviate the imbalanced learning caused by the long-tail phenomena, we propose a simple yet effective resampling method, NMS Resampling, to re-balance the data distribution. Our method, termed as Forest R-CNN, can serve as a plug-and-play module being applied to most object recognition models for recognizing more than 1000 categories. Extensive experiments are performed on the large vocabulary dataset LVIS. Compared with the Mask R-CNN baseline, the Forest R-CNN significantly boosts the performance with 11.5% and 3.9% AP improvements on the rare categories and overall categories, respectively. Moreover, we achieve state-of-the-art results on the LVIS dataset. Code is available at https://github.com/JialianW/Forest_RCNN.
Prestige of scholarly book publishers: an investigation into criteria, processes, and practices across countries
Venue: arXiv cs.DL 2021
Year: 2021

Numerous national research assessment policies set the goal of promoting "excellence" and incentivise scholars to publish their research in the most prestigious journals or with the most prestigious book publishers. We investigate the practicalities of the assessment of book outputs based on the prestige of book publishers (Denmark, Finland, Flanders, Lithuania, Norway). Additionally, we test whether such judgments are transparent and yield consistent results. We show inconsistencies in the levelling of publishers, such as the same publisher being ranked as prestigious and not-so-prestigious in different states or in consequent years within the same country. Likewise, we find that verification of compliance with the mandatory prerequisites is not always possible because of the lack of transparency. Our findings support doubts about whether the assessment of books based on a judgement about their publisher yields acceptable outcomes. Currently used rankings of publishers focus on evaluating the gatekeeping role of publishers but do not assess other essential stages in scholarly book publishing. Our suggestion for future research is to develop approaches to evaluate books by accounting for the value added to every book at every publishing stage, which is vital for the quality of book outputs from research assessment and scholarly communication perspectives.
Topology optimization considering the distortion in additive manufacturing
Venue: arXiv cs.CE 2021
Year: 2021

Additive manufacturing is a free-form manufacturing technique in which parts are built in a layer-by-layer manner. Laser powder bed fusion is one of the popular techniques used to fabricate metal parts. However, it induces residual stress and distortion during fabrication that adversely affects the mechanical properties and dimensional accuracy of the manufactured parts. Therefore, predicting and avoiding the residual stress and distortion are critical issues. In this study, we propose a topology optimization method that accounts for the distortion. First, we propose a computationally inexpensive analytical model for additive manufacturing that uses laser powder bed fusion and formulated an optimization problem. Next, we approximate the topological derivative of the objective function using an adjoint variable method that is then utilized to update the level set function via a time evolutionary reaction-diffusion equation. Finally, the validity and effectiveness of the proposed optimization method was established using two-dimensional design examples.
Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
Venue: arXiv cs.AI 2021
Year: 2021

With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting game results (win predictions). While this task has great potential in various applications, such as esports live streaming and game commentator AI systems, previous studies fail to investigate the methods to interpret these win predictions. To mitigate this issue, we collected a large-scale dataset that contains real-time game records with rich input features of the popular MOBA game Honor of Kings. For interpretable predictions, we proposed a Two-Stage Spatial-Temporal Network (TSSTN) that can not only provide accurate real-time win predictions but also attribute the ultimate prediction results to the contributions of different features for interpretability. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
Feature Extraction Functions for Neural Logic Rule Learning
Venue: arXiv cs.AI 2021
Year: 2021

Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output. In this paper, we propose feature extracting functions for integrating human knowledge abstracted as logic rules into the predictive behavior of a neural network. These functions are embodied as programming functions, which represent the applicable domain knowledge as a set of logical instructions and provide a modified distribution of independent features on input data. Unlike other existing neural logic approaches, the programmatic nature of these functions implies that they do not require any kind of special mathematical encoding, which makes our method very general and flexible in nature. We illustrate the performance of our approach for sentiment classification and compare our results to those obtained using two baselines.
Heterogeneous Interventions Reduce the Spread of COVID-19 in Simulations on Real Mobility Data
Venue: arXiv cs.SI 2021
Year: 2021

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large-scale lockdowns of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.
Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance
Venue: arXiv cs.CL 2021
Year: 2021

Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of healthcare organization. We measured SciBERT diagnosis sentiment classifier generalizability between medical specialties using EHR sentences from MIMIC-III. Models trained on one specialty performed better on internal test sets than mixed or external test sets (mean AUCs 0.92, 0.87, and 0.83, respectively; p = 0.016). When models are trained on more specialties, they have better test performances (p < 1e-4). Model performance on new corpora is directly correlated to the similarity between train and test sentence content (p < 1e-4). Future studies should assess additional axes of generalization to ensure deep learning models fulfil their intended purpose across institutions, specialties, and practices.
How to build your own ASP-based system?!
Venue: arXiv cs.AI 2021
Year: 2021

Answer Set Programming (ASP) has become a popular and quite sophisticated approach to declarative problem solving. This is arguably due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. Unlike this, the high degree of sophistication of the underlying technology makes it increasingly hard for ASP experts to put ideas into practice. For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system CLINGO can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how meta programming can be used for extending ASP. This is a rather light approach that relies on CLINGO's reification feature to use ASP itself for expressing new functionalities. Unlike this, the major part of this tutorial uses traditional programming (in PYTHON) for manipulating CLINGO via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is CLINGO's new Application class that allows us to draw on CLINGO's infrastructure by customizing processes similar to the one in CLINGO. For instance, we may engage manipulations to programs' abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. Another cross-sectional structure, spanning meta as well as application programming, is CLINGO's intermediate format, ASPIF, that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several non-trivial case-studies.
On the Power of Automata Minimization in Reactive Synthesis
Venue: arXiv cs.FL 2021
Year: 2021

Temporal logic is often used to describe temporal properties in AI applications. The most popular language for doing so is Linear Temporal Logic (LTL). Recently, LTL on finite traces, LTLf, has been investigated in several contexts. In order to reason about LTLf, formulas are typically compiled into deterministic finite automata (DFA), as the intermediate semantic representation. Moreover, due to the fact that DFAs have canonical representation, efficient minimization algorithms can be applied to maximally reduce DFA size, helping to speed up subsequent computations. Here, we present a thorough investigation on two classical minimization algorithms, namely, the Hopcroft and Brzozowski algorithms. More specifically, we show how to apply these algorithms to semi-symbolic (explicit states, symbolic transition functions) automata representation. We then compare the two algorithms in the context of an LTLf-synthesis framework, starting from LTLf formulas. While earlier studies on comparing the two algorithms starting from randomly-generated automata concluded that neither algorithm dominates, our results suggest that starting from LTLf formulas, Hopcroft's algorithm is the best choice in the context of reactive synthesis. Deeper analysis explains why the supposed advantage of Brzozowski's algorithm does not materialize in practice.