OpenRaiser/Intern-Atlas huggingface dataset:
The Ghost of Performance Reproducibility Past
Venue: arXiv cs.DC 2022
Year: 2022

The importance of ensemble computing is well established. However, executing ensembles at scale introduces interesting performance fluctuations that have not been well investigated. In this paper, we trace our experience uncovering performance fluctuations of ensemble applications (primarily constituting a workflow of GROMACS tasks), and unsuccessful attempts, so far, at trying to discern the underlying cause(s) of performance fluctuations. Is the failure to discern the causative or contributing factors a failure of capability? Or imagination? Do the fluctuations have their genesis in some inscrutable aspect of the system or software? Does it warrant a fundamental reassessment and rethinking of how we assume and conceptualize performance reproducibility? Answers to these questions are not straightforward, nor are they immediate or obvious. We conclude with a discussion about the performance of ensemble applications and ruminate over the implications for how we define and measure application performance.
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
Venue: arXiv cs.LG 2022
Year: 2022

With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study, we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844-0.872 vs. 0.598-0.810).
Efficient Motion Modelling with Variable-sized blocks from Hierarchical Cuboidal Partitioning
Venue: arXiv cs.CV 2022
Year: 2022

Motion modelling with block-based architecture has been widely used in video coding where a frame is divided into fixed-sized blocks that are motion compensated independently. This often leads to coding inefficiency as fixed-sized blocks hardly align with the object boundaries. Although hierarchical block-partitioning has been introduced to address this, the increased number of motion vectors limits the benefit. Recently, approximate segmentation of images with cuboidal partitioning has gained popularity. Not only are the variable-sized rectangular segments (cuboids) readily amenable to block-based image/video coding techniques, but they are also capable of aligning well with the object boundaries. This is because cuboidal partitioning is based on a homogeneity constraint, minimising the sum of squared errors (SSE). In this paper, we have investigated the potential of cuboids in motion modelling against the fixed-sized blocks used in scalable video coding. Specifically, we have constructed motion-compensated current frame using the cuboidal partitioning information of the anchor frame in a group-of-picture (GOP). The predicted current frame has then been used as the base layer while encoding the current frame as an enhancement layer using the scalable HEVC encoder. Experimental results confirm 6.71%-10.90% bitrate savings on 4K video sequences.
Opinion Leader Detection in Online Social Networks Based on Output and Input Links
Venue: arXiv cs.SI 2022
Year: 2022

The understanding of how users in a network update their opinions based on their neighbours opinions has attracted a great deal of interest in the field of network science, and a growing body of literature recognises the significance of this issue. In this research paper, we propose a new dynamic model of opinion formation in directed networks. In this model, the opinion of each node is updated as the weighted average of its neighbours opinions, where the weights represent social influence. We define a new centrality measure as a social influence metric based on both influence and conformity. We measure this new approach using two opinion formation models: (i) the Degroot model and (ii) our own proposed model. Previously published research studies have not considered conformity, and have only considered the influence of the nodes when computing the social influence. In our definition, nodes with low in-degree and high out-degree that were connected to nodes with high out-degree and low in-degree had higher centrality. As the main contribution of this research, we propose an algorithm for finding a small subset of nodes in a social network that can have a significant impact on the opinions of other nodes. Experiments on real-world data demonstrate that the proposed algorithm significantly outperforms previously published state-of-the-art methods.
CJaFr-v3 : A Freely Available Filtered Japanese-French Aligned Corpus
Venue: arXiv cs.CL 2022
Year: 2022

We present a free Japanese-French parallel corpus. It includes 15M aligned segments and is obtained by compiling and filtering several existing resources. In this paper, we describe the existing resources, their quantity and quality, the filtering we applied to improve the quality of the corpus, and the content of the ready-to-use corpus. We also evaluate the usefulness of this corpus and the quality of our filtering by training and evaluating some standard MT systems with it.
Learning to Optimize: Balancing Two Conflict Metrics in MB-HTS Networks
Venue: arXiv cs.IT 2022
Year: 2022

For multi-beam high throughput (MB-HTS) geostationary (GEO) satellite networks, the congestion appears when user's demands cannot be fully satisfied. This paper boosts the system performance by formulating and solving the power allocation strategies under the congestion control to admit users. A new multi-objective optimization is formulated to balance the sum data throughput and the satisfied user set. After that, we come up with two different solutions, which efficiently tackle the multi-objective maximization problem: The model-based solution utilizes the weighted sum method to enhance the number of demand-satisfied users, whilst the supervised learning solution offers a low-computational complexity design by inheriting optimization structures as continuous mappings. Simulation results verify that our solutions effectively copes with the congestion and outperforms the data throughput demand than the other previous works.
Maximum $k$-Biplex Search on Bipartite Graphs: A Symmetric-BK Branching Approach
Venue: arXiv cs.DB 2022
Year: 2022

Enumerating maximal $k$-biplexes (MBPs) of a bipartite graph has been used for applications such as fraud detection. Nevertheless, there usually exists an exponential number of MBPs, which brings up two issues when enumerating MBPs, namely the effectiveness issue (many MBPs are of low values) and the efficiency issue (enumerating all MBPs is not affordable on large graphs). Existing proposals of tackling this problem impose constraints on the number of vertices of each MBP to be enumerated, yet they are still not sufficient (e.g., they require to specify the constraints, which is often not user-friendly, and cannot control the number of MBPs to be enumerated directly). Therefore, in this paper, we study the problem of finding $K$ MBPs with the most edges called MaxBPs, where $K$ is a positive integral user parameter. The new proposal well avoids the drawbacks of existing proposals. We formally prove the NP-hardness of the problem. We then design two branch-and-bound algorithms, among which, the better one called FastBB improves the worst-case time complexity to $O^*(γ_k^ n)$, where $O^*$ suppresses the polynomials, $γ_k$ is a real number that relies on $k$ and is strictly smaller than 2, and $n$ is the number of vertices in the graph. For example, for $k=1$, $γ_k$ is equal to $1.754$. We further introduce three techniques for boosting the performance of the branch-and-bound algorithms, among which, the best one called PBIE can further improve the time complexity to $O^*(γ_k^{d^3})$ for large sparse graphs, where $d$ is the maximum degree of the graph. We conduct extensive experiments on both real and synthetic datasets, and the results show that our algorithm is up to four orders of magnitude faster than all baselines and finding MaxBPs works better than finding all MBPs for a fraud detection application.
How Segregation Patterns Affect the Availability of Fair District Plans
Venue: arXiv cs.CY 2022
Year: 2022

We create 4200 synthetic cities which vary in percent minority population and their residential segregation patterns. Of these, 1200 are modeled on existing cities, and 3000 are rectangular grid cities. In each city, we consider single-member voting district plans for a hypothetical city council election. A fair district plan is defined as one where the number of minority-majority districts is proportional to the city-wide minority population. Thus each city is summarized by three traits: minority percent, a measure of segregation, and availability of a fair district plan. We find that when the minority population is around 25%-33%, there is a positive correlation between the degree of segregation and the availability of proportional district plan. Consistently, when the minority population lives in a more diffuse residential pattern, there are fewer available proportional district plans. Finally, we develop a new method to validate runtime and sample size of an ensemble of district plans created by the GerryChain software program.
Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
Venue: arXiv cs.CV 2022
Year: 2022

Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative depth estimation models often fail to recover accurate 3D scene shapes due to the unknown depth shift caused by training with the relative depth data. We tackle this problem here and attempt to estimate accurate scene shapes by training on large-scale relative depth data, and estimating the depth shift. To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes. As the two modules are trained separately, we do not need strictly paired training data. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to improve training with relative depth annotation. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot evaluation. Code is available at: https://git.io/Depth
Assessing the Impact of Execution Environment on Observation-Based Slicing
Venue: arXiv cs.SE 2022
Year: 2022

Program slicing reduces a program to a smaller version that retains a chosen computation, referred to as a slicing criterion. One recent multi-lingual slicing approach, observation-based slicing (ORBS), speculatively deletes parts of the program and then executes the code. If the behavior of the slicing criteria is unchanged, the speculative deletion is made permanent. While this makes ORBS language agnostic, it can lead to the production of some non-intuitive slices. One particular challenge is when the execution environment plays a role. For example, ORBS will delete the line "a = 0" if the memory location assigned to a contains zero before executing the statement, since deletion will not affect the value of a and thus the slicing criterion. Consequently, slices can differ between execution environments due to factors such as initialization and call stack reuse. The technique considered, nVORBS, attempts to ameliorate this problem by validating a candidate slice in n different execution environments. We conduct an empirical study to collect initial insights into how often the execution environment leads to slice differences. Specifically, we compare and contrast the slices produced by seven different instantiations of nVORBS. Looking forward, the technique can be seen as a variation on metamorphic testing, and thus suggests how ideas from metamorphic testing might be used to improve dynamic program analysis.
Predicting IMDb Rating of TV Series with Deep Learning: The Case of Arrow
Venue: arXiv cs.LG 2022
Year: 2022

Context: The number of TV series offered nowadays is very high. Due to its large amount, many series are canceled due to a lack of originality that generates a low audience. Problem: Having a decision support system that can show why some shows are a huge success or not would facilitate the choices of renewing or starting a show. Solution: We studied the case of the series Arrow broadcasted by CW Network and used descriptive and predictive modeling techniques to predict the IMDb rating. We assumed that the theme of the episode would affect its evaluation by users, so the dataset is composed only by the director of the episode, the number of reviews that episode got, the percentual of each theme extracted by the Latent Dirichlet Allocation (LDA) model of an episode, the number of viewers from Wikipedia and the rating from IMDb. The LDA model is a generative probabilistic model of a collection of documents made up of words. Method: In this prescriptive research, the case study method was used, and its results were analyzed using a quantitative approach. Summary of Results: With the features of each episode, the model that performed the best to predict the rating was Catboost due to a similar mean squared error of the KNN model but a better standard deviation during the test phase. It was possible to predict IMDb ratings with an acceptable root mean squared error of 0.55.
Multi-dimensional Racism Classification during COVID-19: Stigmatization, Offensiveness, Blame, and Exclusion
Venue: arXiv cs.CY 2022
Year: 2022

Transcending the binary categorization of racist texts, our study takes cues from social science theories to develop a multi-dimensional model for racism detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of BERT and topic modeling, this categorical detection enables insights into the underlying subtlety of racist discussion on digital platforms during COVID-19. Our study contributes to enriching the scholarly discussion on deviant racist behaviours on social media. First, a stage-wise analysis is applied to capture the dynamics of the topic changes across the early stages of COVID-19 which transformed from a domestic epidemic to an international public health emergency and later to a global pandemic. Furthermore, mapping this trend enables a more accurate prediction of public opinion evolvement concerning racism in the offline world, and meanwhile, the enactment of specified intervention strategies to combat the upsurge of racism during the global public health crisis like COVID-19. In addition, this interdisciplinary research also points out a direction for future studies on social network analysis and mining. Integration of social science perspectives into the development of computational methods provides insights into more accurate data detection and analytics.
Confidence Estimation for Object Detection in Document Images
Venue: arXiv cs.CV 2022
Year: 2022

Deep neural networks are becoming increasingly powerful and large and always require more labelled data to be trained. However, since annotating data is time-consuming, it is now necessary to develop systems that show good performance while learning on a limited amount of data. These data must be correctly chosen to obtain models that are still efficient. For this, the systems must be able to determine which data should be annotated to achieve the best results. In this paper, we propose four estimators to estimate the confidence of object detection predictions. The first two are based on Monte Carlo dropout, the third one on descriptive statistics and the last one on the detector posterior probabilities. In the active learning framework, the three first estimators show a significant improvement in performance for the detection of document physical pages and text lines compared to a random selection of images. We also show that the proposed estimator based on descriptive statistics can replace MC dropout, reducing the computational cost without compromising the performances.
DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy
Venue: arXiv cs.HC 2022
Year: 2022

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.
When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds
Venue: arXiv cs.NI 2022
Year: 2022

In recent years, the Internet of Things (IoT) is studied in the context of the Metaverse to provide users immersive cyber-virtual experiences in mixed reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.
Labeling of Cultural Heritage Collections on the Intersection of Visual Analytics and Digital Humanities
Venue: arXiv cs.HC 2022
Year: 2022

Engaging in interdisciplinary projects on the intersection between visualization and humanities research can be a challenging endeavor. Challenges can be finding valuable outcomes for both domains, or how to apply state-of-the-art visual analytics methods like supervised machine learning algorithms. We discuss these challenges when working with cultural heritage data. Further, there is a gap in applying these methods to intangible heritage. To give a reflection on some interdisciplinary projects, we present three case studies focusing on the labeling of cultural heritage collections, the problems and challenges with the data, the participatory design process, and takeaways for the visualization scholars from these collaborations.
MSWasm: Soundly Enforcing Memory-Safe Execution of Unsafe Code
Venue: arXiv cs.CR 2022
Year: 2022

Most programs compiled to WebAssembly (Wasm) today are written in unsafe languages like C and C++. Unfortunately, memory-unsafe C code remains unsafe when compiled to Wasm -- and attackers can exploit buffer overflows and use-after-frees in Wasm almost as easily as they can on native platforms. Memory-Safe WebAssembly (MSWasm) proposes to extend Wasm with language-level memory-safety abstractions to precisely address this problem. In this paper, we build on the original MSWasm position paper to realize this vision. We give a precise and formal semantics of MSWasm, and prove that well-typed MSWasm programs are, by construction, robustly memory safe. To this end, we develop a novel, language-independent memory-safety property based on colored memory locations and pointers. This property also lets us reason about the security guarantees of a formal C-to-MSWasm compiler -- and prove that it always produces memory-safe programs (and preserves the semantics of safe programs). We use these formal results to then guide several implementations: Two compilers of MSWasm to native code, and a C-to-MSWasm compiler (that extends Clang). Our MSWasm compilers support different enforcement mechanisms, allowing developers to make security-performance trade-offs according to their needs. Our evaluation shows that the overhead of enforcing memory safety in software ranges from 22% (enforcing spatial safety alone) to 198% (enforcing full memory safety) on the PolyBenchC suite. More importantly, MSWasm's design makes it easy to swap between enforcement mechanisms; as fast (especially hardware-based) enforcement techniques become available, MSWasm will be able to take advantage of these advances almost for free.
Simple Rigs Hold Fast
Venue: arXiv cs.DC 2022
Year: 2022

An important use of computational systems is updating the state of an object while preserving some set of invariants. That object might be a file, a row in a database, or perhaps an entry in a distributed system. Its invariants may place limits on its relationships with other objects, and generally include maintaining a unique identity across updates. The system maintaining these invariants is responsible for not equivocating, or presenting conflicting versions of an object. We call this absence of equivocation integrity. Today the system providing integrity to an object also manages its state. Indeed, it has long been assumed that this must be the case, and that only the system managing an object's state can provide it with integrity. We show that this assumption is wrong. Objects can maintain the integrity of one system while having their state fully managed by another.
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
Venue: arXiv cs.CV 2022
Year: 2022

Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks. The current trend is to move towards ever larger models and pretraining datasets. This computational headlong rush does not seem reasonable in the long term to move toward sustainable solutions, and de facto excludes academic laboratories with limited resources. In this work, we propose a new framework, dubbed ViCHA, that efficiently exploits the input data to boost the learning by: (a) a new hierarchical cross-modal alignment loss, (b) new self-supervised scheme based on masked image modeling, (c) leveraging image-level annotations, called Visual Concepts, obtained with existing foundation models such as CLIP to boost the performance of the image encoder. Although pretrained on four times less data, our ViCHA strategy outperforms other approaches on several downstream tasks such as Image-Text Retrieval, VQA, Visual Reasoning, Visual Entailment and Visual Grounding. The code will be made publicly available here: https://github.com/mshukor/ViCHA