OpenRaiser/Intern-Atlas huggingface dataset:
Software Supply Chain Map: How Reuse Networks Expand
Venue: arXiv cs.SE 2022
Year: 2022

Clone-and-own is a typical code reuse approach because of its simplicity and efficiency. Cloned software components are maintained independently by a new owner. These clone-and-own operations can be occurred sequentially, that is, cloned components can be cloned again and owned by other new owners on the supply chain. In general, code reuse is not documented well, consequently, appropriate changes like security patches cannot be propagated to descendant software projects. However, the OpenChain Project defined identifying and tracking source code reuses as responsibilities of FLOSS software staffs. Hence supporting source code reuse awareness is in a real need. This paper studies software reuse relations in FLOSS ecosystem. Technically, clone-and-own reuses of source code can be identified by file-level clone set detection. Since change histories are associated with files, we can determine origins and destinations in reusing across multiple software by considering times. By building software supply chain maps, we find that clone-and-own is prevalent in FLOSS development, and set of files are reused widely and repeatedly. These observations open up future challenges of maintaining and tracking global software genealogies.
Multilingual Event Linking to Wikidata
Venue: arXiv cs.CL 2022
Year: 2022

We present a task of multilingual linking of events to a knowledge base. We automatically compile a large-scale dataset for this task, comprising of 1.8M mentions across 44 languages referring to over 10.9K events from Wikidata. We propose two variants of the event linking task: 1) multilingual, where event descriptions are from the same language as the mention, and 2) crosslingual, where all event descriptions are in English. On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020). In our experiments on the two task variants, we find both biencoder and crossencoder models significantly outperform the BM25+ baseline. Our results also indicate that the crosslingual task is in general more challenging than the multilingual task. To test the out-of-domain generalization of the proposed linking systems, we additionally create a Wikinews-based evaluation set. We present qualitative analysis highlighting various aspects captured by the proposed dataset, including the need for temporal reasoning over context and tackling diverse event descriptions across languages.
Optimally Designing Cybersecurity Insurance Contracts to Encourage the Sharing of Medical Data
Venue: arXiv cs.GT 2022
Year: 2022

Though the sharing of medical data has the potential to lead to breakthroughs in health care, the sharing process itself exposes patients and health care providers to various risks. Patients face risks due to the possible loss in privacy or livelihood that can occur when medical data is stolen or used in non-permitted ways, whereas health care providers face risks due to the associated liability. For medical data, these risks persist even after anonymizing/deidentifying, according to the standards defined in existing legislation, the data sets prior to sharing, because shared medical data can often be deanonymized/reidentified using advanced artificial intelligence and machine learning methodologies. As a result, health care providers are hesitant to share medical data. One possible solution to encourage health care providers to responsibly share data is through the use of cybersecurity insurance contracts. This paper studies the problem of designing optimal cybersecurity insurance contracts, with the goal of encouraging the sharing of the medical data. We use a principal-agent model with moral hazard to model various scenarios, derive the optimal contract, discuss its implications, and perform numerical case studies. In particular, we consider two scenarios: the first scenario is where a health care provider is selling medical data to a technology firm who is developing an artificial intelligence algorithm using the shared data. The second scenario is where a group of health care providers share health data amongst themselves for the purpose of furthering medical research using the aggregated medical data.
Modularity benefits reinforcement learning agents with competing homeostatic drives
Venue: arXiv cs.LG 2022
Year: 2022

The problem of balancing conflicting needs is fundamental to intelligence. Standard reinforcement learning algorithms maximize a scalar reward, which requires combining different objective-specific rewards into a single number. Alternatively, different objectives could also be combined at the level of action value, such that specialist modules responsible for different objectives submit different action suggestions to a decision process, each based on rewards that are independent of one another. In this work, we explore the potential benefits of this alternative strategy. We investigate a biologically relevant multi-objective problem, the continual homeostasis of a set of variables, and compare a monolithic deep Q-network to a modular network with a dedicated Q-learner for each variable. We find that the modular agent: a) requires minimal exogenously determined exploration; b) has improved sample efficiency; and c) is more robust to out-of-domain perturbation.
Information and Communication Technology in Migration: A Framework for Applications, Customization, and Research
Venue: arXiv cs.CY 2022
Year: 2022

This paper addresses the role of Information and Communication Technology (ICT) in migration governance, support, and experience with particular attention to emerging technologies such as artificial intelligence, social media, and virtual reality. We propose a framework for technology use based on user groups and process types. We provide examples of using emerging technologies for migration-related tasks within the context of this framework. We then identify how such technologies can be applied to migration-related tasks, developed for customized use, and improved through research to add new features that can help different migration stakeholders. We suggest a series of possible directions for future research and development to take advantage of specific affordances of those emerging technologies more effectively.
A deep learning algorithm for reducing false positives in screening mammography
Venue: arXiv cs.CV 2022
Year: 2022

Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause unnecessary procedures, patient anxiety, and financial burden. This work demonstrates an AI algorithm that reduces false positives by identifying mammograms not suspicious for breast cancer. We trained the algorithm to determine the absence of cancer using 123,248 2D digital mammograms (6,161 cancers) and performed a retrospective study on 14,831 screening exams (1,026 cancers) from 15 US and 3 UK sites. Retrospective evaluation of the algorithm on the largest of the US sites (11,592 mammograms, 101 cancers) a) left the cancer detection rate unaffected (p=0.02, non-inferiority margin 0.25 cancers per 1000 exams), b) reduced callbacks for diagnostic exams by 31.1% compared to standard clinical readings, c) reduced benign needle biopsies by 7.4%, and d) reduced screening exams requiring radiologist interpretation by 41.6% in the simulated clinical workflow. This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.
Leveraging convergence behavior to balance conflicting tasks in multi-task learning
Venue: arXiv cs.LG 2022
Year: 2022

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjust the importance of each task during the backpropagation. The result of this method is to give more attention to the tasks that are diverging or that are not being benefited during the last iterations, allowing to ensure that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances.
Puzzles of Existential Generalisation from Type-theoretic Perspective
Venue: arXiv cs.LO 2022
Year: 2022

The present paper addresses several puzzles related to the Rule of Existential Generalization, (EG). In solution to these puzzles from the viewpoint of simple type theory, I distinguish (EG) from a modified Rule of Existential Quantifier Introduction which is derivable from (EG). Both these rules are often confused and both are considered as primitive but I show that (EG) itself is derivable from the proper Rule of Existential Quantifier Introduction. Moreover, the latter rule must be primitive in logical systems that treat both total and partial functions, for the universal and the existential quantifiers are not interdefinable in them. An appropriate natural deduction for such a system is deployed. The present logical system is simpler than the system recently proposed and applied by the present author. It utilises an adequate definition of substitution which is capable of handling not only a higher-order quantification, but also (hyper)intensional contexts.
Mortensen Logics
Venue: arXiv cs.LO 2022
Year: 2022

Mortensen introduced a connexive logic commonly known as 'M3V'. M3V is obtained by adding a special conditional to LP. Among its most notable features, besides its being connexive, M3V is negation-inconsistent and it validates the negation of every conditional. But Mortensen has also studied and applied extensively other non-connexive logics, for example, closed set logic, CSL, and a variant of Sette's logic, identified and called 'P2' by Marcos. In this paper, we analyze and compare systematically the connexive variants of CSL and P2, obtained by adding the M3V conditional to them. Our main observations are two. First, that the inconsistency of M3V is exacerbated in the connexive variant of closed set logic, while it is attenuated in the connexive variant of the Sette-like P2. Second, that the M3V conditional is, unlike other conditionals, "connexively stable", meaning that it remains connexive when combined with the main paraconsistent negations.
Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy
Venue: arXiv cs.ET 2022
Year: 2022

To improve predictive models for STEM applications, supplemental physics-based features computed from input parameters are introduced into single and multiple layers of a deep neural network (DNN). While many studies focus on informing DNNs with physics through differential equations or numerical simulation, much may be gained through integration of simplified relationships. To evaluate this hypothesis, a number of thin rectangular plates simply-supported on all edges are simulated for five materials. With plate dimensions and material properties as input features and fundamental natural frequency as the sole output, predictive performance of a purely data-driven DNN-based model is compared with models using additional inputs computed from simplified physical relationships among baseline parameters, namely plate weight, modulus of rigidity, and shear modulus. To better understand the benefit to model accuracy, these additional features are injected into various single and multiple DNN layers, and trained with four different dataset sizes. When these physics-enhanced models are evaluated against independent data of the same materials and similar dimensions to the training sets, supplementation with simplified physics-based parameters provides little reduction in prediction error over the baseline for models trained with dataset sizes of 60 and greater, although small improvement from 19.3% to 16.1% occurs when trained with a sparse size of 30. Conversely, notable accuracy gains occur when the independent test data is of material and dimensions not conforming to the training set. Specifically, when physics-enhanced data is injected into multiple DNN layers, reductions in error from 33.2% to 19.6%, 34.9% to 19.9%, 35.8% to 22.4%, and 43.0% to 28.4% are achieved for training dataset sizes of 261, 117, 60, and 30, respectively, demonstrating attainment of a degree of generalizability.
ViTOL: Vision Transformer for Weakly Supervised Object Localization
Venue: arXiv cs.CV 2022
Year: 2022

Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise. To alleviate the above challenges we introduce the following simple changes through our proposed method ViTOL. We leverage the vision-based transformer for self-attention and introduce a patch-based attention dropout layer (p-ADL) to increase the coverage of the localization map and a gradient attention rollout mechanism to generate class-dependent attention maps. We conduct extensive quantitative, qualitative and ablation experiments on the ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2 localization scores of 70.47% and 73.17% on the two datasets respectively. Code is available on https://github.com/Saurav-31/ViTOL
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression
Venue: arXiv cs.LG 2022
Year: 2022

Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performance computing systems such as public clouds. According to our theoretical findings, due to the cascading compression, the training process has considerable deterioration on the convergence performance. To overcome this limitation, we implement a sign-bit compression-based learning synchronization framework, Marsit. It prevents cascading compression via an elaborate bit-wise operation for unbiased sign aggregation and its specific global compensation mechanism for mitigating compression deviation. The proposed framework retains the same theoretical convergence rate as non-compression mechanisms. Experimental results demonstrate that Marsit reduces up to 35% training time while preserving the same accuracy as training without compression.
YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
Venue: arXiv cs.CV 2022
Year: 2022

We introduce YOLO-pose, a novel heatmap-free approach for joint detection, and 2D multi-person pose estimation in an image based on the popular YOLO object detection framework. Existing heatmap based two-stage approaches are sub-optimal as they are not end-to-end trainable and training relies on a surrogate L1 loss that is not equivalent to maximizing the evaluation metric, i.e. Object Keypoint Similarity (OKS). Our framework allows us to train the model end-to-end and optimize the OKS metric itself. The proposed model learns to jointly detect bounding boxes for multiple persons and their corresponding 2D poses in a single forward pass and thus bringing in the best of both top-down and bottom-up approaches. Proposed approach doesn't require the postprocessing of bottom-up approaches to group detected keypoints into a skeleton as each bounding box has an associated pose, resulting in an inherent grouping of the keypoints. Unlike top-down approaches, multiple forward passes are done away with since all persons are localized along with their pose in a single inference. YOLO-pose achieves new state-of-the-art results on COCO validation (90.2% AP50) and test-dev set (90.3% AP50), surpassing all existing bottom-up approaches in a single forward pass without flip test, multi-scale testing, or any other test time augmentation. All experiments and results reported in this paper are without any test time augmentation, unlike traditional approaches that use flip-test and multi-scale testing to boost performance. Our training codes will be made publicly available at https://github.com/TexasInstruments/edgeai-yolov5 and https://github.com/TexasInstruments/edgeai-yolox
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes
Venue: arXiv cs.CV 2022
Year: 2022

We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competitive results on perspective images, missing CNN architectures as well as training data that follows the distortion of omnidirectional images makes current approaches not applicable to our data. The method predicts bounding boxes of multiple persons directly in omnidirectional images without perspective transformation, which reduces overhead of pre- and post-processing and enables real-time performance. The basic idea is to utilize transfer learning to fine-tune CNNs trained on perspective images with data augmentation techniques for detection in omnidirectional images. We fine-tune two variants of Single Shot MultiBox detectors (SSDs). The first one uses Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1 FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12 datasets, specifically on class person. Random 90-degree rotation and random vertical flipping are used for data augmentation in addition to the methods proposed by original SSD. We reach an average precision (AP) of 67.3 % with moSSD and 74.9 % with resSSD onthe evaluation dataset. To enhance the fine-tuning process, we add a subset of HDA Person dataset and a subset of PIROPOdatabase and reduce the number of perspective images to PASCAL VOC07. The AP rises to 83.2 % for moSSD and 86.3 % for resSSD, respectively. The average inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD using Nvidia Quadro P6000. Our method is applicable to other CNN-based object detectors and can potentially generalize for detecting other objects in omnidirectional images.
An Energy Aware Clustering Scheme for 5G-enabled Edge Computing based IoMT Framework
Venue: arXiv cs.NI 2022
Year: 2022

In recent years, 5G network systems start to offer communication infrastructure for Internet of Things (IoT) applications, especially for health care service pro-viders. In smart health care systems, edge computing enabled Internet of Medical Things (IoMT) is an innovative technology to provide online health care monitor-ing facility to patients. Here, energy consumption, along with extending the lifespan of biosensor network, is a key concern. In this contribution, a Chicken Swarm Optimization algorithm, based on Energy Efficient Multi-objective clus-tering scheme is applied in the context of IoMT system. An effective fitness func-tion is designed for cluster head selection., using multiple objectives, such as re-sidual energy, queuing delay, communication cost, link quality and node centrali-ty. Simulated outcomes of the proposed scheme are compared with the existing schemes in terms of parameters such as cluster formation time, energy consump-tion, network lifetime, throughput and propagation delay.
To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?
Venue: arXiv cs.SE 2022
Year: 2022

Deep Learning (DL) models have been widely used to support code completion. These models, once properly trained, can take as input an incomplete code component (e.g., an incomplete function) and predict the missing tokens to finalize it. GitHub Copilot is an example of code recommender built by training a DL model on millions of open source repositories: The source code of these repositories acts as training data, allowing the model to learn "how to program". The usage of such a code is usually regulated by Free and Open Source Software (FOSS) licenses, that establish under which conditions the licensed code can be redistributed or modified. As of Today, it is unclear whether the code generated by DL models trained on open source code should be considered as "new" or as "derivative" work, with possible implications on license infringements. In this work, we run a large-scale study investigating the extent to which DL models tend to clone code from their training set when recommending code completions. Such an exploratory study can help in assessing the magnitude of the potential licensing issues mentioned before: If these models tend to generate new code that is unseen in the training set, then licensing issues are unlikely to occur. Otherwise, a revision of these licenses urges to regulate how the code generated by these models should be treated when used, for example, in a commercial setting. Highlights from our results show that ~$10% to ~0.1% of the predictions generated by a state-of-the-art DL-based code completion tool are Type-1 clones of instances in the training set, depending on the size of the predicted code. Long predictions are unlikely to be cloned.
Exact and approximate determination of the Pareto set using minimal correction subsets
Venue: arXiv cs.AI 2022
Year: 2022

Recently, it has been shown that the enumeration of Minimal Correction Subsets (MCS) of Boolean formulas allows solving Multi-Objective Boolean Optimization (MOBO) formulations. However, a major drawback of this approach is that most MCSs do not correspond to Pareto-optimal solutions. In fact, one can only know that a given MCS corresponds to a Pareto-optimal solution when all MCSs are enumerated. Moreover, if it is not possible to enumerate all MCSs, then there is no guarantee of the quality of the approximation of the Pareto frontier. This paper extends the state of the art for solving MOBO using MCSs. First, we show that it is possible to use MCS enumeration to solve MOBO problems such that each MCS necessarily corresponds to a Pareto-optimal solution. Additionally, we also propose two new algorithms that can find a (1 + {\varepsilon})-approximation of the Pareto frontier using MCS enumeration. Experimental results in several benchmark sets show that the newly proposed algorithms allow finding better approximations of the Pareto frontier than state-of-the-art algorithms, and with guaranteed approximation ratios.