OpenRaiser/Intern-Atlas huggingface dataset:
JITA4DS: Disaggregated execution of Data Science Pipelines between the Edge and the Data Centre
Venue: arXiv cs.DC 2021
Year: 2021

This paper targets the execution of data science (DS) pipelines supported by data processing, transmission and sharing across several resources executing greedy processes. Current data science pipelines environments provide various infrastructure services with computing resources such as general-purpose processors (GPP), Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Tensor Processing Unit (TPU) coupled with platform and software services to design, run and maintain DS pipelines. These one-fits-all solutions impose the complete externalization of data pipeline tasks. However, some tasks can be executed in the edge, and the backend can provide just in time resources to ensure ad-hoc and elastic execution environments. This paper introduces an innovative composable "Just in Time Architecture" for configuring DCs for Data Science Pipelines (JITA-4DS) and associated resource management techniques. JITA-4DS is a cross-layer management system that is aware of both the application characteristics and the underlying infrastructures to break the barriers between applications, middleware/operating system, and hardware layers. Vertical integration of these layers is needed for building a customizable Virtual Data Center (VDC) to meet the dynamically changing data science pipelines' requirements such as performance, availability, and energy consumption. Accordingly, the paper shows an experimental simulation devoted to run data science workloads and determine the best strategies for scheduling the allocation of resources implemented by JITA-4DS.
Handling Inconsistencies in Tables with Nulls and Functional Dependencies
Venue: arXiv cs.DB 2021
Year: 2021

In this paper we address the problem of handling inconsistencies in tables with missing values (also called nulls) and functional dependencies. Although the traditional view is that table instances must respect all functional dependencies imposed on them, it is nevertheless relevant to develop theories about how to handle instances that violate some dependencies. Regarding missing values, we make no assumptions on their existence: a missing value exists only if it is inferred from the functional dependencies of the table. We propose a formal framework in which each tuple of a table is associated with a truth value among the following: true, false, inconsistent or unknown; and we show that our framework can be used to study important problems such as consistent query answering, table merging, and data quality measures - to mention just a few. In this paper, however, we focus mainly on consistent query answering, a problem that has received considerable attention during the last decades. The main contributions of the paper are the following: (a) we introduce a new approach to handle inconsistencies in a table with nulls and functional dependencies, (b) we give algorithms for computing all true, inconsistent and false tuples, (c) we investigate the relationship between our approach and Four-valued logic in the context of data merging, and (d) we give a novel solution to the consistent query answering problem and compare our solution to that of table repairs.
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence
Venue: arXiv cs.CR 2021
Year: 2021

Artificial Intelligence (AI) and Machine Learning (ML) algorithms can support the span of indicator-level, e.g. anomaly detection, to behavioral level cyber security modeling and inference. This contribution is based on a dataset named BRON which is amalgamated from public threat and vulnerability behavioral sources. We demonstrate how BRON can support prediction of related threat techniques and attack patterns. We also discuss other AI and ML uses of BRON to exploit its behavioral knowledge.
Generation of High-Order Coarse Quad Meshes on CAD Models via Integer Linear Programming
Venue: arXiv cs.CE 2021
Year: 2021

We propose an end-to-end pipeline to robustly generate high-quality, high-order and coarse quadrilateral meshes on CAD models. This kind of mesh enables the use of high-order analysis techniques such as high-order finite element methods or isogeometric analysis. An initial unstructured mesh is generated; this mesh contains a low number of irregular vertices but these are not necessarily aligned, causing a very dense quad layout. A T-mesh is built on the mesh which allows to modify its topology by assigning new integer lengths to the T-mesh arcs. The task of simplifying the quad layout can be formulated as an Integer Linear Program which is solved efficiently using an adequate solver. Finally, a high-order quad mesh is extracted from the optimized topology. Validation on several CAD models shows that our approach is fast, robust, strictly respects the CAD features, and achieves interesting results in terms of coarseness and quality.
A Hypothesis for the Aesthetic Appreciation in Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts. In order to verify this hypothesis, we use multi-variate interactions to represent salient concepts and inessential concepts contained in images. Furthermore, we design a set of operations to revise images towards more beautiful ones. In experiments, we find that the revised images are more aesthetic than the original ones to some extent.
Accelerating XOR-based Erasure Coding using Program Optimization Techniques
Venue: arXiv cs.PL 2021
Year: 2021

Erasure coding (EC) affords data redundancy for large-scale systems. XOR-based EC is an easy-to-implement method for optimizing EC. This paper addresses a significant performance gap between the state-of-the-art XOR-based EC approach (with 4.9 GB/s coding throughput) and Intel's high-performance EC library based on another approach (with 6.7 GB/s). We propose a novel approach based on our observation that XOR-based EC virtually generates programs of a Domain Specific Language for XORing byte arrays. We formalize such programs as straight-line programs (SLPs) of compiler construction and optimize SLPs using various optimization techniques. Our optimization flow is three-fold: 1) reducing operations using grammar compression algorithms; 2) reducing memory accesses using deforestation, a functional program optimization method; and 3) reducing cache misses using the (red-blue) pebble game of program analysis. We provide an experimental library, which outperforms Intel's library with 8.92 GB/s throughput.
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty
Venue: arXiv cs.LG 2021
Year: 2021

Safety and robustness are two desired properties for any reinforcement learning algorithm. CMDPs can handle additional safety constraints and RMDPs can perform well under model uncertainties. In this paper, we propose to unite these two frameworks resulting in robust constrained MDPs (RCMDPs). The motivation is to develop a framework that can satisfy safety constraints while also simultaneously offer robustness to model uncertainties. We develop the RCMDP objective, derive gradient update formula to optimize this objective and then propose policy gradient based algorithms. We also independently propose Lyapunov based reward shaping for RCMDPs, yielding better stability and convergence properties.
Improved Retrieval of Programming Solutions With Code Examples Using a Multi-featured Score
Venue: arXiv cs.SE 2021
Year: 2021

Developers often depend on code search engines to obtain solutions for their programming tasks. However, finding an expected solution containing code examples along with their explanations is challenging due to several issues. There is a vocabulary mismatch between the search keywords (the query) and the appropriate solutions. Semantic gap may increase for similar bag of words due to antonyms and negation. Moreover, documents retrieved by search engines might not contain solutions containing both code examples and their explanations. So, we propose CRAR (Crowd Answer Recommender) to circumvent those issues aiming at improving retrieval of relevant answers from Stack Overflow containing not only the expected code examples for the given task but also their explanations. Given a programming task, we investigate the effectiveness of combining information retrieval techniques along with a set of features to enhance the ranking of important threads (i.e., the units containing questions along with their answers) for the given task and then selects relevant answers contained in those threads, including semantic features, like word embeddings and sentence embeddings, for instance, a Convolutional Neural Network (CNN). CRAR also leverages social aspects of Stack Overflow discussions like popularity to select relevant answers for the tasks. Our experimental evaluation shows that the combination of the different features performs better than each one individually. We also compare the retrieval performance with the state-of-art CROKAGE (Crowd Knowledge Answer Generator), which is also a system aimed at retrieving relevant answers from Stack Overflow. We show that CRAR outperforms CROKAGE in Mean Reciprocal Rank and Mean Recall with small and medium effect sizes, respectively.
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator
Venue: arXiv cs.LG 2021
Year: 2021

In this paper we envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles, through a drone traffic monitor (DTM), that also acts as an orchestrator. Expecting non-IID data distribution, we focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle. This can be done through proper allocation of the wireless resources for addressing learner and data heterogeneity. Thus, we propose a reactive method for the allocation of wireless resources, that happens dynamically each FL round, and is based on each learner's contribution to the general model. In addition to this, we explore the use of static methods that remain constant across all rounds. Since we expect partial work from each learner, we use the FedProx FL algorithm, in the task of computer vision. For testing, we construct a non-IID data distribution of the MNIST and FMNIST datasets among four types of learners, in scenarios that represent the quickly changing environment. The results show that proactive measures are effective and versatile at improving system accuracy, and quickly learning the CO class when underrepresented in the network. Furthermore, the experiments show a tradeoff between FedProx intensity and resource allocation efforts. Nonetheless, a well adjusted FedProx local optimizer allows for an even better overall accuracy, particularly when using deeper neural network (NN) implementations.
Sketch Your Own GAN
Venue: arXiv cs.CV 2021
Year: 2021

Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and specialized knowledge in deep learning. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. In this work, we present a method, GAN Sketching, for rewriting GANs with one or more sketches, to make GANs training easier for novice users. In particular, we change the weights of an original GAN model according to user sketches. We encourage the model's output to match the user sketches through a cross-domain adversarial loss. Furthermore, we explore different regularization methods to preserve the original model's diversity and image quality. Experiments have shown that our method can mold GANs to match shapes and poses specified by sketches while maintaining realism and diversity. Finally, we demonstrate a few applications of the resulting GAN, including latent space interpolation and image editing.
Quantum Continual Learning Overcoming Catastrophic Forgetting
Venue: arXiv cs.LG 2021
Year: 2021

Catastrophic forgetting describes the fact that machine learning models will likely forget the knowledge of previously learned tasks after the learning process of a new one. It is a vital problem in the continual learning scenario and recently has attracted tremendous concern across different communities. In this paper, we explore the catastrophic forgetting phenomena in the context of quantum machine learning. We find that, similar to those classical learning models based on neural networks, quantum learning systems likewise suffer from such forgetting problem in classification tasks emerging from various application scenes. We show that based on the local geometrical information in the loss function landscape of the trained model, a uniform strategy can be adapted to overcome the forgetting problem in the incremental learning setting. Our results uncover the catastrophic forgetting phenomena in quantum machine learning and offer a practical method to overcome this problem, which opens a new avenue for exploring potential quantum advantages towards continual learning.
GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena
Venue: arXiv cs.CL 2021
Year: 2021

Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked. These cross-linguistic differences can lead to ambiguities that are difficult to resolve, especially for sentence-level MT systems. The identification of ambiguity and its subsequent resolution is a challenging task for which currently there aren't any specific resources or challenge sets available. In this paper, we introduce gENder-IT, an English--Italian challenge set focusing on the resolution of natural gender phenomena by providing word-level gender tags on the English source side and multiple gender alternative translations, where needed, on the Italian target side.
Enterprise Analytics using Graph Database and Graph-based Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

In a business-to-business (B2B) customer relationship management (CRM) use case, each client is a potential business organization/company with a solid business strategy and focused and rational decisions. This paper introduces a graph-based analytics approach to improve CRM within a B2B environment. In our approach, in the first instance, we have designed a graph database using the Neo4j platform. Secondly, the graph database has been investigated by using data mining and exploratory analysis coupled with cypher graph query language. Specifically, we have applied the graph convolution network (GCN) to enable CRM analytics to forecast sales. This is the first step towards a GCN-based binary classification based on graph databases in the domain of B2B CRM. We evaluate the performance of the proposed GCN model on graph databases and compare it with Random Forest (RF), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN). The proposed GCN approach is further augmented with the shortest path and eigenvector centrality attribute to significantly improve the accuracy of sales prediction. Experimental results reveal that the proposed graph-based deep learning approach outperforms the Random Forests (RsF) and two deep learning models, i.e., CNN and ANN under different combinations of graph features.
A Data Augmented Approach to Transfer Learning for Covid-19 Detection
Venue: arXiv cs.CV 2021
Year: 2021

Covid-19 detection at an early stage can aid in an effective treatment and isolation plan to prevent its spread. Recently, transfer learning has been used for Covid-19 detection using X-ray, ultrasound, and CT scans. One of the major limitations inherent to these proposed methods is limited labeled dataset size that affects the reliability of Covid-19 diagnosis and disease progression. In this work, we demonstrate that how we can augment limited X-ray images data by using Contrast limited adaptive histogram equalization (CLAHE) to train the last layer of the pre-trained deep learning models to mitigate the bias of transfer learning for Covid-19 detection. We transfer learned various pre-trained deep learning models including AlexNet, ZFNet, VGG-16, ResNet-18, and GoogLeNet, and fine-tune the last layer by using CLAHE-augmented dataset. The experiment results reveal that the CLAHE-based augmentation to various pre-trained deep learning models significantly improves the model efficiency. The pre-trained VCG-16 model with CLAHEbased augmented images achieves a sensitivity of 95% using 15 epochs. AlexNet works show good sensitivity when trained on non-augmented data. Other models demonstrate a value of less than 60% when trained on non-augmented data. Our results reveal that the sample bias can negatively impact the performance of transfer learning which is significantly improved by using CLAHE-based augmentation.
Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets -- Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset.
VinaFood21: A Novel Dataset for Evaluating Vietnamese Food Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Vietnam is such an attractive tourist destination with its stunning and pristine landscapes and its top-rated unique food and drink. Among thousands of Vietnamese dishes, foreigners and native people are interested in easy-to-eat tastes and easy-to-do recipes, along with reasonable prices, mouthwatering flavors, and popularity. Due to the diversity and almost all the dishes have significant similarities and the lack of quality Vietnamese food datasets, it is hard to implement an auto system to classify Vietnamese food, therefore, make people easier to discover Vietnamese food. This paper introduces a new Vietnamese food dataset named VinaFood21, which consists of 13,950 images corresponding to 21 dishes. We use 10,044 images for model training and 6,682 test images to classify each food in the VinaFood21 dataset and achieved an average accuracy of 74.81% when fine-tuning CNN EfficientNet-B0. (https://github.com/nguyenvd-uit/uit-together-dataset)
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
Venue: arXiv cs.CV 2021
Year: 2021

This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the source domain data are labelled, but with few-shot per category, while the target domain data are unlabelled. To address the FS-UDA setting, we develop a general UDA model to solve the following two key issues: the few-shot labeled data per category and the domain adaptation between support and query sets. Our model is general in that once trained it will be able to be applied to various FS-UDA tasks from the same source and target domains. Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation. By proposing a novel concept called similarity patterns (SPs), our model not only effectively considers the spatial relationship of LDs that was ignored in previous FSL methods, but also makes the learned image similarity better serve the required domain alignment. Specifically, we propose a novel IMage-to-class sparse Similarity Encoding (IMSE) method. It learns SPs to extract the local discriminative information for classification and meanwhile aligns the covariance matrix of the SPs for domain adaptation. Also, domain adversarial training and multi-scale local feature matching are performed upon LDs. Extensive experiments conducted on a multi-domain benchmark dataset DomainNet demonstrates the state-of-the-art performance of our IMSE for the novel setting of FS-UDA. In addition, for FSL, our IMSE can also show better performance than most of recent FSL methods on miniImageNet.
Verifying Time Complexity of Binary Search using Dafny
Venue: arXiv cs.SE 2021
Year: 2021

Formal software verification techniques are widely used to specify and prove the functional correctness of programs. However, nonfunctional properties such as time complexity are usually carried out with pen and paper. Inefficient code in terms of time complexity may cause massive performance problems in large-scale complex systems. We present a proof of concept for using the Dafny verification tool to specify and verify the worst-case time complexity of binary search. This approach can also be used for academic purposes as a new way to teach algorithms and complexity.
Explaining Counterexamples with Giant-Step Assertion Checking
Venue: arXiv cs.LO 2021
Year: 2021

Identifying the cause of a proof failure during deductive verification of programs is hard: it may be due to an incorrectness in the program, an incompleteness in the program annotations, or an incompleteness of the prover. The changes needed to resolve a proof failure depend on its category, but the prover cannot provide any help on the categorisation. When using an SMT solver to discharge a proof obligation, that solver can propose a model from a failed attempt, from which a possible counterexample can be derived. But the counterexample may be invalid, in which case it may add more confusion than help. To check the validity of a counterexample and to categorise the proof failure, we propose the comparison between the run-time assertion-checking (RAC) executions under two different semantics, using the counterexample as an oracle. The first RAC execution follows the normal program semantics, and a violation of a program annotation indicates an incorrectness in the program. The second RAC execution follows a novel "giant-step" semantics that does not execute loops nor function calls but instead retrieves return values and values of modified variables from the oracle. A violation of the program annotations only observed under giant-step execution characterises an incompleteness of the program annotations. We implemented this approach in the Why3 platform for deductive program verification and evaluated it using examples from prior literature.