OpenRaiser/Intern-Atlas huggingface dataset:
Boolean Reasoning-Based Biclustering for Shifting Pattern Extraction
Venue: arXiv cs.LG 2021
Year: 2021

Biclustering is a powerful approach to search for patterns in data, as it can be driven by a function that measures the quality of diverse types of patterns of interest. However, due to its computational complexity, the exploration of the search space is usually guided by an algorithmic strategy, sometimes introducing random factors that simplify the computational cost (e.g. greedy search or evolutionary computation). Shifting patterns are specially interesting as they account constant fluctuations in data, i.e. they capture situations in which all the values in the pattern move up or down for one dimension maintaining the range amplitude for all the dimensions. This behaviour is very common in nature, e.g. in the analysis of gene expression data, where a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. Boolean reasoning was recently revealed as an appropriate methodology to address the search for constant biclusters. In this work, this direction is extended to search for more general biclusters that include shifting patterns. The mathematical foundations are described in order to associate Boolean concepts with shifting patterns, and the methodology is presented to show that the induction of shifting patterns by means of Boolean reasoning is due to the ability of finding all inclusion--maximal δ-shifting patterns. Experiments with a real dataset show the potential of our approach at finding biclusters with δ-shifting patterns, which have been evaluated with the mean squared residue (MSR), providing an excellent performance at finding results very close to zero.
Catalogs of C and Python Antipatterns by CS1 Students
Venue: arXiv cs.CY 2021
Year: 2021

Understanding students' programming misconceptions is critical. Doing so depends on identifying the reasons why students make errors when learning a new programming language. Knowing the misconceptions can help students to improve their reflection about their mistakes and also help instructors to design better teaching strategies. In this technical report, we propose catalogs of antipatterns for two programming languages: C and Python. To accomplish this, we analyzed the codes of 166 CS1 engineering students when they were coding solutions to programming exercises. In our results, we catalog 41 CS1 antipatterns from 95 cataloged misconceptions in C and Python. These antipatterns were separated into three catalogs: C, Python, and antipatterns found in code using both programming languages. For each antipattern, we present code examples, students' solutions (if they are present), a possible solution to avoid the antipattern, among other information.
The Cardan grille approach to the Voynich MS taken to the next level
Venue: arXiv cs.CY 2021
Year: 2021

The Voynich MS is an illustrated 15th century manuscript, whose text is written in an unknown alphabet, which has not been translated until today. In 2004 Gordon Rugg published a paper in which he proposed that this text is likely to be meaningless, and could have been composed by an alternative application of a so-called Cardan Grille, namely by moving a piece of cardboard with holes over a large table of word fragments, and writing down the words that thus appear. This paper caused considerable discussion in the circles of people interested in the Voynich MS text, but it has not found many followers, even until today. The present paper takes a closer look at the mechanics of this method. Based on this, a more generic method is proposed, which is considerably simpler, both to set up and to execute. It is shown that the unusual word length distribution of the Voynich MS, which is very close to binomial, could be a consequence of the application of such a method. Furthermore, it is argued that this method could not only be used to create meaningless text, but also to encode meaningful text. A first high-level analysis looks at whether such a method could indeed have been applied to create the Voynich MS text, but this is certainly far from conclusive. The main aim of this paper is to inspire further research of the Voynich MS text into a new direction that has not yet been explored in great detail.
Flexible Educational Software Architecture
Venue: arXiv cs.CY 2021
Year: 2021

EAs.LiT is an e-assessment management and analysis software for which contextual requirements and usage scenarios changed over time. Based on these factors and further development activities, the decision was made to adopt a microservice architecture for EAs.LiT version 2 in order to increase its flexibility to adapt to new and changed circumstances. This architectural style and a few adopted technologies, like RDF as a data format, enabled an eased implementation of various use cases. Thus we consider the microservice architecture productive and recommend it for usage in other educational projects. The specific architecture of EAs.LiT version 2 is presented within this article, targeting to enable other educational projects to adopt it by using our work as a foundation or template.
EduPal leaves no professor behind: Supporting faculty via a peer-powered recommender system
Venue: arXiv cs.CY 2021
Year: 2021

The swift transitions in higher education after the COVID-19 outbreak identified a gap in the pedagogical support available to faculty. We propose a smart, knowledge-based chatbot that addresses issues of knowledge distillation and provides faculty with personalized recommendations. Our collaborative system crowdsources useful pedagogical practices and continuously filters recommendations based on theory and user feedback, thus enhancing the experiences of subsequent peers. We build a prototype for our local STEM faculty as a proof concept and receive favorable feedback that encourages us to extend our development and outreach, especially to underresourced faculty.
Improving Botnet Detection with Recurrent Neural Network and Transfer Learning
Venue: arXiv cs.LG 2021
Year: 2021

Botnet detection is a critical step in stopping the spread of botnets and preventing malicious activities. However, reliable detection is still a challenging task, due to a wide variety of botnets involving ever-increasing types of devices and attack vectors. Recent approaches employing machine learning (ML) showed improved performance than earlier ones, but these ML- based approaches still have significant limitations. For example, most ML approaches can not incorporate sequential pattern analysis techniques key to detect some classes of botnets. Another common shortcoming of ML-based approaches is the need to retrain neural networks in order to detect the evolving botnets; however, the training process is time-consuming and requires significant efforts to label the training data. For fast-evolving botnets, it might take too long to create sufficient training samples before the botnets have changed again. To address these challenges, we propose a novel botnet detection method, built upon Recurrent Variational Autoencoder (RVAE) that effectively captures sequential characteristics of botnet activities. In the experiment, this semi-supervised learning method achieves better detection accuracy than similar learning methods, especially on hard to detect classes. Additionally, we devise a transfer learning framework to learn from a well-curated source data set and transfer the knowledge to a target problem domain not seen before. Tests show that the true-positive rate (TPR) with transfer learning is higher than the RVAE semi-supervised learning method trained using the target data set (91.8% vs. 68.3%).
A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images
Venue: arXiv cs.LG 2021
Year: 2021

In the field of eXplainable AI (XAI), robust "blackbox" algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology -- Influence Score (I-score) -- to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems.
The Relative Consistency of the Axiom of Choice Mechanized Using Isabelle/ZF
Venue: arXiv cs.LO 2021
Year: 2021

The proof of the relative consistency of the axiom of choice has been mechanized using Isabelle/ZF. The proof builds upon a previous mechanization of the reflection theorem. The heavy reliance on metatheory in the original proof makes the formalization unusually long, and not entirely satisfactory: two parts of the proof do not fit together. It seems impossible to solve these problems without formalizing the metatheory. However, the present development follows a standard textbook, Kunen's Set Theory, and could support the formalization of further material from that book. It also serves as an example of what to expect when deep mathematics is formalized.
The Reachability Problem for Petri Nets is Not Primitive Recursive
Venue: arXiv cs.LO 2021
Year: 2021

We provide an Ackermannian complexity lower bound for the reachability problem for checking programs, a model equivalent to Petri nets. Moreover in fixed dimension $2d+4$, we show that the problem is $\mathbb{F}_d$-hard. As a direct corollary, the reachability problem in dimension 10 is not elementary.
DVMark: A Deep Multiscale Framework for Video Watermarking
Venue: arXiv cs.MM 2021
Year: 2021

Video watermarking embeds a message into a cover video in an imperceptible manner, which can be retrieved even if the video undergoes certain modifications or distortions. Traditional watermarking methods are often manually designed for particular types of distortions and thus cannot simultaneously handle a broad spectrum of distortions. To this end, we propose a robust deep learning-based solution for video watermarking that is end-to-end trainable. Our model consists of a novel multiscale design where the watermarks are distributed across multiple spatial-temporal scales. It gains robustness against various distortions through a differentiable distortion layer, whereas non-differentiable distortions, such as popular video compression standards, are modeled by a differentiable proxy. Extensive evaluations on a wide variety of distortions show that our method outperforms traditional video watermarking methods as well as deep image watermarking models by a large margin. We further demonstrate the practicality of our method on a realistic video-editing application.
InfographicVQA
Venue: arXiv cs.CV 2021
Year: 2021

Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic understanding of infographic images by using Visual Question Answering technique.To this end, we present InfographicVQA, a new dataset that comprises a diverse collection of infographics along with natural language questions and answers annotations. The collected questions require methods to jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with emphasis on questions that require elementary reasoning and basic arithmetic skills. Finally, we evaluate two strong baselines based on state of the art multi-modal VQA models, and establish baseline performance for the new task. The dataset, code and leaderboard will be made available at http://docvqa.org
Cloud computing as a platform for monetizing data services: A two-sided game business model
Venue: arXiv cs.GT 2021
Year: 2021

With the unprecedented reliance on cloud computing as the backbone for storing today's big data, we argue in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing the big data through Artificial Intelligence (AI) services. The objective is to enable the cloud to be an active platform that can help big data service providers reach a wider set of customers and cloud users (i.e., data consumers) to be exposed to a larger and richer variety of data to run their data analytic tasks. To achieve this vision, we propose a novel game theoretical model, which consists of a mix of cooperative and competitive strategies. The players of the game are the big data service providers, cloud computing platform, and cloud users. The strategies of the players are modeled using the two-sided market theory that takes into consideration the network effects among involved parties, while integrating the externalities between the cloud resources and consumer demands into the design of the game. Simulations conducted using Amazon and google clustered data show that the proposed model improves the total surplus of all the involved parties in terms of cloud resources provision and monetary profits compared to the current merchant model.
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference
Venue: arXiv cs.CV 2021
Year: 2021

Automatic algorithm-hardware co-design for DNN has shown great success in improving the performance of DNNs on FPGAs. However, this process remains challenging due to the intractable search space of neural network architectures and hardware accelerator implementation. Differing from existing hardware-aware neural architecture search (NAS) algorithms that rely solely on the expensive learning-based approaches, our work incorporates integer programming into the search algorithm to prune the design space. Given a set of hardware resource constraints, our integer programming formulation directly outputs the optimal accelerator configuration for mapping a DNN subgraph that minimizes latency. We use an accuracy predictor for different DNN subgraphs with different quantization schemes and generate accuracy-latency pareto frontiers. With low computational cost, our algorithm can generate quantized networks that achieve state-of-the-art accuracy and hardware performance on Xilinx Zynq (ZU3EG) FPGA for image classification on ImageNet dataset. The solution searched by our algorithm achieves 72.5% top-1 accuracy on ImageNet at framerate 50, which is 60% faster than MnasNet and 135% faster than FBNet with comparable accuracy.
Multimodal Self-Supervised Learning of General Audio Representations
Venue: arXiv cs.SD 2021
Year: 2021

We present a multimodal framework to learn general audio representations from videos. Existing contrastive audio representation learning methods mainly focus on using the audio modality alone during training. In this work, we show that additional information contained in video can be utilized to greatly improve the learned features. First, we demonstrate that our contrastive framework does not require high resolution images to learn good audio features. This allows us to scale up the training batch size, while keeping the computational load incurred by the additional video modality to a reasonable level. Second, we use augmentations that mix together different samples. We show that this is effective to make the proxy task harder, which leads to substantial performance improvements when increasing the batch size. As a result, our audio model achieves a state-of-the-art of 42.4 mAP on the AudioSet classification downstream task, closing the gap between supervised and self-supervised methods trained on the same dataset. Moreover, we show that our method is advantageous on a broad range of non-semantic audio tasks, including speaker identification, keyword spotting, language identification, and music instrument classification.
Classical and Quantum algorithms for generic Syndrome Decoding problems and applications to the Lee metric
Venue: arXiv cs.CR 2021
Year: 2021

The security of code-based cryptography usually relies on the hardness of the syndrome decoding (SD) problem for the Hamming weight. The best generic algorithms are all improvements of an old algorithm by Prange, and they are known under the name of Information Set Decoding (ISD) algorithms. This work aims to extend ISD algorithms' scope by changing the underlying weight function and alphabet size of SD. More precisely, we show how to use Wagner's algorithm in the ISD framework to solve SD for a wide range of weight functions. We also calculate the asymptotic complexities of ISD algorithms both in the classical and quantum case. We then apply our results to the Lee metric, which currently receives a significant amount of attention. By providing the parameters of SD for which decoding in the Lee weight seems to be the hardest, our study could have several applications for designing code-based cryptosystems and their security analysis, especially against quantum adversaries.
Survey: Vitals Screening Techniques for a Safer Environment
Venue: arXiv cs.HC 2021
Year: 2021

With COVID-19 disrupting operations across various sectors of the workforce (e.g., offices, airports, libraries, schools), preventative measures enabling resumption of work are quickly becoming a necessity. In this paper, we present the need for Vitals Screening Techniques (VIST) where more than one vital is screened to ensure the safety of the population (e.g. temperature, heart rate, and blood oxygen levels). VIST can be deployed in crowded environments to provide the new necessary layer of safety. We provide extensive coverage of state-of-art technology that can assist in tackling this emerging problem, and evaluate one of the existing products on the market that employ VIST.
Why AI is Harder Than We Think
Venue: arXiv cs.AI 2021
Year: 2021

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and periods of disappointment, loss of confidence, and reduced funding ("AI winter"). Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.
Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?
Venue: arXiv cs.HC 2021
Year: 2021

The development of educational AI (AIEd) systems has often been motivated by their potential to promote educational equity and reduce achievement gaps across different groups of learners -- for example, by scaling up the benefits of one-on-one human tutoring to a broader audience, or by filling gaps in existing educational services. Given these noble intentions, why might AIEd systems have inequitable impacts in practice? In this chapter, we discuss four lenses that can be used to examine how and why AIEd systems risk amplifying existing inequities. Building from these lenses, we then outline possible paths towards more equitable futures for AIEd, while highlighting debates surrounding each proposal. In doing so, we hope to provoke new conversations around the design of equitable AIEd, and to push ongoing conversations in the field forward.
Realtime Mobile Bandwidth and Handoff Predictions in 4G/5G Networks
Venue: arXiv cs.NI 2021
Year: 2021

Mobile apps are increasingly relying on high-throughput and low-latency content delivery, while the available bandwidth on wireless access links is inherently time-varying. The handoffs between base stations and access modes due to user mobility present additional challenges to deliver a high level of user Quality-of-Experience (QoE). The ability to predict the available bandwidth and the upcoming handoffs will give applications valuable leeway to make proactive adjustments to avoid significant QoE degradation. In this paper, we explore the possibility and accuracy of realtime mobile bandwidth and handoff predictions in 4G/LTE and 5G networks. Towards this goal, we collect long consecutive traces with rich bandwidth, channel, and context information from public transportation systems. We develop Recurrent Neural Network models to mine the temporal patterns of bandwidth evolution in fixed-route mobility scenarios. Our models consistently outperform the conventional univariate and multivariate bandwidth prediction models. For 4G \& 5G co-existing networks, we propose a new problem of handoff prediction between 4G and 5G, which is important for low-latency applications like self-driving strategy in realistic 5G scenarios. We develop classification and regression based prediction models, which achieve more than 80\% accuracy in predicting 4G and 5G handoffs in a recent 5G dataset.
AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions
Venue: arXiv cs.CV 2021
Year: 2021

This paper addresses text recognition for domains with limited manual annotations by a simple self-training strategy. Our approach should reduce human annotation effort when target domain data is plentiful, such as when transcribing a collection of single person's correspondence or a large manuscript. We propose to train a seed system on large scale data from related domains mixed with available annotated data from the target domain. The seed system transcribes the unannotated data from the target domain which is then used to train a better system. We study several confidence measures and eventually decide to use the posterior probability of a transcription for data selection. Additionally, we propose to augment the data using an aggressive masking scheme. By self-training, we achieve up to 55 % reduction in character error rate for handwritten data and up to 38 % on printed data. The masking augmentation itself reduces the error rate by about 10 % and its effect is better pronounced in case of difficult handwritten data.
Semi-Supervised Joint Estimation of Word and Document Readability
Venue: arXiv cs.CL 2021
Year: 2021

Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other. Motivated by our analysis showing that there is a recursive relationship between word and document difficulty, we propose to jointly estimate word and document difficulty through a graph convolutional network (GCN) in a semi-supervised fashion. Our experimental results reveal that the GCN-based method can achieve higher accuracy than strong baselines, and stays robust even with a smaller amount of labeled data.