OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Storage Allocations for Optimal Service Rates
Venue: arXiv cs.IT 2021
Year: 2021

Redundant storage maintains the performance of distributed systems under various forms of uncertainty. This paper considers the uncertainty in node access and download service. We consider two access models under two download service models. In one access model, a user can access each node with a fixed probability, and in the other, a user can access a random fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the randomness associated with the file size is negligible. In the second (large file) model, randomness is associated with both the file size and the system's operations. We focus on the service rate of the system. For a fixed redundancy level, the systems' service rate is determined by the allocation of coded chunks over the storage nodes. We consider quasi-uniform allocations, where coded content is uniformly spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that in the small file model, concentrating the coded content to a minimum-size subset is universally optimal. For the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.
BlockNet Report: Exploring the Blockchain Skills Concept and Best Practice Use Cases
Venue: arXiv cs.CY 2021
Year: 2021

In order to explore the practical potential and needs of interdisciplinary knowledge and competence requirements of Blockchain technology, the project activity "Development of Interdisciplinary Blockchain Skills Concept" starts with the literature review identifying the state of the art of Blockchain in Supply Chain Management and Logistics, Business and Finance, as well as Computer Science and IT-Security. The project activity further explores the academic and industry landscape of existing initiatives in education which offer Blockchain courses. Moreover, job descriptions and adverts are analyzed in order to specify today's competence requirements from enterprises. To discuss and define the future required competence, expert workshops are organized to validate the findings by academic experts. Based on the research outcome and validation, an interdisciplinary approach for Blockchain competence is developed. A second part focuses on the development of the Blockchain Best Practices activity while conducting qualitative empirical research based on case studies with industry representatives. Therefore, company interviews, based on the theoretical basis of Output 1, explore existing Blockchain use cases in different sectors. Due to the interdisciplinary importance of Blockchain technology, these skills will be defined by different perspectives of Blockchain from across multiple mentioned disciplines. The use cases and companies for the interviews will be selected based on various sampling criteria to gain results valid for a broad scale. The analysis of the various use cases will be conducted and defined in a standardized format to identify the key drivers and competence requirements for Blockchain technology applications and their adoption. On the one hand, this approach ensures comparability, on the other hand, it facilitates the development of a structured and systematic framework.
Learning-based WiFi Traffic Load Estimation in NR-U Systems
Venue: arXiv cs.IT 2021
Year: 2021

The unlicensed spectrum has been utilized to make up the shortage on frequency spectrum in new radio (NR) systems. To fully exploit the advantages brought by the unlicensed bands, one of the key issues is to guarantee the fair coexistence with WiFi systems. To reach this goal, timely and accurate estimation on the WiFi traffic loads is an important prerequisite. In this paper, a machine learning (ML) based method is proposed to detect the number of WiFi users on the unlicensed bands. An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users. Moreover, NN is trained online and the related parameters and learning rate of NN are jointly optimized to estimate the number of WiFi users adaptively with high accuracy. Simulation results demonstrate that compared with the conventional Kalman Filter based detection mechanism, the proposed approach has lower complexity and can achieve a more stable and accurate estimation.
Multilevel Topological Interference Management: A TIM-TIN Perspective
Venue: arXiv cs.IT 2021
Year: 2021

The robust principles of treating interference as noise (TIN) when it is sufficiently weak, and avoiding it when it is not, form the background of this work. Combining TIN with the topological interference management (TIM) framework that identifies optimal interference avoidance schemes, we formulate a TIM-TIN problem for multilevel topological interference management, wherein only a coarse knowledge of channel strengths and no knowledge of channel phases is available to transmitters. To address the TIM-TIN problem, we first propose an analytical baseline approach, which decomposes a network into TIN and TIM components, allocates the signal power levels to each user in the TIN component, allocates signal vector space dimensions to each user in the TIM component, and guarantees that the product of the two is an achievable number of signal dimensions available to each user in the original network. Next, a distributed numerical algorithm called ZEST is developed. The convergence of the algorithm is demonstrated, leading to the duality of the TIM-TIN problem (in terms of GDoF). Numerical results are also provided to demonstrate the superior sum-rate performance and fast convergence of ZEST.
End-to-end Generative Zero-shot Learning via Few-shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

Contemporary state-of-the-art approaches to Zero-Shot Learning (ZSL) train generative nets to synthesize examples conditioned on the provided metadata. Thereafter, classifiers are trained on these synthetic data in a supervised manner. In this work, we introduce Z2FSL, an end-to-end generative ZSL framework that uses such an approach as a backbone and feeds its synthesized output to a Few-Shot Learning (FSL) algorithm. The two modules are trained jointly. Z2FSL solves the ZSL problem with a FSL algorithm, reducing, in effect, ZSL to FSL. A wide class of algorithms can be integrated within our framework. Our experimental results show consistent improvement over several baselines. The proposed method, evaluated across standard benchmarks, shows state-of-the-art or competitive performance in ZSL and Generalized ZSL tasks.
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community. In particular, actor-critic methods with a centralized critic and decentralized actors are a common instance of this idea. However, the implications of using a centralized critic in this context are not fully discussed and understood even though it is the standard choice of many algorithms. We therefore formally analyze centralized and decentralized critic approaches, providing a deeper understanding of the implications of critic choice. Because our theory makes unrealistic assumptions, we also empirically compare the centralized and decentralized critic methods over a wide set of environments to validate our theories and to provide practical advice. We show that there exist misconceptions regarding centralized critics in the current literature and show that the centralized critic design is not strictly beneficial, but rather both centralized and decentralized critics have different pros and cons that should be taken into account by algorithm designers.
Detecting Fake News Using Machine Learning : A Systematic Literature Review
Venue: arXiv cs.CY 2021
Year: 2021

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through the online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These news can be propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting fake news is described in this systematic literature review.
Unsupervised Abstractive Summarization of Bengali Text Documents
Venue: arXiv cs.CL 2021
Year: 2021

Abstractive summarization systems generally rely on large collections of document-summary pairs. However, the performance of abstractive systems remains a challenge due to the unavailability of parallel data for low-resource languages like Bengali. To overcome this problem, we propose a graph-based unsupervised abstractive summarization system in the single-document setting for Bengali text documents, which requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. We also provide a human-annotated dataset with document-summary pairs to evaluate our abstractive model and to support the comparison of future abstractive summarization systems of the Bengali Language. We conduct experiments on this dataset and compare our system with several well-established unsupervised extractive summarization systems. Our unsupervised abstractive summarization model outperforms the baselines without being exposed to any human-annotated reference summaries.
Leaf Image-based Plant Disease Identification using Color and Texture Features
Venue: arXiv cs.CV 2021
Year: 2021

Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.
A study of text representations in Hate Speech Detection
Venue: arXiv cs.CL 2021
Year: 2021

The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter. Current EU and US legislation against hateful language, in conjunction with the large amount of data produced in these platforms has led to automatic tools being a necessary component of the Hate Speech detection task and pipeline. In this study, we examine the performance of several, diverse text representation techniques paired with multiple classification algorithms, on the automatic Hate Speech detection and abusive language discrimination task. We perform an experimental evaluation on binary and multiclass datasets, paired with significance testing. Our results show that simple hate-keyword frequency features (BoW) work best, followed by pre-trained word embeddings (GLoVe) as well as N-gram graphs (NGGs): a graph-based representation which proved to produce efficient, very low-dimensional but rich features for this task. A combination of these representations paired with Logistic Regression or 3-layer neural network classifiers achieved the best detection performance, in terms of micro and macro F-measure.
Playing the Blame Game with Robots
Venue: arXiv cs.HC 2021
Year: 2021

Recent research shows -- somewhat astonishingly -- that people are willing to ascribe moral blame to AI-driven systems when they cause harm [1]-[4]. In this paper, we explore the moral-psychological underpinnings of these findings. Our hypothesis was that the reason why people ascribe moral blame to AI systems is that they consider them capable of entertaining inculpating mental states (what is called mens rea in the law). To explore this hypothesis, we created a scenario in which an AI system runs a risk of poisoning people by using a novel type of fertilizer. Manipulating the computational (or quasi-cognitive) abilities of the AI system in a between-subjects design, we tested whether people's willingness to ascribe knowledge of a substantial risk of harm (i.e., recklessness) and blame to the AI system. Furthermore, we investigated whether the ascription of recklessness and blame to the AI system would influence the perceived blameworthiness of the system's user (or owner). In an experiment with 347 participants, we found (i) that people are willing to ascribe blame to AI systems in contexts of recklessness, (ii) that blame ascriptions depend strongly on the willingness to attribute recklessness and (iii) that the latter, in turn, depends on the perceived "cognitive" capacities of the system. Furthermore, our results suggest (iv) that the higher the computational sophistication of the AI system, the more blame is shifted from the human user to the AI system.
Quantum Technologies: A Review of the Patent Landscape
Venue: arXiv cs.DL 2021
Year: 2021

Quantum Technologies is a term that is getting broader with every passing year. Nanotechnology and electronics operate in this realm. With the invention of industry-disrupting algorithms like Shor's algorithm that can break RSA encryption on a quantum computer and Quantum Key Distribution, which offers unconditional security in theory, investment is pouring in. Here we taxonomize and analyze 48,577 patents in this area from 2015 to present captured with a comprehensive query in Relecura's patent database. The author's subject experience, along with the company's AI-based tools and scholarly literature, were utilized to make this highly subjective choice of taxonomy. Though most Patent Landscape Analysis Reports consider a single technology, geography, or company, we have tried to give a holistic overview of these technologies as a whole due to their collaborative and intertwined nature. The physics of each technology and its role in the industry is briefly explained where possible.
E-Health Management Services in Supporting Empowerment
Venue: arXiv cs.CY 2021
Year: 2021

The Web technology provides healthcare providers the ability to broaden their services beyond the usual practices, and thus provides a particular advantageous environment to achieve complex e-health goals. This paper discusses how a Web 2.0 application will help healthcare provider to extend and enhance their services by involving and empowering their customers.Web 2.0 referes to the next generations of Web technology that empowers users to generate contents over Internet. The Web 2.0 also refers to the Web as a platform to perform any task online. The Web 2.0 allows customers to have greater control of information flow from interactions between healthcare providers with its customers and among customers themselves. The study employed quantitative methods to depict expectations of customers in Indonesia towards healthcare services that can offer empowerment and social media and sharing. The questionnaires were fairly distributed to the groups of healthcare staffs and customers (patients) who regularly visit healthcare centres. The survey revealed that features of Web 2.0 in e-health services such as consultation online, sharing in social networks, empowerment in detailing personal health records are highly appreciated by customers. Regardless of the limitations of the survey, the public has responded with a great support for the capabilities of Web 2.0 listed from the questionnaires. The findings provide initial ideas and recommendation for a future direction of prototype of social networks in e-health services.
Tracking e-cigarette warning label compliance on Instagram with deep learning
Venue: arXiv cs.SI 2021
Year: 2021

The U.S. Food & Drug Administration (FDA) requires that e-cigarette advertisements include a prominent warning label that reminds consumers that nicotine is addictive. However, the high volume of vaping-related posts on social media makes compliance auditing expensive and time-consuming, suggesting that an automated, scalable method is needed. We sought to develop and evaluate a deep learning system designed to automatically determine if an Instagram post promotes vaping, and if so, if an FDA-compliant warning label was included or if a non-compliant warning label was visible in the image. We compiled and labeled a dataset of 4,363 Instagram images, of which 44% were vaping-related, 3% contained FDA-compliant warning labels, and 4% contained non-compliant labels. Using a 20% test set for evaluation, we tested multiple neural network variations: image processing backbone model (Inceptionv3, ResNet50, EfficientNet), data augmentation, progressive layer unfreezing, output bias initialization designed for class imbalance, and multitask learning. Our final model achieved an area under the curve (AUC) and [accuracy] of 0.97 [92%] on vaping classification, 0.99 [99%] on FDA-compliant warning labels, and 0.94 [97%] on non-compliant warning labels. We conclude that deep learning models can effectively identify vaping posts on Instagram and track compliance with FDA warning label requirements.
Prototyping Low-Cost Automatic Weather Stations for Natural Disaster Monitoring
Venue: arXiv cs.NI 2021
Year: 2021

Weather events put human lives at risk mostly when people might reside in areas susceptible to natural disasters. Weather monitoring is a pivotal task that is accomplished in vulnerable areas with the support of reliable weather stations. Such stations are front-end equipment typically mounted on a fixed mast structure with a set of digital and magnetic weather sensors connected to a datalogger. While remote sensing from a number of stations is paramount, the cost of professional weather instruments is extremely high. This imposes a challenge for large-scale deployment and maintenance of weather stations for broad natural disaster monitoring. To address this problem, in this paper, we validate the hypothesis that a Low-Cost Automatic Weather Station system (LCAWS) entirely developed from commercial-off-the-shelf and open-source IoT technologies is able to provide data as reliable as a Professional Weather Station (PWS) of reference for natural disaster monitoring. To achieve data reliability, we propose an intelligent sensor calibration method to correct weather parameters. From the experimental results of a 30-day uninterrupted observation period, we show that the results of the calibrated LCAWS sensors have no statistically significant differences with the PWS's results. Together with The Brazilian National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), LCAWS has opened new opportunities towards reducing maintenance cost of its weather observational network.
Finding Nash Equilibria of Two-Player Games
Venue: arXiv cs.GT 2021
Year: 2021

This paper is an exposition of algorithms for finding one or all equilibria of a bimatrix game (a two-player game in strategic form) in the style of a chapter in a graduate textbook. Using labeled "best-response polytopes", we present the Lemke-Howson algorithm that finds one equilibrium. We show that the path followed by this algorithm has a direction, and that the endpoints of the path have opposite index, in a canonical way using determinants. For reference, we prove that a number of notions of nondegeneracy of a bimatrix game are equivalent. The computation of all equilibria of a general bimatrix game, via a description of the maximal Nash subsets of the game, is canonically described using "complementary pairs" of faces of the best-response polytopes.
Regularized Generative Adversarial Network
Venue: arXiv cs.LG 2021
Year: 2021

We propose a framework for generating samples from a probability distribution that differs from the probability distribution of the training set. We use an adversarial process that simultaneously trains three networks, a generator and two discriminators. We refer to this new model as regularized generative adversarial network (RegGAN). We evaluate RegGAN on a synthetic dataset composed of gray scale images and we further show that it can be used to learn some pre-specified notions in topology (basic topology properties). The work is motivated by practical problems encountered while using generative methods in the art world.
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
Venue: arXiv cs.LG 2021
Year: 2021

Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on Federated Deep AUC Maximization (FDAM) is still limited. Compared with standard federated learning (FL) approaches that focus on decomposable minimization objectives, FDAM is more complicated due to its minimization objective is non-decomposable over individual examples. In this paper, we propose improved FDAM algorithms for heterogeneous data by solving the popular non-convex strongly-concave min-max formulation of DAM in a distributed fashion, which can also be applied to a class of non-convex strongly-concave min-max problems. A striking result of this paper is that the communication complexity of the proposed algorithm is a constant independent of the number of machines and also independent of the accuracy level, which improves an existing result by orders of magnitude. The experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets, and on medical chest X-ray images from different organizations. Our experiment shows that the performance of FDAM using data from multiple hospitals can improve the AUC score on testing data from a single hospital for detecting life-threatening diseases based on chest radiographs. The proposed method is implemented in our open-sourced library LibAUC (www.libauc.org) whose github address is https://github.com/Optimization-AI/ICML2021_FedDeepAUC_CODASCA.