Reviewerly/exHarmony huggingface dataset:
Adversarially Robust Streaming via Dense-Sparse Trade-offs

Previous chapter Next chapter Full AccessProceedings Symposium on Simplicity in Algorithms (SOSA)Adversarially Robust Streaming via Dense-Sparse Trade-offsOmri Ben-Eliezer, Talya Eden, and Krzysztof OnakOmri Ben-Eliezer, Talya Eden, and Krzysztof Onakpp.214 - 227Chapter DOI:https://doi.org/10.1137/1.9781611977066.15PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract A streaming algorithm is adversarially robust if it is guaranteed to perform correctly even in the presence of an adaptive adversary. The development and analysis of such algorithms have been a very active topic recently, and several sophisticated frameworks for robustification of classical streaming algorithms have been developed. One of the main open questions in this area is whether efficient adversarially robust algorithms exist for moment estimation problems (e.g., F2-estimation) under the turnstile streaming model, where both insertions and deletions are allowed. So far, the best known space complexity for streams of length m, achieved using differential privacy (DP) based techniques, is of order Õ (m1/2) for computing a constant-factor approximation with high constant probability (the Õ notation hides here terms polynomial in log m and log n, where n is the universe size). In this work, we propose a new simple approach to tracking moments by alternating between two different regimes: a sparse regime, in which we can explicitly maintain the current frequency vector and use standard sparse recovery techniques, and a dense regime, in which we make use of existing DP-based robustification frameworks. The results obtained using our technique break the previous m 1/2 barrier for any fixed p. More specifically, our space complexity for F2-estimation is Õ (m2/5) and for F0-estimation, i.e., counting the number of distinct elements, it is Õ(m1/3). All existing robustness frameworks have their space complexity depend multiplicatively on a parameter λ called the flip number of the streaming problem, where λ = m in turnstile moment estimation. The best known dependence in these frameworks (for constant factor approximation) is of order Õ(λ1/2), and it is known to be tight for certain problems. Again, our approach breaks this barrier, achieving a dependence of order Õ(λ1/2-c(p)) for Fp-estimation, where c(p) > 0 depends only on p. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-706-6 https://doi.org/10.1137/1.9781611977066Book Series Name:ProceedingsBook Code:SOSA22Book Pages:v + 320
Predicting breast cancer using machine learning classifiers and enhancing the output by combining the predictions to generate optimal F1-score

Background: Biomedical field has gained a lot of interest from active researchers today. Treating various diseases prevailing among the world has believed to bring huge insight in the today's research world. Second, advancement in technology has eased the work of researchers to justify their work. Machine learning (ML) is an approach being used by bioengineers today to predict diseases and to even aid them in drug discovery. Methods: Considering both the points, one of the most serious diseases, that is breast cancer here, is predicted using ML approaches. Breast cancer is classified as either benign or malignant which is to be predicted with the help of ML classifiers. A very famous dataset Wisconsin Breast Cancer Dataset is used here and is trained by three classifiers mainly support vector machine, general linear model, and neural network (NNET) against testing dataset. Testing the breast cancer prediction was carried out keeping in mind the accuracy of each of the classifiers. Results: This study is involving a generic code in R language. Conclusions: The study intends to show the usage of NNETs in breast cancer prediction using single-layered structure.
Make4Change: Empowering Unemployed Youth through Digital Fabrication

Recently, there has been a large body of research exploring the possibilities of digital fabrication and making in school context. To truly embrace the spirit of equity and democracy so essential to the maker movement, more research is needed concerning how these activities can be used to empower marginalized and at-risk groups. In this paper, we introduce preliminary insights from Make4Change project, that aims to strengthen the social inclusion and employability of unemployed, young people and immigrants outside the labor market through digital fabrication and making. We use the criteria for empowerment of participants as a tool to reflect the planning, organization, and execution of learning activities and on improving our process moving forward. We believe that insights from Make4Change will be useful to practitioners and researchers looking to empower underrepresented, marginal, or at-risk populations through making.
Theory of (1 + 1) ES on the RIDGE

Previous research proposed the uniform mutation inside the sphere as a new mutation operator for evolution strategies (continuous evolutionary algorithms), with a case study of the elitist algorithm on the SPHERE. For that landscape, one-step success probability and expected progress were estimated analytically, and further proved to converge, as space dimension increases, to the corresponding asymptotics of the algorithm with normal mutation. This article takes the analysis further by considering the RIDGE, an asymmetric landscape almost uncovered in the literature. For the elitist algorithm, estimates of expected progress along the radial and longitudinal axes are derived, then tested numerically against the real behavior of the algorithm on several functions from this class. The global behavior of the algorithm is predicted correctly by iterating the one-step analytical formulas. Moreover, experiments show identical mean value dynamics for the algorithms with uniform and normal mutation, which implies that the derived formulas apply also to the normal case. Essential to the whole analysis is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> , the inclination angle of the RIDGE. The behavior of the algorithm on the SPHERE and HYPERPLANE is also obtained, at the limits of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> interval (0°, 90°].
DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots

We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot. Commonsense knowledge about various topics can be represented as an answer set program. However, to advance the conversation, we need to solve the problem of finding relevant consistent concepts, i.e., find consistent knowledge in the "neighborhood" of the current topic being discussed that can be used to advance the conversation. Traditional ASP solvers will generate the whole answer set which is stripped of all the associations between the various atoms (concepts) and thus cannot be used to find relevant consistent concepts. Similarly, goal-directed implementations of ASP will only find concepts directly relevant to a query. We present the DiscASP system that will find the partial consistent model that is relevant to a given topic in a manner similar to how a human will find it. DiscASP is based on a novel graph-based algorithm for finding stable models of an answer set program. We present the DiscASP algorithm, its implementation, and its application to developing a conversational socialbot.
A novel Deep Neural Network architecture for non-linear system identification

We present a novel Deep Neural Network (DNN) architecture for non-linear system identification. We foster generalization by constraining DNN representational power. To do so, inspired by fading memory systems, we introduce inductive bias (on the architecture) and regularization (on the loss function). This architecture allows for automatic complexity selection based solely on available data, in this way the number of hyper-parameters that must be chosen by the user is reduced. Exploiting the highly parallelizable DNN framework (based on Stochastic optimization methods) we successfully apply our method to large scale datasets.
Hole-filling based on content loss indexed 3D partial convolution network for freehand ultrasound reconstruction

During the 3D reconstruction of ultrasound volume from 2D B-scan ultrasound images, holes are usually found in the reconstructed 3D volumes due to the fast scans. This condition will affect the positioning and judgment of the doctor to the lesion. Hence, in this study, we propose to fill the holes by using a novel content loss indexed 3D partial convolution network for 3D freehand ultrasound volume reconstruction. The network can synthesize novel ultrasound volume structures and reconstruct ultrasound volume with missing regions with variable sizes and at arbitrary locations.First, the 3D partial convolution is introduced into the convolutional layer, which is masked and renormalized to be conditioned on only valid voxels. Then, the mask in the next layer is automatically updated as a part of the forward pass. To better preserve texture and structure details of the reconstruction results, we couple the adversarial loss of the least squares generative adversarial network (LSGAN) with the innovative content loss, which consists of the context loss, the feature-matching loss and the total variation loss. Thereafter, we introduce a novel spectral-normalized LSGAN by adding spectral normalization (SN) to the generator and discriminator of the LSGAN. The proposed method is simple in formulation, and is stable in training.Experiments on public and in-vivo ultrasound datasets and comparisons with popular algorithms demonstrate that the proposed approach can generate high-quality hole-filling results with preserved perceptual image details.Considering the high quality of the hole-filling results, the proposed method can effectively fill the missing regions in the reconstructed 3D ultrasound volume from 2D ultrasound image sequences.
A GA-based algorithm meets the fair ranking problem

Ranking items is a vital component in almost every application dealing with selecting the most suitable items among a pool of candidates. Yet, specific individuals or groups may be systematically disadvantaged in getting the opportunity of appearing on the ranking list. The fair ranking problem aims at mitigating the bias imposed on protected groups (i.e., disadvantaged groups) while preserving the total quality of the ranking list as high as possible. FA*IR is one of the existing algorithms, which finds the exact solutions for only one protected group, considering a given minimum number of protected items at every prefix of a ranking list. However, when an item belongs to more than two protected groups achieving optimal solutions gets more difficult. This paper proposes an algorithm called FARGO, a fair ranking algorithm based on the genetic algorithm (GA) enhanced by the simulated annealing (SA) that is able to handle any number of protected groups. A new objective function is also proposed by incorporating the main goals of the problem, which is utilized as FARGO's fitness function. Furthermore, a novel evaluation metric named Expected Gain Ratio (EGR) is introduced to assess a fair ranking algorithm's output. Experimental results on real-world datasets demonstrate that FARGO attains comparative performance with FA*IR for one protected group and finds near-optimal solutions for more than one protected group in terms of NDCG and EGR. Note that involving other concepts such as exposure is not a matter of this paper and can be an interesting subject for further studies.
Abusing the COVID-19 pan(dem)ic

This chapter explores the influence of the COVID-19 pandemic as a salient current event on the perpetration of online scams. Although online attacks are fairly common during times of social strife, relevant scholarly literature on the topic is scarce. To fill this gap, our work focuses on how the COVID-19 pandemic was used as social context in various scams, what types of online scams the pandemic spurred, and which communicative strategies were employed by the scammers. Content analysis was used to study news stories (N = 1924) related to COVID-19-themed online scams that appeared in international media from January 2020 to May 2020. We found that initial fraud campaigns relied more on gain-based communicative strategies, e.g., offering relevant information, out-of-stock goods, and bogus treatment options. As the social context provided by the pandemic evolved, subsequent online scams also involved loss-based communicative strategies such as threatening to deliberately infect persons or suggesting that fines have been imposed for restriction violations. Overall, scammers used the COVID-19 pandemic as a “way in,” stayed up-to-date on evolving real-life circumstances, and, accordingly, continuously adapted their scam messages to keep them current and increase the perceived credibility of the messages.
Overview of SimpleText 2021 - CLEF Workshop on Text Simplification for Scientific Information Access

Information retrieval has moved from traditional document retrieval in which search is an isolated activity, to modern information access where search and the use of the information are fully integrated. But non-experts tend to avoid authoritative primary sources such as scientific literature due to their complex language, internal vernacular, or lacking prior background knowledge. Text simplification approaches can remove some of these barriers, thereby avoiding that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. The CLEF 2021 SimpleText track addresses the opportunities and challenges of text simplification approaches to improve scientific information access head-on. We aim to provide appropriate data and benchmarks, starting with pilot tasks in 2021, and create a community of NLP and IR researchers working together to resolve one of the greatest challenges of today.
TA-GAN: GAN based Traffic Augmentation for Imbalanced Network Traffic Classification

As the mainstream in network traffic classification (NTC), machine learning (ML) based methods suffer performance degradation due to the imbalance distribution of Internet traffic. Data augmentation methods including the traditional oversampling techniques and the Generative Adversarial Network (GAN) based generation methods are most commonly used to counter the imbalance problem in NTC. However, the former is prone to overfitting and introducing noise. The latter overcomes the above weaknesses, but the quality of the generated traffic samples is difficult to judge. Besides, these methods all divide the imbalanced traffic classification problem into two subproblems, which cannot guarantee the global optimality. In this paper, we propose a GAN based Traffic Augmentation (TA-GAN) for imbalanced traffic classification. TA-GAN is an end-to-end framework that integrates the generation of the minority traffic samples with the training of the target classifier. We design the feedback mechanism to better guide the direction of the sample generation and simultaneously indicate the quality of the synthesized samples. Moreover, the existing deep learning-based NTC methods can be easily adapted to imbalance scenarios with TA-GAN. Comprehensive experiments on the public ISCXVPN2016 dataset demonstrate that TA-GAN effectively mitigates the influence of traffic imbalance (a maximum 14.64% improvement to the minority class' <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> score) and outperforms the state-of-the-art methods.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments.The finite context window of these neural models means, however, that they will be unable to leverage the entire relevant context of large files and packages for any given task.While there are many efforts to extend the context window, we introduce an architectureindependent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixedlength window.Using concrete syntax trees of each source file we extract syntactic hierarchies and integrate them into context window by selectively removing from view more specific, less relevant scopes for a given task.We evaluate this approach on code generation tasks and joint translation of natural language and source code in Python programming language, achieving a new state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark.We also introduce new CodeXGLUE benchmarks for userexperience-motivated tasks: code completion with normalized literals, method body completion/code summarization conditioned on filelevel context.
Beyond Occam’s Razor in System Identification: Double-Descent when Modeling Dynamics

System identification aims to build models of dynamical systems from data. Traditionally, choosing the model requires the designer to balance between two goals of conflicting nature; the model must be rich enough to capture the system dynamics, but not so flexible that it learns spurious random effects from the dataset. It is typically observed that the model validation performance follows a U-shaped curve as the model complexity increases. Recent developments in machine learning and statistics, however, have observed situations where a “double-descent” curve subsumes this U-shaped model-performance curve. With a second decrease in performance occurring beyond the point where the model has reached the capacity of interpolating—i.e., (near) perfectly fitting—the training data. To the best of our knowledge, such phenomena have not been studied within the context of dynamic systems. The present paper aims to answer the question: “Can such a phenomenon also be observed when estimating parameters of dynamic systems?” We show that the answer is yes, verifying such behavior experimentally both for artificially generated and real-world datasets.
Detecting Situations with Stream Reasoning on Health Data Obtained with IoT

The development of Internet of Things (IoT) creates large amount of data usable by decision making systems in various domains. In particular, in the field of health monitoring, it enables to follow the medical state of a patient at home in real-time. A challenge is to interpret these data with a high-level representation model in order to have a better understanding of the medical state of a patient. We propose in this article to use Stream Reasoning associated to an ontological representation of the medical context of a patient to understand her situation. This permits to combine in real time static knowledge stored in an ontology and dynamic information provided by smart sensors. To facilitate this process, we introduce constraints and situations concepts to ease the translation of expert knowledge into logical queries. We provide in this paper an experimental analysis of real body temperature data to illustrate how situations may be detected.
Automatic malaria disease detection from blood cell images using the variational quantum circuit

Variational quantum circuit (VQC) is a quantum-classical (QC) machine learning approach that accommodates quantum processes on a classical computer. Malaria is a worldwide deadly disease caused by Plasmodium parasites. This research designs an effective VQC-based approach to recognize the existence of malaria from the Red blood cell (RBC) image through the classification of the optimized feature set that has been extracted from a set of RBC images. Minimum redundancy maximum relevance (mRMR), and Principal component analysis (PCA), are used to optimize the feature set. Comparing to existing classical approaches we have found that mRMR with our input encoding and parameterized circuit of VQC shows satisfactory performance by using a lower number of features and a lower number of parameters. After ascertaining the presence of malaria from VQC we have also introduced a rule-based expert system to detect the types of malaria. The proposed mechanism is mainly designed to evaluate the potency of quantum machine learning (QML) in near-term quantum computers and using the ten-fold cross-validation the scheme gained an overall accuracy, precision, recall, and specificity of 99.06%, 99.08%, 99.05%, and 99.07% respectively for malaria disease diagnosis.
Fault-Tolerant Coherent $H^\infty$ Control for Linear Quantum Systems

Robustness and reliability are two key requirements for developing practical quantum control systems. The purpose of this article is to design a coherent feedback controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop quantum system has both fault tolerance and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> disturbance attenuation performance. This article first extends the physical realization conditions from the time-invariant case to the time-varying case for linear stochastic quantum systems. By relating the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> control problem to the dissipation properties and the solutions of Riccati differential equations, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> controller for the quantum system is then designed by solving a set of linear matrix inequalities. In particular, an algorithm is employed to introduce additional quantum inputs and to construct the corresponding input matrices to ensure the physical realizability of the quantum controller. Also, we propose a real application of the developed fault-tolerant control strategy to quantum optical systems. A linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values due to the fault processes, can be modeled as a linear Markovian jumping system. It is demonstrated that a quantum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> controller can be designed and implemented using some basic optical components to achieve the desired control goal for this class of systems.