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Revisiting Language Support for Generic Programming: When Genericity Is a Core Design Goal

Generic programming has proven to be an effective means of constructing libraries of reusable software components in languages that support it. Generics-related language design choices play a major role in how conducive generic programming is in practice. Inquiry: Several mainstream programming languages (e.g. Much of the existing literature on supporting generic programming focuses thus on retrofitting generic programming into existing languages and identifying related implementation challenges. Is the programming experience significantly better, or different when programming with a language designed for generic programming without limitations from prior language design choices? Magnolia is representative of an approach to language design rooted in algebraic specifications. The understanding of how to set the ground for generic programming will inform future language design. Context: Generic programming, as defined by Stepanov, is a methodology for writing efficient and reusable algorithms by considering only the required properties of their underlying data types and operations. Generic programming has proven to be an effective means of constructing libraries of reusable software components in languages that support it. Generics-related language design choices play a major role in how conducive generic programming is in practice. Inquiry: Several mainstream programming languages (e.g. Java and C++) were first created without generics; features to support generic programming were added later, gradually. Much of the existing literature on supporting generic programming focuses thus on retrofitting generic programming into existing languages and identifying related implementation challenges. Is the programming experience significantly better, or different when programming with a language designed for generic programming without limitations from prior language design choices? Approach: We examine Magnolia, a language designed to embody generic programming. Magnolia is representative of an approach to language design rooted in algebraic specifications. We repeat a well-known experiment, where we put Magnolia's generic programming facilities under scrutiny by implementing a subset of the Boost Graph Library, and reflect on our development experience. Knowledge: We discover that the idioms identified as key features for supporting Stepanov-style generic programming in the previous studies and work on the topic do not tell a full story. We clarify which of them are more of a means to an end, rather than fundamental features for supporting generic programming. Based on the development experience with Magnolia, we identify variadics as an additional key feature for generic programming and point out limitations and challenges of genericity by property. Grounding: Our work uses a well-known framework for evaluating the generic programming facilities of a language from the literature to evaluate the algebraic approach through Magnolia, and we draw comparisons with well-known programming languages. Importance: This work gives a fresh perspective on generic programming, and clarifies what are fundamental language properties and their trade-offs when considering supporting Stepanov-style generic programming. The understanding of how to set the ground for generic programming will inform future language design.

Authors: Benjamin Chetioui, Jaakko Järvi, Magne Haveraaen.

Little Tricky Logic: Misconceptions in the Understanding of LTL

All these uses demand that the user have a clear understanding of what an LTL specification means. This paper addresses the gap with a first study of LTL misconceptions. Concretely, we decompose "understanding LTL" into three questions. Therefore, we also study the relationship between formulas and specific traces. These findings are already resulting in changes to the Alloy modeling language. Grounding: Our findings are grounded in the responses to our survey rounds. Round 4 adds deep support for our misconceptions via talk-aloud surveys. Our survey instruments can serve as a starting point for other studies.

Context: Linear Temporal Logic (LTL) has been used widely in verification. Its importance and popularity have only grown with the revival of temporal logic synthesis, and with new uses of LTL in robotics and planning activities. All these uses demand that the user have a clear understanding of what an LTL specification means. Inquiry: Despite the growing use of LTL, no studies have investigated the misconceptions users actually have in understanding LTL formulas. This paper addresses the gap with a first study of LTL misconceptions. Approach: We study researchers' and learners' understanding of LTL in four rounds (three written surveys, one talk-aloud) spread across a two-year timeframe. Concretely, we decompose "understanding LTL" into three questions. A person reading a spec needs to understand what it is saying, so we study the mapping from LTL to English. A person writing a spec needs to go in the other direction, so we study English to LTL. However, misconceptions could arise from two sources: a misunderstanding of LTL's syntax or of its underlying semantics. Therefore, we also study the relationship between formulas and specific traces. Knowledge: We find several misconceptions that have consequences for learners, tool builders, and designers of new property languages. These findings are already resulting in changes to the Alloy modeling language. We also find that the English to LTL direction was the most common source of errors; unfortunately, this is the critical "authoring" direction in which a subtle mistake can lead to a faulty system. We contribute study instruments that are useful for training learners (whether academic or industrial) who are getting acquainted with LTL, and we provide a code book to assist in the analysis of responses to similar-style questions. Grounding: Our findings are grounded in the responses to our survey rounds. Round 1 used Quizius to identify misconceptions among learners in a way that reduces the threat of expert blind spots. Rounds 2 and 3 confirm that both additional learners and researchers (who work in formal methods, robotics, and related fields) make similar errors. Round 4 adds deep support for our misconceptions via talk-aloud surveys. Importance This work provides useful answers to two critical but unexplored questions: in what ways is LTL tricky and what can be done about it? Our survey instruments can serve as a starting point for other studies.

Authors: Ben Greenman, Sam Saarinen, Tim Nelson, Shriram Krishnamurthi.

Repeatable random permutation set

Besides, RPS could take DST as a special case when all items occur in the same order. However, the repetition of items is not allowed in RPS. Based on these properties, a decision support system application is simulated to show the effectiveness of R2PS. Based on Dempster-Shafer evidence theory (DST), random permutation set (RPS) is proposed by replacing combinatorial number with permutation number and therefore incorporating order information. Besides, RPS could take DST as a special case when all items occur in the same order. However, the repetition of items is not allowed in RPS. To address this issue, we propose repeatable random permutation set (R2PS) which takes the repetition of items into consideration. The right and left junctional sum combination rules are proposed and their properties including consistency, pseudo-Matthew effect and associativity are researched. Based on these properties, a decision support system application is simulated to show the effectiveness of R2PS.

Authors: Wenran Yang, Yong Deng.

Spam Review Detection Using Deep Learning

In many online sites, there are options for posting reviews, and thus creating scopes for fake paid reviews or untruthful reviews. These concocted reviews can mislead the general public and put them in a perplexity whether to believe the review or not. Prominent machine learning techniques have been introduced to solve the problem of spam review detection. Our focus in this article is to detect any deceptive text reviews.

A robust and reliable system of detecting spam reviews is a crying need in todays world in order to purchase products without being cheated from online sites. In many online sites, there are options for posting reviews, and thus creating scopes for fake paid reviews or untruthful reviews. These concocted reviews can mislead the general public and put them in a perplexity whether to believe the review or not. Prominent machine learning techniques have been introduced to solve the problem of spam review detection. The majority of current research has concentrated on supervised learning methods, which require labeled data - an inadequacy when it comes to online review. Our focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network (RNN) that is Long Short-Term Memory (LSTM). We have also applied some traditional machine learning classifiers such as Nave Bayes (NB), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) to detect spam reviews and finally, we have shown the performance comparison for both traditional and deep learning classifiers.

Authors: G. M. Shahariar, Swapnil Biswas, Faiza Omar, Faisal Muhammad Shah, Samiha Binte Hassan.

Photon Rings Around Warped Black Holes

The black hole photon ring is a prime target for upcoming space-based VLBI missions seeking to image the fine structure of astrophysical black holes. Recent work has identified a number of emergent symmetries related to the intricate self-similar structure of the photon ring. Here, we explore this web of interrelated phenomena in an exactly soluble example that arises as an approximation to the near-extremal Kerr black hole. We show explicitly that the geometric optics approximation reproduces the eikonal limit of the exact QNM spectrum, as well as the approximate "near-ring" wavefunctions. The black hole photon ring is a prime target for upcoming space-based VLBI missions seeking to image the fine structure of astrophysical black holes. The classical Lyapunov exponents of the corresponding nearly bound null geodesics control the quasinormal ringing of a perturbed black hole as it settles back down to equilibrium, and they admit a holographic interpretation in terms of quantum Ruelle resonances of the microstate dual to the Kerr black hole. Recent work has identified a number of emergent symmetries related to the intricate self-similar structure of the photon ring. Here, we explore this web of interrelated phenomena in an exactly soluble example that arises as an approximation to the near-extremal Kerr black hole. The self-dual warped AdS$_3$ geometry has a photon ring as well as $\mathsf{SL}(2,\mathbb{R})$ isometries and an exactly calculable quasinormal mode (QNM) spectrum. We show explicitly that the geometric optics approximation reproduces the eikonal limit of the exact QNM spectrum, as well as the approximate "near-ring" wavefunctions. The $\mathsf{SL}(2,\mathbb{R})$ isometries are directly related to the emergent conformal symmetry of the photon ring in black hole images but are distinct from a recently discussed conformal symmetry of the eikonal QNM spectrum. The equivalence of the classical QNM spectrum -- and thus the photon ring -- to the quantum Ruelle resonances in the context of a spacetime with a putative holographic dual suggests that the photon ring of a warped black hole is indeed part of the black hole hologram.

Authors: Daniel Kapec, Alexandru Lupsasca, Andrew Strominger.

On local well-posedness of nonlinear dispersive equations with partially regular data

This makes it possible to extract a different integrability/regularity of the data from each variable.

We revisit the local well-posedness theory of nonlinear Schr\"odinger and wave equations in Sobolev spaces $H^s$ and $\dot{H}^s$, $0< s\leq 1$. The theory has been well established over the past few decades under Sobolev initial data regular with respect to all spatial variables. But here, we reveal that the initial data do not need to have complete regularity like Sobolev spaces, but only partially regularity with respect to some variables is sufficient. To develop such a new theory, we suggest a refined Strichartz estimate which has a different norm for each spatial variable. This makes it possible to extract a different integrability/regularity of the data from each variable.

Authors: Youngwoo Koh, Yoonjung Lee, Ihyeok Seo.

Physically Adversarial Attacks and Defenses in Computer Vision: A Survey

The current adversarial attacks in computer vision can be divided into digital attacks and physical attacks according to their different attack forms. Compared with digital attacks, which generate perturbations in the digital pixels, physical attacks are more practical in the real world. To establish a taxonomy, we organize the current physical attacks from attack tasks, attack forms, and attack methods, respectively. Thus, readers can have a systematic knowledge about this topic from different aspects. Based on the above survey, we finally discuss the challenges of this research field and further outlook the future direction. Although Deep Neural Networks (DNNs) have been widely applied in various real-world scenarios, they are vulnerable to adversarial examples. The current adversarial attacks in computer vision can be divided into digital attacks and physical attacks according to their different attack forms. Compared with digital attacks, which generate perturbations in the digital pixels, physical attacks are more practical in the real world. Owing to the serious security problem caused by physically adversarial examples, many works have been proposed to evaluate the physically adversarial robustness of DNNs in the past years. In this paper, we summarize a survey versus the current physically adversarial attacks and physically adversarial defenses in computer vision. To establish a taxonomy, we organize the current physical attacks from attack tasks, attack forms, and attack methods, respectively. Thus, readers can have a systematic knowledge about this topic from different aspects. For the physical defenses, we establish the taxonomy from pre-processing, in-processing, and post-processing for the DNN models to achieve a full coverage of the adversarial defenses. Based on the above survey, we finally discuss the challenges of this research field and further outlook the future direction.

Authors: Xingxing Wei, Bangzheng Pu, Jiefan Lu, Baoyuan Wu.

Active CT Reconstruction with a Learned Sampling Policy

Computed tomography (CT) is a widely-used imaging technology that assists clinical decision-making with high-quality human body representations. To reduce the radiation dose posed by CT, sparse-view and limited-angle CT are developed with preserved image quality. With such a design, we achieve better performances on the NIH-AAPM dataset over popular uniform sampling, especially when the number of views is small. Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.

Computed tomography (CT) is a widely-used imaging technology that assists clinical decision-making with high-quality human body representations. To reduce the radiation dose posed by CT, sparse-view and limited-angle CT are developed with preserved image quality. However, these methods are still stuck with a fixed or uniform sampling strategy, which inhibits the possibility of acquiring a better image with an even reduced dose. In this paper, we explore this possibility via learning an active sampling policy that optimizes the sampling positions for patient-specific, high-quality reconstruction. To this end, we design an \textit{intelligent agent} for active recommendation of sampling positions based on on-the-fly reconstruction with obtained sinograms in a progressive fashion. With such a design, we achieve better performances on the NIH-AAPM dataset over popular uniform sampling, especially when the number of views is small. Finally, such a design also enables RoI-aware reconstruction with improved reconstruction quality within regions of interest (RoI's) that are clinically important. Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.

Authors: Ce Wang, Kun Shang, Haimiao Zhang, Shang Zhao, Dong Liang, S. Kevin Zhou.

Channel-Aware Pretraining of Joint Encoder-Decoder Self-Supervised Model for Telephonic-Speech ASR

The joint encoder-decoder self-supervised model extends the HuBERT model with a Transformer decoder. HuBERT performs clustering of features and predicts the class of every input frame. In simple pooling, which is our baseline, there is no way to identify the channel information. To incorporate channel information, we have proposed non-overlapping cluster IDs for speech from different channels. This paper proposes a novel technique to obtain better downstream ASR performance from a joint encoder-decoder self-supervised model when trained with speech pooled from two different channels (narrow and wide band). The joint encoder-decoder self-supervised model extends the HuBERT model with a Transformer decoder. HuBERT performs clustering of features and predicts the class of every input frame. In simple pooling, which is our baseline, there is no way to identify the channel information. To incorporate channel information, we have proposed non-overlapping cluster IDs for speech from different channels. Our method gives a relative improvement of ~ 5% over the joint encoder-decoder self-supervised model built with simple pooling of data, which serves as our baseline.

Authors: Vrunda N. Sukhadia, A. Arunkumar, S. Umesh.

A Data-Driven Approach to Quantum Cross-Platform Verification

Cross-platform verification becomes increasingly challenging as the system's dimensionality increases, and has so far remained intractable for continuous variable quantum systems. In this Letter, we develop a data-driven approach, working with limited noisy data and suitable for continuous variable quantum states. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data.

The task of testing whether two uncharacterized devices behave in the same way, known as cross-platform verification, is crucial for benchmarking quantum simulators and near-term quantum computers. Cross-platform verification becomes increasingly challenging as the system's dimensionality increases, and has so far remained intractable for continuous variable quantum systems. In this Letter, we develop a data-driven approach, working with limited noisy data and suitable for continuous variable quantum states. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data. The network can be trained offline with classically simulated data, and is demonstrated here on non-Gaussian quantum states for which cross-platform verification could not be achieved with previous techniques. It can also be applied to cross-platform verification of quantum dynamics and to the problem of experimentally testing whether two quantum states are equivalent up to Gaussian unitary transformations.

Authors: Ya-Dong Wu, Yan Zhu, Ge Bai, Yuexuan Wang, Giulio Chiribella.