OpenRaiser/Intern-Atlas huggingface dataset:
On the Algorithmic Content of Quantum Measurements
Venue: arXiv cs.CC 2021
Year: 2021

We show that given a quantum measurement, for an overwhelming majority of pure states, no meaningful information is produced. This is independent of the number of outcomes of the quantum measurement. Due to conservation inequalities, such random noise cannot be processed into coherent data.
Reconstruction of Sparse Signals under Gaussian Noise and Saturation
Venue: arXiv cs.LG 2021
Year: 2021

Most compressed sensing algorithms do not account for the effect of saturation in noisy compressed measurements, though saturation is an important consequence of the limited dynamic range of existing sensors. The few algorithms that handle saturation effects either simply discard saturated measurements, or impose additional constraints to ensure consistency of the estimated signal with the saturated measurements (based on a known saturation threshold) given uniform-bounded noise. In this paper, we instead propose a new data fidelity function which is directly based on ensuring a certain form of consistency between the signal and the saturated measurements, and can be expressed as the negative logarithm of a certain carefully designed likelihood function. Our estimator works even in the case of Gaussian noise (which is unbounded) in the measurements. We prove that our data fidelity function is convex. We moreover, show that it satisfies the condition of Restricted Strong Convexity and thereby derive an upper bound on the performance of the estimator. We also show that our technique experimentally yields results superior to the state of the art under a wide variety of experimental settings, for compressive signal recovery from noisy and saturated measurements.
DEFT: Distilling Entangled Factors by Preventing Information Diffusion
Venue: arXiv cs.LG 2021
Year: 2021

Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks (IB). Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order to provide an insight into the problem, we develop an annealing test to calculate the information freezing point (IFP), which is a transition state to freeze information into the latent variables. We also explore these clues or inductive biases for separating the entangled factors according to the differences in the IFP distributions. We found the existing approaches suffer from the information diffusion problem, according to which the increased information diffuses in all latent variables. Based on this insight, we propose a novel disentanglement framework, termed the distilling entangled factor (DEFT), to address the information diffusion problem by scaling backward information. DEFT applies a multistage training strategy, including multigroup encoders with different learning rates and piecewise disentanglement pressure, to disentangle the factors stage by stage. We evaluate DEFT on three variants of dSprite and SmallNORB, which show low-variance and high-level disentanglement scores. Furthermore, the experiment under the correlative factors shows incapable of TC-based approaches. DEFT also exhibits a competitive performance in the unsupervised setting.
Evaluating the robustness of source code plagiarism detection tools to pervasive plagiarism-hiding modifications
Venue: arXiv cs.SE 2021
Year: 2021

Source code plagiarism is a common occurrence in undergraduate computer science education. In order to identify such cases, many source code plagiarism detection tools have been proposed. A source code plagiarism detection tool evaluates pairs of assignment submissions to detect indications of plagiarism. However, a plagiarising student will commonly apply plagiarism-hiding modifications to source code in an attempt to evade detection. Subsequently, prior work has implied that currently available source code plagiarism detection tools are not robust to the application of pervasive plagiarism-hiding modifications. In this article, 11 source code plagiarism detection tools are evaluated for robustness against plagiarism-hiding modifications. The tools are evaluated with data sets of simulated undergraduate plagiarism, constructed with source code modifications representative of undergraduate students. The results of the performed evaluations indicate that currently available source code plagiarism detection tools are not robust against modifications which apply fine-grained transformations to the source code structure. Of the evaluated tools, JPlag and Plaggie demonstrates the greatest robustness to different types of plagiarism-hiding modifications. However, the results also indicate that graph-based tools (specifically those that compare programs as program dependence graphs) show potentially greater robustness to pervasive plagiarism-hiding modifications.
Discovering conservation laws from trajectories via machine learning
Venue: arXiv cs.LG 2021
Year: 2021

Invariants and conservation laws convey critical information about the underlying dynamics of a system, yet it is generally infeasible to find them from large-scale data without any prior knowledge or human insight. We propose ConservNet to achieve this goal, a neural network that spontaneously discovers a conserved quantity from grouped data where the members of each group share invariants, similar to a general experimental setting where trajectories from different trials are observed. As a neural network trained with a novel and intuitive loss function called noise-variance loss, ConservNet learns the hidden invariants in each group of multi-dimensional observables in a data-driven, end-to-end manner. Our model successfully discovers underlying invariants from the simulated systems having invariants as well as a real-world double pendulum trajectory. Since the model is robust to various noises and data conditions compared to baseline, our approach is directly applicable to experimental data for discovering hidden conservation laws and further, general relationships between variables.
Nature-Inspired Optimization Algorithms: Research Direction and Survey
Venue: arXiv cs.NE 2021
Year: 2021

Nature-inspired algorithms are commonly used for solving the various optimization problems. In past few decades, various researchers have proposed a large number of nature-inspired algorithms. Some of these algorithms have proved to be very efficient as compared to other classical optimization methods. A young researcher attempting to undertake or solve a problem using nature-inspired algorithms is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some score over others. In this paper, an attempt has been made to summarize various leading research proposals that shall pave way for any new entrant to easily understand the journey so far. Here, we classify the nature-inspired algorithms as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, widely acknowledged nature-inspired algorithms namely- ACO, ABC, EAM, FA, FPA, GA, GSA, JAYA, PSO, SFLA, TLBO and WCA, have been studied. The purpose of this review is to present an exhaustive analysis of various nature-inspired algorithms based on its source of inspiration, basic operators, control parameters, features, variants and area of application where these algorithms have been successfully applied. It shall also assist in identifying and short listing the methodologies that are best suited for the problem.
Generate and Revise: Reinforcement Learning in Neural Poetry
Venue: arXiv cs.CL 2021
Year: 2021

Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems.
The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness
Venue: arXiv cs.CY 2021
Year: 2021

Measures of algorithmic fairness often do not account for human perceptions of fairness that can substantially vary between different sociodemographics and stakeholders. The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification. It supports (i) studying human perceptions of fairness and (ii) comparing these human perceptions with measures of algorithmic fairness. The framework includes fairness scenario generation, fairness perception elicitation and fairness perception analysis. We demonstrate the FairCeptron framework by applying it to a hypothetical university admission context where we collect human perceptions of fairness in the presence of minorities. An implementation of the FairCeptron framework is openly available, and it can easily be adapted to study perceptions of algorithmic fairness in other application contexts. We hope our work paves the way towards elevating the role of studies of human fairness perceptions in the process of designing algorithmic decision making systems.
APS: A Large-Scale Multi-Modal Indoor Camera Positioning System
Venue: arXiv cs.CV 2021
Year: 2021

Navigation inside a closed area with no GPS-signal accessibility is a highly challenging task. In order to tackle this problem, recently the imaging-based methods have grabbed the attention of many researchers. These methods either extract the features (e.g. using SIFT, or SOSNet) and map the descriptive ones to the camera position and rotation information, or deploy an end-to-end system that directly estimates this information out of RGB images, similar to PoseNet. While the former methods suffer from heavy computational burden during the test process, the latter suffers from lack of accuracy and robustness against environmental changes and object movements. However, end-to-end systems are quite fast during the test and inference and are pretty qualified for real-world applications, even though their training phase could be longer than the former ones. In this paper, a novel multi-modal end-to-end system for large-scale indoor positioning has been proposed, namely APS (Alpha Positioning System), which integrates a Pix2Pix GAN network to reconstruct the point cloud pair of the input query image, with a deep CNN network in order to robustly estimate the position and rotation information of the camera. For this integration, the existing datasets have the shortcoming of paired RGB/point cloud images for indoor environments. Therefore, we created a new dataset to handle this situation. By implementing the proposed APS system, we could achieve a highly accurate camera positioning with a precision level of less than a centimeter.
Plotting time: On the usage of CNNs for time series classification
Venue: arXiv cs.CV 2021
Year: 2021

We present a novel approach for time series classification where we represent time series data as plot images and feed them to a simple CNN, outperforming several state-of-the-art methods. We propose a simple and highly replicable way of plotting the time series, and feed these images as input to a non-optimized shallow CNN, without any normalization or residual connections. These representations are no more than default line plots using the time series data, where the only pre-processing applied is to reduce the number of white pixels in the image. We compare our method with different state-of-the-art methods specialized in time series classification on two real-world non public datasets, as well as 98 datasets of the UCR dataset collection. The results show that our approach is very promising, achieving the best results on both real-world datasets and matching / beating the best state-of-the-art methods in six UCR datasets. We argue that, if a simple naive design like ours can obtain such good results, it is worth further exploring the capabilities of using image representation of time series data, along with more powerful CNNs, for classification and other related tasks.
Polynomial Linear System Solving with Random Errors: new bounds and early termination technique
Venue: arXiv cs.SC 2021
Year: 2021

This paper deals with the polynomial linear system solving with errors (PLSwE) problem. Specifically, we focus on the evaluation-interpolation technique for solving polynomial linear systems and we assume that errors can occur in the evaluation step. In this framework, the number of evaluations needed to recover the solution of the linear system is crucial since it affects the number of computations. It depends on the parameters of the linear system (degrees, size) and on a bound on the number of errors. Our work is part of a series of papers about PLSwE aiming to reduce this number of evaluations. We proved in [Guerrini et al., Proc. ISIT'19] that if errors are randomly distributed, the bound of the number of evaluations can be lowered for large error rate. In this paper, following the approach of [Kaltofen et al., Proc. ISSAC'17], we improve the results of [Guerrini et al., Proc. ISIT'19] in two directions. First, we propose a new bound of the number of evaluations, lowering the dependency on the parameters of the linear system, based on work of [Cabay, Proc. SYMSAC'71]. Second, we introduce an early termination strategy in order to handle the unnecessary increase of the number of evaluations due to overestimation of the parameters of the system and on the bound on the number of errors.
Toward Semantic Representation of Middleware Services
Venue: arXiv cs.SE 2021
Year: 2021

Middleware is middle tier software that supports communications between two or more different applications, and between applications and shared services. Managing the complexity and heterogeneity of distributed infrastructures is the important role of middleware so that it can provide the simple programing environment for the developer of distributed application Middleware supports communications, information exchange, objects management, sending messages, in addition to provides many functions to build distributed systems. Many classification and definition has been provided for middleware, there is a great need to give a global look to middleware and their factors. Ontology for Middleware services is proposed in this paper, whose intention is to make a global look to middleware and enrich middleware services description. Using this ontology, we can discover and classify incoming services to their appropriate types according to their specification and characteristics. The proposed ontology is helpful for users to find their suitable services according to their own preferences. Besides this is an example, a parser has been used to classify many middleware service files.
Challenges in Net Neutrality Violation Detection: A Case Study of Wehe Tool and Improvements
Venue: arXiv cs.CY 2021
Year: 2021

We consider the problem of detecting deliberate traffic discrimination on the Internet. Given the complex nature of the Internet, detection of deliberate discrimination is not easy to detect, and tools developed so far suffer from various limitations. We study challenges in detecting the violations (focusing on the HTTPS traffic) and discuss possible mitigation approaches. We focus on `Wehe,' the most recent tool developed to detect net-neutrality violations. Wehe hosts traffic from all services of interest in a common server and replays them to mimic the behavior of the traffic from original servers. Despite Wehe's vast utility and possible influences over policy decisions, its mechanisms are not yet validated by others. In this work, we highlight critical weaknesses in Wehe where its replay traffic is not being correctly classified as intended services by the network middleboxes. We validate this observation using a commercial traffic shaper. We propose a new method in which the SNI parameter is set appropriately in the initial TLS handshake to overcome this weakness. Using commercial traffic shapers, we validate that SNI makes the replay traffic gets correctly classified as the intended traffic by the middleboxes. Our new approach thus provides a more realistic method for detecting neutrality violations of HTTPS traffic.
The Rise of Technology in Crime Prevention: Opportunities, Challenges and Practitioners Perspectives
Venue: arXiv cs.CY 2021
Year: 2021

Criminal activity is a prevalent issue in contemporary culture and society, with most nations facing unacceptable levels of crime. Technological innovation has been one of the main driving forces leading to the continuous improvement of crime control and crime prevention strategies (e.g. GPS tracking and tagging, video surveillance, etc.). Given this, it is a moral obligation for the research community to consider how the contemporary technological developments (i.e. Internet of Things (IoT), Machine Learning, Edge Computing)might help reduce crime worldwide. In line with this, this paper provides a discussion of how a sample of contemporary hardware and software-based technologies might help further reduce criminal actions. After a thorough analysis of a wide array of technologies and a number of workshops with organisations of interest, we believe that the adoption of novel technologies by vulnerable individuals, victim support organisations and law enforcement can help reduce the occurrence of criminal activity.
Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross-disciplinary Ethical Algorithm Research
Venue: arXiv cs.CY 2021
Year: 2021

The ethical consequences of, constraints upon and regulation of algorithms arguably represent the defining challenges of our age, asking us to reckon with the rise of computational technologies whose potential to radically transforming social and individual orders and identity in unforeseen ways is already being realised. Yet despite the multidisciplinary impact of this algorithmic turn, there remains some way to go in motivating the crossdisciplinary collaboration that is crucial to advancing feasible proposals for the ethical design, implementation and regulation of algorithmic and automated systems. In this work, we provide a framework to assist cross-disciplinary collaboration by presenting a Four C's Framework covering key computational considerations researchers across such diverse fields should consider when approaching these questions: (i) computability, (ii) complexity, (iii) consistency and (iv) controllability. In addition, we provide examples of how insights from ethics, philosophy and population ethics are relevant to and translatable within sciences concerned with the study and design of algorithms. Our aim is to set out a framework which we believe is useful for fostering cross-disciplinary understanding of pertinent issues in ethical algorithmic literature which is relevant considering the feasibility of ethical algorithmic governance, especially the impact of computational constraints upon algorithmic governance.
Amazon Product Recommender System
Venue: arXiv cs.IR 2021
Year: 2021

The number of reviews on Amazon has grown significantly over the years. Customers who made purchases on Amazon provide reviews by rating the product from 1 to 5 stars and sharing a text summary of their experience and opinion of the product. The ratings of a product are averaged to provide an overall product rating. We analyzed what ratings score customers give to a specific product (a music track) in order to build a recommender model for digital music tracks on Amazon. We test various traditional models along with our proposed deep neural network (DNN) architecture to predict the reviews rating score. The Amazon review dataset contains 200,000 data samples; we train the models on 70% of the dataset and test the performance of the models on the remaining 30% of the dataset.
Convolutional Neural Network Interpretability with General Pattern Theory
Venue: arXiv cs.LG 2021
Year: 2021

Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefits, such as more accountable usage, better algorithm maintenance and improvement. The complexity of dataset structure may contribute to the difficulty in solving interpretability problem arising from DNN's black-box mechanism. Thus, we propose to use pattern theory formulated by Ulf Grenander, in which data can be described as configurations of fundamental objects that allow us to investigate convolutional neural network's (CNN) interpretability in a component-wise manner. Specifically, U-Net-like structure is formed by attaching expansion blocks (EB) to ResNet, allowing it to perform semantic segmentation-like tasks at its EB output channels designed to be compatible with pattern theory's configurations. Through these modules, some heatmap-based explainable artificial intelligence (XAI) methods will be shown to extract explanations w.r.t individual generators that make up a single data sample, potentially reducing the impact of dataset's complexity to interpretability problem. The MNIST-equivalent dataset containing pattern theory's elements is designed to facilitate smoother entry into this framework, along which the theory's generative aspect is naturally presented.
Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits
Venue: arXiv cs.CY 2021
Year: 2021

While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.