OpenRaiser/Intern-Atlas huggingface dataset:
Information Access Equality on Network Generative Models
Venue: arXiv cs.SI 2021
Year: 2021

It is well known that networks generated by common mechanisms such as preferential attachment and homophily can disadvantage the minority group by limiting their ability to establish links with the majority group. This has the effect of limiting minority nodes' access to information. We present the results of an empirical study on the equality of information access in network models with different growth mechanisms and spreading processes. For growth mechanisms, we focus on the majority/minority dichotomy, homophily, preferential attachment, and diversity. For spreading processes, we investigate simple vs. complex contagions, different transmission rates within and between groups, and various seeding conditions. We observe two phenomena. First, information access equality is a complex interplay between network structures and the spreading processes. Second, there is a trade-off between equality and efficiency of information access under certain circumstances (e.g., when inter-group edges are low and information transmits asymmetrically). Our findings can be used to make recommendations for mechanistic design of social networks with information access equality.
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?
Venue: arXiv cs.LG 2021
Year: 2021

Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent 'best practices' around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a "ground truth" or "gold standard" of training data is available, it is especially relevant to discuss issues around the equally-important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise.
Design Smells in Deep Learning Programs: An Empirical Study
Venue: arXiv cs.SE 2021
Year: 2021

Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural network (DNN) and then training it on a dataset. This process requires that developers make multiple architectural (e.g., type, size, number, and order of layers) and configuration (e.g., optimizer, regularization methods, and activation functions) choices that affect the quality of the DL models, and consequently software quality. An under-specified or poorly-designed DL model may train successfully but is likely to perform poorly when deployed in production. Design smells in DL programs are poor design and-or configuration decisions taken during the development of DL components, that are likely to have a negative impact on the performance (i.e., prediction accuracy) and then quality of DL based software systems. In this paper, we present a catalogue of 8 design smells for a popular DL architecture, namely deep Feedforward Neural Networks which is widely employed in industrial applications. The design smells were identified through a review of the existing literature on DL design and a manual inspection of 659 DL programs with performance issues and design inefficiencies. The smells are specified by describing their context, consequences, and recommended refactorings. To provide empirical evidence on the relevance and perceived impact of the proposed design smells, we conducted a survey with 81 DL developers. In general, the developers perceived the proposed design smells as reflective of design or implementation problems, with agreement levels varying between 47\% and 68\%.
Polarized skylight orientation determination artificial neural network
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.
Symbol Elimination for Parametric Second-Order Entailment Problems (with Applications to Problems in Wireless Network Theory)
Venue: arXiv cs.LO 2021
Year: 2021

We analyze possibilities of second-order quantifier elimination for formulae containing parameters -- constants or functions. For this, we use a constraint resolution calculus obtained from specializing the hierarchical superposition calculus. If saturation terminates, we analyze possibilities of obtaining weakest constraints on parameters which guarantee satisfiability. If the saturation does not terminate, we identify situations in which finite representations of infinite saturated sets exist. We identify situations in which entailment between formulae expressed using second-order quantification can be effectively checked. We illustrate the ideas on a series of examples from wireless network research.
An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels
Venue: arXiv cs.LG 2021
Year: 2021

We consider the problem of training robust and accurate deep neural networks (DNNs) when subject to various proportions of noisy labels. Large-scale datasets tend to contain mislabeled samples that can be memorized by DNNs, impeding the performance. With appropriate handling, this degradation can be alleviated. There are two problems to consider: how to distinguish clean samples and how to deal with noisy samples. In this paper, we present Ensemble Noise-robust K-fold Cross-Validation Selection (E-NKCVS) to effectively select clean samples from noisy data, solving the first problem. For the second problem, we create a new pseudo label for any sample determined to have an uncertain or likely corrupt label. E-NKCVS obtains multiple predicted labels for each sample and the entropy of these labels is used to tune the weight given to the pseudo label and the given label. Theoretical analysis and extensive verification of the algorithms in the noisy label setting are provided. We evaluate our approach on various image and text classification tasks where the labels have been manually corrupted with different noise ratios. Additionally, two large real-world noisy datasets are also used, Clothing-1M and WebVision. E-NKCVS is empirically shown to be highly tolerant to considerable proportions of label noise and has a consistent improvement over state-of-the-art methods. Especially on more difficult datasets with higher noise ratios, we can achieve a significant improvement over the second-best model. Moreover, our proposed approach can easily be integrated into existing DNN methods to improve their robustness against label noise.
Proof Generation in CDSAT
Venue: arXiv cs.LO 2021
Year: 2021

The main ideas in the CDSAT (Conflict-Driven Satisfiability) framework for SMT are summarized, leading to approaches to proof generation in CDSAT.
An Inverse QSAR Method Based on Linear Regression and Integer Programming
Venue: arXiv cs.LG 2021
Year: 2021

Recently a novel framework has been proposed for designing the molecular structure of chemical compounds using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). In the framework, we first define a feature vector $f(C)$ of a chemical graph $C$ and construct an ANN that maps $x=f(C)$ to a predicted value $η(x)$ of a chemical property $π$ to $C$. After this, we formulate an MILP that simulates the computation process of $f(C)$ from $C$ and that of $η(x)$ from $x$. Given a target value $y^*$ of the chemical property $π$, we infer a chemical graph $C^\dagger$ such that $η(f(C^\dagger))=y^*$ by solving the MILP. In this paper, we use linear regression to construct a prediction function $η$ instead of ANNs. For this, we derive an MILP formulation that simulates the computation process of a prediction function by linear regression. The results of computational experiments suggest our method can infer chemical graphs with around up to 50 non-hydrogen atoms.
CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference
Venue: arXiv cs.AR 2021
Year: 2021

A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference. It leverages a novel charge-domain multiply-and-accumulate (MAC) mechanism and circuitry to achieve superior linearity under process variations compared to conventional IMC designs. The adopted semi-parallel architecture efficiently stores filters from multiple CNN layers by sharing eight standard 6T SRAM cells with one charge-domain MAC circuit. Moreover, up to six levels of bit-width of weights with two encoding schemes and eight levels of input activations are supported. A 7-bit charge-injection SAR (ciSAR) analog-to-digital converter (ADC) getting rid of sample and hold (S&H) and input/reference buffers further improves the overall energy efficiency and throughput. A 65-nm prototype validates the excellent linearity and computing accuracy of CAP-RAM. A single 512x128 macro stores a complete pruned and quantized CNN model to achieve 98.8% inference accuracy on the MNIST data set and 89.0% on the CIFAR-10 data set, with a 573.4-giga operations per second (GOPS) peak throughput and a 49.4-tera operations per second (TOPS)/W energy efficiency.
MSE Loss with Outlying Label for Imbalanced Classification
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose mean squared error (MSE) loss with outlying label for class imbalanced classification. Cross entropy (CE) loss, which is widely used for image recognition, is learned so that the probability value of true class is closer to one by back propagation. However, for imbalanced datasets, the learning is insufficient for the classes with a small number of samples. Therefore, we propose a novel classification method using the MSE loss that can be learned the relationships of all classes no matter which image is input. Unlike CE loss, MSE loss is possible to equalize the number of back propagation for all classes and to learn the feature space considering the relationships between classes as metric learning. Furthermore, instead of the usual one-hot teacher label, we use a novel teacher label that takes the number of class samples into account. This induces the outlying label which depends on the number of samples in each class, and the class with a small number of samples has outlying margin in a feature space. It is possible to create the feature space for separating high-difficulty classes and low-difficulty classes. By the experiments on imbalanced classification and semantic segmentation, we confirmed that the proposed method was much improved in comparison with standard CE loss and conventional methods, even though only the loss and teacher labels were changed.
COSPEX: A Program Comprehension Tool for Novice Programmers
Venue: arXiv cs.SE 2021
Year: 2021

Developers often encounter unfamiliar code during software maintenance which consumes a significant amount of time for comprehension, especially for novice programmers. Automated techniques that analyze a source code and present key information to the developers can lead to an effective comprehension of the code. Researchers have come up with automated code summarization techniques that focus on code summarization by generating brief summaries rather than aiding its comprehension. Existing debuggers represent the execution states of the program but they do not show the complete execution at a single point. Studies have revealed that the effort required for program comprehension can be reduced if novice programmers are provided with worked examples. Hence, we propose COSPEX (Comprehension using Summarization via Program Execution) - an Atom plugin that dynamically extracts key information for every line of code executed and presents it to the developers in the form of an interactive example-like dynamic information instance. As a preliminary evaluation, we presented 14 undergraduates having Python programming experience up to 1 year with a code comprehension task in a user survey. We observed that COSPEX helped novice programmers in program comprehension and improved their understanding of the code execution. The source code and tool are available at: https://bit.ly/3utHOBM, and the demo on Youtube is available at: https://bit.ly/2Sp08xQ.
SOCluster- Towards Intent-based Clustering of Stack Overflow Questions using Graph-Based Approach
Venue: arXiv cs.SE 2021
Year: 2021

Stack Overflow (SO) platform has a huge dataset of questions and answers driven by interactions between users. But the count of unanswered questions is continuously rising. This issue is common across various community Question & Answering platforms (Q&A) such as Yahoo, Quora and so on. Clustering is one of the approaches used by these communities to address this challenge. Specifically, Intent-based clustering could be leveraged to answer unanswered questions using other answered questions in the same cluster and can also improve the response time for new questions. It is here, we propose SOCluster, an approach and a tool to cluster SO questions based on intent using a graph-based clustering approach. We selected four datasets of 10k, 20k, 30k & 40k SO questions without code-snippets or images involved, and performed intent-based clustering on them. We have done a preliminary evaluation of our tool by analyzing the resultant clusters using the commonly used metrics of Silhouette coefficient, Calinkski-Harabasz Index, & Davies-Bouldin Index. We performed clustering for 8 different threshold similarity values and analyzed the intriguing trends reflected by the output clusters through the three evaluation metrics. At 90% threshold similarity, it shows the best value for the three evaluation metrics on all four datasets. The source code and tool are available for download on Github at: https://github.com/Liveitabhi/SOCluster, and the demo can be found here: https://youtu.be/uyn8ie4h3NY.
An NLG pipeline for a legal expert system: a work in progress
Venue: arXiv cs.CL 2021
Year: 2021

We present the NLG component for L4, a prototype domain-specific language (DSL) for drafting laws and contracts. As a concrete use case, we describe a pipeline for a legal expert system created from L4 code. The NLG component is used in two steps. The first step is to create an interview, whose answers are processed into a query for an automated reasoner. The second step is to render the answers of the reasoner in natural language.
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning is vulnerable to adversarial examples. Many defenses based on randomized neural networks have been proposed to solve the problem, but fail to achieve robustness against attacks using proxy gradients such as the Expectation over Transformation (EOT) attack. We investigate the effect of the adversarial attacks using proxy gradients on randomized neural networks and demonstrate that it highly relies on the directional distribution of the loss gradients of the randomized neural network. We show in particular that proxy gradients are less effective when the gradients are more scattered. To this end, we propose Gradient Diversity (GradDiv) regularizations that minimize the concentration of the gradients to build a robust randomized neural network. Our experiments on MNIST, CIFAR10, and STL10 show that our proposed GradDiv regularizations improve the adversarial robustness of randomized neural networks against a variety of state-of-the-art attack methods. Moreover, our method efficiently reduces the transferability among sample models of randomized neural networks.
Dynamical System Parameter Identification using Deep Recurrent Cell Networks
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear time-invariant dynamical systems, the topic of damping factor identification is studied. By utilizing a six-layer deep neural network with different recurrent cells, namely GRUs, LSTMs or BiLSTMs; and by feeding input-output sequence pairs captured from a dynamical system simulator, we search for an effective deep recurrent architecture in order to resolve damping factor identification problem. Our study results show that, although previously not utilized for this task in the literature, bidirectional gated recurrent cells (BiLSTMs) provide better parameter identification results when compared to unidirectional gated recurrent memory cells such as GRUs and LSTM. Thus, indicating that an input-output sequence pair of finite length, collected from a dynamical system and when observed anachronistically, may carry information in both time directions for prediction of a dynamical systems parameter.
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

After the 2017 TuSimple Lane Detection Challenge, its evaluation based on accuracy and F1 score has become the de facto standard to measure the performance of lane detection methods. In this work, we conduct the first large-scale empirical study to evaluate the robustness of state-of-the-art lane detection methods under physical-world adversarial attacks in autonomous driving. We evaluate 4 major types of lane detection approaches with the conventional evaluation and end-to-end evaluation in autonomous driving scenarios and then discuss the security proprieties of each lane detection model. We demonstrate that the conventional evaluation fails to reflect the robustness in end-to-end autonomous driving scenarios. Our results show that the most robust model on the conventional metrics is the least robust in the end-to-end evaluation. Although the competition dataset and its metrics have played a substantial role in developing performant lane detection methods along with the rapid development of deep neural networks, the conventional evaluation is becoming obsolete and the gap between the metrics and practicality is critical. We hope that our study will help the community make further progress in building a more comprehensive framework to evaluate lane detection models.
Convolutional LSTM models to estimate network traffic
Venue: arXiv cs.NI 2021
Year: 2021

Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute on-going large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration - details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration - is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.
A Latency-Aware Real-Time Video Surveillance Demo: Network Slicing for Improving Public Safety
Venue: arXiv cs.NI 2021
Year: 2021

We report the automated deployment of 5G services across a latency-aware, semidisaggregated, and virtualized metro network. We summarize the key findings in a detailed analysis of end-to-end latency, service setup time, and soft-failure detection time.
A Temporal Logic for Strategic Hyperproperties
Venue: arXiv cs.LO 2021
Year: 2021

Hyperproperties are commonly used in computer security to define information-flow policies and other requirements that reason about the relationship between multiple computations. In this paper, we study a novel class of hyperproperties where the individual computation paths are chosen by the strategic choices of a coalition of agents in a multi-agent system. We introduce HyperATL*, an extension of computation tree logic with path variables and strategy quantifiers. HyperATL* can express strategic hyperproperties, such as that the scheduler in a concurrent system has a strategy to avoid information leakage. HyperATL* is particularly useful to specify asynchronous hyperproperties, i.e., hyperproperties where the speed of the execution on the different computation paths depends on the choices of the scheduler. Unlike other recent logics for the specification of asynchronous hyperproperties, our logic is the first to admit decidable model checking for the full logic. We present a model checking algorithm for HyperATL* based on alternating word automata and show that our algorithm is asymptotically optimal by providing a matching lower bound. We have implemented a prototype model checker for a fragment of HyperATL*, able to check various security properties on small programs.
Semantic Segmentation Alternative Technique: Segmentation Domain Generation
Venue: arXiv cs.CV 2021
Year: 2021

Detecting objects of interest in images was always a compelling task to automate. In recent years this task was more and more explored using deep learning techniques, mostly using region-based convolutional networks. In this project we propose an alternative semantic segmentation technique making use of Generative Adversarial Networks. We consider semantic segmentation to be a domain transfer problem. Thus, we train a feed forward network (FFNN) to receive as input a seed real image and generate as output its segmentation mask.
Automated Malware Design for Cyber Physical Systems
Venue: arXiv cs.CR 2021
Year: 2021

The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.