OpenRaiser/Intern-Atlas huggingface dataset:
Classical to Quantum Software Migration Journey Begins: A Conceptual Readiness Model
Venue: arXiv cs.SE 2022
Year: 2022

With recent advances in the development of more powerful quantum computers, the re-search area of quantum software engineering is emerging. Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it has been drawing increasing attention recently to provide concepts, principles, and guidelines to address the ongoing challenges of quantum software development. The importance of the topic motivated us to voice out a call for action to develop a readiness model that will help an organization assess its capability of migration from classic software engineering to quan-tum software engineering. The proposed model will be based on the existing multivocal literature, industrial empirical study, understanding of the process areas, challenging factors and enablers that could impact the quantum software engineering process. We believe that the proposed model will provide a roadmap for software development organizations to measure their readiness concerning to transformation from classic to quantum software engineering by suggesting best practices and highlighting important process areas, challeng-es, and enablers.
Empirically grounded agent-based policy evaluation of the adoption of sustainable lighting under the European Ecodesign Directive
Venue: arXiv cs.MA 2022
Year: 2022

Twelve years ago, the European Union began with the gradual phase-out of energy-inefficient incandescent light bulbs under the Ecodesign Directive. In this work, we implement an agent-based simulation to model the consumer behaviour in the EU lighting market with the goal to explain consumer behaviour and explore alternative policies. Agents are based on the Consumat II model, have individual preferences based on empirical market research, gather experience from past actions, and socially interact with each other in a dynamic environment. Our findings suggest that the adoption of energy-friendly lighting alternatives was hindered by a low level of consumer interest combined with high-enough levels of satisfaction about incandescent bulbs and that information campaigns can partially address this. These findings offer insight into both individual-level driving forces of behaviour and society-level outcomes in a niche market. With this, our work demonstrates the strengths of agent-based models for policy generation and evaluation.
Personalized Federated Learning with Communication Compression
Venue: arXiv cs.LG 2022
Year: 2022

In contrast to training traditional machine learning (ML) models in data centers, federated learning (FL) trains ML models over local datasets contained on resource-constrained heterogeneous edge devices. Existing FL algorithms aim to learn a single global model for all participating devices, which may not be helpful to all devices participating in the training due to the heterogeneity of the data across the devices. Recently, Hanzely and Richtárik (2020) proposed a new formulation for training personalized FL models aimed at balancing the trade-off between the traditional global model and the local models that could be trained by individual devices using their private data only. They derived a new algorithm, called Loopless Gradient Descent (L2GD), to solve it and showed that this algorithms leads to improved communication complexity guarantees in regimes when more personalization is required. In this paper, we equip their L2GD algorithm with a bidirectional compression mechanism to further reduce the communication bottleneck between the local devices and the server. Unlike other compression-based algorithms used in the FL-setting, our compressed L2GD algorithm operates on a probabilistic communication protocol, where communication does not happen on a fixed schedule. Moreover, our compressed L2GD algorithm maintains a similar convergence rate as vanilla SGD without compression. To empirically validate the efficiency of our algorithm, we perform diverse numerical experiments on both convex and non-convex problems and using various compression techniques.
Universal Backdoor Attacks Detection via Adaptive Adversarial Probe
Venue: arXiv cs.CV 2022
Year: 2022

Extensive evidence has demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks, which motivates the development of backdoor attacks detection. Most detection methods are designed to verify whether a model is infected with presumed types of backdoor attacks, yet the adversary is likely to generate diverse backdoor attacks in practice that are unforeseen to defenders, which challenge current detection strategies. In this paper, we focus on this more challenging scenario and propose a universal backdoor attacks detection method named Adaptive Adversarial Probe (A2P). Specifically, we posit that the challenge of universal backdoor attacks detection lies in the fact that different backdoor attacks often exhibit diverse characteristics in trigger patterns (i.e., sizes and transparencies). Therefore, our A2P adopts a global-to-local probing framework, which adversarially probes images with adaptive regions/budgets to fit various backdoor triggers of different sizes/transparencies. Regarding the probing region, we propose the attention-guided region generation strategy that generates region proposals with different sizes/locations based on the attention of the target model, since trigger regions often manifest higher model activation. Considering the attack budget, we introduce the box-to-sparsity scheduling that iteratively increases the perturbation budget from box to sparse constraint, so that we could better activate different latent backdoors with different transparencies. Extensive experiments on multiple datasets (CIFAR-10, GTSRB, Tiny-ImageNet) demonstrate that our method outperforms state-of-the-art baselines by large margins (+12%).
A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks
Venue: arXiv cs.RO 2022
Year: 2022

Robots working in real environments need to adapt to unexpected changes to avoid failures. This is an open and complex challenge that requires robots to timely predict and identify the causes of failures to prevent them. In this paper, we present a causal method that will enable robots to predict when errors are likely to occur and prevent them from happening by executing a corrective action. First, we propose a causal-based method to detect the cause-effect relationships between task executions and their consequences by learning a causal Bayesian network (BN). The obtained model is transferred from simulated data to real scenarios to demonstrate the robustness and generalization of the obtained models. Based on the causal BN, the robot can predict if and why the executed action will succeed or not in its current state. Then, we introduce a novel method that finds the closest state alternatives through a contrastive Breadth-First-Search if the current action was predicted to fail. We evaluate our approach for the problem of stacking cubes in two cases; a) single stacks (stacking one cube) and; b) multiple stacks (stacking three cubes). In the single-stack case, our method was able to reduce the error rate by 97%. We also show that our approach can scale to capture multiple actions in one model, allowing to measure timely shifted action effects, such as the impact of an imprecise stack of the first cube on the stacking success of the third cube. For these complex situations, our model was able to prevent around 75% of the stacking errors, even for the challenging multiple-stack scenario. Thus, demonstrating that our method is able to explain, predict, and prevent execution failures, which even scales to complex scenarios that require an understanding of how the action history impacts future actions.
On the Energy Consumption of Different Dataframe Processing Libraries -- An Exploratory Study
Venue: arXiv cs.SE 2022
Year: 2022

Background: The energy consumption of machine learning and its impact on the environment has made energy efficient ML an emerging area of research. However, most of the attention stays focused on the model creation and the training and inferencing phase. Data oriented stages like preprocessing, cleaning and exploratory analysis form a critical part of the machine learning workflow. However, the energy efficiency of these stages have gained little attention from the researchers. Aim: Our study aims to explore the energy consumption of different dataframe processing libraries as a first step towards studying the energy efficiency of the data oriented stages of the machine learning pipeline. Method: We measure the energy consumption of 3 popular libraries used to work with dataframes, namely Pandas, Vaex and Dask for 21 different operations grouped under 4 categories on 2 datasets. Results: The results of our analysis show that for a given dataframe processing operation, the choice of library can indeed influence the energy consumption with some libraries consuming 202 times lesser energy over others. Conclusion: The results of our study indicates that there is a potential for optimizing the energy consumption of the data oriented stages of the machine learning pipeline and further research is needed in the direction.
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints
Venue: arXiv cs.CV 2022
Year: 2022

Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with densely captured input images. However, the geometry of NeRF is extremely under-constrained given sparse views, resulting in significant degradation of novel view synthesis quality. Inspired by self-supervised depth estimation methods, we propose StructNeRF, a solution to novel view synthesis for indoor scenes with sparse inputs. StructNeRF leverages the structural hints naturally embedded in multi-view inputs to handle the unconstrained geometry issue in NeRF. Specifically, it tackles the texture and non-texture regions respectively: a patch-based multi-view consistent photometric loss is proposed to constrain the geometry of textured regions; for non-textured ones, we explicitly restrict them to be 3D consistent planes. Through the dense self-supervised depth constraints, our method improves both the geometry and the view synthesis performance of NeRF without any additional training on external data. Extensive experiments on several real-world datasets demonstrate that StructNeRF surpasses state-of-the-art methods for indoor scenes with sparse inputs both quantitatively and qualitatively.
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing
Venue: arXiv cs.LG 2022
Year: 2022

Amazon Customer Service provides real-time support for millions of customer contacts every year. While bot-resolver helps automate some traffic, we still see high demand for human agents, also called subject matter experts (SMEs). Customers outreach with questions in different domains (return policy, device troubleshooting, etc.). Depending on their training, not all SMEs are eligible to handle all contacts. Routing contacts to eligible SMEs turns out to be a non-trivial problem because SMEs' domain eligibility is subject to training quality and can change over time. To optimally recommend SMEs while simultaneously learning the true eligibility status, we propose to formulate the routing problem with a nonparametric contextual bandit algorithm (K-Boot) plus an eligibility control (EC) algorithm. K-Boot models reward with a kernel smoother on similar past samples selected by $k$-NN, and Bootstrap Thompson Sampling for exploration. EC filters arms (SMEs) by the initially system-claimed eligibility and dynamically validates the reliability of this information. The proposed K-Boot is a general bandit algorithm, and EC is applicable to other bandits. Our simulation studies show that K-Boot performs on par with state-of-the-art Bandit models, and EC boosts K-Boot performance when stochastic eligibility signal exists.
Solving the Job Shop Scheduling Problem with Ant Colony Optimization
Venue: arXiv cs.MA 2022
Year: 2022

The Job Shop Schedule Problem (JSSP) refers to the ability of an agent to allocate tasks that should be executed in a specified time in a machine from a cluster. The task allocation can be achieved from several methods, however, this report it is explored the ability of the Ant Colony Optimization to generate feasible solutions for several JSSP instances. This proposal models the JSSP as a complete graph since disjunct models can prevent the ACO from exploring all the search space. Several instances of the JSSP were used to evaluate the proposal. Results suggest that the algorithm can reach optimum solutions for easy and harder instances with a selection of parameters.
Continuous Planning for Inertial-Aided Systems
Venue: arXiv cs.RO 2022
Year: 2022

Inertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories which aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the RRT* planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks.
Prototyping a Serial Number Based Authentication Model for a Computer in a Wireless Local Area Network
Venue: arXiv cs.CR 2022
Year: 2022

With the increase of wireless LAN usage in homes and enterprises due to its numerous benefits, authenticating the ever increasing number of devices and their users has become a challenge to proprietors of such kind networks. A MAC address, a physical network address that is used as basis for this study, has a copy of its value in the system software that can be spoofed and altered rendering the address not unique, not secure and unreliable. On the contrary, a computers serial number is hard-coded in the system hardware only and therefore cannot be spoofed and altered making it unique, secure and reliable. The research, therefore, was aimed at designing a model that demonstrates how a computers serial number can be used for authenticating a computer in a wireless local area network. In order to achieve the research objective, the study examined the inbuilt access and use of a computers serial number prototype model as an alternative method of authenticating devices in a network. Design science research methodology that involved design and development, demonstration and model evaluation was employed. A Serial Number Based Authentication Prototype or SNAP was therefore designed using state chart and flow chart diagrams based on dynamic programming, developed over evolutionary prototyping and test run on a static experimental design using Java Development Kit and MySQL platforms to demonstrate, as proof of concept, that a computers serial number can be used to authenticate a computer in a wireless local area network. From the test runs whose outcomes were the binary values yes or no, it was found out that SNAP can actually allow or deny, enable or disable a computer in a network based on the computers serial number. The researcher therefore, recommends that the prototype be scaled up, then adopted as a network device authentication method.
Edge Coverage Path Planning for Robot Mowing
Venue: arXiv cs.RO 2022
Year: 2022

Thanks to the rapid evolvement of robotic technologies, robot mowing is emerging to liberate humans from the tedious and time-consuming landscape work. Traditionally, robot mowing is perceived as a "Coverage Path Planning" problem, with a simplification that converts non-convex obstacles into convex obstacles. Besides, the converted obstacles are commonly dilated by the robot's circumcircle for collision avoidance. However when applied to robot mowing, an obstacle in a lawn is usually non-convex, imagine a garden on the lawn, such that the mentioned obstacle processing methods would fill in some concave areas so that they are not accessible to the robot anymore and hence produce inescapable uncut areas along the lawn edge, which dulls the landscape's elegance and provokes rework. To shrink the uncut area around the lawn edge we hereby reframe the problem into a brand new problem, named the "Edge Coverage Path Planning" problem that is dedicated to path planning with the objective to cover the edge. Correspondingly, we propose two planning methods, the "big and small disk" and the "sliding chopstick" planning method to tackle the problem by leveraging image morphological processing and computational geometry skills. By validation, our proposed methods can outperform the traditional "dilation-by-circumcircle" method.
Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders
Venue: arXiv cs.CV 2022
Year: 2022

Chromosome analysis and identification from metaphase images is a critical part of cytogenetics based medical diagnosis. It is mainly used for identifying constitutional, prenatal and acquired abnormalities in the diagnosis of genetic diseases and disorders. The process of identification of chromosomes from metaphase is a tedious one and requires trained personnel and several hours to perform. Challenge exists especially in handling touching, overlapping and clustered chromosomes in metaphase images, which if not segmented properly would result in wrong classification. We propose a method to automate the process of detection and segmentation of chromosomes from a given metaphase image, and in using them to classify through a Deep CNN architecture to know the chromosome type. We have used two methods to handle the separation of overlapping chromosomes found in metaphases - one method involving watershed algorithm followed by autoencoders and the other a method purely based on watershed algorithm. These methods involve a combination of automation and very minimal manual effort to perform the segmentation, which produces the output. The manual effort ensures that human intuition is taken into consideration, especially in handling touching, overlapping and cluster chromosomes. Upon segmentation, individual chromosome images are then classified into their respective classes with 95.75\% accuracy using a Deep CNN model. Further, we impart a distribution strategy to classify these chromosomes from the given output (which typically could consist of 46 individual images in a normal scenario for human beings) into its individual classes with an accuracy of 98\%. Our study helps conclude that pure manual effort involved in chromosome segmentation can be automated to a very good level through image processing techniques to produce reliable and satisfying results.
Bias Impact Analysis of AI in Consumer Mobile Health Technologies: Legal, Technical, and Policy
Venue: arXiv cs.CY 2022
Year: 2022

Today's large-scale algorithmic and automated deployment of decision-making systems threatens to exclude marginalized communities. Thus, the emergent danger comes from the effectiveness and the propensity of such systems to replicate, reinforce, or amplify harmful existing discriminatory acts. Algorithmic bias exposes a deeply entrenched encoding of a range of unwanted biases that can have profound real-world effects that manifest in domains from employment, to housing, to healthcare. The last decade of research and examples on these effects further underscores the need to examine any claim of a value-neutral technology. This work examines the intersection of algorithmic bias in consumer mobile health technologies (mHealth). We include mHealth, a term used to describe mobile technology and associated sensors to provide healthcare solutions through patient journeys. We also include mental and behavioral health (mental and physiological) as part of our study. Furthermore, we explore to what extent current mechanisms - legal, technical, and or normative - help mitigate potential risks associated with unwanted bias in intelligent systems that make up the mHealth domain. We provide additional guidance on the role and responsibilities technologists and policymakers have to ensure that such systems empower patients equitably.
Boosting Robustness Verification of Semantic Feature Neighborhoods
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural networks have been shown to be vulnerable to adversarial attacks that perturb inputs based on semantic features. Existing robustness analyzers can reason about semantic feature neighborhoods to increase the networks' reliability. However, despite the significant progress in these techniques, they still struggle to scale to deep networks and large neighborhoods. In this work, we introduce VeeP, an active learning approach that splits the verification process into a series of smaller verification steps, each is submitted to an existing robustness analyzer. The key idea is to build on prior steps to predict the next optimal step. The optimal step is predicted by estimating the certification velocity and sensitivity via parametric regression. We evaluate VeeP on MNIST, Fashion-MNIST, CIFAR-10 and ImageNet and show that it can analyze neighborhoods of various features: brightness, contrast, hue, saturation, and lightness. We show that, on average, given a 90 minute timeout, VeeP verifies 96% of the maximally certifiable neighborhoods within 29 minutes, while existing splitting approaches verify, on average, 73% of the maximally certifiable neighborhoods within 58 minutes.
Data Innovation in Demography, Migration and Human Mobility
Venue: arXiv cs.CY 2022
Year: 2022

With the consolidation of the culture of evidence-based policymaking, the availability of data has become central to policymakers. Nowadays, innovative data sources offer an opportunity to describe demographic, mobility, and migratory phenomena more accurately by making available large volumes of real-time and spatially detailed data. At the same time, however, data innovation has led to new challenges (ethics, privacy, data governance models, data quality) for citizens, statistical offices, policymakers and the private sector. Focusing on the fields of demography, mobility, and migration studies, the aim of this report is to assess the current state of data innovation in the scientific literature as well as to identify areas in which data innovation has the most concrete potential for policymaking. Consequently, this study has reviewed more than 300 articles and scientific reports, as well as numerous tools, that employed non-traditional data sources to measure vital population events (mortality, fertility), migration and human mobility, and the population change and population distribution. The specific findings of our report form the basis of a discussion on a) how innovative data is used compared to traditional data sources; b) domains in which innovative data have the greatest potential to contribute to policymaking; c) the prospects of innovative data transition towards systematically contributing to official statistics and policymaking.