OpenRaiser/Intern-Atlas huggingface dataset:
Instance generation tool for on-demand transportation problems
Venue: arXiv cs.SI 2022
Year: 2022

We present REQreate, a tool to generate instances for on-demand transportation problems. Such problems consist of optimizing the routes of vehicles according to passengers' demand for transportation under space and time restrictions (requests). REQreate is flexible and can be configured to generate instances for a large number of problems in this problem class. In this paper, we demonstrate this by generating instances for the Dial-a-Ride Problem (DARP) and On-demand Bus Routing Problem (ODBRP). In most of the literature, researchers either test their algorithms with instances based on artificial networks or perform real-life case studies on instances derived from a specific city or region. Furthermore, locations of requests for on-demand transportation problems are mostly randomly chosen according to a uniform distribution. The aim of REQreate is to overcome these non-realistic and overfitting shortcomings. Rather than relying on either artificial or limited data, we retrieve real-world street networks from OpenStreetMaps (OSM). To the best of our knowledge, this is the first tool to make use of real-life networks to generate instances for an extensive catalogue of existing and upcoming on-demand transportation problems. Additionally, we present a simple method that can be embedded in the instance generation process to produce distinct urban mobility patterns. We perform an analysis with real life datasets reported by rideshare companies and compare them with properties of synthetic instances generated with REQreate. Another contribution of this work is the introduction of the concept of instance similarity that serves as support to create diverse benchmark sets, in addition to properties (size, dynamism, urgency and geographic dispersion) that could be used to comprehend what affects the performance of algorithms.
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm
Venue: arXiv cs.LG 2022
Year: 2022

Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of high-performance attack generation methods, most of them directly add perturbations to original data and measure them using L_p norms; this can break the major structure of data, thus, creating invalid attacks. In this paper, we propose a black-box attack, which, instead of modifying original data, modifies latent features of data extracted by an autoencoder; then, we measure noises in semantic space to protect the semantics of data. We trained autoencoders on MNIST and CIFAR-10 datasets and found optimal adversarial perturbations using a genetic algorithm. Our approach achieved a 100% attack success rate on the first 100 data of MNIST and CIFAR-10 datasets with less perturbation than FGSM.
DSR: Towards Drone Image Super-Resolution
Venue: arXiv cs.CV 2022
Year: 2022

Despite achieving remarkable progress in recent years, single-image super-resolution methods are developed with several limitations. Specifically, they are trained on fixed content domains with certain degradations (whether synthetic or real). The priors they learn are prone to overfitting the training configuration. Therefore, the generalization to novel domains such as drone top view data, and across altitudes, is currently unknown. Nonetheless, pairing drones with proper image super-resolution is of great value. It would enable drones to fly higher covering larger fields of view, while maintaining a high image quality. To answer these questions and pave the way towards drone image super-resolution, we explore this application with particular focus on the single-image case. We propose a novel drone image dataset, with scenes captured at low and high resolutions, and across a span of altitudes. Our results show that off-the-shelf state-of-the-art networks witness a significant drop in performance on this different domain. We additionally show that simple fine-tuning, and incorporating altitude awareness into the network's architecture, both improve the reconstruction performance.
Two-level Closed Loops for RAN Slice Resources Management Serving Flying and Ground-based Cars
Venue: arXiv cs.NI 2022
Year: 2022

Flying and ground-based cars require various services such as autonomous driving, remote pilot, infotainment, and remote diagnosis. Each service requires specific Quality of Service (QoS) and network features. Therefore, network slicing can be a solution to fulfill the requirements of various services. Some services, such as infotainment, may have similar requirements to serve flying and ground-based cars. Therefore, some slices can serve both kinds of cars. However, when network slice resource sharing is too aggressive, slices can not meet QoS requirements, where resource under-provisioning causes the violation of QoS, and resource over-provisioning causes resource under-utilization. We propose two closed loops for managing RAN slice resources for cars to address these challenges. First, we present an auction mechanism for allocating Resource Block (RB) to the tenants who provide services to the cars using slices. Second, we design one closed loop that maps slices and services of tenants to virtual Open Distributed Units (vO-DUs) and assigns RB to vO-DUs for management purposes. Third, we design another closed loop for intra-slices RB scheduling to serve cars. Fourth, we present a reward function that interconnects these two closed loops to satisfy the time-varying demands of cars at each slice while meeting QoS requirements in terms of delay. Finally, we design distributed deep reinforcement learning approach to maximize the formulated reward function. The simulation results show that our approach satisfies more than 90% vO-DUs resource constraints and network slice requirements.
Social Diversity for ATL Repair
Venue: arXiv cs.SE 2022
Year: 2022

Model transformations play an essential role in the Model-Driven Engineering paradigm. Writing a correct transformation program requires to be proficient with the source and target modeling languages, to have a clear understanding of the mapping between the elements of the two, as well as to master the transformation language to properly describe the transformation. Transformation programs are thus complex and error-prone, and finding and fixing errors in such programs typically involve a tedious and time-consuming effort by developers. In this paper, we propose a novel search-based approach to automatically repair transformation programs containing many semantic errors. To prevent the fitness plateaus and the single fitness peak limitations, we leverage the notion of social diversity to promote repair patches tackling errors that are less covered by the other patches of the population. We evaluate our approach on 71 semantically incorrect transformation programs written in ATL, and containing up to five semantic errors simultaneously. The evaluation shows that integrating social diversity when searching for repair patches allows to improve the quality of those patches and to speed up the convergence even when up to five semantic errors are involved.
Collective phototactic robotectonics
Venue: arXiv cs.RO 2022
Year: 2022

Cooperative task execution, a hallmark of eusociality, is enabled by local interactions between the agents and the environment through a dynamically evolving communication signal. Inspired by the collective behavior of social insects whose dynamics is modulated by interactions with the environment, we show that a robot collective can successfully nucleate a construction site via a trapping instability and cooperatively build organized structures. The same robot collective can also perform de-construction with a simple change in the behavioral parameter. These behaviors belong to a two-dimensional phase space of cooperative behaviors defined by agent-agent interaction (cooperation) along one axis and the agent-environment interaction (collection and deposition) on the other. Our behavior-based approach to robot design combined with a principled derivation of local rules enables the collective to solve tasks with robustness to a dynamically changing environment and a wealth of complex behaviors.
DiVa: An Accelerator for Differentially Private Machine Learning
Venue: arXiv cs.AR 2022
Year: 2022

The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential privacy (DP) is rapidly gaining momentum in the industry as a practical standard for privacy protection. Despite DP's importance, however, little has been explored within the computer systems community regarding the implication of this emerging ML algorithm on system designs. In this work, we conduct a detailed workload characterization on a state-of-the-art differentially private ML training algorithm named DP-SGD. We uncover several unique properties of DP-SGD (e.g., its high memory capacity and computation requirements vs. non-private ML), root-causing its key bottlenecks. Based on our analysis, we propose an accelerator for differentially private ML named DiVa, which provides a significant improvement in compute utilization, leading to 2.6x higher energy-efficiency vs. conventional systolic arrays.
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning
Venue: arXiv cs.CV 2022
Year: 2022

Parotid gland tumor is a common type of head and neck tumor. Segmentation of the parotid glands and tumors by MR images is important for the treatment of parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently deep learning has developed rapidly, which can handle complex problems. However, most of the current deep learning methods for processing medical images are still based on supervised learning. Compared with natural images, medical images are difficult to acquire and costly to label. Contrastive learning, as an unsupervised learning method, can more effectively utilize unlabeled medical images. In this paper, we used a Transformer-based contrastive learning method and innovatively trained the contrastive learning network with transfer learning. Then, the output model was transferred to the downstream parotid segmentation task, which improved the performance of the parotid segmentation model on the test set. The improved DSC was 89.60%, MPA was 99.36%, MIoU was 85.11%, and HD was 2.98. All four metrics showed significant improvement compared to the results of using a supervised learning model as a pre-trained model for the parotid segmentation network. In addition, we found that the improvement of the segmentation network by the contrastive learning model was mainly in the encoder part, so this paper also tried to build a contrastive learning network for the decoder part and discussed the problems encountered in the process of building.
A Multi-Objective approach to the Electric Vehicle Routing Problem
Venue: arXiv cs.NE 2022
Year: 2022

The electric vehicle routing problem (EVRP) has garnered great interest from researchers and industrialists in an attempt to move from fuel-based vehicles to healthier and more efficient electric vehicles (EVs). While it seems that the EVRP should not be much different from traditional vehicle routing problems (VRPs), challenges like limited cruising time, long charging times, and limited availability of charging facilities for electric vehicles makes all the difference. Previous works target logistics and delivery-related solutions wherein a homogeneous fleet of commercial EVs have to return to the initial point after making multiple stops. On the opposing front, we solve a personal electric vehicle routing problem and provide an optimal route for a single vehicle in a long origin-destination (OD) trip. We perform multi-objective optimization - minimizing the total trip time and the cumulative cost of charging. In addition, we incorporate external and real-life elements like traffic at charging stations, detour distances for reaching a charging station, and variable costs of electricity at different charging stations into the problem formulation. In particular, we define a multi-objective mixed integer non-linear programming (MINLP) problem and obtain a feasible solution using the $ε$-constraint algorithm. We further implement meta-heuristic techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to obtain the most optimal route and hence, the objective values. The experiment is carried out for multiple self-generated data instances and the results are thereby compared.
What happens to psychological safety when going remote?
Venue: arXiv cs.SE 2022
Year: 2022

Psychological safety is a precondition for learning and success in software teams. Companies such as SavingsBank, which is discussed in this article, have developed good practices to facilitate psychological safety, most of which depend on face-to-face interaction. However, what happens to psychological safety when working remotely? In this article, we explore how Norwegian software developers experienced pandemic and post-pandemic remote work and describe simple behaviors and attitudes related to psychological safety. We pay special attention to the hybrid work mode, in which team members alternate days in the office with days working from home. Our key takeaway is that spontaneous interaction in the office facilitates psychological safety, while remote work increases the thresholds for both spontaneous interaction and psychological safety. We recommend that software teams synchronize their office presence to increase chances for spontaneous interaction in the office while benefitting from focused work while at home.
Another Use of SMOTE for Interpretable Data Collaboration Analysis
Venue: arXiv cs.LG 2022
Year: 2022

Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data. To construct the collaboration representations, each institution generates and shares a shareable anchor dataset and centralizes its intermediate representation. Although, random anchor dataset functions well for DC analysis in general, using an anchor dataset whose distribution is close to that of the raw dataset is expected to improve the recognition performance, particularly for the interpretable DC analysis. Based on an extension of the synthetic minority over-sampling technique (SMOTE), this study proposes an anchor data construction technique to improve the recognition performance without increasing the risk of data leakage. Numerical results demonstrate the efficiency of the proposed SMOTE-based method over the existing anchor data constructions for artificial and real-world datasets. Specifically, the proposed method achieves 9 percentage point and 38 percentage point performance improvements regarding accuracy and essential feature selection, respectively, over existing methods for an income dataset. The proposed method provides another use of SMOTE not for imbalanced data classifications but for a key technology of privacy-preserving integrated analysis.
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
Venue: arXiv cs.SD 2022
Year: 2022

Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e.g., by highlighting potentially relevant time-frequency bins in a spectrogram or time-pitch bins in a piano roll. This can be difficult to understand, particularly for musicologists without technical knowledge. To address this issue, we focus on more human-friendly explanations based on high-level musical concepts. Our research targets trained systems (post-hoc explanations) and explores two approaches: a supervised one, where the user can define a musical concept and test if it is relevant to the system; and an unsupervised one, where musical excerpts containing relevant concepts are automatically selected and given to the user for interpretation. We demonstrate both techniques on an existing symbolic composer classification system, showcase their potential, and highlight their intrinsic limitations.
GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
Venue: arXiv cs.CL 2022
Year: 2022

The dialogue-based relation extraction (DialogRE) task aims to predict the relations between argument pairs that appear in dialogue. Most previous studies utilize fine-tuning pre-trained language models (PLMs) only with extensive features to supplement the low information density of the dialogue by multiple speakers. To effectively exploit inherent knowledge of PLMs without extra layers and consider scattered semantic cues on the relation between the arguments, we propose a Guiding model with RelAtional Semantics using Prompt (GRASP). We adopt a prompt-based fine-tuning approach and capture relational semantic clues of a given dialogue with 1) an argument-aware prompt marker strategy and 2) the relational clue detection task. In the experiments, GRASP achieves state-of-the-art performance in terms of both F1 and F1c scores on a DialogRE dataset even though our method only leverages PLMs without adding any extra layers.
Computing Maximum Fixed Point Solutions over Feasible Paths in Data Flow Analyses
Venue: arXiv cs.SE 2022
Year: 2022

The control flow graph (CFG) representation of a procedure used by virtually all flow-sensitive program analyses, admits a large number of infeasible control flow paths i.e., these paths do not occur in any execution of the program. Hence the information reaching along infeasible paths in an analysis is spurious. This affects the precision of the conventional maximum fixed point (MFP) solution of the data flow analysis, because it includes the information reaching along all control flow paths. The existing approaches for removing this imprecision are either specific to a data flow problem with no straightforward generalization or involve control flow graph restructuring which may exponentially blow up the size of the CFG. We lift the notion of MFP solution to define the notion of feasible path MFP (FPMFP) solutions that exclude the data flowing along known infeasible paths. The notion of FPMFP is generic and does not involve CFG restructuring. Instead, it takes externally supplied information about infeasible paths and lifts any data flow analysis to an analysis that maintains the distinctions between different paths where these distinctions are beneficial, and ignores them where they are not. Thus it gets the benefit of a path-sensitive analysis where it is useful without performing a conventional path-sensitive analysis. We evaluated the proposed feasible path MFP solutions for reaching definitions analysis and potentially uninitialized variable analysis on 30 benchmarks. The evaluation results indicate that precision improvement in these two analyses respectively reduce the number def-use pairs by up to 13.6% (average 2.87%, geometric mean 1.75%), and reduce the potentially uninitialized variable alarms by up to 100% (average 18.5%, geo. mean 3%). We found that the FPMFP computation time was 2.9X of MFP computation time on average.
Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention
Venue: arXiv cs.CV 2022
Year: 2022

Although convolutional representation of multiscale sparse tensor demonstrated its superior efficiency to accurately model the occupancy probability for the compression of geometry component of dense object point clouds, its capacity for representing sparse LiDAR point cloud geometry (PCG) was largely limited. This is because 1) fixed receptive field of the convolution cannot characterize extremely and unevenly distributed sparse LiDAR points very well; and 2) pretrained convolutions with fixed weights are insufficient to dynamically capture information conditioned on the input. This work therefore suggests the neighborhood point attention (NPA) to tackle them, where we first use k nearest neighbors (kNN) to construct adaptive local neighborhood; and then leverage the self-attention mechanism to dynamically aggregate information within this neighborhood. Such NPA is devised as a NPAFormer to best exploit cross-scale and same-scale correlations for geometric occupancy probability estimation. Compared with the anchor using standardized G-PCC, our method provides >17% BD-rate gains for lossy compression, and >14% bitrate reduction for lossless scenario using popular LiDAR point clouds in SemanticKITTI and Ford datasets. Compared with the state-of-the-art (SOTA) solution using attention optimized octree coding method, our approach requires much less decoding runtime with about 640 times speedup on average, while still presenting better compression efficiency.
Untangling the Dueling Expert Witnesses: Comparing Ensemble Methods in Pennsylvania's Redistricting Plans
Venue: arXiv cs.CY 2022
Year: 2022

Ensembles of random legislative districts are a valuable tool for assessing whether a proposed district plan is an outlier or gerrymander. Expert witnesses have presented these in litigation using various methods, and unsurprisingly, they often disagree. Recent open source methods now permit independent validation of expert witness testimony. Here, we compare ensembles for the Pennsylvania House and Congressional districts calculated using "Redist" and "Gerrychain" further incorporating constraints restricting county and municipal boundary splitting, as required by Pennsylvania for legal plans. We compare results to expert witness testimony submitted by Republican and Democratic parties. We confirm some of the testimony but could not reproduce all of it, struggling with metrics based on a heuristic "sum of votes index" rathern than a straightforward average of metrics across multiple elections. We recommend against relying on analytics based on summing votes from multiple elections to create vote incides and derivative metrics as these are inherently poorly behaved. To promote transparency, we recommend that where possible, expert witness testimony be based solely on publicly available election data as opposed to proprietary data closely held by political parties.