OpenRaiser/Intern-Atlas huggingface dataset:
$\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-Additive Hadamard Codes
Venue: arXiv cs.IT 2023
Year: 2023

The $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-additive codes are subgroups of $\mathbb{Z}_2^{α_1} \times \mathbb{Z}_4^{α_2} \times \mathbb{Z}_8^{α_3}$, and can be seen as linear codes over $\mathbb{Z}_2$ when $α_2=α_3=0$, $\mathbb{Z}_4$-additive or $\mathbb{Z}_8$-additive codes when $α_1=α_3=0$ or $α_1=α_2=0$, respectively, or $\mathbb{Z}_2\mathbb{Z}_4$-additive codes when $α_3=0$. A $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard code is a Hadamard code which is the Gray map image of a $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-additive code. In this paper, we generalize some known results for $\mathbb{Z}_2\mathbb{Z}_4$-linear Hadamard codes to $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard codes with $α_1 \neq 0$, $α_2 \neq 0$, and $α_3 \neq 0$. First, we give a recursive construction of $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-additive Hadamard codes of type $(α_1,α_2, α_3;t_1,t_2, t_3)$ with $t_1\geq 1$, $t_2 \geq 0$, and $t_3\geq 1$. Then, we show that in general the $\mathbb{Z}_4$-linear, $\mathbb{Z}_8$-linear and $\mathbb{Z}_2\mathbb{Z}_4$-linear Hadamard codes are not included in the family of $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard codes with $α_1 \neq 0$, $α_2 \neq 0$, and $α_3 \neq 0$. Actually, we point out that none of these nonlinear $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard codes of length $2^{11}$ is equivalent to a $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard code of any other type, a $\mathbb{Z}_2\mathbb{Z}_4$-linear Hadamard code, or a $\mathbb{Z}_{2^s}$-linear Hadamard code, with $s\geq 2$, of the same length $2^{11}$.
On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment
Venue: arXiv cs.LG 2023
Year: 2023

Multi-Agent RL or MARL is one of the complex problems in Autonomous Driving literature that hampers the release of fully-autonomous vehicles today. Several simulators have been in iteration after their inception to mitigate the problem of complex scenarios with multiple agents in Autonomous Driving. One such simulator--SMARTS, discusses the importance of cooperative multi-agent learning. For this problem, we discuss two approaches--MAPPO and MADDPG, which are based on-policy and off-policy RL approaches. We compare our results with the state-of-the-art results for this challenge and discuss the potential areas of improvement while discussing the explainability of these approaches in conjunction with waypoints in the SMARTS environment.
From Agile to DevOps, Holistic Approach for Faster and Efficient Software Product Release Management
Venue: arXiv cs.SE 2023
Year: 2023

Release management is one of the most important software processes and is a set of processes that includes the compilation, configuration, and management of software versions in different environments. In recent years, changes in processes, technologies, and tools and changes in practices and understanding have paved the way for more effective, efficient, sustainable, reusable models and methods in this field. The purpose of this study is to examine the DevOps idea to produce a flow, highlight their benefits, and investigate with a model how these philosophies, which are two of the most important processes and methods in software development today, can reveal an effective release management process. What has been learned from the research is how the agile and DevOps practices, which have become widespread in recent years, can be positioned in a general flow in the release management process, although there are different practices, flows, disciplines, and technology. Sharing a case study on these issues in future studies and an experience sharing research where the flow is applied as a case study will reveal positive feedback on the real-life application and results of the flow and the model. Further, a literature review studies in which deficiencies in the literature are identified will be useful in determining the gaps in the process.
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning
Venue: arXiv cs.LG 2023
Year: 2023

Active learning is a powerful method for training machine learning models with limited labeled data. One commonly used technique for active learning is BatchBALD, which uses Bayesian neural networks to find the most informative points to label in a pool set. However, BatchBALD can be very slow to compute, especially for larger datasets. In this paper, we propose a new approximation, k-BALD, which uses k-wise mutual information terms to approximate BatchBALD, making it much less expensive to compute. Results on the MNIST dataset show that k-BALD is significantly faster than BatchBALD while maintaining similar performance. Additionally, we also propose a dynamic approach for choosing k based on the quality of the approximation, making it more efficient for larger datasets.
Understanding Context to Capture when Reconstructing Meaningful Spaces for Remote Instruction and Connecting in XR
Venue: arXiv cs.HC 2023
Year: 2023

Recent technological advances are enabling HCI researchers to explore interaction possibilities for remote XR collaboration using high-fidelity reconstructions of physical activity spaces. However, creating these reconstructions often lacks user involvement with an overt focus on capturing sensory context that does not necessarily augment an informal social experience. This work seeks to understand social context that can be important for reconstruction to enable XR applications for informal instructional scenarios. Our study involved the evaluation of an XR remote guidance prototype by 8 intergenerational groups of closely related gardeners using reconstructions of personally meaningful spaces in their gardens. Our findings contextualize physical objects and areas with various motivations related to gardening and detail perceptions of XR that might affect the use of reconstructions for remote interaction. We discuss implications for user involvement to create reconstructions that better translate real-world experience, encourage reflection, incorporate privacy considerations, and preserve shared experiences with XR as a medium for informal intergenerational activities.
RF+clust for Leave-One-Problem-Out Performance Prediction
Venue: arXiv cs.NE 2023
Year: 2023

Per-instance automated algorithm configuration and selection are gaining significant moments in evolutionary computation in recent years. Two crucial, sometimes implicit, ingredients for these automated machine learning (AutoML) methods are 1) feature-based representations of the problem instances and 2) performance prediction methods that take the features as input to estimate how well a specific algorithm instance will perform on a given problem instance. Non-surprisingly, common machine learning models fail to make predictions for instances whose feature-based representation is underrepresented or not covered in the training data, resulting in poor generalization ability of the models for problems not seen during training.In this work, we study leave-one-problem-out (LOPO) performance prediction. We analyze whether standard random forest (RF) model predictions can be improved by calibrating them with a weighted average of performance values obtained by the algorithm on problem instances that are sufficiently close to the problem for which a performance prediction is sought, measured by cosine similarity in feature space. While our RF+clust approach obtains more accurate performance prediction for several problems, its predictive power crucially depends on the chosen similarity threshold as well as on the feature portfolio for which the cosine similarity is measured, thereby opening a new angle for feature selection in a zero-shot learning setting, as LOPO is termed in machine learning.
Rig Inversion by Training a Differentiable Rig Function
Venue: arXiv cs.GR 2023
Year: 2023

Rig inversion is the problem of creating a method that can find the rig parameter vector that best approximates a given input mesh. In this paper we propose to solve this problem by first obtaining a differentiable rig function by training a multi layer perceptron to approximate the rig function. This differentiable rig function can then be used to train a deep learning model of rig inversion.
ESWORD: Implementation of Wireless Jamming Attacks in a Real-World Emulated Network
Venue: arXiv cs.NI 2023
Year: 2023

Wireless jamming attacks have plagued wireless communication systems and will continue to do so going forward with technological advances. These attacks fall under the category of Electronic Warfare (EW), a continuously growing area in both attack and defense of the electromagnetic spectrum, with one subcategory being electronic attacks. Jamming attacks fall under this specific subcategory of EW as they comprise adversarial signals that attempt to disrupt, deny, degrade, destroy, or deceive legitimate signals in the electromagnetic spectrum. While jamming is not going away, recent research advances have started to get the upper hand against these attacks by leveraging new methods and techniques, such as machine learning. However, testing such jamming solutions on a wide and realistic scale is a daunting task due to strict regulations on spectrum emissions. In this paper, we introduce eSWORD, the first large-scale framework that allows users to safely conduct real-time and controlled jamming experiments with hardware-in-the-loop. This is done by integrating eSWORD into the Colosseum wireless network emulator that enables large-scale experiments with up to 50 software-defined radio nodes. We compare the performance of eSWORD with that of real-world jamming systems by using an over-the-air wireless testbed (ensuring safe measures were taken when conducting experiments). Our experimental results demonstrate that eSWORD follows similar patterns in throughput, signal-to-noise ratio, and link status to real-world jamming experiments, testifying to the high accuracy of the emulated eSWORD setup.
Weakly-Supervised Questions for Zero-Shot Relation Extraction
Venue: arXiv cs.CL 2023
Year: 2023

Zero-Shot Relation Extraction (ZRE) is the task of Relation Extraction where the training and test sets have no shared relation types. This very challenging domain is a good test of a model's ability to generalize. Previous approaches to ZRE reframed relation extraction as Question Answering (QA), allowing for the use of pre-trained QA models. However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. Our technique can successfully translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. On tail entity extraction, we outperform the previous state-of-the-art by more than 16 F1 points without using gold question templates. On the RE-QA dataset where no previous baseline for relation extraction exists, our proposed algorithm comes within 0.7 F1 points of a system that uses gold question templates. Our model also outperforms the state-of-the-art ZRE baselines on the FewRel and WikiZSL datasets, showing that QA models no longer need template questions to match the performance of models specifically tailored to the ZRE task. Our implementation is available at https://github.com/fyshelab/QA-ZRE.
Unpacking the Essential Tension of Knowledge Recombination: Analyzing the Impact of Knowledge Spanning on Citation Counts and Disruptive Innovation
Venue: arXiv cs.SI 2023
Year: 2023

Drawing on the theories of knowledge recombination, we aim to unpack the essential tension between tradition and innovation in scientific research. Using the American Physical Society data and computational methods, we analyze the impact of knowledge spanning on both citation counts and disruptive innovation. The findings show that knowledge spanning has a U-shaped impact on disruptive innovation. In contrast, there is an inverted U-shaped relationship between knowledge spanning and citation counts, and the inverted U-shaped effect is moderated by team size. This study contributes to the theories of knowledge recombination by suggesting that both intellectual conformism and knowledge recombination can lead to disruptive innovation. That is, when evaluating the quality of scientific research with disruptive innovation, the essential tension seems to disappear.
Topological Understanding of Neural Networks, a survey
Venue: arXiv cs.LG 2023
Year: 2023

We look at the internal structure of neural networks which is usually treated as a black box. The easiest and the most comprehensible thing to do is to look at a binary classification and try to understand the approach a neural network takes. We review the significance of different activation functions, types of network architectures associated to them, and some empirical data. We find some interesting observations and a possibility to build upon the ideas to verify the process for real datasets. We suggest some possible experiments to look forward to in three different directions.
Analysis of UAV Corridors in Cellular Networks
Venue: arXiv cs.IT 2023
Year: 2023

In this article, we introduce a new mathematical framework for the analysis and design of UAV corridors in cellular networks, while considering a realistic network deployment, antenna radiation pattern, and propagation channel model. By leveraging quantization theory, we optimize the electrical tilts of existing ground cellular base stations to maximize the coverage of both legacy ground users and UAVs flying along specified aerial routes. Our practical case study shows that the optimized network results in a cell partitioning that significantly differs from the usual hexagonal pattern, and that it can successfully guarantee coverage all over the UAV corridors without degrading the perceived signal strength on the ground.
A deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobility
Venue: arXiv cs.RO 2023
Year: 2023

Urban air mobility is the new mode of transportation aiming to provide a fast and secure way of travel by utilizing the low-altitude airspace. This goal cannot be achieved without the implementation of new flight regulations which can assure safe and efficient allocation of flight paths to a large number of vertical takeoff/landing aerial vehicles. Such rules should also allow estimating the effective capacity of the low-altitude airspace for planning purposes. Path planning is a vital subject in urban air mobility which could enable a large number of UAVs to fly simultaneously in the airspace without facing the risk of collision. Since urban air mobility is a novel concept, authorities are still working on the redaction of new flight rules applicable to urban air mobility. In this study, an autonomous UAV path planning framework is proposed using a deep reinforcement learning approach and a deep deterministic policy gradient algorithm. The objective is to employ a self-trained UAV to reach its destination in the shortest possible time in any arbitrary environment by adjusting its acceleration. It should avoid collisions with any dynamic or static obstacles and avoid entering prior permission zones existing on its path. The reward function is the determinant factor in the training process. Thus, two different reward function compositions are compared and the chosen composition is deployed to train the UAV by coding the RL algorithm in python. Finally, numerical simulations investigated the success rate of UAVs in different scenarios providing an estimate of the effective airspace capacity.
Low-Resource Compositional Semantic Parsing with Concept Pretraining
Venue: arXiv cs.CL 2023
Year: 2023

Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve the capabilities of a voice assistant by adding a new domain, the underlying semantic parsing model needs to be retrained using thousands of annotated examples from the new domain, which is time-consuming and expensive. In this work, we present an architecture to perform such domain adaptation automatically, with only a small amount of metadata about the new domain and without any new training data (zero-shot) or with very few examples (few-shot). We use a base seq2seq (sequence-to-sequence) architecture and augment it with a concept encoder that encodes intent and slot tags from the new domain. We also introduce a novel decoder-focused approach to pretrain seq2seq models to be concept aware using Wikidata and use it to help our model learn important concepts and perform well in low-resource settings. We report few-shot and zero-shot results for compositional semantic parsing on the TOPv2 dataset and show that our model outperforms prior approaches in few-shot settings for the TOPv2 and SNIPS datasets.
Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators
Venue: arXiv cs.LG 2023
Year: 2023

Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One key challenge in designing hardware accelerators for GCNs is the vast size and randomness in their data access patterns which greatly reduces the effectiveness of the limited on-chip cache. Aimed at improving the effectiveness of the cache by mitigating the irregular data accesses, prior studies often employ the vertex tiling techniques used in traditional graph processing applications. While being effective at enhancing the cache efficiency, those approaches are often sensitive to the tiling configurations where the optimal setting heavily depends on target input datasets. Furthermore, the existing solutions require manual tuning through trial-and-error or rely on sub-optimal analytical models. In this paper, we propose Slice-and-Forge (SnF), an efficient hardware accelerator for GCNs which greatly improves the effectiveness of the limited on-chip cache. SnF chooses a tiling strategy named feature slicing that splits the features into vertical slices and processes them in the outermost loop of the execution. This particular choice results in a repetition of the identical computational patterns over irregular graph data over multiple rounds. Taking advantage of such repetitions, SnF dynamically tunes its tile size. Our experimental results reveal that SnF can achieve 1.73x higher performance in geomean compared to prior work on multi-engine settings, and 1.46x higher performance in geomean on small scale settings, without the need for off-line analyses.
A predictive physics-aware hybrid reduced order model for reacting flows
Venue: arXiv cs.LG 2023
Year: 2023

In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orthogonal Decomposition (POD) combined with deep learning architectures. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients, based on recursive (RNN) and convolutional (CNN) neural networks. From each architecture, different models have been created to understand the behavior of each parameter of the neural network. Results show that these architectures are able to predict the temporal coefficients of the POD modes, as well as the whole snapshots. The RNN shows lower prediction error for all the variables analyzed. The model was also found capable of predicting more complex simulations showing transfer learning capabilities.
What Drives Virtual Influencer's Impact?
Venue: arXiv cs.CY 2023
Year: 2023

In the midst of the influencer marketing boom, more and more companies are shifting resources from real to virtual (or computer-generated) influencers. But while virtual influencers have the potential to engage consumers and drive action, some posts resonate and boost sales, while others do not. What makes some virtual influencer posts more impactful? This work examines how including someone else in photos shapes consumer responses to virtual influencers' posts. A multimethod investigation, combining automated image and text analysis of thousands of social media posts with controlled experiments, demonstrates that companion presence boosts impact. These effects are driven by trust. Companion presence makes virtual influencers seem more human, which makes them seem more trustworthy, and thus increases the impact of their posts. Taken together, the findings shed light on how others' presence shapes responses to virtual influencer content, reveal a psychological mechanism through which companions affect consumer perceptions, and provide actionable insights for designing more impactful social media content.
Learning Effective Strategies for Moving Target Defense with Switching Costs
Venue: arXiv cs.GT 2023
Year: 2023

Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker's ability to perform reconnaissance for exploiting a system's vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker's motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a system and how each of these vulnerabilities can be exploited by an attacker. In this work, we aim to create algorithms that generate effective Moving Target Defense strategies that do not rely on prior knowledge about the attackers. Our work assumes that the only way the defender receives information about its own reward is via interaction with the attacker in a repeated game setting. Depending on the amount of information that can be obtained from the interactions, we devise two different algorithms using multi-armed bandit formulation to identify efficient strategies. We then evaluate our algorithms using data mined from the National Vulnerability Database to showcase that they match the performance of the state-of-the-art techniques, despite using a lot less amount of information.
Explainable Deep Reinforcement Learning: State of the Art and Challenges
Venue: arXiv cs.LG 2023
Year: 2023

Interpretability, explainability and transparency are key issues to introducing Artificial Intelligence methods in many critical domains: This is important due to ethical concerns and trust issues strongly connected to reliability, robustness, auditability and fairness, and has important consequences towards keeping the human in the loop in high levels of automation, especially in critical cases for decision making, where both (human and the machine) play important roles. While the research community has given much attention to explainability of closed (or black) prediction boxes, there are tremendous needs for explainability of closed-box methods that support agents to act autonomously in the real world. Reinforcement learning methods, and especially their deep versions, are such closed-box methods. In this article we aim to provide a review of state of the art methods for explainable deep reinforcement learning methods, taking also into account the needs of human operators - i.e., of those that take the actual and critical decisions in solving real-world problems. We provide a formal specification of the deep reinforcement learning explainability problems, and we identify the necessary components of a general explainable reinforcement learning framework. Based on these, we provide a comprehensive review of state of the art methods, categorizing them in classes according to the paradigm they follow, the interpretable models they use, and the surface representation of explanations provided. The article concludes identifying open questions and important challenges.