OpenRaiser/Intern-Atlas huggingface dataset:
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
Venue: arXiv cs.LG 2023
Year: 2023

The proliferation and ubiquity of temporal data across many disciplines has sparked interest for similarity, classification and clustering methods specifically designed to handle time series data. A core issue when dealing with time series is determining their pairwise similarity, i.e., the degree to which a given time series resembles another. Traditional distance measures such as the Euclidean are not well-suited due to the time-dependent nature of the data. Elastic metrics such as dynamic time warping (DTW) offer a promising approach, but are limited by their computational complexity, non-differentiability and sensitivity to noise and outliers. This thesis proposes novel elastic alignment methods that use parametric \& diffeomorphic warping transformations as a means of overcoming the shortcomings of DTW-based metrics. The proposed method is differentiable \& invertible, well-suited for deep learning architectures, robust to noise and outliers, computationally efficient, and is expressive and flexible enough to capture complex patterns. Furthermore, a closed-form solution was developed for the gradient of these diffeomorphic transformations, which allows an efficient search in the parameter space, leading to better solutions at convergence. Leveraging the benefits of these closed-form diffeomorphic transformations, this thesis proposes a suite of advancements that include: (a) an enhanced temporal transformer network for time series alignment and averaging, (b) a deep-learning based time series classification model to simultaneously align and classify signals with high accuracy, (c) an incremental time series clustering algorithm that is warping-invariant, scalable and can operate under limited computational and time resources, and finally, (d) a normalizing flow model that enhances the flexibility of affine transformations in coupling and autoregressive layers.
How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment
Venue: arXiv cs.HC 2023
Year: 2023

As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, independently learning Python through 45 code-authoring tasks using Codex, an LLM-based code generator. We explore several questions related to how learners used these code generators and provide an analysis of the properties of the written prompts and the generated code. Specifically, we explore (A) the context in which learners use Codex, (B) what learners are asking from Codex, (C) properties of their prompts in terms of relation to task description, language, and clarity, and prompt crafting patterns, (D) the correctness, complexity, and accuracy of the AI-generated code, and (E) how learners utilize AI-generated code in terms of placement, verification, and manual modifications. Furthermore, our analysis reveals four distinct coding approaches when writing code with an AI code generator: AI Single Prompt, where learners prompted Codex once to generate the entire solution to a task; AI Step-by-Step, where learners divided the problem into parts and used Codex to generate each part; Hybrid, where learners wrote some of the code themselves and used Codex to generate others; and Manual coding, where learners wrote the code themselves. The AI Single Prompt approach resulted in the highest correctness scores on code-authoring tasks, but the lowest correctness scores on subsequent code-modification tasks during training. Our results provide initial insight into how novice learners use AI code generators and the challenges and opportunities associated with integrating them into self-paced learning environments. We conclude with various signs of over-reliance and self-regulation, as well as opportunities for curriculum and tool development.
FlexCast: genuine overlay-based atomic multicast
Venue: arXiv cs.DC 2023
Year: 2023

Atomic multicast is a communication abstraction where messages are propagated to groups of processes with reliability and order guarantees. Atomic multicast is at the core of strongly consistent storage and transactional systems. This paper presents FlexCast, the first genuine overlay-based atomic multicast protocol. Genuineness captures the essence of atomic multicast in that only the sender of a message and the message's destinations coordinate to order the message, leading to efficient protocols. Overlay-based protocols restrict how process groups can communicate. Limiting communication leads to simpler protocols and reduces the amount of information each process must keep about the rest of the system. FlexCast implements genuine atomic multicast using a complete DAG overlay. We experimentally evaluate FlexCast in a geographically distributed environment using gTPC-C, a variation of the TPC-C benchmark that takes into account geographical distribution and locality. We show that, by exploiting genuineness and workload locality, FlexCast outperforms well-established atomic multicast protocols without the inherent communication overhead of state-of-the-art non-genuine multicast protocols.
From OCEL to DOCEL -- Datasets and Automated Transformation
Venue: arXiv cs.DB 2023
Year: 2023

Object-centric event data represent processes from the point of view of all the involved object types. This perspective has gained interest in recent years as it supports the analysis of processes that previously could not be adequately captured, due to the lack of a clear case notion as well as an increasing amount of output data that needs to be stored. Although publicly available event logs are crucial artifacts for researchers to develop and evaluate novel process mining techniques, the currently available object-centric event logs have limitations in this regard. Specifically, they mainly focus on control-flow and rarely contain objects with attributes that change over time, even though this is not realistic, as the attribute values of objects can be altered during their lifecycle. This paper addresses this gap by providing two means of establishing object-centric datasets with dynamically evolving attributes. First, we provide event log generators, which allow researchers to generate customized, artificial logs with dynamic attributes in the recently proposed DOCEL format. Second, we propose and evaluate an algorithm to convert OCEL logs into DOCEL logs, which involves the detection of event attributes that capture evolving object information and the creation of dynamic attributes from these. Through these contributions, this paper supports the advancement of object-centric process analysis by providing researchers with new means to obtain relevant data to use during the development of new techniques.
VoiceLens: Controllable Speaker Generation and Editing with Flow
Venue: arXiv cs.SD 2023
Year: 2023

Currently, many multi-speaker speech synthesis and voice conversion systems address speaker variations with an embedding vector. Modeling it directly allows new voices outside of training data to be synthesized. GMM based approaches such as Tacospawn are favored in literature for this generation task, but there are still some limitations when difficult conditionings are involved. In this paper, we propose VoiceLens, a semi-supervised flow-based approach, to model speaker embedding distributions for multi-conditional speaker generation. VoiceLens maps speaker embeddings into a combination of independent attributes and residual information. It allows new voices associated with certain attributes to be \textit{generated} for existing TTS models, and attributes of known voices to be meaningfully \textit{edited}. We show in this paper, VoiceLens displays an unconditional generation capacity that is similar to Tacospawn while obtaining higher controllability and flexibility when used in a conditional manner. In addition, we show synthesizing less noisy speech from known noisy speakers without re-training the TTS model is possible via solely editing their embeddings with a SNR conditioned VoiceLens model. Demos are available at sos1sos2sixteen.github.io/voicelens.
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Venue: arXiv cs.CV 2023
Year: 2023

Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing works introduce too many additional learnable parameters to assign different pruning rates across different layers in CNN or cannot control the compression rate explicitly. Since too narrow network blocks information flow for training, automatic pruning rate setting cannot explore a high pruning rate for a specific layer. To overcome these limitations, we propose a novel framework named Layer Adaptive Progressive Pruning (LAPP), which gradually compresses the network during initial training of a few epochs from scratch. In particular, LAPP designs an effective and efficient pruning strategy that introduces a learnable threshold for each layer and FLOPs constraints for network. Guided by both task loss and FLOPs constraints, the learnable thresholds are dynamically and gradually updated to accommodate changes of importance scores during training. Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer. What's more, in order to maintain the expressive power of the pruned layer, before training starts, we introduce an additional lightweight bypass for each convolutional layer to be pruned, which only adds relatively few additional burdens. Our method demonstrates superior performance gains over previous compression methods on various datasets and backbone architectures. For example, on CIFAR-10, our method compresses ResNet-20 to 40.3% without accuracy drop. 55.6% of FLOPs of ResNet-18 are reduced with 0.21% top-1 accuracy increase and 0.40% top-5 accuracy increase on ImageNet.
An Investigation of Distribution Alignment in Multi-Genre Speaker Recognition
Venue: arXiv cs.SD 2023
Year: 2023

Multi-genre speaker recognition is becoming increasingly popular due to its ability to better represent the complexities of real-world applications. However, a major challenge is the significant shift in the distribution of speaker vectors across different genres. While distribution alignment is a common approach to address this challenge, previous studies have mainly focused on aligning a source domain with a target domain, and the performance of multi-genre data is unknown. This paper presents a comprehensive study of mainstream distribution alignment methods on multi-genre data, where multiple distributions need to be aligned. We analyze various methods both qualitatively and quantitatively. Our experiments on the CN-Celeb dataset show that within-between distribution alignment (WBDA) performs relatively better. However, we also found that none of the investigated methods consistently improved performance in all test cases. This suggests that solely aligning the distributions of speaker vectors may not fully address the challenges posed by multi-genre speaker recognition. Further investigation is necessary to develop a more comprehensive solution.
Federated Learning Under Restricted User Availability
Venue: arXiv cs.LG 2023
Year: 2023

Federated Learning (FL) is a decentralized machine learning framework that enables collaborative model training while respecting data privacy. In various applications, non-uniform availability or participation of users is unavoidable due to an adverse or stochastic environment, the latter often being uncontrollable during learning. Here, we posit a generic user selection mechanism implementing a possibly randomized, stationary selection policy, suggestively termed as a Random Access Model (RAM). We propose a new formulation of the FL problem which effectively captures and mitigates limited participation of data originating from infrequent, or restricted users, at the presence of a RAM. By employing the Conditional Value-at-Risk (CVaR) over the (unknown) RAM distribution, we extend the expected loss FL objective to a risk-aware objective, enabling the design of an efficient training algorithm that is completely oblivious to the RAM, and with essentially identical complexity as FedAvg. Our experiments on synthetic and benchmark datasets show that the proposed approach achieves significantly improved performance as compared with standard FL, under a variety of setups.
Towards a Theory of Maximal Extractable Value II: Uncertainty
Venue: arXiv cs.GT 2023
Year: 2023

Maximal Extractable Value (MEV) is value extractable by temporary monopoly power commonly found in decentralized systems. This extraction stems from a lack of user privacy upon transaction submission and the ability of a monopolist validator to reorder, add, and/or censor transactions. There are two main directions to reduce MEV: reduce the flexibility of the miner to reorder transactions by enforcing ordering rules and/or introduce a competitive market for the right to reorder, add, and/or censor transactions. In this work, we unify these approaches via \emph{uncertainty principles}, akin to those found in harmonic analysis and physics. This provides a quantitative trade-off between the freedom to reorder transactions and the complexity of an economic payoff to a user in a decentralized network. This trade off is analogous to the Nyquist-Shannon sampling theorem and demonstrates that sequencing rules in blockchains need to be application specific. Our results suggest that neither so-called fair ordering techniques nor economic mechanisms can individually mitigate MEV for arbitrary payoff functions.
On LCP codes over a mixed ring alphabet
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we introduce a standard generator matrix for mixed-alphabet linear codes over finite chain rings. Furthermore, we show that, when one has a linear complementary pair (LCP) of mixed-alphabet linear codes, both codes are weakly-free. Additionally, we establish that any mixed-alphabet product group code is separable. Thus, if one has a pair $\{C, D\}$ of mixed-alphabet product group codes over a finite chain ring that forms a LCP, it follows that $C$ and the Euclidean dual of $D$ are permutation equivalent.
Urdu Poetry Generated by Using Deep Learning Techniques
Venue: arXiv cs.CL 2023
Year: 2023

This study provides Urdu poetry generated using different deep-learning techniques and algorithms. The data was collected through the Rekhta website, containing 1341 text files with several couplets. The data on poetry was not from any specific genre or poet. Instead, it was a collection of mixed Urdu poems and Ghazals. Different deep learning techniques, such as the model applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU), have been used. Natural Language Processing (NLP) may be used in machine learning to understand, analyze, and generate a language humans may use and understand. Much work has been done on generating poetry for different languages using different techniques. The collection and use of data were also different for different researchers. The primary purpose of this project is to provide a model that generates Urdu poems by using data completely, not by sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as in the base paper. The results have shown good accuracy in the poems generated by the model.
Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic Uncertainty
Venue: arXiv cs.RO 2023
Year: 2023

Global navigation satellite systems (GNSS) denied environments/conditions require unmanned aerial vehicles (UAVs) to energy-efficiently and reliably fly. To this end, this study presents perception-and-energy-aware motion planning for UAVs in GNSS-denied environments. The proposed planner solves the trajectory planning problem by optimizing a cost function consisting of two indices: the total energy consumption of a UAV and the perception quality of light detection and ranging (LiDAR) sensor mounted on the UAV. Before online navigation, a high-fidelity simulator acquires a flight dataset to learn energy consumption for the UAV and heteroscedastic uncertainty associated with LiDAR measurements, both as functions of the horizontal velocity of the UAV. The learned models enable the online planner to estimate energy consumption and perception quality, reducing UAV battery usage and localization errors. Simulation experiments in a photorealistic environment confirm that the proposed planner can address the trade-off between energy efficiency and perception quality under heteroscedastic uncertainty. The open-source code is released at https://gitlab.com/ReI08/perception-energy-planner.
Spring-IMU Fusion Based Proprioception for Feedback Control of Soft Manipulators
Venue: arXiv cs.RO 2023
Year: 2023

This paper presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space based algorithm for closed-loop control. A gradient descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads.
Integration of Polyimide Flexible PCB Wings in Northeastern Aerobat
Venue: arXiv cs.RO 2023
Year: 2023

The principal aim of this Master's thesis is to propel the optimization of the membrane wing structure of the Northeastern Aerobat through origami techniques and enhancing its capacity for secure hovering within confined spaces. Bio-inspired drones offer distinctive capabilities that pave the way for innovative applications, encompassing wildlife monitoring, precision agriculture, search and rescue operations, as well as the augmentation of residential safety. The evolved noise-reduction mechanisms of birds and insects prove advantageous for drones utilized in tasks like surveillance and wildlife observation, ensuring operation devoid of disturbances. Traditional flying drones equipped with rotary or fixed wings encounter notable constraints when navigating narrow pathways. While rotary and fixed-wing systems are conventionally harnessed for surveillance and reconnaissance, the integration of onboard sensor suites within micro aerial vehicles (MAVs) has garnered interest in vigilantly monitoring hazardous scenarios in residential settings. Notwithstanding the agility and commendable fault tolerance exhibited by systems such as quadrotors in demanding conditions, their inflexible body structures impede collision tolerance, necessitating operational spaces free of collisions. Recent years have witnessed an upsurge in integrating soft and pliable materials into the design of such systems; however, the pursuit of aerodynamic efficiency curtails the utilization of excessively flexible materials for rotor blades or propellers. This thesis introduces a design that integrates polyimide flexible PCBs into the wings of the Aerobat and employs guard design incorporating feedback-driven stabilizers, enabling stable hovering flights within Northeastern's Robotics-Inspired Study and Experimentation (RISE) cage.
Automata Quest: NCAs as a Video Game Life Mechanic
Venue: arXiv cs.HC 2023
Year: 2023

We study life over the course of video game history as represented by their mechanics. While there have been some variations depending on genre or "character type", we find that most games converge to a similar representation. We also examine the development of Conway's Game of Life (one of the first zero player games) and related automata that have developed over the years. With this history in mind, we investigate the viability of one popular form of automata, namely Neural Cellular Automata, as a way to more fully express life within video game settings and innovate new game mechanics or gameplay loops.
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Venue: arXiv cs.LG 2023
Year: 2023

Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as dynamic service composition, job scheduling, and offloading, as well as service adaptation. While Deep RL offers many benefits, understanding the decision-making of Deep RL is challenging because its learned decision-making policy essentially appears as a black box. Yet, understanding the decision-making of Deep RL is key to help service developers perform debugging, support service providers to comply with relevant legal frameworks, and facilitate service users to build trust. We introduce Chat4XAI to facilitate the understanding of the decision-making of Deep RL by providing natural-language explanations. Compared with visual explanations, the reported benefits of natural-language explanations include better understandability for non-technical users, increased user acceptance and trust, as well as more efficient explanations. Chat4XAI leverages modern AI chatbot technology and dedicated prompt engineering. Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API and evaluate the fidelity and stability of its explanations using an adaptive service exemplar.
Methods of quantifying specialized knowledge and network rewiring
Venue: arXiv cs.SI 2023
Year: 2023

Technological innovations are a major driver of economic development that depend on the exchange of knowledge and ideas among those with unique but complementary specialized knowledge and knowhow. However, measurement of specialized knowledge embedded in technologists, scientists and entrepreneurs in the knowledge economy presents an empirical challenge as both the exchange of knowledge and knowledge itself remain difficult to observe. We develop novel measures of specialized knowledge using a unique dataset of longitudinal records of participation at technology-focused meetup events in two regional knowledge economics. Our measures of specialized knowledge can be further used to quantify the extend of knowledge spillover and network rewiring and uncover underlying social mechanisms that contribute to the development of increasingly complex and differentiated networks in maturing knowledge economies. We apply these methods in the context of the rapid morphogenesis of emerging regional technology economies in New York City and Los Angeles.