OpenRaiser/Intern-Atlas huggingface dataset:
When is a DAO Decentralized?
Venue: arXiv cs.DC 2023
Year: 2023

While previously a nascent theoretical construct, decentralized autonomous organizations have grown rapidly in recent years. DAOs typically emerge around the management of decentralized financial applications and thus benefit from the rapid growth of innovation in this sector. In response, global regulators increasingly voice the intent to regulate these activities. This may impose an excessive compliance burden on DAOs, unless they are deemed sufficiently decentralized to be regulated. Yet, decentralization is an abstract concept with scarce legal precedence. We investigate dimensions of decentralization through thematic analysis, combining extant literature with a series of expert interviews. We propose a definition of 'sufficient decentralization' and present a general framework for the assessment of decentralization. We derive five dimensions for the assessment of decentralization in DAOs: Token-weighted voting, Infrastructure, Governance, Escalation and Reputation. We present a discretionary sample application of the framework and five propositions on the future regulation and supervision of DAOs. We contribute new practical insights on the topic of compliance and decentralized organizations to the growing discourse on the application of blockchain technology in information systems and management disciplines
Classification of news spreading barriers
Venue: arXiv cs.CL 2023
Year: 2023

News media is one of the most effective mechanisms for spreading information internationally, and many events from different areas are internationally relevant. However, news coverage for some news events is limited to a specific geographical region because of information spreading barriers, which can be political, geographical, economic, cultural, or linguistic. In this paper, we propose an approach to barrier classification where we infer the semantics of news articles through Wikipedia concepts. To that end, we collected news articles and annotated them for different kinds of barriers using the metadata of news publishers. Then, we utilize the Wikipedia concepts along with the body text of news articles as features to infer the news-spreading barriers. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that the proposed approach using Wikipedia concepts based semantic knowledge offers better performance than the usual for classifying the news-spreading barriers.
Benchmarking Faithfulness: Towards Accurate Natural Language Explanations in Vision-Language Tasks
Venue: arXiv cs.CL 2023
Year: 2023

With deep neural models increasingly permeating our daily lives comes a need for transparent and comprehensible explanations of their decision-making. However, most explanation methods that have been developed so far are not intuitively understandable for lay users. In contrast, natural language explanations (NLEs) promise to enable the communication of a model's decision-making in an easily intelligible way. While current models successfully generate convincing explanations, it is an open question how well the NLEs actually represent the reasoning process of the models - a property called faithfulness. Although the development of metrics to measure faithfulness is crucial to designing more faithful models, current metrics are either not applicable to NLEs or are not designed to compare different model architectures across multiple modalities. Building on prior research on faithfulness measures and based on a detailed rationale, we address this issue by proposing three faithfulness metrics: Attribution-Similarity, NLE-Sufficiency, and NLE-Comprehensiveness. The efficacy of the metrics is evaluated on the VQA-X and e-SNLI-VE datasets of the e-ViL benchmark for vision-language NLE generation by systematically applying modifications to the performant e-UG model for which we expect changes in the measured explanation faithfulness. We show on the e-SNLI-VE dataset that the removal of redundant inputs to the explanation-generation module of e-UG successively increases the model's faithfulness on the linguistic modality as measured by Attribution-Similarity. Further, our analysis demonstrates that NLE-Sufficiency and -Comprehensiveness are not necessarily correlated to Attribution-Similarity, and we discuss how the two metrics can be utilized to gain further insights into the explanation generation process.
Statute-enhanced lexical retrieval of court cases for COLIEE 2022
Venue: arXiv cs.IR 2023
Year: 2023

We discuss our experiments for COLIEE Task 1, a court case retrieval competition using cases from the Federal Court of Canada. During experiments on the training data we observe that passage level retrieval with rank fusion outperforms document level retrieval. By explicitly adding extracted statute information to the queries and documents we can further improve the results. We submit two passage level runs to the competition, which achieve high recall but low precision.
Serverless Approach to Sensitivity Analysis of Computational Models
Venue: arXiv cs.DC 2023
Year: 2023

Digital twins are virtual representations of physical objects or systems used for the purpose of analysis, most often via computer simulations, in many engineering and scientific disciplines. Recently, this approach has been introduced to computational medicine, within the concept of Digital Twin in Healthcare (DTH). Such research requires verification and validation of its models, as well as the corresponding sensitivity analysis and uncertainty quantification (VVUQ). From the computing perspective, VVUQ is a computationally intensive process, as it requires numerous runs with variations of input parameters. Researchers often use high-performance computing (HPC) solutions to run VVUQ studies where the number of parameter combinations can easily reach tens of thousands. However, there is a viable alternative to HPC for a substantial subset of computational models - serverless computing. In this paper we hypothesize that using the serverless computing model can be a practical and efficient approach to selected cases of running VVUQ calculations. We show this on the example of the EasyVVUQ library, which we extend by providing support for many serverless services. The resulting library - CloudVVUQ - is evaluated using two real-world applications from the computational medicine domain adapted for serverless execution. Our experiments demonstrate the scalability of the proposed approach.
Uncharted Territory: Energy Attacks in the Battery-less Internet of Things
Venue: arXiv cs.CR 2023
Year: 2023

We study how ambient energy harvesting may be used as an attack vector in the battery-less Internet of Things (IoT). Battery-less IoT devices rely on ambient energy harvesting and are employed in a multitude of applications, including safety-critical ones such as biomedical implants. Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging energy buffers. Through an independent exploratory study and a follow-up systematic analysis, we demonstrate that by exerting limited control on ambient energy one can create situations of livelock, denial of service, and priority inversion, without physical device access. We call these situations energy attacks. Using concepts of approximate intermittent computing and machine learning, we design a technique that can detect energy attacks with 92%+ accuracy, that is, up to 37% better than the baselines, and with up to one fifth of their energy overhead. Crucially, by design, our technique does not cause any additional energy failure compared to the regular intermittent processing. We conclude with directions to inspire defense techniques and a discussion on the feasibility of energy attacks.
Effective implementation of the High Performance Conjugate Gradient benchmark on GraphBLAS
Venue: arXiv cs.DC 2023
Year: 2023

Applications in High-Performance Computing (HPC) environments face challenges due to increasing complexity. Among them, the increasing usage of sparse data pushes the limits of data structures and programming models and hampers the efficient usage of existing, highly parallel hardware. The GraphBLAS specification tackles these challenges by proposing a set of data containers and primitives, coupled with a semantics based on abstract algebraic concepts: this allows multiple applications on sparse data to be described with a small set of primitives and benefit from the many optimizations of a compile-time-known algebraic specification. Among HPC applications, the High Performance Conjugate Gradient (HPCG) benchmark is an important representative of a large body of sparse workloads, and its structure poses several programmability and performance challenges. This work tackles them by proposing and evaluating an implementation on GraphBLAS of HPCG, highlighting the main changes to its kernels. The results for shared memory systems outperforms the reference, while results in distributed systems highlight fundamental limitations of GraphBLAS-compliant implementations, which suggests several future directions.
Stochastic Code Generation
Venue: arXiv cs.CL 2023
Year: 2023

Large language models pre-trained for code generation can generate high-quality short code but often struggle with generating coherent long code and understanding higher-level or system-level specifications. This issue is also observed in language modeling for long text generation, and one proposed solution is the use of a latent stochastic process. This approach involves generating a document plan and then producing text that is consistent with it. In this study, we investigate whether this technique can be applied to code generation to improve coherence. We base our proposed encoder and decoder on the pre-trained GPT-2 based CodeParrot model and utilize the APPS dataset for training. We evaluate our results using the HumanEval benchmark and observe that the modified Time Control model performs similarly to CodeParrot on this evaluation.
Exploring Blockchain Technology through a Modular Lens: A Survey
Venue: arXiv cs.CR 2023
Year: 2023

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications.
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network architecture, noise level, denoising steps, training image size, and optimizer/scheduler. We show that tuning these hyperparameters allows us to achieve better performance on both distortion and perceptual scores. We also propose a U-Net based latent diffusion model which performs diffusion in a low-resolution latent space while preserving high-resolution information from the original input for the decoding process. Compared to the previous latent-diffusion model which trains a VAE-GAN to compress the image, our proposed U-Net compression strategy is significantly more stable and can recover highly accurate images without relying on adversarial optimization. Importantly, these modifications allow us to apply diffusion models to various image restoration tasks, including real-world shadow removal, HR non-homogeneous dehazing, stereo super-resolution, and bokeh effect transformation. By simply replacing the datasets and slightly changing the noise network, our model, named Refusion, is able to deal with large-size images (e.g., 6000 x 4000 x 3 in HR dehazing) and produces good results on all the above restoration problems. Our Refusion achieves the best perceptual performance in the NTIRE 2023 Image Shadow Removal Challenge and wins 2nd place overall.
'That Darned Sandstorm': A Study of Procedural Generation through Archaeological Storytelling
Venue: arXiv cs.AI 2023
Year: 2023

Procedural content generation has been applied to many domains, especially level design, but the narrative affordances of generated game environments are comparatively understudied. In this paper we present our first attempt to study these effects through the lens of what we call a generative archaeology game that prompts the player to archaeologically interpret the generated content of the game world. We report on a survey that gathered qualitative and quantitative data on the experiences of 187 participants playing the game Nothing Beside Remains. We provide some preliminary analysis of our intentional attempt to prompt player interpretation, and the unintentional effects of a glitch on the player experience of the game.
Predicting dynamic, motion-related changes in B0 field in the brain at a 7 T MRI using a subject-specific fine-tuned U-net
Venue: arXiv cs.CV 2023
Year: 2023

Subject movement during the magnetic resonance examination is inevitable and causes not only image artefacts but also deteriorates the homogeneity of the main magnetic field (B0), which is a prerequisite for high quality data. Thus, characterization of changes to B0, e.g. induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-tuned the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data. Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method. We therefore conclude that it is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of magnetic resonance acquisitions without the use of navigators.
Search-Space Pruning with Int-Splits for Faster QBF Solving
Venue: arXiv cs.LO 2023
Year: 2023

In many QBF encodings, sequences of Boolean variables stand for binary representations of integer variables. Examples are state labels in bounded model checking or actions in planning problems. Often not the full possible range is used, e.g., for representing six different states, three Boolean variables are required, rendering two of the eight possible assignments irrelevant for the solution of the problem. As QBF solvers do not have any domain-specific knowledge on the formula they process, they are not able to detect this pruning opportunity. In this paper, we introduce the idea of int-splits, which provide domain-specific information on integer variables to QBF solvers. This is particularly appealing for parallel Divide-and-Conquer solving which partitions the search space into independently solvable sub-problems. Using this technique, we reduce the number of generated sub-problems from a full expansion to only the required subset. We then evaluate how many resources int-splits save in problems already well suited for D&C. In that context, we provide a reference implementation that splits QBF formulas into many sub-problems with or without int-splits and merges results. We finally propose a comment-based optional syntax extension to (Q)DIMACS that includes int-splits and is suited for supplying proposed guiding paths natively to D&C solvers.
Scenario Approach for Parametric Markov Models
Venue: arXiv cs.LO 2023
Year: 2023

In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis.
Multimodal Short Video Rumor Detection System Based on Contrastive Learning
Venue: arXiv cs.CV 2023
Year: 2023

With the rise of short video platforms as prominent channels for news dissemination, major platforms in China have gradually evolved into fertile grounds for the proliferation of fake news. However, distinguishing short video rumors poses a significant challenge due to the substantial amount of information and shared features among videos, resulting in homogeneity. To address the dissemination of short video rumors effectively, our research group proposes a methodology encompassing multimodal feature fusion and the integration of external knowledge, considering the merits and drawbacks of each algorithm. The proposed detection approach entails the following steps: (1) creation of a comprehensive dataset comprising multiple features extracted from short videos; (2) development of a multimodal rumor detection model: first, we employ the Temporal Segment Networks (TSN) video coding model to extract video features, followed by the utilization of Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) to extract textual features. Subsequently, the BERT model is employed to fuse textual and video features; (3) distinction is achieved through contrast learning: we acquire external knowledge by crawling relevant sources and leverage a vector database to incorporate this knowledge into the classification output. Our research process is driven by practical considerations, and the knowledge derived from this study will hold significant value in practical scenarios, such as short video rumor identification and the management of social opinions.
Applications of Uncalibrated Image Based Visual Servoing in Micro- and Macroscale Robotics
Venue: arXiv cs.RO 2023
Year: 2023

We present a robust markerless image based visual servoing method that enables precision robot control without hand-eye and camera calibrations in 1, 3, and 5 degrees-of-freedom. The system uses two cameras for observing the workspace and a combination of classical image processing algorithms and deep learning based methods to detect features on camera images. The only restriction on the placement of the two cameras is that relevant image features must be visible in both views. The system enables precise robot-tool to workspace interactions even when the physical setup is disturbed, for example if cameras are moved or the workspace shifts during manipulation. The usefulness of the visual servoing method is demonstrated and evaluated in two applications: in the calibration of a micro-robotic system that dissects mosquitoes for the automated production of a malaria vaccine, and a macro-scale manipulation system for fastening screws using a UR10 robot. Evaluation results indicate that our image based visual servoing method achieves human-like manipulation accuracy in challenging setups even without camera calibration.
Synthetic Data from Diffusion Models Improves ImageNet Classification
Venue: arXiv cs.CV 2023
Year: 2023

Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models of natural images can be used for generative data augmentation, helping to improve challenging discriminative tasks? We show that large-scale text-to image diffusion models can be fine-tuned to produce class conditional models with SOTA FID (1.76 at 256x256 resolution) and Inception Score (239 at 256x256). The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines.
A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy
Venue: arXiv cs.LG 2023
Year: 2023

Recurrent Neural Networks are classes of Artificial Neural Networks that establish connections between different nodes form a directed or undirected graph for temporal dynamical analysis. In this research, the laser induced breakdown spectroscopy (LIBS) technique is used for quantitative analysis of aluminum alloys by different Recurrent Neural Network (RNN) architecture. The fundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employed to generate the LIBS plasma for the prediction of constituent concentrations of the aluminum standard samples. Here, Recurrent Neural Networks based on different networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (Simple RNN), and as well as Recurrent Convolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU are utilized for concentration prediction. Then a comparison is performed among prediction by classical machine learning methods of support vector regressor (SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Linear Regression, and k-Nearest Neighbor (KNN) algorithm. Results showed that the machine learning tools based on Convolutional Recurrent Networks had the best efficiencies in prediction of the most of the elements among other multivariate methods.