OpenRaiser/Intern-Atlas huggingface dataset:
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
Venue: arXiv cs.CV 2023
Year: 2023

Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. Existing medical VQA methods tend to encode medical images and learn the correspondence between visual features and questions without exploiting the spatial, semantic, or medical knowledge behind them. This is partially because of the small size of the current medical VQA dataset, which often includes simple questions. Therefore, we first collected a comprehensive and large-scale medical VQA dataset, focusing on chest X-ray images. The questions involved detailed relationships, such as disease names, locations, levels, and types in our dataset. Based on this dataset, we also propose a novel baseline method by constructing three different relationship graphs: spatial relationship, semantic relationship, and implicit relationship graphs on the image regions, questions, and semantic labels. The answer and graph reasoning paths are learned for different questions.
Leveraging Prior Knowledge in Reinforcement Learning via Double-Sided Bounds on the Value Function
Venue: arXiv cs.LG 2023
Year: 2023

An agent's ability to leverage past experience is critical for efficiently solving new tasks. Approximate solutions for new tasks can be obtained from previously derived value functions, as demonstrated by research on transfer learning, curriculum learning, and compositionality. However, prior work has primarily focused on using value functions to obtain zero-shot approximations for solutions to a new task. In this work, we show how an arbitrary approximation for the value function can be used to derive double-sided bounds on the optimal value function of interest. We further extend the framework with error analysis for continuous state and action spaces. The derived results lead to new approaches for clipping during training which we validate numerically in simple domains.
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages
Venue: arXiv cs.IR 2023
Year: 2023

Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.
A Differentiable Signed Distance Representation for Continuous Collision Avoidance in Optimization-Based Motion Planning
Venue: arXiv cs.RO 2023
Year: 2023

This paper proposes a new set of conditions for exactly representing collision avoidance constraints within optimization-based motion planning algorithms. The conditions are continuously differentiable and therefore suitable for use with standard nonlinear optimization solvers. The method represents convex shapes using a support function representation and is therefore quite general. For collision avoidance involving polyhedral or ellipsoidal shapes, the proposed method introduces fewer variables and constraints than existing approaches. Additionally the proposed method can be used to rigorously ensure continuous collision avoidance as the vehicle transitions between the discrete poses determined by the motion planning algorithm. Numerical examples demonstrate how this can be used to prevent problems of corner cutting and passing through obstacles which can occur when collision avoidance is only enforced at discrete time steps.
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification
Venue: arXiv cs.LG 2023
Year: 2023

Graph Neural Networks (GNNs) have proven to be powerful in many graph-based applications. However, they fail to generalize well under heterophilic setups, where neighbor nodes have different labels. To address this challenge, we employ a confidence ratio as a hyper-parameter, assuming that some of the edges are disassortative (heterophilic). Here, we propose a two-phased algorithm. Firstly, we determine edge coefficients through subgraph matching using a supplementary module. Then, we apply GNNs with a modified label propagation mechanism to utilize the edge coefficients effectively. Specifically, our supplementary module identifies a certain proportion of task-irrelevant edges based on a given confidence ratio. Using the remaining edges, we employ the widely used optimal transport to measure the similarity between two nodes with their subgraphs. Finally, using the coefficients as supplementary information on GNNs, we improve the label propagation mechanism which can prevent two nodes with smaller weights from being closer. The experiments on benchmark datasets show that our model alleviates over-smoothing and improves performance.
Persian topic detection based on Human Word association and graph embedding
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods.
Improving User Controlled Table-To-Text Generation Robustness
Venue: arXiv cs.CL 2023
Year: 2023

In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator. Such generation models usually learn from carefully selected cell combinations (clean cell selections); however, in practice users may select unexpected, redundant, or incoherent cell combinations (noisy cell selections). In experiments, we find that models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. We propose a fine-tuning regime with additional user-simulated noisy cell selections. Models fine-tuned with the proposed regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases; and achieve comparable state-of-the-art performance on the ToTTo dataset.
Weighted First Order Model Counting with Directed Acyclic Graph Axioms
Venue: arXiv cs.AI 2023
Year: 2023

Statistical Relational Learning (SRL) integrates First-Order Logic (FOL) and probability theory for learning and inference over relational data. Probabilistic inference and learning in many SRL models can be reduced to Weighted First Order Model Counting (WFOMC). However, WFOMC is known to be intractable ($\mathrm{\#P_1-}$ complete). Hence, logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called domain liftable. Recent line of works have shown the two-variable fragment of FOL, extended with counting quantifiers ($\mathrm{C^2}$) to be domain-liftable. However, many properties of real-world data can not be modelled in $\mathrm{C^2}$. In fact many ubiquitous properties of real-world data are inexressible in FOL. Acyclicity is one such property, found in citation networks, genealogy data, temporal data e.t.c. In this paper we aim to address this problem by investigating the domain liftability of directed acyclicity constraints. We show that the fragment $\mathrm{C^2}$ with a Directed Acyclic Graph (DAG) axiom, i.e., a predicate in the language is axiomatized to represent a DAG, is domain-liftable. We present a method based on principle of inclusion-exclusion for WFOMC of $\mathrm{C^2}$ formulas extended with DAG axioms.
Recent Trends in Cross-Border Data Access by Law Enforcement Agencies
Venue: arXiv cs.CY 2023
Year: 2023

Access to online data has long been important for law enforcement agencies in their collection of electronic evidence and investigation of crimes. These activities have also long involved cross-border investigations and international cooperation between agencies and jurisdictions. However, technological advances such as cloud computing have complicated the investigations and cooperation arrangements. Therefore, several new laws have been passed and proposed both in the United States and the European Union for facilitating cross-border crime investigations in the context of cloud computing. These new laws and proposals have also brought many new legal challenges and controversies regarding extraterritoriality, data protection, privacy, and surveillance. With these challenges in mind and with a focus on Europe, this paper reviews the recent trends and policy initiatives for cross-border data access by law enforcement agencies.
Advanced Image Quality Assessment for Hand- and Fingervein Biometrics
Venue: arXiv cs.CV 2023
Year: 2023

Natural Scene Statistics commonly used in non-reference image quality measures and a deep learning based quality assessment approach are proposed as biometric quality indicators for vasculature images. While NIQE and BRISQUE if trained on common images with usual distortions do not work well for assessing vasculature pattern samples' quality, their variants being trained on high and low quality vasculature sample data behave as expected from a biometric quality estimator in most cases (deviations from the overall trend occur for certain datasets or feature extraction methods). The proposed deep learning based quality metric is capable of assigning the correct quality class to the vaculature pattern samples in most cases, independent of finger or hand vein patterns being assessed. The experiments were conducted on a total of 13 publicly available finger and hand vein datasets and involve three distinct template representations (two of them especially designed for vascular biometrics). The proposed (trained) quality measures are compared to a several classical quality metrics, with their achieved results underlining their promising behaviour.
Towards Measuring and Scoring Speaker Diarization Fairness
Venue: arXiv cs.SD 2023
Year: 2023

Speaker diarization, or the task of finding "who spoke and when", is now used in almost every speech processing application. Nevertheless, its fairness has not yet been evaluated because there was no protocol to study its biases one by one. In this paper we propose a protocol and a scoring method designed to evaluate speaker diarization fairness. This protocol is applied on a large dataset of spoken utterances and report the performances of speaker diarization depending on the gender, the age, the accent of the speaker and the length of the spoken sentence. Some biases induced by the gender, or the accent of the speaker were identified when we applied a state-of-the-art speaker diarization method.
Technical Dimensions of Programming Systems
Venue: arXiv cs.PL 2023
Year: 2023

Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art. In computer science, much has been said and done to allow comparison of **programming languages**, yet no similar theory exists for *programming systems*; we believe that programming systems deserve a theory too. We present a framework of *technical dimensions* which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them. We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions. Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems. Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants.
Black Boxes, White Noise: Similarity Detection for Neural Functions
Venue: arXiv cs.PL 2023
Year: 2023

Similarity, or clone, detection has important applications in copyright violation, software theft, code search, and the detection of malicious components. There is now a good number of open source and proprietary clone detectors for programs written in traditional programming languages. However, the increasing adoption of deep learning models in software poses a challenge to these tools: these models implement functions that are inscrutable black boxes. As more software includes these DNN functions, new techniques are needed in order to assess the similarity between deep learning components of software. Previous work has unveiled techniques for comparing the representations learned at various layers of deep neural network models by feeding canonical inputs to the models. Our goal is to be able to compare DNN functions when canonical inputs are not available -- because they may not be in many application scenarios. The challenge, then, is to generate appropriate inputs and to identify a metric that, for those inputs, is capable of representing the degree of functional similarity between two comparable DNN functions. Our approach uses random input with values between -1 and 1, in a shape that is compatible with what the DNN models expect. We then compare the outputs by performing correlation analysis. Our study shows how it is possible to perform similarity analysis even in the absence of meaningful canonical inputs. The response to random inputs of two comparable DNN functions exposes those functions' similarity, or lack thereof. Of all the metrics tried, we find that Spearman's rank correlation coefficient is the most powerful and versatile, although in special cases other methods and metrics are more expressive. We present a systematic empirical study comparing the effectiveness of several similarity metrics using a dataset of 56,355 classifiers collected from GitHub. This is accompanied by a sensitivity analysis that reveals how certain models' training related properties affect the effectiveness of the similarity metrics. To the best of our knowledge, this is the first work that shows how similarity of DNN functions can be detected by using random inputs. Our study of correlation metrics, and the identification of Spearman correlation coefficient as the most powerful among them for this purpose, establishes a complete and practical method for DNN clone detection that can be used in the design of new tools. It may also serve as inspiration for other program analysis tasks whose approaches break in the presence of DNN components.
Defending against the nothing-at-stake problem in multi-threaded blockchains
Venue: arXiv cs.CR 2023
Year: 2023

In blockchain systems, the scarcity of a resource is used as a Sybil protection mechanism. In Proof-of-Work blockchains, that resource is computing power. In the event of a fork, the scarcity of this resource theoretically prevents miners from producing blocks on both branches of a fork. In Proof-of-Stake blockchains, because that resource is token stake, the computational cost of creating a block is negligible. In the event of a fork, and if no specific measures have been taken, rational block producers should extend both branches of the fork. In blockchains with sequential block production, a punishment mechanism known as slashing is often cited as a protection against the nothing-at-stake problem. However, in the context of a blockchain with parallel block production, it seems that slashing is not sufficient against the numerous divergence opportunities. In this paper, we propose a novel protection against the nothing-at-stake problem that takes the most out of BFT and Nakamoto-based consensus. By combining those approaches, we wish to scale up blockchains by allowing parallel block production without reconciliation.
An Accurate Model to Estimate 5G Propagation Path Loss for the Indoor Environment
Venue: arXiv cs.IT 2023
Year: 2023

This paper presents a new large-scale propagation path loss model to design a fifth-generation (5G) wireless communication system for indoor environments. Simulations for the indoor environment, for all polarization at non-line-of-sight (NLOS) and line-of-sight (LOS), which are performed per meter over a distance of 47 m between each of the separated transmitter antenna (TX) and the receiver antenna (RX) positions to compare better the proposed extensive flexible path loss model with previous models. All the simulations are conducted at the Abu-rayhan buildings at the Amirkabir University of Technology. The results demonstrated that the simple presented model with a single parameter denoted ZMS can predict the expansive path loss over distance more accurately. The values of the path loss exponent (PLE) for the LOS scenario are simulated and achieved at 3.63, 1.81, and 3.42 for the V-H, V-V, and V-Omni antenna polarizations, and for NLOS is 6.11, 4.21, and 5.23 at the 28 GHz frequency for all the polarization antenna type V-H, V-V, and V-Omni, appropriately.
UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios
Venue: arXiv cs.CV 2023
Year: 2023

Stereo matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, such as KITTI and Scene Flow. UAVs (Unmanned Aerial Vehicles) are commonly utilized for surface observation, and their captured images are frequently used for detailed 3D reconstruction due to high resolution and low-altitude acquisition. At present, the mainstream supervised learning network requires a significant amount of training data with ground-truth labels to learn model parameters. However, due to the scarcity of UAV stereo matching datasets, the learning-based network cannot be applied to UAV images. To facilitate further research, this paper proposes a novel pipeline to generate accurate and dense disparity maps using detailed meshes reconstructed by UAV images and LiDAR point clouds. Through the proposed pipeline, this paper constructs a multi-resolution UAV scenario dataset, called UAVStereo, with over 34k stereo image pairs covering 3 typical scenes. As far as we know, UAVStereo is the first stereo matching dataset of UAV low-altitude scenarios. The dataset includes synthetic and real stereo pairs to enable generalization from the synthetic domain to the real domain. Furthermore, our UAVStereo dataset provides multi-resolution and multi-scene images pairs to accommodate a variety of sensors and environments. In this paper, we evaluate traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios and offering suggestions for future research. The dataset is available at https://github.com/rebecca0011/UAVStereo.git
Control Flow Duplication for Columnar Arrays in a Dynamic Compiler
Venue: arXiv cs.PL 2023
Year: 2023

Columnar databases are an established way to speed up online analytical processing (OLAP) queries. Nowadays, data processing (e.g., storage, visualization, and analytics) is often performed at the programming language level, hence it is desirable to also adopt columnar data structures for common language runtimes. While there are frameworks, libraries, and APIs to enable columnar data stores in programming languages, their integration into applications typically requires developer interference. In prior work, researchers implemented an approach for *automated* transformation of arrays into columnar arrays in the GraalVM JavaScript runtime. However, this approach suffers from performance issues on smaller workloads as well as on more complex nested data structures. We find that the key to optimizing accesses to columnar arrays is to identify queries and apply specific optimizations to them. In this paper, we describe novel compiler optimizations in the GraalVM Compiler that optimize queries on columnar arrays. At JIT compile time, we identify loops that access potentially columnar arrays and duplicate them in order to specifically optimize accesses to columnar arrays. Additionally, we describe a new approach for creating columnar arrays from arrays consisting of complex objects by performing **multi-level storage transformation**. We demonstrate our approach via an implementation for JavaScript `Date` objects. [ full abstract at https://doi.org/10.22152/programming-journal.org/2023/7/9 ]