OpenRaiser/Intern-Atlas huggingface dataset:
Cyber Security Requirements for Platforms Enhancing AI Reproducibility
Venue: arXiv cs.CR 2023
Year: 2023

Scientific research is increasingly reliant on computational methods, posing challenges for ensuring research reproducibility. This study focuses on the field of artificial intelligence (AI) and introduces a new framework for evaluating AI platforms for reproducibility from a cyber security standpoint to address the security challenges associated with AI research. Using this framework, five popular AI reproducibility platforms; Floydhub, BEAT, Codalab, Kaggle, and OpenML were assessed. The analysis revealed that none of these platforms fully incorporates the necessary cyber security measures essential for robust reproducibility. Kaggle and Codalab, however, performed better in terms of implementing cyber security measures covering aspects like security, privacy, usability, and trust. Consequently, the study provides tailored recommendations for different user scenarios, including individual researchers, small laboratories, and large corporations. It emphasizes the importance of integrating specific cyber security features into AI platforms to address the challenges associated with AI reproducibility, ultimately advancing reproducibility in this field. Moreover, the proposed framework can be applied beyond AI platforms, serving as a versatile tool for evaluating a wide range of systems and applications from a cyber security perspective.
Low Latency of object detection for spikng neural network
Venue: arXiv cs.CV 2023
Year: 2023

Spiking Neural Networks, as a third-generation neural network, are well-suited for edge AI applications due to their binary spike nature. However, when it comes to complex tasks like object detection, SNNs often require a substantial number of time steps to achieve high performance. This limitation significantly hampers the widespread adoption of SNNs in latency-sensitive edge devices. In this paper, our focus is on generating highly accurate and low-latency SNNs specifically for object detection. Firstly, we systematically derive the conversion between SNNs and ANNs and analyze how to improve the consistency between them: improving the spike firing rate and reducing the quantization error. Then we propose a structural replacement, quantization of ANN activation and residual fix to allevicate the disparity. We evaluate our method on challenging dataset MS COCO, PASCAL VOC and our spike dataset. The experimental results show that the proposed method achieves higher accuracy and lower latency compared to previous work Spiking-YOLO. The advantages of SNNs processing of spike signals are also demonstrated.
The Maximum Cover with Rotating Field of View
Venue: arXiv cs.CG 2023
Year: 2023

Imagine a polygon-shaped platform $P$ and only one static spotlight outside $P$; which direction should the spotlight face to light most of $P$? This problem occurs in maximising the visibility, as well as in limiting the uncertainty in localisation problems. More formally, we define the following maximum cover problem: "Given a convex polygon $P$ and a Field Of View (FOV) with a given centre and inner angle $ϕ$; find the direction (an angle of rotation $θ$) of the FOV such that the intersection between the FOV and $P$ has the maximum area". In this paper, we provide the theoretical foundation for the analysis of the maximum cover with a rotating field of view. The main challenge is that the function of the area $A_ϕ(θ)$, with the angle of rotation $θ$ and the fixed inner angle $ϕ$, cannot be approximated directly. We found an alternative way to express it by various compositions of a function $A_θ(ϕ)$ (with a restricted inner angle $ϕ$ and a fixed direction $θ$). We show that $A_θ(ϕ)$ has an analytical solution in the special case of a two-sector intersection and later provides a constrictive solution for the original problem. Since the optimal solution is a real number, we develop an algorithm that approximates the direction of the field of view, with precision $\varepsilon$, and complexity $\mathcal{O}(n(\log{n}+(\log{\varepsilon})/ϕ))$.
Perception for Humanoid Robots
Venue: arXiv cs.RO 2023
Year: 2023

Purpose of Review: The field of humanoid robotics, perception plays a fundamental role in enabling robots to interact seamlessly with humans and their surroundings, leading to improved safety, efficiency, and user experience. This scientific study investigates various perception modalities and techniques employed in humanoid robots, including visual, auditory, and tactile sensing by exploring recent state-of-the-art approaches for perceiving and understanding the internal state, the environment, objects, and human activities. Recent Findings: Internal state estimation makes extensive use of Bayesian filtering methods and optimization techniques based on maximum a-posteriori formulation by utilizing proprioceptive sensing. In the area of external environment understanding, with an emphasis on robustness and adaptability to dynamic, unforeseen environmental changes, the new slew of research discussed in this study have focused largely on multi-sensor fusion and machine learning in contrast to the use of hand-crafted, rule-based systems. Human robot interaction methods have established the importance of contextual information representation and memory for understanding human intentions. Summary: This review summarizes the recent developments and trends in the field of perception in humanoid robots. Three main areas of application are identified, namely, internal state estimation, external environment estimation, and human robot interaction. The applications of diverse sensor modalities in each of these areas are considered and recent significant works are discussed.
Human Kinematics-inspired Skeleton-based Video Anomaly Detection
Venue: arXiv cs.CV 2023
Year: 2023

Previous approaches to detecting human anomalies in videos have typically relied on implicit modeling by directly applying the model to video or skeleton data, potentially resulting in inaccurate modeling of motion information. In this paper, we conduct an exploratory study and introduce a new idea called HKVAD (Human Kinematic-inspired Video Anomaly Detection) for video anomaly detection, which involves the explicit use of human kinematic features to detect anomalies. To validate the effectiveness and potential of this perspective, we propose a pilot method that leverages the kinematic features of the skeleton pose, with a specific focus on the walking stride, skeleton displacement at feet level, and neck level. Following this, the method employs a normalizing flow model to estimate density and detect anomalies based on the estimated density. Based on the number of kinematic features used, we have devised three straightforward variant methods and conducted experiments on two highly challenging public datasets, ShanghaiTech and UBnormal. Our method achieves good results with minimal computational resources, validating its effectiveness and potential.
A New Centralized Multi-Node Repair Scheme of MSR codes with Error-Correcting Capability
Venue: arXiv cs.IT 2023
Year: 2023

Minimum storage regenerating (MSR) codes, with the MDS property and the optimal repair bandwidth, are widely used in distributed storage systems (DSS) for data recovery. In this paper, we consider the construction of $(n,k,l)$ MSR codes in the centralized model that can repair $h$ failed nodes simultaneously with $e$ out $d$ helper nodes providing erroneous information. We first propose the new repair scheme, and give a complete proof of the lower bound on the amount of symbols downloaded from the helped nodes, provided that some of helper nodes provide erroneous information. Then we focus on two explicit constructions with the repair scheme proposed. For $2\leq h\leq n-k$, $k+2e\leq d \leq n-h$ and $d\equiv k+2e \;(\mod{h})$, the first one has the UER $(h, d)$-optimal repair property, and the second one has the UER $(h, d)$-optimal access property. Compared with the original constructions (Ye and Barg, IEEE Tran. Inf. Theory, Vol. 63, April 2017), our constructions have improvements in three aspects: 1) The proposed repair scheme is more feasible than the one-by-one scheme presented by Ye and Barg in a parallel data system; 2) The sub-packetization is reduced from $\left(\operatorname{lcm}(d-k+1, d-k+2,\cdots, d-k+h)\right)^n$ to $\left((d-2e-k+h)/h\right)^n$, which reduces at least by a factor of $(h(d-k+h))^n$; 3) The field size of the first construction is reduced to $|\mathbb{F}| \geq n(d-2e-k+h)/h$, which reduces at least by a factor of $h(d-k+h)$. Small sub-packetization and small field size are preferred in practice due to the limited storage capacity and low computation complexity in the process of encoding, decoding and repairing.
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension
Venue: arXiv cs.CL 2023
Year: 2023

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60\% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.
A user study of visualisations of spatio-temporal eye tracking data
Venue: arXiv cs.HC 2023
Year: 2023

Eye movements have a spatial (where people look), but also a temporal (when people look) component. Various types of visualizations have been proposed that take this spatio-temporal nature of the data into account, but it is unclear how well each one can be interpreted and whether such interpretation depends on the question asked about the data or the nature of the data-set that is being visualised. In this study, four spatio-temporal visualization techniques for eye movements (chord diagram, scanpath, scarfplot, space-time cube) were compared in a user study. Participants (N = 25) answered three questions (what region first, what region most, which regions most between) about each visualization, which was based on two types of data-sets (eye movements towards adverts, eye movements towards pairs of gambles). Accuracy of the answers depended on a combination of the data-set, the question that needed to answered, and the type of visualization. For most questions, the scanpath, which did not use area of interest (AOI) information, resulted in lower accuracy than the other graphs. This suggests that AOIs improve the information conveyed by graphs. No effects of experience with reading graphs (for work or not for work) or education on accuracy of the answer was found. The results therefore suggest that there is no single best visualisation of the spatio-temporal aspects of eye movements. When visualising eye movement data, a user study may therefore be beneficial to determine the optimal visualization of the data-set and research question at hand.
Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data
Venue: arXiv cs.LG 2023
Year: 2023

In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data. In this work, we address this limitation by providing an approach for inferring latent graphs that capture the intrinsic data relationships. By leveraging graph-based representations, our approach facilitates the seamless propagation of information throughout the graph, effectively incorporating global and local knowledge. Through evaluations on biomedical tabular datasets, we compare the capabilities of our approach to other contemporary methods. Our work demonstrates the significance of inter-instance relationship discovery as practical means for constructing robust latent graphs to enhance semi-supervised learning techniques. The experiments show that the proposed methodology outperforms contemporary state-of-the-art methods for (semi-)supervised learning on three biomedical datasets.
Partial Transport for Point-Cloud Registration
Venue: arXiv cs.CV 2023
Year: 2023

Point cloud registration plays a crucial role in various fields, including robotics, computer graphics, and medical imaging. This process involves determining spatial relationships between different sets of points, typically within a 3D space. In real-world scenarios, complexities arise from non-rigid movements and partial visibility, such as occlusions or sensor noise, making non-rigid registration a challenging problem. Classic non-rigid registration methods are often computationally demanding, suffer from unstable performance, and, importantly, have limited theoretical guarantees. The optimal transport problem and its unbalanced variations (e.g., the optimal partial transport problem) have emerged as powerful tools for point-cloud registration, establishing a strong benchmark in this field. These methods view point clouds as empirical measures and provide a mathematically rigorous way to quantify the `correspondence' between (the transformed) source and target points. In this paper, we approach the point-cloud registration problem through the lens of optimal transport theory and first propose a comprehensive set of non-rigid registration methods based on the optimal partial transportation problem. Subsequently, leveraging the emerging work on efficient solutions to the one-dimensional optimal partial transport problem, we extend our proposed algorithms via slicing to gain significant computational efficiency, resulting in fast and robust non-rigid registration algorithms. We demonstrate the effectiveness of our proposed methods and compare them against baselines on various 3D and 2D non-rigid registration problems where the source and target point clouds are corrupted by random noise.
ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction & Survival
Venue: arXiv cs.LG 2023
Year: 2023

Prostate cancer is a prevalent malignancy among men aged 50 and older. Current diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific Antigen levels, and Digital Rectal Examinations (DRE). However, these methods suffer from a significant rate of false positive results. This study focuses on the development and validation of an intelligent mathematical model utilizing Artificial Neural Networks (ANNs) to enhance the early detection of prostate cancer. The primary objective of this research paper is to present a novel mathematical model designed to aid in the early detection of prostate cancer, facilitating prompt intervention by healthcare professionals. The model's implementation demonstrates promising potential in reducing the incidence of false positives, thereby improving patient outcomes. Furthermore, we envision that, with further refinement, extensive testing, and validation, this model can evolve into a robust, marketable solution for prostate cancer detection. The long-term goal is to make this solution readily available for deployment in various screening centers, hospitals, and research institutions, ultimately contributing to more effective cancer screening and patient care.
Nash Equilibria of Two-Player Matrix Games Repeated Until Collision
Venue: arXiv cs.GT 2023
Year: 2023

We introduce and initiate the study of a natural class of repeated two-player matrix games, called Repeated-Until-Collision (RUC) games. In each round, both players simultaneously pick an action from a common action set $\{1, 2, \dots, n\}$. Depending on their chosen actions, they derive payoffs given by $n \times n$ matrices $A$ and $B$, respectively. If their actions collide (i.e., they pick the same action), the game ends, otherwise, it proceeds to the next round. Both players want to maximize their total payoff until the game ends. RUC games can be interpreted as pursuit-evasion games or repeated hide-and-seek games. They also generalize hand cricket, a popular game among children in India. We show that under mild assumptions on the payoff matrices, every RUC game admits a Nash equilibrium (NE). Moreover, we show the existence of a stationary NE, where each player chooses their action according to a probability distribution over the action set that does not change across rounds. Remarkably, we show that all NE are effectively the same as the stationary NE, thus showing that RUC games admit an almost unique NE. Lastly, we also show how to compute (approximate) NE for RUC games.
Projection based fuzzy least squares twin support vector machine for class imbalance problems
Venue: arXiv cs.LG 2023
Year: 2023

Class imbalance is a major problem in many real world classification tasks. Due to the imbalance in the number of samples, the support vector machine (SVM) classifier gets biased toward the majority class. Furthermore, these samples are often observed with a certain degree of noise. Therefore, to remove these problems we propose a novel fuzzy based approach to deal with class imbalanced as well noisy datasets. We propose two approaches to address these problems. The first approach is based on the intuitionistic fuzzy membership, termed as robust energy-based intuitionistic fuzzy least squares twin support vector machine (IF-RELSTSVM). Furthermore, we introduce the concept of hyperplane-based fuzzy membership in our second approach, where the final classifier is termed as robust energy-based fuzzy least square twin support vector machine (F-RELSTSVM). By using this technique, the membership values are based on a projection based approach, where the data points are projected on the hyperplanes. The performance of the proposed algorithms is evaluated on several benchmark and synthetic datasets. The experimental results show that the proposed IF-RELSTSVM and F-RELSTSVM models outperform the baseline algorithms. Statistical tests are performed to check the significance of the proposed algorithms. The results show the applicability of the proposed algorithms on noisy as well as imbalanced datasets.
AutoEncoding Tree for City Generation and Applications
Venue: arXiv cs.CV 2023
Year: 2023

City modeling and generation have attracted an increased interest in various applications, including gaming, urban planning, and autonomous driving. Unlike previous works focused on the generation of single objects or indoor scenes, the huge volumes of spatial data in cities pose a challenge to the generative models. Furthermore, few publicly available 3D real-world city datasets also hinder the development of methods for city generation. In this paper, we first collect over 3,000,000 geo-referenced objects for the city of New York, Zurich, Tokyo, Berlin, Boston and several other large cities. Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation. Specifically, we first propose a novel Spatial-Geometric Distance (SGD) metric to measure the similarity between building layouts and then construct a binary tree over the raw geometric data of building based on the SGD metric. Next, we present a tree-structured network whose encoder learns to extract and merge spatial information from bottom-up iteratively. The resulting global representation is reversely decoded for reconstruction or generation. To address the issue of long-dependency as the level of the tree increases, a Long Short-Term Memory (LSTM) Cell is employed as a basic network element of the proposed AETree. Moreover, we introduce a novel metric, Overlapping Area Ratio (OAR), to quantitatively evaluate the generation results. Experiments on the collected dataset demonstrate the effectiveness of the proposed model on 2D and 3D city generation. Furthermore, the latent features learned by AETree can serve downstream urban planning applications.
OPPO: An Ontology for Describing Fine-Grained Data Practices in Privacy Policies of Online Social Networks
Venue: arXiv cs.CR 2023
Year: 2023

Privacy policies outline the data practices of Online Social Networks (OSN) to comply with privacy regulations such as the EU-GDPR and CCPA. Several ontologies for modeling privacy regulations, policies, and compliance have emerged in recent years. However, they are limited in various ways: (1) they specifically model what is required of privacy policies according to one specific privacy regulation such as GDPR; (2) they provide taxonomies of concepts but are not sufficiently axiomatized to afford automated reasoning with them; and (3) they do not model data practices of privacy policies in sufficient detail to allow assessing the transparency of policies. This paper presents an OWL Ontology for Privacy Policies of OSNs, OPPO, that aims to fill these gaps by formalizing detailed data practices from OSNS' privacy policies. OPPO is grounded in BFO, IAO, OMRSE, and OBI, and its design is guided by the use case of representing and reasoning over the content of OSNs' privacy policies and evaluating policies' transparency in greater detail.
Clinical Trial Recommendations Using Semantics-Based Inductive Inference and Knowledge Graph Embeddings
Venue: arXiv cs.AI 2023
Year: 2023

Designing a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical trial records. Here, we propose a novel recommendation methodology, based on neural embeddings trained on a first-of-a-kind knowledge graph of clinical trials. We addressed several important research questions in this context, including designing a knowledge graph (KG) for clinical trial data, effectiveness of various KG embedding (KGE) methods for it, a novel inductive inference using KGE, and its use in generating recommendations for clinical trial design. We used publicly available data from clinicaltrials.gov for the study. Results show that our recommendations approach achieves relevance scores of 70%-83%, measured as the text similarity to actual clinical trial elements, and the most relevant recommendation can be found near the top of list. Our study also suggests potential improvement in training KGE using node semantics.
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
Venue: arXiv cs.LG 2023
Year: 2023

With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising execution platform due to their fine-grained parallelism, low-power consumption, reconfigurability, and concurrent execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between the non-trivial FPGA development efforts and rapid emergence of new GNN models. In this paper, we propose GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment, and FPGA implementations of 6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with distinct topologies and scales. The results show that GNNHLS achieves up to 50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy reduction.