OpenRaiser/Intern-Atlas huggingface dataset:
Melody: A Platform for Linked Open Data Visualisation and Curated Storytelling
Venue: arXiv cs.HC 2023
Year: 2023

Data visualisation and storytelling techniques help experts highlight relations between data and share complex information with a broad audience. However, existing solutions targeted to Linked Open Data visualisation have several restrictions and lack the narrative element. In this article we present MELODY, a web interface for authoring data stories based on Linked Open Data. MELODY has been designed using a novel methodology that harmonises existing Ontology Design and User Experience methodologies (eXtreme Design and Design Thinking), and provides reusable User Interface components to create and publish web-ready article-alike documents based on data retrievable from any SPARQL endpoint. We evaluate the software by comparing it with existing solutions, and we show its potential impact in projects where data dissemination is crucial.
LM4HPC: Towards Effective Language Model Application in High-Performance Computing
Venue: arXiv cs.LG 2023
Year: 2023

In recent years, language models (LMs), such as GPT-4, have been widely used in multiple domains, including natural language processing, visualization, and so on. However, applying them for analyzing and optimizing high-performance computing (HPC) software is still challenging due to the lack of HPC-specific support. In this paper, we design the LM4HPC framework to facilitate the research and development of HPC software analyses and optimizations using LMs. Tailored for supporting HPC datasets, AI models, and pipelines, our framework is built on top of a range of components from different levels of the machine learning software stack, with Hugging Face-compatible APIs. Using three representative tasks, we evaluated the prototype of our framework. The results show that LM4HPC can help users quickly evaluate a set of state-of-the-art models and generate insightful leaderboards.
Integer Linear Programming Modeling of Addition Sequences With Additional Constraints for Evaluation of Power Terms
Venue: arXiv cs.DM 2023
Year: 2023

In this work, an integer linear programming (ILP) based model is proposed for the computation of a minimal cost addition sequence for a given set of integers. Since exponents are additive under multiplication, the minimal length addition sequence will provide an economical solution for the evaluation of a requested set of power terms. This is turn, finds application in, e.g., window-based exponentiation for cryptography and polynomial evaluation. Not only is an optimal model proposed, the model is extended to consider different costs for multipliers and squarers as well as controlling the depth of the resulting addition sequence.
Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields
Venue: arXiv cs.CV 2023
Year: 2023

Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method which efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with $k$ features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.
ethp2psim: Evaluating and deploying privacy-enhanced peer-to-peer routing protocols for the Ethereum network
Venue: arXiv cs.CR 2023
Year: 2023

Network-level privacy is the Achilles heel of financial privacy in cryptocurrencies. Financial privacy amounts to achieving and maintaining blockchain- and network-level privacy. Blockchain-level privacy recently received substantial attention. Specifically, several privacy-enhancing technologies were proposed and deployed to enhance blockchain-level privacy. On the other hand, network-level privacy, i.e., privacy on the peer-to-peer layer, has seen far less attention and development. In this work, we aim to provide a peer-to-peer network simulator, ethp2psim, that allows researchers to evaluate the privacy guarantees of privacy-enhanced broadcast and message routing algorithms. Our goal is two-fold. First, we want to enable researchers to implement their proposed protocols in our modular simulator framework. Second, our simulator allows researchers to evaluate the privacy guarantees of privacy-enhanced routing algorithms. Finally, ethp2psim can help choose the right protocol parameters for efficient, robust, and private deployment.
Self-Supervised Image Captioning with CLIP
Venue: arXiv cs.CV 2023
Year: 2023

Image captioning, a fundamental task in vision-language understanding, seeks to generate accurate natural language descriptions for provided images. Current image captioning approaches heavily rely on high-quality image-caption pairs, which can be hard to obtain for many domains. To address this, we introduce a self-supervised image captioning method. After learning an initial signal from a small labeled dataset, our method transitions to self-supervised learning on unlabeled data, leveraging the auxiliary task of enhancing the CLIP relevance between images and generated captions. Remarkably, despite utilizing less than 2% of the labeled COCO dataset, our method delivers a performance comparable to state-of-the-art models trained on the complete dataset. Human evaluations further reveal that our method produces captions with greater distinctiveness and informativeness, two attributes inherently challenging to achieve through supervised learning.
Continual Learning for Out-of-Distribution Pedestrian Detection
Venue: arXiv cs.CV 2023
Year: 2023

A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection. While recent pedestrian detection models have achieved impressive performance on various datasets, they remain sensitive to shifts in the distribution of the inference data. Our method adopts and modifies Elastic Weight Consolidation to a backbone object detection network, in order to penalize the changes in the model weights based on their importance towards the initially learned task. We show that when trained with one dataset and fine-tuned on another, our solution learns the new distribution and maintains its performance on the previous one, avoiding catastrophic forgetting. We use two popular datasets, CrowdHuman and CityPersons for our cross-dataset experiments, and show considerable improvements over standard fine-tuning, with a 9% and 18% miss rate percent reduction improvement in the CrowdHuman and CityPersons datasets, respectively.
DeepStream: Bandwidth Efficient Multi-Camera Video Streaming for Deep Learning Analytics
Venue: arXiv cs.NI 2023
Year: 2023

Deep learning video analytic systems process live video feeds from multiple cameras with computer vision models deployed on edge or cloud. To optimize utility for these systems, which usually corresponds to query accuracy, efficient bandwidth management for the cameras competing for the fluctuating network resources is crucial. We propose DeepStream, a bandwidth efficient multi-camera video streaming system for deep learning video analytics. DeepStream addresses the challenge of limited and fluctuating bandwidth resources by offering several tailored solutions. We design a novel Regions of Interest detection (ROIDet) algorithm which can run in real time on resource constraint devices, such as Raspberry Pis, to remove spatial redundancy in video frames and reduce the amount of data to be transmitted. We also propose a content-aware bandwidth optimization framework and an Elastic Transmission Mechanism that exploits correlations among video contents. We implement DeepStream on Raspberry Pis and a desktop computer. Evaluations on real-world datasets show that DeepStream's ROIDet algorithm saves up to 54\% bandwidth with less than 1\% accuracy drop. Additionally,DeepStream improves utility by up to 23\% compared to baselines under the same bandwidth conditions.
Exploiting Inferential Structure in Neural Processes
Venue: arXiv cs.LG 2023
Year: 2023

Neural Processes (NPs) are appealing due to their ability to perform fast adaptation based on a context set. This set is encoded by a latent variable, which is often assumed to follow a simple distribution. However, in real-word settings, the context set may be drawn from richer distributions having multiple modes, heavy tails, etc. In this work, we provide a framework that allows NPs' latent variable to be given a rich prior defined by a graphical model. These distributional assumptions directly translate into an appropriate aggregation strategy for the context set. Moreover, we describe a message-passing procedure that still allows for end-to-end optimization with stochastic gradients. We demonstrate the generality of our framework by using mixture and Student-t assumptions that yield improvements in function modelling and test-time robustness.
Examining Lower Latency Routing with Overlay Networks
Venue: arXiv cs.NI 2023
Year: 2023

In today's rapidly expanding digital landscape, where access to timely online content is paramount to users, the underlying network infrastructure and latency performance significantly influence the user experience. We present an empirical study of the current Internet's connectivity and the achievable latencies to propose better routing paths if available. Understanding the severity of the non-optimal internet topology with RIPE Atlas stats, we conduct practical experiments to demonstrate that local traffic from the San Diego area to the University of California, San Diego reaches up to Los Angeles before serving responses. We examine the traceroutes and build an experimental overlay network to constrain the San Diego traffic within the city to get better round-trip time latencies.
One-class systems seamlessly fit in the forward-forward algorithm
Venue: arXiv cs.LG 2023
Year: 2023

The forward-forward algorithm presents a new method of training neural networks by updating weights during an inference, performing parameter updates for each layer individually. This immediately reduces memory requirements during training and may lead to many more benefits, like seamless online training. This method relies on a loss ("goodness") function that can be evaluated on the activations of each layer, of which can have a varied parameter size, depending on the hyperparamaterization of the network. In the seminal paper, a goodness function was proposed to fill this need; however, if placed in a one-class problem context, one need not pioneer a new loss because these functions can innately handle dynamic network sizes. In this paper, we investigate the performance of deep one-class objective functions when trained in a forward-forward fashion. The code is available at \url{https://github.com/MichaelHopwood/ForwardForwardOneclass}.
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
Venue: arXiv cs.RO 2023
Year: 2023

Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i.e., distribution shift). Interactive fleet learning (IFL) mitigates distribution shift by allowing robots to access remote human supervisors during task execution and learn from them over time, but different supervisors may demonstrate the task in different ways. Recent work proposes Implicit Behavior Cloning (IBC), which is able to represent multimodal demonstrations using energy-based models (EBMs). In this work, we propose Implicit Interactive Fleet Learning (IIFL), an algorithm that builds on IBC for interactive imitation learning from multiple heterogeneous human supervisors. A key insight in IIFL is a novel approach for uncertainty quantification in EBMs using Jeffreys divergence. While IIFL is more computationally expensive than explicit methods, results suggest that IIFL achieves a 2.8x higher success rate in simulation experiments and a 4.5x higher return on human effort in a physical block pushing task over (Explicit) IFL, IBC, and other baselines.
Probability of Error for Optimal Codes in a Reconfigurable Intelligent Surface Aided URLLC System
Venue: arXiv cs.IT 2023
Year: 2023

The lower bound on the decoding error probability for the optimal code given a signal-to-noise ratio and a code rate are investigated in this letter for the reconfigurable intelligent surface (RIS) communication system over a Rician fading channel at the short blocklength regime, which is the key characteristic of ultra-reliable low-latency communications (URLLC) to meet the need for strict adherence to quality of service (QoS) requirements. Sphere packing technique is used to derive our main results. The Wald sequential t-test lemma and the Gaussian-Chebyshev quadrature are the main tools to obtain the closed-form expression for the lower bound. Numerical results are provided to validate our results and demonstrate the tightness of our results compared to the Polyanskiy-Poor-Verdu (PPV) bound.
C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
Venue: arXiv cs.CL 2023
Year: 2023

Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system. Consequently, they often correlate poorly with human evaluations. To address this issue, we propose a novel model-agnostic approach that leverages Conditional Pointwise Mutual Information (C-PMI) to measure the turn-level interaction between the system and the user based on a given evaluation dimension. Experimental results on the widely used FED dialogue evaluation dataset demonstrate that our approach significantly improves the correlation with human judgment compared with existing evaluation systems. By replacing the negative log-likelihood-based scorer with our proposed C-PMI scorer, we achieve a relative 62.6% higher Spearman correlation on average for the FED evaluation metric. Our code is publicly available at https://github.com/renll/C-PMI.
Errorless Robust JPEG Steganography Using Steganographic Polar Codes
Venue: arXiv cs.CR 2023
Year: 2023

Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression is proposed. The method evaluates the behavior of DCT coefficients after recompression using the local JPEG encoder to select robust coefficients and sets the other coefficients as wet cost. Combining the lattice embedding scheme, the method is errorless by construction. However, the authors only concern with the success rate under theoretical embedding, while the success rate of the implementation with practical steganographic codes is not verified. In this letter, we implement the method with two steganographic codes, i.e., steganographic polar code and syndrome-trellis code. By analyzing the possibility of success embedding of two steganographic codes under wet paper embedding, we discover that steganographic polar code achieves success embedding with a larger number of wet coefficients compared with syndrome-trellis code, which makes steganographic polar code more suitable under the errorless robust embedding paradigm. The experimental results show that the combination of steganographic polar code and errorless robust embedding achieves a higher success rate compared with the implementation with syndrome-trellis code under close security performance.
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?
Venue: arXiv cs.CL 2023
Year: 2023

For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation. However, it is unclear which aspects of segmentation affect their understanding. This study assesses the robustness of PLMs against various disrupted segmentation caused by noise. An evaluation framework for subword segmentation, named Contrastive Lexical Semantic (CoLeS) probe, is proposed. It provides a systematic categorization of segmentation corruption under noise and evaluation protocols by generating contrastive datasets with canonical-noisy word pairs. Experimental results indicate that PLMs are unable to accurately compute word meanings if the noise introduces completely different subwords, small subword fragments, or a large number of additional subwords, particularly when they are inserted within other subwords.
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
Venue: arXiv cs.CL 2023
Year: 2023

In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a long way to go when it comes to logical reasoning. Although some methods for it have been put forward, they either are designed in a quite complicated way or rely too much on external structures. In this paper, we proposed IDOL (InDicator-Oriented Logic Pre-training), an easy-to-understand but highly effective further pre-training task which logically strengthens the pre-trained models with the help of 6 types of logical indicators and a logically rich dataset LGP (LoGic Pre-training). IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2.0 while keeping competitive general language understanding ability through testing on tasks in GLUE. Besides, at the beginning of the era of large language models, we take several of them like ChatGPT into comparison and find that IDOL still shows its advantage.
A Highly Accurate Query-Recovery Attack against Searchable Encryption using Non-Indexed Documents
Venue: arXiv cs.CR 2023
Year: 2023

Cloud data storage solutions offer customers cost-effective and reduced data management. While attractive, data security issues remain to be a core concern. Traditional encryption protects stored documents, but hinders simple functionalities such as keyword search. Therefore, searchable encryption schemes have been proposed to allow for the search on encrypted data. Efficient schemes leak at least the access pattern (the accessed documents per keyword search), which is known to be exploitable in query recovery attacks assuming the attacker has a significant amount of background knowledge on the stored documents. Existing attacks can only achieve decent results with strong adversary models (e.g. at least 20% of previously known documents or require additional knowledge such as on query frequencies) and they give no metric to evaluate the certainty of recovered queries. This hampers their practical utility and questions their relevance in the real-world. We propose a refined score attack which achieves query recovery rates of around 85% without requiring exact background knowledge on stored documents; a distributionally similar, but otherwise different (i.e., non-indexed), dataset suffices. The attack starts with very few known queries (around 10 known queries in our experiments over different datasets of varying size) and then iteratively recovers further queries with confidence scores by adding previously recovered queries that had high confidence scores to the set of known queries. Additional to high recovery rates, our approach yields interpretable results in terms of confidence scores.
Insertion-Only Dynamic Connectivity in General Disk Graphs
Venue: arXiv cs.CG 2023
Year: 2023

Let $S \subseteq \mathbb{R}^2$ be a set of $n$ \emph{sites} in the plane, so that every site $s \in S$ has an \emph{associated radius} $r_s > 0$. Let $D(S)$ be the \emph{disk intersection graph} defined by $S$, i.e., the graph with vertex set $S$ and an edge between two distinct sites $s, t \in S$ if and only if the disks with centers $s$, $t$ and radii $r_s$, $r_t$ intersect. Our goal is to design data structures that maintain the connectivity structure of $D(S)$ as $S$ changes dynamically over time. We consider the incremental case, where new sites can be inserted into $S$. While previous work focuses on data structures whose running time depends on the ratio between the smallest and the largest site in $S$, we present a data structure with $O(α(n))$ amortized query time and $O(\log^6 n)$ expected amortized insertion time.
Diversity of Expertise is Key to Scientific Impact: a Large-Scale Analysis in the Field of Computer Science
Venue: arXiv cs.DL 2023
Year: 2023

Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors' expertise. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics we defined, reflecting the diversity of expertise, are significantly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement
Venue: arXiv cs.CL 2023
Year: 2023

Disentangling uncorrelated information in speech utterances is a crucial research topic within speech community. Different speech-related tasks focus on extracting distinct speech representations while minimizing the affects of other uncorrelated information. We present a large-scale speech corpus to facilitate the research of speech representation disentanglement. 3D-Speaker contains over 10,000 speakers, each of whom are simultaneously recorded by multiple Devices, locating at different Distances, and some speakers are speaking multiple Dialects. The controlled combinations of multi-dimensional audio data yield a matrix of a diverse blend of speech representation entanglement, thereby motivating intriguing methods to untangle them. The multi-domain nature of 3D-Speaker also makes it a suitable resource to evaluate large universal speech models and experiment methods of out-of-domain learning and self-supervised learning. https://3dspeaker.github.io/
A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction
Venue: arXiv cs.SE 2023
Year: 2023

Is there a statistical difference between Naive Bayes and Random Forest in terms of recall, f-measure, and precision for predicting software defects? By utilizing systematic literature review and meta-analysis, we are answering this question. We conducted a systematic literature review by establishing criteria to search and choose papers, resulting in five studies. After that, using the meta-data and forest-plots of five chosen papers, we conducted a meta-analysis to compare the two models. The results have shown that there is no significant statistical evidence that Naive Bayes perform differently from Random Forest in terms of recall, f-measure, and precision.