OpenRaiser/Intern-Atlas huggingface dataset:
Identifying Periods of Cyclical Stress in University Students Using Wearables In-the-Wild
Venue: arXiv cs.CY 2024
Year: 2024

University students encounter various forms of stress during their academic journey, including cyclical stress associated with final exams. Supporting their well-being means helping them manage their stress levels. In this study, we used a wearable health-tracking ring on a cohort of 103 Japanese university students for up to 28 months in the wild. The study aimed to investigate whether group-wide biomarkers of stress can be identified in a sample having similar daily schedules and whether these occurrences can be pinpointed to specific periods of the academic year. We found population-wide increased stress markers during exams, New Year's, and job hunting season, a Japanese job market peculiarity. Our results highlight the available potential of unobtrusive, in-situ detection of the current mental state of university student populations using off-the-shelf wearables from noisy data, with significant implications for the well-being of the users. Our approach and method of analysis allows for monitoring the student body's stress level without singling out individuals and therefore represents a privacy-preserving method. This way, new and sudden stress increases can be recognized, which can help identify the stressor and inform the design and introduction of counter measures.
Cooperative Backscatter Communications with Reconfigurable Intelligent Surfaces: An APSK Approach
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, a novel amplitude phase shift keying (APSK) modulation scheme for cooperative backscatter communications aided by a reconfigurable intelligent surface (RIS-CBC) is presented, according to which the RIS is configured to modulate backscatter information onto unmodulated or PSK-modulated signals impinging on its surface via APSK. We consider both passive and active RISs, with the latter including an amplification unit at each reflecting element. In the passive (resp. active) RIS-CBC-APSK, backscatter information is conveyed through the number of RIS reflecting elements being on the ON state (resp. active mode) and their phase shift values. By using the optimal APSK constellation to ensure that reflected signals from the RIS undergo APSK modulation, a bit-mapping mechanism is presented. Assuming maximum-likelihood detection, we also present closed-form upper bounds for the symbol error rate (SER) performance for both passive and active RIS-CBC-APSK schemes over Rician fading channels. In addition, we devise a low-complexity detector that can achieve flexible trade-offs between performance and complexity. Finally, we extend RIS-CBC-APSK to multiple-input single-output scenarios and present an alternating optimization approach for the joint design of transmit beamforming and RIS reflection. Our extensive simulation results on the SER performance corroborate our conducted performance analysis and showcase the superiority of the proposed RIS-CBC-APSK schemes over the state-of-the-art RIS-CBC benchmarks.
Real-time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision
Venue: arXiv cs.RO 2024
Year: 2024

Perceiving a three-dimensional (3D) scene with multiple objects while moving indoors is essential for vision-based mobile cobots, especially for enhancing their manipulation tasks. In this work, we present an end-to-end pipeline with instance segmentation, feature matching, and point-set registration for egocentric robots with binocular vision, and demonstrate the robot's grasping capability through the proposed pipeline. First, we design an RGB image-based segmentation approach for single-view 3D semantic scene segmentation, leveraging common object classes in 2D datasets to encapsulate 3D points into point clouds of object instances through corresponding depth maps. Next, 3D correspondences of two consecutive segmented point clouds are extracted based on matched keypoints between objects of interest in RGB images from the prior step. In addition, to be aware of spatial changes in 3D feature distribution, we also weigh each 3D point pair based on the estimated distribution using kernel density estimation (KDE), which subsequently gives robustness with less central correspondences while solving for rigid transformations between point clouds. Finally, we test our proposed pipeline on the 7-DOF dual-arm Baxter robot with a mounted Intel RealSense D435i RGB-D camera. The result shows that our robot can segment objects of interest, register multiple views while moving, and grasp the target object. The source code is available at https://github.com/mkhangg/semantic_scene_perception.
ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding
Venue: arXiv cs.CL 2024
Year: 2024

With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical. However, the current approaches for aligning the LLMs output with expected safety usually require substantial training efforts, e.g., high-quality safety data and expensive computational resources, which are costly and inefficient. To this end, we present reverse prompt contrastive decoding (ROSE), a simple-yet-effective method to directly boost the safety of existing instruction-tuned LLMs without any additional training. The principle of ROSE is to improve the probability of desired safe output via suppressing the undesired output induced by the carefully-designed reverse prompts. Experiments on 6 safety and 2 general-purpose tasks show that, our ROSE not only brings consistent and significant safety improvements (up to +13.8% safety score) upon 5 types of instruction-tuned LLMs, but also benefits the general-purpose ability of LLMs. In-depth analyses explore the underlying mechanism of ROSE, and reveal when and where to use it.
Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing
Venue: arXiv cs.SE 2024
Year: 2024

In this paper, we propose shifting the focus of robustness evaluation for Neural Program Repair (NPR) techniques toward naturally-occurring data transformations. To accomplish this, we first examine the naturalness of semantic-preserving transformations through a two-stage human study. This study includes (1) interviews with senior software developers to establish concrete criteria for evaluating the naturalness of these transformations, and (2) a survey involving 10 developers to assess the naturalness of 1,178 transformations, i.e., pairs of original and transformed programs, applied to 225 real-world bugs. Our findings show that only 60% of these transformations are deemed natural, while 20% are considered unnatural, with strong agreement among annotators. Moreover, the unnaturalness of these transformations significantly impacts both their applicability to benchmarks and the conclusions drawn from robustness testing. Next, we conduct natural robustness testing on NPR techniques to assess their true effectiveness against real-world data variations. Our experimental results reveal a substantial number of prediction changes in NPR techniques, leading to significant reductions in both plausible and correct patch rates when comparing performance on the original and transformed datasets. Additionally, we observe notable differences in performance improvements between NPR techniques, suggesting potential biases on NPR evaluation introduced by limited datasets. Finally, we propose an LLM-based metric to automate the assessment of transformation naturalness, ensuring the scalability of natural robustness testing.
Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching
Venue: arXiv cs.LG 2024
Year: 2024

The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To address this limitation, we propose a novel offloading architecture, called joint data deepening-and-prefetching (JD2P), which is feature-by-feature offloading comprising two key techniques. The first one is data deepening, where each data sample's features are sequentially offloaded in the order of importance determined by the data embedding technique such as principle component analysis (PCA). Offloading is terminated once the already transmitted features are sufficient for accurate data classification, resulting in a reduction in the amount of transmitted data. The criteria to offload data are derived for binary and multi-class classifiers, which are designed based on support vector machine (SVM) and deep neural network (DNN), respectively. The second one is data prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. We evaluate the effectiveness of JD2P through experiments using the MNIST dataset, and the results demonstrate its significant reduction in expected energy consumption compared to several benchmarks without degrading learning accuracy.
AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization
Venue: arXiv cs.CV 2024
Year: 2024

Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied. This paper presents a novel adversarial attack strategy, AICAttack (Attention-based Image Captioning Attack), designed to attack image captioning models through subtle perturbations on images. Operating within a black-box attack scenario, our algorithm requires no access to the target model's architecture, parameters, or gradient information. We introduce an attention-based candidate selection mechanism that identifies the optimal pixels to attack, followed by a customised differential evolution method to optimise the perturbations of pixels' RGB values. We demonstrate AICAttack's effectiveness through extensive experiments on benchmark datasets against multiple victim models. The experimental results demonstrate that our method outperforms current leading-edge techniques by achieving consistently higher attack success rates.
LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation
Venue: arXiv cs.CL 2024
Year: 2024

The rise of multimodal misinformation on social platforms poses significant challenges for individuals and societies. Its increased credibility and broader impact compared to textual misinformation make detection complex, requiring robust reasoning across diverse media types and profound knowledge for accurate verification. The emergence of Large Vision Language Model (LVLM) offers a potential solution to this problem. Leveraging their proficiency in processing visual and textual information, LVLM demonstrates promising capabilities in recognizing complex information and exhibiting strong reasoning skills. In this paper, we first investigate the potential of LVLM on multimodal misinformation detection. We find that even though LVLM has a superior performance compared to LLMs, its profound reasoning may present limited power with a lack of evidence. Based on these observations, we propose LEMMA: LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation. LEMMA leverages LVLM intuition and reasoning capabilities while augmenting them with external knowledge to enhance the accuracy of misinformation detection. Our method improves the accuracy over the top baseline LVLM by 7% and 13% on Twitter and Fakeddit datasets respectively.
Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model
Venue: arXiv cs.LG 2024
Year: 2024

Interactions among large number of entities is naturally high-dimensional and incomplete (HDI) in many big data related tasks. Behavioral characteristics of users are hidden in these interactions, hence, effective representation of the HDI data is a fundamental task for understanding user behaviors. Latent factor analysis (LFA) model has proven to be effective in representing HDI data. The performance of an LFA model relies heavily on its training process, which is a non-convex optimization. It has been proven that incorporating local curvature and preprocessing gradients during its training process can lead to superior performance compared to LFA models built with first-order family methods. However, with the escalation of data volume, the feasibility of second-order algorithms encounters challenges. To address this pivotal issue, this paper proposes a mini-block diagonal hessian-free (Mini-Hes) optimization for building an LFA model. It leverages the dominant diagonal blocks in the generalized Gauss-Newton matrix based on the analysis of the Hessian matrix of LFA model and serves as an intermediary strategy bridging the gap between first-order and second-order optimization methods. Experiment results indicate that, with Mini-Hes, the LFA model outperforms several state-of-the-art models in addressing missing data estimation task on multiple real HDI datasets from recommender system. (The source code of Mini-Hes is available at https://github.com/Goallow/Mini-Hes)
DB-LLM: Accurate Dual-Binarization for Efficient LLMs
Venue: arXiv cs.LG 2024
Year: 2024

Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment. Quantization emerges as one of the most effective methods for improving the computational efficiency of LLMs. However, existing ultra-low-bit quantization always causes severe accuracy drops. In this paper, we empirically relieve the micro and macro characteristics of ultra-low bit quantization and present a novel Dual-Binarization method for LLMs, namely DB-LLM. For the micro-level, we take both the accuracy advantage of 2-bit-width and the efficiency advantage of binarization into account, introducing Flexible Dual Binarization (FDB). By splitting 2-bit quantized weights into two independent sets of binaries, FDB ensures the accuracy of representations and introduces flexibility, utilizing the efficient bitwise operations of binarization while retaining the inherent high sparsity of ultra-low bit quantization. For the macro-level, we find the distortion that exists in the prediction of LLM after quantization, which is specified as the deviations related to the ambiguity of samples. We propose the Deviation-Aware Distillation (DAD) method, enabling the model to focus differently on various samples. Comprehensive experiments show that our DB-LLM not only significantly surpasses the current State-of-The-Art (SoTA) in ultra-low bit quantization (eg, perplexity decreased from 9.64 to 7.23), but also achieves an additional 20\% reduction in computational consumption compared to the SOTA method under the same bit-width. Our code will be released soon.
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Venue: arXiv cs.CV 2024
Year: 2024

Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. pathologies). In this work, we propose Weakly Supervised ROI Proposal Networks (WSRPN), a new method for generating bounding box proposals on the fly using a specialized region of interest-attention (ROI-attention) module. WSRPN integrates well with classic backbone-head classification algorithms and is end-to-end trainable with only image-label supervision. We experimentally demonstrate that our new method outperforms existing methods in the challenging task of disease localization in chest X-ray images. Code: https://github.com/philip-mueller/wsrpn
Gaussian Broadcast on Grids
Venue: arXiv cs.IT 2024
Year: 2024

Motivated by the classical work on finite noisy automata (Gray 1982, Gács 2001, Gray 2001) and by the recent work on broadcasting on grids (Makur, Mossel, and Polyanskiy 2022), we introduce Gaussian variants of these models. These models are defined on graded posets. At time $0$, all nodes begin with $X_0$. At time $k\ge 1$, each node on layer $k$ computes a combination of its inputs at layer $k-1$ with independent Gaussian noise added. When is it possible to recover $X_0$ with non-vanishing correlation? We consider different notions of recovery including recovery from a single node, recovery from a bounded window, and recovery from an unbounded window. Our main interest is in two models defined on grids: In the infinite model, layer $k$ is the vertices of $\mathbb{Z}^{d+1}$ whose sum of entries is $k$ and for a vertex $v$ at layer $k \ge 1$, $X_v=α\sum (X_u + W_{u,v})$, summed over all $u$ on layer $k-1$ that differ from $v$ exactly in one coordinate, and $W_{u,v}$ are i.i.d. $\mathcal{N}(0,1)$. We show that when $α<1/(d+1)$, the correlation between $X_v$ and $X_0$ decays exponentially, and when $α>1/(d+1)$, the correlation is bounded away from $0$. The critical case when $α=1/(d+1)$ exhibits a phase transition in dimension, where $X_v$ has non-vanishing correlation with $X_0$ if and only if $d\ge 3$. The same results hold for any bounded window. In the finite model, layer $k$ is the vertices of $\mathbb{Z}^{d+1}$ with nonnegative entries with sum $k$. We identify the sub-critical and the super-critical regimes. In the sub-critical regime, the correlation decays to $0$ for unbounded windows. In the super-critical regime, there exists for every $t$ a convex combination of $X_u$ on layer $t$ whose correlation is bounded away from $0$. We find that for the critical parameters, the correlation is vanishing in all dimensions and for unbounded window sizes.
Thinking Outside the Black Box: Insights from a Digital Exhibition in the Humanities
Venue: arXiv cs.DL 2024
Year: 2024

One of the main goals of Open Science is to make research more reproducible. There is no consensus, however, on what exactly "reproducibility" is, as opposed for example to "replicability", and how it applies to different research fields. After a short review of the literature on reproducibility/replicability with a focus on the humanities, we describe how the creation of the digital twin of the temporary exhibition "The Other Renaissance" has been documented throughout, with different methods, but with constant attention to research transparency, openness and accountability. A careful documentation of the study design, data collection and analysis techniques helps reflect and make all possible influencing factors explicit, and is a fundamental tool for reliability and rigour and for opening the "black box" of research.
Cluster Metric Sensitivity to Irrelevant Features
Venue: arXiv cs.LG 2024
Year: 2024

Clustering algorithms are used extensively in data analysis for data exploration and discovery. Technological advancements lead to continually growth of data in terms of volume, dimensionality and complexity. This provides great opportunities in data analytics as the data can be interrogated for many different purposes. This however leads challenges, such as identification of relevant features for a given task. In supervised tasks, one can utilise a number of methods to optimise the input features for the task objective (e.g. classification accuracy). In unsupervised problems, such tools are not readily available, in part due to an inability to quantify feature relevance in unlabeled tasks. In this paper, we investigate the sensitivity of clustering performance noisy uncorrelated variables iteratively added to baseline datasets with well defined clusters. We show how different types of irrelevant variables can impact the outcome of a clustering result from $k$-means in different ways. We observe a resilience to very high proportions of irrelevant features for adjusted rand index (ARI) and normalised mutual information (NMI) when the irrelevant features are Gaussian distributed. For Uniformly distributed irrelevant features, we notice the resilience of ARI and NMI is dependent on the dimensionality of the data and exhibits tipping points between high scores and near zero. Our results show that the Silhouette Coefficient and the Davies-Bouldin score are the most sensitive to irrelevant added features exhibiting large changes in score for comparably low proportions of irrelevant features regardless of underlying distribution or data scaling. As such the Silhouette Coefficient and the Davies-Bouldin score are good candidates for optimising feature selection in unsupervised clustering tasks.
Private Interdependent Valuations: New Bounds for Single-Item Auctions and Matroids
Venue: arXiv cs.GT 2024
Year: 2024

We study auction design within the widely acclaimed model of interdependent values, introduced by Milgrom and Weber [1982]. In this model, every bidder $i$ has a private signal $s_i$ for the item for sale, and a public valuation function $v_i(s_1,\ldots,s_n)$ which maps every vector of private signals (of all bidders) into a real value. A recent line of work established the existence of approximately-optimal mechanisms within this framework, even in the more challenging scenario where each bidder's valuation function $v_i$ is also private. This body of work has primarily focused on single-item auctions with two natural classes of valuations: those exhibiting submodularity over signals (SOS) and $d$-critical valuations. In this work we advance the state of the art on interdependent values with private valuation functions, with respect to both SOS and $d$-critical valuations. For SOS valuations, we devise a new mechanism that gives an improved approximation bound of $5$ for single-item auctions. This mechanism employs a novel variant of an "eating mechanism", leveraging LP-duality to achieve feasibility with reduced welfare loss. For $d$-critical valuations, we broaden the scope of existing results beyond single-item auctions, introducing a mechanism that gives a $(d+1)$-approximation for any environment with matroid feasibility constraints on the set of agents that can be simultaneously served. Notably, this approximation bound is tight, even with respect to single-item auctions.
Attack Tree Generation via Process Mining
Venue: arXiv cs.CR 2024
Year: 2024

Attack Trees are a graphical model of security used to study threat scenarios. While visually appealing and supported by solid theories and effective tools, one of their main drawbacks remains the amount of effort required by security experts to design them from scratch. This work aims to remedy this by providing a method for the automatic generation of Attack Trees from attack logs. The main original feature of our approach w.r.t existing ones is the use of Process Mining algorithms to synthesize Attack Trees, which allow users to customize the way a set of logs are summarized as an Attack Tree, for example by discarding statistically irrelevant events. Our approach is supported by a prototype that, apart from the derivation and translation of the model, provides the user with an Attack Tree in the RisQFLan format, a tool used for quantitative risk modeling and analysis with Attack Trees. We illustrate our approach with the case study of attacks on a communication protocol, produced by a state-of-the-art protocol analyzer.