OpenRaiser/Intern-Atlas huggingface dataset:
Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey
Venue: arXiv cs.NI 2023
Year: 2023

The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a large amount of work has been presented and studied in almost every field. Since 2012, when the convolution neural networks have been reintroduced in the context of \textit{ImagNet} competition, DL continued to achieve superior performance in many challenging tasks and problems. Wireless communications, in general, and medium access control (MAC) techniques, in particular, were among the fields that were heavily affected by this improvement. MAC protocols play a critical role in defining the performance of wireless communication systems. At the same time, the community lacks a comprehensive survey that collects, analyses, and categorizes the recent work in ML-inspired MAC techniques. In this work, we fill this gap by surveying a long line of work in this era. We solidify the impact of machine learning on wireless MAC protocols. We provide a comprehensive background to the widely adopted MAC techniques, their design issues, and their taxonomy, in connection with the famous application domains. Furthermore, we provide an overview of the ML techniques that have been considered in this context. Finally, we augment our work by proposing some promising future research directions and open research questions that are worth further investigation.
Language identification as improvement for lip-based biometric visual systems
Venue: arXiv cs.CV 2023
Year: 2023

Language has always been one of humanity's defining characteristics. Visual Language Identification (VLI) is a relatively new field of research that is complex and largely understudied. In this paper, we present a preliminary study in which we use linguistic information as a soft biometric trait to enhance the performance of a visual (auditory-free) identification system based on lip movement. We report a significant improvement in the identification performance of the proposed visual system as a result of the integration of these data using a score-based fusion strategy. Methods of Deep and Machine Learning are considered and evaluated. To the experimentation purposes, the dataset called laBial Articulation for the proBlem of the spokEn Language rEcognition (BABELE), consisting of eight different languages, has been created. It includes a collection of different features of which the spoken language represents the most relevant, while each sample is also manually labelled with gender and age of the subjects.
Inseq: An Interpretability Toolkit for Sequence Generation Models
Venue: arXiv cs.CL 2023
Year: 2023

Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to democratize access to interpretability analyses of sequence generation models. Inseq enables intuitive and optimized extraction of models' internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. We showcase its potential by adopting it to highlight gender biases in machine translation models and locate factual knowledge inside GPT-2. Thanks to its extensible interface supporting cutting-edge techniques such as contrastive feature attribution, Inseq can drive future advances in explainable natural language generation, centralizing good practices and enabling fair and reproducible model evaluations.
Theoretical Limits of Backscatter Communications
Venue: arXiv cs.IT 2023
Year: 2023

Backscatter communication is a hot candidate for future IoT systems. It offers the possibility for connectivity with tiny amounts of energy that can be easily obtained from energy harvesting. This is possible as backscatter devices do not actively transmit electromagnetic waves. Instead they only reflect existing electromagnetic waves by changing the antenna load. This fact leads to significant differences compared to classical communication wrt. the modulation schemes and achievable data rates. However, to our best knowledge nobody has so far systematically analyzed the achievable data rates and transmit ranges for different parameter configurations. Within this paper we derive theoretical bounds for backscatter communications based on classical information theory. We then use these bounds to analyze how different parameters - e.g. the distance, the frequency, or the transmit power - affect the achievable data rates. The bounds are derived for mono-static configuration, as well as for bi-static configurations. This allows feasibility analyses for different use-cases that are currently discussed in 3GPP and IEEE 802.
Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition
Venue: arXiv cs.LG 2023
Year: 2023

We develop novel methodology for active feature acquisition (AFA), the study of how to sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes acquisition costs whilst still yielding accurate predictions. The AFA framework can be useful in a myriad of domains, including health care applications where the cost of acquiring additional features for a patient (in terms of time, money, risk, etc.) can be weighed against the expected improvement to diagnostic performance. Previous approaches for AFA have employed either: deep learning RL techniques, which have difficulty training policies in the AFA MDP due to sparse rewards and a complicated action space; deep learning surrogate generative models, which require modeling complicated multidimensional conditional distributions; or greedy policies, which fail to account for how joint feature acquisitions can be informative together for better predictions. In this work we show that we can bypass many of these challenges with a novel, nonparametric oracle based approach, which we coin the acquisition conditioned oracle (ACO). Extensive experiments show the superiority of the ACO to state-of-the-art AFA methods when acquiring features for both predictions and general decision-making.
Cross-modal Contrastive Learning for Multimodal Fake News Detection
Venue: arXiv cs.LG 2023
Year: 2023

Automatic detection of multimodal fake news has gained a widespread attention recently. Many existing approaches seek to fuse unimodal features to produce multimodal news representations. However, the potential of powerful cross-modal contrastive learning methods for fake news detection has not been well exploited. Besides, how to aggregate features from different modalities to boost the performance of the decision-making process is still an open question. To address that, we propose COOLANT, a cross-modal contrastive learning framework for multimodal fake news detection, aiming to achieve more accurate image-text alignment. To further improve the alignment precision, we leverage an auxiliary task to soften the loss term of negative samples during the contrast process. A cross-modal fusion module is developed to learn the cross-modality correlations. An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations. Finally, we evaluate the COOLANT and conduct a comparative study on two widely used datasets, Twitter and Weibo. The experimental results demonstrate that our COOLANT outperforms previous approaches by a large margin and achieves new state-of-the-art results on the two datasets.
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail
Venue: arXiv cs.LG 2023
Year: 2023

We present a novel dataset collected by ASOS (a major online fashion retailer) to address the challenge of predicting customer returns in a fashion retail ecosystem. With the release of this substantial dataset we hope to motivate further collaboration between research communities and the fashion industry. We first explore the structure of this dataset with a focus on the application of Graph Representation Learning in order to exploit the natural data structure and provide statistical insights into particular features within the data. In addition to this, we show examples of a return prediction classification task with a selection of baseline models (i.e. with no intermediate representation learning step) and a graph representation based model. We show that in a downstream return prediction classification task, an F1-score of 0.792 can be found using a Graph Neural Network (GNN), improving upon other models discussed in this work. Alongside this increased F1-score, we also present a lower cross-entropy loss by recasting the data into a graph structure, indicating more robust predictions from a GNN based solution. These results provide evidence that GNNs could provide more impactful and usable classifications than other baseline models on the presented dataset and with this motivation, we hope to encourage further research into graph-based approaches using the ASOS GraphReturns dataset.
On Differentially Private Online Predictions
Venue: arXiv cs.LG 2023
Year: 2023

In this work we introduce an interactive variant of joint differential privacy towards handling online processes in which existing privacy definitions seem too restrictive. We study basic properties of this definition and demonstrate that it satisfies (suitable variants) of group privacy, composition, and post processing. We then study the cost of interactive joint privacy in the basic setting of online classification. We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound. This demonstrates a stark difference with more restrictive notions of privacy such as the one studied by Golowich and Livni (2021), where only a double exponential overhead on the mistake bound is known (via an information theoretic upper bound).
Eliciting Information from participants with Competing Incentives and Dependent Beliefs
Venue: arXiv cs.GT 2023
Year: 2023

In this paper, we study belief elicitation about an uncertain future event, where the reports will affect a principal's decision. We study two problems that can arise in this setting: (1) Agents may have an interest in the outcome of the principal's decision. We show that with intrinsic competing incentives (an interest in a decision that is internal to an agent) truthfulness cannot be guaranteed and there is a fundamental tradeoff between how much the principal allows reports to influence the decision, how much budget the principal has, and the degree to which a mechanism can be manipulated. Furthermore, we show that the Quadratic Scoring Rule is worst-case optimal in minimizing the degree of manipulation. In contrast, we obtain positive results and truthful mechanisms in a setting where the competing incentives stem instead from a rational briber who wants to promote a particular decision. We show that the budget required to achieve this robustness scales with the sum of squares of the degree to which agent reports can influence the decision. (2) We study the setting where the future event is only observed conditionally on the decision taken. We give a category of mechanisms that are truthful when agent beliefs are independent but fails with dependent beliefs, and show how to resolve this through a decoupling method.
Tight Algorithms for Connectivity Problems Parameterized by Modular-Treewidth
Venue: arXiv cs.DS 2023
Year: 2023

We study connectivity problems from a fine-grained parameterized perspective. Cygan et al. (TALG 2022) obtained algorithms with single-exponential running time $α^{tw} n^{O(1)}$ for connectivity problems parameterized by treewidth ($tw$) by introducing the cut-and-count-technique, which reduces connectivity problems to locally checkable counting problems. In addition, the bases $α$ were proven to be optimal assuming the Strong Exponential-Time Hypothesis (SETH). As only sparse graphs may admit small treewidth, these results do not apply to graphs with dense structure. A well-known tool to capture dense structure is the modular decomposition, which recursively partitions the graph into modules whose members have the same neighborhood outside of the module. Contracting the modules yields a quotient graph describing the adjacencies between modules. Measuring the treewidth of the quotient graph yields the parameter modular-treewidth, a natural intermediate step between treewidth and clique-width. We obtain the first tight running times for connectivity problems parameterized by modular-treewidth. For some problems the obtained bounds are the same as relative to treewidth, showing that we can deal with a greater generality in input structure at no cost in complexity. We obtain the following randomized algorithms for graphs of modular-treewidth $k$, given an appropriate decomposition: Steiner Tree can be solved in time $3^k n^{O(1)}$, Connected Dominating Set can be solved in time $4^k n^{O(1)}$, Connected Vertex Cover can be solved in time $5^k n^{O(1)}$, Feedback Vertex Set can be solved in time $5^k n^{O(1)}$. The first two algorithms are tight due to known results and the last two algorithms are complemented by new tight lower bounds under SETH.
IoT-Enabled Low-Cost Fog Computing System with Online Machine Learning for Accurate and Low-Latency Heart Monitoring in Rural Healthcare Settings
Venue: arXiv cs.HC 2023
Year: 2023

Healthcare services in rural areas face numerous challenges due to the high cost of treatment and a lack of appropriate services. The application of Internet of Things (IoT) technology has shown potential in mitigating these issues. This article discusses the potential of Internet of Things (IoT) and fog computing to reduce healthcare costs and improve patient outcomes. The use of these technologies in cardiovascular health informatics is explored, along with the economic thought process of hospital decision-makers and end-of-life practices in intensive care units. Remote monitoring using IoT devices is highlighted as a promising way to detect health issues before they become serious, leading to earlier interventions and improved health outcomes. The use of fog computing in healthcare is also discussed, with a focus on its ability to provide real-time data processing, analysis, and decision-making capabilities. The article presents a novel architecture for Device-as-a-Service, utilizing both fog and cloud computing to improve the efficiency and accuracy of ECG device processing, and concludes that it has the potential to reduce costs by up to 80% in the Iranian market. The adoption of fog computing in healthcare is acknowledged to present significant challenges, such as security and privacy concerns,
A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Increasing attention is being diverted to data-efficient problem settings like Open Vocabulary Semantic Segmentation (OVSS) which deals with segmenting an arbitrary object that may or may not be seen during training. The closest standard problems related to OVSS are Zero-Shot and Few-Shot Segmentation (ZSS, FSS) and their Cross-dataset variants where zero to few annotations are needed to segment novel classes. The existing FSS and ZSS methods utilize fully supervised pixel-labelled seen classes to segment unseen classes. Pixel-level labels are hard to obtain, and using weak supervision in the form of inexpensive image-level labels is often more practical. To this end, we propose a novel unified weakly supervised OVSS pipeline that can perform ZSS, FSS and Cross-dataset segmentation on novel classes without using pixel-level labels for either the base (seen) or the novel (unseen) classes in an inductive setting. We propose Weakly-Supervised Language-Guided Segmentation Network (WLSegNet), a novel language-guided segmentation pipeline that i) learns generalizable context vectors with batch aggregates (mean) to map class prompts to image features using frozen CLIP (a vision-language model) and ii) decouples weak ZSS/FSS into weak semantic segmentation and Zero-Shot segmentation. The learned context vectors avoid overfitting on seen classes during training and transfer better to novel classes during testing. WLSegNet avoids fine-tuning and the use of external datasets during training. The proposed pipeline beats existing methods for weak generalized Zero-Shot and weak Few-Shot semantic segmentation by 39 and 3 mIOU points respectively on PASCAL VOC and weak Few-Shot semantic segmentation by 5 mIOU points on MS COCO. On a harder setting of 2-way 1-shot weak FSS, WLSegNet beats the baselines by 13 and 22 mIOU points on PASCAL VOC and MS COCO, respectively.
Enhancing Vulnerability Prioritization: Data-Driven Exploit Predictions with Community-Driven Insights
Venue: arXiv cs.CR 2023
Year: 2023

The number of disclosed vulnerabilities has been steadily increasing over the years. At the same time, organizations face significant challenges patching their systems, leading to a need to prioritize vulnerability remediation in order to reduce the risk of attacks. Unfortunately, existing vulnerability scoring systems are either vendor-specific, proprietary, or are only commercially available. Moreover, these and other prioritization strategies based on vulnerability severity are poor predictors of actual vulnerability exploitation because they do not incorporate new information that might impact the likelihood of exploitation. In this paper we present the efforts behind building a Special Interest Group (SIG) that seeks to develop a completely data-driven exploit scoring system that produces scores for all known vulnerabilities, that is freely available, and which adapts to new information. The Exploit Prediction Scoring System (EPSS) SIG consists of more than 170 experts from around the world and across all industries, providing crowd-sourced expertise and feedback. Based on these collective insights, we describe the design decisions and trade-offs that lead to the development of the next version of EPSS. This new machine learning model provides an 82\% performance improvement over past models in distinguishing vulnerabilities that are exploited in the wild and thus may be prioritized for remediation.
Reinforcement Learning with Depreciating Assets
Venue: arXiv cs.AI 2023
Year: 2023

A basic assumption of traditional reinforcement learning is that the value of a reward does not change once it is received by an agent. The present work forgoes this assumption and considers the situation where the value of a reward decays proportionally to the time elapsed since it was obtained. Emphasizing the inflection point occurring at the time of payment, we use the term asset to refer to a reward that is currently in the possession of an agent. Adopting this language, we initiate the study of depreciating assets within the framework of infinite-horizon quantitative optimization. In particular, we propose a notion of asset depreciation, inspired by classical exponential discounting, where the value of an asset is scaled by a fixed discount factor at each time step after it is obtained by the agent. We formulate a Bellman-style equational characterization of optimality in this context and develop a model-free reinforcement learning approach to obtain optimal policies.
Soft-Search: Two Datasets to Study the Identification and Production of Research Software
Venue: arXiv cs.DL 2023
Year: 2023

Software is an important tool for scholarly work, but software produced for research is in many cases not easily identifiable or discoverable. A potential first step in linking research and software is software identification. In this paper we present two datasets to study the identification and production of research software. The first dataset contains almost 1000 human labeled annotations of software production from National Science Foundation (NSF) awarded research projects. We use this dataset to train models that predict software production. Our second dataset is created by applying the trained predictive models across the abstracts and project outcomes reports for all NSF funded projects between the years of 2010 and 2023. The result is an inferred dataset of software production for over 150,000 NSF awards. We release the Soft-Search dataset to aid in identifying and understanding research software production: https://github.com/si2-urssi/eager
Taylor TD-learning
Venue: arXiv cs.LG 2023
Year: 2023

Many reinforcement learning approaches rely on temporal-difference (TD) learning to learn a critic. However, TD-learning updates can be high variance. Here, we introduce a model-based RL framework, Taylor TD, which reduces this variance in continuous state-action settings. Taylor TD uses a first-order Taylor series expansion of TD updates. This expansion allows Taylor TD to analytically integrate over stochasticity in the action-choice, and some stochasticity in the state distribution for the initial state and action of each TD update. We include theoretical and empirical evidence that Taylor TD updates are indeed lower variance than standard TD updates. Additionally, we show Taylor TD has the same stable learning guarantees as standard TD-learning with linear function approximation under a reasonable assumption. Next, we combine Taylor TD with the TD3 algorithm, forming TaTD3. We show TaTD3 performs as well, if not better, than several state-of-the art model-free and model-based baseline algorithms on a set of standard benchmark tasks.
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
Venue: arXiv cs.LG 2023
Year: 2023

Link prediction is central to many real-world applications, but its performance may be hampered when the graph of interest is sparse. To alleviate issues caused by sparsity, we investigate a previously overlooked phenomenon: in many cases, a densely connected, complementary graph can be found for the original graph. The denser graph may share nodes with the original graph, which offers a natural bridge for transferring selective, meaningful knowledge. We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions. We develop a framework to effectively leverage the structural prior in this setting. We first create an intersection subgraph using the shared nodes between the two graphs, then transfer knowledge from the source-enriched intersection subgraph to the full target graph. In the second step, we consider two approaches: a modified label propagation, and a multi-layer perceptron (MLP) model in a teacher-student regime. Experimental results on proprietary e-commerce datasets and open-source citation graphs show that the proposed workflow outperforms existing transfer learning baselines that do not explicitly utilize the intersection structure.
Image-Based Virtual Try-on System With Clothing-Size Adjustment
Venue: arXiv cs.CV 2023
Year: 2023

The conventional image-based virtual try-on method cannot generate fitting images that correspond to the clothing size because the system cannot accurately reflect the body information of a person. In this study, an image-based virtual try-on system that could adjust the clothing size was proposed. The size information of the person and clothing were used as the input for the proposed method to visualize the fitting of various clothing sizes in a virtual space. First, the distance between the shoulder width and height of the clothing in the person image is calculated based on the coordinate information of the key points detected by OpenPose. Then, the system changes the size of only the clothing area of the segmentation map, whose layout is estimated using the size of the person measured in the person image based on the ratio of the person and clothing sizes. If the size of the clothing area increases during the drawing, the details in the collar and overlapping areas are corrected to improve visual appearance.