OpenRaiser/Intern-Atlas huggingface dataset:
Cell-Edge Performance Booster in 6G: Cell-Free Massive MIMO vs. Reconfigurable Intelligent Surface
Venue: arXiv cs.IT 2023
Year: 2023

User experience in mobile communications is vulnerable to worse quality at the cell edge, which cannot be compensated by enjoying excellent service at the cell center, according to the principle of risk aversion in behavioral economics. Constrained by weak signal strength and substantial inter-cell interference, the cell edge is always a major bottleneck of any mobile network. Due to their possibility for empowering the next-generation mobile system, reconfigurable intelligent surface (RIS) and cell-free massive MIMO (CFmMIMO) have recently attracted a lot of focus from academia and industry. In addition to a variety of technological advantages, both are highly potential to boost cell-edge performance. To the authors' best knowledge, a performance comparison of RIS and CFmMIMO, especially on the cell edge, is still missing in the literature. To fill this gap, this paper establishes a fair scenario and demonstrates extensive numerical results to clarify their behaviors at the cell edge.
Qubo model for the Closest Vector Problem
Venue: arXiv cs.CR 2023
Year: 2023

In this paper we consider the closest vector problem (CVP) for lattices $Λ\subseteq \mathbb{Z}^n$ given by a generator matrix $A\in \mathcal{M}_{n\times n}(\mathbb{Z})$. Let $b>0$ be the maximum of the absolute values of the entries of the matrix $A$. We prove that the CVP can be reduced in polynomial time to a quadratic unconstrained binary optimization (QUBO) problem in $O(n^2(\log(n)+\log(b)))$ binary variables, where the length of the coefficients in the corresponding quadratic form is $O(n(\log(n)+\log(b)))$.
Sound Dynamic Deadlock Prediction in Linear Time
Venue: arXiv cs.PL 2023
Year: 2023

Deadlocks are one of the most notorious concurrency bugs, and significant research has focused on detecting them efficiently. Dynamic predictive analyses work by observing concurrent executions, and reason about alternative interleavings that can witness concurrency bugs. Such techniques offer scalability and sound bug reports, and have emerged as an effective approach for concurrency bug detection, such as data races. Effective dynamic deadlock prediction, however, has proven a challenging task, as no deadlock predictor currently meets the requirements of soundness, high-precision, and efficiency. In this paper, we first formally establish that this tradeoff is unavoidable, by showing that (a) sound and complete deadlock prediction is intractable, in general, and (b) even the seemingly simpler task of determining the presence of potential deadlocks, which often serve as unsound witnesses for actual predictable deadlocks, is intractable. The main contribution of this work is a new class of predictable deadlocks, called sync(hronization)-preserving deadlocks. Informally, these are deadlocks that can be predicted by reordering the observed execution while preserving the relative order of conflicting critical sections. We present two algorithms for sound deadlock prediction based on this notion. Our first algorithm SPDOffline detects all sync-preserving deadlocks, with running time that is linear per abstract deadlock pattern, a novel notion also introduced in this work. Our second algorithm SPDOnline predicts all sync-preserving deadlocks that involve two threads in a strictly online fashion, runs in overall linear time, and is better suited for a runtime monitoring setting. We implemented both our algorithms and evaluated their ability to perform offline and online deadlock-prediction on a large dataset of standard benchmarks.
On the Importance of Contrastive Loss in Multimodal Learning
Venue: arXiv cs.LG 2023
Year: 2023

Recently, contrastive learning approaches (e.g., CLIP (Radford et al., 2021)) have received huge success in multimodal learning, where the model tries to minimize the distance between the representations of different views (e.g., image and its caption) of the same data point while keeping the representations of different data points away from each other. However, from a theoretical perspective, it is unclear how contrastive learning can learn the representations from different views efficiently, especially when the data is not isotropic. In this work, we analyze the training dynamics of a simple multimodal contrastive learning model and show that contrastive pairs are important for the model to efficiently balance the learned representations. In particular, we show that the positive pairs will drive the model to align the representations at the cost of increasing the condition number, while the negative pairs will reduce the condition number, keeping the learned representations balanced.
Assessing Perceived Fairness from Machine Learning Developer's Perspective
Venue: arXiv cs.LG 2023
Year: 2023

Fairness in machine learning (ML) applications is an important practice for developers in research and industry. In ML applications, unfairness is triggered due to bias in the data, curation process, erroneous assumptions, and implicit bias rendered within the algorithmic development process. As ML applications come into broader use developing fair ML applications is critical. Literature suggests multiple views on how fairness in ML is described from the users perspective and students as future developers. In particular, ML developers have not been the focus of research relating to perceived fairness. This paper reports on a pilot investigation of ML developers perception of fairness. In describing the perception of fairness, the paper performs an exploratory pilot study to assess the attributes of this construct using a systematic focus group of developers. In the focus group, we asked participants to discuss three questions- 1) What are the characteristics of fairness in ML? 2) What factors influence developers belief about the fairness of ML? and 3) What practices and tools are utilized for fairness in ML development? The findings of this exploratory work from the focus group show that to assess fairness developers generally focus on the overall ML application design and development, i.e., business-specific requirements, data collection, pre-processing, in-processing, and post-processing. Thus, we conclude that the procedural aspects of organizational justice theory can explain developers perception of fairness. The findings of this study can be utilized further to assist development teams in integrating fairness in the ML application development lifecycle. It will also motivate ML developers and organizations to develop best practices for assessing the fairness of ML-based applications.
Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media
Venue: arXiv cs.SI 2023
Year: 2023

Social media enables the rapid spread of many kinds of information, from memes to social movements. However, little is known about how information crosses linguistic boundaries. We apply causal inference techniques on the European Twitter network to quantify multilingual users' structural role and communication influence in cross-lingual information exchange. Overall, multilinguals play an essential role; posting in multiple languages increases betweenness centrality by 13%, and having a multilingual network neighbor increases monolinguals' odds of sharing domains and hashtags from another language 16-fold and 4-fold, respectively. We further show that multilinguals have a greater impact on diffusing information less accessible to their monolingual compatriots, such as information from far-away countries and content about regional politics, nascent social movements, and job opportunities. By highlighting information exchange across borders, this work sheds light on a crucial component of how information and ideas spread around the world.
High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection
Venue: arXiv cs.CV 2023
Year: 2023

Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called \textit{HSI-ShipDetectionNet} that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle marine ship detection dataset and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of recall, and mean average precision (mAP) while being lightweight and suitable for deployment on resource-limited platforms.
Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions
Venue: arXiv cs.SE 2023
Year: 2023

Large language models (LLMs), such as OpenAI's Codex, have demonstrated their potential to generate code from natural language descriptions across a wide range of programming tasks. Several benchmarks have recently emerged to evaluate the ability of LLMs to generate functionally correct code from natural language intent with respect to a set of hidden test cases. This has enabled the research community to identify significant and reproducible advancements in LLM capabilities. However, there is currently a lack of benchmark datasets for assessing the ability of LLMs to generate functionally correct code edits based on natural language descriptions of intended changes. This paper aims to address this gap by motivating the problem NL2Fix of translating natural language descriptions of code changes (namely bug fixes described in Issue reports in repositories) into correct code fixes. To this end, we introduce Defects4J-NL2Fix, a dataset of 283 Java programs from the popular Defects4J dataset augmented with high-level descriptions of bug fixes, and empirically evaluate the performance of several state-of-the-art LLMs for the this task. Results show that these LLMS together are capable of generating plausible fixes for 64.6% of the bugs, and the best LLM-based technique can achieve up to 21.20% top-1 and 35.68% top-5 accuracy on this benchmark.
Markov Games with Decoupled Dynamics: Price of Anarchy and Sample Complexity
Venue: arXiv cs.GT 2023
Year: 2023

This paper studies the finite-time horizon Markov games where the agents' dynamics are decoupled but the rewards can possibly be coupled across agents. The policy class is restricted to local policies where agents make decisions using their local state. We first introduce the notion of smooth Markov games which extends the smoothness argument for normal form games to our setting, and leverage the smoothness property to bound the price of anarchy of the Markov game. For a specific type of Markov game called the Markov potential game, we also develop a distributed learning algorithm, multi-agent soft policy iteration (MA-SPI), which provably converges to a Nash equilibrium. Sample complexity of the algorithm is also provided. Lastly, our results are validated using a dynamic covering game.
Multimedia Distribution Process Tracking for Android and iOS
Venue: arXiv cs.MM 2023
Year: 2023

The crime of illegally filming and distributing images or videos worldwide is increasing day by day. With the increasing penetration rate of smartphones, there has been a rise in crimes involving secretly taking pictures of people's bodies and distributing them through messengers. However, little research has been done on these related issue. The crime of distributing media using the world's popular messengers, WhatsApp and Telegram, is continuously increasing. It is also common to see criminals distributing illegal footage through various messengers to avoid being caught in the investigation network. As these crimes increase, there will continue to be a need for professional investigative personnel, and the time required for criminal investigations will continue to increase. In this paper, we propose a multimedia forensic method for tracking footprints by checking the media information that changes when images and videos shot with a smartphone are transmitted through instant messengers. We have selected 11 of the world's most popular instant messengers and two secure messengers. In addition, we selected the most widely used Android and iOS operating systems for smartphones. Through this study, we were able to confirm that it is possible to trace footprints related to the distribution of instant messengers by analyzing transmitted images and videos. Thus, it was possible to determine which messengers were used to distribute the video when it was transmitted through multiple messengers.
StoryChat: Designing a Narrative-Based Viewer Participation Tool for Live Streaming Chatrooms
Venue: arXiv cs.HC 2023
Year: 2023

Live streaming platforms and existing viewer participation tools enable users to interact and engage with an online community, but the anonymity and scale of chat usually result in the spread of negative comments. However, only a few existing moderation tools investigate the influence of proactive moderation on viewers' engagement and prosocial behavior. To address this, we developed StoryChat, a narrative-based viewer participation tool that utilizes a dynamic graphical plot to reflect chatroom negativity. We crafted the narrative through a viewer-centered (N=65) iterative design process and evaluated the tool with 48 experienced viewers in a deployment study. We discovered that StoryChat encouraged viewers to contribute prosocial comments, increased viewer engagement, and fostered viewers' sense of community. Viewers reported a closer connection between streamers and other viewers because of the narrative design, suggesting that narrative-based viewer engagement tools have the potential to encourage community engagement and prosocial behaviors.
SGDP: A Stream-Graph Neural Network Based Data Prefetcher
Venue: arXiv cs.OS 2023
Year: 2023

Data prefetching is important for storage system optimization and access performance improvement. Traditional prefetchers work well for mining access patterns of sequential logical block address (LBA) but cannot handle complex non-sequential patterns that commonly exist in real-world applications. The state-of-the-art (SOTA) learning-based prefetchers cover more LBA accesses. However, they do not adequately consider the spatial interdependencies between LBA deltas, which leads to limited performance and robustness. This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP). Specifically, SGDP models LBA delta streams using a weighted directed graph structure to represent interactive relations among LBA deltas and further extracts hybrid features by graph neural networks for data prefetching. We conduct extensive experiments on eight real-world datasets. Empirical results verify that SGDP outperforms the SOTA methods in terms of the hit ratio by 6.21%, the effective prefetching ratio by 7.00%, and speeds up inference time by 3.13X on average. Besides, we generalize SGDP to different variants by different stream constructions, further expanding its application scenarios and demonstrating its robustness. SGDP offers a novel data prefetching solution and has been verified in commercial hybrid storage systems in the experimental phase. Our codes and appendix are available at https://github.com/yyysjz1997/SGDP/.
Improving Spectral Efficiency via Pilot Assignment and Subarray Selection Under Realistic XL-MIMO Channels
Venue: arXiv cs.IT 2023
Year: 2023

The main requirements for 5G and beyond connectivity include a uniform high quality of service, which can be attained in crowded scenarios by extra-large MIMO (XL-MIMO) systems. Another requirement is to support increasing connected users in (over)crowded machine-type communication (mMTC). In such scenarios, pilot assignment (PA) becomes paramount to reduce pilot contamination and consequently improve spectral efficiency (SE). We propose a novel quasi-optimal low-complexity iterative pilot assignment strategy for XL-MIMO systems based on a genetic algorithm (GA). The proposed GA-based PA procedure turns the quality of service more uniform, taking into account the normalized mean-square error (NMSE) of channel estimation from each candidate of the population. The simulations reveal that the proposed iterative procedure minimizes the channel estimation NMSE averaged over the UEs. The second procedure is the subarray (SA) selection. In XL-MIMO systems, commonly, a UE is close to a SA antenna subset such that a sufficient data rate can be achieved if only a specific SA serves that UE. Thus, a SA selection procedure is investigated to make the system scalable by defining the maximum number of UEs each SA can help. Hence, the SA clusters are formatted based on the PA decision. Furthermore, we introduce an appropriate channel model for XL-MIMO, which considers a deterministic LoS component with a distance-dependent probability of existence combined with a stochastic spatially correlated Rayleigh NLoS fading component. The developed simulations and analyses rely on this suitable channel model under realistic assumptions of pilot contamination and correlated channels.
ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application
Venue: arXiv cs.RO 2023
Year: 2023

This paper demonstrates how OpenAI's ChatGPT can be used in a few-shot setting to convert natural language instructions into a sequence of executable robot actions. The paper proposes easy-to-customize input prompts for ChatGPT that meet common requirements in practical applications, such as easy integration with robot execution systems and applicability to various environments while minimizing the impact of ChatGPT's token limit. The prompts encourage ChatGPT to output a sequence of predefined robot actions, represent the operating environment in a formalized style, and infer the updated state of the operating environment. Experiments confirmed that the proposed prompts enable ChatGPT to act according to requirements in various environments, and users can adjust ChatGPT's output with natural language feedback for safe and robust operation. The proposed prompts and source code are open-source and publicly available at https://github.com/microsoft/ChatGPT-Robot-Manipulation-Prompts
Spiking Neural Networks for Detecting Satellite-Based Internet-of-Things Signals
Venue: arXiv cs.IT 2023
Year: 2023

With the rapid growth of IoT networks, ubiquitous coverage is becoming increasingly necessary. Low Earth Orbit (LEO) satellite constellations for IoT have been proposed to provide coverage to regions where terrestrial systems cannot. However, LEO constellations for uplink communications are severely limited by the high density of user devices, which causes a high level of co-channel interference. This research presents a novel framework that utilizes spiking neural networks (SNNs) to detect IoT signals in the presence of uplink interference. The key advantage of SNNs is the extremely low power consumption relative to traditional deep learning (DL) networks. The performance of the spiking-based neural network detectors is compared against state-of-the-art DL networks and the conventional matched filter detector. Results indicate that both DL and SNN-based receivers surpass the matched filter detector in interference-heavy scenarios, owing to their capacity to effectively distinguish target signals amidst co-channel interference. Moreover, our work highlights the ultra-low power consumption of SNNs compared to other DL methods for signal detection. The strong detection performance and low power consumption of SNNs make them particularly suitable for onboard signal detection in IoT LEO satellites, especially in high interference conditions.
Factify 2: A Multimodal Fake News and Satire News Dataset
Venue: arXiv cs.CL 2023
Year: 2023

The internet gives the world an open platform to express their views and share their stories. While this is very valuable, it makes fake news one of our society's most pressing problems. Manual fact checking process is time consuming, which makes it challenging to disprove misleading assertions before they cause significant harm. This is he driving interest in automatic fact or claim verification. Some of the existing datasets aim to support development of automating fact-checking techniques, however, most of them are text based. Multi-modal fact verification has received relatively scant attention. In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles. Factify 2 has 50,000 new data instances. Similar to FACTIFY 1.0, we have three broad categories - support, no-evidence, and refute, with sub-categories based on the entailment of visual and textual data. We also provide a BERT and Vison Transformer based baseline, which achieves 65% F1 score in the test set. The baseline codes and the dataset will be made available at https://github.com/surya1701/Factify-2.0.
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of training method because they don't require accessing sensitive attributes during test time in contrast to post-processing methods. While this has been extensively studied in classical machine learning models, their impact on deep neural networks remains unclear. Recent research has shown that adding fairness constraints to the objective function leads to severe over-fitting to fairness criteria in large models, and how to solve this challenge is an important open question. To tackle this, we leverage the wisdom and power of pre-training and fine-tuning and develop a simple but novel framework to train fair neural networks in an efficient and inexpensive way -- last-layer fine-tuning alone can effectively promote fairness in deep neural networks. This framework offers valuable insights into representation learning for training fair neural networks.
No-Existence Of Generalize Diffusion
Venue: arXiv cs.CC 2023
Year: 2023

We show that given two arbitrary states $\ketψ,\ketϕ$ it is impossible to compute the transformation: $ \ketψ\ketϕ \mapsto \ketψ\left( \mathbb{I} - 2 \ketψ\braψ \right)\ketϕ $ The contradiction of the existence of such operator follows by showing that using it, two players can compute the disjoints of their sets in a single round and $O\left( \sqrt{n} \right)$ communication complexity, which shown by Braverman to be impossible \cite{Braverman}.