OpenRaiser/Intern-Atlas huggingface dataset:
Deep Neural Networks Fused with Textures for Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modelling using the long short-term memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.
Bengali Fake Reviews: A Benchmark Dataset and Detection System
Venue: arXiv cs.CL 2024
Year: 2024

The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. This paper introduces the Bengali Fake Review Detection (BFRD) dataset, the first publicly available dataset for identifying fake reviews in Bengali. The dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. To convert non-Bengali words in a review, a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali. We have conducted rigorous experimentation using multiple deep learning and pre-trained transformer language models to develop a reliable detection system. Finally, we propose a weighted ensemble model that combines four pre-trained transformers: BanglaBERT, BanglaBERT Base, BanglaBERT Large, and BanglaBERT Generator . According to the experiment results, the proposed ensemble model obtained a weighted F1-score of 0.9843 on 13390 reviews, including 1339 actual fake reviews and 5356 augmented fake reviews generated with the nlpaug library. The remaining 6695 reviews were randomly selected from the 7710 non-fake instances. The model achieved a 0.9558 weighted F1-score when the fake reviews were augmented using the bnaug library.
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection
Venue: arXiv cs.LG 2024
Year: 2024

Thalassemia is a heritable blood disorder which is the outcome of a genetic defect causing lack of production of hemoglobin polypeptide chains. However, there is less understanding of the precise frequency as well as sharing in these areas. Knowing about the frequency of thalassemia occurrence and dependable mutations is thus a significant step in preventing, controlling, and treatment planning. Here, Political Tangent Search Optimizer based Transfer Learning (PTSO_TL) is introduced for thalassemia detection. Initially, input data obtained from a particular dataset is normalized in the data normalization stage. Quantile normalization is utilized in the data normalization stage, and the data are then passed to the feature fusion phase, in which Weighted Euclidean Distance with Deep Maxout Network (DMN) is utilized. Thereafter, data augmentation is performed using the oversampling method to increase data dimensionality. Lastly, thalassemia detection is carried out by TL, wherein a convolutional neural network (CNN) is utilized with hyperparameters from a trained model such as Xception. TL is tuned by PTSO, and the training algorithm PTSO is presented by merging of Political Optimizer (PO) and Tangent Search Algorithm (TSA). Furthermore, PTSO_TL obtained maximal precision, recall, and f-measure values of about 94.3%, 96.1%, and 95.2%, respectively.
Mechanistic Modeling and Analysis of Thermal Radiation in Conventional, Microwave-assisted, and Hybrid Freeze Drying for Biopharmaceutical Manufacturing
Venue: arXiv cs.CE 2024
Year: 2024

In freeze drying, thermal radiation has a significant effect on the drying process of vials located near the corner and edge of the trays, resulting in non-uniformity of the products. Understanding and being able to predict the impact of thermal radiation are therefore critical to accurate determination of the drying process endpoint given the variation in heat transfer of each vial. This article presents a new mechanistic model that describes complex thermal radiation during primary drying in conventional, microwave-assisted, and hybrid freeze drying. Modeling of thermal radiation employs the diffuse gray surface model and radiation network approach, which systematically and accurately incorporates simultaneous radiation exchange between every surface including the chamber wall and vials, allowing the framework to be seamlessly applied for analyzing various freeze-dryer designs. Model validation with data from the literature shows accurate prediction of the drying times for all vials, including inner, edge, and corner vials. The validated model is demonstrated for thermal radiation analysis and parametric studies to guide the design and optimization of freeze dryers.
Robust mmWave/sub-THz multi-connectivity using minimal coordination and coarse synchronization
Venue: arXiv cs.IT 2024
Year: 2024

This study investigates simpler alternatives to coherent joint transmission for supporting robust connectivity against signal blockage in mmWave/sub-THz access networks. By taking an information-theoretic viewpoint, we demonstrate analytically that with a careful design, full macrodiversity gains and significant SNR gains can be achieved through canonical receivers and minimal coordination and synchronization requirements at the infrastructure side. Our proposed scheme extends non-coherent joint transmission by employing a special form of diversity to counteract artificially induced deep fades that would otherwise make this technique often compare unfavorably against standard transmitter selection schemes. Additionally, the inclusion of an Alamouti-like space-time coding layer is shown to recover a significant fraction of the optimal performance. Our conclusions are based on a statistical single-user multi-point intermittent block fading channel model that, although simplified, enables rigorous ergodic and outage rate analysis, while also considering timing offsets due to imperfect delay compensation. In addition, we validate our theoretical approach by means of deterministic ray-tracing simulations that capture the essential features of next generation mmWave/sub-THz communications.
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
Venue: arXiv cs.LG 2024
Year: 2024

Deep Reinforcement Learning (DRL) has made significant advancements in various fields, such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal policies through interactions with their environments. However, the application of DRL in safety-critical domains presents challenges, particularly concerning the safety of the learned policies. DRL agents, which are focused on maximizing rewards, may select unsafe actions, leading to safety violations. Runtime safety monitoring is thus essential to ensure the safe operation of these agents, especially in unpredictable and dynamic environments. This paper introduces SMARLA, a black-box safety monitoring approach specifically designed for DRL agents. SMARLA utilizes machine learning to predict safety violations by observing the agent's behavior during execution. The approach is based on Q-values, which reflect the expected reward for taking actions in specific states. SMARLA employs state abstraction to reduce the complexity of the state space, enhancing the predictive capabilities of the monitoring model. Such abstraction enables the early detection of unsafe states, allowing for the implementation of corrective and preventive measures before incidents occur. We quantitatively and qualitatively validated SMARLA on three well-known case studies widely used in DRL research. Empirical results reveal that SMARLA is accurate at predicting safety violations, with a low false positive rate, and can predict violations at an early stage, approximately halfway through the execution of the agent, before violations occur. We also discuss different decision criteria, based on confidence intervals of the predicted violation probabilities, to trigger safety mechanisms aiming at a trade-off between early detection and low false positive rates.
Meta-learning in healthcare: A survey
Venue: arXiv cs.LG 2024
Year: 2024

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies are different. This survey discusses meta-learning broad applications in the healthcare domain to provide insight into how and where it can address critical healthcare challenges. We first describe the theoretical foundations and pivotal methods of meta-learning. We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies. Finally, we highlight the current challenges in meta-learning research, discuss the potential solutions, and provide future perspectives on meta-learning in healthcare.
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking
Venue: arXiv cs.IR 2024
Year: 2024

When learning to rank from user interactions, search and recommender systems must address biases in user behavior to provide a high-quality ranking. One type of bias that has recently been studied in the ranking literature is when sensitive attributes, such as gender, have an impact on a user's judgment about an item's utility. For example, in a search for an expertise area, some users may be biased towards clicking on male candidates over female candidates. We call this type of bias group membership bias. Increasingly, we seek rankings that are fair to individuals and sensitive groups. Merit-based fairness measures rely on the estimated utility of the items. With group membership bias, the utility of the sensitive groups is under-estimated, hence, without correcting for this bias, a supposedly fair ranking is not truly fair. In this paper, first, we analyze the impact of group membership bias on ranking quality as well as merit-based fairness metrics and show that group membership bias can hurt both ranking and fairness. Then, we provide a correction method for group bias that is based on the assumption that the utility score of items in different groups comes from the same distribution. This assumption has two potential issues of sparsity and equality-instead-of-equity; we use an amortized approach to address these. We show that our correction method can consistently compensate for the negative impact of group membership bias on ranking quality and fairness metrics.
Shaping a Smarter Electromagnetic Landscape: IAB, NCR, and RIS in 5G Standard and Future 6G
Venue: arXiv cs.IT 2024
Year: 2024

The main objective of 5G and beyond networks is to provide an optimal user experience in terms of throughput and reliability, irrespective of location and time. To achieve this, traditional fixed macro base station deployments are being replaced by more innovative and flexible solutions, such as wireless backhaul and relays. This article focuses on the evolution and standardization of these advancements, which are shaping the electromagnetic landscape. Specifically, we explore Integrated Access and Backhaul (IAB) nodes, which offer a cost-efficient and agile alternative to fiber backhaul. We also discuss Network-Controlled Repeaters (NCRs) and the emergence of Reconfigurable Intelligent Surfaces (RIS) actively adapting the wireless environment. The article provides an overview of the 5G features and ongoing developments in 3GPP Release 18 related to these intelligent EM entities, highlighting the expected evolution of future wireless networks in terms of architecture, operations, and control signals.
Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations
Venue: arXiv cs.DB 2024
Year: 2024

Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such geospatial videos on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative, build-filter-observe paradigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3x, while maintaining up to 97.1% accuracy compared to unoptimized execution.
GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
Venue: arXiv cs.CR 2024
Year: 2024

Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control or data-flow patterns, such as re-entrancy and integer overflow. However, a recent study on Web3 security bugs revealed that about 80% of these bugs cannot be audited by existing tools due to the lack of domain-specific property description and checking. Given recent advances in Large Language Models (LLMs), it is worth exploring how Generative Pre-training Transformer (GPT) could aid in detecting logicc vulnerabilities. In this paper, we propose GPTScan, the first tool combining GPT with static analysis for smart contract logic vulnerability detection. Instead of relying solely on GPT to identify vulnerabilities, which can lead to high false positives and is limited by GPT's pre-trained knowledge, we utilize GPT as a versatile code understanding tool. By breaking down each logic vulnerability type into scenarios and properties, GPTScan matches candidate vulnerabilities with GPT. To enhance accuracy, GPTScan further instructs GPT to intelligently recognize key variables and statements, which are then validated by static confirmation. Evaluation on diverse datasets with around 400 contract projects and 3K Solidity files shows that GPTScan achieves high precision (over 90%) for token contracts and acceptable precision (57.14%) for large projects like Web3Bugs. It effectively detects ground-truth logic vulnerabilities with a recall of over 70%, including 9 new vulnerabilities missed by human auditors. GPTScan is fast and cost-effective, taking an average of 14.39 seconds and 0.01 USD to scan per thousand lines of Solidity code. Moreover, static confirmation helps GPTScan reduce two-thirds of false positives.
POSIT: Promotion of Semantic Item Tail via Adversarial Learning
Venue: arXiv cs.IR 2024
Year: 2024

In many recommendations, a handful of popular items (e.g., movies / television shows, news, etc.) can be dominant in recommendations for many users. However, we know that in a large catalog of items, users are likely interested in more than what is popular. The dominance of popular items may mean that users will not see items that they would probably enjoy. In this paper, we propose a technique to overcome this problem using adversarial machine learning. We define a metric to translate the user-level utility metric in terms of an advantage/disadvantage over items. We subsequently used that metric in an adversarial learning framework to systematically promote disadvantaged items. Distinctly, our method integrates a small-capacity model to produce semantically meaningful weights, leading to an algorithm that identifies and promotes a semantically similar item within the learning process. In the empirical study, we evaluated the proposed technique on three publicly available datasets and seven competitive baselines. The result shows that our proposed method not only improves the coverage, but also, surprisingly, improves the overall performance.
XAI in Automated Fact-Checking? The Benefits Are Modest and There's No One-Explanation-Fits-All
Venue: arXiv cs.HC 2024
Year: 2024

The massive volume of online information along with the issue of misinformation has spurred active research in the automation of fact-checking. Like fact-checking by human experts, it is not enough for an automated fact-checker to just be accurate, but also be able to inform and convince the user of the validity of its predictions. This becomes viable with explainable artificial intelligence (XAI). In this work, we conduct a study of XAI fact-checkers involving 180 participants to determine how users' actions towards news and their attitudes towards explanations are affected by the XAI. Our results suggest that XAI has limited effects on users' agreement with the veracity prediction of the automated fact-checker and on their intent to share news. However, XAI nudges users towards forming uniform judgments of news veracity, thereby signaling their reliance on the explanations. We also found polarizing preferences towards XAI and raise several design considerations on them.
PURL: Safe and Effective Sanitization of Link Decoration
Venue: arXiv cs.CR 2024
Year: 2024

While privacy-focused browsers have taken steps to block third-party cookies and mitigate browser fingerprinting, novel tracking techniques that can bypass existing countermeasures continue to emerge. Since trackers need to share information from the client-side to the server-side through link decoration regardless of the tracking technique they employ, a promising orthogonal approach is to detect and sanitize tracking information in decorated links. To this end, we present PURL (pronounced purel-l), a machine-learning approach that leverages a cross-layer graph representation of webpage execution to safely and effectively sanitize link decoration. Our evaluation shows that PURL significantly outperforms existing countermeasures in terms of accuracy and reducing website breakage while being robust to common evasion techniques. PURL's deployment on a sample of top-million websites shows that link decoration is abused for tracking on nearly three-quarters of the websites, often to share cookies, email addresses, and fingerprinting information.
RecycleGPT: An Autoregressive Language Model with Recyclable Module
Venue: arXiv cs.CL 2024
Year: 2024

Existing large language models have to run K times to generate a sequence of K tokens. In this paper, we present RecycleGPT, a generative language model with fast decoding speed by recycling pre-generated model states without running the whole model in multiple steps. Our approach relies on the observation that adjacent tokens in a sequence usually have strong correlations and the next token in a sequence can be reasonably guessed or inferred based on the preceding ones. Experiments and analysis demonstrate the effectiveness of our approach in lowering inference latency, achieving up to 1.4x speedup while preserving high performance.
Near-optimal pilot assignment in cell-free massive MIMO
Venue: arXiv cs.IT 2024
Year: 2024

Cell-free massive MIMO systems are currently being considered as potential enablers of future (6G) technologies for wireless communications. By combining distributed processing and massive MIMO, they are expected to deliver improved user coverage and efficiency. A possible source of performance degradation in such systems is pilot contamination, which contributes to causing interference during uplink training and affects channel estimation negatively. Contamination occurs when the same pilot sequence is assigned to more than one user. This is in general inevitable, as the number of mutually orthogonal pilot sequences corresponds to only a fraction of the coherence interval. We introduce a new algorithm for pilot assignment and analyze its performance both from a theoretical perspective and in computational experiments. We show that it has an approximation ratio close to 1 for a plausibly large number of orthogonal pilot sequences, as well as low computational complexity under massive parallelism. We also show that, on average, it outperforms other methods in terms of per-user SINR and throughput on the uplink.
Toward Zero-Shot Instruction Following
Venue: arXiv cs.CL 2024
Year: 2024

This work proposes a challenging yet more realistic setting for zero-shot cross-task generalization: zero-shot instruction following, presuming the existence of a paragraph-style task definition while no demonstrations exist. To better learn the task supervision from the definition, we propose two strategies: first, to automatically find out the critical sentences in the definition; second, a ranking objective to force the model to generate the gold outputs with higher probabilities when those critical parts are highlighted in the definition. The joint efforts of the two strategies yield state-of-the-art performance on the Super-NaturalInstructions. Our code is available on GitHub.
Asynchronous Evolution of Deep Neural Network Architectures
Venue: arXiv cs.NE 2024
Year: 2024

Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients) are idle much of the time, waiting for the next generation to be created. Evolutionary neural architecture search (ENAS), a class of EAs that optimizes the architecture and hyperparameters of deep neural networks, is particularly vulnerable to this issue. This paper proposes a generic asynchronous evaluation strategy (AES) that is then adapted to work with ENAS. AES increases throughput by maintaining a queue of up to $K$ individuals ready to be sent to the workers for evaluation and proceeding to the next generation as soon as $M<<K$ individuals have been evaluated. A suitable value for $M$ is determined experimentally, balancing diversity and efficiency. To showcase the generality and power of AES, it was first evaluated in eight-line sorting network design (a single-population optimization task with limited evaluation-time variability), achieving an over two-fold speedup. Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed. It was then scaled up to ENAS for image captioning (a multi-population open-ended-optimization task), resulting in an over two-fold speedup. In all problems, a multifold performance improvement was observed, suggesting that AES is a promising method for parallelizing the evolution of complex systems with long and variable evaluation times, such as those in ENAS.