OpenRaiser/Intern-Atlas huggingface dataset:
Textual analysis of End User License Agreement for red-flagging potentially malicious software
Venue: arXiv cs.SE 2024
Year: 2024

New software and updates are downloaded by end users every day. Each dowloaded software has associated with it an End Users License Agreements (EULA), but this is rarely read. An EULA includes information to avoid legal repercussions. However,this proposes a host of potential problems such as spyware or producing an unwanted affect in the target system. End users do not read these EULA's because of length of the document and users find it extremely difficult to understand. Text summarization is one of the relevant solution to these kind of problems. This require a solution which can summarize the EULA and classify the EULA as "Benign" or "Malicious". We propose a solution in which we have summarize the EULA and classify the EULA as "Benign" or "Malicious". We extract EULA text of different sofware's then we classify the text using eight different supervised classifiers. we use ensemble learning to classify the EULA as benign or malicious using five different text summarization methods. An accuracy of $95.8$\% shows the effectiveness of the presented approach.
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems
Venue: arXiv cs.CL 2024
Year: 2024

The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models. Based on measurements shown on different datasets, we demonstrate that RAG-based constructions are more efficient than models produced with FN. We point out that connecting RAG and FN is not trivial, because connecting FN models with RAG can cause a decrease in performance. Furthermore, we outline a simple RAG-based architecture which, on average, outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity. This shows the significant advantage of RAG over FN in terms of hallucination, which is not offset by the fact that the average 8% better METEOR score of FN models indicates greater creativity compared to RAG.
A Sophisticated Framework for the Accurate Detection of Phishing Websites
Venue: arXiv cs.CR 2024
Year: 2024

Phishing is an increasingly sophisticated form of cyberattack that is inflicting huge financial damage to corporations throughout the globe while also jeopardizing individuals' privacy. Attackers are constantly devising new methods of launching such assaults and detecting them has become a daunting task. Many different techniques have been suggested, each with its own pros and cons. While machine learning-based techniques have been most successful in identifying such attacks, they continue to fall short in terms of performance and generalizability. This paper proposes a comprehensive methodology for detecting phishing websites. The goal is to design a system that is capable of accurately distinguishing phishing websites from legitimate ones and provides generalized performance over a broad variety of datasets. A combination of feature selection, greedy algorithm, cross-validation, and deep learning methods have been utilized to construct a sophisticated stacking ensemble classifier. Extensive experimentation on four different phishing datasets was conducted to evaluate the performance of the proposed technique. The proposed algorithm outperformed the other existing phishing detection models obtaining accuracy of 97.49%, 98.23%, 97.48%, and 98.20% on dataset-1 (UCI Phishing Websites Dataset), dataset-2 (Phishing Dataset for Machine Learning: Feature Evaluation), dataset-3 (Phishing Websites Dataset), and dataset-4 (Web page phishing detection), respectively. The high accuracy values obtained across all datasets imply the models' generalizability and effectiveness in the accurate identification of phishing websites.
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
Venue: arXiv cs.AI 2024
Year: 2024

This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other. The manuscript covers in a simple way classical algorithms for solving the problem and includes a review of recent developments in graph neural networks and quantum algorithms. The review concludes with benchmarks for testing classical as well as new learning, and quantum algorithms.
The Human Factor in Detecting Errors of Large Language Models: A Systematic Literature Review and Future Research Directions
Venue: arXiv cs.CL 2024
Year: 2024

The launch of ChatGPT by OpenAI in November 2022 marked a pivotal moment for Artificial Intelligence, introducing Large Language Models (LLMs) to the mainstream and setting new records in user adoption. LLMs, particularly ChatGPT, trained on extensive internet data, demonstrate remarkable conversational capabilities across various domains, suggesting a significant impact on the workforce. However, these models are susceptible to errors - "hallucinations" and omissions, generating incorrect or incomplete information. This poses risks especially in contexts where accuracy is crucial, such as legal compliance, medicine or fine-grained process frameworks. There are both technical and human solutions to cope with this isse. This paper explores the human factors that enable users to detect errors in LLM outputs, a critical component in mitigating risks associated with their use in professional settings. Understanding these factors is essential for organizations aiming to leverage LLM technology efficiently, guiding targeted training and deployment strategies to enhance error detection by users. This approach not only aims to optimize the use of LLMs but also to prevent potential downstream issues stemming from reliance on inaccurate model responses. The research emphasizes the balance between technological advancement and human insight in maximizing the benefits of LLMs while minimizing the risks, particularly in areas where precision is paramount. This paper performs a systematic literature research on this research topic, analyses and synthesizes the findings, and outlines future research directions. Literature selection cut-off date is January 11th 2024.
Helpful or Harmful? Exploring the Efficacy of Large Language Models for Online Grooming Prevention
Venue: arXiv cs.CR 2024
Year: 2024

Powerful generative Large Language Models (LLMs) are becoming popular tools amongst the general public as question-answering systems, and are being utilised by vulnerable groups such as children. With children increasingly interacting with these tools, it is imperative for researchers to scrutinise the safety of LLMs, especially for applications that could lead to serious outcomes, such as online child safety queries. In this paper, the efficacy of LLMs for online grooming prevention is explored both for identifying and avoiding grooming through advice generation, and the impact of prompt design on model performance is investigated by varying the provided context and prompt specificity. In results reflecting over 6,000 LLM interactions, we find that no models were clearly appropriate for online grooming prevention, with an observed lack of consistency in behaviours, and potential for harmful answer generation, especially from open-source models. We outline where and how models fall short, providing suggestions for improvement, and identify prompt designs that heavily altered model performance in troubling ways, with findings that can be used to inform best practice usage guides.
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Venue: arXiv cs.HC 2024
Year: 2024

Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId's success factors, limitations, and its generalizability to other crowdsourced science domains.
Towards Comprehensive Multimodal Perception: Introducing the Touch-Language-Vision Dataset
Venue: arXiv cs.CV 2024
Year: 2024

Tactility provides crucial support and enhancement for the perception and interaction capabilities of both humans and robots. Nevertheless, the multimodal research related to touch primarily focuses on visual and tactile modalities, with limited exploration in the domain of language. Beyond vocabulary, sentence-level descriptions contain richer semantics. Based on this, we construct a touch-language-vision dataset named TLV (Touch-Language-Vision) by human-machine cascade collaboration, featuring sentence-level descriptions for multimode alignment. The new dataset is used to fine-tune our proposed lightweight training framework, STLV-Align (Synergistic Touch-Language-Vision Alignment), achieving effective semantic alignment with minimal parameter adjustments (1%). Project Page: https://xiaoen0.github.io/touch.page/.
An Admission Control Algorithm for Isochronous and Asynchronous Traffic in IEEE 802.11ad MAC
Venue: arXiv cs.NI 2024
Year: 2024

Due to availability of large amount of bandwidth in the 60 GHz band and support of contention-free channel access called Service Period (SP), the IEEE 802.11ad/ay Wi-Fi standard is well suited for low latency and high data rate applications. IEEE 802.11ad supports two types of SP user traffic: isochronous and asynchronous. These user traffic need guaranteed SP duration before their respective deadlines. Hence, admission control plays an important role in an IEEE 802.11ad system. In an earlier work, we studied admission control and scheduling of isochronous and asynchronous traffic in an IEEE 802.11ad system, but we assumed the asynchronous requests to be periodic to keep the algorithm simple. That assumption resulted in overallocation of resource and potential degradation of performance. In this paper, we present an admission control algorithm which does not make such assumption and yet still maintains a linear run time complexity and allocates resources to the requests in a proportional fair manner. We provide arguments to establish correctness of the algorithm in terms of guaranteeing SP allocation to the requests before their respective deadlines.
Scaling Behavior of Machine Translation with Large Language Models under Prompt Injection Attacks
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are increasingly becoming the preferred foundation platforms for many Natural Language Processing tasks such as Machine Translation, owing to their quality often comparable to or better than task-specific models, and the simplicity of specifying the task through natural language instructions or in-context examples. Their generality, however, opens them up to subversion by end users who may embed into their requests instructions that cause the model to behave in unauthorized and possibly unsafe ways. In this work we study these Prompt Injection Attacks (PIAs) on multiple families of LLMs on a Machine Translation task, focusing on the effects of model size on the attack success rates. We introduce a new benchmark data set and we discover that on multiple language pairs and injected prompts written in English, larger models under certain conditions may become more susceptible to successful attacks, an instance of the Inverse Scaling phenomenon (McKenzie et al., 2023). To our knowledge, this is the first work to study non-trivial LLM scaling behaviour in a multi-lingual setting.
Gamified GUI testing with Selenium in the IntelliJ IDE: A Prototype Plugin
Venue: arXiv cs.SE 2024
Year: 2024

Software testing is a crucial phase in software development, enabling the detection of issues and defects that may arise during the development process. Addressing these issues enhances software applications' quality, reliability, user experience, and performance. Graphical User Interface (GUI) testing, one such technique, involves mimicking a regular user's interactions with an application to identify defects. However, GUI testing is often underutilized due to its perceived repetitiveness, error-proneness, and lack of immediate feedback on test quality. In recent years, gamification-incorporating game elements in non-game contexts to boost interest, motivation, and engagement-has gained traction in various fields, including software engineering and education. This paper presents GIPGUT: a prototype of a gamification plugin for IntelliJ IDEA, an Integrated Development Environment (IDE) that supports scripted GUI testing. The plugin enhances testers' engagement with typically monotonous and tedious tasks through achievements, rewards, and profile customization. A preliminary prototype evaluation was conducted with a small group of users to assess its usability and the impact of gamification on the GUI testing process. The results indicate high usability and positive reception of the gamification elements. However, due to the limited sample size of participants, further research is necessary to understand the plugin's effectiveness fully.
MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Tasks such as autonomous navigation, 3D reconstruction, and object recognition near the water surfaces are crucial in marine robotics applications. However, challenges arise due to dynamic disturbances, e.g., light reflections and refraction from the random air-water interface, irregular liquid flow, and similar factors, which can lead to potential failures in perception and navigation systems. Traditional computer vision algorithms struggle to differentiate between real and virtual image regions, significantly complicating tasks. A virtual image region is an apparent representation formed by the redirection of light rays, typically through reflection or refraction, creating the illusion of an object's presence without its actual physical location. This work proposes a novel approach for segmentation on real and virtual image regions, exploiting synthetic images combined with domain-invariant information, a Motion Entropy Kernel, and Epipolar Geometric Consistency. Our segmentation network does not need to be re-trained if the domain changes. We show this by deploying the same segmentation network in two different domains: simulation and the real world. By creating realistic synthetic images that mimic the complexities of the water surface, we provide fine-grained training data for our network (MARVIS) to discern between real and virtual images effectively. By motion & geometry-aware design choices and through comprehensive experimental analysis, we achieve state-of-the-art real-virtual image segmentation performance in unseen real world domain, achieving an IoU over 78% and a F1-Score over 86% while ensuring a small computational footprint. MARVIS offers over 43 FPS (8 FPS) inference rates on a single GPU (CPU core). Our code and dataset are available here https://github.com/jiayi-wu-umd/MARVIS.
NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways
Venue: arXiv cs.ET 2024
Year: 2024

A physical-layer modulator is a vital component for an IoT gateway to map the symbols to signals. However, due to the soldered hardware chipsets on the gateway's motherboards or the diverse toolkits on different platforms for the software radio, the existing solutions either have limited extensibility or are platform-specific. Such limitation is hard to ignore when modulation schemes and hardware platforms have become extremely diverse. This paper presents a new paradigm of using neural networks as an abstraction layer for physical layer modulators in IoT gateway devices, referred to as NN-defined modulators. Our approach addresses the challenges of extensibility and portability for multiple technologies on various hardware platforms. The proposed NN-defined modulator uses a model-driven methodology rooted in solid mathematical foundations while having native support for hardware acceleration and portability to heterogeneous platforms. We conduct the evaluation of NN-defined modulators on different platforms, including Nvidia Jetson Nano and Raspberry Pi. Evaluations demonstrate that our NN-defined modulator effectively operates as conventional modulators and provides significant efficiency gains (up to $4.7\times$ on Nvidia Jetson Nano and $1.1\times$ on Raspberry Pi), indicating high portability. Furthermore, we show the real-world applications using our NN-defined modulators to generate ZigBee and WiFi packets, which are compliant with commodity TI CC2650 (ZigBee) and Intel AX201 (WiFi NIC), respectively.
Influence of Personality and Communication Behavior of a Conversational Agent on User Experience and Social Presence in Augmented Reality
Venue: arXiv cs.HC 2024
Year: 2024

A virtual embodiment can benefit conversational agents, but it is unclear how their personalities and non-verbal behavior influence the User Experience and Social Presence in Augmented Reality (AR). We asked 30 users to converse with a virtual assistant who gives recommendations about city activities. The participants interacted with two different personalities: Sammy, a cheerful blue mouse, and Olive, a serious green human-like agent. Each was presented with two body languages - happy/friendly and annoyed/unfriendly. We conclude how agent representation and humor affect User Experience aspects, and that body language is significant in the evaluation and perception of the AR agent.
Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as text generation, summarization, and classification. Given that such models are trained on a humongous amount of online knowledge, we hypothesize that LLMs can assess whether driving scenarios generated by autonomous driving testing techniques are realistic, i.e., being aligned with real-world driving conditions. To test this hypothesis, we conducted an empirical evaluation to assess whether LLMs are effective and robust in performing the task. This reality check is an important step towards devising LLM-based autonomous driving testing techniques. For our empirical evaluation, we selected 64 realistic scenarios from \deepscenario--an open driving scenario dataset. Next, by introducing minor changes to them, we created 512 additional realistic scenarios, to form an overall dataset of 576 scenarios. With this dataset, we evaluated three LLMs (\gpt, \llama, and \mistral) to assess their robustness in assessing the realism of driving scenarios. Our results show that: (1) Overall, \gpt achieved the highest robustness compared to \llama and \mistral, consistently throughout almost all scenarios, roads, and weather conditions; (2) \mistral performed the worst consistently; (3) \llama achieved good results under certain conditions; and (4) roads and weather conditions do influence the robustness of the LLMs.
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
Venue: arXiv cs.LG 2024
Year: 2024

Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead to catastrophic results. We propose a policy gradient based approach that is robust to adversarial agents which can send arbitrary values to the server. Under this setting, our results form the first global convergence guarantees with general parametrization. These results demonstrate resilience with adversaries, while achieving optimal sample complexity of order $\tilde{\mathcal{O}}\left( \frac{1}{Nε^2} \left( 1+ \frac{f^2}{N}\right)\right)$, where $N$ is the total number of agents and $f<N/2$ is the number of adversarial agents.
ViTCN: Vision Transformer Contrastive Network For Reasoning
Venue: arXiv cs.CV 2024
Year: 2024

Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency. However, abstract reasoning remains a challenge for these models, Can AI really thinking like a human? still be a question yet to be answered. Raven Progressive Matrices (RPM) is a metric designed to assess human reasoning capabilities. It presents a series of eight images as a problem set, where the participant should try to discover the underlying rules among these images and select the most appropriate image from eight possible options that best completes the sequence. This task always be used to test human reasoning abilities and IQ. Zhang et al proposed a dataset called RAVEN which can be used to test Machine Learning model abstract reasoning ability. In this paper, we purposed Vision Transformer Contrastive Network which build on previous work with the Contrastive Perceptual Inference network (CoPiNet), which set a new benchmark for permutationinvariant models Raven Progressive Matrices by incorporating contrast effects from psychology, cognition, and education, and extends this foundation by leveraging the cutting-edge Vision Transformer architecture. This integration aims to further refine the machine ability to process and reason about spatial-temporal information from pixel-level inputs and global wise features on RAVEN dataset.