OpenRaiser/Intern-Atlas huggingface dataset:
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension
Venue: arXiv cs.CL 2023
Year: 2023

ChatGPT has shown its great power in text processing, including its reasoning ability from text reading. However, there has not been any direct comparison between human readers and ChatGPT in reasoning ability related to text reading. This study was undertaken to investigate how ChatGPTs (i.e., ChatGPT and ChatGPT Plus) and Chinese senior school students as ESL learners exhibited their reasoning ability from English narrative texts. Additionally, we compared the two ChatGPTs in the reasoning performances when commands were updated elaborately. The whole study was composed of three reasoning tests: Test 1 for commonsense inference, Test 2 for emotional inference, and Test 3 for causal inference. The results showed that in Test 1, the students outdid the two ChatGPT versions in local-culture-related inferences but performed worse than the chatbots in daily-life inferences. In Test 2, ChatGPT Plus excelled whereas ChatGPT lagged behind in accuracy. In association with both accuracy and frequency of correct responses, the students were inferior to the two chatbots. Compared with ChatGPTs' better performance in positive emotions, the students showed their superiority in inferring negative emotions. In Test 3, the students demonstrated better logical analysis, outdoing both chatbots. In updating command condition, ChatGPT Plus displayed good causal reasoning ability while ChatGPT kept unchanged. Our study reveals that human readers and ChatGPTs have their respective advantages and disadvantages in drawing inferences from text reading comprehension, unlocking a complementary relationship in text-based reasoning.
Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking
Venue: arXiv cs.CV 2023
Year: 2023

Multi-Object Tracking (MOT) remains a vital component of intelligent video analysis, which aims to locate targets and maintain a consistent identity for each target throughout a video sequence. Existing works usually learn a discriminative feature representation, such as motion and appearance, to associate the detections across frames, which are easily affected by mutual occlusion and background clutter in practice. In this paper, we propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets, so as to achieve robust data association in the tracking process. For the detections without being associated, we design a novel single-shot feature learning module to extract discriminative features of each detection, which can efficiently associate targets between adjacent frames. For the tracklets being lost several frames, we design a novel multi-shot feature learning module to extract discriminative features of each tracklet, which can accurately refind these lost targets after a long period. Once equipped with a simple data association logic, the resulting VisualTracker can perform robust MOT based on the single-shot and multi-shot feature representations. Extensive experimental results demonstrate that our method has achieved significant improvements on MOT17 and MOT20 datasets while reaching state-of-the-art performance on DanceTrack dataset.
Decentralized Energy Marketplace via NFTs and AI-based Agents
Venue: arXiv cs.NI 2023
Year: 2023

The paper introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technology and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. The proposed framework uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a transparent and secure trading environment. Leveraging Federated Deep Reinforcement Learning (FDRL), the system promotes collaborative and adaptive energy management strategies, maintaining user privacy. A notable innovation is the use of smart contracts, ensuring high efficiency and integrity in energy transactions. Extensive evaluations demonstrate the system's scalability and the effectiveness of the FDRL method in optimizing energy distribution. This research significantly contributes to developing sophisticated decentralized smart grid infrastructures. Our approach broadens potential blockchain and AI applications in sustainable energy systems and addresses incentive alignment and transparency challenges in traditional energy trading mechanisms. The implementation of this paper is publicly accessible at \url{https://github.com/RasoulNik/DEM}.
Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing
Venue: arXiv cs.CL 2023
Year: 2023

Understanding how humans process natural language has long been a vital research direction. The field of natural language processing (NLP) has recently experienced a surge in the development of powerful language models. These models have proven to be invaluable tools for studying another complex system known to process human language: the brain. Previous studies have demonstrated that the features of language models can be mapped to fMRI brain activity. This raises the question: is there a commonality between information processing in language models and the human brain? To estimate information flow patterns in a language model, we examined the causal relationships between different layers. Drawing inspiration from the workspace framework for consciousness, we hypothesized that features integrating more information would more accurately predict higher hierarchical brain activity. To validate this hypothesis, we classified language model features into two categories based on causal network measures: 'low in-degree' and 'high in-degree'. We subsequently compared the brain prediction accuracy maps for these two groups. Our results reveal that the difference in prediction accuracy follows a hierarchical pattern, consistent with the cortical hierarchy map revealed by activity time constants. This finding suggests a parallel between how language models and the human brain process linguistic information.
A Novel VAPT Algorithm: Enhancing Web Application Security Trough OWASP top 10 Optimization
Venue: arXiv cs.CR 2023
Year: 2023

This research study is built upon cybersecurity audits and investigates the optimization of an Open Web Application Security Project (OWASP) Top 10 algorithm for Web Applications (WA) security audits using Vulnerability Assessment and Penetration Testing (VAPT) processes. The study places particular emphasis on enhancing the VAPT process by optimizing the OWASP algorithm. To achieve this, the research utilizes desk documents to gain knowledge of WA cybersecurity audits and their associated tools. It also delves into archives to explore VAPT processes and identify techniques, methods, and tools for VAPT automation. Furthermore, the research proposes a prototype optimization that streamlines the two steps of VAPT using the OWASP Top 10 algorithm through an experimental procedure. The results are obtained within a virtual environment, which employs black box testing methods as the primary means of data acquisition and analysis. In this experimental setting, the OWASP algorithm demonstrates an impressive level of precision, achieving a precision rate exceeding 90%. It effectively covers all researched vulnerabilities, thus justifying its optimization. This research contributes significantly to the enhancement of the OWASP algorithm and benefits the offensive security community. It plays a crucial role in ensuring compliance processes for professionals and analysts in the security and software development fields.
Causal Fairness-Guided Dataset Reweighting using Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, and several fairness notions based on the on Pearl's causal framework have been proposed. In this paper, we construct a reweighting scheme of datasets to address causal fairness. Our approach aims at mitigating bias by considering the causal relationships among variables and incorporating them into the reweighting process. The proposed method adopts two neural networks, whose structures are intentionally used to reflect the structures of a causal graph and of an interventional graph. The two neural networks can approximate the causal model of the data, and the causal model of interventions. Furthermore, reweighting guided by a discriminator is applied to achieve various fairness notions. Experiments on real-world datasets show that our method can achieve causal fairness on the data while remaining close to the original data for downstream tasks.
An Interpretation of E-HA$^w$ inside HA$^w$
Venue: arXiv cs.LO 2023
Year: 2023

Higher Type Arithmetic (HA$^w$) is a first-order many-sorted theory. It is a conservative extension of Heyting Arithmetic obtained by extending the syntax of terms to all of System T: the objects of interest here are the functionals of higher types. While equality between natural numbers is specified by the axioms of Peano, how can equality between functionals be defined? From this question, different versions of HA$^w$ arise, such as an extensional version (E-HA$^w$) and an intentional version (I-HA$^w$). In this work, we will see how the study of partial equivalence relations leads us to design a translation by parametricity from E-HA$^w$ to HA$^w$.
Graph Neural Networks for Pressure Estimation in Water Distribution Systems
Venue: arXiv cs.LG 2023
Year: 2023

Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDN hydraulics. However, pure physics-based simulations involve several challenges, e.g. partially observable data, high uncertainty, and extensive manual configuration. Thus, data-driven approaches have gained traction to overcome such limitations. In this work, we combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. First, we propose a new data generation method using a mathematical simulation but not considering temporal patterns and including some control parameters that remain untouched in previous works; this contributes to a more diverse training data. Second, our training strategy relies on random sensor placement making our GNN-based estimation model robust to unexpected sensor location changes. Third, a realistic evaluation protocol considers real temporal patterns and additionally injects the uncertainties intrinsic to real-world scenarios. Finally, a multi-graph pre-training strategy allows the model to be reused for pressure estimation in unseen target WDNs. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$_2$O and a MAPE of 7%, surpassing the performance of previous studies. Likewise, it outperformed previous approaches on other WDN benchmarks, showing a reduction of absolute error up to approximately 52% in the best cases.
$P_c\varepsilonκ_{max}$-Means++: Adapt-$P$ Driven by Energy and Distance Quality Probabilities Based on $κ$-Means++ for the Stable Election Protocol (SEP)
Venue: arXiv cs.NI 2023
Year: 2023

The adaptive probability $P_{\text{\tiny{adp}}}$ formalized in Adapt-$P$ is developed based on the remaining number of SNs $ζ$ and optimal clustering $κ_{\text{\tiny{max}}}$, yet $P_{\text{\tiny{adp}}}$ does not implement the probabilistic ratios of energy and distance factors in the network. Furthermore, Adapt-$P$ does not localize cluster-heads in the first round properly because of its reliance on distance computations defined in LEACH, that might result in uneven distribution of cluster-heads in the WSN area and hence might at some rounds yield inefficient consumption of energy. This paper utilizes \nolinebreak{$k$\small{-}means\small{++}} and Adapt-$P$ to propose \nolinebreak{$P_{\text{c}} κ_{\text{\tiny{max}}}$\small{-}means\small{++}} clustering algorithm that better manages the distribution of cluster-heads and produces an enhanced performance. The algorithm employs an optimized cluster-head election probability $P_\text{c}$ developed based on energy-based $P_{η(j,i)}$ and distance-based $P\!\!\!_{ψ(j,i)}$ quality probabilities along with the adaptive probability $P_{\text{\tiny{adp}}}$, utilizing the energy $\varepsilon$ and distance optimality $d\!_{\text{\tiny{opt}}}$ factors. Furthermore, the algorithm utilizes the optimal clustering $κ_{\text{\tiny{max}}}$ derived in Adapt-$P$ to perform adaptive clustering through \nolinebreak{$κ_{\text{\tiny{max}}}$\small{-}means\small{++}}. The proposed \nolinebreak{$P_{\text{c}} κ_{\text{\tiny{max}}}${\small{-}}means{\small{++}}} is compared with the energy-based algorithm \nolinebreak{$P_η\varepsilon κ_{\text{\tiny{max}}}${\small{-}}means{\small{++}}} and distance-based \nolinebreak{$P_ψd_{\text{\tiny{opt}}} κ_{\text{\tiny{max}}}${\small{-}}means{\small{++}}} algorithm, and has shown an optimized performance in term of residual energy and stability period of the network.
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries. Our two-step approach starts from training a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents through a chain-of-thought reasoning process. Subsequently, we blend the mined QA pairs with a conversational dataset to fine-tune the LLM as a chatbot, thereby enriching its domain-specific expertise and conversational capabilities. We also developed a new evaluation benchmark which comprises four domain-specific text corpora and associated human-crafted QA pairs for testing. Our model shows remarkable performance improvement over generally aligned LLM and surpasses domain-adapted models directly fine-tuned on domain corpus. In particular, LLMiner achieves this with minimal human intervention, requiring only 600 seed instances, thereby providing a pathway towards self-improvement of LLMs through model-synthesized training data.
QKD Entity Source Authentication: Defense-in-Depth for Post Quantum Cryptography
Venue: arXiv cs.CR 2023
Year: 2023

Quantum key distribution (QKD) was conceived by Charles Bennett and Gilles Brassard in December of 1984. In the ensuing 39 years QKD systems have been deployed around the world to provide secure encryption for terrestrial as well as satellite communication. In 2016 the National Institute of Standards and Technology (NIST) began a program to standardize a series of quantum resistant algorithms to replace our current encryption standards thereby protecting against future quantum computers breaking public key cryptography. This program is known as post quantum cryptography or PQC. One of the tenets of cybersecurity is to use an approach that simultaneously provides multiple protections known as defense-in-depth. This approach seeks to avoid single points of failure. The goal of this paper is to examine the suitability of a hybrid QKD / PQC defense-in-depth strategy. A focus of the paper will be to examine the sufficiency of initial QKD hardware authentication (entity source authentication) which is necessary to guard against man-in-the-middle attacks.
Optimal Path Planning for Aerial Load Transportation in Complex Environments using PSO-Improved Artificial Potential Fields
Venue: arXiv cs.RO 2023
Year: 2023

In this article, we investigate the optimal path planning for aerial load transportation in complex, dynamic, and static environments using Particle Swarm Optimization (PSO). A hierarchical optimal control system is designed for a quadrotor equipped with a cable-suspended payload, employing Euler-Lagrange equations of motion. To navigate through obstacles, an improved artificial potential field combined with the PSO algorithm is used to determine the shortest path for a virtual point, acting as a leader. This leader guides the system toward the target point while avoiding collisions with both fixed and moving obstacles. The gravitational and repulsion coefficient forces using various PSO methods are fine-tuned to achieve the best trajectory and minimize time duration. The identified point serves as the desired location for quadrotor position control, based on a sliding mode strategy. Finally, we present numerical results to demonstrate the successful transportation of the payload by the system.
Cactus Representations in Polylogarithmic Max-flow via Maximal Isolating Mincuts
Venue: arXiv cs.DS 2023
Year: 2023

A cactus representation of a graph, introduced by Dinitz et al. in 1976, is an edge sparsifier of $O(n)$ size that exactly captures all global minimum cuts of the graph. It is a central combinatorial object that has been a key ingredient in almost all algorithms for the connectivity augmentation problems and for maintaining minimum cuts under edge insertions (e.g. [NGM97], [CKL+22], [Hen97]). This sparsifier was generalized to Steiner cactus for a vertex set $T$, which can be seen as a vertex sparsifier of $O(|T|)$ size that captures all partitions of $T$ corresponding to a $T$-Steiner minimum cut, and also hypercactus, an analogous concept in hypergraphs. These generalizations further extend the applications of cactus to the Steiner and hypergraph settings. In a long line of work on fast constructions of cactus and its generalizations, a near-linear time construction of cactus was shown by [Karger and Panigrahi 2009]. Unfortunately, their technique based on tree packing inherently does not generalize. The state-of-the-art algorithms for Steiner cactus and hypercactus are still slower than linear time by a factor of $Ω(|T|)$ [DV94] and $Ω(n)$ [CX17], respectively. We show how to construct both Steiner cactus and hypercactus using polylogarithmic calls to max flow, which gives the first almost-linear time algorithms of both problems. The constructions immediately imply almost-linear-time connectivity augmentation algorithms in the Steiner and hypergraph settings, as well as speed up the incremental algorithm for maintaining minimum cuts in hypergraphs by a factor of $n$. The key technique behind our result is a novel variant of the influential isolating mincut technique [LP20, AKL+21] which we called maximal isolating mincuts. This technique makes the isolating mincuts to be "more balanced" which, we believe, will likely be useful in future applications.
Cost/benefit analysis model for implementing virtual reality in construction companies
Venue: arXiv cs.CY 2023
Year: 2023

Immersive technologies (ImT), like Virtual Reality (VR), have several potential applications in the construction industry. However, the absence of a cost-benefit analysis discourages construction decision-makers from implementing these technologies. In this study, we proposed a primary model for conducting a cost-benefit analysis for implementing virtual reality in construction companies. The cost and benefit factors were identified through a literature review and considered input variables for the model, and then using synthetic data, a Monte Carlo simulation was performed to generate a distribution of outcome. Given the uncertainty in input parameters, this distribution reflected the potential range of total net benefit. Considering synthetic data and input factors obtained only through literature and assumptions, VR implementation could be a promising decision based on the results. This study's results would benefit decision-makers in construction companies about the costs and benefits of implementing VR and other researchers interested in this field.
Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in Kolkata, India: A Machine Learning Approach
Venue: arXiv cs.LG 2023
Year: 2023

Type 2 diabetes mellitus represents a prevalent and widespread global health concern, necessitating a comprehensive assessment of its risk factors. This study aimed towards learning whether there is any differential impact of age, Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes mellitus in males and females in Kolkata, West Bengal, India based on a sample observed from the out-patient consultation department of Belle Vue Clinic in Kolkata. Various machine learning models like Logistic Regression, Random Forest, and Support Vector Classifier, were used to predict the risk of diabetes, and performance was compared based on different predictors. Our findings indicate a significant age-related increase in risk of diabetes for both males and females. Although exercising and BMI was found to have significant impact on the risk of Type 2 diabetes in males, in females both turned out to be statistically insignificant. For both males and females, predictive models based on WhtR demonstrated superior performance in risk assessment compared to those based on BMI. This study sheds light on the gender-specific differences in the risk factors for Type 2 diabetes, offering valuable insights that can be used towards more targeted healthcare interventions and public health strategies.
Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence
Venue: arXiv cs.CY 2023
Year: 2023

Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms to recidivism algorithms, when they are designed without diversity in mind. Thus, we must take action to promote diversity among those in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. While the persistence of diverse populations has been studied in engineering, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI by surveying students in ML/AI courses. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from other STEM fields, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field. Our findings suggest that short-term intentional persistence is associated with academic enrollment factors such as major and level of study. Long-term intentional persistence is correlated with measures of professional role confidence. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence, and women generally care more about this. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field.