OpenRaiser/Intern-Atlas huggingface dataset:
Negative-Weight Single-Source Shortest Paths in Near-Linear Time: Now Faster!
Venue: arXiv cs.DS 2023
Year: 2023

In this work we revisit the fundamental Single-Source Shortest Paths (SSSP) problem with possibly negative edge weights. A recent breakthrough result by Bernstein, Nanongkai and Wulff-Nilsen established a near-linear $O(m \log^8(n) \log(W))$-time algorithm for negative-weight SSSP, where $W$ is an upper bound on the magnitude of the smallest negative-weight edge. In this work we improve the running time to $O(m \log^2(n) \log(nW) \log\log n)$, which is an improvement by nearly six log-factors. Some of these log-factors are easy to shave (e.g. replacing the priority queue used in Dijkstra's algorithm), while others are significantly more involved (e.g. to find negative cycles we design an algorithm reminiscent of noisy binary search and analyze it with drift analysis). As side results, we obtain an algorithm to compute the minimum cycle mean in the same running time as well as a new construction for computing Low-Diameter Decompositions in directed graphs.
SciKGTeX -- A LaTeX Package to Semantically Annotate Contributions in Scientific Publications
Venue: arXiv cs.DL 2023
Year: 2023

Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-actionable way and enable more efficient, computer-assisted workflows for many research activities. Crowd-sourcing approaches are used frequently to build and maintain such scientific knowledge graphs. To contribute to scientific knowledge graphs, researchers need simple and easy-to-use solutions to generate new knowledge graph elements and establish the practice of semantic representations in scientific communication. In this paper, we present a workflow for authors of scientific documents to specify their contributions with a LaTeX package, called SciKGTeX, and upload them to a scientific knowledge graph. The SciKGTeX package allows authors of scientific publications to mark the main contributions of their work directly in LaTeX source files. The package embeds marked contributions as metadata into the generated PDF document, from where they can be extracted automatically and imported into a scientific knowledge graph, such as the ORKG. This workflow is simpler and faster than current approaches, which make use of external web interfaces for data entry. Our user evaluation shows that SciKGTeX is easy to use, with a score of 79 out of 100 on the System Usability Scale, as participants of the study needed only 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. Further testing shows that the embedded contributions can be successfully uploaded to ORKG within ten seconds. SciKGTeX simplifies the process of manual semantic annotation of research contributions in scientific articles. Our workflow demonstrates how a scientific knowledge graph can automatically ingest research contributions from document metadata.
A Framework for Successful Corporate Cloud Transformation
Venue: arXiv cs.SE 2023
Year: 2023

Corporate Cloud transformation is expected to continue to grow double-digit each of the next few years. This growth is augmented by digital transformation, which itself is gaining huge momentum due to the recent consumer behavior trends and especially the COVID pandemic. It is also estimated that globally billions of dollars are wasted due to efficiencies in the way cloud migrations are launched and handled. This paper discusses a framework using which organizations can successfully execute cloud transformation.
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service. Since users include people with critical information needs like students or patients engaging with chatbots, the safety of these systems is of prime importance. Therefore, a clear understanding of the capabilities and limitations of LLMs is necessary. To this end, we systematically evaluate toxicity in over half a million generations of ChatGPT, a popular dialogue-based LLM. We find that setting the system parameter of ChatGPT by assigning it a persona, say that of the boxer Muhammad Ali, significantly increases the toxicity of generations. Depending on the persona assigned to ChatGPT, its toxicity can increase up to 6x, with outputs engaging in incorrect stereotypes, harmful dialogue, and hurtful opinions. This may be potentially defamatory to the persona and harmful to an unsuspecting user. Furthermore, we find concerning patterns where specific entities (e.g., certain races) are targeted more than others (3x more) irrespective of the assigned persona, that reflect inherent discriminatory biases in the model. We hope that our findings inspire the broader AI community to rethink the efficacy of current safety guardrails and develop better techniques that lead to robust, safe, and trustworthy AI systems.
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning
Venue: arXiv cs.LG 2023
Year: 2023

Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success. Existing trial outcome prediction models were not designed to model the relations among similar trials, capture the progression of features and designs of similar trials, or address the skewness of trial data which causes inferior performance for less common trials. To fill the gap and provide accurate trial outcome prediction, we propose Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first identifies trial topics to cluster the multi-sourced trial data into relevant trial topics. It then generates trial embeddings and organizes them by topic and time to create clinical trial sequences. With the consideration of each trial sequence as a task, it uses a meta-learning strategy to achieve a point where the model can rapidly adapt to new tasks with minimal updates. In particular, the topic discovery module enables a deeper understanding of the underlying structure of the data, while sequential learning captures the evolution of trial designs and outcomes. This results in predictions that are not only more accurate but also more interpretable, taking into account the temporal patterns and unique characteristics of each trial topic. We demonstrate that SPOT wins over the prior methods by a significant margin on trial outcome benchmark data: with a 21.5\% lift on phase I, an 8.9\% lift on phase II, and a 5.5\% lift on phase III trials in the metric of the area under precision-recall curve (PR-AUC).
Those Aren't Your Memories, They're Somebody Else's: Seeding Misinformation in Chat Bot Memories
Venue: arXiv cs.CL 2023
Year: 2023

One of the new developments in chit-chat bots is a long-term memory mechanism that remembers information from past conversations for increasing engagement and consistency of responses. The bot is designed to extract knowledge of personal nature from their conversation partner, e.g., stating preference for a particular color. In this paper, we show that this memory mechanism can result in unintended behavior. In particular, we found that one can combine a personal statement with an informative statement that would lead the bot to remember the informative statement alongside personal knowledge in its long term memory. This means that the bot can be tricked into remembering misinformation which it would regurgitate as statements of fact when recalling information relevant to the topic of conversation. We demonstrate this vulnerability on the BlenderBot 2 framework implemented on the ParlAI platform and provide examples on the more recent and significantly larger BlenderBot 3 model. We generate 150 examples of misinformation, of which 114 (76%) were remembered by BlenderBot 2 when combined with a personal statement. We further assessed the risk of this misinformation being recalled after intervening innocuous conversation and in response to multiple questions relevant to the injected memory. Our evaluation was performed on both the memory-only and the combination of memory and internet search modes of BlenderBot 2. From the combinations of these variables, we generated 12,890 conversations and analyzed recalled misinformation in the responses. We found that when the chat bot is questioned on the misinformation topic, it was 328% more likely to respond with the misinformation as fact when the misinformation was in the long-term memory.
Design and Analysis of Index codes for 3-Group NOMA in Vehicular Adhoc Networks
Venue: arXiv cs.IT 2023
Year: 2023

Index coding (IC) is a source coding technique employed to improve spectral utilisation, where the source node aims to satisfy users' demands by making minimum transmissions. Non-orthogonal multiple access (NOMA) is integral to the radio access technique used in 5G networks. Index-coded NOMA (IC-NOMA) transmission scheme in Vehicular Adhoc Networks (VANETs) involves applying NOMA principles on index-coded data to avoid network congestion and to improve spectral efficiency compared to conventional IC systems. In this work, a spectral efficient transmission scheme called 3-Group IC-NOMA is proposed, and an innovative index code design that fits with NOMA decoding principles to obtain improved spectral efficiency is developed. Through exhaustive analytical studies, we demonstrate that the proposed transmission scheme always supports higher rates than the conventional IC systems and requires less power to achieve an information rate at least as good as conventional IC systems.
Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature
Venue: arXiv cs.CL 2023
Year: 2023

We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large language model to engage in meaningful interactions with Astronomy papers using in-context prompting. To optimize for efficiency, we employ a distillation technique that effectively reduces the size of the original input paper by 50\%, while maintaining the paragraph structure and overall semantic integrity. We then explore the model's responses using a multi-document context (ten distilled documents). Our findings indicate that GPT-4 excels in the multi-document domain, providing detailed answers contextualized within the framework of related research findings. Our results showcase the potential of large language models for the astronomical community, offering a promising avenue for further exploration, particularly the possibility of utilizing the models for hypothesis generation.
Collaborative Machine Learning Model Building with Families Using Co-ML
Venue: arXiv cs.HC 2023
Year: 2023

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering alternative ideas and approaches that can arise when learners work together; consequently, it often precludes encountering critical issues in ML around data representation and diversity that can surface when different perspectives are manifested in a group-constructed data set. To address this issue, we created Co-ML -- a tablet-based app for learners to collaboratively build ML image classifiers through an end-to-end, iterative model-building process. In this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case study of a family (two children 11 and 14-years-old working with their parents) using Co-ML in a facilitated introductory ML activity at home. We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality. We discuss how a distributed collaborative process, in which individuals can take on different model-building responsibilities, provides a rich context for children and adults to learn ML dataset design.
World City Networks and Multinational Firms: An Analysis of Economic Ties Over a Decade
Venue: arXiv cs.SI 2023
Year: 2023

One perspective to view the economic development of cities is through the presence of multinational firms; how subsidiaries of various organizations are set up throughout the globe and how cities are connected to each other through these networks of multinational firms. Analysis of these networks can reveal interesting economical and spatial trends, as well as help us understand the importance of cities in national and regional economic development. This paper aims to study networks of cities formed due to the linkages of multinational firms over a decade (from 2010 to 2019). More specifically we are interested in analyzing the growth and stability of various cities in terms of the connections they form with other cities over time. Our results can be summarized into two key findings: First, we ascertain the central position of several cities due to their economically stable connections; Second, we successfully identify cities that have evolved over the past decade as the presence of multinational firms has increased in these cities.
A Topology by Geometrization for Sub-Iterated Immediate Snapshot Message Adversaries and Applications to Set-Agreement
Venue: arXiv cs.DC 2023
Year: 2023

The Iterated Immediate Snapshot model (IIS) is a central model in the message adversary setting. We consider general message adversaries whose executions are arbitrary subsets of the executions of the IIS message adversary. We present a new topological approach for such general adversaries, based upon geometric simplicial complexes. We are able to define a topology directly on the considered sets of executions, which gives both simpler and more powerful ways of using topology for distributed computability. As application of this new framework, we present a complete characterization and lower bounds for solving set-agreement for general sub-IIS message adversaries.
DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification
Venue: arXiv cs.LG 2023
Year: 2023

Brain-inspired hyperdimensional computing (HDC) has been recently considered a promising learning approach for resource-constrained devices. However, existing approaches use static encoders that are never updated during the learning process. Consequently, it requires a very high dimensionality to achieve adequate accuracy, severely lowering the encoding and training efficiency. In this paper, we propose DistHD, a novel dynamic encoding technique for HDC adaptive learning that effectively identifies and regenerates dimensions that mislead the classification and compromise the learning quality. Our proposed algorithm DistHD successfully accelerates the learning process and achieves the desired accuracy with considerably lower dimensionality.
chatClimate: Grounding Conversational AI in Climate Science
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a limited time is essential and difficult. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai and demonstrate its ability to answer challenging questions accurately in three different QA scenarios: asking from 1) GPT-4, 2) chatClimate, and 3) hybrid chatClimate. The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the hybrid chatClimate provided more accurate answers, highlighting the effectiveness of our solution. This approach can be easily scaled for chatbots in specific domains, enabling the delivery of reliable and accurate information.
Analyzing the Impact of Elusive Faults on Blockchain Reliability
Venue: arXiv cs.CR 2023
Year: 2023

Blockchain recently became very popular due to its use in cryptocurrencies and potential application in various domains (e.g., retail, healthcare, insurance). The smart contract is a key part of blockchain systems and specifies an agreement between transaction participants. Nowadays, smart contracts are being deployed carrying residual faults, including severe vulnerabilities that lead to different types of failures at runtime. Fault detection tools can be used to detect faults that may then be removed from the code before deployment. However, in the case of smart contracts, the common opinion is that tools are immature and ineffective. In this work, we carry out a fault injection campaign to empirically analyze the runtime impact that realistic faults present in smart contracts may have on the reliability of blockchain systems. We place particular attention on the faults that elude popular smart contract verification tools and show if and in which ways the faults lead the blockchain system to fail at runtime. Results show general poor detection and, to some extent, complementary performance by the three tools used. The results also show that several elusive faults are responsible for severe blockchain failures.
Optimal Testing of Generalized Reed-Muller Codes in Fewer Queries
Venue: arXiv cs.CC 2023
Year: 2023

A local tester for an error correcting code $C\subseteq Σ^{n}$ is a tester that makes $Q$ oracle queries to a given word $w\in Σ^n$ and decides to accept or reject the word $w$. An optimal local tester is a local tester that has the additional properties of completeness and optimal soundness. By completeness, we mean that the tester must accept with probability $1$ if $w\in C$. By optimal soundness, we mean that if the tester accepts with probability at least $1-ε$ (where $ε$ is small), then it must be the case that $w$ is $O(ε/Q)$-close to some codeword $c\in C$ in Hamming distance. We show that Generalized Reed-Muller codes admit optimal testers with $Q = (3q)^{\lceil{ \frac{d+1}{q-1}\rceil}+O(1)}$ queries. Here, for a prime power $q = p^{k}$, the Generalized Reed-Muller code, RM[n,q,d], consists of the evaluations of all $n$-variate degree $d$ polynomials over $\mathbb{F}_q$. Previously, no tester achieving this query complexity was known, and the best known testers due to Haramaty, Shpilka and Sudan(which is optimal) and due to Ron-Zewi and Sudan(which was not known to be optimal) both required $q^{\lceil{\frac{d+1}{q-q/p} \rceil}}$ queries. Our tester achieves query complexity which is polynomially better than by a power of $p/(p-1)$, which is nearly the best query complexity possible for generalized Reed-Muller codes. The tester we analyze is due to Ron-Zewi and Sudan, and we show that their basic tester is in fact optimal. Our methods are more general and also allow us to prove that a wide class of testers, which follow the form of the Ron-Zewi and Sudan tester, are optimal. This result applies to testers for all affine-invariant codes (which are not necessarily generalized Reed-Muller codes).
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Recently, multi-interest models, which extract interests of a user as multiple representation vectors, have shown promising performances for sequential recommendation. However, none of existing multi-interest recommendation models consider the Out-Of-Distribution (OOD) generalization problem, in which interest distribution may change. Considering multiple interests of a user are usually highly correlated, the model has chance to learn spurious correlations between noisy interests and target items. Once the data distribution changes, the correlations among interests may also change, and the spurious correlations will mislead the model to make wrong predictions. To tackle with above OOD generalization problem, we propose a novel multi-interest network, named DEep Stable Multi-Interest Learning (DESMIL), which attempts to de-correlate the extracted interests in the model, and thus spurious correlations can be eliminated. DESMIL applies an attentive module to extract multiple interests, and then selects the most important one for making final predictions. Meanwhile, DESMIL incorporates a weighted correlation estimation loss based on Hilbert-Schmidt Independence Criterion (HSIC), with which training samples are weighted, to minimize the correlations among extracted interests. Extensive experiments have been conducted under both OOD and random settings, and up to 36.8% and 21.7% relative improvements are achieved respectively.
Generative Adversarial Networks-Driven Cyber Threat Intelligence Detection Framework for Securing Internet of Things
Venue: arXiv cs.CR 2023
Year: 2023

While the benefits of 6G-enabled Internet of Things (IoT) are numerous, providing high-speed, low-latency communication that brings new opportunities for innovation and forms the foundation for continued growth in the IoT industry, it is also important to consider the security challenges and risks associated with the technology. In this paper, we propose a two-stage intrusion detection framework for securing IoTs, which is based on two detectors. In the first stage, we propose an adversarial training approach using generative adversarial networks (GAN) to help the first detector train on robust features by supplying it with adversarial examples as validation sets. Consequently, the classifier would perform very well against adversarial attacks. Then, we propose a deep learning (DL) model for the second detector to identify intrusions. We evaluated the proposed approach's efficiency in terms of detection accuracy and robustness against adversarial attacks. Experiment results with a new cyber security dataset demonstrate the effectiveness of the proposed methodology in detecting both intrusions and persistent adversarial examples with a weighted avg of 96%, 95%, 95%, and 95% for precision, recall, f1-score, and accuracy, respectively.