Reviewerly/exHarmony huggingface dataset:
Adaptive Provisioning In-band Network Telemetry at Computing Power Network [invited]

In-band Network Telemetry (INT) is proposed to detect networks via injecting specific probes to collect the hop-by-hop metadata within programmable switches. But there exist multiple challenges to conducting INT at Computing Power Network, such as control decisions of different INT frequencies, and the unforeseeable INT query workloads. In this study, we formulate an online non-linear time-varying integer programming problem that aims to maximize the overall quality of service through both frequency selection and INT query workload distribution. To achieve this, we propose an online learning, INTService, which utilizes a primal-dual mechanism to make fractional decisions. At last, extensive evaluations show that our proposed INTService exhibits up-lift performance 40% on average over other state-of-the-art algorithms.
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise

In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole Slide Images, with a focus on the task of selective classification, where the model should reject the classification in situations in which it is uncertain. We conduct our experiments on tile-level under the aspects of domain shift and label noise, as well as on slide-level. In our experiments, we compare Deep Ensembles, Monte-Carlo Dropout, Stochastic Variational Inference, Test-Time Data Augmentation as well as ensembles of the latter approaches. We observe that ensembles of methods generally lead to better uncertainty estimates as well as an increased robustness towards domain shifts and label noise, while contrary to results from classical computer vision benchmarks no systematic gain of the other methods can be shown. Across methods, a rejection of the most uncertain samples reliably leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.
Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs’ cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system’s effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities.
Probability Prediction for Graduate Admission Using CNN-LSTM Hybrid Algorithm

Currently, the prediction of student admissions still uses conventional machine learning algorithms where there is no algorithm for optimization. This study aims to produce a model that can predict student acceptance of ownership more optimally by using an optimization hybrid learning algorithm, namely the Convolutional Neural Network Long Short Term Memory (CNN-LSTM). This study uses the Microsoft Team Data Science Process method which consists of business understanding, data acquisition &amp; understanding, modeling, and implementation as well as using the acceptance dataset obtained from the kaggle.com website as much as 500 data. The results showed that the CNN-LSTM hybrid learning model could optimize the prediction of students' chances of success in exposure as evidenced by the evaluation results of RMSE of 6.31%, MAE of 4.4%, and R2 of 80.52%. This model is implemented in a website application using the Python language, the Django framework, and the MySQL database.
Crude Oil Price Forecasting Using Long Short-Term Memory and Support Vector Regression

Crude oil or petroleum is a non-renewable energy source derived from organic materials whose formation process is lengthy. Crude oil is a commodity whose prices often fluctuate. When there is a fluctuation, a nation's economy will be affected. The crude oil price datasets are categorized as non-linear. This research used two models to compare the performance of those two models to find the best model to predict Brent crude oil prices. The models used in this research are Long Short-Term Memory (LSTM) and Support Vector Regression (SVR). Those two methods are widely used for a similar case, such as forecasting the stock price. The dataset used in this study is the price of Brent crude oil from May 1987 to May 2022. The result of this study indicates that the deep learning algorithm, LSTM, performs better in forecasting the price of Brent crude oil with a root mean squared error value of 1.543.&#x0D; Index Terms—Crude Oil, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Deep Learning, Forecasting.
Inclusive Privacy Control at Home for Smart Health

The Trust in Home: Rethinking Interface Design in IoT (THRIDI) project initiated a community discussion and collaboration among a multidisciplinary group of experts and early-career researchers in a series of design workshops. The THRIDI workshops have been designed based on the Human-Data Interaction (HDI) framework. The workshops use several methods ranging from card sorting to scenario analysis, to elicit discussion of privacy perceptions in smart home settings and the current barriers to achieving more trustable interactions with IoT devices. We demonstrate how creative workshops are useful in enabling critical reflection and knowledge exchange in a multidisciplinary context providing a useful bridge between radically different disciplines. The workshops brought together diverse viewpoints, and participants often emphasised the need for meaningful engagement with under-represented and marginalised stakeholders across the entire technology design, development, and deployment processes. This chapter reports on the findings related to inclusivity that have arisen from discussions on health-related technologies in smart homes.
Default Logic as a Species of Causal Reasoning

We will show that Reiter's default logic can be viewed as a particular instantiation of causal reasoning. This will be demonstrated by establishing back and forth translations between default theories and causal theories of the causal calculus under a particular nonmonotonic semantics of causal theories that will be called a default semantics. Moreover, it will be shown that Pearl's structural equation models can be viewed as default causal theories in this sense. We will discuss also some global consequences this representation could have for establishing a general role of causation in nonmonotonic reasoning.
Case based Reasoning Technique in Digital Diagnostic System for Lung Cancer Detection

An efficient method for creating digital diagnostic systems for the early diagnosis of lung cancer is case-based reasoning (CBR). Artificial intelligence in the form of CBR employs knowledge from the past, or "cases," to answer contemporary problems. In the context of lung cancer detection, CBR systems use patterns and relationships identified in previous diagnostic cases to make predictions about new cases. CBR systems have several advantages over other approaches to lung cancer detection, including increased accuracy and reduced false positive rates. CBR systems can also be easily updated with new information, allowing them to continually improve over time. Despite these advantages, there are also challenges associated with the use of CBR in lung cancer detection. For example, CBR systems may be biased by the training data they are provided, leading to incorrect predictions. Additionally, CBR systems may have difficulty handling complex relationships and patterns in the data, leading to reduced accuracy. The use of CBR techniques in digital diagnostic systems for lung cancer detection has the potential to provide more accurate and reliable diagnostic results, while also reducing the time and resources required for traditional diagnostic methods. To fully exploit the potential of CBR for lung cancer detection, additional study and development in this field are required.
DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization

In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping. DISA, for the first time, eliminates the dependence of the convergent step-size range on the Euclidean norm of the linear mapping, while inheriting the advantages of the classic Primal-Dual Proximal Splitting Algorithm (PD-PSA): simple structure and easy implementation. This indicates that DISA can be executed without prior knowledge of the norm, and tiny step-sizes can be avoided when the norm is large. Additionally, we prove sublinear and linear convergence rates of DISA under general convexity and metric subregularity, respectively. Moreover, we provide a variant of DISA with approximate proximal mapping and prove its global convergence and sublinear convergence rate. Numerical experiments corroborate our theoretical analyses and demonstrate a significant acceleration of DISA compared to existing PD-PSAs.
Quantum-assisted Monte Carlo algorithms for fermions

Quantum computing is a promising way to systematically solve the longstanding computational problem, the ground state of a many-body fermion system. Many efforts have been made to realise certain forms of quantum advantage in this problem, for instance, the development of variational quantum algorithms. A recent work by Huggins et al. [1] reports a novel candidate, i.e. a quantum-classical hybrid Monte Carlo algorithm with a reduced bias in comparison to its fully-classical counterpart. In this paper, we propose a family of scalable quantum-assisted Monte Carlo algorithms where the quantum computer is used at its minimal cost and still can reduce the bias. By incorporating a Bayesian inference approach, we can achieve this quantum-facilitated bias reduction with a much smaller quantum-computing cost than taking empirical mean in amplitude estimation. Besides, we show that the hybrid Monte Carlo framework is a general way to suppress errors in the ground state obtained from classical algorithms. Our work provides a Monte Carlo toolkit for achieving quantum-enhanced calculation of fermion systems on near-term quantum devices.
Vision-Based Jigsaw Puzzle Solving with a Robotic Arm

This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images.
Precise Traits from Sloppy Components: Perception and the Origin of Phenotypic Response

Organisms perceive their environment and respond. The origin of perception–response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain a memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception–response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection.
Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City

With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM’s Translation Capability

Large, multilingual language models exhibit surprisingly good zero- or few-shot machine translation capabilities, despite having never seen the intentionally-included translation examples provided to typical neural translation systems. We investigate the role of incidental bilingualism—the unintentional consumption of bilingual signals, including translation examples—in explaining the translation capabilities of large language models, taking the Pathways Language Model (PaLM) as a case study. We introduce a mixed-method approach to measure and understand incidental bilingualism at scale. We show that PaLM is exposed to over 30 million translation pairs across at least 44 languages. Furthermore, the amount of incidental bilingual content is highly correlated with the amount of monolingual in-language content for non-English languages. We relate incidental bilingual content to zero-shot prompts and show that it can be used to mine new prompts to improve PaLM’s out-of-English zero-shot translation quality. Finally, in a series of small-scale ablations, we show that its presence has a substantial impact on translation capabilities, although this impact diminishes with model scale.
19th International Workshop on Mining and Learning with Graphs (MLG)

The 19th International Workshop on Mining and Learning with Graphs (MLG) is held in Long Beach, CA, USA and is co-located with the Tenth International Workshop on Deep Learning on Graphs (DLG) as part of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. In doing so, we aim to better understand the overarching principles and the limitations of our current methods, and to inspire research on new algorithms and techniques for mining and learning with graphs. Topics of interest include, but are not limited to, graph mining, graph machine learning, statistical relational learning, social network analysis, and network science. The target audience spans researchers and practitioners across academia, government, and industry.
arXivEdits: Understanding the Human Revision Process in Scientific Writing

Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance.However, prior work studying the human editing process in this domain mainly focused on the abstract or introduction sections, resulting in an incomplete picture.In this work, we provide a complete computational framework for studying text revision in scientific writing.We first introduce ARXIVEDITS, a new annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1,000 sentence pairs.It supports our datadriven analysis to unveil the common strategies practiced by researchers for revising their papers.To scale up the analysis, we also develop automatic methods to extract revision at document-, sentence-, and word-levels.A neural CRF sentence alignment model trained on our corpus achieves 93.8 F1, enabling the reliable matching of sentences between different versions.We formulate the edit extraction task as a span alignment problem, and our proposed method extracts more fine-grained and explainable edits, compared to the commonly used diff algorithm.An intention classifier trained on our dataset achieves 78.9 F1 on the finegrained intent classification task.Our data and system are released at tiny.one/arxivedits.
LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs.In this paper, we argue that existing GNNbased EA methods inherit the inborn defects from their neural network lineage: weak scalability and poor interpretability.Inspired by recent studies, we reinvent the Label Propagation algorithm to effectively run on KGs and propose a non-neural EA framework -LightEA, consisting of three efficient components: (i) Random Orthogonal Label Generation, (ii) Three-view Label Propagation, and (iii) Sparse Sinkhorn Iteration.According to the extensive experiments on public datasets, LightEA has impressive scalability, robustness, and interpretability.With a mere tenth of time consumption, LightEA achieves comparable results to state-of-the-art methods across all datasets and even surpasses them on many.
iCompass at WANLP 2022 Shared Task: ARBERT and MARBERT for Multilabel Propaganda Classification of Arabic Tweets

Arabic propaganda detection in Arabic was carried out using transformers pre-trained models ARBERT, MARBERT. They were fine-tuned for the down-stream task in hand ‘subtask 1’, multilabel classification of Arabic tweets. Submitted model was MARBERT the got 0.597 micro F1 score and got the fifth rank.
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization

Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).Despite their potential, our understanding of the factors influencing end-task performance and the robustness of in-context learning remains limited.This paper aims to bridge this knowledge gap by investigating the reliance of LLMs on shortcuts or spurious correlations within prompts.Through comprehensive experiments on classification and extraction tasks, we reveal that LLMs are "lazy learners" that tend to exploit shortcuts in prompts for downstream tasks.Additionally, we uncover a surprising finding that larger models are more likely to utilize shortcuts in prompts during inference.Our findings provide a new perspective on evaluating robustness in in-context learning and pose new challenges for detecting and mitigating the use of shortcuts in prompts.
Few-shot In-context Learning on Knowledge Base Question Answering

Question answering over knowledge bases is considered a difficult problem due to the challenge of generalizing to a wide variety of possible natural language questions.Additionally, the heterogeneity of knowledge base schema items between different knowledge bases often necessitates specialized training for different knowledge base question-answering (KBQA) datasets.To handle questions over diverse KBQA datasets with a unified trainingfree framework, we propose KB-BINDER, which for the first time enables few-shot incontext learning over KBQA tasks.Firstly, KB-BINDER leverages large language models like Codex to generate logical forms as the draft for a specific question by imitating a few demonstrations.Secondly, KB-BINDER grounds on the knowledge base to bind the generated draft to an executable one with BM25 score matching.The experimental results on four public heterogeneous KBQA datasets show that KB-BINDER can achieve a strong performance with only a few in-context demonstrations.Especially on GraphQA and 3-hop MetaQA, KB-BINDER can even outperform the state-of-the-art trained models.On GrailQA and WebQSP, our model is also on par with other fully-trained models.We believe KB-BINDER can serve as an important baseline for future research.