Reviewerly/exHarmony huggingface dataset:
Attention Based Stopword Generation for Neural Network Based Text Processing

Stopwords are used to improve the accuracy of document classification and retrieval. We believe that setting appropriate stopwords improves classification accuracy. However, in our preliminary experiments, in document classification tasks using BERT, existing stopword lists are not effective for improving classification accuracy. In this method, words with high attention in misclassified input documents and low attention in correctly classified documents are treated as stopwords. We conduct experiments to confirm the effectiveness of our stopword generation methods. Our experimental results show that there are cases using stopwords generated by our methods that improve the classification accuracy. We find that the classification accuracy was higher when focusing on attention differences than when focusing only on attention in incorrect documents.
A Machine Learning Approach to Enterprise Matchmaking Using Multilabel Text Classification Based on Semi-structured Website Content

Finding the right business partner to drive innovation or acquire technology transfer is a labor and time-intensive process. To simplify this process, there is a need for improved methods of automated matchmaking that can quickly identify the best potential collaboration partners. This paper presents a novel approach for semi-automated business matchmaking between companies and research institutes, that is applied to a first case study. For this purpose, we compare two transformer-based text classification models and evaluate how dataset quality affects few-shot learning performance. Flair's TARS classifier performed very well in our use case, requiring only 40 examples per class to achieve an F1 score of about 90%. This is already very close to the Hugging Face standard text classifier, which achieved an F1 score of 92% with much more annotation effort. The results show that few-shot learning models like TARS can achieve accurate results even with few training samples compared to regular transformer-based language models. Our novel approach allows the time-consuming and labor-intensive task of manual partner matchmaking to be significantly reduced.
A correct-by-construction conversion from lambda calculus to combinatory logic

Abstract This pearl defines a translation from well-typed lambda terms to combinatory logic, where both the preservation of types and the correctness of the translation are enforced statically.
Fast Correlated DNA Subsequence Search via Graph-Based Representation

Numerous fields employ correlation-based searches to provide insights about relationships in a database. Herein we propose a correlated DNA subsequence search for DNA databases to extract DNA subsequences that frequently co-occur with a query. Typical search methods must evaluate the entire database for potential candidates, which is computationally expensive. To overcome this, we propose a threshold-based algorithm to achieve efficient computation. The proposed algorithm extracts all DNA subsequences whose correlation is more significant than the threshold and excludes unlikely candidates. Experiments involving real-world DNA databases show that the proposed method achieves a faster search than baseline algorithms while maintaining a high accuracy.
Digital Index Card Creation and Management for Memorizing What You See on the Web

This paper proposes a method of enabling users to memorize important information obtained from daily Web browsing by letting them manage their browsing history as cards. People always encounter a lot of information on websites, but most of it is forgotten even if needed later. Therefore, we implemented a memory retention support system based on card creation and management. This system allows users to make cards semi-manually using their website browsing history. The system displays the cards in an easy-to-view manner and provides management functions. By creating and organizing the cards that summarize their daily browsing activities and reviewing the cards they collected, users can realize what they value and recall necessary information more easily. The results of the user study in which the participants used the system for a Web search task demonstrated that the proposed semi-manual card creation has positive effects on memory retention after four days.
WISHFUL - Website Extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage

Extracting information from diverse websites is increasingly important, especially for analyzing vast data sets to detect trends, gain insights. By studying job ads, researchers can monitor employer demand shifts, assisting policymakers in aiding affected workers and industries. However, extraction faces challenges like varied website formats, dynamic content, and duplicate data. This study introduces a method for extracting data from diverse private university websites involving keyword identification, website categorization, and extraction pipelines.
BabiaXR: Facilitating experiments about XR data visualization

BabiaXR is a toolset for conducting experiments about 3D data visualizations in extended reality (XR) in the browser. BabiaXR provides both components for building complex data visualizations, and for easily transforming them into scenes suitable for running experiments with subjects. For data visualization, it provides components to retrieve, filter, select, and visualize data. To facilitate empirical experiments with human subjects, it provides components for showing information to subjects, controlling their interaction with data, and recording their reactions. This enables the easy transformation of a certain data visualization scene into an experiment directly usable by subjects. BabiaXR is extensible, based on the A-Frame JavaScript framework for XR. As such, it is easily composable with other A-Frame components, and complex data visualization scenes can be created by using only HTML constructs. BabiaXR can be used in any XR device supporting WebXR, and with limited capabilities also in desktop and mobile devices.
European Artificial Intelligence Act: an AI security approach

Purpose The purpose of this paper is to highlight the key technical challenges that derive from the recently proposed European Artificial Intelligence Act and specifically, to investigate the applicability of the requirements that the AI Act mandates to high-risk AI systems from the perspective of AI security. Design/methodology/approach This paper presents the main points of the proposed AI Act, with emphasis on the compliance requirements of high-risk systems. It matches known AI security threats with the relevant technical requirements, it demonstrates the impact that these security threats can have to the AI Act technical requirements and evaluates the applicability of these requirements based on the effectiveness of the existing security protection measures. Finally, the paper highlights the necessity for an integrated framework for AI system evaluation. Findings The findings of the EU AI Act technical assessment highlight the gap between the proposed requirements and the available AI security countermeasures as well as the necessity for an AI security evaluation framework. Originality/value AI Act, high-risk AI systems, security threats, security countermeasures.
How Resilient is Privacy-preserving Machine Learning Towards Data-Driven Policy? Jakarta COVID-19 Patient Study Case

With the rise of personal data law in various countries, data privacy has recently become an essential issue. One of the well-known techniques used in overcoming privacy issues during analysis is differential privacy. However, many studies have shown that differential privacy decreased the machine learning model performance. It becomes problematic for any organization like the government to draw a policy from accurate insights from citizen statistics while maintaining citizen privacy. This study reviews differential privacy in machine learning algorithms and evaluates its performance on real COVID-19 patient data, using Jakarta, Indonesia as a case study. Besides that, we also validate our study with two additional datasets, the public Adult dataset from University of California, Irvine, and an Indonesia socioeconomic dataset. We find that using differential privacy tends to reduce accuracy and may lead to model failure in imbalanced data, particularly in more complex models such as random forests. The finding emphasizes differential privacy usage in government is practical for the trustworthy government but with distinct challenges. We discuss limitations and recommendations for any organization that works with personal data to leverage differential privacy in the future.
Utility-preserving Federated Learning

We investigate the concept of utility-preserving federated learning (UPFL) in the context of deep neural networks. We theoretically prove and experimentally validate that UPFL achieves the same accuracy as centralized training independent of the data distribution across the clients. We demonstrate that UPFL can fully take advantage of the momentum and weight decay techniques compared to centralized training, but it incurs substantial communication overhead. Ordinary federated learning, on the other hand, provides much higher communication efficiency, but it can partially benefit from the aforementioned techniques to improve utility. Given that, we propose a method called weighted gradient accumulation to gain more benefit from the momentum and weight decay akin to UPFL, while providing practical communication efficiency similar to ordinary federated learning.
Differentially Private Logistic Regression with Sparse Solutions

LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions. Differentially private versions of LASSO logistic regression have been developed, but generally produce dense solutions, reducing the intrinsic utility of the LASSO penalty. In this paper, we present a differentially private method for sparse logistic regression that maintains hard zeros. Our key insight is to first train a non-private LASSO logistic regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection. To demonstrate our method's performance, we run experiments on synthetic and real-world datasets.
The Adversarial Implications of Variable-Time Inference

Machine learning (ML) models are known to be vulnerable to a number of attacks that target the integrity of their predictions or the privacy of their training data. To carry out these attacks, a black-box adversary must typically possess the ability to query the model and observe its outputs (e.g., labels). In this work, we demonstrate, for the first time, the ability to enhance such decision-based attacks. To accomplish this, we present an approach that exploits a novel side channel in which the adversary simply measures the execution time of the algorithm used to post-process the predictions of the ML model under attack. The leakage of inference-state elements into algorithmic timing side channels has never been studied before, and we have found that it can contain rich information that facilitates superior timing attacks that significantly outperform attacks based solely on label outputs. In a case study, we investigate leakage from the non-maximum suppression (NMS) algorithm, which plays a crucial role in the operation of object detectors. In our examination of the timing side-channel vulnerabilities associated with this algorithm, we identified the potential to enhance decision-based attacks. We demonstrate attacks against the YOLOv3 detector, leveraging the timing leakage to successfully evade object detection using adversarial examples, and perform dataset inference. Our experiments show that our adversarial examples exhibit superior perturbation quality compared to a decision-based attack. In addition, we present a new threat model in which dataset inference based solely on timing leakage is performed. To address the timing leakage vulnerability inherent in the NMS algorithm, we explore the potential and limitations of implementing constant-time inference passes as a mitigation strategy.
Lookin' Out My Backdoor! Investigating Backdooring Attacks Against DL-driven Malware Detectors

Given their generalization capabilities,deep learning algorithms may represent a powerful weapon in the arsenal of antivirus developers. Nevertheless, recent works in different domains (e.g., computer vision) have shown that such algorithms are susceptible to backdooring attacks, namely training-time attacks that aim toteach a deep neural network to misclassify inputs containing a specific trigger. This work investigates the resilience of deep learning models for malware detection against backdooring attacks. In particular, we devise two classes of attacks for backdooring a malware detector that targets the update process of the underlying deep learning classifier. While the first and most straightforward approach relies onsuperficial triggers made of static byte sequences, the second attack we propose employslatent triggers, namely specific feature configurations in the latent space of the model. The latent triggers may be produced by different byte sequences in the binary inputs, rendering the triggerdynamic in the input space and thus more challenging to detect.
Model Selection for Continuous Operation of Automated Vulnerability Assessment System

Software vulnerabilities have emerged as a critical concern, leading to substantial financial losses for individuals, corporations, and diverse stakeholders. The application of machine learning models in automating vulnerability assessment has gained traction, yet the pervasive challenge of concept drift often hinders their efficacy. Concept drift, manifested as shifts in statistical data properties over time or influenced by external factors, undermines model accuracy and necessitates continuous adaptation. This paper addresses the pressing issue of concept drift in the context of automated vulnerability assessment. Our contribution lies in proposing a novel methodology to mitigate concept drift-induced performance degradation. We advocate for incorporating interval-based training, where models are learned during periods devoid of concept drift, and a repository of diverse models is established. Subsequently, model selection is guided by a dynamic evaluation process using a modest amount of new concept data. Through comprehensive experimentation, we illuminate our approach's prowess in swiftly accommodating evolving data distributions, thereby safeguarding the accuracy of vulnerability assessment models.
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile

Differential privacy (DP) is the prevailing technique for protecting user data in machine learning models. However, deficits to this framework include a lack of clarity for selecting the privacy budget ε and a lack of quantification for the privacy leakage for a particular data row by a particular trained model. We make progress toward these limitations and a new perspective by which to visualize DP results by studying a privacy metric that quantifies the extent to which a model trained on a dataset using a DP mechanism is ''covered'' by each of the distributions resulting from training on neighboring datasets. We connect this coverage metric to what has been established in the literature and use it to rank the privacy of individual samples from the training set in what we call a privacy profile. We additionally show that the privacy profile can be used to probe an observed transition to indistinguishability that takes place in the neighboring distributions as ε decreases, which we suggest is a tool that can enable the selection of ε by the ML practitioner wishing to make use of DP.
Membership Inference Attacks Against Semantic Segmentation Models

Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this can constitute a severe privacy violation. In this work we attempt to address an existing knowledge gap by conducting an exhaustive study of membership inference attacks and defences in the domain of semantic image segmentation. Our findings indicate that for certain threat models, these learning settings can be considerably more vulnerable than the previously considered classification settings. We quantitatively evaluate the attacks on a number of popular model architectures across a variety of semantic segmentation tasks, demonstrating that membership inference attacks in this domain can achieve a high success rate and defending against them may result in unfavourable privacy-utility trade-offs or increased computational costs.
When Papers Choose Their Reviewers: Adversarial Machine Learning in Peer Review

Academia is thriving like never before. Thousands of papers are submitted to conferences on hot research topics, such as artificial intelligence and computer vision. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems employ statistical topic models from machine learning to characterize the content of papers and automate their assignment to reviewers.
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models

Differentially Private Stochastic Gradient Descent (DP-SGD) limits the amount of private information deep learning models can memorize during training. This is achieved by clipping and adding noise to the model's gradients, and thus networks with more parameters require proportionally stronger perturbation. As a result, large models have difficulties learning useful information, rendering training with DP-SGD exceedingly difficult on more challenging training tasks. Recent research has focused on combating this challenge through training adaptations such as heavy data augmentation and large batch sizes. However, these techniques further increase the computational overhead of DP-SGD and reduce its practical applicability. In this work, we propose using the principle of sparse model design to solve precisely such complex tasks with fewer parameters, higher accuracy, and in less time, thus serving as a promising direction for DP-SGD. We achieve such sparsity by design by introducing equivariant convolutional networks for model training with Differential Privacy. Using equivariant networks, we show that small and efficient architecture design can outperform current state-of-the-art with substantially lower computational requirements. On CIFAR-10, we achieve an increase of up to 9% in accuracy while reducing the computation time by more than 85%. Our results are a step towards efficient model architectures that make optimal use of their parameters and bridge the privacy-utility gap between private and non-private deep learning for computer vision.
Dictionary Attack on IMU-based Gait Authentication

We present a novel adversarial model for authentication systems that use gait patterns recorded by the inertial measurement unit (IMU) built into smartphones. The attack idea is inspired by and named after the concept of a dictionary attack on knowledge (PIN or password) based authentication systems. In particular, this work investigates whether it is possible to build a dictionary of IMUGait patterns and use it to launch an attack or find an imitator who can actively reproduce IMUGait patterns that match the target's IMUGait pattern. Nine physically and demographically diverse individuals walked at various levels of four predefined controllable and adaptable gait factors (speed, step length, step width, and thigh-lift), producing 178 unique IMUGait patterns. Each pattern attacked a wide variety of user authentication models. The deeper analysis of error rates (before and after the attack) challenges the belief that authentication systems based on IMUGait patterns are the most difficult to spoof; further research is needed on adversarial models and associated countermeasures.
An observational study on the microclimate and soil thermal regimes under solar photovoltaic arrays

The high demand for low-carbon energy sources to mitigate climate change has prompted a rapid increase in ground-mounted solar parks. The implementation of photovoltaic (PV) significantly impacted the local climate and ecosystem, which are both poorly understood. To investigate the effects of a typical solar park on the Gobi ecological system, local microclimate and soil thermal regimes were measured year-round under and between PV arrays, at an applied solar park sited in Xinjiang, China. Our results demonstrated their seasonal and diurnal changes. Under solar PV arrays, the mean annual net radiation and wind speed decreased by 92.68 % and 50.53 % respectively. In contrast, PV panels caused an increase of the rear sides air by 10.12 % with 0.87 °C. South-facing PV panels reduced wind speed with the prevailing northerly wind below. In addition, the relative humidity rapidly decreased when snow covered the ground, but slightly increased from April to September. We found the soil under PV panels was cooler and tended to be a sink of energy during spring and summer whereas was more often a source during autumn and winter compared with the soil between PV panels. Observed data developed the understanding of the energy processes of solar parks in Gobi ecosystems and provided evidence to support the sustainable management of the solar park.