Reviewerly/exHarmony huggingface dataset:
In-Context Unlearning: Language Models as Few Shot Unlearners

Machine unlearning, the study of efficiently removing the impact of specific training points on the trained model, has garnered increased attention of late, driven by the need to comply with privacy regulations like the Right to be Forgotten. Although unlearning is particularly relevant for LLMs in light of the copyright issues they raise, achieving precise unlearning is computationally infeasible for very large models. To this end, recent work has proposed several algorithms which approximate the removal of training data without retraining the model. These algorithms crucially rely on access to the model parameters in order to update them, an assumption that may not hold in practice due to computational constraints or when the LLM is accessed via API. In this work, we propose a new class of unlearning methods for LLMs we call ''In-Context Unlearning'', providing inputs in context and without having to update model parameters. To unlearn a particular training instance, we provide the instance alongside a flipped label and additional correctly labelled instances which are prepended as inputs to the LLM at inference time. Our experimental results demonstrate that these contexts effectively remove specific information from the training set while maintaining performance levels that are competitive with (or in some cases exceed) state-of-the-art unlearning methods that require access to the LLM parameters.
Combining Decentralized IDentifiers with Proof of Membership to Enable Trust in IoT Networks

The Self-Sovereign Identity (SSI) is a decentralized paradigm enabling full control over the data used to build and prove the identity. In Internet of Things networks with security requirements, the Self-Sovereign Identity can play a key role and bring benefits with respect to centralized identity solutions. The challenge is to make the SSI compatible with resource-constraint IoT networks. In line with this objective, the paper proposes and discusses an alternative (mutual) authentication process for IoT nodes under the same administration domain. The main idea is to combine the Decentralized IDentifier (DID)-based verification of private key ownership with the verification of a proof that the DID belongs to an evolving trusted set. The solution is built around the proof of membership notion. The paper analyzes two membership solutions, a novel solution designed by the Authors based on Merkle trees and a second one based on the adaptation of Boneh, Boyen and Shacham (BBS) group signature scheme. The paper concludes with a performance estimation and a comparative analysis.
Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities

Environmental, social, and governance issues have gained significant prominence recently, particularly with a growing emphasis on environmental protection. In the realm of heightened environmental concerns, unmanned aerial vehicles have emerged as pivotal assets in addressing transportation challenges with a sustainable perspective. This study focuses on enhancing unmanned aerial vehicles’ object detection proficiency within the realm of sustainable transportation. The proposed method refines the YOLOv7 E-ELAN model, tailored explicitly for traffic scenarios. Leveraging strides in deep learning and computer vision, the adapted model demonstrates enhancements in mean average precision, outperforming the original on the VisDrone2019 dataset. This approach, encompassing model component enhancements and refined loss functions, establishes an efficacious strategy for precise unmanned aerial vehicles object detection. This endeavor aligns seamlessly with environmental, social, and governance principles. Moreover, it contributes to the 11th Sustainable Development Goal by fostering secure urban spaces. As unmanned aerial vehicles have become integral to public safety and surveillance, enhancing detection algorithms cultivates safer environments for residents. Sustainable transport encompasses curbing traffic congestion and optimizing transportation systems, where unmanned aerial vehicle-based detection plays a pivotal role in managing traffic flow, thereby supporting extended Sustainable Development Goal 11 objectives. The efficient utilization of unmanned aerial vehicles in public transit significantly aids in reducing carbon footprints, corresponding to the “Environmental Sustainability” facet of Environmental, Social, and Governance principles.
Impact of task constraints on a 3D visuomotor tracking task in virtual reality

Objective: The aim of the present study was to evaluate the impact of different task constraints on the participants’ adaptation when performing a 3D visuomotor tracking task in a virtual environment. Methods: Twenty-three voluntary participants were tested with the HTC Vive Pro Eye VR headset in a task that consisted of tracking a virtual target moving in a cube with an effector controlled with the preferred hand. Participants had to perform 120 trials according to three task constraints (i.e., gain, size, and speed), each performed according to four randomized conditions. The target-effector distance and elbow range of movement were measured. Results: The results showed an increase in the distance to the target when the task constraints were the strongest. In addition, a change in movement kinematics was observed, involving an increase in elbow amplitude as task constraints increased. It also appeared that the depth dimension played a major role in task difficulty and elbow amplitude and coupling in the tracking task. Conclusion: This research is an essential step towards characterizing interactions with a 3D virtual environment and showing how virtual constraints can facilitate arm’s involvement in the depth dimension.
An innovative mixed reality approach for maxillofacial osteotomies and repositioning

Abstract Craniomaxillofacial surgeries are performed using custom-made physical cutting guides and resin dental splints that present several drawbacks (e.g. time and cost required for their design and production). The literature commonly provides augmented/mixed reality (AR/MR) solutions for assisting maxillofacial osteotomies and repositioning without any interactive guide. This work proposes a new MR application, useful for osteotomy and repositioning, providing interactive, fast, and intuitive feedback to the surgeon, who is then supported in performing the bone fragment resection and replacement frame by frame. The proposed application speeds up the surgery and reduces under/overshooting errors. Moreover, the idea of integrating osteotomy and repositioning assistance in the same MR application is rarely found in the literature. It is an entirely novel approach to craniomaxillofacial surgery. The MR application has been designed with a three-button menu. The “App Start” calibrates the app, the “Osteotomy Mode” visualises the holograms of the cutting lines and drilling points, and the “Repositioning Mode” visualises the step-by-step real-time feedback to precisely support the surgeon placing the osteotomised bone fragment towards the final pre-planned position. The MR app has been developed in Unity and deployed on Microsoft HoloLens V2. A laboratory test bench was realised to validate the accuracy of the proposed MR-based approach. The validation protocol consists of two tasks to test the osteotomy and repositioning modes using a 3D-printed skull phantom. For osteotomy, the accuracy is 0.89 mm (genioplasty), 1.24 mm (maxillary osteotomy), 1.33 mm (orthognathic surgery), and 2.89 mm (mandibular angle osteotomy). For repositioning, the accuracy is 0.6 mm (anteroposterior deviation), 0.7 mm (mediolateral deviation), and 0.6° (angular deviation).
Promoting Women’s Empowerment: Linkages Between Financial Development, Employment and Economic Growth in Selected African Economies

Women’s empowerment (WE) is one of the main challenges to economic development in African countries. Scholars have criticized national development initiatives to focus on well-being issues and treat women as the passive beneficiaries of welfare-enhancing assistance. This study uses panel fully modified least square and vector error correction model to investigate the impact of interactive effects of WE and financial development (FD) on employment and economic growth based on the World Bank income group as follows: upper-middle-income countries (UMIC), lower-middle-income countries (LMIC), and low-income countries (LIC). The findings show that the interactive effects of WE and FD on output and employment is not significant in the long run in LMIC and LIC. Despite the significance of these interactive effects in UMIC, the coefficient is less than one. Also, the causality result in the UMIC shows feedback between WE and economic growth. And that FD has a direct influence on growth in LMIC, while WE has a direct influence on employment in LIC.
Detection of code smells using machine learning techniques combined with data-balancing methods

Code smells are prevalent issues in software design that arise when implementation or design principles are violated. These issues manifest as symptoms or anomalies in the source code. Timely identification of code smells plays a crucial role in enhancing software quality and facilitating software maintenance. Previous studies have shown that code smell detection can be accomplished through the utilization of machine learning (ML) methods. However, despite their increasing popularity, research suggests that the suitability of these methods are not always appropriate due to the problem of imbalanced data. Consequently, the effectiveness of ML models may be negatively affected. This study aims to propose a novel method for detecting code smells by employing five ML algorithms, namely decision tree (DT), k-nearest neighbors (K-NN), support vector machine (SVM), XGboost (XGB), and multi-layer perceptron (MLP). Additionally, to tackle the challenge of imbalanced data, the proposed method incorporates the random oversampling technique. Experiments were conducted in this study using four datasets that encompassed code smells, specifically god-class, data-class, long-method, and feature-envy. The experimental outcomes were evaluated and compared using various performance metrics. Upon comparing the outcomes of our models on both the balanced and original datasets, we found that the XGB model achieved the highest accuracy of 100% for detecting the data class and long method on the original datasets. In contrast, the highest accuracy of 100% was obtained for the data class and long method using DT, SVM, and XGB models on the balanced datasets. According to the empirical findings, there is significant promise in using ML techniques for the accurate prediction of code smells.
Virtual reality as an aged care technology – opportunities and limitations from a qualitative study

Abstract The metaverse, a virtual, three-dimensional and interactive world that is accessible through virtual reality (VR) technology, is gaining interest in healthcare and medical research. VR, as its precursor, already offers opportunity for healthcare itself, due to its capabilities to overcome distances, create safe, customized environments and allow immersive, powerful experiences. Older adults, who often suffer from reduced mobility, could thus greatly benefit from VR. This qualitative study explores the perceptions of older adults and their caregivers, formal and informal, on VR and its potential use in aged care. The study reveals that all stakeholder groups foresee potential of VR as an entertaining and therapeutic technology, but that they also worry about VRs’ impact on loneliness, the cognitive capabilities needed to use it, as well as its safety for older adults. Our study identifies clear opportunities for VR technology in aged care and points to important prejudices that need to be dismantled before VR will be widely adopted as a technology in aged care.
Pashto offensive language detection: a benchmark dataset and monolingual Pashto BERT

Social media platforms have become inundated with offensive language. This issue must be addressed for the growth of online social networks (OSNs) and a healthy online environment. While significant research has been devoted to identifying toxic content in major languages like English, this remains an open area of research in the low-resource Pashto language. This study aims to develop an AI model for the automatic detection of offensive textual content in Pashto. To achieve this goal, we have developed a benchmark dataset called the Pashto Offensive Language Dataset (POLD), which comprises tweets collected from Twitter and manually classified into two categories: “offensive” and “not offensive”. To discriminate these two categories, we investigated the classic deep learning classifiers based on neural networks, including CNNs and RNNs, using static word embeddings: Word2Vec, fastText, and GloVe as features. Furthermore, we examined two transfer learning approaches. In the first approach, we fine-tuned the pre-trained multilingual language model, XLM-R, using the POLD dataset, whereas, in the second approach, we trained a monolingual BERT model for Pashto from scratch using a custom-developed text corpus. Pashto BERT was then fine-tuned similarly to XLM-R. The performance of all the deep learning and transformer learning models was evaluated using the POLD dataset. The experimental results demonstrate that our pre-trained Pashto BERT model outperforms the other models, achieving an F1-score of 94.34% and an accuracy of 94.77%.
Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification

Medical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat; medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support early diagnosis, correct analysis, and treatment planning. Computed Tomography (CT) scans are generally utilized to detect and classify PCs. Deep learning (DL) approaches have demonstrated the ability to support the diagnosis and detection of several medical conditions, containing PC. Convolutional Neural Networks (CNNs) are a kind of DL approach generally employed for image analysis that is trained to automatically learn and extract features in medical images. So, this study purposes a new Sparrow Search Algorithm with Stacked Deep Learning based Medical Image Analysis for Pancreatic Cancer Detection and Classification (SSASDL-PCDC) technique on CT images. The purpose of the study is to design an SSASDL-PCDC technique to achieve improved pancreatic cancer detection performance. In addition, the SSASDL-PCDC technique applies Harris Hawks Optimization (HHO) with a densely connected networks (DenseNet) model for the feature extraction process. Moreover, convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM) approach was utilized for PC detection and classification. Furthermore, Sparrow Search Algorithm (SSA) is used to adjust the hyperparameter values of the CNN-BiLSTM technique. To evaluate the effectiveness of the SSASDL-PCDC technique, extensive experiments were executed on a comprehensive database of pancreatic CT images. The simulation outcome value depicted that the SSASDL-PCDC technique with maximum sensitivity of 99.26%, specificity of 99.26%, and accuracy of 99.26%.
Piezocatalytic degradation of 2,4-dichlorophenol in a water environment by a g-C<sub>3</sub>N<sub>4</sub>/CdS heterojunction catalyst: interfacial electric field boosting mechanism

CdS modified GCN has been explored as a high-efficiency piezocatalyst for 2,4-DCP degradation.
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the emerging interdisciplinary field of adversarial attacks on LLMs, a subfield of trustworthy ML, combining the perspectives of Natural Language Processing and Security. Prior work has shown that even safety-aligned LLMs (via instruction tuning and reinforcement learning through human feedback) can be susceptible to adversarial attacks, which exploit weaknesses and mislead AI systems, as evidenced by the prevalence of `jailbreak' attacks on models like ChatGPT and Bard. In this survey, we first provide an overview of large language models, describe their safety alignment, and categorize existing research based on various learning structures: textual-only attacks, multi-modal attacks, and additional attack methods specifically targeting complex systems, such as federated learning or multi-agent systems. We also offer comprehensive remarks on works that focus on the fundamental sources of vulnerabilities and potential defenses. To make this field more accessible to newcomers, we present a systematic review of existing works, a structured typology of adversarial attack concepts, and additional resources, including slides for presentations on related topics at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL'24).
StockWatch: Novo Nordisk Shares Soar as Ozempic Aces Phase III CKD Trial

GEN EdgeVol. 5, No. 1 TopicsStockWatch: Novo Nordisk Shares Soar as Ozempic Aces Phase III CKD TrialPhase III trial in chronic kidney disease ends early based on efficacy, further raising price of stock that has nearly doubled over the past yearAlex PhilippidisAlex PhilippidisAlex Philippidis is Senior Business Editor of GEN.Search for more papers by this authorPublished Online:12 Oct 2023https://doi.org/10.1089/genedge.5.1.137AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 5Issue 1Jan 2023 Information© 2023 by GEN PublishingTo cite this article:Alex Philippidis.StockWatch: Novo Nordisk Shares Soar as Ozempic Aces Phase III CKD Trial.GEN Edge.Jan 2023.708-713.http://doi.org/10.1089/genedge.5.1.137Published in Volume: 5 Issue 1: October 12, 2023PDF download
Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation

Nowadays, contrastive self-supervised learning has been widely incorporated into sequential recommender systems. However, most existing contrastive sequential recommender systems simply emphasize the overall information of interaction sequences, thereby neglecting the special periodic patterns of user behavior. In this study, we propose that users exhibit emanative periodicity towards a group of correlated items, i.e., user behavior follow a certain periodic pattern while their interests may shift from one item to other related items over time. In light of this observation, we present a hierarchical contrastive learning framework to model EmAnative periodicity for SEquential Recommendation (referred to as EASE). Specifically, we design dual-channel contrastive strategy from the perspective of correlation and periodicity to capture emanative periodic patterns. Furthermore, we extend the traditional binary contrastive loss with hierarchical constraint to handle hierarchical contrastive samples, thus preserving the inherent hierarchical information of correlation and periodicity. Comprehensive experiments conducted on five datasets substantiate the effectiveness of our proposed EASE in improving sequential recommendation.
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings.
Incorporating Social-Aware User Preference for Video Recommendation

Modeling user interest accurately is crucial to recommendation systems. Existing works capture user interest from historical behaviors. Due to the sparsity and noise in user behavior data, behavior based models learn incomplete and sometimes inaccurate preference patterns and easily suffer from the cold user problem. In this work, we propose a social graph enhanced framework for behavior based models, namely Social4Rec. The social graph, involving multiple relation types, is extracted to find users with similar interests. It is challenging due to the trivial and sparse relations in social graph. To address the sparse relations issue, we first propose a Cluster-Calibrate-Merge network (CCM) to discover interest groups satisfying three properties: intrinsic self-organizing patterns through cluster layer, robustness to sparse relations through knowledge distillation of calibrator layer. We then use the averaged user interest representation within each group from CCM to complete each user behavior embedding and obtain relation specific interest aware embedding. To alleviate the trivial relation problem, relation specific interest aware embedding are aggregated among relation types through attention mechanism to obtain the interest aware social embedding for each user. It is combined with user behavior embedding to derive the matching score between the user and item. Both offline and online experiments on our video platform, which is one of the biggest video recommendation platforms with nearly one billion users over the world, demonstrate the superiority of our method, especially for cold users. The codes are available at https://github.com/xuanjixiao/onerec .