Reviewerly/exHarmony huggingface dataset:
Multi-dimensional task recognition for human-robot teaming: literature review

Human-robot teams collaborating to achieve tasks under various conditions, especially in unstructured, dynamic environments will require robots to adapt autonomously to a human teammate's state. An important element of such adaptation is the robot's ability to infer the human teammate's tasks. Environmentally embedded sensors (e.g., motion capture and cameras) are infeasible in such environments for task recognition, but wearable sensors are a viable task recognition alternative. Human-robot teams will perform a wide variety of composite and atomic tasks, involving multiple activity components (i.e., gross motor, fine-grained motor, tactile, visual, cognitive, speech and auditory) that may occur concurrently. A robot's ability to recognize the human's composite, concurrent tasks is a key requirement for realizing successful teaming. Over a hundred task recognition algorithms across multiple activity components are evaluated based on six criteria: sensitivity, suitability, generalizability, composite factor, concurrency and anomaly awareness. The majority of the reviewed task recognition algorithms are not viable for human-robot teams in unstructured, dynamic environments, as they only detect tasks from a subset of activity components, incorporate non-wearable sensors, and rarely detect composite, concurrent tasks across multiple activity components.
Predicting Compliance of Security Policies: Norms and Sanctions

ABSTRACTThis paper is motivated by the need to find reasons for the divergent findings reported in many studies that assessed the effects of social norms on IS security policy compliance, and answers multiple calls for additional research on this subject. In doing so, this paper contributes to the existing information security literature by presenting an integrated model that draws from the General Deterrence Theory, the Social Norms, and the Normality perspective. Specifically, this study investigates the effect of formal sanctions on information security policies compliance mediated by subjective, descriptive, and moral norms. The data were collected from the employees of five organizations in the Southern United States using the survey method. Quantitative analysis of the sampled data was conducted by using PLS to assess the various hypotheses. This study shows that descriptive and moral norms mediate the relationships between formal sanctions and information security compliance. The paper highlights the influence of moral norms on how IT users respond to IS security policies.KEYWORDS: Security policies compliancesocial normsorganizational punishmentmoral normspolicies complianceinformation systems security Disclosure statementNo potential conflict of interest was reported by the author(s).
The development and testing of a pictogram signaling advertising in online videos

Although influencer marketing has become an important advertising strategy, it has one major challenge: its lack of transparency. Young people often struggle to distinguish commercial from non-commercial content, making them particularly susceptible to influencer marketing. In response to the Dutch Media Act, we aim to develop and test a Kijkwijzer pictogram that that clearly signals advertising (i.e. influencer marketing) in online videos to minors (8–18 year-olds). The project comprised three phases: (1) an inventory phase including a cocreation workshop, (2) a survey (N = 248) gaining insights into minors' associations with a selection of pictograms, and (3) a preregistered online experiment (N = 656) to compare the effectiveness of selected pictograms in increasing advertising literacy. The cocreation workshop and the survey resulted in three pictograms that were associated with advertising and sponsored content and deemed appropriate by the minors to signal influencer marketing in online videos. However, results of the online experiment showed no effects of these pictograms on conceptual and affective advertising literacy. Overall, the findings show the difficulty of creating one effective pictogram that is preferred by all age groups, and the value of the different research phases.
Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks

Abstract For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance. Prevalent meta-learning approaches focus on obtaining good hyperparameter configurations with a limited computational budget for a completely new task based on the results obtained from the prior tasks. This paper proposes a new formulation of the tuning problem, called consolidated learning , more suited to practical challenges faced by model developers, in which a large number of predictive models are created on similar datasets. In such settings, we are interested in the total optimization time rather than tuning for a single task. We show that a carefully selected static portfolio of hyperparameter configurations yields good results for anytime optimization, while maintaining the ease of use and implementation. Moreover, we point out how to construct such a portfolio for specific domains. The improvement in the optimization is possible due to the more efficient transfer of hyperparameter configurations between similar tasks. We demonstrate the effectiveness of this approach through an empirical study for the XGBoost algorithm and the newly created metaMIMIC benchmarks of predictive tasks extracted from the MIMIC-IV medical database. In the paper, we show that the potential of consolidated learning is considerably greater due to its compatibility with many machine learning application scenarios.
ProPack: Executing Concurrent Serverless Functions Faster and Cheaper

The serverless computing model has been on the rise in recent years due to a lower barrier to entry and elastic scalability. However, our experimental evidence suggests that multiple serverless computing platforms suffer from serious performance inefficiencies when a high number of concurrent function instances are invoked, which is a desirable capability for parallel applications. To mitigate this challenge, this paper introduces ProPack, a novel solution that provides higher performance and yields cost savings for end users running applications with high concurrency. ProPack leverages insights obtained from experimental study to build a simple and effective analytical model that mitigates the scalability bottleneck. Our evaluation on multiple serverless platforms including AWS Lambda and Google confirms that ProPack can improve average performance by 85% and save cost by 66%. ProPack provides significant improvement (over 50%) over the state-of-the-art serverless workload manager such as Pywren, and is also, effective at mitigating the concurrency bottleneck for FuncX, a recent on-premise serverless execution platform for parallel applications.
Spatial frequency enhanced salient object detection

Many salient object detection (SOD) methods based on convolutional neural networks utilize spatial frequency information from images to obtain salient features. However, most of these methods do not extract and/or exploit spatial frequency information effectively. This results in suboptimal detection performance in complex natural and human social scenes. In this work, we first propose an effective and flexible spatial frequency enhancement (SFE) module based on generalized Oct-convolution. SFE can effectively extract and incorporate multiple spatial frequency information from in-stage and cross-stage feature maps and output an arbitrary number of comprehensive and compact frequency features. Then we design a spatial frequency enhanced network (SFENet) adopting two SFE modules to refine high- and low-frequency salient features end-to-end. High- and low-frequency salient features are then integrated into a final full-band saliency prediction. Experiments show that SFENet can ensure the integrity and sharpness of salient regions in complex backgrounds on five benchmark datasets.
CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.
t-SNE Based on Halton Sequence Initialized Butterfly Optimization Algorithm

t-distributed stochastic neighbor embedding (t-SNE), as a type of dimensionality reduction algorithm, is usually used to visualize and explore high-dimensional data. By comparing with other dimensionality reduction algorithms, t-SNE has the best effect for data visualization. If it is applied to n-dimensional data, intelligent mapping can be successfully done from n-dimensional data to 3-dimensional data, or even 2-dimensional data, and the probability distribution of the original data can be properly kept even after the dimensionality reduction process. t-SNE is not a linear dimensionality reduction technique, and it is good at processing nonlinear structural data, thus, it is easy for t-SNE algorithm to obtain complex manifold structures from high-dimensional data. Usually, t-SNE does not have a unique optimal solution, and the number of iterations and the convergence of its objective function are the main factors which can affect its visualization effect. Generally, gradient descent (GD) method is used to solve the problem of minimum Kullback-Leiber (KL) divergence of the objective function. However, due to the disadvantage of GD method that can easily fall into local optimal value, in this paper, Halton sequence initialized butterfly optimization algorithm (HSBOA) has been combined with traditional t-SNE algorithm so as to improve the optimization accuracy and convergence speed, and to effectively help t-SNE jump out of the local optimal value trap during the optimization process.
Exploring the Efficacy of BERT in Bengali NLP: A Study on Sentiment Analysis and Aspect Detection

The development of accurate sentiment analysis and aspect detection for the Bengali language is crucial due to the rise of Bengali language usage in digital media. Sentiment analysis and aspect detection are essential tasks in Natural Language Processing (NLP) as they allow us to extract meaningful information from textual data. In this study, we explore the performance of advanced NLP techniques in Bengali text classification tasks, specifically sentiment analysis and aspect detection. To achieve this, we compare the performance of the Bidirectional Encoder Representations from Transformers (BERT) model with Bi-LSTM, LSTM, and GRU models. We collect two Bengali datasets and preprocess them to be compatible with the input format required by BERT. The model is then trained and tested on the preprocessed data. Our results show that the BERT model outperforms the traditional Bi-LSTM, LSTM, and GRU models with a 92.5% accuracy in sentiment classification and 90.4% accuracy in aspect detection. The precision, recall, and F1-score values further support the superior performance of BERT. Our study highlights the effectiveness of using advanced NLP techniques such as BERT in text classification tasks for the Bengali language. This opens up new avenues for future work in the field of Bengali NLP, specifically in the area of sentiment analysis and aspect detection.
A Bird's Eye View on Quantum Computing: Current and Future Trends

Quantum computing is a potentially highly-disruptive technology for several domains across the Computer Science field. However, many technological challenges still prevent the construction of a fault-tolerant and reliable quantum computer. These challenges sparked enormous interest in the scientific community, which led to the development of several independent strands of research. Consequently, research in the quantum computing domain is fragmented, making it highly specialized. This can lead to missed opportunities in identifying valuable research directions and contributions from neighboring research areas. For these reasons, this paper provides an overview of the current state of play in quantum computing for different quantum research areas at different layers of the development stack. Furthermore, for each of them, we highlight possible new outlooks that could be pivotal in the near future.
Depth-Aware Feature Pyramid Network for Semantic Segmentation

Object segmentation based on multi-sensor fusion is a critical technique in autonomous vehicles, providing several benefits, including increased accuracy, robustness to adverse conditions, heightened situational awareness, and efficient processing. In this paper, we introduce a novel feature fusion-based object segmentation model named Depth-Aware Feature Pyramid Network that integrates RGB and depth information using a multi-scale feature fusion mechanism. As a result, the proposed algorithm can dynamically fuse features from multiple modalities, namely RGB and depth, to perform object segmentation with depth awareness. To validate the performance of the proposed algorithm, we conducted experiments on the Cityscapes benchmark and achieved a 72.4% mean Intersection over Union (mIOU), outperforming related object segmentation methods for autonomous vehicles.
Authentication and Access Control in Cloud-Based Systems

Every organization places a high priority on maintaining the privacy and safety of sensitive data. Tokens are used to store sensitive information to prove one’s authenticity prior to accessing system resources and services. JSON Web Token (JWT) is one such token that contains user information and is generated on the server side and issued to users for authentication purposes. However, there is still a gap in the existing research, which can be addressed with the proposed token revocation scheme in this work. The token revocation scheme ensures that the tokens belonging to deactivated users in the system are invalidated to address the issue of unauthorized users reusing tokens. RBAC is employed in this paradigm to enable access control, which strengthens security by granting users different levels of permission to access system resources. With this introduced framework, users are guaranteed to not use the previous role that was assigned to them.
A Content Recommendation Platform for People with Intellectual Disability

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. On this concept, an interactive web-based recommendation platform is proposed, aiming to provide personalized suggestions for information and entertainment, including creative and educational activities, tailored to the special users’ needs of this population. The recommended items concern publicly available web-content, suggested according to the individual user interests, through a user-friendly environment.
How Reliable are the Deep Learning-based Anomaly Detectors? A Comprehensive Reliability Analysis of Autoencoder-based Anomaly Detectors

Autoencoders have been successfully used for detecting anomalies in various intelligent systems. Generally, the autoencoder-based anomaly detection system detects a data point as anomalous if its reconstruction loss is higher than the set threshold. However, the autoencoder-based anomaly detection system lacks generalizability and reliability. Theoretically, autoencoders should produce reconstruction loss higher than the set threshold for all out-of-distribution data points. However, it has been noted that the autoencoders produce lower reconstruction loss for some data points and higher for some specific data points that are out of distribution. For example, autoencoders produce lower reconstruction loss than the set threshold for the input image with motion blur perturbation. In contrast, it produces a higher reconstruction loss for the input image with impulse noise. Motivated by the above-mentioned problem statement, this paper aims to investigate the autoencoders’ reliability in detecting various anomalies. Our experimental results found that autoencoders are highly data-specific, and the detection results are highly unreliable for various anomalies. We proposed a framework with designed reliability evaluation metrics to help assess the reliability of autoencoder-based anomaly detectors. We reported the analysis of the reliability of autoencoders in terms of their reconstruction loss against perturbed versus normal images.
Instant Messaging Meets Video Conferencing: Studying the Performance of IM Video Calls

Video conferencing applications typically use UDP and often implement their own congestion control. Research studying these custom algorithms generally finds that they do react to congestion and can hold their own against competing TCP flows. However, these works focus on applications specializing in video conferencing, neglecting those that only offer video conferencing as one of many features, such as many instant messengers. While these instant messaging-based video call applications (IMVCAs) may opt to use standardized frameworks, such as WebRTC, their actual implementations and behaviors are wildly unknown. In this paper, we thus set out to study the behavior of three popular IMVCAs, analyzing their interplay with TCP and the impact on QoE. We find that the surveyed IMVCAs (Signal, Telegram, WhatsApp) are TCP-friendly, i.e., they do not choke TCP. However, their per-app behavior differs significantly and no app equals the other: Signal and Telegram, e.g., take TCP-friendliness too far, yielding up to 90 % of their bandwidth. This results in severe QoE detriments in the form of drastically reduced sending rates and visual quality. As Signal is known to use WebRTC, this finding suggests that the current variant might be too conservative for coexisting with TCP. In contrast, WhatsApp counters congestion by filling queues to avoid losing bandwidth. Overall, IMVCAs do not keep up with the performance of specialized applications.
Bias in Internet Measurement Platforms

Network operators and researchers frequently use Internet measurement platforms (IMPs), such as RIPE Atlas, RIPE RIS, or RouteViews for, e.g., monitoring network performance, detecting routing events, topology discovery, or route optimization. To interpret the results of their measurements and avoid pitfalls or wrong generalizations, users must understand a platform's limitations. To this end, this paper studies an important limitation of IMPs, the bias, which exists due to the non-uniform deployment of the vantage points. Specifically, we introduce a generic framework to systematically and comprehensively quantify the multi-dimensional (e.g., across location, topology, network types, etc.) biases of IMPs. Using the framework and open datasets, we perform a detailed analysis of biases in IMPs that confirms well-known (to the domain experts) biases and sheds light on less-known or unexplored biases. To facilitate IMP users to obtain awareness of and explore bias in their measurements, as well as further research and analyses (e.g., methods for mitigating bias), we publicly share our code and data, and provide online tools (API, Web app, etc.) that calculate and visualize the bias in measurement setups.
Deep Generative Replay for Multivariate Time-Series Monitoring with Variational Autoencoders

Multivariate time-series (MTS) play a crucial role in network monitoring and analysis problems. We explore the usage of generative AI for MTS data modeling, in particular for the sake of knowledge replay. Knowledge replay mechanisms help in leveraging past experiences to enhance learning, mitigate forgetting, promote generalization, and enable the transfer of knowledge across different tasks or domains. Using a VAE-based deep architecture for data modeling, we incorporate a Deep Generative Replay (DGR) approach to transfer previously learned latent representations into future learning tasks, enabling continual learning in MTS problems. We study the generative characteristics of VAE-based models on top of a multi-dimensional network monitoring dataset collected from an operational mobile Internet Service Provider (ISP), portraying its usage in the context of DGR learning tasks.
Comparative Study of Bert Models and Roberta in Transformer based Question Answering

Using deep learning technique, transformer-based self-supervised pre-trained models have revolutionised the idea of transfer learning in natural language processing (NLP). The self-attention mechanism makes transformers more prevalent in transfer learning across broad range of NLP tasks. In this study, seven prominent models are compared., including the Bertbase- uncased, Distilbert-base-cased, Distilbert-base-uncased, and Roberta models, in terms of their effectiveness (using 3 epochs). Bert base cased, Bert-medium-squad2-distilled, and Electra-basesquad2 on the Stanford Question Answering Dataset were utilised models with two epochs (SQuAD). The analysis shows that in three epochs, Roberta models provide the most accuracy, while Distilbert-base cased models provide the maximum accuracy when compared to Bert-base uncased, Distilbert-base uncased, and Distilbert base cased models. While Electra-base-squad2 performs better than Bert base cased and Bert-medium-squad2- distilled in cases with two epoches. Although all Bert and Roberta models take a long time to run, increasing accuracy requires more time to train the dataset.