Reviewerly/exHarmony huggingface dataset:
Intermediate certificate suppression in post-quantum TLS

Quantum computing advances threaten the security of today's public key infrastructure, and have led to the pending standardization of alternative, quantum-resistant key encapsulation and digital signature cryptography schemes. Unfortunately, authentication algorithms based on the new post-quantum (PQ) cryptography create significant performance bottlenecks for TLS due to larger certificate chains which introduce additional packets and round-trips. The TLS handshake slowdown will be unacceptable to many applications, and detrimental to the broader adoption of quantum safe cryptography standards. In this paper, we propose a novel framework for Intermediate Certificate Authority (ICA) certificate suppression in TLS that reduces the authentication message size and prevents excessive round-trip delays. Our approach utilizes an approximate membership query (AMQ) data structure (probabilistic filter) to advertise known ICA certs to remote TLS endpoints so that unnecessary ICA certificates are omitted from the TLS handshake exchange. We showcase the extend of the PQ authentication overhead challenge in TLS, and evaluate the feasibility of AMQ filters for ICA suppression in terms of space and computational overhead. Finally, we experimentally evaluate the potential gains form our approach and showcase a 70% reduction in exchanged ICA cert data that translates to 15--50 MB of savings in PQ TLS and for certain Web-based application scenarios.
Audio Data-driven Anomaly Detection for Induction Motor Based on Generative Adversarial Networks

This paper presents an anomaly detection model for an induction motor. The proposed one uses a Generative Adversarial Networks(GANs) model, which receives audio data from multiple microphones as its input. The audio signals from each microphone are transformed into mel-spectrograms used as the model's input. The anomaly cases considered in this paper are bearing faults that occurred at the front and rear bearings. One thousand train data were obtained from the normal target motor running at various operating points. The 200 test data were selected from data obtained from three target motors (i.e., normal, front bearing fault, rear bearing fault conditions) at different operating points. The proposed method is a GANomaly[1]-based approach, and contextual loss is defined as an anomaly score. As a result, 0.9822 Area Under the Receiver Operating Characteristic (AUROC) and 92.52 % accuracy were achieved for the untrained anomaly data of front and rear bearing faults. The model training was performed with PyTorch in a Python environment.
Multi-Branch ResNet-Transformer Based Deep Hybrid Approach for Short-term Spatio-Temporal Solar Irradiance Forecasting

Motivated by existing challenges on how to effectively model the spatial correlation and similar time-varying patterns among neighboring solar sites as well as alleviate performance degradation due to overfitting of conventional neural networks such as long short-term memory (LSTM) caused by long temporal inputs, we propose a spatio-temporal framework comprised of concatenated multi-branch residual networks and transformers (ResTrans). Using 17 years of data from 12 different solar sites in Philadelphia, USA, we demonstrate that the proposed approach exhibits enhanced performance compared to hybrid benchmark architectures such as single-branch ResTrans and multi-branch ResNet-LSTM (ResLSTM), single-branch ResLSTM, and CNN-LSTM.
A statistical analysis of the tangible fine art book collection at an academic library

This study examines circulation and interlibrary loan data for the fine art print books collection at an academic library in the southeastern United States. By employing a quantitative analysis using descriptive and inferential statistics, a better understanding of the relationships between the circulation of subtypes of titles within the LC “N” (fine arts) call number range, user groups (faculty, graduate students, undergraduate students), and the publication date emerges. The findings of this study show that borrowers prefer books with more recent publication dates, most fine art books circulate one to two times within 10 years, and undergraduate students borrow most of all user groups. The results of the study also promote the understanding that users of the fine art book collection continue to rely heavily upon print books. It also helps to inform future collections decisions and communicate to art liaison librarians best practices for targeting user needs in this area of the collection. The tangible fine art book collection remains essential to the research and scholarship of faculty, graduate students, and undergraduate students on campus and within the academic community at large.
Perspective: leveraging the electronic health record to improve sleep in the hospital

Inpatient sleep loss can worsen health outcomes, including delirium and falls. Sleep disruptions in the hospital often originate from provider-patient interactions ordered electronically through computerized provider order entry. These orders contain clinical decision support systems with default schedules. These defaults are often around-the-clock, may not align with patients' needs, and cause iatrogenic sleep loss. Optimizing clinical decision support in the electronic health record can decrease unnecessary sleep disruptions and influence sleep-friendly decision-making.Kadura S, Siala T, Arora VM. Perspective: Leveraging the electronic health record to improve sleep in the hospital. J Clin Sleep Med. 2023;19(2):421-423.
Proposing a Post-anthropocentric HAI through the Perception of Sentient Entities as Trans-objects

This research explores an emerging phenomenon of perceived sentience in human-agent interaction (HAI) discourse where an anthropocentric bias is observed. We propose an alternative post-anthropocentric lens through developing a framework to help understand the perception of sentient entities as possible trans-objects. The term ’post-anthropocentric lens’ is coined to represent emerging future interactive objects where HAI may be imagined overlapping with post-human philosophy bridging the limitations of an anthropocentric design philosophy for HAI. Our proposal uses a research through design approach to explore possible future imaginings for object-object interaction among social agents and humans. We conclude that it is possible to define future emerging characteristics of autonomous agents through a decentralised post-anthropocentric approach towards HAI.
User Involvement in Training Smart Home Agents

Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation.
A novel text generation algorithm based on few-shot dataset

The current text generation generally requires understanding the contextual derivation of corpus based on a large amount of data. In fact, it is very difficult because of the limited data. We develop a novel text generation algorithm based on few-shot learning data which uses a dual-channel decoder and can smoothly perform copying and generating for the predicted words. Experimental results show that this proposed algorithm's efficiency.
Short text classification method based on enhanced word vector and ACBiGRU model

In the process of short text classification, the problems of sparse features and fuzzy semantics are often encountered, and the word vectors trained by traditional methods cannot accurately express text features. To enhance the expression effect of text features, this paper introduces part-of-speech features to enhance the semantic expression of features. On this basis, an ACBiGRU classification model based on enhanced word vectors is proposed. The model takes the enhanced word vector as input, uses the CNN network to extract the features initially and highlight the local features of the text, a then uses the BiGRU network to learn and combined with the attention mechanism to highlight key information.
Inclusion in Data: Incorporating DEIA Components into Research Data Services Practices

The RDAP Summit 2022 theme of "Envisioning an Inclusive Data Future" reeled in various talks which provided ample avenues for incorporating DEIA components into data management strategies as well as outreach to diverse communities. This commentary will focus on a few of the strategies and initiatives discussed in presentations and talks at the conference that could and potentially should be integrated into established Research Data Services programs.
Recurrent context-aware multi-stage network for single image deraining

Single image rain streak removal is extremely necessary since rainy images can seriously affect many computer vision systems. In this paper, we propose a novel recurrent context-aware multi-stage network (ReCMN) for image rain removal that gradually predicts clean derained results. Specifically, the ReCMN introduces a multi-stage strategy to perform contextual relationship modeling. Firstly, we use the densely residual extraction block (DREB) to guide feature extraction. Then, a multi-scale context aggregation block (MCAB) is designed to utilize the long-distance dependencies and multiple scale features, which can fuse features of different levels to fully exploit contextual information. Finally, we develop a parallel attention block (PAB) to capture the channel and spatial information and only pass effective feature representation. Experimental results demonstrate that our method outperforms several state-of-the-art methods, based on both synthetic datasets and real-world rainy images.
A multi-method approach to compare presence, fear induction and desensitization in survival horror games within the reality-virtuality-continuum

Virtual environments and games are often used to evoke positive emotions. Contrary the survival horror genre aims to induce negative feelings in players. The effects of playing fear-inducing games in virtual reality (VR) is rather unexplored, since research mainly focuses on positive emotions. To investigate the relationship between immersion, presence and negative emotion induction, we compared repeated horror game usage between playing on desktop computers, in VR, and smart substitutional reality (SSR), which supplements VR with additional haptic and thermal stimuli. Conducting a longitudinal study utilizing questionnaires, observations and physiological measurements, we expected an increase of fear using VR and SSR due to the increased immersion. Physiological data was not analyzed due to huge data loss, while observations and self-reports revealed contradictory results. Behavioral data showed stronger expression of fear in VR and SSR. Presence was increased in the VR and SSR groups compared to PC, further a mediation of emotion induction via presence was confirmed. Altogether, the reception of horror games within VR or SSR is associated with strong emotional reactions for selected individuals. Future research should take methodological lessons learned into account.
Decoding Authorship: Is There Really no Place for an Algorithmic Author Under Copyright Law?

Abstract The third technological revolution is dragging the world, incrementally but surely, into an era where Artificial Intelligent (AI) is taking over cultural production from human beings. Nevertheless, authorship remains largely defined in humanistic terms across Western cultures as a projection of the image of the “romantic author” emanating from the 18th century. Although it is not spelt out in the law, judges do seem to demonstrate remarked reluctance to grant authorship status to AI. The developments in three important areas may enable a reconstruction of the human-centric authorship ideology: a structural pro-corporate prejudice, a low threshold for originality, and the judicial avoidance of aesthetic assessment. Distilled to its essence, AI can be as equally creative as human beings as they follow the same laws of cultural production. In dealing with the resulting ownership issue of AI authorship, an altered “work made for hire” doctrine is proposed as a promising solution design template.
UMOTMA: Underwater multiple object tracking with memory aggregation

Underwater multi-object tracking (UMOT) is an important technology in marine animal ethology. It is affected by complex factors such as scattering, background interference, and occlusion, which makes it a challenging computer vision task. As a result, the stable continuation of trajectories among different targets has been the key to the tracking performance of UMOT tasks. To solve such challenges, we propose an underwater multi-object tracking algorithm based on memory aggregation (UMOTMA) to effectively associate multiple frames with targets. First, we propose a long short-term memory (LSTM)-based memory aggregation module (LSMAM) to enhance memory utilization between multiple frames. Next, LSMAM embeds LSTM into the transformer structure to save and aggregate features between multiple frames. Then, an underwater image enhancement module M E is introduced to process the original underwater images, which improves the quality and visibility of the underwater images so that the model can extract better features from the images. Finally, LSMAM and M E are integrated with a backbone network to implement the entire algorithm framework, which can fully utilize the historical information of the tracked targets. Experiments on the UMOT datasets and the underwater fish school datasets show that UMOTMA generally outperforms existing models and can maintain the stability of the target trajectory while ensuring high-quality detection. The code is available via Github.
Image-based finite-time visual servoing of a quadrotor for tracking a moving target

This paper proposes an image-based visual servoing control method for a moving target of a quadrotor UAV (QUAV). Firstly, the dynamic image model with moving target parameters is established based on the image moment features in the virtual camera plane. For the unpredictability of the moving target in space, we use a high-order differentiator to estimate the state parameters of the moving target. In order to solve the problem of image depth information caused by a monocular camera, we derive a nonlinear finite-time linear velocity observer from the virtual image plane, which can not only estimate the linear velocity information of QUAV but also avoid the measurement of image depth. Based on the above information, we design the global finite-time controller and use Lyapunov theory to prove the finite-time stability of the system. Finally, the numerical simulations verify the convergence of the proposed control scheme, and the ROS gazebo simulations demonstrate the improved performance of the proposed control scheme in tracking error.
Advanced Enhancement Techniques for Breast Cancer Classification in Mammographic Images

Background: Breast cancer is one of the most significant health problems in the world. Early diagnosis of breast cancer is very important for treatment. Image enhancement techniques have been used to improve the captured images for quick and accurate diagnosis. These techniques include median filtering, edge enhancement, dilation, erosion, and contrast-limited adaptive histogram equalization. Although these techniques have been used in many studies, their results have not reached optimum values based on image properties and the methods used for feature extraction and classification. Methods: In this study, enhancement techniques were implemented to guarantee the best image enhancement. They were applied to 319 images collected from the Mammographic Image Analysis Society (MIAS) database. The Gabor filter and local binary pattern were used as feature extraction methods together with support vector machine (SVM), linear discriminant analysis (LDA), and nearest neighbor (KNN) classifiers. Results: The experimental work indicates that by merging the features of the Gabor filter and local binary pattern, the results were 97.8%, 100%, and 94.6% for normal/abnormal and 85.1%, 88.7%, and 81.9% for benign/malignant using the SVM, LDA, and KNN classifiers, respectively. Conclusion: The best results were obtained by combining the features of the two tested strategies and using LDA as a classifier.
Regular and polyregular theories of reduplication

We explore the generative capacity of morphological theories of reduplication. We computationally classify theories of reduplication using a hierarchy of string-to-string function classes. Reduplication as a process requires only the regular class of functions. We show that various morphological theories necessarily treat it as a more expressive polyregular function, while others maintain regularity. We discuss the significance of this formal result for reduplicative functions and recognition.
A Bona Fide Turing Test

The constantly rising demand for human-like conversational agents and the accelerated development of natural language processing technology raise expectations for a breakthrough in intelligent machine research and development. However, measuring intelligence is impossible without a proper test. Alan Turing proposed a test for machine intelligence based on imitation and unconstrained conversations between a machine and a human. To the best of our knowledge, no one has ever conducted Turing’s test as Turing prescribed, even though the Turing Test has been a bone of contention for more than seventy years. Conducting a bona fide Turing Test will contribute to machine intelligence evaluation research and has the potential to advance AI researchers in their ultimate quest, developing an intelligent machine.
An Investigation of Employees’ Intention to Comply with Information Security System—A Mixed Approach Based on Regression Analysis and fsQCA

Employee security compliance behavior has become an important safeguard to protect the security of corporate information assets. Focusing on human factors, this paper discusses how to regulate and guide employees' compliance with information security systems through effective methods. Based on protection motivation theory (PMT), a model of employees' intention to comply with the information security system was constructed. A questionnaire survey was adopted to obtain 224 valid data points, and SPSS 26.0 was applied to verify the hypotheses underlying the research model. Then, based on the results of a regression analysis, fuzzy set qualitative comparative analysis (fsQCA) was used to explore the conditional configurations that affect employees' intention to comply with the information security system from a holistic perspective. The empirical results demonstrated that perceived severity, perceived vulnerability, response efficacy, and self-efficacy all positively influenced the employees' intention to comply with the information security system; while rewards and response costs had a negative effect. Threat appraisal had a greater effect on employees' intention to comply with the information security system compared to response appraisal. The fsQCA results showed that individual antecedent conditions are not necessary to influence employees' intention to comply with an information security system. Seven pathways exist that influence an employees' intention to comply with an information security system, with reward, self-efficacy, and response cost being the core conditions having the highest probability of occurring in each configuration of pathways, and with perceived severity and self-efficacy appearing in the core conditions of configurations with an original coverage greater than 40%. Theoretically, this study discusses the influence of the elements of PMT on employees' intention to comply with an information security system, reveals the mechanism of influence of the combination of the influencing factors on the outcome variables, and identifies the core factors and auxiliary factors in the condition configurations, providing a new broader perspective for the study of information security compliance behavior and providing some theoretical support for strengthening enterprise security management. Practically, targeted suggestions are proposed based on the research results, to increase the intention of enterprise employees to comply with information security systems, thereby improving the effectiveness of enterprise information security management and the degree of information security in enterprises.
Proof-of-Stake in Algorand

In the last few years, a number of blockchain-based online platforms decided to use consensus procedures other than Proof of Work (PoW), originally adopted by Bitcoin. An alternative protocol, which attracted much attention, is the so-called Proof-of-Stake (PoS), which unlike PoW consensus is not based on solving an energy-consuming cryptopuzzle, but rather on the amount of currency units owned by a user. Different platforms adopted alternative versions of PoS. In this article, we investigate a version of PoS inspired by Algorand, which embodies a number of specific features. Indeed, to confirm a new block in the chain Algorand introduces three steps: proposal, selection , and confirmation of a block. Each step is performed by randomly selected users, where draws are based on PoS. We find explicit solutions for individual money demand, under the main simplifying assumption that a user is rewarded by the system only for the first role that she's drawn for. We do so by considering both exogenous and endogenous money supply. We also discuss the monetary equilibrium of the system, an important element in the analysis because a long-lasting disequilibrium of the economy may cause disappointment and induce some users to leave the system. Our findings suggest that an equilibrium seems to be more likely to take place if users have heterogeneous, rather than homogeneous, preferences.
Domain-Specific Visual Language for Data Engineering Quality

Data engineering pipelines process large amounts of information, and ensuring that the quality and integrity of the data is maintained throughout is critical for technical, business, and social reasons. Conventional data quality assurance approaches require a large amount of fine-grained testing code, which is laborious, easy to get out of sync, and inscrutable to non-technical stakeholders. An executable higher-level visual approach to expressing quality requirements can serve as a shared representation of these constraints and their implications for all parties, eliminating repetition while increasing accessibility and maintainability. We present a visual programming language for expressing data quality requirements within a pipeline declaratively, structured as a diagram of compositional data flow, transformation, and validation steps.