Reviewerly/exHarmony huggingface dataset:
Physicians’ Online Writing Language Style and Patient Satisfaction: The Mediator of Depth of Physician–Patient Interactions

Online health counseling (OHC) is increasingly important in modern healthcare. This development has attracted considerable attention from researchers. However, the reality of the lack of physician-patient communication and dissatisfaction with online health services remains prevalent, and more research is needed to raise awareness about important issues related to OHC services, especially in terms of patient satisfaction and depth of interaction (i.e., the product of the number of interactions and the relevance of the content). This study constructs an empirical model to explore the relationship between physicians' online writing language style (inclusive language and emojis), depth of physician-patient interactions, and patient satisfaction. The study obtained 5064 online health counseling records from 337 pediatricians and analyzed them using text mining and empirical methods. The results showed that physicians' inclusive language (β = 0.3198, p < 0.05) and emojis (β = 0.6059, p < 0.01) had a positive impact on patient satisfaction. In addition, the depth of the physician-patient interaction partially mediated this effect. This study promotes a better understanding of the mechanisms of physician-patient interactions in online settings and has important implications for how online physicians and platforms can better provide online healthcare services.
An Intelligent Conversational Agent for the Legal Domain

An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user’s query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&amp;A) retrieval is to create labelled Q&amp;A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it.
REEGAT: RoBERTa Entity Embedding and Graph Attention Networks Enhanced Sentence Representation for Relation Extraction

Relation extraction is one of the most important intelligent information extraction technologies, which can be used to construct and optimize services in intelligent communication systems (ICS). One issue with the existing relation extraction approaches is that they use one-sided sentence embedding as their final prediction vector, which degrades relation extraction performance. The innovative relation extraction model REEGAT (RoBERTa Entity Embedding and Graph Attention networks enhanced sentence representation) that we present in this paper, incorporates the concept of enhanced word embedding from graph neural networks. The model first uses RoBERTa to obtain word embedding and PyTorch embedding to obtain relation embedding. Then, the multi-headed attention mechanism in GAT (graph attention network) is introduced to weight the word embedding and relation embedding to enrich further the meaning conveyed by the word embedding. Finally, the entity embedding component is used to obtain sentence representation by pooling the word embedding from GAT and the entity embedding from named entity recognition. The weighted and pooled word embedding contains more relational information to alleviate the one-sided problem of sentence representation. The experimental findings demonstrate that our model outperforms other standard methods.
SimoSet: A 3D Object Detection Dataset Collected from Vehicle Hybrid Solid-State LiDAR

Three-dimensional (3D) object detection based on point cloud data plays a critical role in the perception system of autonomous driving. However, this task presents a significant challenge in terms of its practical implementation due to the absence of point cloud data from automotive-grade hybrid solid-state LiDAR, as well as the limitations regarding the generalization ability of data-driven deep learning methods. In this paper, we introduce SimoSet, the first vehicle view 3D object detection dataset composed of automotive-grade hybrid solid-state LiDAR data. The dataset was collected from a university campus, contains 52 scenes, each of which are 8 s long, and provides three types of labels for typical traffic participants. We analyze the impact of the installation height and angle of the LiDAR on scanning effect and provide a reference process for the collection, annotation, and format conversion of LiDAR data. Finally, we provide baselines for LiDAR-only 3D object detection.
Designing a Leveled Conversational Teachable Agent for English Language Learners

Given the considerable importance of the English language as a common method of global communication, supporting and engaging learning environments for studying English as a second language are essential. In this study, we propose an interactive agent that considers differences in learners’ skill levels to help motivate learners and support independent study. The proposed agent is an AI chatbot that can vary the complexity of its natural language interactions as learners’ skills improve and engage in unstructured conversations with users. We first collected a dataset of English conversations among children and then acquired data on learners’ utterances of words and sentences using an interactive interface for skill evaluation. Based on these datasets, we generated a set of personalized conversational agents by using algorithms to evaluate speakers’ pronunciation and sentences. Finally, an expert in English language learning evaluated the interactive system to ensure that it could support learners with different levels of proficiency and that the application may be expected to help motivate students to learn. The results showed that the interactive teachable agent could help motivate students who may not otherwise be interested in learning by using a more personalized approach tailored to their skill level.
Wart-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model

Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve the interpretability of the model training process, an effective heuristic algorithm, i.e., the covariance matrix adaptation evolution strategy (CMA-ES), is incorporated as the training method of the dendritic neuron model. Two common datasets of wart-treatment efficacy, i.e., the cryotherapy dataset and the immunotherapy dataset, are used to verify the effectiveness of the proposed method. The proposed CMA-ES-based dendritic neuron model achieves promising results, with average classification accuracies of 0.9012 and 0.8654 on the two datasets, respectively. The experimental results indicate that the proposed method achieves better or more competitive prediction results than six common machine learning models. In addition, the trained dendritic neuron model can be simplified using a dendritic pruning mechanism. Finally, an effective wart-treatment efficacy prediction method based on a dendritic neuron model, which can provide decision support for physicians, is proposed in this paper.
Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing

Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, there are still deficiencies with these approaches, such as obvious artificial recovery traces and the distortion of image processing results. This paper proposes a novel enhanced CycleGAN network with an adaptive dark channel prior for unpaired single-image dehazing. First, a Wave-Vit semantic segmentation model is utilized to achieve the adaption of the dark channel prior (DCP) to accurately recover the transmittance and atmospheric light. Then, the scattering coefficient derived from both physical calculations and random sampling means is utilized to optimize the rehazing process. Bridged by the atmospheric scattering model, the dehazing/rehazing cycle branches are successfully combined to form an enhanced CycleGAN framework. Finally, experiments are conducted on reference/no-reference datasets. The proposed model achieved an SSIM of 94.9% and a PSNR of 26.95 on the SOTS-outdoor dataset and obtained an SSIM of 84.71% and a PSNR of 22.72 on the O-HAZE dataset. The proposed model significantly outperforms typical existing algorithms in both objective quantitative evaluation and subjective visual effect.
Label embedding asymmetric discrete hashing for efficient cross-modal retrieval

Given the exponential growth of multimedia data, how to swiftly and accurately retrieve information has grown in popularity. Among retrieval techniques, supervised hashing stands out due to its low memory footprint and relatively precise accuracy. Prior theoretical studies often inserted high-order tags into binary code learning, treating them as independent entities. Nevertheless, such approaches frequently neglect the latent category correlations revealed by the label information. Additionally, in terms of optimization, some algorithms employ a bit-by-bit scheme, leading to time-consuming, while others adopt a relaxation-based strategy, producing quantization inaccuracy. To address these issues, we formulate a novel, two-step hashing strategy, termed Label Embedding Asymmetric Discrete Hashing (LEADH). In this study, we provide an asymmetric technique to protect the discrete binary code constraints. Compared with the symmetric model, this method significantly reduces time consumption. In particular, a label-binary mutual mapping architecture is specifically recommended. This model can fully explore and utilize multi-label semantic information to provide better discriminative learned binary codes. Furthermore, to minimize quantization errors, an efficient and effective discrete optimization module based on augmented Lagrangian multipliers is elaborately designed. Extensive experimentation and theoretical study support our model's superiority. Compared to the sub-optimal method, our LEADH achieves an improvement of 2.6%, 1.8%, and 1.1% on Wiki, MIRFlickr, and NUS-WIDE, respectively.
A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning

The need to improve motion planning techniques for manipulator robots, and new effective strategies to manipulate different objects to perform more complex tasks, is crucial for various real-world applications where robots cooperate with humans. This paper proposes a novel framework that aims to improve the motion planning of a robotic agent (a manipulator robot) through semantic knowledge-based reasoning. The Semantic Web Rule Language (SWRL) was used to infer new knowledge based on the known environment and the robotic system. Ontological knowledge, e.g., semantic maps, were generated through a deep neural network, trained to detect and classify objects in the environment where the robotic agent performs. Manipulation constraints were deduced, and the environment corresponding to the agent's manipulation workspace was created so the planner could interpret it to generate a collision-free path. For reasoning with the ontology, different SPARQL queries were used. The proposed framework was implemented and validated in a real experimental setup, using the planning framework ROSPlan to perform the planning tasks. The proposed framework proved to be a promising strategy to improve motion planning of robotics systems, showing the benefits of artificial intelligence, for knowledge representation and reasoning in robotics.
Gamma error correction algorithm for phase shift profilometry based on polar angle average

Phase errors caused by the gamma nonlinear response of the phase shift profilometry (PSP) severely affect 3D measurement accuracy. To tackle this, a simple phase error compensation technique based on polar angle average is presented in this paper to improve the accuracy. Wrapped phase values are regarded as polar angles, and a polar axis distribution map can be obtained in the polar coordinate. Practically, the polar axis distribution (PAD) of the undistorted wrapped phase is uniform. When there are nonlinear effects in an PSP, the polar axis is unevenly distributed. The difference between adjacent polar angles is computed and then rearranged with specifically labels. After that, the even PAD diagram is generated, and the smooth phase can be further recovered. Compared with conventional nonlinearity correction methods, the proposed method corrects the error without any auxiliary conditions. Its effectiveness and robustness are verified in simulations and experiments.
Optimal Security Notion for Decentralized Multi-Client Functional Encryption

Research on (Decentralized) Multi-Client Functional Encryption (or (D)MCFE) is very active, with interesting constructions, especially for the class of inner products. However, the security notions have been evolving over the time. While the target of the adversary in distinguishing ciphertexts is clear, legitimate scenarios that do not consist of trivial attacks on the functionality are less obvious. In this paper, we wonder whether only trivial attacks are excluded from previous security games. And, unfortunately, this was not the case. We then propose a stronger security notion, with a large definition of admissible attacks, and prove it is optimal: any extension of the set of admissible attacks is actually a trivial attack on the functionality, and not against the specific scheme. In addition, we show that all the previous constructions are insecure w.r.t. this new security notion. Eventually, we propose new DMCFE schemes for the class of inner products that provide the new features and achieve this stronger security notion.
Subversion-Resilient Authenticated Encryption Without Random Oracles

In 2013, the Snowden revelations have shown subversion of cryptographic implementations to be a relevant threat. Since then, the academic community has been pushing the development of models and constructions to defend against adversaries able to arbitrarily subvert cryptographic implementations. To capture these strong capabilities of adversaries, Russell, Tang, Yung, and Zhou (CCS’17) proposed CPA-secure encryption in a model that utilizes a trusted party called a watchdog testing an implementation before use to detect potential subversion. This model was used to construct subversion-resilient implementations of primitives such as random oracles by Russell, Tang, Yung, and Zhou (CRYPTO’18) or signature schemes by Chow et al. (PKC’19) but primitives aiming for a CCA-like security remained elusive in any watchdog model. In this work, we present the first subversion-resilient authenticated encryption scheme with associated data (AEAD) without making use of random oracles. At the core of our construction are subversion-resilient PRFs, which we obtain from weak PRFs in combination with the classical Naor–Reingold transformation. We revisit classical constructions based on PRFs to obtain subversion-resilient MACs, where both tagging and verification are subject to subversion, as well as subversion-resilient symmetric encryption in the form of stream ciphers. Finally, we observe that leveraging the classical Encrypt-then-MAC approach yields subversion-resilient AEAD. Our results are based on the trusted amalgamation model by Russell, Tang, Yung, and Zhou (ASIACRYPT’16) and the assumption of honest key generation.
A Forkcipher-Based Pseudo-Random Number Generator

Good randomness is needed for most cryptographic applications. In practice pseudo-random number generators (PRNGs) are employed. $$\texttt {CTR\_DRBG}$$ is a popular choice and among the recommended PRNGs by NIST. It is defined for use with primitives like AES or TDEA, which are not always suited for lightweight applications. In this work we propose $$\texttt{FCRNG}$$ , a new PRNG, similar to $$\texttt {CTR\_DRBG}$$ , that is optimized for the lightweight setting (e.g. the Internet of Things). Our $$\texttt{FCRNG}$$ construction utilizes the expanding and tweakable forkcipher primitive instantiated with $$\textsf{ForkSkinny}$$ , which was introduced by Andreeva et al. at ASIACRYPT 2019. $$\texttt{FCRNG}$$ employs internally a forkcipher-based counter-style mode $$\mathsf {\texttt {FCTR}}$$ . We propose two $$\mathsf {\texttt {FCTR}}$$ variants: $$\texttt {FCTR\hbox {-}c}$$ for optimized speed and $$\texttt {FCTR\hbox {-}T}$$ for optimized security. We then show that $$\texttt{FCRNG}$$ with $$\textsf{ForkSkinny}$$ can be 33% faster than $$\texttt {CTR\_DRBG}$$ when instantiated with the AES blockcipher. $$\texttt{FCRNG}$$ achieves also a better security bound in the robustness security game - first introduced by Dodis et al. at CCS’13 and now the standard security goal for PRNGs. Contrary to the CRYPTO 2020 security bound by Hoang and Shen established for $$\texttt {CTR\_DRBG}$$ , the security of our construction with $$\texttt {FCTR\hbox {-}T}$$ does not degrade with the length of the random inputs, nor the amount of requested output pseudorandom bits. $$\texttt{FCRNG}$$ passes all tests of the NIST test suite for pseudorandom number generators.
Recommendation for a Holistic Secure Embedded ISA Extension

Embedded systems are a cornerstone of the ongoing digitization of our society, ranging from expanding markets around IoT and smart-X devices over to sensors in autonomous driving, medical equipment or critical infrastructures. Since a vast amount of embedded systems are safety-critical (e.g., due to their operation site), security is a necessity for their operation. However, unlike mobile, desktop, and server systems, where adversaries typically only act have remote access, embedded systems typically face attackers with physical access. Thus embedded system require an additional set of defense techniques, preferably leveraging hardware acceleration to minimize the impact on their stringent operation constraints. Over the last decade numerous defenses have been explored, however, they have often been analyzed in isolation. In this work, we first systematically analyze the state of the art in defenses for both software exploitation and fault attacks on embedded systems. We then carefully design a holistic instruction set extension to augment the RISC-V instruction set architecture with instructions to deter against th e threats analyzed in this work. Moreover we implement our design using the gem5 simulator system and a binary translation approach to arm software with our instruction set extension. Finally, we evaluate performance overhead on the MiBench2 benchmark suite. Our evaluation demonstrates a ROM overhead increase of 20% to defeat the aforementioned attacks.
A Deep Ensemble Network for Sentiment Analysis in Bi-Lingual Low-Resource Languages

Sentiment analysis (SA) is the systematic identification, extraction, quantification, and study of affective states and subjective information using natural language processing. It is widely used for analysing users’ feedback, such as reviews or social posts. Recently, SA has been one of the favourite research domains in NLP due to their wide range of applications, including E-commerce, healthcare, hotel business and others. Many machine learning and deep learning-based models exist to predict the sentiment of the user’s post. However, the sentiment analysis in low-resource languages like Kannada, Malayalam, Telugu and Tamils received very fewer attention due to language complexity and the low availability of required resources. This research fills the gap by proposing an ensemble model for predicting the sentiment of code-mixed Kannada and Malayalam languages. The ensemble of transformer based models achieved a promising weighted F 1 -score of 0.66 for Kannada code-mixed language. In contrast, the ensemble model of the deep learning framework performed best by achieving a weighted F 1 -score of 0.72 for the Malayalam dataset, outperforming existing research.
Privacy Preserving Data Analysis with the Encode, Shuffle, Analyse Architecture in Vehicular Data Sharing

In recent years, vehicles have become smarter, with more data being collected and analyzed. With further digitalisation, the importance of data within and around vehicles is only going to increase, allowing stakeholders to generate new and more insights from that data. With this, the threat of privacy invasion and the de-identification of vehicles and vehicle users becomes more pressing. In this work, we implement a prototype of a variant of distributed differential privacy (DP), called Encode, Shuffle, and Analyse (ESA) architecture, on a real-world vehicular dataset for vehicle energy consumption analysis. An analysis for energy consumption standard statistics and a basic neural network prediction model are set up to elaborate the utility, privacy trade-off. The results are then compared to the same analysis in a non-private setting. This work contributes to the basis of knowledge by providing a prototype of the ESA architecture in vehicle data analysis for energy demand prediction in urban areas. The results identify important parameters to balance privacy and utility and provides actionable insights for regulators and vehicle manufacturers to preserve vehicle privacy. Future work should further investigate possible attack scenarios to achieve a higher understanding of the privacy guarantees of this architecture.