Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of the Glass Transition Temperature Gradient on the Nonlinear Viscoelastic Behavior in Reinforced Elastomers

We analyze the influence of a glass transition gradient near the particle surfaces on the nonlinear mechanical behavior of reinforced elastomers. We studied systems consisting of grafted silica particles dispersed in a cross-linked poly(ethyl acrylate) matrix. Both particle/matrix interfaces and dispersion states of the particles have been precisely characterized. On the basis of previous studies on the same systems evidencing a glass transition gradient around the particles, we show that the precocious nonlinear mechanical behavior of these filled systems is related to the strain-softening of the glassy polymer shell surrounding the particles surfaces. Comparing strain-softening and melting of the glassy polymer shell, we observe that the strain-softening is enhanced by strong local stress amplifications.
Co-designing Socially Assistive Sidekicks for Motion-based AAC

Augmentative and alternative communication (AAC) devices enable speech-based communication. However, AAC devices do not support nonverbal communication, which allows people to take turns, regulate conversation dynamics, and express intentions. Nonverbal communication requires motion, which is often challenging for AAC users to produce due to motor constraints. In this work, we explore how socially assistive robots, framed as “sidekicks,” might provide augmented communicators (ACs) with a nonverbal channel of communication to support their conversational goals. We developed and conducted an accessible co-design workshop that involved two ACs, their caregivers, and three motion experts. We identified goals for conversational support, co-designed prototypes depicting possible sidekick forms, and enacted different sidekick motions and behaviors to achieve speakers’ goals. We contribute guidelines for designing sidekicks that support ACs according to three key parameters: attention, precision, and timing. We show how these parameters manifest in appearance and behavior and how they can guide future designs for augmented nonverbal communication.
Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction

Air pollution is an important environmental issue of increasing concern, which impacts human health. Accurate air quality prediction is crucial for avoiding people suffering from serious air pollution. Most of the prior works focus on capturing the temporal trend of air quality for each monitoring station. Recent deep learning based methods also model spatial dependencies among neighboring stations. However, we observe that besides geospatially adjacent stations, the stations which share similar functionalities or consistent temporal patterns could also have strong dependencies. In this paper, we propose an Attentive Temporal Graph Convolutional Network (ATGCN) to model diverse inter-station relationships for air quality prediction of citywide stations. Specifically, we first encode three types of relationships among stations including spatial adjacency, functional similarity, and temporal pattern similarity into graphs. Then we design parallel encoding modules, which respectively incorporate attentive graph convolution operations into the Gated Recurrent Units (GRUs) to iteratively aggregate features from related stations with different graphs. Furthermore, augmented with an attention-based fusion unit, decoding modules with a similar structure to the encoding modules are designed to generate multi-step predictions for all stations. The experiments on two real-world datasets demonstrate the superior performance of our model beyond state-of-the-art methods.
Diverse User Preference Elicitation with Multi-Armed Bandits

Personalized recommender systems rely on knowledge of user preferences to produce recommendations. While those preferences are often obtained from past user interactions with the recommendation catalog, in some situations such observations are insufficient or unavailable. The most widely studied case is with new users, although other similar situations arise where explicit preference elicitation is valuable. At the same time, a seemingly disparate challenge is that there is a well-known popularity bias in many algorithmic approaches to recommender systems. The most common way of addressing this challenge is diversification, which tends to be applied to the output of a recommender algorithm, prior to items being presented to users. We tie these two problems together, showing a tight relationship. Our results show that popularity bias in preference elicitation contributes to popularity bias in recommendation. In particular, most elicitation methods directly optimize only for the relevance of recommendations that would result from collected preferences. This focus on recommendation accuracy biases the preferences collected. We demonstrate how diversification can instead be applied directly at elicitation time. Our model diversifies the preferences elicited using Multi-Armed Bandits, a classical exploration-exploitation framework from reinforcement learning. This leads to a broader understanding of users' preferences, and improved diversity and serendipity of recommendations, without necessitating post-hoc debiasing corrections.
Deconstructed Trustee Theory: Disentangling Trust in Body and Identity in Multi-Robot Distributed Systems

This paper introduces and justifies (through an n=210 online human subject study) Deconstructed Trustee Theory, a theory of human robot trust that factors the representation of trustee into robot body and robot identity in order to differentially model perceived trustworthiness of robot body and identity. This theory predicts (a) that different levels of trustworthiness can be attributed to a robot body and a robot identity, (b) that divergence between levels of perceived trustworthiness of body and identity may be effected by communication policies that reveal the potential for phenomena such as re-embodiment, co-embodiment, and agent migration in multi-robot systems, and (c) that perceived trustworthiness of body and identity may further diverge and be refined through moral cognitive processes triggered on observation of blameworthy actions. CCS CONCEPTS • Human-centered computing $\rightarrow$User studies; Natural language interfaces; • Computer systems organization $\rightarrow$Robotics;
“I think you are doing a bad job!” : The Effect of Blame Attribution by a Robot in Human-Robot Collaboration

Robots will increasingly collaborate with human partners necessitating research into how robots negotiate negative collaborative outcomes. This study investigates the effect of blame attribution on trust assessments in human-robot collaboration. Participants (n=60) collaboratively played a game with a humanoid robot in one of four conditions in a 2 (blame correctness: correct vs. incorrect) by 2 (blame target: human vs. robot) between-subjects experiment. Results show that people evaluate a robot more positively when it blames itself for collaborative failures, especially, it seems, in the case of incorrect self-blame. Our findings indicate a need to further research on effective communication strategies for robots that need to negotiate collaborative failures without compromising the trust relationships with its human partner. CCS CONCEPTS • Human-centered computing →Empirical studies in collaborative and social computing;• Computer systems organization →Robotics.
Enabling Sector Scheduling for 5G-CPE Dense Networks

5G customer premise equipment (5G-CPE) is an IoT gateway technology that integrates 5G and Wi-Fi and therefore can provide Wi-Fi connection for IoT devices and meanwhile benefit from the advantages of 5G. With the increasing number of IoT devices, transmission collisions and hidden/exposed terminal problems on the Wi-Fi connection side become more and more serious. Conventional mechanisms cannot solve these problems well. In this paper, we propose a Wi-Fi sector (Wi-FiS) design, which is compatible with Wi-Fi, to solve them fundamentally. Wi-FiS divides the whole coverage area of Wi-Fi into multiple sectors and utilizes beamforming technology and sector-based scheduling to improve system performance of Wi-Fi dense networks. For a single-cell network, Wi-FiS differentiates uplink and downlink operations and totally excludes collision in downlink. For a multicell network, Wi-FiS can avoid hidden and exposed terminal problems, while enabling parallel transmissions among multiple cells. We then develop a theoretical model to analyze Wi-FiS’s throughput. Extensive simulations verify that our theoretical model is very accurate and Wi-FiS can improve system throughput of Wi-Fi dense networks significantly.
Output Feedback NCS of DoS Attacks Triggered by Double-Ended Events

In recent years, the research of the network control system under the event triggering mechanism subjected to network attacks has attracted foreign and domestic scholars’ wide attention. Among all kinds of network attacks, denial-of-service (DoS) attack is considered the most likely to impact the performance of NCS significantly. +e existing results on event triggering do not assess the occurrence of DoS attacks and controller changes, which will reduce the control performance of the addressed system. Aiming at the network control system attacked by DoS, this paper combines double-ended elastic event trigger control, DoS attack, and quantitative feedback control to study the stability of NCS with quantitative feedback of DoS attack triggered by a double-ended elastic event. Simulation examples show that this method can meet the requirements of control performance and counteract the known periodic DoS attacks, which save limited resources and improve the system’s antijamming ability.
Low Rank Correlation Representation and Clustering

Correlation learning is a technique utilized to find a common representation in cross-domain and multiview datasets. However, most existing methods are not robust enough to handle noisy data. As such, the common representation matrix learned could be influenced easily by noisy samples inherent in different instances of the data. In this paper, we propose a novel correlation learning method based on a low-rank representation, which learns a common representation between two instances of data in a latent subspace. Specifically, we begin by learning a low-rank representation matrix and an orthogonal rotation matrix to handle the noisy samples in one instance of the data so that a second instance of the data can linearly reconstruct the low-rank representation. Our method then finds a similarity matrix that approximates the common low-rank representationmatrix much better such that a rank constraint on the Laplacian matrix would reveal the clustering structure explicitly without any spectral postprocessing. Extensive experimental results on ORL, Yale, Coil-20, Caltech 101-20, and UCI digits datasets demonstrate that our method has superior performance than other state-of-the-art compared methods in six evaluation metrics.
Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.
Anticoncept Drift Method for Malware Detector Based on Generative Adversarial Network

The number of new malware has been increasing year by year, and the construction of the malware sample space is also changing with time. The existing research studies on malware detection mainly focus on how to improve detection performance and how to effectively detect the evasion malware and improve the detection performance of adversarial samples, while ignoring the concept drift of malware samples over time. The concept drift of the sample will lead to the aging of the detector model, thus resulting in the reduction of the detection accuracy. Concerning this problem, we proposed a malware sample generator based on auxiliary classifier GAN, according to the malware samples generated, to train the detection model. In this paper, the API call sequence is used as a feature to train the improved generative adversarial network, and the trained generator model is used to generate samples that simulate concept drift for the purpose of training detection models. Meanwhile, using the detection results of the detector as the training set again, the generator is used to generate samples, so as to repeatedly train the detection model and improve the anticoncept drift performance of the monitoring model. In this paper, real malware samples and generated samples are used to train the detector model, and malware samples are segmented in a linear time sequence as test sets to verify the effectiveness of the proposed method. The results reveal that the framework can maintain good detection accuracy and effectively mitigate the aging of the detector in a longer time dimension.
Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.
A Novel Improved Reversible Visible Image Watermarking Algorithm Based on Grad-CAM and JND

With the rapid access convenience of content brought by 5G technology, the integrity protection of content becomes more important. (e reversible visible watermarking algorithm has attracted more attention due to its effective content protection. In this paper, a novel improved reversible visible image watermarking scheme based on gradient-weighted class activation mapping (Grad-CAM) and the just noticeable difference (JND) model has been presented. (e proposed region of interest (ROI) selection strategy is used to locate the main protected body of images for watermark embedding. Divide the watermark and ROI into nonoverlapping blocks in the same way and then embed the classified two types of watermark blocks into corresponding ROI blocks with the JND model. (e optimal bit positions for watermark embedding can be selected adaptively with JND threshold and achieve the tradeoff between the watermark visibility and watermarked image quality. For lossless image recovery and watermark extraction, the recovery information is reversibly hidden into watermarked image. In the experiments, the same process of grayscale images is used to each channel separately for color images watermarking. Besides, there are six aspects in this paper to estimate the proposed scheme; with the comparison to other reversible visible watermarking schemes, experimental results demonstrate the effectiveness of our proposed scheme.
Chilling Temperature Storage Changes the Inorganic Phosphate Pool Distribution in Cherimoya (Annona cherimola) Fruit

Phophorous nuclear magnetic resonance ( 31 P-NMR) spectroscopy was used to study the vacuolar and cytoplasmic pH and the inorganic phosphate (Pi) pool distribution in 'Fino de Jete' cherimoya ( Annona cherimola Mill.) fruit stored at a chilling temperature (6 oC). Fruit stored at the ripening temperature (20 oC) for 3 days were used as a control. 31 P-NMR results confirmed that 6 oC storage caused cytoplasmic acidosis (a decrease of 0.72 ± 0.08 pH units) and a notable increase in the amount of Pi in the cytoplasm. Spectra of perchloric acid extracts also revealed that storage at 6 oC was associated with an increase in the total amount of Pi and phosphorylated metabolites. Moreover, perfusion experiments with a phosphate medium confirmed the preferential accumulation of Pi in the cytoplasm in chilled tissues. Specific activation of phospho- enolpyruvate carboxylase (PEPC) (32.1 ± 1.7 µmol·min -1 ·mg -1
Energy Management Strategy Using Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

In this paper, an energy management strategy for electric vehicles equipped with fuel cell (FC), battery (BAT), and supercapacitor (SC) is considered, aiming at improving the whole performance under a framework of vehicle to network application. In detail, based on wavelet transform and equivalent consumption minimization strategy (ECMS), the demand power of vehicles is optimized to enhance the lifespan of fuel cell, fuel economy, and dynamic performance of electric vehicles. e wavelet transform is used to separate the high-frequency power in order to provide a peak power and recycle the braking energy. e equivalent consumption minimization strategy is used to distribute the low-frequency power to fuel cell and battery for minimizing the hydrogen consumption. Obtained results are studied using an advanced vehicle simulator, and its effectiveness of the strategy is confirmed, which provides a fundamental control method for the IOV application.
Image Encryption Scheme Based on Block Scrambling, Closed-Loop Diffusion, and DNA Molecular Mutation

A new image encryption scheme is proposed with a combination of block scrambling, closed-loop diffusion, and DNAmolecular mutation. -e new chaotic block scrambling mechanism is put forward to replace the traditional swapping rule by combining the rectangular-ambulatory-plane cyclic shift with the bidirectional random disorganization. -e closed-loop diffusion strategy is designed to form a feedback system, which improves the anti-interference capacity of the algorithm. To further destroy the blocks characteristics and eliminate the correlations among adjacent blocks, two efficient methods of DNA molecular mutation are adopted in the mutation stage. Moreover, the proposed algorithm possesses a large key space and the keys are highly related with the plaintext image. Experimental results demonstrate that the suggested image encryption strategy is practicable and has strong ability against a variety of common attacks.
Order-Revealing Encryption Scheme with Comparison Token for Cloud Computing

Order-preserving encryption (OPE) is a basic paradigm for the outsourced database where the order of plaintexts is kept in ciphertexts. OPE enables efficient order comparison execution while providing privacy protection. Unfortunately, almost all the previous OPE schemes either require numerous rounds of interactions or reveal more information about the encrypted database (e.g., the most significant bit). Order-revealing encryption (ORE) as a generalization is an encryption scheme where the order of plaintexts can be evaluated by running a comparison algorithm. )erefore, it is desirable to design an efficient ORE scheme which addresses above efficiency and security issues. In this paper, we propose a noninteractive ORE scheme from prefix encoding and Bloom filter techniques. )e proposed scheme is an encryption scheme where a cloud service provider cannot evaluate the order of plaintexts until a comparison token is provided. )e security analysis illustrates that our scheme achieves ideal security with frequency hiding. Furthermore, we illustrate a secure range query scheme through designing an encrypted tree structure named PORE tree from the above ORE scheme. )e PORE tree reveals the order between different nodes and leaves encrypted data items in the same node incomparable even after query execution. Finally, the experimental evaluation shows the high efficiency of the proposed ORE scheme and range query scheme.
Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.