Miracle-dz/UniFAR-FTUs huggingface dataset:
A deep transfer‐learning‐based dynamic reinforcement learning for intelligent tightening system

Reinforcement learning (RL) has been widely applied in the static environment with standard reward functions. For intelligent tightening tasks, it is a challenge to transform expert knowledge into a recognizable mathematical expression for RL agents. Changing assembly standards make the model repeat learning updated knowledge with a high time‐cost. In addition, as the difficulty and low accuracy of designing reward functions, the RL model itself also limits its application in the complex and dynamic engineering environment. To solve the above problems, a deep transfer‐learning‐based dynamic reinforcement learning (DRL‐DTL) is presented and applied in the intelligent tightening system. Specifically, a deep convolution transfer‐learning model (DCTL) is presented to build a mathematical mapping between agents of the model and subjective knowledge, which endows agents to learn from human knowledge efficiently. Then, a dynamic expert library is established to improve the adaptability of algorithm to the changing environment. And an inverse RL based on prior knowledge is presented to acquire reward functions. Experiments are conducted on a tightening assembly system and the results show that the tightening robot with the proposed model can inspect quality problems during the tightening process autonomously and make an adjustment decision based on the optimal policy that the agent calculates.
Computational thinking: Enhancing STEAM and engineering education, from theory to practice

This publication provides an editorial introduction to a special issue of Computer Applications in Engineering Education journal entitled Computational Thinking for STEM and Engineering Education. It provides introductory remarks to the issue and a commentary on the published material, as well as presents the views of the special issue editors in the field of science, technology, engineering and mathematics (STEM) education and research. This special issue is structured as follows. It contains articles related to the topics of computational thinking (CT), STEM, and engineering education. It starts with three overview articles based on systematic reviews, giving a structured overview of CT concepts in terms of practical educational approaches in introductory science, technology, engineering, arts and mathematics (STEAM) education. Subsequent articles present practical approaches to CT and STEAM education in such educational areas as general engineering, programming and software engineering, physical computing, robotics, electronics, mechanical, and power engineering.
Examining the Effects of Media-Generated Stereotypes on Receivers' Trust and Attitude in Pakistan. Moderating Influence of Ethnicity and Gender

There is a long-standing debate about the effects of media-generated stereotypes on receivers’ trust and attitude. However, there is insufficient consensus about their influence on the media receiver’s ecological perspective in determining their extent of trust and attitudes. Drawing an analogy from Differential Susceptibility to Media Effect Model (hereafter DSMM) notion that media effects are conditional and are contingent on differential-susceptibility, this study examines the influence of dispositional and social susceptibility to media. To do so, the study validates the influence of media user’s gender (dispositional susceptibility) and ethnicity (social susceptibility) in determining the outcomes of media-generated stereotypes, media trust (MT), and attitude towards media organization (AO). The survey method has been employed to collect data through a self-administered questionnaire from 1061 university students in public sector institutions in Pakistan. The results provide empirical evidence that media-generated stereotypes are a substantially negative predictor of media trust and attitudes towards the media organization. The results also validate that the influence of the stereotyping manifested by the receiver’s ecological perspective such as ethnicity and gender are crucial determinants of the receiver’s trust and attitudes. Managerially, the study urges that journalistic practices must be more ethnoculturally inclusive, to cope with the contemporary media landscape.
Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis

Deep learning represents a powerful set of techniques for profiling sidechannel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various countermeasures. Considering that deep learning techniques commonly have a plethora of hyperparameters to tune, it is clear that such top attack results can come with a high price in preparing the attack. This is especially problematic as the side-channel community commonly uses random search or grid search techniques to look for the best hyperparameters.In this paper, we propose to use reinforcement learning to tune the convolutional neural network hyperparameters. In our framework, we investigate the Q-Learning paradigm and develop two reward functions that use side-channel metrics. We mount an investigation on three commonly used datasets and two leakage models where the results show that reinforcement learning can find convolutional neural networks exhibiting top performance while having small numbers of trainable parameters. We note that our approach is automated and can be easily adapted to different datasets. Several of our newly developed architectures outperform the current state-of-the-art results. Finally, we make our source code publicly available. https://github.com/AISyLab/Reinforcement-Learning-for-SCA
Arrangement of band structure for organic-inorganic photovoltaics embedded with silicon nanowire arrays grown on indium tin oxide glass

Silicon nanowires (SiNWs) arrays grown vertically on an indium tin oxide glass substrate by the vapor liquid solid process are covered with poly (3-hexylthiophene) (P3HT) mixed with [6,6]-phenyl-C60-butyric acid methyl ester (PCBM) to make into a hybrid solar cell. With proper control of the nanowire size and arrangement of the band structure suitable for charge carrier transport, the P3HT/SiNWs solar cell can have a much better energy conversion efficiency than the P3HT+PCBM solar cell. Poor band structure arrangement can lead to band barrier and enhanced electron-hole pair recombination and much lower efficiency even with more light absorption.
SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process

The Social Internet of Things (SIoT) means that every node can use a set of nodes that are considered as friends to search for a specific service. However, this is a slow process because each node is required to manage a high number of friends. Thus, the SIoT issue consists of how to select the right friends that improve the network navigability. The enhancement of the network navigability boosts the search for a service to be rapid but not guaranteed. Furthermore, sending requests from the shortest paths involves the rapid search, but the network lifetime can be reduced due to the number of requests that can be transmitted and processed by the nodes that have low power energy. This paper proposes a new approach that improves the network navigability, speeds up the search process, and increases the network lifetime. This approach aims at creating groups dynamically by nodes where each group has a master node, second, using a consensus algorithm between master nodes to agree with a specific capability, finally adopting a friendship selection method to create a social network. Thus, the friends will be sorted periodically for the objective of creating simultaneously a balance between the energy consumption and the rapid search process. Simulation results on the Brightkite location-based online social network dataset demonstrate that our proposal outperforms baseline methods in terms of some parameters of network navigability, path length to reach the providers, and network lifetime.
A New and Improved Reduction Proof of Cascade PRF

The preﬁx-free security of a cascade function based on a c -bit compression function f is reduced to the q -query PRF security of f with a tightness gap (cid:96)q where q represents the maximum number of queries to the cascade and (cid:96) represents the length of the longest query. A two-stage proof for this reduction was ﬁrst given by Bellare et al. in FOCS-96 for an adaptive distinguisher, and later a similar two-stage reduction was proved in CRYPTO-14 by Gazi et al. for a non-adaptive distinguisher. In this paper we prove a direct single-stage reduction with a tightness gap of σ (the total length of all queries). This is an improvement over existing reductions whenever the lengths of queries vary widely. In the case of non-adaptive preﬁx-free security, we also show a reduction proof which reduces PRF advantage of the cascade to two terms – (i) a q -query PRF security of f with a tightness gap of q (without a factor of (cid:96) ) and (ii) a single query PRF security of f with a tightness gap of σ . We further extend to a more general ﬁner reduction to multiple terms over diﬀerent limits on the queries to f . All these reductions can be easily extended to a multiuser setup . In particular, we reduce multiuser preﬁx-free PRF security of the cascade to a single user q max -query PRF security of f with a tightness gap σ (the total length of all queries to all users), where q max is the maximum number of queries allowed to any user. We have shown similar improved bounds (with respect to query complexity) for non-adaptive multiuser PRF security. In addition to immediate applications to multiuser security of HMAC and NMAC , our improved analysis has the following useful applications:
Estimating α-Rank by Maximizing Information Gain

Game theory has been increasingly applied in settings where the game is not known outright, but has to be estimated by sampling. For example, meta-games that arise in multi-agent evaluation can only be accessed by running a succession of expensive experiments that may involve simultaneous deployment of several agents. In this paper, we focus on α-rank, a popular game-theoretic solution concept designed to perform well in such scenarios. We aim to estimate the α-rank of the game using as few samples as possible. Our algorithm maximizes information gain between an epistemic belief over the α-ranks and the observed payoff. This approach has two main benefits. First, it allows us to focus our sampling on the entries that matter the most for identifying the α-rank. Second, the Bayesian formulation provides a facility to build in modeling assumptions by using a prior over game payoffs. We show the benefits of using information gain as compared to the confidence interval criterion of ResponseGraphUCB, and provide theoretical results justifying our method.
Malicious Data Frame Injection Attack Without Seizing Association in IEEE 802.11 Wireless LANs

In the infrastructure mode of IEEE 802.11 wireless local area networks (LANs), an access point (AP) provides wireless networking to multiple wireless client nodes in the shared medium. Client nodes that have established an association with an AP fully trust the AP, and exchange data packets with other nodes on the Internet through the associated AP. If the AP has malicious intent or an attacker deprives the AP, the associated nodes are exposed to the potential danger of security attacks such as a denial of service, data traffic sniffing and spoofing, and malware attacks. These association extortion based attacks can be detected by monitoring frames at the medium access control (MAC) layer. In this article, we propose a malicious-frame-injection based attack without seizing the association between an AP and client nodes. The proposed attack performs wireless jamming, MAC frame sniffing, and spoofing successively in the shared wireless medium. We have implemented the proposed attack using software-defined radio (SDR) on a real-world experimental testbed with off-the-shelf nodes and performed the attack on hypertext transfer protocol (HTTP) communication using transmission control protocol (TCP) transport protocol to demonstrate its wireless LAN security risk.
Thermal conductivities of amorphous polymers

Although at the present time no precise agreement between theory and experiment exists for amorphous polymers, as might be expected on the limited understanding of the liquid state, nevertheless certain trends in the thermal conductivity of these materials may be predicted with some confidence. This paper considers the influence of two factors, temperature and polymer density, with a view to providing guidance to engineers concerned with the manipulation and use of amorphous polymers.
Integration of GNSS and BLE Technology With Inertial Sensors for Real-Time Positioning in Urban Environments

The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.
Radiation damage to proteins: an electron paramagnetic resonance study

Exposure of a wide range of proteins to ionizing radiation at low temperatures has led to the following generalizations: (i) Electrons are ejected indiscriminately from any part of the protein. The residual radical cations largely become localized on the backbone amide units, and are trapped thereon by the N–H proton loss. (ii) These centres are characterized by their 14N hyperfine coupling constants and by coupling to the unique C–H proton. This varies in a manner that is characteristic of α-helical regions and β-sheet regions of the protein. (iii) The ejected electrons, in contrast, are very mobile through the protein until they reach good electron traps, such as transition metal ions or disulfide linkages.
Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.
Pairwise Coarse Registration of Indoor Point Clouds Using 2D Line Features

Registration is essential for terrestrial LiDAR (light detection and ranging) scanning point clouds. The registration of indoor point clouds is especially challenging due to the occlusion and self-similarity of indoor structures. This paper proposes a 4 degrees of freedom (4DOF) coarse registration method that fully takes advantage of the knowledge that the equipment is levelled or the inclination compensated for by a tilt sensor in data acquisition. The method decomposes the 4DOF registration problem into two parts: (1) horizontal alignment using ortho-projected images and (2) vertical alignment. The ortho-projected images are generated using points between the floor and ceiling, and the horizontal alignment is achieved by the matching of the source and target ortho-projected images using the 2D line features detected from them. The vertical alignment is achieved by making the height of the floor and ceiling in the source and target points equivalent. Two datasets, one with five stations and the other with 20 stations, were used to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved 80% and 63% successful registration rates (SRRs) in a simple scene and a challenging scene, respectively. The SRR in the simple scene is only lower than that of the keypoint-based four-point congruent set (K4PCS) method. The SRR in the challenging scene is better than all five comparison methods. Even though the proposed method still has some limitations, the proposed method provides an alternative to solve the indoor point cloud registration problem.
OntoConnect: results for OAEI 2020

The results of OntoConnect, an Ontology alignment system, in the Ontology Alignment Evaluation Initiative (OAEI) 2020 campaign is reported in this paper. OntoConnect is a domain-independent schema alignment system that combines syntactic similarity and structural similarity between classes/concepts to align the classes/concepts from the source and target ontologies. This paper describes the participation of OntoConnect at OAEI 2020 and discusses its methodology and results on the Anatomy dataset.
Privacy-Preserving Privacy Profile Proposal Protocol

Many web-based and mobile applications and services allow users to indicate their preferences regarding whether and how their personal information can be used or reused by the application itself, by the service provider, and/or by third parties. The number of possible conﬁgurations that constitute a user’s preference proﬁle can be overwhelming to a typical user. This report describes a practical, privacy-preserving technique for reducing the burden users face when specifying their preferences by oﬀering users data-driven recommendations for fully-speciﬁed preference proﬁles based on their inputs for just a few settings. The feasibility of the approach is demonstrated by a browser-based prototype application that relies on secure multi-party computation and uses the web-compatible JIFF library as the backbone for managing communications between the client application and the recommendation service. The principal algorithms used for generating proposed preference proﬁles are k -means clustering (for privacy-preserving analysis of preference proﬁle data across multiple users) and k -nearest neighbors (for selecting a proposed preference proﬁle to recommend to the user).
Spike Neural Network Learning Algorithm Based on an Evolutionary Membrane Algorithm

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. Compared with the traditional neural network, the spiking neural network (SNN) has better biogenesis and stronger computing power. In this paper, an SNN learning model based on an evolutionary membrane algorithm is proposed to solve the problem of supervised classification. The proposed algorithm uses the P system’s object, reaction rules, and membrane structure to solve these problems. Specifically, the proposed algorithm can automatically adjust the learning parameters of the network by adjusting the synaptic weight in the learning stage of the spiking neural model according to different application data, providing a better solution model for balance exploration and exploitation. In the simulation experiment, effectiveness verification research is carried out. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving twelve supervised classification benchmark problems through learning curves and quantified classification results.
Stock market decision support modeling with tree-based AdaBoost ensemble machine learning models

Forecasting stock market behavior has received tremendous attention from investors and researchers for a very long time due to its potential profitability. Predicting stock market behavior is regarded as one of the extremely challenging applications of time series forecasting. While there is divided opinion on the efficiency of markets, numerous empirical studies which are widely accepted have shown that the stock market is predictable to some extent. Statistical based methods and machine learning models are used to forecast and analyze the stock market. Machine learning (ML) models typically perform better than those of statistical and econometric models. In addition, performance of ensemble ML models is typically superior to those of individual ML models. In this paper, we study and compare the efficiency of treebased ensemble ML models (namely, Bagging classifier, Random Forest (RF), Extra trees classifier (ET), AdaBoost of Bagging (ADA_of_BAG), AdaBoost of RandomForest (ADA_of_RF), and AdaBoost of ExtraTrees (ADA_of_ET)). Stock data randomly collected from three different stock exchanges were used for the study. Forty technical indicators were computed and used as input features. The data set was spilt into training and test sets. The performance of the models was evaluated with the test set using accuracy, precision, recall, F1-score, specificity and AUC metrics. Kendall W test of concordance was used to rank the performance of the different models. The experimental results indicated that AdaBoost of Bagging (ADA_of_BAG) model was the highest performer among the tree-based ensemble models studied. Also, boosting of the bagging ensemble models improved the performance of the bagging ensemble models.