Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Justice and Public Cooperation Intention: Mediating Role of Political Trust and Moderating Effect of Outcome Dependence

Cooperation is vital to human evolution and the development of society. In addition, social justice is one of humanity’s long pursuits. Based on social exchange theory and system justification theory, we built and tested a comprehensive mediated moderation model of the relationship between social justice and public cooperation intention via the mediation of political trust and with the moderation of outcome dependence. This research consisted of two studies using laboratory experiment (N = 320) and field survey (N = 1240) methods. Data were collected from participants located in China. The results showed that (1) both competence-based trust and motive-based trust mediated the relationship between social justice (i.e., distributive justice and procedural justice) and public cooperation intention; (2) outcome dependence moderated the relationship between social justice and public cooperation intention; and (3) the moderation of outcome dependence functioned through the mediating effect of competence-based and motive-based trust. The theoretical and practical significance of these findings is discussed.
Three-dimensional segmentation of cortical sulci using active models

We propose a method for the segmentation of cerebral sulci, representing them by surfaces. This method is based on the computation of the differential characteristics of MRI data. The computation of curvature information, using the Lvv operator, allows one to differentiate sulcal and gyral regions, resulting in a global detection of the cortical scheme. The analytical description of a particular sulcus is obtained by initializing an active model on its trace upon the brain surface. The result is a surface representing the buried part of the sulcus. The 'snake-spline' model allows one to define an algorithm which is simpler and more robust than the classical snake. This method of segmentation yields good results for the 3D segmentation and visualization of cortical sulci.
Alethea: A Provably Secure Random Sample Voting Protocol

In random sample voting, only a randomly chosen subset of all eligible voters are selected to vote. This poses new security challenges for the voting protocol used. In particular, one must ensure that the chosen voters were randomly selected while preserving their anonymity. Moreover, the small number of selected voters leaves little room for error and only a few manipulations of the votes may significantly change the outcome. We propose Alethea, the first random sample voting protocol that satisfies end-to-end verifiability and receipt-freeness. Our protocol makes explicit the distinction between human voters and their devices. This allows for more fine-grained statements about the required capabilities and trust assumptions of each agent than is possible in previous work. We define new security properties related to the randomness and anonymity of the sample group and the probability of undetected manipulations. We prove correctness of the protocol and its properties both using traditional paper and pen proofs and with tool support.
The temporal advantage for reloading vs. uploading conscious representations decays over time

Abstract In motion-induced blindness (MIB), a static target superimposed on a global moving pattern frequently disappears and reappears into consciousness. We previously reported an intriguing illusory temporal reversal whereby a new stimulus onset (e.g. a dot flash) presented during MIB triggers an early reappearance of the target, yet is systematically perceived as occurring after the target reappearance. This illusion implies that the unconscious target representation can be quickly reactivated, with a temporal advantage for its conscious reloading as compared to the conscious uploading of a newly presented visual stimulus. However, it remains unclear whether the temporal advantage for conscious representation reloading strengthens, decays, or remains constant over time after we lose the initial conscious access to the stimulus. To address this question, we examined the relation between the duration of MIB and the percentage of illusory temporal reversals, and we found a negative correlation between the two measures, both between and within observers. The results suggest that although the unconscious target representation retains a certain level of activation during MIB, the temporal advantage for reloading its preexisting representation into consciousness decays over time.
Contribution of Local Analysis Techniques for the Characterization of Iron and Alloying Elements in Nitrides: Consequences on the Precipitation Process in Fe–Si and Fe–Cr Nitrided Alloys

Atom Probe Tomography (APT), Transmission Electron Microscopy (TEM), and 3D mechanical calculations in complex geometry and anisotropic strain fields were employed to study the role of minor elements in the precipitation process of silicon and chromium nitrides in nitrided Fe–Si and Fe–Cr alloys, respectively. In nitrided Fe–Si alloys, an original sequence of Si3N4 precipitation was highlighted. Al–N clusters form first and act as nucleation sites for amorphous Si3N4 nitrides. This novel example of particle-simulated nucleation opens a new way to control Si3N4 precipitation in Fe–Si alloys. In nitrided Fe–Cr alloys, both the presence of iron in chromium nitrides and excess nitrogen in the ferritic matrix are unquestionably proved. Only a certain part of the so-called excess nitrogen is shown to be explained by the elastic accommodation of the misfit between nitride and the ferritic matrix. The presence of immobile excess nitrogen trapped at interfaces can be highly suspected.
A 60 GHz 360° Phase Shifter with 2.7° Phase Resolution and 1.4° RMS Phase Error in a 40-nm CMOS Technology

This paper presents a vector modulator based active phase shifter for the 56–65 GHz frequency band in a 40-nm digital CMOS technology. The design comprises a magnetically-enhanced quadrature hybrid with better than 1-degree phase imbalance and 18 dB tuning range variable gain amplifiers with transformer broadband matching to generate 360° full-range phase tuning. It achieves 2.7° phase resolution for the first time. The rms phase error of the phase shifter is 1.4° and the rms gain error is 0.13 dB at 60 GHz. The insertion loss is 0.4 dB. The power consumption is 38 mW.
Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.
A Novel Latin-Square-Based Secret Sharing for M2M Communications

Machine-to-machine (M2M) communication, an automated communications technology for the equipment or devices, holds great promise in every corner of the modern society, such as civil transportation, smart homes, smart grids, and industrial automation. The M2M technology is still in its infancy, and further development and deployment of M2M systems hinges on establishing an efficient and secure information management system with a satisfactory security level. In this paper, we extend the idea of <inline-formula><tex-math notation="LaTeX">$(t,n)$</tex-math> </inline-formula> secret sharing for information transmission in M2M with high security and efficiency. Specifically, a secret is divided into <inline-formula><tex-math notation="LaTeX">$2k$</tex-math></inline-formula> shares, and then, transmitted through <inline-formula><tex-math notation="LaTeX">$2k$</tex-math></inline-formula> node-disjoint paths constructed by the Latin square. Note that in our scheme, the secret key is simultaneously transmitted along with the encrypted message through these <inline-formula><tex-math notation="LaTeX">$2k$</tex-math></inline-formula> paths from the source node to the destination node, which greatly improves the efficiency of point-to-point communications in M2M systems. Furthermore, owing to the properties of <inline-formula><tex-math notation="LaTeX">$(t,n)$</tex-math> </inline-formula> secret sharing, the security of M2M communications is guaranteed. In addition, to avoid dishonest participants, verifiable secret sharing is supported in the proposed scheme. Sufficient theoretical proof and performance analysis demonstrate that our scheme is secure and efficient for M2M communications.
Subjective Well-Being Decreasing With Age: New Research on Children Over 8.

An increasing number of scientific publications have provided data from different countries suggesting subjective well-being (SWB) continuously decreases during adolescence. A review of these publications reveals authors have used dissimilar scales in diverse countries. Using data from the international Children's Worlds project (N = 48,040), involving 15 countries, a comparative analysis was performed to determine how mean scores evolve with different SWB scales from the age of 8 onwards. The results support the hypothesis that the tendency of SWB to decrease with age starts at around 10 years of age in most countries, while also confirming that different psychometric scales display different levels of sensitivity to diverse sociocultural contexts and more than one should be used in any research on children and adolescents' SWB.
Deterring the spread of misinformation on social network sites: A social cognitive theory‐guided intervention

As more individuals turn to social network sites (SNSs) for information, the spread of misinformation in these sites is becoming a greater concern. Not only can misinformation cause individual users anxiety and harm, but it can also prevent SNSs from realizing their full potential as trustworthy sources of information. This study proposed and tested an intervention‐based strategy that was designed to discourage behavior that promotes the spread of misinformation. Guided by the social cognitive theory (SCT), the intervention sought to modify users' outcome expectations by presenting them with a message that highlighted the negative consequences of misinformation. To investigate the effectiveness of this intervention message, a classical experiment was conducted on‐line with 131 college‐student participants. In the study's experimental group, the ANOVA results showed that the intervention effectively reduced the total number of “Likes” and “Shares” for postings that provided misinformation. Future development and testing of this SCT‐guided, outcome‐expectations‐based intervention is promising.
Miniaturized Dual-Band Differential Filter Using Dual-Mode Dielectric Resonator

In this letter, a dual-mode dielectric resonator (DR) which is directly mounted on the bottom of metal cavity is investigated and utilized to design differential bandpass filter (BPF) with dual-band response. The bottom metal plate acts as an electric wall for a pair of dominant modes, resulting in a half-size reduction of the DR. According to the electric-field distributions of dominant modes and Ampere’s right-hand screw rule, the proposed dual-mode DR that owns two orthogonal half-TE $11\delta $ dominant modes can be still differentially excited conveniently and applied to construct dual-band differential-mode (DM) frequency response in the design of differential BPF. Meanwhile, the common-mode response can be suppressed to a great extent in a wide frequency range. A transmission zero between the two DM passbands can be generated to improve the band-to-band isolation. Finally, a differential DR BPF with dual-band response is given to verify the proposed idea.
Cross-Entropy Training of DNN Ensemble Acoustic Models for Low-Resource ASR

Deep neural networks (DNNs) have shown a great promise in exploiting out-of-language data, particularly for under-resourced languages. The common trend is to merge data from various source languages to train a multilingual DNN and then reuse the hidden layers as language-independent feature extractors for a low-resource target language. While there is a consensus that using as much data from various languages results in a better and more general multilingual DNN, employing only source languages similar to the target language has proven effective. In this study, we propose a novel framework for multilingual DNN training, which employs all the available training data and exploits complementary information from individual source languages at the same time. Toward this goal, we borrow the idea of an ensemble with one generalist and many specialists. The generalist is derived from a multilingual DNN acoustic model trained on all available multilingual data; the specialists are the DNNs derived from the source languages individually. Then, the constituents in the ensemble are combined using weighted averaging schemes, where the combination weights are trained to minimize the cross-entropy objective function. In this framework, we seek for complementary information among the constituents while it is possible to get at least the performance equal to the baseline. Moreover, unlike previous well-known system combination schemes, only one model is required during decoding. We successfully examined two combination methodologies and demonstrated their usefulness in different scenarios using the multilingual GlobalPhone dataset. It is observed that, specifically, speech recognition systems developed in low-resource settings profit from the proposed strategy.
Inductive Kernel Low-rank Decomposition with Priors: A Generalized Nystrom Method

Low-rank matrix decomposition has gained great popularity recently in scaling up kernel methods to large amounts of data. However, some limitations could prevent them from working effectively in certain domains. For example, many existing approaches are intrinsically unsupervised, which does not incorporate side information (e.g., class labels) to produce task specific decompositions; also, they typically work "transductively", i.e., the factorization does not generalize to new samples, so the complete factorization needs to be recomputed when new samples become available. To solve these problems, in this paper we propose an "inductive"-flavored method for low-rank kernel decomposition with priors. We achieve this by generalizing the Nystrom method in a novel way. On the one hand, our approach employs a highly flexible, nonparametric structure that allows us to generalize the low-rank factors to arbitrarily new samples; on the other hand, it has linear time and space complexities, which can be orders of magnitudes faster than existing approaches and renders great efficiency in learning a low-rank kernel decomposition. Empirical results demonstrate the efficacy and efficiency of the proposed method.
Query‐centric scientific topic evolution extraction

Researchers in academia and industry face a deluge of data in our digital world. In this paper, we investigate a novel problem, query‐centric scientific topic evolution. Using heterogeneous graph mining techniques we construct a topic evolution tree (TET) from massive collections of scientific publications, enabling students and researchers to explore the foundation of research topics outside their specialization. Prior research has focused mainly on citation relationships; in this study we employed multiple types of relationships, including authorship, citation, publishing venue, and the contributions authors, papers, and venues have made to a specific topic. We examine multiple restricted meta‐paths in constructing a TET covering topics from the MeSH vocabulary for biomedical research.
Experimental Analysis of Popular Smartphone Apps Offering Anonymity, Ephemerality, and End-to-End Encryption

As social networking takes to the mobile world, smartphone apps provide users with ever-changing ways to interact with each other. Over the past couple of years, an increasing number of apps have entered the market offering end-to-end encryption, self-destructing messages, or some degree of anonymity. However, little work thus far has examined the properties they offer. To this end, this paper presents a taxonomy of 18 of these apps: we first look at the features they promise in their appeal to broaden their reach and focus on 8 of the more popular ones. We present a technical evaluation, based on static and dynamic analysis, and identify a number of gaps between the claims and reality of their promises.
A 3-D Energy-Harvesting-Aware Routing Scheme for Space Nanosatellite Networks

Space wireless networks composed of a large number of low-cost miniaturized nanosatellites enable many promising astronautical applications such as the distributed and cooperative sensing. Data collection via multihop relay is critical for nanosatellite networks. However, due to the small size of onboard solar panels and limited attitude control, energy harvesting (EH) capability of nanosatellites is quite low and unstable. In addition, the space networks have 3-D topologies due to various orbit altitudes. In this paper, we propose a novel 3-D EH-aware routing (3DEHR) scheme for nanosatellite networks. The main idea is to establish the 3-D artificial potential field (APF) based on the EH capabilities and geographic positions of neighboring nanosatellites. A nanosatellite node determines the next hop among its neighbors based on the composite force direction obtained from the APF for data forwarding. We also design the methods in 3DEHR to solve the routing void problem and to circumvent sensitive nodes such as malicious satellites. Simulation results show that 3DEHR outperforms the existing routing schemes in hop stretch and packet delivery rate. Since the routing paths are planned based on the EH capabilities of nanosatellites, the harvested energy in the network is fully utilized and the network lifetime is extended. 3DEHR is suitable for nanosatellite networks in space and other 3-D ad hoc networks with dynamic EH capabilities and topologies.
De-sensitization Design and Analysis for Highly Integrated RFSoC and DRAM Stacked-Die Design

For 3G market, the demand drives continuously on highly-integrated, low-cost radios solution. Beyond the development of RFSoC, DRAM SiP is an attractive solution to the entry-level market. Integrating LPDDR3-1066 Mbps die by stacking with cellular and connectivity radios SoC die, the world's 1st 3G RFSoC with DRAM SiP solution was presented in this paper using low-cost and low-profile 2-layer wire-bond TFBGA package. In this design, RDL routing is used for interconnects between RFSoC and DRAM, as well as interconnects for fan-out of all peripheral I/Os. Therefore, the interference from DRAM and RDL routing to RF is the bottleneck of system integration. In order to characterize DRAM noise, an inductor probe was designed to measure the noise level from DRAM and its RDL. Based on the measured data, the stacking placement of RFSoC and DRAM can be determined preliminarily. Furthermore, a test vehicle was developed to verify the DRAM noise effect on RF PLL because its LC tank VCO is sensitive to the interference. From the experiment, a tone near VCO frequency with measured DRAM noise magnitude level is injected into DRAM and then causes spur occurred on LO and results in RF performance degradation, e.g. RX desensitization, TX spectrum mask failure and EVM degradation, etc. In this paper, a simplified equivalent model was proposed to estimate aforementioned coupling effect and performed good correlation between simulation and measurement. Besides, chip-package-PCB co-design flow was also applied to deal with the interference and de-sensitization problem in RFSoC design and this generalized methodology can widely be applied to all kinds of system-on-chip (SoC) / system-in-package (SiP) designs for cellular and connectivity applications.
Railway Subgrade Defect Automatic Recognition Method Based on Improved Faster R-CNN

Railway subgrade defect is the serious threat to train safety. Vehicle-borne GPR method has become the main railway subgrade detection technology with its advantages of rapidness and nondestructiveness. However, due to the large amount of detection data and the variety in defect shape and size, defect recognition is a challenging task. In this work, the method based on deep learning is proposed to recognize defects from the ground penetrating radar (GPR) profile of subgrade detection data. Based on the Faster R-CNN framework, the improvement strategies of feature cascade, adversarial spatial dropout network (ASDN), Soft-NMS, and data augmentation have been integrated to improve recognition accuracy, according to the characteristics of subgrade defects. The experimental results indicates that compared with traditional SVM+HOG method and the baseline Faster R-CNN, the improved model can achieve better performance. The model robustness is demonstrated by a further comparison experiment of various defect types. In addition, the improvements to model performance of each improvement strategy are verified by an ablation experiment of improvement strategies. This paper tries to explore the new thinking for the application of deep learning method in the field of railway subgrade defect recognition.
Comparing the Normalized Difference Vegetation Index with the Google Street View Measure of Vegetation to Assess Associations between Greenness, Walkability, Recreational Physical Activity, and Health in Ottawa, Canada

The manner in which features of the built environment, such as walkability and greenness, impact participation in recreational activities and health are complex. We analyzed survey data provided by 282 Ottawa adults in 2016. The survey collected information on participation in recreational physical activities by season, and whether these activities were performed within participants’ neighbourhoods. The SF-12 instrument was used to characterize their overall mental and physical health. Measures of active living environment, and the satellite derived Normalized Difference Vegetation Index (NDVI) and Google Street View (GSV) greenness indices were assigned to participants’ residential addresses. Logistic regression and least squares regression were used to characterize associations between these measures and recreational physical activity, and self-reported health. The NDVI was not associated with participation in recreational activities in either the winter or summer, or physical or mental health. In contrast, the GSV was positively associated with participation in recreational activities during the summer. Specifically, those in the highest quartile spent, on average, 5.4 more hours weekly on recreational physical activities relative to those in the lowest quartile (p = 0.01). Active living environments were associated with increased utilitarian walking, and reduced reliance on use of motor vehicles. Our findings provide support for the hypothesis that neighbourhood greenness may play an important role in promoting participation in recreational physical activity during the summer.