Miracle-dz/UniFAR-FTUs huggingface dataset:
GPSKey: GPS based Secret Key Establishment for Intra-Vehicle Environment

In order to overcome such limitations, context-based pairing schemes, where they extract secret key from common observations (e.g., visual channel [29], ambient audio [9], [14], timing of detected events [11], wireless signal strength [12], [33] and wireless channel interference [19], [22]) without requiring human interactions are proposed. However, these schemes present two shortcomings: (1) The surrounding environment may not consistently provide enough randomness for effective key generations. For example, [11] requires a user to generate additional events to reduce pairing time. (2) Some channels may not available in a normal vehicle. For example, audio signals in a vehicle can be easily interfered with by noises and vibrations.
Explaining Toxic Text via Knowledge Enhanced Text Generation

Warning: This paper contains content that is offensive and may be upsetting.Biased or toxic speech can be harmful to various demographic groups. Therefore, it is not only important for models to detect these speech, but to also output explanations of why a given text is toxic. Previous literature has mostly focused on classifying and detecting toxic speech, and existing efforts on explaining stereotypes in toxic speech mainly use standard text generation approaches, resulting in generic and repetitive explanations. Building on these prior works, we introduce a novel knowledge-informed encoder-decoder framework to utilize multiple knowledge sources to generate implications of biased text.Experiments show that our knowledge informed models outperform prior state-of-the-art models significantly, and can generate detailed explanations of stereotypes in toxic speech compared to baselines, both quantitatively and qualitatively.
Technology Startups and University-Based Entrepreneurial Ecosystems in the Universities of Golestan Province

University startup accelerator programs have exponentially increased over the past decade; however, their role in the university entrepreneurial ecosystem is ambiguous. This research aims to determine the Effectiveness of Technological Startups on University Entrepreneurship Ecosystems by Rough Set Theory. This is particularly the case when entrepreneurship education (EE) integration is considered, raising questions about such startup enablers' validity, measurement, and outcomes. The target population in the qualitative section was similar research and academic experts at the entrepreneurial level. However, the target population in the qualitative section, 20 managers and deputies at various academic levels in Golestan province, were analyzed according to the requirement Rough theory; much of the population is acceptable. In this study, 9 research proposals for the development of technological startups and 5 components of the entrepreneurial ecosystem at the university level were determined by screening selected studies. Due to the confirmation of the theoretical adequacy limit based on Delphi analysis, they entered the analysis phase of Rough Theory. The results showed that the most effective role of the university as an entrepreneurial ecosystem is to strengthen the level of educational and research functions. If the educational and research functions in entrepreneurial ecosystems move towards technology, it can connect industry with the university and provide the basis for sustainable development.
VARIABILITY AND COMPLEXITY OF SHOULDER MOTION DURING WHEELCHAIR PROPULSION

Researchers have quantified shoulder motion during manual wheelchair propulsion using basic kinematics, such as peak joint angle and velocity. Yet, little has been done to examine the variability of upper body motion patterns during wheelchair propulsion, even though studies have suggested that lack of variability in biological systems maybe indicative of pathology. Phase portraits, which are graphical representations of position relative to velocity, can be used to explore the dynamics of a system over multiple cycles. Recently, new techniques have been developed to examine changes in variability and complexity in the shape of phase portraits. Variability was quantified by examining fluctuations of the centroid of each phase portrait over multiple cycles, specifically by calculating the confidence area and drift of the centroid. Complexity of the portrait was quantified by determining the portrait shape’s frequency content using Fourier-based methods. In this preliminary study, phase portraits of shoulder flexion-extension angular position versus angular velocity were examined as function of propulsion speed. Nine experienced manual wheelchair users propelled on a stationary roller at fast (1.1 m/s) and slow (0.7 m/s) speeds for three minutes at each speed. Variability parameters had mixed results with propulsion speed. There was a trend for the centroid area to increase with speed (p = 0.06); whereas, there was no significant change in centroid drift (p = 0.2). Complexity of the phase portrait shape decreased significantly with speed (p < 0.05). These results support prior work that propulsion speed impacts shoulder biomechanics. Future work needs to determine if variability and complexity metrics are sensitive to pathology.
Computational modelling of structural battery composites

Batteries and surrounding structures (e.g. battery modules and packs) in electrical vehicles and devices are often designed in a way that prevents the electro-chemically active part of the battery cells from being exposed to mechanical loads during operation/service. This means that the energy storage capability is added as a monofunctional addition to the system (i.e. it only provides one functionality, storing energy). Hence, one of the main drawbacks of the existing technology is its energy storage to weight ratio, in terms of the complete system. A viable route to improve this ratio is to develop energy storage solutions with the ability to sustain mechanical loads. Indeed, by adding this additional functionality, such solutions offer significant system mass and volume savings and allow for innovative future design of electric vehicles and devices. The structural battery composite material is made from carbon fibre reinforced structural battery electrolyte (SBE), and exploits the multifunctional capability of the material constituents to facilitate electrical energy storage in structural components. Due to its inherent multifunctionality, the physical phenomena occurring within the material during operation will interact. Further, due to the fact that the studied material is intended to perform multiple functions some of the couplings between the physical processes are expected to be more pronounced, and critical to design, as compared to conventional batteries. Hence, to accurately predict and evaluate the combined performance of structural batteries, coupled multiphysics models are needed. In this thesis, a computational modelling framework to predict the coupled thermo-electro-chemo-mechanical performance of structural batteries is developed. The framework is utilized to study the essential couplings between the physical processes and numerical predictions are compared favourably with experimental data. It is shown that two-way coupling between the electro-chemical and mechanical processes is important to account for when evaluating the combined electro-chemo-mechanical performance of structural batteries. Further, it is shown that the convective contribution to the mass flux of ions in the SBE, as well as the thermal effects during operations are crucial to consider when evaluating the combined performance. Moreover, the framework is extended to study an electro-chemically driven actuator and sensor utilizing carbon fibre-SBE electrodes. Finally, in addition to the modelling work a laminated structural battery with unprecedented multifunctional (i.e. combined mechanical and electrochemical) performance is manufactured and characterized, featuring an energy density of 24 Wh kg − 1 and an elastic modulus of 25 GPa and tensile strength exceeding 300 MPa.
C4: Contrastive Cross-Language Code Clone Detection

During software development, developers introduce code clones by reusing existing code to improve programming productivity. Considering the detrimental effects on software maintenance and evolution, many techniques are proposed to detect code clones. Existing approaches are mainly used to detect clones written in the same programming language. However, it is common to develop programs with the same functionality but in different programming languages to support various platforms. In this paper, we propose a new approach named C4, referring to Contrastive Cross-language Code Clone detection model. It can detect cross-language clones with learned representations effectively. C4 exploits the pre-trained model CodeBERT to convert programs in different languages into high-dimensional vector representations. In addition, we fine tune the C4 model through a constrastive learning objective that can effectively recognize clone pairs and non-clone pairs. To evaluate the effectiveness of our approach, we conduct extensive experiments on the dataset proposed by CLCDSA. Experimental results show that C4 achieves scores of 0.94, 0.90, and 0.92 in terms of precision, recall and F-measure and substantially outperforms the state-of-the-art baselines.
Effects of tillage and irrigation method on energy use and greenhouse gas emissions of corn production

: In this study, the effect of tillage and irrigation method on energy use, energy output, energy use efficiency (EUE), net energy gain (NEG), specific energy (SE), energy productivity (EP), and greenhouse gas (GHG) emissions of corn production was evaluated. The research was conducted in a split plot design with three replicates in Fars province, Iran. Main plots were irrigation methods including surface irrigation (SI) (gated pipe), drip irrigation (DI), and sprinkler irrigation (SPI). Tillage methods including no-till (NT), reduced tillage (RT), and conventional tillage (CT) were considered as sub plots. Results showed that irrigation method had significant effect on energy use, energy output, energy use efficiency, net energy gain, specific energy, energy productivity, and greenhouse gas (GHG) emissions of corn production, while tillage method had significant influence only on energy use, energy output, and net energy gain. SI had the highest input energy (165856 MJ ha -1 ) and GHG emission (29376 kg CO 2 e ha -1 ) followed by SPI (117662 MJ ha -1 and 20363 kg CO 2 e ha -1 ) and DI (88597 MJ ha -1 and 15025 kg CO 2 e ha -1 ). DI and SPI reduced input energy by 45% and 29% and GHG emissions by 48.9% and 30.7%, respectively compared to SI. NT reduced the energy input and increased the energy output by 2.3% and 9.8% respectively, compared to the CT. Results of this study also showed that corn production cropping system involving an efficient irrigation method (DI) and conservation tillage (CoT) would be an energy efficient cropping system with low environmental pollution risks. Citation: 2022. Effects of tillage and irrigation method on energy use and greenhouse gas emissions of corn production. Agricultural Engineering International: CIGR Journal, 24(1): 102-112.
Droughts, Deluges, and (River) Diversions: Valuing Market-Based Water Reallocation

This paper develops and applies a method to value water trading on a river network. The framework relies on regulatory variation in diversion caps to identify production functions for irrigated farms, then uses the estimated shadow values to assess the market’s reallocation. I apply this framework to the largest water market in human history, located in southeastern Australia. Observed water trading increased output by 4–6 percent from 2007 to 2015, equivalent to avoiding an 8–12 percent uniform decline in water resources. Reallocation and average surplus both increase substantially during drought, implying that water markets can be most valuable when climatic variability is most severe. (JEL D23, D24, Q12, Q15, Q25, Q54)
New Method of Fault Detection and Location for EHV Cables with Transmission Lines

A new method of accurate fault location for EHV cable with transmission line is presented. Based on the proposed criterion, the method can discriminate the fault happened in EHV cables from transmission lines, then the fault location can be determined by using the variation of voltage and current obtained from both ends of the connecting lines. The method is also suitable for more complex system such as EHV transmission line-cable-transmission line with only a little changes in the criterion. Simulation results show that the proposed new method does not depend on the data synchronization at both ends of the line and can provide accurate result even if the source impedances and fault resistance are different. The proposed method is easy to implement without false-root identification.
What’s next

Teaching machines to perceive the overall quality of speech and estimate the underlying acoustic characteristics of the environment in which the signal was captured without a clean reference signal (i.e. non intrusively) is a challenging task that has many real-world applications. These include audio forensics, hearing aids, speaker diarization and automatic speech recognition (ASR). In the context of ASR, an important application is for the purpose of data augmentation and selection, where an algorithm like Non-Intrusive Speech Analysis (NISA) can be used for analyzing field data to estimate the distributions of various acoustic parameters present and then to use those distributions to select appropriate data for ASR model training as well as for the purpose of configuring and automating data augmentation strategies.
A difference technique to avoid interface errors in measurement of high-conductance thermal insulation

Lower limits of measurement are prescribed within all steady-state test methods for thermal insulation. The limit, typically 0.1 m2 K/W, is largely required because of the increasing significance of interface resistance. We have previously proposed the use of a difference method, in conjunction with flexible buffer materials, to minimize the effects of interface resistance and facilitate measurement of rigid materials below these limits. We have now studied this approach at higher thermal resistances and incorporated a refinement to include a known reference specimen in the difference measurement, which largely eliminates the residual resistance terms. Specimens of expanded polystyrene and cast acrylic were measured in a conventional heat flow meter apparatus using two alternative silicone buffer materials: one solid and the other a sponge. Analysis also included earlier measurements of 12 more highly conducting specimens. Across all of these, thermal resistance values obtained by the difference method were lower between 0.008 and 0.016 m2 K/W, attributable to removing the contribution of interface resistance.
A remark on genotype selection in plant breeding projects

Abstract One of the main problems in plant breeding is the selection of the best genotypes. Most often the selection is made using yield as a main trait of continuous type, and ignoring the other traits of discrete type. Here, a simple procedure is proposed for dealing with the selection problem using not only the yield, but also an auxiliary discrete trait. The method is based on transforming the continuous variable into a discrete one and testing the dependence of variables with the use of contingency tables. The procedure is illustrated by a real unreplicated experiment with winter wheat.
Dynamics of corticocortical brain functional connectivity relevant to therapeutic response to biologics in inflammatory arthritis

Objectives: Brain function controls cognition and pain perception. The dynamics of brain function also affects physiopsychological status in individuals with neuropsychiatric diseases or chronic pain, and could be evaluated by functional magnetic resonance imaging (fMRI). The brain functional connectivity is aberrant in inflammatory arthritis (IA) including rheumatoid arthritis and spondyloarthritis. We aimed to evaluate the effects of dynamic functional connectivity on clinical course of IA in the context of therapeutic responsiveness to biologics using resting-state fMRI. Methods: Resting-state fMRI data from 64 patients with IA consisting of two cohorts were used. Dynamic functional connectivity was derived as a correlation coefficient of the windowed blood oxygen level-dependent signal time series. We determined representative whole-brain functional connectivity patterns by k-means++ cluster analysis, and evaluated the association of their occurrence probability and therapeutic outcomes with biologics. Results: Four distinct clusters of dynamic functional connectivity in IA patients were determined. In the first cohort, occurrence probability of cluster 3 was associated with favorable therapeutic response in disease activity and patients' global assessment. This finding was validated by the second cohort. The whole-brain functional coordination of the cluster 3 indicated significantly increased corticocortical connectivity, and probabilistically decreased after therapy in treatment-effective patients compared to -ineffective patients. Conclusion: Dynamic functional connectivity, in particular, frequent emergence of corticocortical connections was associated with clinical outcomes in patients with IA. Our study suggested that the coherence of corticocortical interactions is related to top-down modulation of pain and therapeutic satisfaction, and give significant perspective for brain function in IA management.
PREDICTION INTERVALS OF AN ORDERED OBSERVATION FROM ONE-PARAMETER EXPONENTIAL DISTRIBUTION BASED ON MULTIPLE TYPE II CENSORED SAMPLES

ABSTRACT This paper provides a suitable pivotal quantity to present the prediction interval of the j th future ordered-observation in a sample of size n from one-parameter exponential distribution in case of a multiple type II censored sample. In addition, we also discuss the approximate prediction interval, the best linear unbiased estimate and approximate maximum likelihood estimate of X j based on the censored sample. As in applications, the total duration time in a life test and the failure time of a j-out-of-n system may be predicted. Finally, a simulated study and three illustrative examples are included.
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes

Many scenes in real life can be abstracted to the sparse reward visual scenes, where it is difficult for an agent to tackle the task under the condition of only accepting images and sparse rewards. We propose to decompose this problem into two sub-problems: the visual representation and the sparse reward. To address them, a novel framework IAMMIR combining the self-supervised representation learning with the intrinsic motivation is presented. For visual representation, a representation driven by a combination of the imageaugmented forward dynamics and the reward is acquired. For sparse rewards, a new type of intrinsic reward is designed, the Momentum Memory Intrinsic Reward (MMIR). It utilizes the difference of the outputs from the current model (online network) and the historical model (target network) to present the agent's state familiarity. Our method is evaluated on the visual navigation task with sparse rewards in Vizdoom. Experiments demonstrate that our method achieves the state of the art performance in sample efficiency, at least 2 times faster than the existing methods reaching 100% success rate.
Dataset Pruning: Reducing Training Data by Examining Generalization Influence

The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural costs. This poses crucial questions that, do all training data contribute to model's performance? How much does each individual training sample or a sub-training-set affect the model's generalization, and how to construct the smallest subset from the entire training data as a proxy training set without significantly sacrificing the model's performance? To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct the smallest subset of training data that yields strictly constrained generalization gap. The empirically observed generalization gap of dataset pruning is substantially consistent with our theoretical expectations. Furthermore, the proposed method prunes 40% training examples on the CIFAR-10 dataset, halves the convergence time with only 1.3% test accuracy decrease, which is superior to previous score-based sample selection methods.
Farnsworth – Munsell 100 hue test associations with recurrent pituitary adenomas

Purpose. To evaluate associations between the recurrence of PA and color contrast sensitivity by using F-M 100 hue test. Methods. 132 eyes of 66 patients with PA, 18 eyes of 9 patients with recurrent PA and 198 eyes of 99 healthy people were included in the study. Color contrast sensitivity was analysed by using Farnsworth - Munsell 100 hue test. Results. In the group of patients with non-recurrent PA the error score was 63,62 (mean rank), while in the group of patients who had the recurrence of PA - 84,72 (mean rank) (p=0,03). Conclusion. The results of F-M 100 hue test were statistically significant better in the non-recurrent PA group compared to the group of patients with recurrent PA.
Energy and entropy in the geometrical trinity of gravity

All energy is gravitational energy. That is the consequence of the equivalence principle, according to which gravity is the universal interaction. The physical charges of this interaction have remained undisclosed, but the Advent of the Geometrical Trinity opened a new approach to this foundational problem. Here it is shown to provide a background-independent uniﬁcation of the previous, non-covariant approaches of Bergmann-Thomson, Cooperstock, Einstein, von Freud, Landau-Lifshitz, Papapetrou and Weinberg. First, the Noether currents are derived for a generic Palatini theory of gravity coupled with generic matter ﬁelds, and then the canonical i.e. the unique charges are robustly derived and analysed, particularly in the metric teleparallel and the symmetric teleparallel versions of General Relativity. These results, and their application to black holes and gravitational waves, are new.
The Davis-Chandrasekhar-Fermi Method Revisited

Despite the rich observational results on interstellar magnetic fields in star-forming regions, it is still unclear how dynamically significant the magnetic fields are at varying physical scales, because direct measurement of the field strength is observationally difficult. The Davis-Chandrasekhar-Fermi (DCF) method has been the most commonly used method to estimate the magnetic field strength from polarization data. It is based on the assumption that gas turbulent motion is the driving source of field distortion via linear Alfvén waves. In this work, using MHD simulations of star-forming clouds, we test the validity of the assumption underlying the DCF method by examining its accuracy in the real 3D space. Our results suggest that the DCF relation between turbulent kinetic energy and magnetic energy fluctuation should be treated as a statistical result instead of a local property. We then develop and investigate several modifications to the original DCF method using synthetic observations, and propose new recipes to improve the accuracy of DCF-derived magnetic field strength. We further note that the biggest uncertainty in the DCF analysis may come from the linewidth measurement instead of the polarization observation, especially since the line-of-sight gas velocity can be used to estimate the gas volume density, another critical parameter in the DCF method.