Miracle-dz/UniFAR-FTUs huggingface dataset:
A Secure Trust-Based Key Distribution With Self-Healing for Internet of Things

Internet of things (IoT) can enable cyber-physical objects to communicate with one another and realize real-time living-needs control such as for vehicles, smart phones, refrigerators, healthcare gadgets and air-conditioners. Of the applications of IoT, collecting and receiving health-related data securely is the most crucial and significant use in the fields of community and hospital healthcare, where any single communication failure or data loss might cause a life damage risk. To address this problem, self-healing mechanism can be used for facilitating secure communications and recovering from the lost data. In this paper, a new key distribution with self-healing for IoT objects of community healthcare is proposed, and the underlying system is composed of two layers. In the top layer, the new scheme implements both deterministic security link and access control which are based on polynomial-based methods. In the bottom layer, we propose a trust-based key distribution scheme with self-healing and a singular value decomposition (SVD) based authentication method. Security and performance analysis show that our protocol can be performed more efficiently in data communication. In addition, results obtained from both security analysis and simulations indicate that our scheme is more suitable for IoT networks.
You Don’t Bend It Like Beckham if You’re Female and Reminded of It: Stereotype Threat Among Female Football Players

Originally, the stereotype threat effect – poorer performance due to a fear of fulfilling a negative stereotype about one’s group – was demonstrated for cognitive tasks (e.g. Steele and Aronson, 1995, or Steele, 1997). Drawing on the widespread stereotype of women being unable to play football we experimentally tested (N = 80) whether a respective threat affected female football players’ goal scoring precision, i.e. a complex and demanding motor task. Those participants who were reminded of the stereotype scored significantly less hits than those not reminded. Additionally, deviations from the instruction during task execution (e.g. shooting from another distance than demanded or using the wrong foot) were recorded. Stereotype threat did not affect this comparatively more cognitive task of following instructions correctly. In order to explore underlying mechanisms of the observed stereotype effect, several potential mediators, e.g. measures of cognitive interference, or collective identification, were tested. None emerged as an unquestionable link between threat and motor performance. We discuss, however, why collective identification – in comparison to cognitive demand – appears to be the more promising explanatory concept.
Computing across Trust Boundaries using Distributed Homomorphic Cryptography

In this work, we advance the conceptual and technical aspects of Secure Multiparty Computation (SMC). We approach SMC as a computational problem and propose a novel formulation of this problem in terms of trust boundaries. From this formulation, we derive a general framework that enables a more comprehensive characterization of both the SMC problem and its solutions. Existing SMC solutions are commonly seen as diametrically different and incompatible, but we show how they can be mapped to particular instances of our framework, hence enabling their analysis under a common and unified basis. In this framework, the core component of an SMC solution is a distributed homomorphic cryptosystem. We show that the features this cryptosystem provides determine the need for interaction and overall efficiency of the corresponding SMC solutions. Based on this analysis, we introduce a practical instantiation of our framework by proposing a distributed version of the BrakerskiFan-Vercauteren (BFV) lattice-based homomorphic cryptosystem. We analyze the security, noise overhead, and computational costs of this scheme. Due to its conceptual simplicity and efficiency, our solution has great potential for addressing highly relevant scenarios, such as secure data-sharing and machine-learning. Hence, this work constitutes a step forward in secure computation, by enabling computation across trust boundaries.
Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study.
Convolutional Recurrent Neural Network Based Progressive Learning for Monaural Speech Enhancement

Recently, progressive learning has shown its capacity to improve speech quality and speech intelligibility when it is combined with deep neural network (DNN) and long short-term memory (LSTM) based monaural speech enhancement algorithms, especially in low signal-to-noise ratio (SNR) conditions. Nevertheless, due to a large number of parameters and high computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is essential to significantly reduce both the number of parameters and the computational load for practical applications. For this purpose, we propose a novel progressive learning framework with causal convolutional recurrent neural networks called PL-CRNN, which takes advantage of both convolutional neural networks and recurrent neural networks to drastically reduce the number of parameters and simultaneously improve speech quality and speech intelligibility. Numerous experiments verify the effectiveness of the proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of objective measurements. Compared with PL-DNN, PL-LSTM, and CRNN, the proposed PL-CRNN algorithm can reduce the number of parameters up to 93%, 97%, and 92%, respectively.
Estimation of Object Functions Using Convolutional Neural Network

In recent years, a tremendous research effort has been made in the area of generic object recognition. However, both an object’s name and function are important for robots to comprehend objects. Object functions refer to “the purpose that something has or the job that someone or something does”. Various elements (e.g., the physical information, material, appearance and human interaction) independently or mutually form object functions. There are many researches on object functions using human-object interaction, while there are few using appearance. However, it can be believed that object functions may be formed by appearance. In this paper, we propose a new method to estimate object functions from appearance on images. Our approach is to estimate object functions using convolutional neural network(CNN), which has ability to learn rich mid-level features. In our method, we add adaptation layers for object function estimation formed by full-connection to the CNN which is pre-trained on the ImageNet2013 with 1000 object classes. Experimental results show that the classification rates of two of three functions of objects such as ”cuttable” and ”movable” are over 80% and that the appearance is closely related to object functions.
Cortical Plasticity Induced by Anodal Transcranial Pulsed Current Stimulation Investigated by Combining Two-Photon Imaging and Electrophysiological Recording

Anodal-transcranial pulsed current stimulation (a-tPCS) has been used in human studies to modulate cortical excitability or improve behavioral performance in recent years. Multiple studies show crucial roles of astrocytes in cortical plasticity. The calcium activity in astrocytes could regulate synaptic transmission and synaptic plasticity. Whether the astrocytic activity is involved in a-tPCS-induced cortical plasticity is presently unknown. The purpose of this study is to investigate the calcium responses in neurons and astrocytes evoked by a-tPCS with different current intensities, and thereby provides some indication of the mechanisms underlying a-tPCS-induced cortical plasticity. Two-photon calcium imaging was used to record the calcium responses of neurons and astrocytes in mouse somatosensory cortex. Local field potential (LFP) evoked by sensory stimulation was used to assess the effects of a-tPCS on plasticity. We found that long-duration a-tPCS with high-intensity current could evoke large-amplitude calcium responses in both neurons and astrocytes, whereas long-duration a-tPCS with low-intensity current evoked large-amplitude calcium responses only in astrocytes. The astrocytic Ca2+ elevations are driven by noradrenergic-dependent activation of the alpha-1 adrenergic receptors (A1ARs), while the intense Ca2+ responses of neurons are driven by action potentials. LFP recordings demonstrated that low-intensity a-tPCS led to enhancement of cortical excitability while high-intensity a-tPCS resulted in diminution of cortical excitability. The results provide some evidence that the enhancement of a-tPCS-induced cortical excitability might be partly associated with calcium elevation in astrocytes, whereas the diminution of a-tPCS-induced cortical excitability might be caused by excessive calcium activity in neurons. These findings indicate that the appropriate current intensity should be used in the application of a-tPCS.
Reducing uncertainty in simulation estimates of the surface tension through a two-scale finite-size analysis: thicker is better

Recent simulation studies of the surface tension γ, and other properties of thin free-standing films, have revealed unexpected finite size effects in which the variance of the properties vary monotonically with the in-plane width of the films, complicating the extrapolation of estimates of film properties to the thermodynamic limit. We carried out molecular dynamics simulations to determine the origin of this phenomenon, and to address the practical problem of developing a more reliable methodology for estimating γ in the thermodynamic limit. We find that there are two distinct finite size effects that must be addressed in a finite size analysis of γ in thin films. The first finite size scale is the in-plane width of the films and the second scale is the simulation cell size in the transverse direction. Increasing the first scale enhances fluctuations in γ, measured by the standard deviation of their distribution, while increasing the second reduces γ fluctuations due to a corresponding increased ‘freedom’ of the film to fluctuate out of plane. We find that using progressively large simulation cells in the transverse direction, while keeping the film width fixed to an extent in which the full bulk liquid zone is developed, allows us to obtain a smooth extrapolation to the thermodynamic limit, enabling a reduction of the γ uncertainty to a magnitude on the order of 1% for systems having a reasonably large size, i.e., O (1 μm).
Mathematical Modelling and Comparative Study of Elliptical and Circular Capacitive Pressure Microsensor

A lot of work on clamped circular, square and rectangular shaped capacitive pressure sensor has been carried out. However, to the best of our knowledge, no elaborate work has been performed on mathematical formulation and simulation of clamped elliptical shaped capacitive pressure sensor in literature. This paper describes the mathematical modelling and simulation of normal mode clamped elliptical shaped capacitive pressure sensor. The study of various performance parameters like maximum diaphragm deflection, capacitance variation, mechanical sensitivity, capacitive sensitivity and non-linearity is also carried out for operating pressure range of 0kPa – 18 kPa. For circular capacitive pressure sensor, the operating pressure range is modified according to physical dimensions i.e. thickness and radius of diaphragm, separation gap between plates and maximum deflection. For same overlapping area between plates (10000π mm2), the comparison of elliptical shapes of different eccentricities with circular shape diaphragm is carried out. The thickness of diaphragm is taken as2 μm and separation gap is 1 μm in all the designs which are used in this work. In this comparative study, it is observed that elliptical shaped capacitive pressure sensors have better linearity than circular diaphragm pressure sensor.
Anisotropic strain effects in small-twist-angle graphene on graphite

The direct experimental probing of locally varying lattice parameters and anisotropic lattice deformations in atomic multilayers is extremely challenging. Here, we develop a new combined numerical/graphical method for the analysis of irregular moire superstructures measured by scanning tunneling microscopy (STM) on a small-twist-angle ($\sim$0.6$^{\circ}$) graphene on highly oriented pyrolytic graphite (gr/HOPG). We observe distorted moire patterns with a spatially varying period in annealed gr/HOPG. The nanoscale modulation of the moire period observed by STM reflects a locally strained (and sheared) graphene with anisotropic variation of the lattice parameters. We use a specific algorithm based on a rigid lattice Fourier method, which is able to reconstruct the irregular and distorted moire patterns emerging from strain-induced lattice deformations. Our model is universal and can be used to study different moire patterns occurring in two-dimensional van der Waals heterostructures. Additionally, room temperature scanning tunneling spectroscopy measurements show electronic states at the Dirac point, localized on moire hills, which increase significantly the apparent corrugation of the moire pattern. The measured topography is compared to classical molecular dynamics simulations. Density functional theory (DFT) calculations confirm that an AAB stacked trilayer region itself can contribute electronic states near the Fermi-level, in agreement with the measured peak in the local density of states. Furthermore, CMD calculations reveal direction-dependent bond alternations ($\sim$0.5$\%$) around the stacking regions, induced by shear strain, which could influence electronic properties.
Korta rapporter – Short communications An exceptionally large Willow Warbler Phylloscopus trochilus

In the morning of 13 May 2010, ÅL trapped a Willow Warbler Phylloscopus trochilus (Figure 1) in a mistnet at Rörumstrand, near Simrishamn in southernmost Sweden (55.644 dec°N, 14.267 dec°E). Already when taking it out of the net it was clear that this was an unusually large individual. This was confirmed by its measurements: wing length 78.0 mm (to the nearest half mm, maximum chord, Svensson 1992, Figure 2), tail length 60.0 mm (to the nearest half mm, from the base of tail feathers to the tip of longest tail feather, Svensson 1992, Figure 2), and tarsus 23.5 mm (to the nearest 0.1 mm, with foot and tibiotarsus held perpendicular to tarsometatarsus, measuring the distance between the extreme bending points, Alatalo & Lundberg 1986). It had a fat score of 3 on a scale from 0 to 6 (Pettersson & Hasselquist 1985) and weighed 10.8 g. The bird was ringed and released soon after processing, and was not seen or retrapped again. Male Willow Warblers have on average more than 5 mm longer wings than females, with only a small overlap (Williamson 1976, Bensch et al. 2009). Due to its very long wing (see below), the bird was most certainly a male. Further, the bird was probably on migration, since the local breedFigure 1. The large Willow Warbler Phylloscopus trochilus trapped on 13 May 2010. Den stora lövsångare som fångades 13 maj 2010.
Auralisation of acoustical product properties for technical systems in virtual environments

Today product development is dominated by reducing time and costs, which can be contradictory to the required high quality standards of products. The advantages of using computer-based tools enable the simulation of product properties and their optimisation before first physical prototypes are built. The presentation of those simulation results using (extended) Virtual Reality (VR) technologies is very helpful especially if both the simulation tools and the VR representation are multimodal (e.g. visualisation extended by acoustics) so that an enhanced immersion in the virtual scene becomes possible. Furthermore, this enables an easy comprehension and understanding of complex contexts and relations, which otherwise might be only clear for experts. Current VR-systems are often limited by only providing visual information. Even if a large amount of visual information is covered very well, only geometry and geometry-related behaviour (e.g. motion = geometry changes in time) or abstract information can be transferred. However, in many cases product development has to consider the acoustical behaviour of technical systems. Applications of this can be found in the noise engineering (noise control or noise limitation of products) as well as in the sound design engineering of products (so called ”brand sound”). Due to the increasing importance of acoustics and the expectation of a more realistic immersion and presence in VR environments the sense of perception should be extended. For this a research group was built in Ilmenau consisting of mechanical engineers as well as acoustical engineers at Ilmenau University of Technology in the engineering design group as well at the Fraunhofer Institute for Digital Media Technology (IDMT). The goal of the research work is to add acoustical representations to VR-systems for technical applications, integrating the acoustical analysis and synthesis in the early phases of engineering design process by means of virtual prototyping. For the audio-visual representations in VR environments there are two important fields being investigated by the research group: • Research and development of product models including acoustical behaviour for steady and non-steady states of the product, • Research and development of methods and models for the adaptation of the acoustical behaviour of products in a spatial, immersive and interactive audio reproduction system for VR environments. This contribution discusses the use of a particular VR-system using the Wave Field Synthesis (WFS) technology for the sound reproduction. Besides this, necessary simulation methods as well as models of sound generation and transmission through the structure will be discussed. Those simulation models and methods are used for the representation of acoustical product properties for real-time auralisation via WFS. This provides means for the optimisation and modification of system parameters. A pick-and-place unit (see figure 1) was taken as an example of an ongoing product development process, findings using this example are in the focus of this paper.
Cardiorespiratory fitness and hippocampal volume predict faster episodic associative learning in older adults

Declining episodic memory is common among otherwise healthy older adults, in part due to negative effects of aging on hippocampal circuits. However, there is significant variability between individuals in severity of aging effects on the hippocampus and subsequent memory decline. Importantly, variability may be influenced by modifiable protective physiological factors such as cardiorespiratory fitness (CRF). More research is needed to better understand which aspects of cognition that decline with aging benefit most from CRF. The current study evaluated the relation of CRF with learning rate on the episodic associative learning (EAL) task, a task designed specifically to target hippocampal‐dependent relational binding and to evaluate learning with repeated occurrences. Results show higher CRF was associated with faster learning rate. Larger hippocampal volume was also associated with faster learning rate, though hippocampal volume did not mediate the relationship between CRF and learning rate. Furthermore, to support the distinction between learning item relations and learning higher‐order sequences, which declines with aging but is largely reliant on extra‐hippocampal learning systems, we found learning rate on the EAL task was not related to motor sequence learning on the alternating serial reaction time task. Motor sequence learning was also not correlated with hippocampal volume. Thus, for the first time, we show that both higher CRF and larger hippocampal volume in healthy older adults are related to enhanced rate of relational memory acquisition.
Nearshore coral growth declining on the Mesoamerican Barrier Reef System

Anthropogenic global change and local stressors are impacting coral growth and survival worldwide, altering the structure and function of coral reef ecosystems. Here, we show that skeletal extension rates of nearshore colonies of two abundant and widespread Caribbean corals (Siderastrea siderea, Pseudodiploria strigosa) declined across the Belize Mesoamerican Barrier Reef System (MBRS) over the past century, while offshore coral conspecifics exhibited relatively stable extension rates over the same temporal interval. This decline has caused nearshore coral extension rates to converge with those of their historically slower growing offshore coral counterparts. For both species, individual mass coral bleaching events were correlated with low rates of skeletal extension within specific reef environments, but no single bleaching event was correlated with low skeletal extension rates across all reef environments. We postulate that the decline in skeletal extension rates for nearshore corals is driven primarily by the combined effects of long‐term ocean warming and increasing exposure to higher levels of land‐based anthropogenic stressors, with acute thermally induced bleaching events playing a lesser role. If these declining trends in skeletal growth of nearshore S. siderea and P. strigosa continue into the future, the structure and function of these critical nearshore MBRS coral reef systems is likely to be severely impaired.
Sensitivity to Interaural Phase in Older Hearing-Impaired Listeners Correlates With Nonauditory Trail Making Scores and With a Spatial Auditory Task of Unrelated Peripheral Origin

Interaural phase difference (IPD) discrimination upper frequency limits and just-noticeable differences (JNDs), interaural level difference (ILD) JNDs, and diotic intensity JNDs were measured for 20 older hearing-impaired listeners with matched moderate sloping to severe sensorineural hearing losses. The JNDs were measured using tone stimuli at 500 Hz. In addition to these auditory tests, the participants completed a cognitive test (Trail Making Test). Significant performance improvements in IPD discrimination were observed across test sessions. Strong correlations were found between IPD and ILD discrimination performance. Very strong correlations were observed between IPD discrimination and Trail Making performance as well as strong correlations between ILD discrimination and Trail Making performance. These relationships indicate that interindividual variability in IPD discrimination performance did not exclusively reflect deficits specific to any auditory processing, including early auditory processing of temporal information. The observed relationships between spatial audition and cognition may instead be attributable to a modality-general spatial processing deficit and/or individual differences in global processing speed.