Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotation and Translation Invariant Palmprint Recognition With Biologically Inspired Transform

Extracting rotation and translation invariant features is a difficult task for palmprint recognition. Traditional methods have difficulty in dealing with palmprint images degraded by those variations. Studies have shown that neurons at higher levels exhibit an increasing degree of invariance to above mentioned image variations. Moreover, primary visual cortex(V1) is believed to give stronger responses to light bars of certain directions. Based on these observations, a biologically inspired transform feature extractor, namely BIT, for palmprint recognition is proposed in this paper. BIT involves two stages, which mimics visual information processing in V1. In the first stage, we build an orientation edge detector to highlight the edges response in each direction. The orientation edge detector is primarily composed of a phase congruency based edge detector and a bipolar filter. After that, a local spatial frequency detector produces a response, converting rotation factors of orientation edges into a horizontal shifted map. In the second stage, the orientation edge detector and local spatial frequency detector are applied again, which converts shifted map into an invariant pixel in feature map. Extensive experimental results not only show that our method is robust to image variations including rotation and translation, but also illustrate the effectiveness and discriminability of the extracted invariant palmprint features in recognition problems.
Scale-factor analysis of a geometrically compensated (100) single-crystal silicon vibratory ring gyroscope

In this paper, we present the rate measurement results, especially the scale factor in a mode-matched geometrically compensated (100) single crystal silicon (SCS) vibratory ring gyroscope (VRG) using closed-loop driving and open-loop sensing test. Compared to the non-compensated (100) VRG with uniform radial width, the smaller frequency split (Δf) of 9 Hz was obtained in the compensated one with non-uniform radial width and optimized suspending beams in simulations. By using electrostatic tuning with multiple electrodes, we realized mode-matching in the as-fabricated compensated VRG with Δf of 126 Hz. Then, considering the dimensions, parameters of read-out circuit and mode-rotation caused by electrostatic tuning, a theoretical model of scale factor is derived. This model adequately considers effect of mode-rotation and dimensions on scale factor of a VRG, especially its radial width, ring radius and capacitance gap, which well explained the reason for the measured values of scale factor and bias stability.
A Surgical-Oriented Liver Segmentation Approach Using Deep Learning

Accurate organ segmentation is nowadays considered indispensable in typical surgical pipelines. Over the last couple of decades, researchers and clinicians have been driven to achieve the most efficient segmentation technique that would allow radiologists and surgeons to have easy access to organ measurements and visualization. Although several implementations and systems have been implemented, there still lies plenty of space in creating a more robust, accurate, reliable, and fast segmentation algorithm. Since automated segmentation algorithms have less user interaction (minimizing the probability of performance variation) and are usually preferred by the clinicians, the aim of this paper is to provide a state-of-art automated solution for accurately segmenting the liver and liver tumor from CT volumes. The implementation utilizes a deep learning hybrid network that blends features extracted from both 2D and 3D convolutions to enhance the overall performance of the algorithm, in addition to some pre-processing and postprocessing. We tested this network on both public and private data and it showed promising results that would be highly applicable in surgical setups. Finally, we briefly discuss how these results are relevant in a typical surgical application.
Frequency Domain Analysis of the Minerva Medica Temple by means of the Motion Magnification Methodology

Historical monuments in the urban environment are affected by anthropic vibrations, therefore the health monitoring of those structures is mandatory. The frequency domain analysis is the standard procedure to infer the presence of damages, but usually it requires expensive equipment, time consuming elaborations, trained personnel. Moreover, especially if the monument to be investigated is a large building, many devices are necessary. In order to face all these difficulties, we propose the recently developed motion magnification methodology. It is based on a processing procedure that plays the role of a microscope for slight motions within a video. In recent years, experimental studies explored this methodology applied to simple geometries, demonstrating its potentialities and reliability thorough comparison with conventional and accurate vibration instruments, such as accelerometers and lasers. However, the study of large ancient monuments is lacking. Our case-study, the so-called Temple of Minerva Medica located between the railway tracks and a tramway in the center of Rome, intends to fill the gap.
Diversity and Evenness: A Unifying Notation and Its Consequences

Three commonly used measures of diversity, Simpson's index, Shannon's entropy, and the total number of species, are related to Renyi's definition of a generalized entropy. A unified concept of diversity is presented, according to which there is a continuum of possible diversity measures. In a sense which becomes apparent, these measures provide estimates of the effective number of species present, and differ only in their tendency to include or to ignore the relatively rarer species. The notion of the diversity of a community as opposed to that of a sample is examined, and is related to the asymptotic form of the species—abundance curve. A new and plausible definition of evenness is derived.
Trade and the conservation status of the family Psittacidae in Venezuela

Summary An assessment of trade in parrots throughout Venezuela, 1988–1989, reveals alarmingly high internal and international levels. The national trade has main outlets in major cities, but is now compounded by the use of feathers for Indian artifacts sold to tourists. International trade involves illegal export chiefly from the Orinoco Delta, the majority of such birds (65,000–75,000) destined for Guyana. The large macaws suffer badly from both types of trade, but owing to its restricted range the endemic Amazona barbadensis is perhaps the most critically threatened species. Other species are assessed and, along with key sites, identified in priority order for remedial action, which should include more detailed field studies, rigorous trade data analysis, exchange of trade data with major neighbours, census and monitoring technique improvements, educational campaigns, and strict breeding facility control.
HRTF Individualization using Deep Learning

The research presented in this paper focuses on Head-Related Transfer Function (HRTF) individualization using deep learning techniques. HRTF individualization is paramount for accurate binaural rendering, which is used in XR technologies, tools for the visually impaired, and many other applications. The rising availability of public HRTF data currently allows experimentation with different input data formats and various computational models. Accordingly, three research directions are investigated here: (1) extraction of predictors from user data; (2) unsupervised learning of HRTFs based on autoencoder networks; and (3) synthesis of HRTFs from anthropometric data using deep multilayer perceptrons and principal component analysis. While none of the aforementioned investigations has shown outstanding results to date, the knowledge acquired throughout the development and troubleshooting phases highlights areas of improvement which are expected to pave the way to more accurate models for HRTF individualization.
Evaluation of wheat and maize evapotranspiration determination by direct use of the Penman–Monteith equation in a semi-arid region

The Penman–Monteith (PM) equation was introduced as one of the most reliable equations to determine crop ETc, without using crop coefficient or ETo values. In this study, the PM equation was evaluated using lysimeters in a semi-arid region for wheat and maize. Different equations for aerodynamic resistance (r a) and canopy resistance (r c) were tested in the PM equation and they were ranked using statistical analysis. It was shown that the combined method of r a and r c in FAO-56 does not lead to a good prediction of ETc for wheat and maize in comparison with the lysimeter-measured data. The results indicated that a modified equation for r c was the most accurate method for both wheat and maize. Using this equation, the suggested model of FAO-56 and another investigation for r a led to the best results for wheat and maize, respectively. Furthermore, it was shown that the previously modified equation for r c was newly modified as a function of vapor pressure deficit (VPD) and the results were as accurate as before. Therefore, an equation as a function of VPD can be used when solar radiation (R n) is not available easily.
High-Availability Service Chain Realization Theory

We propose a novel approach to realize service chains with specific availability and latency requirements by dimensioning a NFV infrastructure (NFVI). Our contribution leads to a theory that is based on extending SDN capacity computation to the domain of NFV. We do so by expanding upon the requirements of availability of service chains (SCs), projecting these onto a NFVI. A key contribution of our theoretical model is the inclusion of delay bounds while traversing a SC. The combination of availability, delay bounds and computing SC granularity is achieved through modeling SCs as directed acyclic graphs in a NFVI, and acted upon using network calculus, eventually leading to a SC realization theorem. This SC realization theorem answers a question as to how many SCs of a particular type and granularity can be provisioned on a particular DC. A second fast approach towards computing SCs realization on a NFVI is also presented. The theorems are verified numerically for DCs of size upto 12,228 servers (nodes). Results for scalability and latency are presented.
A Stochastic Attention CNN Model for Rumor Stance Classification

Rumor stance classification is a task for identifying different stances about specific social media posts, and it is considered as an important step to prevent rumors from spreading. However, most previous studies for the rumor stance classification, which focus on content features of posts, ignore the textual information granularity and reading (or writing) habits of the public. In social networks, people may have different stances towards unverified posts with different reading habits. Based on this observation, we propose a Stochastic Attention Convolutional Neural Net (SACNN) under different textual information granularity to catch different habits of the public for the rumor stance classification task. Specifically, being treated as view-windows of readers, convolutional kernels in a SACNN contain trainable convolutional kernels and stochastic untrainable convolutional kernels, and different sizes of the stochastic convolutional kernels are used to simulate casual online reading habits and to extract course-grained features such as phrase features. After the convolutional layer, fine-grained features such as keywords can be extracted by a pooling layer. The experiments show that the average accuracy and F1 score of our proposed model are respectively 0.26% and 0.46% higher than the state-of-the-art results on the rumor stance classification task on PHEME dataset.
Cyber Attack Detection for a Nonlinear Binary Crude Oil Distillation Column

Cyber security for Industrial Control Systems (ICS) is increasingly becoming an area of research as advance network technology continues to evolve providing extended connectivity between cyber world and control system hardware in a plant. In this paper, we present attack detection and isolation technique for sensor attacks on a binary distillation column as an important ICS field which can be targeted by attackers. At first, we present a hybrid model of a DC where the DC has been designed in Aspen Plus Dynamics and the control system has been implemented using Simulink. Then, the mathematical model of various sensor attacks which have been considered for the study are presented. Following that an attack detection and isolation technique based on state estimation using Luenberger observer has been presented. Finally we present the results illustrating effect of sensor attacks on DC performance along with validating the effectiveness of the proposed attack detection and isolation method.
Detection Thresholds of Tactile Perception in Virtual Environments

We investigate the detection thresholds of tactile perception in virtual environments. We estimate the absolute detection thresholds for electrovibration stimuli under different parameters of excitation signals (waveform and frequency). Experimental results and subjective comments suggest that participants’ tactile sensitivities to electrovibration stimuli are decreased in virtual environments. The frequency significantly affects the absolute detection thresholds, and lower voltages can be perceived under 60Hz for the square wave than the sinusoidal wave. Our findings provide a foundation to future studies aiming at modelling and simulating desired tactile perception of objects in virtual reality.
Introducing Mental Workload Assessment for the Design of Virtual Reality Training Scenarios

Training is one of the major use cases of Virtual Reality (VR) due to the flexibility and reproducibility of VR simulations. However, the use of the user’s cognitive state, and in particular mental workload (MWL), remains largely unexplored in the design of training scenarios. In this paper, we propose to consider MWL for the design of complex training scenarios involving multiple parallel tasks in VR. The proposed approach is based on the assessment of the MWL elicited by each potential task configuration in the training application. Following the assessment, the resulting model is then used to create training scenarios able to modulate the user’s MWL over time. This approach is illustrated by a VR flight training simulator based on the Multi-Attribute Task Battery II, which solicits different cognitive resources, able to generate 12 different tasks configurations. A first user study (N = 38) was conducted to assess the MWL for each task configuration using self-reports and performance measurements. This assessment was then used to generate three training scenarios in order to induce different levels of MWL over time. A second user study (N = 14) confirmed that the proposed approach was able to induce the expected mental workload over time for each training scenario. These results pave the way to further studies exploring how MWL modulation can be used to improve VR training applications.
Demonstrating COLIBRI VR, an Open-Source Toolkit to Render Real-World Scenes in Virtual Reality

This demonstration showcases an open-source toolkit we developed in the Unity game engine to enable authors to render real-world photographs in virtual reality (VR) with motion parallax and view-dependent highlights. First, we illustrate the toolset’s capabilities by using it to display interactive, photorealistic renderings of a museum’s mineral collection. Then, we invite audience members to be rendered in VR using our toolkit, thus providing a live, behind-the-scenes look at the process.
Categorization of Contact Events as Intended or unintended using Pre-Contact Kinematic Features

Contact events are essential to the execution of manipulation tasks in virtual reality because they mark the different phases of an interaction, e.g. initial hand-object or tool-object contact. Generally, the nature of contact events can be distinguished in two categories: intended and unintended. Whereas intended contacts typically signalize the successful execution of a task, unintended contacts are the result of bad motor planning or execution, or are caused by accidental movements.Here we explore the use of kinematic features to distinguish intended and unintended contacts during an object placing task executed in virtual reality. A handheld object was placed over a horizontal surface with a vertical planar movement. The experimental setup enabled us to generate unintended contacts by triggering unexpected interruptions during the placing movement.Experimental results show significant differences between distributions of the kinematic features across intended and unintended contacts. These results indicate that the proposed kinematic features enable a robust distinction between intended and unintended contacts independent of substantial variations of movement properties (amplitude, duration, velocity).
Can We Build a Search Engine over Spark?

Search engines have to deal with a huge amount of data in scalable and efficient ways to produce effective search results. In this paper, we address the problem of building an efficient and scalable experimental search engine over Spark, an in-memory distributed big data processing framework. The proposed system, SparkIR, can serve as a research framework for conducting information retrieval (IR) experiments. SparkIR supports document-based partitioning scheme for indexing and document-at-a-time (DAAT) for query evaluation. Moreover, it offers static pruning (using champion list) to improve the retrieval efficiency. We evaluated the performance of SparkIR using ClueWeb12-B13 collection that contains about 50M English Web pages. Experiments over different subsets of the collection showed that SparkIR exhibits reasonable efficiency and scalability performance overall for both indexing and retrieval.
Effect of Color Enhancement on Early Detection of Skin Cancer using Convolutional Neural Network

The prevalence of skin cancer is significantly increasing each year due to the damage of the ozone layer in the atmosphere that makes more ultraviolet radiation passing through. Knowing this situation, it is important to develop a simple image processing technique that can be used in the early detection of skin cancer. The skin cancer detection becomes highly active research since 2016 due to the ISIC has released a large skin cancer image dataset. Several types of research propose hand-crafted image processing with machine learning, but the technique is a little bit complicated. The aim of this study is to investigate the effect of simple image processing technique, contrast enhancement using CLAHE and MSRCR as contrast enhancements with CNN. The results show that compares to MSRCR, CLAHE is more suitable to be used in color image enhancement for early detection of skin cancer using CNN. But, the original and CLAHE-enhanced dataset give the same accuracy in the training and validation. The main contribution of this study is that the image contrast enhancement is not required for the skin cancer screening purpose.
N-gram Approximation of LSTM Recurrent Language Models for Single-pass Recognition of Hungarian Call Center Conversations

Modeling the less constrained grammar and word order of conversational speech poses a great challenge to conventional back-off n-gram language models (BNLMs). Recurrent Neural Network Language Models (RNNLMs) can provide much better predictions, however, in real-time Automatic Speech Recognition (ASR) systems (e.g. speech dictation) the process delay due to two-pass decoding can not be tolerated. In this paper, we investigate n-gram-based language modeling techniques that can be applied in a single-pass ASR system to approximate the performance of a RNNLM. Perplexity and word error rate (WER) of BNLMs, BNLM approximation of RNNLMs (RNNBNLM) and RNN n-grams are compared on our Hungarian ASR task. Rich morphology of agglutinative languages (like Hungarian) is often handled by using subword language models, hence we evaluated subword BNLMs, RNN-BNLM and RNN ngrams, as well. It was found that a subword RNN-BNLM can approach the performance of a RNN 4-gram model, and recover roughly 40% of the RNNLM perplexity reduction. All in all, we managed to improve WER of our call center speech transcription system 8% relative without affecting its real-time operation.
Diploidization in Gossypium hirsutum as Indicated by Glandless-Stem and Boll Inheritance

The genotype of all but one of the tested stocks of amphidiploid G. hirsutum (2AhDh) and G. barbadense (2AbDb) is Gl1Gl1 for glanded stem and boll and gl1gl1 for glandless stem and boll. Stock Z101 of G. barbadense has Gl1Gl1 and Gl5 Gl5, its duplicate. Genes for glanded stem occur in diploid Gossypium relatives of the A and D genomes of the amphidiploids; new amphidiploids involving these diploids show Gl genes at two loci, one in each genome. The genes Gl5 arb and Gl1thu were transferred to the A and D genomes, respectively, from G. arboreum (2A2) and G. thurberi (2D1) of amphidiploid 2A2D1. Gl1ari as transferred to the Dh genome from G. aridum (2D4) was allelic with Gl1 of G. hirsutum; its allelism and known D diploid origin place the Gl1 locus on the Dh genomes of G. hirsutum. Failure to find Gl genes at two loci, one per genome, in G. hirsutum was attributed to diploidization. In G. barbadense diploidization probably occurred by mutation of Gl5 of the Ah genome to gl, while Gl1 of the Dh remained active. This possibility for diploidization in G. hirsutum could not be positively excluded. However, the absence of a Gl gene at an Ah locus could be due to a loss of chromatin containing the Gl5 locus in the settling-down of an ancestral amphidiploid into present-day, strictly-bivalent pairing. Supporting evidence, taken from the literature on Gossypium, was discussed to support changes in chromatin during the evolutionary process of G. hirsutum.
Multi-Lane Capsule Network for Classifying Images With Complex Background

Capsule Network (CapsNet) is a novel structure for deep neural network, mapping the region of target instance to vectors and matrices rather than scalars. This process is enabled by dynamic routing algorithm which help CapsNet to achieve more robust capacity with fewer parameters than traditional CNNs. However, one drawback of Capsule is that it turns to account for everything in the image, which leads to a poor performance when the backgrounds are too varied to model with a reasonable sized net. We proposed a multi-lane capsule network with strict-squash (MLSCN) to solve this problem. In MLSCN, we designed a novel Capsule based network structure, replaced the Squash function and optimized the implementation of dropout. To validate modification proposed in the paper, we conducted extensive experiments on three public datasets which include MNIST, affNIST and CIFAR10. Ablation experiments were also conducted to analyze contributions of each component of MLSCN. Experimental results show that MLSCN outperforms the original CapsNet in multiple benchmarks. Our model boosts the classification accuracy of CIFAR10 about 17% with negligible parameter increase compare to original CapsNet. Besides, the proposed model achieves an accuracy of 65.37%, while original CapsNet is only 41.62% on MNIST-BC.