Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sentiment Analysis Dataset for Trustworthiness Evaluation

While deep learning models have greatly improved the performance of most artificial intelligence tasks, they are often criticized to be untrustworthy due to the black-box problem. Consequently, many works have been proposed to study the trustworthiness of deep learning. However, as most open datasets are designed for evaluating the accuracy of model outputs, there is still a lack of appropriate datasets for evaluating the inner workings of neural networks. The lack of datasets obviously hinders the development of trustworthiness research. Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability. To evaluate these factors, our dataset contains diverse annotations about the challenging distribution of instances, manual adversarial instances and sentiment explanations. Several evaluation metrics are further proposed for interpretability and robustness. Based on the dataset and metrics, we conduct comprehensive comparisons for the trustworthiness of three typical models, and also study the relations between accuracy, robustness and interpretability. We release this trustworthiness evaluation dataset at \url{https://github/xyz} and hope our work can facilitate the progress on building more trustworthy systems for real-world applications.
A New Basis for Sparse Principal Component Analysis

Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a $p \times k$ matrix) is approximately sparse. We propose a method that presumes the $p \times k$ matrix becomes approximately sparse after a $k \times k$ rotation. The simplest version of the algorithm initializes with the leading $k$ principal components. Then, the principal components are rotated with an $k \times k$ orthogonal rotation to make them approximately sparse. Finally, soft-thresholding is applied to the rotated principal components. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. One consequence is that a sparse component need not to be a leading eigenvector, but rather a mixture of them. In this way, we propose a new (rotated) basis for sparse PCA. In addition, our approach avoids"deflation"and multiple tuning parameters required for that. Our sparse PCA framework is versatile; for example, it extends naturally to a two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We provide evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications -- sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of social networks, we demonstrate the modern usefulness of sparse PCA in exploring multivariate data.
In-situ visualization of natural hazards with Galaxy and Material Point Method

—Visualizing regional-scale landslides is the key to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a limited subset of simulation data and are not scalable to rendering exascale models with billions of particles. In-situ visualization is a technique of rendering simulation data in real-time, i.e., rendering visuals in tandem while the simulation is running. In this study, we develop a scalable N:M interface architecture to visualize regional-scale landslides. We demonstrate the scalability of the architecture by simulating the long runout of the 2014 Oso landslide using the Material Point Method coupled with the Galaxy ray tracing engine rendering 4.2 million material points as spheres. In-situ visualization has an amortized runtime increase of 2% compared to non-visualized simulations. The developed approach can achieve in-situ visualization of regional-scale landslides with billions of particles with minimal impact on the simulation process.
Learning Cell Nuclei Segmentation Using Labels Generated With Classical Image Analysis Methods

Creating manual annotations in a large number of images is a tedious bottleneck that limits deep learning use in many applications. Here, we present a study in which we used the output of a classical image analysis pipelineas labels when training a convolutional neural network(CNN). This may not only reduce the time experts spend annotating images but it may also lead to an improvement of results when compared to the output from the classical pipeline used in training. Inour application, i.e.,cell nuclei segmentation,we generated the annotations using CellProfiler(a tool for developing classical image analysis pipelines for biomedical applications)and trained on them a U-Net-based CNN model. The best model achieved a 0.96 dice-coefficient of the segmented Nuclei and a 0.84 object-wise Jaccard indexwhich was better than the classical method used for generating the annotations by 0.02and 0.34, respectively. Our experimental results show that in this application, not only such training is feasiblebut also thatthe deep learning segmentationsare a clear improvement compared to the output from the classical pipelineused for generating the annotations.
AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

Abstract. An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).
Development of new gas chromatography/mass spectrometry procedure for the determination of hexahydrophthalic anhydride in unsaturated polyester resins

This paper reports the first contribution to the determination of the hexahydrophthalic anhydride in unsaturated polyester resins by automated head space/solid-phase micro extraction and gas chromatography/mass spectrometry analysis. After a preliminary reaction of the acid anhydride with water to form carboxylic acids, a rapid on-sample derivatization using trimethyloxonium tetrafluoroborate was used. A new autosampler platform was proposed in this study by using the Multi Fiber Exchange device. The limits of detection for one mg of unsaturated polyester resin were 2.9 pg for the cis-1,2-cyclohexanedicarboxylic acid and 8.0 pg for trans-1,2-cyclohexanedicarboxylic acid, whereas the limits of quantification were 14.5 and 29.7 pg, respectively. The equilibrium and kinetics of this substance vs solid-phase microextraction are theoretically evaluated and discussed.
Enhancing Security between V2v, V2i, V2r Using Cryptosystem for Vanet

: Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of VANETs over the past few years. VANET consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. VANET developed an advanced traffic signaling system for message transmission.so when data transmission from source to destination node various attack and introduces in system and reduce PDR, Information Loss. In these paper we will work on The suggested cryptosystem like MD5 and ECC using proposed scheme increase efficiency, latency, Packet Delivery ratio, Authentication, Scalability and security between communication node.
Non‐polar (11$ \bar 2 $0) InGaN quantum dots with short exciton lifetimes grown by metal‐organic vapour phase epitaxy

InGaN quantum dot (QD) structures have been grown by metal organic vapour phase epitaxy (MOVPE) on non-polar (11-20) GaN surfaces by employing an anneal step in nitrogen immediately after the growth of the InGaN. Here, we compare the growth of such structures on pseudo-substrates grown using an epitaxial lateral overgrowth (ELOG) technique and on pseudo-substrates grown using a simpler in situ SiNx interlayer. The less complex defect reduction approach results in a significantly higher defect density, but does not detrimentally effect the QD formation. For both types of pseudo-substrate, sharp peaks with resolution limited widths are observed in both cathodoluminescence at 9 K and micro-photoluminescence at 4.2 K. The QDs demonstrate significantly reduced exciton lifetimes compared to structures grown on c-plane, which has advantages for possible applications in single photon sources. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Chemical analysis and potential use of the tree Combretum erythrophyllum grown on gold and uranium mine tailings seepage

Combretum erythrophyllum is a southern African riparian tree, which is widely used in traditional medicine to combat microbial infections. More than seven flavonoids with antimicrobial properties, including apigenin, genkwanin, rhamnocitrin and kaempferol, have been previously isolated from methanolic extracts of leaf material. The species occurs naturally in the Witwatersrand Basin gold fields, and is included in experimental trials of acid and salt-tolerant trees for seepage control purposes around gold mine tailings at AngloGold Ashanti Ltd (the Mine Woodlands Project). In this study, C. erythrophyllum, harvested from the mine woodland trials, was investigated as a source of secondary metabolites for the control of food-borne pathogens. The phenolic profiles of methanolic leaf extracts from trees growing on acid mine drainage and non-impacted surrounding soils were compared to those from a natural population, using high performance liquid chromatography. The concentrations of apigenin, genkwanin and kaempferol are currently being determined in dry leaf material. These extracts were evaluated for their ability to inhibit the growth of Listeria monocytogenes and Candida albicans biofilm formation. No significant differences between the phenolic profiles of samples from different substrata or regions could be discerned. Selected extracts of both mine and natural populations were able to significantly inhibit biofilm formation in the case of C. albicans. The activity against L. monocytogenes was substantially lower. Micrographs obtained using confocal laser scanning microscopy confirmed the inhibitory effects. These results demonstrate the potential of C. erythrophyllum as a good candidate for application in food safety. Such commercial attributes can add value to C. erythrophyllum and other species used for mine rehabilitation, and provide a basis for the development of secondary industries based on renewable natural resources alongside mine closure.
Data Mining and Machine Learning: Implementation model, Applications, Challenges and Issue in Sentiment Analysis System - A Survey

: Sentiment analysis is the process of determining the opinion or filling of a piece of text. Companies across the word implementing machine learning to do it automatically. It is a supper use of opinion inside the text. Once we understand how the customer fills after analyzing their comments and reviews. One can identify what kind of things they like or dislike and built things like recommendation system. Or more target marketing systems for them. This paper discussed the different implantation model of sentiment analysis system with their advantage and disadvantages. This paper also included the some real world application where sentiment analysis system can be used. Different implementation challenges and other issues related to sentiment analysis system explored in this paper.
A mm-Wave Coupler Design

An unsymmetric coupler has been designed for a planar mn-wave (120 GHz) structure. Measurement results, which verify the design, are also presented. The coupler design is based on three requirements: matching to the feeding waveguide, proper phase advance (namely half the phase advance of other cells) and surface fields comparable to those of the inner cells (to control dark current). This design differs from convent’ional coupler designs by two points. First, the structure is open and special care is necessary t,o avoid the excit.ation of TEM-waves. Second, because of the small size of the structure, the geometry is adapted to micro-mechanic fabricational techniques (e.g. LIGA)
Learning CNN Strategy for Voice Disorder Classification and Detection

: Voice disorder is the abnormal tone of voice which is produced by vocal cord infecting the viruses. If it is not detected in time then it will lead to critical conditions like permanent loss of voice or facing some other problems related to voice. In some patients the voice disorder frequently occurs but due to costly medical policies they ignore it or delay taking treatment. For that various novel techniques are used like SVM, DNN, ANN and CNN. Most of the researchers work on SVM as a deep learning technique to detect the voice disorder, but CNN is also a very efficient method for voice disorder detection. So in this paper we discussed the CNN method which is another option for voice disorder detection.in this paper we will study about the CNN methodology which gives the accuracy 92-95% using Saarbrücken voice database.
On the Usability of Self Sovereign Identity Solutions

In the absence of a unique identity layer on the Internet, many identity solutions have evolved over time—examples include standalone username and password pairs, Single Sign On, and Federated Identity Management. Privacy and security risks for identity owners and liability for identity issuers and verifiers, however, are still alarmingly present. Self-Sovereign Identity (SSI) solutions are new technologies that recognize the need to keep user identity privately stored in user-owned devices, securely verified by identity issuers, and only revealed to verifiers and relying parties as needed. Many commercial SSI solutions are already available to users, issuers, and verifiers. As other researchers have pointed out, usability remains a pressing unknown in the existing SSI solutions. We study five of the most commonly used SSI solutions: uPort, Connect.me, Trinsic, Jolocom, and ShoCard (now PingID) with respect to their usability. We identify some concrete usability problems and suggest ways to resolve them. Our work recognizes that identifying, prioritizing, and implementing the non-functional requirement of usability in SSI solutions is essential for their adoption.
Kalimatun Sawa’ as The Basis of Religious Tolerance (Interpretation of Nurcholish Madjid’s Thoughts Based on Paul Ricoeur’s Hermeneutics)

This paper aims to understand deeply the thoughts of Nurcholish Madjid about kalimatun sawa’ as the basis of religious tolerance. The approach used is Paul Ricoeur’s Hermeneutics. The main issues that will be examined include interpretation theory in Paul Ricoeur’s Hermeneutics, Nurcholish Madjid’s thoughts about kalimatun sawa’, as well as the application of interpretation theory in understanding kalimatun sawa’. Through this understanding, it is expected to find the meaning of kalimatun sawa’ as well as its contribution to religious tolerance in Indonesia.
Build your own tensor network library: DMRjulia I. Basic library for the density matrix renormalization group

An introduction to the density matrix renormalization group is contained here, including coding examples. The focus of this code is on basic operations involved in tensor network computations, and this forms the foundation of the DMRjulia library. Algorithmic complexity, measurements from the matrix product state, convergence to the ground state, and other relevant features are also discussed. The present document covers the implementation of operations for dense tensors into the Julia language. The code can be used as an educational tool to understand how tensor network computations are done in the context of entanglement renormalization or as a template for other codes in low level languages. A comprehensive Supplemental Material is meant to be a "Numerical Recipes" style introduction to the core functions and a simple implementation of them. The code is fast enough to be used in research and can be used to make new algorithms.
First Responders Got Wings: UAVs to the Rescue of Localization Operations in Beyond 5G Systems

Natural and human-made disasters have dramatically increased in recent decades. Given the strong relationship between first responders' localization time and the final number of deaths, the modernization of search and rescue operations has become imperative. In this context, unmanned-aerial-vehicle-based solutions are the most promising candidates to take on the localization challenge by leveraging emerging technologies such as artificial Intelligence, reconfigurable intelligent surfaces, and orthogonal time-frequency space modulations. In this article, we capitalize on such recently available techniques by shedding light on the main challenges and future opportunities to boost the localization performance of state-of-the-art techniques to give birth to unprecedentedly effective missing victims localization solutions.
The air-water interface of condensed water microdroplets does not produce H$_2$O$_2$

Recent reports on the production of hydrogen peroxide (H$_2$O$_2$) on the surface of condensed water microdroplets without the addition of catalysts or additives have sparked significant interest. The underlying mechanism is speculated to be ultrahigh electric fields at the air-water interface; smaller droplets present higher interfacial area and produce higher (detectable) H$_2$O$_2$ yields. Herein, we present an alternative explanation for these experimental observations. We compare H$_2$O$_2$ production in water microdroplets condensed from vapor produced via (i) heating water to 50-70 {\deg}C and (ii) ultrasonic humidification (as exploited in the original report). Water microdroplets condensed after heating do not show any enhancement in the H$_2$O$_2$ level in comparison to the bulk water, regardless of droplet size or the substrate wettability. In contrast, those condensed after ultrasonic humidification produce significantly higher H$_2$O$_2$ quantities. We conclude that the ultrasonication of water contributes to the H$_2$O$_2$ production, not droplet interfacial effects.
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms

Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2D image analysis/ understanding or 3D point cloud modeling and segmentation algorithms to detect (i.e., recognize and localize) road potholes from vision sensor data, e.g., RGB images and/or depth/disparity images. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large well-annotated dataset for CNN training. In this regard, while computer vision-based methods were the mainstream research trend in the past decade, machine learning-based methods were merely discussed. Recently, we published the first stereo vision-based road pothole detection dataset and a novel disparity transformation algorithm, whereby the damaged and undamaged road areas can be highly distinguished. However, there are no benchmarks currently available for state-of-the-art (SoTA) CNNs trained using either disparity images or transformed disparity images. Therefore, in this paper, we first discuss the SoTA CNNs designed for semantic segmentation and evaluate their performance for road pothole detection with extensive experiments. Additionally, inspired by graph neural network (GNN), we propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation. Our experiments compare GAL-DeepLabv3+, our best-performing implementation, with nine SoTA CNNs on three modalities of training data: RGB images, disparity images, and transformed disparity images. The experimental results suggest that our proposed GAL-DeepLabv3+ achieves the best overall pothole detection accuracy on all training data modalities. The source code, dataset, and benchmark are publicly available at mias.group/GAL-Pothole-Detection.
Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention

Automatically describing video, or video captioning, has been widely studied in the multimedia field. This paper proposes a new task of sensor-augmented egocentric-video captioning, a newly constructed dataset for it called MMAC Captions, and a method for the newly proposed task that effectively utilizes multi-modal data of video and motion sensors, or inertial measurement units (IMUs). While conventional video captioning tasks have difficulty in dealing with detailed descriptions of human activities due to the limited view of a fixed camera, egocentric vision has greater potential to be used for generating the finer-grained descriptions of human activities on the basis of a much closer view. In addition, we utilize wearable-sensor data as auxiliary information to mitigate the inherent problems in egocentric vision: motion blur, self-occlusion, and out-of-camera-range activities. We propose a method for effectively utilizing the sensor data in combination with the video data on the basis of an attention mechanism that dynamically determines the modality that requires more attention, taking the contextual information into account. We compared the proposed sensor-fusion method with strong baselines on the MMAC Captions dataset and found that using sensor data as supplementary information to the egocentric-video data was beneficial, and that our proposed method outperformed the strong baselines, demonstrating the effectiveness of the proposed method.