Reviewerly/exHarmony huggingface dataset:
Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area

Loess is an important soil type that is widespread in the Loess Plateau of northwest China. However, mining exploitation, beneficiation, and metallurgy have led to inorganic contamination of soils that threatens the health of residents. The regular absorption peak shift of near-infrared (NIR) spectra in loessal soils represents a new method of soil environmental assessment based on field reflectance spectroscopy and hyperspectral remote sensing. Specifically, the NIR features of loessal soil will shift in response to changes in the soil composition and microstructure induced by heavy metal pollution. This study collected 27 samples from notable regions in the study area. Mid-infrared (MIR) spectral analysis, NIR spectral analysis, modified seven-step Tessier sequential extraction, and X-ray diffraction were used to analyze the band shift phenomenon of MIR and NIR features. The alignment of NIR bands was determined via the correlation between NIR and MIR bands associated with the vibration variations of the hydroxyl group. The correlations established by NIR band positions and exchangeable Cd cations were also analyzed. The results were then discussed according to the mineralogical characteristics of the heavy metal cations adsorbed on the surface and interlayer sites of clay minerals. These results can be used as a reference for the application of NIR technology to detecting heavy metal contamination in the soil of mining regions.
Deep Learning Based Mammogram Image Classification

Breast cancer is one of the type of cancer that appears most commonly in women. Mortality of women who are suffering from breast cancer is very high so it is required to detect the cancer in the very early stage and treat the breast cancer patients to save their life. This research helps the radiologist to identify the mammogram image as normal or abnormal. We have applied Convolution neural networks and built different models with different combinations of layers for the purpose of identifying the mammogram image as normal and abnormal. We have conducted experiments on different deep learning models such as alexnet, inception network and Visual Geometry Group 16. We have conducted experiments on raw images and on the processed images. We obtained different performance accuracy for each model on two different datasets such as Mammograms-MIAS and Curated Breast Imaging Subset of DDSM.
DSC_NEXT: Document-Level Sentiment ClassificatioN with EXTensional-Information

Document-level sentiment classification is to assign an overall sentiment polarity to an opinion document. Some researchers have already realized that, in addition to document texts, extensional-information such as product features and user preferences can be quite useful. Many previous studies represent them as ID-type extensional-information and incorporate them into deep learning models. However, they ignore the descriptive extensional information that is also useful for document representations. This paper covers the following aspects: (1) introduces the Description of Opinion Target (DOT), a new extensional-information for document-level sentiment classification, (2) builds the Document-level Sentiment ClassificatioN with EXTensional-information (DSC_NEXT) dataset which consists of three datasets: IMDB_NEXT, Yelp_NEXT and CMRDB_NEXT and (3) validates the effectiveness of DOT by performing experiments based on current state-of-the-art (SOTA) document-level sentiment analysis methods. Implications for using extensional-information in neural network models are also considered.
Detection Accuracy for Evaluating Compositional Explanations of Units

The recent success of deep learning models in solving complex problems and in different domains has increased interest in understanding what they learn. Therefore, different approaches have been employed to explain these models, one of which uses human-understandable concepts as explanations. Two examples of methods that use this approach are Network Dissection [5] and Compositional explanations [23]. The former explains units using atomic concepts, while the latter makes explanations more expressive, replacing atomic concepts with logical forms. While intuitively, logical forms are more informative than atomic concepts, it is not clear how to quantify this improvement, and their evaluation is often based on the same metric that is optimized during the search-process and on the usage of hyper-parameters to be tuned. In this paper, we propose to use as evaluation metric the Detection Accuracy, which measures units’ consistency of detection of their assigned explanations. We show that this metric (1) evaluates explanations of different lengths effectively, (2) can be used as a stopping criterion for the compositional explanation search, eliminating the explanation length hyper-parameter, and (3) exposes new specialized units whose length 1 explanations are the perceptual abstractions of their longer explanations. Code available at https://github.com/KRLGroup/detacc-compexp .
Fast Conditional Network Compression Using Bayesian HyperNetworks

We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g., a context involving only a subset of classes or a context where only limited compute resource is available. To solve this, we propose an efficient Bayesian framework to compress a given large network into much smaller size tailored to meet each contextual requirement. We employ a hypernetwork to parameterize the posterior distribution of weights given conditional inputs and minimize a variational objective of this Bayesian neural network. To further reduce the network sizes, we propose a new input-output group sparsity factorization of weights to encourage more sparseness in the generated weights. Our methods can quickly generate compressed networks with significantly smaller sizes than baseline methods.
Blockchain Technology Concept for Improving Supply Chain Traceability in the Ivory Market

Supply chain traceability is gaining momentum as a means to gain visibility across the supply chain. In order to curb poaching in wildlife sector and harvesting of resources such as ivory, there is need to introduce full proof technologies. This article proposes use of blockchain in tracing the supply chain of ivory and other wildlife products, from source to destination. The article is based on literature review on wildlife practices. Ivory trade participants were identified and mapped into a blockchain model using blockchain modeling techniques. The proposed blockchain approach allows transaction recording as blocks and visibility to relevant participants.
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer

Text style transfer aims to change the style of sentences while preserving the semantic meanings. Due to the lack of parallel data, the Denoising Auto-Encoder (DAE) is widely used in this task to model distributions of different sentence styles. However, because of the conflict between the target of the conventional denoising procedure and the target of style transfer task, the vanilla DAE can not produce satisfying enough results. To improve the transferability of the model, most of the existing works combine DAE with various complicated unsupervised networks, which makes the whole system become over-complex. In this work, we design a novel DAE model named Style-Enhanced DAE (SE-DAE), which is specifically designed for the text style transfer task. Compared with previous complicated style-transfer models, our model do not consist of any complicated unsupervised networks, but only relies on the high-quality pseudo-parallel data generated by a novel data refinement mechanism. Moreover, to alleviate the conflict between the targets of the conventional denoising procedure and the style transfer task, we propose another novel style denoising mechanism, which is more compatible with the target of the style transfer task. We validate the effectiveness of our model on two style benchmark datasets. Both automatic evaluation and human evaluation show that our proposed model is highly competitive compared with previous strong the state of the art (SOTA) approaches and greatly outperforms the vanilla DAE.
A Feature Fusion Based Dense Estimation Model for 3D Human Pose

With the development of technology, especially the rise of some practical application technology such as AR, VR, and human-computer interaction, 3D human pose estimation has shown more and more extensive prospects. However, in existing 3D human pose estimation methods, there are still some issues. For example, the lack of multi-scale and diversity in the analysis of human posture leads to insufficient refinement in fitting and segmentation of body parts, and eventually causing unsatisfactory performance. In this paper, we propose a feature-fusion-based dense estimation model (FFD model) for 3D human pose estimation to solve the above problems. Specifically, we design a feature fusion module obtaining multi-scale feature information, which not only captures the overall information about the human posture, but also retains the position information and detailed information of the human body part. Furthermore, we use a dense feature extraction backbone network to expand the diversity of receptive fields, thus making the human pose estimation more refined and precise. Experiments on the COCO dataset show the superiority of FFD model compared with the baseline model.
Mapping the Photoresponse of the Quantum-Dot Based Photon-Number-Resolving Detector

Efficient and versatile photon-number resolving detectors are critical to the development of future communication systems. The quantum-dot, optically-gated, field-effect transistor (QDOGFET) is one such detector. Utilizing quantum dots (QDs), tiny islands of semiconductor, imbedded in a transistor, QDOGFETs have been shown to exhibit single-photon sensitivity and photon-number-resolving (PNR) capabilities. A photon is detected when it photocharges a QD, which alters the amount of current flowing through the transistor by screening the gate field. Crucial to the resolving power is that each charged QD produce the same response, regardless of its location within the active area of the device. Here, we investigate the extent spatial nonuniformities in the QDOGFET’s response to light limit its ability to distinguish different numbers of photons. By using an optical-scanning microscope (OSM), contour plots of a QDOGFET’s response are acquired that show that the device exhibits localized “hotspots” where it is particularly sensitive to photons. The spatial resolution of the microscope is enhanced by capping the QDOGFET with a solid-immersion lens (SIL). We present experimental results that show how the hotspots depend on bias conditions and help decipher the root cause of the nonuniformities.
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator

As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities. Designed and trained from certain statistical distributions of data, AI's deep neural networks (DNNs) remain vulnerable to deceptive inputs that violate a DNN’s statistical, predictive assumptions. Before being fed into a neural network, however, most existing adversarial examples cannot maintain malicious functionality when applied to an affine transformation. For practical purposes, maintaining that malicious functionality serves as an important measure of the robustness of adversarial attacks. To help DNNs learn to defend themselves more thoroughly against attacks, we propose an affine-invariant adversarial attack, which can consistently produce more robust adversarial examples over affine transformations. For efficiency, we propose to disentangle current affine-transformation strategies from the Euclidean geometry coordinate plane with its geometric translations, rotations and dilations; we reformulate the latter two in polar coordinates. Afterwards, we construct an affine-invariant gradient estimator by convolving the gradient at the original image with derived kernels, which can be integrated with any gradient-based attack methods. Extensive experiments on ImageNet, including some experiments under physical condition, demonstrate that our method can significantly improve the affine invariance of adversarial examples and, as a byproduct, improve the transferability of adversarial examples, compared with alternative state-of-the-art methods.
Protecting Web Application Code and Sensitive Data with Symmetric and Identity-Based Cryptosystems

How to protect the security of web application code and sensitive data has become one of the primary concerns in web services. In this paper, symmetric cryptosystem combined with identity-based public key cryptosystem is proposed to protect web application programs and sensitive data. The key generation center generates the private and public key pairs for the web server and users, which are used to implement identity authentication and data integrity. When web application code and sensitive data are transmitted between the web server and the user's browser, a random session key is generated for encrypting the web application code and sensitive data. Meanwhile, a digital signature is generated and added to the encrypted program code and sensitive data. The security analysis shows that the proposed security scheme can ensure the confidentiality, integrity and authentication of web application code and sensitive data.
Croplook: Crop Trading Portal for Kapatagan Upland Farmers Association (Kufa) with SMS Notification and Data Statistics to Exhibit the Most and Least Profitable Crops

Crop Trading involves the buying and selling of crops that have been produced from the farming industries. It gives greater access for consumers or buyers to various crops, often at more affordable prices. With the use of technology, the farmers will be able to adopt modern ways of farming like this portal, as it gives more opportunities for farmers to offer their crops and expand their accessibility to buyers. This study aimed to provide a website for farmers and buyers who have problems in terms of crop trading. The researchers used Rapid Application Development as a method in making the project possible. This methodology helped the team to give much faster development and produce a high-quality system. Identifying the errors through testing and going back to design and development to find and fix the mistakes to avoid application failure. After thorough research and development, the researchers were able to meet the objectives of the application. The website will showcase different crops as what farmers are input provided their necessary information for easy transactions for buyers, showing currently available and unavailable crops in a community, SMS notification for farmers to notify if some buyers reserved their products, and a message for customers about their reservation of crops, an exhibition of most and the least profitable crops to provide support for farmers' decisions with the used of the statistical chart and chat box to enable farmers and customers to communicate.
Blokzincirde Anonim ve Devredilemez Biyometrik Dijital Kimlik

The first user centric Identity Management system on the Bitcoin Blockchain was introduced in 2017 by Augot et al.However, Bitcoin is not an anonymous cryptocurrency, therefore, privacy is not guaranteed.Hence, one aspect of the identity, namely biometrics should be processed in a privacy preserving manner and as encrypted.This way, non-transferability is guaranteed in addition to the anonymous processing of the most important personal identifier, namely sensitive biometric data.In fact, the first anonymous biometric identification system that guarantees these notions was described in 2018 on top of Zerocoin protocol.In this system, biometric data (fingerprint,face, iris), are stored as encrypted on the Blockchain.If there is an incoming identification request from the user to access a service, the biometric matching is performed by the nodes/miners in the encrypted domain and if a match is found, it is recorded on the Blockchain.In summary, through anonymous transfers, service providers complete the anonymous biometric identification procedure and provides the necessary access to the service.In this research article, we evalute recent privacy coins of Zerocash and Monero in addition to Zerocoin, and examine whether there exists better solutions in biometric based Identity Management systems with respect to efficiency and privacy.First, we describe anonymous biometric identification/authentication systems based on anonymous cryptocurrencies and then we modify them slightly to obtain anonymous Digital ID.Finally, we compare the analyzed privacy coins in order to find the cheapest solution.Initial results show that Cryptonote based Monero provides the most ideal system, leading to the evaluation of RingCT based systems guaranteeing a higher security level.
An Efficient Obstacle Detection Scheme for Low-Altitude UAVs Using Google Maps

Unmanned aerial vehicles have shown great potential in fast shipping and delivery, including delivering emergency support and services to the disaster (natural or manmade) hit areas where manual reach is infeasible. For accurate and effective emergency service delivery at the adverse sites, the UAVs need to fly close to the ground. Due to the low-altitude flight, there may be many stationary obstacles (e.g., trees and buildings) on the path of a UAV. Detecting these obstacles is crucial for successful mission accomplishment and evading crash. The existing obstacle detection methods limit the flying speed of a UAV due to the latency in processing and analysing the in-flight sensed data. To mitigate this, we propose to equip the UAV with the prior information of the obstacles on its trajectory. In case of an obstacle, the UAV slows down to avoid the obstacle; otherwise, it travels with a much higher speed. As experiment, we fed the UAVs with the satellite images from the Google Maps. It is observed that the proposed approach improves the overall flying speed of the UAVs to a great extent.
Simple contrapositive assumption-based argumentation part II: Reasoning with preferences

Simple contrapositive assumption-based argumentation frameworks are a general setting for structured argumentation, providing a robust approach to reasoning with arguments and counter-arguments. In this paper we extend these frameworks with priorities and introduce some new results concerning the Dung's semantics of the resulting formalisms.
Cardinal Correlated Oversampling for Detection of Malicious Web Links Using Machine Learning

The problem with malicious websites is growing day by day as it leads to the black listing of websites. The unauthorized websites are gathering the user’s database information and their assets. Few of the URLs are completely used as a host webpage to publish unrelated web content that signifies cyber-attacks. Cracking the presence of malicious website still pertains as open task due to the lack of web characteristics for malicious and benign websites. To overcome this problem, we are using machine learning techniques for detecting the malicious content and web links. Backgrounding the above, this paper used malicious webpage dataset extracted from UCI dataset repository for predicting the level of mushroom edibility. The categorization of malicious webpage classes is achieved in five ways. Firstly, the dataset consisting of 21 features with 1781 records and is preprocessed with encoding, feature scaling and missing values. Secondly, raw dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed. Thirdly, the cardinality free malicious dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed. Fourth, the correlated free malicious dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed. Fifth, the oversampled malicious dataset is fitted to all the classifiers with and without the presence of feature scaling and the performance is analyzed with precision, recall, accuracy, running time and F-score. Implementation analysis portrays that the decision tree classifier for raw, Cardinality reduced dataset tends to retain the accuracy with 97.1% before and after feature scaling. The random forest classifier with correlated free dataset tends to retain the 96.6% accuracy before and after feature scaling. Decision tree classifier for oversampled dataset tends to retain the accuracy with 98.4% before and after feature scaling. From the above analysis decision tree classifier is found to be more efficient in its accuracy with all raw, cardinality free and oversampled dataset.
Depth Guided Auto-Encoder Network for Human Pose Estimation

Human pose estimation (HPE) from the monocular image has been a recent hot topic which has great potential applications on health, safety and security monitoring. However, the estimation accuracy and the requirement of large scale training dataset are two main challenges in this area. In this paper, we introduce a depth guided auto-encoder (AE) network for human pose estimation from the monocular image. Specifically, the Image-to-Depth Auto-Encoder of the network is obtained during the pre-train stage using depth data. The embedding learnt from this AE is then used to train parameters of the decoder in the network for pose estimation. The learned AE from depth data aims to increase the human pose estimation accuracy. Furthermore, we can pre-train the AE using depth information from synthetic data which greatly reduces the need for training data. For evaluation, the proposed approach is applied to SURREAL which is a synthetic dataset. Our depth guided auto-encoder network outperforms the one without it. Experimental results demonstrate the effectiveness of the approach in this paper.
Educational Program for the Development of Digital Competencies of Teachers of Social Sciences in Secondary Vocational Education

The aim of the paper is to point out to a new educational program, which was created as one of the outputs of the TAČR ÉTA project (project number TL01000192) at the Czech Technical University in Prague, Masaryk Institute of Advanced Studies within the research grant of the Czech Republic []. The educational program develops the digital competencies of teachers, is focused on working with new technologies and the application of digital technologies in the educational process.