Reviewerly/exHarmony huggingface dataset:
Iso-dip Contouring: An Innovative Approach to Exploration for Concealed, Structure-controlled, Hydrothermal, Vein-type Uranium Mineralisation in Kanchankayi Sector of Kurlagere-Gogi-Gundanahalli Fault Zone, Yadgir District, Karnataka

Kanchankayi is located in the central part of the 55 km long Kurlagere-Gogi-Gundanahalli (KG) fault of Meso-Neoproterozoic Bhima basin of Southern India. Intensive integrated exploration in Kanchankayi has established a concealed, fracture-controlled, low-grade uranium deposit. Uranium mineralisation is mostly confined within the fractured limestone of Shahabad Formation and partly within fractured basement granite. Tectonic movements along the KG fault in Kanchankayi area have resulted in the northward thrusting of basement granite over the Bhima sediments forming a reverse fault geometry. Structural mapping and subsurface exploration has established that the uranium mineralisation is mainly confined to the fault zone. In general, the fault zone is characterised by steep southerly dipping to vertical limestone beds. Iso-dip contouring was attempted on the beds having equal angle of dip, with the premise that steep southerly dipping and vertical beds will reveal the trend and location of the fault zone on the surface. The iso-dip contour map reveals that the contour depicting the fault zone initially follows the basement-sediment contact and further NE i.e. towards Kanchankayi and transgresses into the Bhima sediments. This suggests that the fault zone, instead of following the basement-sediment contact, moves within the basin in Kanchankayi. Recent sub-surface exploration in Kanchankayi has corroborated this interpretation. The innovative approach in utilizing structural mapping and iso-dip contouring has not only given a new dimension to the exploration programme at Kanchankayi but also provided a model technique for exploration of concealed, structure-controlled, hydrothermal, vein-type uranium mineralisation in parts of Bhima basin.
On the Security of Some Recently Proposed Certificateless Signcryption Schemes

A certificateless signcryption (CL-SC) scheme is an important cryptographic primitive which provides the goals of a signature scheme (i.e. the unforgeability) and an encryption scheme (i.e. the confidentiality) both at once, in a certificateless setting. The certificateless public key cryptography (CL-PKC) setting, makes it possible to overcome the problems of the conventional public key infrastructure (i.e. the certificates management) and the ID-Based public key cryptography (i.e. the key escrow problem), concurrently. Recently, Caixue [4], Shan [16] and Ullah et al. [17] have proposed CL-SC schemes. In this paper, these schemes are analyzed. Some attacks are designed which show that Caixue's scheme is easily forgeable and Shan's scheme is forgeable against a malicious key generation center (KGC). Moreover, it is shown that Ullah et al.'s scheme has basic errors in its algorithms, as it does not even satisfy the correctness of the verification algorithm and it is not a CL-SC scheme at all.
Image super‐resolution with parallel convolution attention network

Abstract In recent years, deep convolutional neural networks (CNNs) have achieved a lot of outstanding results in super‐resolution with superior ability. However, the majority of CNNs only use a series of convolution kernels with the same size to extract features. This will cause limited receptive fields. In this work, we propose a parallel convolution attention network (PCAN) to extract features in an effective way. Specifically, a pair of parallel convolutions (PCs) with different kernel sizes is used in one layer in our network, which can extract features within different receptive fields, thereby making full use of the multiscale information. Meanwhile, we apply a channel‐spatial attention (CSA) module in each parallel convolution block to calculate and fuse channel attention and spatial attention. The obtained attention maps emphasize useful features. Experimental results demonstrate the superiority of our PCAN in comparison with the state‐of‐the‐art methods.
Investigating Back-Translation in Tibetan-Chinese Neural Machine Translation

Abstract In recent years, the proposal of neural network has provided new idea for solving natural language processing, and at the same time, neural machine translation has become the frontier method of machine translation. In low-resource languages, due to the sparse bilingual data, the model needs more high-quality data, and the translation quality fails to achieve the desired effect. In this paper, experiments on neural network machine translation based on attention are conducted on Tibetan-Chinese language pairs, and transfer learning method combined with back translation method is used to alleviate the problem of insufficient Tibetan-Chinese parallel corpus. Experimental results show that the proposed transfer learning combined with back translation method is simple and effective. Compared with traditional translation methods, the translation effect is significantly improved. From the analysis of translation, it can be seen that the citation of Tibetan-Chinese neural machine translation is smoother, which is greatly improved compared to the translation without back translation. At the same time, there are common deficiencies in neural machine translation such as inadequate translation and low translation loyalty.
A Machine Learning Based Approach for Next-Day Photovoltaic Power Forecasting

In this paper we propose a different strategy to produce a 24-hours ahead PV power forecasts. The main idea consists in developing a set of simple univariate models based on Least square Support Vector Regression (LsSVR), each of the developed models will forecast the PV power of each 30 minutes of the next day. To compare LsSVR results and also to demonstrate the generic aspect of the proposed strategy, we use the Feed Forward Neural Network (FFNN) as benchmark model. We provide a comprehensive evaluation of the proposed approach using publicly available PV power database from Ausgrid utility.
Data Sharing Attribute-Based Secure with Efficient Revocation in Cloud Computing

In recent days, cloud computing is one of the emerging fields. It is a platform to maintain the data and privacy of the users. To process and regulate the data with high security, the access control methods are used. The cloud environment always faces several challenges such as robustness, security issues and so on. Conventional methods like Cipher text-Policy Attribute-Based Encryption (CP-ABE) are reflected in providing huge security, but still, the problem exists like the non-existence of attribute revocation and minimum efficient. Hence, this research work particularly on the attribute-based mechanism to maximize efficiency. Initially, an objective coined out in this work is to define the attributes for a set of users. Secondly, the data is to be re-encrypted based on the access policies defined for the particular file. The re-encryption process renders information to the cloud server for verifying the authenticity of the user even though the owner is offline. The main advantage of this work evaluates multiple attributes and allows respective users who possess those attributes to access the data. The result proves that the proposed Data sharing scheme helps for Revocation under a fine-grained attribute structure.
RETRACTED: Development of Art Industry Park Based on FPGA and Wireless Sensor

The fast ascent of innovative ventures park has become another pattern. Generally speaking, the act of park improvement has been a long way in front of its speculations, and the absence of hypothetical direction has become a bottleneck in confining the quick and sound advancement of innovative ventures. By drawing an exercise from the new financial topography and utilizing the improved 'center outskirts' model, the article examines the Art Zone arrangement system and uncovers its advancement issues. Along these lines, a few proposals are advanced: from one viewpoint, the focal region ought to hold the wellsprings of imagination by securing the inventiveness, controlling the rents and building a stage run the specialty of modern remote sensor organizations Industrial Wireless Sensor Networks (IWSNs). In this manner, given the business' needs, examine distinctive remote correspondence conventions, fundamental engineering, and open exploration that have been characterized. Furthermore, flow difficulties here of examination are talked about. To assess the real utilization of Industrial Wireless Sensor Networks IWSNs from research ventures in collaboration with neighborhood mechanical accomplices. To put it plainly, the viewpoint for the eventual fate of using the further turn of events and Industrial Wireless Sensor Networks (IWSNs) of this article will be the sketch.
Face Recognition Using The Basic Components Analysis Algorithm

Abstract Since all the things surrounding us have characteristics, therefore, of course, there are several characteristics and features of the human face that distinguish it and know it from others, making it a distinct organism with certain features that can be classified and identified on its basis. Since the detection and cutting of faces from the image is a critical problem that has gained importance in recent times, they play a major role in facial recognition systems. In this research, we present a new method for identifying faces using the Principal Component Analysis PCA algorithm, through the passage of the image in several stages, starting with the stage of obtaining the image (taking it); then, the face detection phase of the original image, and aligning the image (i.e. adjusting the face angle to the camera angle). After that, we enter the image with the stage of extracting the important basic features of the image; then, we match the required image with the available image store. The proposed algorithm was tested with several images and faces and faces without faces were successfully recognized. The proposed algorithm is characterized by a high efficiency in the detected faces. The accuracy of this algorithm is more than 95% in the detection faces. The proposed algorithm is a prerequisite for any system that uses the face as the main feature.
Applying FastPhotoStyle to Synthetic Data for Military Vehicle Detection

Object detection is one of the main task for the deep learning applications. Deep learning performance has already exceeded human's detection ability, in the case when there are lots of data for training deep neural networks. In the case of military fields, there are needs to resolve the data shortage problem to employ deep learning system efficiently with benefits. Generating the synthetic data can be a solution, but the domain gap between the synthetic and real data is still an obstacle for training the model. In this paper, we propose a method for decreasing the domain gap by applying style transfer techniques to synthetic data for military vehicle detection. Utilizing FastPhotoStyle to the synthetic data aids efficiently improving the accuracy of object detection when the real data is insufficiency for training. Specifically, we show that stylization which enables artificial data more realistic diminishes the domain gap by evaluating the visualization of their distributions using principal component analysis and Fréchet inception distance score. As a result, the performance has been improved about 8% in the AP@50 metric for stylized synthetic data.
Identifying Criminal Suspects on Social Networks

Identifying suspects of crimes on social networks is one of the most relevant tasks in the analysis of this type of network. Most of the computational methods focused on this task involve supervised machine learning, and, therefore, require previously labeled datasets that inform, among the registered people, messages and/or conversations, which ones are suspects. However, in practice, this type of information is not available, for several reasons, among which, it is rare or even protected by secrecy guaranteed by law. This limitation makes it very difficult to effectively use these methods in real situations. Hence, the present work raises the hypothesis that the use of a controlled vocabulary on the field of application can make it possible the identification of suspects in social networks, without the need for previously labeled datasets. In order to search for experimental evidence that points to the validity of the hypothesis raised, this article proposes a generic method that uses a controlled vocabulary with categorized terms, according to a certain domain (e.g., pedophilia, cyberbullying, terrorism, etc.), to analyze messages exchanged on social networks, in order to identify criminal suspects. The results obtained in a preliminary experiment in pedophilia domain showed signs of adequacy of the proposed method.
Flexible Access Control over Privacy-Preserving Cloud Data Processing

Cloud computing provides an efficient and convenient platform for cloud users to store, process, and control their data (such as cybersecurity education-related data). Cloud overcomes the bottlenecks of resource-constrained devices and greatly releases storage and computing burdens on users. However, due to the lack of full trust in cloud service providers, cloud users generally prefer to outsource their sensitive data in an encrypted form, which seriously complicates data processing, analysis, as well as access control. Homomorphic encryption (HE) as a single key system cannot flexibly control data sharing and access after encrypted data processing. How to realize various computations over encrypted data in an efficient way and at the same time flexibly control the access to data processing results has been an important challenging issue. In this chapter, we propose a privacy-preserving data processing system to support several basic operations over outsourced encrypted data under the cooperation of a data service provider (DSP) and a computation party (CP). In addition, attribute-based encryption (ABE) is also applied to support flexible access control of processing results of encrypted data. Our schemes provide an efficient measure for secure data analytics to preserve the privacy of sensitive course data, e.g., course feedback, survey inputs, examination statistical data, exercises about security-related data for intrusion/malware detection and integrated personal data processing, etc. All of them can be applied into the education of cybersecurity.
Modernizing k‐nearest neighbors

The k‐nearest neighbors (k‐NN) method is one of the oldest statistical/machine learning techniques. It is included in virtually every major package, such as caret , parsnip , mlr3 and scikit‐learn . Yet those packages do not go beyond the basics. With today's high‐speed computation capability, k‐NN can be made much more powerful. Here, we present directions in which that can be done.
Traffic Parameters Acquisition System using Faster R-CNN Deep Learning based algorithm

Traffic parameters survey is important for proper control of traffic lights on the roads. Computer vision is one of the tools that offer greater advantages and lower cost compared to other alternatives. Particularly among the computer vision algorithms, the use of Deep Learning stands out against the traditional methods of image processing, due to the varying conditions of the environment. In the present paper, vehicle detection is performed by using a Deep Learning based algorithm, running the system trained under different environments for which the system was not trained. Later, an area of interest is defined in the image to be analyzed where, based on the detected vehicles, the necessary parameters of each of the routes of interest will be obtained. The parameters detection includes obtaining the queue lengths, estimating the average number of passengers in the region of interest and detecting the number of vehicles detected according to their type.
The axiomatic and the operational approaches to resource theories of magic do not coincide

Stabilizer operations (SO) occupy a prominent role in fault-tolerant quantum computing. They are defined operationally by the use of Clifford gates, Pauli measurements, and classical control. These operations can be efficiently simulated on a classical computer, a result which is known as the Gottesman–Knill theorem. However, an additional supply of magic states is enough to promote them to a universal, fault-tolerant model for quantum computing. To quantify the needed resources in terms of magic states, a resource theory of magic has been developed. SO are considered free within this theory; however, they are not the most general class of free operations. From an axiomatic point of view, these are the completely stabilizer-preserving (CSP) channels, defined as those that preserve the convex hull of stabilizer states. It has been an open problem to decide whether these two definitions lead to the same class of operations. In this work, we answer this question in the negative, by constructing an explicit counter-example. This indicates that recently proposed stabilizer-based simulation techniques of CSP maps are strictly more powerful than Gottesman–Knill-like methods. The result is analogous to a well-known fact in entanglement theory, namely, that there is a gap between the operationally defined class of local operations and classical communication and the axiomatically defined class of separable channels.
A New Statistical Method for Anomaly Detection in Distributed Systems

Distributed computing systems are increasing in popularity and being widely used as a new way of large-scale data processing. However, to achieve a reliable and efficient performance in a distributed environment, it is important to deal with system anomalies as soon as they are encountered. In this paper, two novel anomaly detection algorithms will be introduced and compared with previous anomaly detection algorithms. These novel algorithms are devised based on data summarization and error prediction in comparison with previously extracted data. The result of our experiments show that the proposed methods exhibit higher performance in terms of precision and accuracy.
Detection of Malicious URLs Using Deep Learning Approach

A phishing attack is one of the cyberbullying activities over the Internet. Most of the phishing websites try to imitate the legitimate websites. Web contents of phishing websites and URL features mimic the legitimate website and URL. Existing techniques for detecting and analyzing these malicious URLs are very much time consuming and costly due to their complexities. Traditional black and white listing is used for the detection of malicious websites. But this technique is not effective for real-time application. Various other techniques for the detection of malicious URL have been designed from machine learning approaches such as Support Vector Machines, Random Forest, etc. In such approaches, lexical properties of the URL are considered using the Bag-of-words feature. But it does not provide information about semantics and sequential information. It requires a considerable amount of manual feature engineering. Further, it cannot handle new features. To deal with the above limitations we focus on a real-time and language-independent phishing detection model by analyzing the anatomy of the URLs. We first detect the static and dynamic features manually. After getting the feature values, we find the lexical features of URL using Convolutional Neural Network (CNN) which can capture characters and words of the URL. After that, we merge features that are manually extracted and features obtained from CNN. Subsequently, we apply the bidirectional Long Short Term Memory (LSTM) model to generate the sequence of information of the URL. Thus, a hybrid model of CNN and Bidirectional LSTM are used for the detection of malicious URLs. We analyze the URL without accessing the web content of the corresponding website. It eliminates the time latency. We compare the performance of our network with some other existing ones and try to obtain better results.
Concept for Safety-Related Development of Deep Neural Networks in the Automotive

The performance of Deep Neural Networks in field of computer vision as most attractive segment of current trends in automotive is not just remarkable, but also quite beneficial in comparison to the classic SW approaches, since many hundred thousand lines of codes can be easily replaced. On the other side, a completely different development process makes them not fully conform to the requirements of current standards for functional safety in automotive, ISO 26262 and SOTIF. Analyzing Deep Learning Networks from the perspective of classic SW approach was the first try to address the drawbacks and deviations from the current standards. Although worthwhile, such studies didn't provide concepts, how to develop DNNs in a systematic, transparent and assessable way.This paper introduces a novel concept for the development of deep neural networks considering their characteristics, overcoming the drawback with tool evaluation process of wide established frameworks (such as TensorFlow, Keras etc.) and providing a systematic, traceable and assessable solution.
Performance Evaluation of M/D/1 Queuing Model on Hyperledger Fabric

Hyperledger Fabric is a private blockchain intended to be confidentiality, security, scalability, transparency, and access control service to the enterprise. The impact of the configuration factors influences the performance of Hyperledger Fabric because of a modular architecture. It does not provide to find the performance metrics of each processing phase likes endorsing peer, ordering service, and committing peer through the experiments of the formal Software Development Kit (SDK). In this paper, the performance metric of ordering service is presented in Hyperledger Fabric by using M/D/1 queuing model. The utilization is evaluated based on the random transaction arrival rate to a single orderer node and the deterministic service rate to determine the addition of a new orderer node. The number of orderer nodes could be decided based on the model result to improve the real system performance.
Analysis of Sentiment Classification of Hotel Reviews Based on Multinomial Naive Bayes

Hotel online reviews have an important influence on other users’ purchasing decisions, which also has the characteristics of large volume, fast growth, many types, and low value density. To assist consumers in purchasing decisions, an effective method is needed to quickly mine the true emotions of other customers from these review data, so as to conduct a classified evaluation of hotels, or provide recommendation basis. Therefore, a multinomial Naive Bayesian sentiment classification model for hotel reviews is proposed in this paper. On the basis of the characteristics of the hotel reviews, the stop word list was expanded by word frequency statistics. Removal of more irrelevant stop words is beneficial for noise information filtering during classification. Besides, aiming at the dimensional disaster problem caused by the bag of words model, the dimensionality reduction is carried out by using the information gain method. The experimental results show that after using the extended hotel stop word list, it has little effect on the classification results, but the efficiency has been improved obviously. Furthermore, the model can achieve better classification results than ordinary multinomial Naive Bayes classifiers.