Reviewerly/exHarmony huggingface dataset:
Remote Sensing and Deep Learning for Sustainable Mining

This paper brings together advances in remote sensing and deep learning for mineral mapping in a sustainable way. In more detail, we propose a multisensor feature fusion approach to integrate heterogeneous RGB, multispectral, and hyperspectral images for sustainable mining. The proposed approach is composed of two main steps; Feature extraction and classification. In the feature extraction step, we develop a three-stream convolutional neural network to extract high-level information from the input multisensor data. In the classification step, we develop a multisensor composite kernel approach to perform fusion and mapping simultaneously. The proposed approach produces very high quality classification maps with exceptional results in terms of classification accuracies.
Learning Simulator: A simulation software for animal and human learning

behavioral biologists, ethologists, and psychologists, and it can also be useful to students and teachers in these fields.
WITHDRAWN: Blockchain integrated IIOT – Future of IOT

In the current world, we are witnessing forthe speedy growth of Industrial Internet of Things (IIoT)due to the rapid development of technologies.But, the three tiers network topology and limited resources of edge computing impose several difficultiesin conventional method of data storage, communication, and reliability. In distributed IIoT, we use blockchain’s distributed trust and allocated ledger and this is used as an effective method for various IOT applications. Blockchain technology enables new business models, innovative organisational forms and work processes. It has also been widely used in hierarchical corporations andindependentcompanies. Blockchain has opened the door for many new possibilities in the way value can be directly transferred between participants in a convenient and trusted manner. With smart contracts, execution of workflows will be used for automation of regulatory workflows. Reporting and monitoring of required data, checking of compliances, and approval processes will become easier than ever.
A Two-sample Nonparametric Test for Circular Data– its Exact Distribution and Performance

A nonparametric test labelled 'Rao Spacing-frequencies test' is explored and developed for testing whether two circular samples come from the same population. Its exact distribution and performance relative to comparable tests such as the Wheeler-Watson test and the Dixon test in small samples, are discussed. Although this test statistic is shown to be asymptotically normal, as one would expect, this large sample distribution does not provide satisfactory approximations for small to moderate samples. Exact critical values for small samples are obtained and tables provided here, using combinatorial techniques, and asymptotic critical regions are assessed against these. For moderate sample sizes in-between i.e. when the samples are too large making combinatorial techniques computationally prohibitive but yet asymptotic regions do not provide a good approximation, we provide a simple Monte Carlo procedure that gives very accurate critical values. As is well-known, the large number of usual rank-based tests are not applicable in the context of circular data since the values of such ranks depend on the arbitrary choice of origin and the sense of rotation used (clockwise or anti-clockwise). Tests that are invariant under the group of rotations, depend on the data through the so-called 'spacing frequencies', the frequencies of one sample that fall in between the spacings (or gaps) made by the other. The Wheeler-Watson, Dixon, and the proposed Rao tests are of this form and are explicitly useful for circular data, but they also have the added advantage of being valid and useful for comparing any two samples on the real line. Our study and simulations establish the 'Rao spacing-frequencies test' as a desirable, and indeed preferable test in a wide variety of contexts for comparing two circular samples, and as a viable competitor even for data on the real line. Computational help for implementing any of these tests, is made available online "TwoCircles" R package and is part of this paper.
Effects of Class Imbalance Problem in Convolutional Neural Network Based Image Classification

Convolutional Neural Networks (CNNs) have revolutionized the image classification tasks to a greater extent. Various research works reported the high accuracy of CNNs in image classification problems related to science and engineering. It has been found that many image classification problems carry an additional challenge of imbalanced classes in the dataset. In absence of a suitable analysis of the effect of imbalanced image data on the performance of CNNs, the current study thoroughly analyzed the same. CNN models are evaluated in terms of Sensitivity, Specificity, G-Mean and Bookmaker Informedness with varying degrees of imbalance ratio. The experimental results have revealed that the performance of CNN is severely affected by increased imbalance ratio.
Evaluation of Dimensionality Reduction Techniques for Load Profiling Application in Smart Grid Environment

The traditional power grid system is evolving toward a smart grid system, which will improve the user experience. However, this system is capable of generating high-dimensional data at a very high sample rate. A common technique for reducing high-dimensional data is dimensionality reduction. With this technique, we are able to reduce the data to a lower dimension, making it suitable for smart grid applications including transmission, storage, and visualization. Linear dimensionality reduction techniques are mostly explored in the context of smart grid applications. Due to the nonlinear nature of data generation in the smart grid, we anticipate that the nonlinear dimensionality reduction techniques can perform better. This work evaluates different nonlinear dimensionality reduction techniques and compares them with principal component analysis, which is a widely used linear dimensionality reduction technique in the smart grid environment. We use the visualization of load profile data and adjusted rank index (ARI) for comparison of dimensionality reduction techniques. Load profiling is an important task to complement the demand-side management and tariff selection. The visual depiction of the load profiles and ARI suggest that the nonlinear dimensionality reduction techniques perform better compared with linear dimensionality reduction techniques.
Website Defacer Classification: A Finite Mixture Model Approach

Hackers often engage in website defacement early in their criminal careers to establish a reputation. Some hackers become increasingly prolific and launch a large number of attacks against their targets, whereas others only launch a few attacks before eventually desisting from a life of crime. A better understanding of why some hackers launch a large number of attacks, while others do not, will assist in the implementation of targeted intervention strategies. Therefore, the current study, using a sample of 119 active hackers, seeks to answer two research questions: (1) Are there different groups of website defacers based on attack volume? (2) Which observed hacker-level characteristics can be used to predict latent class membership? We find that two unique groups of website defacers exist: low-volume defacers (69%) and high-volume defacers (31%). Social media presence, the content of the defacement, and the type of defacement are all predictive of latent class membership. Policy implications are discussed.
A combined syntactic-semantic embedding model based on lexicalized tree-adjoining grammar

This paper presents a joint syntactic-semantic embedding model which not only uses syntactic information to enrich the word embeddings but also generates distributed representations for the syntactic structures themselves. The syntactic input to our model comes from a Lexicalized Tree-Adjoining Grammar parser. The word embeddings from our model outperform the Skip-gram embeddings in several word similarity and sentiment classification experiments. The syntactic structure embeddings help improve a transition-based dependency parser by a clear margin.
GPS2Vec: Pre-Trained Semantic Embeddings for Worldwide GPS Coordinates

GPS coordinates are fine-grained location indicators that are difficult to be effectively utilized by classifiers in geo-aware applications. Previous GPS encoding methods concentrate on generating hand-crafted features for small areas of interest. However, many real world applications require a machine learning model, analogous to the pre-trained ImageNet model for images, that can efficiently generate semantically-enriched features for planet-scale GPS coordinates. To address this issue, we propose a novel two-level grid-based framework, termed GPS2Vec, which is able to extract geo-aware features in real-time for locations worldwide. The Earth’s surface is first discretized by the Universal Transverse Mercator (UTM) coordinate system. Each UTM zone is then considered as a local area of interest that is further divided into fine-grained cells to perform the initial GPS encoding. We train a neural network in each UTM zone to learn the semantic embeddings from the initial GPS encoding. The training labels can be automatically derived from large-scale geotagged documents such as tweets, check-ins, and images that are available from social sharing platforms. We conducted comprehensive experiments on three geo-aware applications, namely place semantic annotation, geotagged image classification, and next location prediction. Experimental results demonstrate the effectiveness of our approach, as prediction accuracy improves significantly based on a simple multi-feature early fusion strategy with deep neural networks, including both CNNs and RNNs.
OldSlavNet: A scalable Early Slavic dependency parser trained on modern language data

Historical languages are increasingly being modelled computationally. Syntactically annotated texts are often a sine-qua-non in their modelling, but parsing of pre-modern language varieties faces great data sparsity, intensified by high levels of orthographic variation. In this paper we present a good-quality Early Slavic dependency parser, attained via manipulation of modern Slavic data to resemble the orthography and morphosyntax of pre-modern varieties. The tool can be deployed to expand historical treebanks, which are crucial for data collection and quantification, and beneficial to downstream NLP tasks and historical text mining.
The Jam Station: Gamifying Collaborative Musical Experiences Through Algorithmic Assessment

The Jam Station is a collaborative musical experience (CME), with embedded sensors to detect, evaluate, and reinforce collaboration among players of all skill levels. The system is also gamified, consisting of four instruments that face a centrally located, vertically oriented, display that visualizes various game states. Playing any of the instruments affect the game as musical collaboration is assessed and visualized on the display. The display also includes a "progress bar'' which fills up the more players collaborate musically, and to "win'' the game, the progress bar must be full, initiating a multi-color light show for the players. By creating an environment focused primarily on collaboration, we aim to support collaborative music-making for musicians of all skill levels.
Two-Party Secure Computation for Any Polynomial Function on Ciphertexts under Different Secret Keys

Multikey fully homomorphic encryption proposed by Lopez-Alt et al. (STOC12) is a significant primitive that allows one to perform computation on the ciphertexts encrypted by multiple different keys independently. Then, several schemes were constructed based on decisional small polynomial ratio or learning with errors. These schemes all require an expansion algorithm to transform a ciphertext under a single key into an encryption of the same message under a set of keys. To achieve the expansion algorithm without interaction with these key-keepers, their encryption algorithm not only outputs a ciphertext of a plaintext but also exports auxiliary information generated from the randomness used in the former encryption process. Beyond that, the size of the ciphertext encrypted by multiple keys increases linearly or quadratically in the number of participants. In this paper, we studied the problem whether someone can directly perform arbitrary computation on ciphertexts encrypted by different keys without any auxiliary information in the output of the encryption algorithm and an increase in the size of the ciphertext in the expansion algorithm. To this end, we proposed a novel and simple scheme of secure computation on ciphertexts under two different keys directly without any auxiliary information. In other words, each party just provides its own ciphertexts encrypted by the GSW scheme (CRYPTO13). In the procedure of executing evaluation on these ciphertexts, the size of the new ciphertext remains the same as that of the GSW ciphertext.
Efficient 4-Way Vectorizations of the Montgomery Ladder

We propose two new algorithms for 4-way vectorization of the well known Montgomery ladder over elliptic curves of Montgomery form. The first algorithm is suitable for variable base scalar multiplication. In comparison to the previous work by Hisil <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> <xref ref-type="bibr" rid="ref17" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[17]</xref> , it eliminates a number of non-multiplication operations at the cost of a single multiplication by a curve constant. Implementation results show this trade-off to be advantageous. The second algorithm is suitable for fixed base scalar multiplication and provides clear speed improvement over a previous vectorization strategy due to Costigan and Schwabe (2009). The well known Montgomery curves Curve25519 and Curve448 are part of the TLS protocol, version 1.3. For these two curves, we provide constant time assembly implementations of the new algorithms. Additionally, for the algorithm of Hisil <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> <xref ref-type="bibr" rid="ref17" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[17]</xref> , we provide improved implementations for Curve25519 and new implementation for Curve448. Timings results on the Haswell and Skylake processors indicate that in practice the new algorithms are to be preferred over previous methods for scalar multiplication on these curves.
Narrative review: Social media use by employees and the risk to institutional and personal information security compliance in South Africa

Social media platforms have become essential to organisations in developing countries as they can offer a business advantage. This comes with security risks and privacy concerns as numerous scientific literatures have testified. Although the majority of employees are using social media privately and at the workplace (using the same device such as a smartphone), some organisations have not effectively established information security awareness programmes to protect their electronic information backbone. It is a fact that professional hackers are prowling constantly to gain access to systems of organisations and sometimes employees make naïve mistakes that can open the door to cyberattacks, which exploit vulnerabilities in the organisation’s system. The current Coronavirus Disease 2019 (COVID-19) crisis is a prime example where employees and students are encouraged to work from home. No organisation does have complete control over the security measures each employee has in place for his or her private connection. This study applied a desktop review to identify the cyber risks associated with social media use at the workplace. A scoping literature review gathered the data following a qualitative approach. The theories of reasoned action and deterrence were used as a theoretical foundation for the study. A model is proposed to enhance employee information security compliance when using social media at the workplace and demonstrates how awareness strategies can be employed to improve employee information security compliance. It is recommended that organisations implement methods to minimise social media risks to ensure that the integrity of information is preserved through these awareness programmes to employees.
Improving object detection in paintings based on time contexts

This paper proposes a novel approach to object detection for the Cultural Heritage domain, which relies on combining Deep Learning and semantic metadata about candidate objects extracted from existing sources such as Wikidata, dictionaries, or Google NGram. Working with cultural heritage presents challenges not present in every-day images. In computer vision, object detection models are usually trained with datasets whose classes are not imaginary concepts, and have neither symbolic nor time-specific dimensions. Apart from this conceptual problem, the paintings are limited in number and represent the same concept in potentially very different styles. Finally, the metadata associated with the images is often poor or inexistent, which makes it hard to properly train a model. Our approach can improve the precision of object detection by placing the classes detected by a neural network model in time, based on the dates of their first known use. By taking into account the time of inception of objects such as the TV, cell phone, or scissors, and the appearance of some objects in the geographical space that corresponds to a painting (e.g. bananas or broccoli in 15th century Europe), we can correct and refine the detected objects based on their chronologic probability.
The Application of Blockchain for the Management of Engineering Projects in Power System

As the government becomes more stringent in the supervision of engineering projects in power system, power grid companies have higher standards for the efficiency and accuracy of the project investments. However, the low authenticity of the data in current engineering projects, and the lack of the data transparency among different project departments hinder the investment developing toward precision. Meanwhile, the non-standardized project management process, and the traditional project investment statistics methods hardly meet current standards. To addresses the key issues in the investment management process of such engineering projects in the power grid, this paper proposes a management framework, which incorporates blockchain technology with the advance features of decentralization, non-repudiation and traceability. A systematic block chain structure is established, where all participants in the project management process are treated as nodes, and different transaction types are integrated into the proposed blockchain. In the end, based on typical engineering project scenarios, application cases are given to provide new ideas for the application of blockchain technology for the management of engineering projects in the power system.
Mediative Fuzzy Logic of Sugeno-Tsk Model For The Diagnosis of Diabetes

Abstract Fuzzy Logic (FL) is very beneficial in medical field. But due to the consideration of membership function only; it cannot give an appropriate result in prese nt era of contradiction. So, we need to consider favourable as well as unfavourable cases together as the Intuitionistic Fuzzy Logic (IFL) does. But what happens if we have imperfect information that cannot be dealt with the IFL. When there-exist a contradiction in the expert knowledge, then we have to propose a Mediative Fuzzy Logic (MFL) based Sugeno’s inference system for the diagnosis of diabetes. In the present research paper, we have proposed a new approach to the diagnosis of diabetes, we have collected certain information from Pima Indians Diabetes Database (PIDD) as input variables and we used MFL based inference system for the diagnosis of diabetes.
Long-term recurrent convolutional network violent Behaviour recognition with attention mechanism

Violent behavior recognition is an important direction of behavior recognition research. For traditional violent behavior recognition algorithms, there is too much background information when processing video information, which will cause greater interference in feature extraction, so the recognition accuracy is not high. Improved on the basis of effective recurrent convolutional network, a long-term recurrent convolutional network with attention mechanism is proposed. In the video preprocessing stage, a variety of attention mechanisms are introduced. In the feature extraction stage, the lightweight end-to-side neural network architecture GhostNet and convLSTM are selected to build a long-term recurrent convolutional network. The global average pooling and fully connected layer are used in the classification process. The combined approach realizes the classification of behaviours. The final results show that in the Hockey dataset, the algorithm in this paper has increased by 0.4% compared to LRCN, in the RWF-2000 dataset with more samples, it has increased by 10.5% compared to LRCN, and has increased by 1.75% compared to I3D, indicating that the algorithm in this paper can effectively suppress the background information. Interference, improve the performance of the algorithm.
UEFI Trusted Computing Vulnerability Analysis Based on State Transition Graph

In the face of increasingly serious firmware attacks, it is of great significance to analyze the vulnerability security of UEFI. This paper first introduces the commonly used trusted authentication mechanisms of UEFI. Then, aiming at the loopholes in the process of UEFI trust verification in the startup phase, combined with the state transition diagram, PageRank algorithm and Bayesian network theory, the analysis model of UEFI trust verification startup vulnerability is constructed. And according to the example to verify the analysis. Through the verification and analysis of the data obtained, the vulnerable attack paths and key vulnerable nodes are found. Finally, according to the analysis results, security enhancement measures for UEFI are proposed.
DNN-Based Semantic Extraction: Fast Learning from Multispectral Signatures

In this paper, we present three methods that reduce the computational time of training Deep Neural Networks with multispectral images, optimize the resource occupation of the dataset, and obtain high performance for reduced datasets. In the first two methods, we reduce the dimension of the input data with either histograms of pixel intensity or Bag-of-Words. Then we train a Convolutional Neural Network with either histograms or Bag-of-Words and we achieve an accelerated training. Moreover, storing the image patches from the dataset in the form of histograms or Bag-of-Words reduced the memory storage significantly. In the last method, we subsample the training dataset randomly to 50%, 20% and 10% of the original dataset, thus training a Convolutional Neural Network on a smaller number of samples (in the form of histograms or Bag-of-Words), and the classification performance is almost unaffected. This is an important achievement, as there are few labelled datasets for Earth Observation and the number of images in these datasets is small. Our results show that the training time is reduced by a maximum of 387 times and the datasets with histograms or Bag-of-Words occupy 633 times less space.