Reviewerly/exHarmony huggingface dataset:
Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India

In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.
A systematic review of context-aware technology use in foreign language learning

As context plays a critical role in foreign language (FL) learning, context-aware technology is a promising tool for supporting FL learning. The present study examined studies published between 2000 and 2018 to understand the trends of technology and related research and the current issues with context-aware technology in FL learning. This review included 75 empirical studies and 13 system-descriptive studies. The empirical studies were further examined in terms of publication year, methodology, participants, languages, device types, and the effectiveness of the technology based on FL learning results. Most of the studies indicated positive effects of the technology in the affective, linguistic, socio-cultural, and cognitive domains. The review also found issues with the reviewed studies, including the research methods, research sustainability, and practicability of the technology. The review results showed that the number of publications per year fluctuated rather than increased over the studied period. Students’ perceptions of the use of context-aware technology in FL were the most common focus of the reviewed research; far fewer empirical studies were found that assessed students’ actual language learning outcomes. The results of this study suggest that more long-term, pedagogy-informed empirical studies based on students’ actual learning outcomes are required to verify the effectiveness of context-aware technology on FL learning and to help effectively integrate such technology into learning in the future.
Image watermarking based on matrix decomposition and gyrator transform in invariant integer wavelet domain

In this paper, an image watermarking algorithm with high security and robustness using matrix decomposition and Gyrator transform is proposed, which is developed in the invariant integer wavelet domain. The novelty of this paper is shown below. We propose the redistributed invariant integer wavelet transform, which has real reversibility and can transform an image into the invariant domain where the pixel values are all integers. Moreover, a new method for finding the singular value of an image matrix by QR decomposition and singular value decomposition (SVD) is given. Compared with SVD, it has lower time complexity. In addition, a new dual encryption scheme based on Gyrator transform and QR decomposition is introduced. This encryption scheme has reversibility and high security. The simulation results show that the proposed encryption algorithm can effectively resist statistical attack and differential attack. Moreover, the proposed watermarking scheme performs well in resisting geometric attacks and has obvious advantages over the existing related algorithms. In addition, this watermarking algorithm satisfies the invisibility, security, robustness and capacity requirements of the watermarking algorithm.
Predicting dementia with routine care EMR data

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.
A verified optimizer for Quantum circuits

We present VOQC, the first fully verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, a simple quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. SQIR uses a semantics of matrices of complex numbers, which is the standard for quantum computation, but treats matrices symbolically in order to reason about programs that use an arbitrary number of quantum bits. SQIR's careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, including full-circuit transformations from cutting-edge optimizers.
Learning to Personalize in Appearance-Based Gaze Tracking

Personal variations severely limit the performance of appearance-based gaze tracking. Adapting to these variations using standard neural network model-adaption methods is difficult. The problems range from overfitting, due to small amounts of training data, to underfitting, due to restrictive model architectures. We tackle these problems by introducing SPatial Adaptive GaZe Estimator (SPAZE ). By modeling personal variations as a low-dimensional latent parameter space, SPAZE provides just enough adaptability to capture the range of personal variations without being prone to overfitting. Calibrating SPAZE for a new person reduces to solving a small and simple optimization problem. SPAZE achieves an error of 2.70 degrees on the MPIIGaze dataset, improving on the state-of-the-art by 14 %. We contribute to gaze tracking research by empirically showing that personal variations are well-modeled as a 3-dimensional latent parameter space for each eye. We show that this low-dimensionality is expected by examining model-based approaches to gaze tracking.
PAC-GAN: Packet Generation of Network Traffic using Generative Adversarial Networks

Generative adversarial networks (GANs) have proven extremely successful in creating artificial yet highly realistic media data such as images, text, audio and videos. In this paper, we adapt and describe a GAN method for creating network traffic data at the IP packet layer. Generating realistic network traffic is essential for development and testing of techniques in Cyber and network security related tasks such as anomaly or intrusion detection. The effectiveness of such network monitoring techniques depends directly on the traffic data. Despite this, traffic generation is often considered low priority because it is difficult. Using GANs, we prototype and prove feasibility in the generation of real traffic flows such as ICMP Pings, DNS queries, and HTTP web requests. Using Convolutional Neural Network (CNN) GANs, we propose an alternative encoding of network traffic data into the CNN model. Experiments show our generated traffic can be successfully transmitted through the Internet eliciting desired responses from the network. The work described in this paper is the first step in demonstrating successful creation and transmission of network traffic; it forms the basis for future GAN based traffic generators at scale.
Public key encryption with equality test in the standard model

Public key encryption with equality test (PKEET) is a cryptosystem that allows a tester who has trapdoors issued by one or more users Ui to perform equality tests on ciphertexts encrypted using public key(s) of Ui. Since this feature has a lot of practical applications including search on encrypted data, several PKEET schemes have been proposed so far. However, to the best of our knowledge, all the existing proposals are proven secure only under the hardness of number-theoretic problems and/or the random oracle heuristics. In this paper, we show that this primitive can be achieved not only generically from well-established other primitives but also even without relying on the random oracle heuristics. More precisely, our generic construction for PKEET employs a two-level hierarchical identity-based encryption scheme, which is selectively secure against chosen plaintext attacks, a strongly unforgeable one-time signature scheme and a cryptographic hash function. Our generic approach toward PKEET has several advantages over all the previous works; it directly leads the first standard model construction and also directly implies the first lattice-based construction. Finally, we show how to extend our approach to the identity-based setting.
Attention-aware perceptual enhancement nets for low-resolution image classification

Classifying low-resolution (LR) images is notoriously challenging because of their noisy representation and limited information. Existing approaches mainly solve this challenge by training carefully designed architectures on LR datasets or by employing an image-resizing algorithm in a straightforward manner. However, the performance improvements of these methods are usually limited or even trivial in the case of LR images. In this work, we address the LR image classification problem by developing an end-to-end architecture that internally elevates representations of an LR image to “super-resolved” ones. This approach imparts characteristics similar to those of high-resolution (HR) images and is thus more discriminative and representative for image classification. For this purpose, we propose an innovative unified framework, named Attention-aware Perceptual Enhancement Nets (APEN), which integrates perceptual enhancement and an attention mechanism in an end-to-end manner for LR image classification. Specifically, the framework includes a perceptual enhancement network to generate super-resolved images from LR images. In addition, a novel attention mechanism is presented to highlight informative regions, while restricting the semantic deviation of super-resolved images. Additionally, we designed a feature rectification strategy to promote the adaptability of category decision. Experiments conducted on publicly available datasets demonstrate the superiority of our method against state-of-the-art methods on both LR and HR datasets.
Challenges and performance metrics for security operations center analysts: a systematic review

The increasing use of Security Operations Centers (SOCs) by organisations as a part of their cyber security strategy has led to several studies aiming to understand and improve SOC operations. However, to the best of our knowledge, there is no systematic literature review on the challenges faced by SOC analysts or on metrics for measuring analysts performance. To this end, we conducted a Systematic Literature Review (SLR) in accordance with the guidelines for undertaking SLR and analyzed papers published on SOCs between 2008 and 2018. We provide a comprehensive overview of the challenges faced by SOC analysts and of the metrics suggested in the literature for measuring analysts performance. In addition, we present a mapping between the challenges and existing performance metrics showing how the effectiveness of an analyst in addressing a particular challenge could be measured. We also discuss the drawbacks of the existing metrics and suggest directions for improvement. Our findings will enable SOC analysts and managers, as well as the academic community to gain a better understanding of the challenges impeding the performance of SOC analysts, and how analysts performance could be measured and improved.
Research and Implementation of Machine Learning Classifier Based on KNN

Abstract Machine learning classifier is an important part of pattern recognition system; it is also an important research field of machine learning. The main research object of this paper is K data mining (KNN, K Nearest Neighbor) classification method, using KNN to classify the data, and compare the classification results. The research work of this paper mainly discusses the implementation of KNN-based machine learning classifier, mainly focusing on the theoretical analysis of K-data mining, algorithm implementation, and implementing KNN-based machine learning classifier.
How to Efficiently Handle Complex Values? Implementing Decision Diagrams for Quantum Computing

Quantum computing promises substantial speedups by exploiting quantum mechanical phenomena such as superposition and entanglement. Corresponding design methods require efficient means of representation and manipulation of quantum functionality. In the classical domain, decision diagrams have been successfully employed as a powerful alternative to straightforward means such as truth tables. This motivated extensive research on whether decision diagrams provide similar potential in the quantum domain-resulting in new types of decision diagrams capable of substantially reducing the complexity of representing quantum states and functionality. From an implementation perspective, many concepts and techniques from the classical domain can be re-used in order to implement decision diagrams packages for the quantum realm. However, new problems-namely how to efficiently handle complex numbers-arise. In this work, we propose a solution to overcome these problems. Experimental evaluations confirm that this yields improvements of orders of magnitude in the runtime needed to create and to utilize these decision diagrams. The resulting implementation is publicly available as a quantum DD package at http://iic.jku.at/eda/research/quantum_dd.
Few-Shot Learning With Geometric Constraints

In this article, we consider the problem of few-shot learning for classification. We assume a network trained for base categories with a large number of training examples, and we aim to add novel categories to it that have only a few, e.g., one or five, training examples. This is a challenging scenario because: 1) high performance is required in both the base and novel categories; and 2) training the network for the new categories with a few training examples can contaminate the feature space trained well for the base categories. To address these challenges, we propose two geometric constraints to fine-tune the network with a few training examples. The first constraint enables features of the novel categories to cluster near the category weights, and the second maintains the weights of the novel categories far from the weights of the base categories. By applying the proposed constraints, we extract discriminative features for the novel categories while preserving the feature space learned for the base categories. Using public data sets for few-shot learning that are subsets of ImageNet, we demonstrate that the proposed method outperforms prevalent methods by a large margin.
On the Linguistic Representational Power of Neural Machine Translation Models

Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure captured within the learned representations, which is an important aspect in translating morphologically rich languages? (ii) Do the representations capture long-range dependencies, and effectively handle syntactically divergent languages? (iii) Do the representations capture lexical semantics? We conduct a thorough investigation along several parameters: (i) Which layers in the architecture capture each of these linguistic phenomena; (ii) How does the choice of translation unit (word, character, or subword unit) impact the linguistic properties captured by the underlying representations? (iii) Do the encoder and decoder learn differently and independently? (iv) Do the representations learned by multilingual NMT models capture the same amount of linguistic information as their bilingual counterparts? Our data-driven, quantitative evaluation illuminates important aspects in NMT models and their ability to capture various linguistic phenomena. We show that deep NMT models trained in an end-to-end fashion, without being provided any direct supervision during the training process, learn a non-trivial amount of linguistic information. Notable findings include the following observations: (i) Word morphology and part-of-speech information are captured at the lower layers of the model; (ii) In contrast, lexical semantics or non-local syntactic and semantic dependencies are better represented at the higher layers of the model; (iii) Representations learned using characters are more informed about word-morphology compared to those learned using subword units; and (iv) Representations learned by multilingual models are richer compared to bilingual models.
ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion

We study the task of learning entity and relation embeddings in knowledge graphs for predicting missing links. Previous translational models on link prediction make use of translational properties but lack enough expressiveness, while the convolution neural network based model (ConvE) takes advantage of the great nonlinearity fitting ability of neural networks but overlooks translational properties. In this paper, we propose a new knowledge graph embedding model called ParamE which can utilize the two advantages together. In ParamE, head entity embeddings, relation embeddings and tail entity embeddings are regarded as the input, parameters and output of a neural network respectively. Since parameters in networks are effective in converting input to output, taking neural network parameters as relation embeddings makes ParamE much more expressive and translational. In addition, the entity and relation embeddings in ParamE are from feature space and parameter space respectively, which is in line with the essence that entities and relations are supposed to be mapped into two different spaces. We evaluate the performances of ParamE on standard FB15k-237 and WN18RR datasets, and experiments show ParamE can significantly outperform existing state-of-the-art models, such as ConvE, SACN, RotatE and D4-STE/Gumbel.
Interactive Fiction Games: A Colossal Adventure

A hallmark of human intelligence is the ability to understand and communicate with language. Interactive Fiction games are fully text-based simulation environments where a player issues text commands to effect change in the environment and progress through the story. We argue that IF games are an excellent testbed for studying language-based autonomous agents. In particular, IF games combine challenges of combinatorial action spaces, language understanding, and commonsense reasoning. To facilitate rapid development of language-based agents, we introduce Jericho, a learning environment for man-made IF games and conduct a comprehensive study of text-agents across a rich set of games, highlighting directions in which agents can improve.
Perceived Organizational Support, Alternative Job Opportunity, Organizational Commitment, Job Satisfaction and Turnover Intention: A Moderated-mediated Model

Abstract Background and Purpose: This article tested a structural model that examines the mediating role of organizational commitment on the link between perceived organizational support, perceived alternative job opportunities, and turnover intention, and the moderating role of job satisfaction on the proposed relationships. Methodology: Using convenience sampling technique, a self-administered survey was conducted on a pool of Jordanian small and medium sized enterprises (SMEs). The obtained data (n=270) were analyzed with contemporary variance-based structural equation modelling (PLS-SEM) software SmartPLS v3. Results: Findings revealed that organizational commitment mediates the association between perceived organizational support and turnover intention, perceived alternative job opportunities and turnover intention. In addition, job satisfaction did not moderate the associations between organizational support, perceived alternative job opportunity and organizational commitment. Conclusion: The present study is among the first to show the mediating mechanism of organizational commitment on the link between perceived organizational support, perceived alternative job opportunity and turnover intention. Theoretical and practical implications are drawn, before pointing to potential future research directions that build on the evidence-based positions argued for in this study.