Reviewerly/exHarmony huggingface dataset:
An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images

Superresolution (SR) has provided an effective solution to the increasing need for high-resolution images in remote sensing applications. Among various SR methods, deep learning-based SR (DLSR) has made a significant breakthrough. However, supervised DLSR methods require a considerable amount of training data, which is hardly available in the remote sensing field. To address this issue, some research works have recently proposed and revealed the capability of deep learning in unsupervised SR. This article presents an efficient unsupervised SR (EUSR) deep learning model using dense skip connections, which boosts the reconstruction performance in parallel with the reduction of computational burden. To do this, several blocks containing densely connected convolutional layers are implemented to increase the depth of the model. Some skip connections also concatenate feature maps of different blocks to enable better SR performance. Moreover, a bottle-neck block abstracts the feature maps in fewer feature maps to remarkably reduce the computational burden. According to our experiments, the proposed EUSR leads to better results than the state-of-the-art DLSR method in terms of reconstruction quality with less computational burden. Furthermore, results indicate that the EUSR is more robust than its rival in dealing with images of different classes and larger sizes.
Image extrapolation based on multi-column convolutional attention network

Image inpainting is based on the generation of branches against the network. Many recent methods of deep learning have shown great progress in challenging tasks that repair large numbers of missing areas in the map. These methods can be visually restored to a reasonable image and structure, but still produce a distorted structure or a fuzzy structure that is different in the surrounding area. In the past year, image expansion has begun to slowly enter our field of vision. Previous research based on mathematical methods started to get noticed, but the challenge of extrapolating this work is very huge, so we propose multi-column convolution. The attention network is trained to produce a blank gap in the image, and the depth learning method of image expansion is very promising and proved to be feasible.
GeoBalance: An Excel VBA program for mass balance calculation in geosciences

Mass balance calculation is a fundamental approach in geosciences. In petrology and geochemistry, it is widely used for quantitative characterization of phase transition and mass transfer. In a routine petrological practice, this method is commonly used to calculate proportions of the minerals formed from initial or parental bulk composition, or to quantify the reaction coefficients for minerals involved in a chemical reaction that achieves equilibria. In this paper, we present a new mass balance calculation program, GeoBalance, which is written with Visual Basic for Applications (VBA) and built in a macro-enabled Excel™ worksheet. The working algorithm is based on solving the least square problem using pseudo-inverse of matrix and singular value decomposition (SVD) of matrix. Both unconstrained and constrained solution methods are implemented in the program and can be chosen by the user. In addition, input data (e.g. compositions of the minerals) can be different data types, such as cations, oxides, moles of oxides, or moles of mineral endmembers. As a result of calculation, phase proportions are presented as percentages on 100%-normalized basis with residuals (for each chemical component), and a sum of squared estimate of errors (SSE). Monte Carlo simulation of error propagation for evaluating calculation uncertainty is also implemented as an optional function. An additional advantage of our program is that it enables users to perform mass balance calculations for complicated systems with a flexible number of phases and/or chemical components.
Detection method of phishing email based on persuasion principle

“Phishing emails” are phishing emails with illegal links that direct users to pages of some real websites that are spoofed, or pages where real HTML has been inserted with dangerous HTML code, so as to deceive users' private information such as bank or credit card account numbers, email account numbers, and passwords. People are the most vulnerable part of security. Phishing emails use human weaknesses to attack. This article describes the application of the principle of persuasion in phishing emails, and based on the existing methods, this paper proposes a phishing email detection method based on the persuasion principle. The principle of persuasion principle is to count whether the corresponding word of the feature appears in the mail. The feature is selected using an information gain algorithm, and finally 25 features are selected for detection. Finally experimentally verified, accuracy rate reached 99.6%.
Collaborative Modelling

Modelling is a fundamental activity in software engineering, which is often performed in collaboration. For this purpose, on-line tools running on the cloud are frequently used. However, recent advances in Natural Language Processing have fostered the emergence of chatbots, which are increasingly used for all sorts of software engineering tasks, including modelling. To evaluate to what extent chatbots are suitable for collaborative modelling, we conducted an experimental study with 54 participants, to evaluate the usability of a modelling chatbot called SOCIO, comparing it with the on-line tool Creately. We employed a within-subjects cross-over design of 2 sequences and 2 periods. Usability was determined by attributes of efficiency, effectiveness, satisfaction and quality of the results. We found that SOCIO saved time and reduced communication effort over Creately. SOCIO satisfied users to a greater extent than Creately, while in effectiveness results were similar. With respect to diagram quality, SOCIO outperformed Creately in terms of precision, while solutions with Creately had better recall and perceived success. However, in terms of accuracy and error scores, both tools were similar.
The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain

We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data were mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, brains were cyto- and myelo-architectonically annotated to create individual 3D atlases. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze the accuracy of our tracer segmentation and brain registration approaches. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

We introduce 'POLAR' — a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold – hot, soft – hard). The core idea of our approach is to transform existing, pre-trained word embeddings via semantic differentials to a new "polar" space with interpretable dimensions defined by such polar opposites. Our framework also allows for selecting the most discriminative dimensions from a set of polar dimensions provided by an oracle, i.e., an external source. We demonstrate the effectiveness of our framework by deploying it to various downstream tasks, in which our interpretable word embeddings achieve a performance that is comparable to the original word embeddings. We also show that the interpretable dimensions selected by our framework align with human judgement. Together, these results demonstrate that interpretability can be added to word embeddings without compromising performance. Our work is relevant for researchers and engineers interested in interpreting pre-trained word embeddings.
Machine Learning Based Activity Recognition To Identify Wasteful Activities In Production

Lean Management focusses on the elimination of wasteful activities in production. Whilst numerous methods such as value stream analysis or spaghetti diagrams exist to identify transport, inventory, defects, overproduction or waiting, the waste of human motion is difficult to detect. Activity recognition attempts to categorize human activities using sensor data. Human activity recognition (HAR) is already used in the consumer domain to detect human activities such as walking, climbing stairs or running. This paper presents an approach to transfer the human activity recognition methods to production in order to detect wasteful motion in production processes and to evaluate workplaces. Using sensor data from ordinary smartphones, long-term short-term memory networks (LSTM) are used to classify human activities. Additional to the LSTM-network, the paper contributes a labeled data set for supervised learning. The paper demonstrates how activity recognition can be included in learning factory training starting from the generation of training data to the analysis of the results.
A Comparative Study between State-of-the-Art Object Detectors for Traffic Light Detection

In this paper, we evaluate some commonly used state-of-the-art object detectors, namely, Faster RCNN and YOLO, for traffic light detection. We choose the Bosch Small Traffic Light Dataset which is considered to be a challenging benchmark for this purpose. The model architecture, parameters are discussed in detail, and are altered to detect and classify even traffic lights that are indistinguishable to the human eye. We present the results for these optimized models along with the baseline results. Our experimental study shows that Faster RCNN model outperforms YOLO in terms of the precision obtained. However, when real-time deployment is considered, YOLO performs the best.
Salient Slices: Improved Neural Network Training and Performance with Image Entropy

As a training and analysis strategy for convolutional neural networks (CNNs), we slice images into tiled segments and use, for training and prediction, segments that both satisfy an information criterion and contain sufficient content to support classification. In particular, we use image entropy as the information criterion. This ensures that each tile carries as much information diversity as the original image and, for many applications, serves as an indicator of usefulness in classification. To make predictions, a probability aggregation framework is applied to probabilities assigned by the CNN to the input image tiles. This technique, which we call Salient Slices, facilitates the use of large, high-resolution images that would be impractical to analyze unmodified; provides data augmentation for training, which is particularly valuable when image availability is limited; and the ensemble nature of the input for prediction enhances its accuracy.
Learning-based models to detect runtime phishing activities using URLs

Phishing websites are fraudulent sites that impersonate a trusted party to gain access to sensitive information of an individual person or organization. Traditionally, phishing website detection is done through the usage of blacklist databases. However, due to the current, rapid development of global networking and communication technologies, there are numerous websites and it has become difficult to classify based on traditional methods since new websites are created every second. In this paper, we are proposing a real-time, anti-phishing system. In the first step, we extract the lexical and host-based properties of a website. In the second step, we combine URL (Uniform Resource Locator) features, NLP and host-based properties to train the machine learning and deep learning models. Our detection model is able to detect phishing URLs with a detection rate of 94.89%.
Stratigraphic discriminatory potential of heavy mineral analysis for the Neogene sediments of Belgium

The Neogene units of Belgium cannot always be easily distinguished based on visual inspection and correlation across the basin is not straightforward. To aid in the stratigraphic interpretation of units, the discriminatory potential of heavy minerals has been determined. In this study, heavy mineral composition is combined with grain size analysis, providing information on the bulk sediment. Based on heavy mineral composition important interpretations could be made, such as (1) a different provenance between the Dessel Member and the Hageland Diest sand, making it improbable that they were deposited at the same time, (2) the Kasterlee-sensu-Gulinck unit of the eastern Antwerp Campine should be redefined as a lower Mol Formation unit or as a lateral equivalent of the typical Kasterlee Formation to the west, affected strongly by southern continental sediment input, and (3) the Waubach Member in the Ruhr Valley Graben should be split into two separate units, with the upper unit correlated with the Mol Formation and the lower unit, possibly the Inden Formation, correlated with the Diest Formation and Kasterlee Formation. The ‘X’ unit of the Maaseik core is likely a local transitional unit which cannot be directly correlated with a unit in the Campine Basin.
Uncertainty Visualisation: An Interactive Visual Survey

There exists a gulf between the rhetoric in visualisation research about the significance of uncertainty and the inclusion of representations of uncertainty in visualisations used in practice. The graphical representation of uncertainty information has emerged as a problem of great importance in visualisation research. This contribution presents a survey of 286 uncertainty visualisation research publications. All publications are categorised with regard to publication type, publication venue, application domain, target user, and evaluation type. We present an interactive web-based browser that facilitates easy visual search and exploration of the publications included in the survey. We conclude that uncertainty visualisation is severely limited by the quality and scope of uncertainty data, by the limited confidence in the data, and by the perceptual and cognitive confusion that the graphical representation of the data can generate.
Bio-inspired algorithms: principles, implementation, and applications to wireless communication

Bio-inspired computing-based techniques are gaining importance in the current engineering research domain, particularly for solving complex optimization problems. These are stochastic search techniques which are developed to achieve near-optimal solutions to large-scale optimization problems. The conventional mathematical optimization methods often fail as the solutions are trapped in local optima, which gave rise to the development of alternate derivative free metaheuristic global optimization techniques. In recent times, the bio-inspired algorithms have been explored for various applications and their performance is as good as or better than that of conventional techniques. The main focus of this work is to introduce the important bio-inspired techniques available in the literature. These are not exhaustive and many new optimization algorithms inspired by natural processes are developed day-by-day. The method of development, implementation and the corresponding step-wise description is highlighted using flow diagrams and application of these techniques for selected areas of wireless communication is discussed.
Edge-centric inferential modeling &amp; analytics

This work contributes to a real-time, edge-centric inferential modeling and analytics methodology introducing the fundamental mechanisms for (i) predictive models update and (ii) diverse models selection in distributed computing. Our objective in edge-centric analytics is the time-optimized model caching and selective forwarding at the network edge adopting optimal stopping theory, where communication overhead is significantly reduced as only inferred knowledge and sufficient statistics are delivered instead of raw data obtaining high quality of analytics. Novel model selection algorithms are introduced to fuse the inherent models' diversity over distributed edge nodes to support inferential analytics tasks to end-users/analysts, and applications in real-time. We provide statistical learning modeling and establish the corresponding mathematical analyses of our mechanisms along with comprehensive performance and comparative assessment using real data from different domains and showing its benefits in edge computing.
VIDPSO: Victim item deletion based PSO inspired sensitive pattern hiding algorithm for dense datasets

Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving data mining (PPDM) includes various sensitive pattern hiding (SPH) techniques, which ensures that sensitive patterns do not get revealed when data mining models are applied on shared datasets. In the process of hiding sensitive patterns, some of the non-sensitive patterns also become infrequent. SPH techniques thus affect the results of data mining models. Maintaining a balance between data privacy and data utility is an NP-hard problem because it requires the selection of sensitive items for deletion and also the selection of transactions containing these items such that side effects of deletion are minimal. There are various algorithms proposed by researchers that use evolutionary approaches such as genetic algorithm(GA), particle swarm optimization (PSO) and ant colony optimization (ACO). These evolutionary SPH algorithms mask sensitive patterns through the deletion of sensitive transactions. Failure in the sensitive patterns masking and loss of data have been the biggest challenges for such algorithms. The performance of evolutionary algorithms further gets degraded when applied on dense datasets. In this research paper, victim item deletion based PSO inspired evolutionary algorithm named VIDPSO is proposed to sanitize the dense datasets. In the proposed algorithm, each particle of the population consists of n number of sub-particles derived from pre-calculated victim items. The proposed algorithm has a high exploration capability to search the solution space for selecting optimal transactions. Experiments conducted on real and synthetic dense datasets depict that VIDPSO algorithm performs better vis-a-vis GA, PSO and ACO based SPH algorithms in terms of hiding failure with minimal loss of data.
Questionator-Automated Question Generation using Deep Learning

Due to a boom in the amount of data generated every day, there is a need for automation in the education domain where it is humanly impossible for a single individual to make sense out of the data even for a simple task such as generating questions for a quiz or a test. Automatic question generation for textual inputs is valuable in academics where answering questions helps students to learn and improve their understanding of their field of study. Automatic question generation finds application in dialog systems or virtual assistants where asking questions is an important part of interactions between humans and machines. In this paper, we propose a state-of-the-art solution using a pipeline that utilizes natural language processing and image captioning techniques capable of generating questions not only for textual but also for visual inputs. Along with the question, distractors for the generated questions and their answers are also created.
Training Discrete-Valued Neural Networks with Sign Activations Using Weight Distributions

Since resource-constrained devices hardly benefit from the trend towards ever-increasing neural network (NN) structures, there is growing interest in designing more hardware-friendly NNs. In this paper, we consider the training of NNs with discrete-valued weights and sign activation functions that can be implemented more efficiently in terms of inference speed, memory requirements, and power consumption. We build on the framework of probabilistic forward propagations using the local reparameterization trick, where instead of training a single set of NN weights we rather train a distribution over these weights. Using this approach, we can perform gradient-based learning by optimizing the continuous distribution parameters over discrete weights while at the same time perform backpropagation through the sign activation. In our experiments, we investigate the influence of the number of weights on the classification performance on several benchmark datasets, and we show that our method achieves state-of-the-art performance.
EyesGAN: Synthesize human face from human eyes

Face recognition recently has achieved remarkable success in many fields, especially in mobile payment, authentication, criminal investigation, and city management. However, face occlusion is still the key problem in person identification, such as in the field of anti-terrorism, criminal cases and public security. To solve this problem, an improved end-to-end deep generative adversarial network (named EyesGAN) has been proposed to synthesize human face from human eyes in this paper, which can be used as a potential scheme for masked face recognition. BicycleGAN is chosen as the baseline and effective improvements have been achieved. First, the self-attentional mechanism is introduced so that the improved model can more effectively learn about the internal mapping between human eyes and face. Second, the perceptual loss is applied to guide the model cyclic training and help with updating the network parameters so that the synthesized face can be of higher-similarity to the ground truth face. Third, EyesGAN has been designed by getting the utmost out of the performance of the perceptual loss and the self-attentional mechanism in GANs. A dataset of eyes-to-face synthesis has been reconstructed based on the public face datasets for training and testing. Finally, the faces synthesized by EyesGAN have been quantitatively and qualitatively compared with the results of existing methods. Extensive experiments demenstrate that our proposed method has performed better than the state-of-the-art methods in terms of Average Euclidean Distance, Average Cosine Similarity, Synthesis Accuracy Percentage, Fréchet Inception Distance, and Baidu face recognition rate (the accuracy achieved 96.1% on 615 test data of CelebA database). In this paper, the feasibility of synthesizing human face from human eyes has been explored, and the attention map shows that our network can predict other parts of the face from eyes.