Reviewerly/exHarmony huggingface dataset:
Chimera: A Bridge Between Big Data Analytics and Semantic Technologies

In the last decades, Knowledge Graph (KG) empowered analytics have been used to extract advanced insights from data. Several companies integrated legacy relational databases with semantic technologies using Ontology-Based Data Access (OBDA). In practice, this approach enables the analysts to write SPARQL queries both over KGs and SQL relational data sources by making transparent most of the implementation details. However, the volume of data is continuously increasing, and a growing number of companies are adopting distributed storage platforms and distributed computing engines. There is a gap between big data and semantic technologies. Ontop, one of the reference OBDA systems, is limited to legacy relational databases, and the compatibility with the big data analytics engine Apache Spark is still missing. This paper introduces Chimera, an open-source software suite that aims at filling such a gap. Chimera enables a new type of round-tripping data science pipelines. Data Scientists can query data stored in a data lake using SPARQL through Ontop and SparkSQL while saving the semantic results of such analysis back in the data lake. This new type of pipelines semantically enriches data from Spark before saving them back.
Automatically classifying crime scene images using machine learning methodologies

Over the last several decades, forensic science has undergone a digital transformation whereby laboratories have become heavily reliant on information technology. Although computer-based tools have helped automate processes in forensic science, the digital transformation is not without serious technical challenges. Ever-increasing volumes, diversity, and complexity of data has overwhelmed forensic practitioners despite technological advances. In particular, it is increasingly difficult for forensic practitioners to successfully conduct the manual content labelling and cataloguing of crime scene image databases. In this paper, we investigate the application of two different machine learning classifier models on a rich collection of real-world forensic casework images of drug-related offences sourced from the Australian Federal Police illicit drug database, as proof-of-concepts for the automatic classification of crime scene images. The casework database includes 97,287 illicit drug-related casework images that have been labelled into well-defined categories by AFP personnel. The first model is based on a Support Vector Machine (SVM) classifier combined with Bag-of-Visual-Words (BoVW) visual dictionaries derived from various local image feature descriptors. The second model is based on a hierarchical Deep Convolution Neural Network (DCNN) method called Tree-CNN. Experimental results based on a subset of 60,520 images from the illicit drug database reveal average True Positive classification rates for the BoVW-based and Tree-CNN models of 66.48% and 89.17%, respectively. Furthermore, a closer assessment of the classification results suggests that the Tree-CNN model has the greatest potential for further development and use in real-world applications.
The word as a unit of internal predictability

Abstract A long-standing problem in linguistics is how to define word . Recent research has focused on the incompatibility of diverse definitions, and the challenge of finding a definition that is crosslinguistically applicable. In this study I take a different approach, asking whether one structure is more word-like than another based on the concepts of predictability and information. I hypothesize that word constructions tend to be more “internally predictable” than phrase constructions, where internal predictability is the degree to which the entropy of one constructional element is reduced by mutual information with another element. I illustrate the method with case studies of complex verbs in German and Murrinhpatha, comparing verbs with selectionally restricted elements against those built from free elements. I propose that this method identifies an important mathematical property of many word-like structures, though I do not expect that it will solve all the problems of wordhood.
25GMAC/sec/axon photonic neural networks with 7GHz bandwidth optics through channel response-aware training

We present a channel response-aware Photonic Neural Network (PNN) and demonstrate experimentally its resilience in Inter-Symbol Interference (ISI) when implemented in an integrated neuron. The trained PNN model performs at 25GMAC/sec/axon using only 7GHz-bandwidth photonic axons with 97.37% accuracy in the MNIST dataset.
PRO-GAGE: A High Performance Compact GAGE Hash Function Processor for Small Space Technology

There is a growing demand for small devices for aerospace and near-orbit applications such as CubeSats, airships, and others, which demand comprehensive functionality, computational capability and reliability. These small systems depend on limited power budgets, sufficient computational power, and accuracy. Secure and reliable operation of these devices is also a fundamental matter as the interest (from both private and public sectors) in exploring space is continuously increasing. At present, open space is considered an insecure environment and therefore increases the demand for constrained security primitives on these flying devices. However, many such features require significant power, which affects the battery lifetime. In this paper, we propose and evaluate the area, power and performance of a novel High Performance Compact low-power GAGE hash function processor called PRO-GAGE. The results show PRO-GAGE is 29x faster and 7x more power-efficient than the reported implementation of GAGE hash function on the AVR ATMEGA 328p micro-controller.
RETRACTED: A Novel approach in Hybrid Median Filtering for Denoising Medical images

Abstract Image denoising is a key pre-processing step in medical image analysis. At current, deep learning-based models have shown a great promise, which outperformed many conventional methods over the past three decades. Speckle noise removal is a major issue in preserving all the delicate details and the edges in ultrasound image processing as it degrades the visual evaluation of ultrasound images. The multiplicative behavior of speckle-noise is converted into additive by using log transform as the additive noise removal is easy as compared to multiplicative noise. An innovative approach for denoising highly distorted images affected by speckle noise is proposed. This paper presents a result of significant work in image denoising and exploring several thresholding methods of denoising images such as SureShrink, VisuShrink and BayesShrink. The results of different approaches of wavelet-based image denoising methods are tabulated to find the best method. The main aim is to show the result of wavelet coefficients on a new basis, so that the noise can be minimized or removed from the data.
Organizing the Design Thinking Toolbox: Supporting the Requirements Elicitation Decision Making

Context: Requirements Elicitation is an essential part of the software development process. Design Thinking (DT) arises as an alternative approach for supporting the Requirements Elicitation step. Empirical studies have shown that the use of DT in the software development process can be beneficial. The literature offers a wide variety of DT techniques. However, there is a lack of studies that mention which information is needed to use the techniques and which results they generate. Objective: Motivated by this need, this work aims to assist the software engineers in selecting which DT techniques are more suitable to support the requirements elicitation step. Method: We performed a literature review to identify DT techniques that can be used for the requirements elicitation. Next, we analysed and modelled the techniques identified using SADT diagrams. We grouped the DT techniques into ten categories according to their main purpose. Based on the SADT diagrams, we elaborated a comparative table for these categories, showing for each technique: input, control, mechanism, and output terms. We carried out a feasibility study to verify whether the comparison tables among the DT techniques provide the necessary information for software engineers. Results: Our preliminary results showed that the comparative tables, describing the inputs, control, mechanism, and outputs, helped software engineers to select the DT technique according to their goals. Conclusion: The results provided evidence that the DT techniques comparison table is promising in the context of requirements elicitation decision making to choose the most appropriate DT techniques.
GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images

Convolutional neural network (CNN) has achieved superior performance on the computer-aided diagnosis for histopathological images. Although the spatial arrangement of cells of various types in histopathological images is an important characteristic for the diagnosis of cancers, CNN cannot explicitly capture this spatial structure information. This challenge can be overcome by constructing the graph data on histopathological images and learning the graph representation with valuable spatial correlations in the graph convolutional network (GCN). However, the current GCN models for histopathological images usually require a complicated preprocessing process or prior experience of node selection for graph construction. Moreover, there is a lack of learning architecture that can perform feature selection to refine features in the GCN. In this work, we propose a group quadratic graph convolutional network (GQ-GCN), which adopts CNN to extract features from histopathological images for further adaptively graph construction. In particular, the group graph convolutional network (G-GCN) is developed to implement both feature selection and compression of graph representation. In addition, the quadratic operation is specifically embedded into the graph convolution to enhance the representation ability of a single neuron for complex data. The experimental results on two public breast histopathological image datasets indicate the effectiveness of the proposed GQ-GCN.
Secure and Optimal Secret Sharing Scheme for Color Images

Due to contemporary communication trends, the amount of multimedia data created and transferred in 5G networks has reached record levels. Multimedia applications communicate an enormous quantity of images containing private data that tend to be attacked by cyber-criminals and later used for illegal reasons. Security must consider and adopt the new and unique features of 5G/6G platforms. Cryptographic procedures, especially secret sharing (SS), with some extraordinary qualities and capacities, can be conceived to handle confidential data. This paper has developed a secured (k, k) multiple secret sharing (SKMSS) scheme with Hybrid Optimal SIMON ciphers. The proposed SKMSS method constructs a set of noised components generated securely based on performing hash and block ciphers over the secret image itself. The shares are created and safely sent after encrypting them through the Hybrid Optimal SIMON ciphers based on the noised images. This is a lightweight cryptography method and helps reduce computation complexity. The hybrid Particle Swarm Optimization-based Cuckoo Search Optimization Algorithm generates the keys based on the analysis of the peak signal to noise ratio value of the recovered secret images. In this way, the quality of the secret image is also preserved even after performing more computations upon securing the images.
General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet, an efficient and highly generalized deep learning neural network for P- and S-wave phase picking. The software organically combines multiple modules including application terminal interface, docker container, data visualization, SSH protocol data transmission and other auxiliary modules. Characterized by a series of technologically powerful functions, the software is highly convenient for all users. To obtain the P- and S-wave picks, one only needs to prepare three-component seismic data as input and customize some parameters in the interface. In particular, the software can automatically identify complex waveforms (i.e. continuous or truncated waves) and support multiple types of input data such as SAC, MSEED, NumPy array, etc. A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software. The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data, thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.
GPU-Accelerated implementation of a genetically optimized image encryption algorithm

This paper presents a GPU-accelerated implementation of an image encryption algorithm. The algorithm uses the concepts of a modified XOR cipher to encrypt and decrypt the images, with an encryption pad, generated using the shared secret key and some initialization vectors. It uses a genetically optimized pseudo-random generator that outputs a stream of random bytes of the specified length. The proposed algorithm is subjected to a number of theoretical, experimental, and mathematical analyses, to examine its performance and security against a number of possible attacks, using the following metrics - histogram analysis, correlation analysis, information entropy analysis, NPCR and UACI. The performance analysis carried out shows an average speedup-ratio of 3.489 for encryption, and 4.055 for decryption operation, between the serial and parallel implementations using GPU. The algorithm aims to provide better performance benchmarks, which can significantly improve the experience in the relevant use-cases, like real-time media applications.
Deep Model Compression via Two-Stage Deep Reinforcement Learning

Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications. For example, employing deep neural networks on mobile systems requires the design of accurate yet fast CNN for low latency in classification and object detection. To fulfill the need, we aim at obtaining CNN models with both high testing accuracy and small size to address resource constraints in many embedded devices. In particular, this paper focuses on proposing a generic reinforcement learning-based model compression approach in a two-stage compression pipeline: pruning and quantization. The first stage of compression, i.e., pruning, is achieved via exploiting deep reinforcement learning (DRL) to co-learn the accuracy and the FLOPs updated after layer-wise channel pruning and element-wise variational pruning via information dropout. The second stage, i.e., quantization, is achieved via a similar DRL approach but focuses on obtaining the optimal bits representation for individual layers. We further conduct experimental results on CIFAR-10 and ImageNet datasets. For the CIFAR-10 dataset, the proposed method can reduce the size of VGGNet by \(9\times \) from 20.04 MB to 2.2 MB with a slight accuracy increase. For the ImageNet dataset, the proposed method can reduce the size of VGG-16 by \(33\times \) from 138 MB to 4.14 MB with no accuracy loss.
Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

The world health organization (WHO) terms dengue as a serious illness that impacts almost half of the world's population and carries no specific treatment.Early and accurate detection of spread in affected regions can save precious lives.Despite the severity of the disease, a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams.However, the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data, as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are required.This research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache Hadoop.The method consists of two main parts: the first part collects data from social media using Apache Flume, while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache Hive.To overcome the noisy and unstructured nature of the data, the process of extracting information is characterized by pre and post-filtration phases.As a result, only with the integration of Flume and Hive with filtration and polarity analysis, can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social network.We introduce how the Apache Hadoop ecosystem -Flume and Hivecan provide a sentiment analysis capability by storing and processing large amounts of data.An important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.
Explainability in Human-Robot Teaming

In human-robot teaming, one of the crucial keys for the team’s success is that the robot and human teammates can collaborate accordingly in a coordinated manner. Each teammate should be aware of what the other teammate is going to perform and likely to need. In this context, a robot is expected to understand human teammate intention and performance and explain its actions and decisions and its rationale to its teammate. In addition, the capability to model the expectation of a human teammate empowers the robot to collaborate with human understandably and expectedly, leading to effective teaming. Through forming mental modelling, the robot can understand the impact of its own behaviour on the mental model of the human. In addition, the desirable traits in human-robot teaming, including fluent behaviour, adaptability, trust-building, effective communication, and explainability, can be achieved through mental modelling. In this work, we introduce a scenario for human-robot teaming considering all the five desirable traits in teaming with the main focus on explainability and effective communication. Using a general model reconciliation, the expectation of the human teammate of the robot can be modelled, and the explanation can be generated. In a considered scenario including Care-O-bot 4 service robot and a human teammate, we assume that the robot detects the human’s current task (analysing his body gesture) and predicts his following action and his expectation from the robot. In a reciprocal interdependence task, the robot coordinates his behaviour and acts accordingly by picking up the relevant tool. Through explanation and communication robot further offers the outcome of his decision to the human teammate and adapts its action by handing the tool to the human upon his desire.
To i-vote or Not to i-vote: Drivers and Barriers to the Implementation of Internet Voting

This paper investigates the drivers and barriers of internet voting and the implications of a global pandemic for the development of the respective technology. In contrast to the expected uptake in the early 2000s of internet voting, the technology is still rather seldomly used in election systems around the world. The paper at hand explores the different forces that drive or impede internet voting adoption from a political, social, legal, organizational, contextual, economic and technological perspective. In an exploratory approach, 18 expert interviews and extensive complementary desk research were conducted.The findings identified 15 general drivers and 15 general barriers for the process of internet voting adoption. The evidence suggests that for a large part, the political features, trust and perception are the most pivotal factors to internet voting development.
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation

Previous state-of-the-art deep generative models improve fine-grained image generation quality by designing hierarchical model structures and synthesizing images across multiple stages. The learning process is typically performed without any supervision in object categories. To address this issue, while at the same time to alleviate the level of complexity of both model design and training, we propose a Single-Stage Controllable GAN (SSCGAN) for conditional fine-grained image synthesis in a semi-supervised setting. Considering the fact that fine-grained object categories may have subtle distinctions and shared attributes, we take into account three factors of variation for generative modeling: class-independent content, cross-class attributes and class semantics, and associate them with different variables. To ensure disentanglement among the variables, we maximize mutual information between the class-independent variable and synthesized images, map real data to the latent space of a generator to perform consistency regularization of cross-class attributes, and incorporate class semantic-based regularization into a discriminator’s feature space. We show that the proposed approach delivers a single-stage controllable generator and high-fidelity synthesized images of fine-grained categories. SSC-GAN establishes state-of-the-art semi-supervised image synthesis results across multiple fine-grained datasets.
TAGAT: Type-Aware Graph Attention neTworks for reasoning over knowledge graphs

With the rapid development of knowledge graphs (KGs), various AI-related applications have been affected positively. However, even though some KGs are relatively large, they still suffer from incompleteness. This has mighty promoted the development of reasoning over KGs to complement them. However, most existing reasoning methods only focus on semantics in the KG, ignoring potential or valuable information hidden in it, the most typical ones are neighborhood information and type information. This limits not only the reasoning performance but also the interpretability of the embedding space. To this end, we propose a Type-Aware Graph Attention neTworks (TAGAT) for the reasoning task over KGs. Except for combining type-related information during the embedding process, TAGAT further adopts a hierarchical attention mechanism to realize the perception of type and neighborhood information meticulously. For each entity, different attention levels respectively focus on considering the contribution of different relations, different types under each relation and different entity of each type under each relation. Moreover, the embedding space of TAGAT is constrained by the type information and naturally has better type-related interpretability, which greatly complements the defects of existing KGC models. Extensive experiments validate the high reasoning performance and the relatively ideal interpretability of our model.
GPUCorrel: A GPU accelerated Digital Image Correlation software written in Python

This article presents an open-source Integrated Digital Image Correlation (I-DIC) software written in Python using CUDA-enabled GPUs designed to run at high (1–100 Hz) frequency. The field computation is performed using a global approach and the result is a projection of the real field in a user-defined base of fields. This software can be used in many applications and one use in experimental mechanics is demonstrated by driving a bi-axial tensile test on a cruciform specimen.