Reviewerly/exHarmony huggingface dataset:
Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face

As unmanned coal mining technology gradually replaces the traditional mechanized coal mining technology, shearer operation mode is changed from local control to remote control in intelligent fully mechanized mining face. In remote control mode, it is difficult to protect the personnel who straying into the shearer operator space without observation and reminder from the shearer operator. Hence, it is necessary to establish an intelligent personnel detection method to protect the safety of coal miners in intelligent unmanned mining face. The environment of low and non-uniform illumination in fully mechanized coal mining face has seriously restricted the application of personnel detection technology based on visible light imaging. Therefore, a personnel detection method based on infrared thermal imaging is proposed in this paper to solve the disadvantages of using visible light imaging in downhole applications. On this basis, a spatiotemporal guided filter is proposed to harmonize the relationship between edge-preserving and noise-removing. Then, an improved Lucas-Kanade method based on the adaptive-size window is utilized to achieve a more robust personnel detection. Moreover, the personnel detection in the shearer operating space is realized based on epipolar geometry and morphology processing. Finally, the laboratory experiment and industrial test are carried out to evaluate the proposed method, and the results indicate the feasibility and superiority of the proposed methods which show considerable application prospects.
Rubik-Net: Learning Spatial Information via Rotation-Driven Convolutions for Brain Segmentation

The accurate segmentation of brain tissue in Magnetic Resonance Image (MRI) slices is essential for assessing neurological conditions and brain diseases. However, it is challenging to segment MRI slices because of the low contrast between different brain tissues and the partial volume effect. 2-Dimensional (2-D) convolutional networks cannot handle such volumetric image data well because they overlook spatial information between MRI slices. Although 3-Dimensional (3-D) convolutions capture volumetric spatial information, they have not been fully exploited to enhance representative ability of deep networks; moreover, they may lead to overfitting with insufficient training data. In this paper, we propose a novel convolutional mechanism, termed Rubik convolution, to capture multi-dimensional information between MRI slices. Rubik convolution rotates the axis of a set of consecutive slices, enabling 2-D convolution kernels to extract features of each axial plane simultaneously. Next, feature maps are rotated back to fuse multidimensional information by the Max-View-Maps. Furthermore, we propose an efficient 2-D convolutional network, namely Rubik-Net, where the residual connections and the bottleneck structure are used to enhance information transmission and reduce the number of network parameters. The Rubik-Net shows promising results on iSeg2017, iSeg2019, IBSR and BrainWeb datasets in terms of segmentation accuracy. In particular, we achieved the best results in 95th percentile Hausdorff distance and average surface distance in cerebrospinal fluid segmentation on the most challenging iSeg2019 dataset. The experiments indicate that Rubik-Net improves the accuracy and efficiency of medical image segmentation. Moreover, Rubik convolution can be easily embedded into existing 2-D convolutional networks.
Reproducible Hyperparameter Optimization

A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead to a large variance in outcomes due to nondeterministic model training during hyperparameter optimization. The variation in outcomes poses a problem both for reproducibility of the hyperparameter search itself and comparisons of different methods each optimized using hyperparameter search. In addition, the fact that hyperparameter search may result in nonoptimal hyperparameter settings may affect other studies, since hyperparameter settings are often copied from previously published research. To remedy this issue, we define the mean prediction error across model training runs as the objective for the hyperparameter search. We then propose a hypothesis testing procedure that makes inference on the mean performance of each hyperparameter setting and results in an equivalence class of hyperparameter settings that are not distinguishable in performance. We further embed this procedure into a group sequential testing framework to increase efficiency in terms of the average number of model training replicates required. Empirical results on machine learning benchmarks show that at equal computation the proposed method reduces the variation in hyperparameter search outcomes by up to 90% while resulting in equal or lower mean prediction errors when compared to standard random search and Bayesian optimization. Moreover, the sequential testing framework successfully reduces computation while preserving performance of the method. The code to reproduce the results is available online and in the supplementary materials.
PRS-P2P: A Prosumer Recommender System for Secure P2P Energy Trading using Q-Learning Towards 6G

In this paper, we present a secure Peer-to-Peer (P2P) trading system for residential houses that aims to maximize energy sharing and improve the operational effectiveness of utility vendors (UVs) in the Smart Grid (SG) environment. Here, we propose PRS-P2P scheme, i.e., a Prosumer Recommender System (PRS) for P2P Energy Trading (ET) using reinforcement learning and blockchain towards 6G. A reinforcement learning-based (Q-learning) algorithm is proposed to improve the decision-making process of the buyer (i.e., consumer) for the seller (i.e., prosumers, who can generate and consume energy) selection from multiple seller participants in a model-free way. During P2P-ET, data flow among different devices, which raises concern for several issues such as security, latency, and others. So, the proposed PRS-P2P scheme uses ethereum blockchain and 6G network, where the buyer sends a request for P2P-ET to the selected seller and trade executed securely using Smart Contract (SC) based on the ethereum blockchain. Then, it employs InterPlanetary File System (IPFS) for energy data management at low cost and performs real-time settlement of trade with low latency. To justify the efficacy of the proposed PRS-P2P scheme, experimental results are compared to the existing approaches concerning different metrics like timesteps for rewards, penalties, and low communication latency.
Image Classification with Artificial Intelligence: Cats vs Dogs

With the rise of the techniques including big data and artificial intelligence, we now have a higher-level technology in solving image classification problems, and increase the accuracy of image classification by a remarkable amount. To compare and analyze the classification performance from different machine learning and deep learning, this paper implemented support vector machine and convolutional neural network to solve the classical Cats vs Dogs problem, and compared how different parameters affect CNN. The lessons are summarized and presented in this paper.
Time series model for real-time forecasting of Australian photovoltaic solar farms power output

Intermittent electrical power output from grid-connected solar farms causes intermittent and uncertain requirements for dispatchable power to balance power supply and demand. Accurate forecasting of electrical power output from solar farms can improve managing power generators connected to the grid. To forecast the electrical power output, a time series model is developed for two solar farms in Australia. The forecast model consists of a Fourier series that models seasonality and an autoregressive moving-average component that models the difference between the observed electrical power outputs and the Fourier series. Persistence detection is added to the model to improve forecast performance on clear days. Using minutely data, the model forecasts the electrical power output seven minutes ahead at every five-minute interval to comply with the requirements of the Australian Energy Market Operator (AEMO). Based on a 30-day testing period, the normalized mean absolute error (NMAE) skills of the time series model are 10.9% and 13.2% lower than those of the clear sky index persistence (CSIP) model. However, the normalized root mean squared error (NRMSE) skills of the time series model are approximately 3% and 12% higher than those of CSIP and the model currently used by AEMO, respectively. As the NRMSE skills are more indicative than the NMAE skills in reducing large forecast errors that would reduce electricity grid stability, the results suggest that AEMO can improve the management of the electricity grid with an inexpensive tool by adopting the developed model to forecast electrical power output of solar farms.
On the relevance of the metadata used in the semantic segmentation of indoor image spaces

• Quantitatively evaluate the usefulness of contextual information for a U-Net. • Prove that learning systems rely heavily on contextual info for identification tasks. • The Importance of Metadata Applied to Semantic Segmentation for Indoor Scenes. • Deploying an efficient and robust FCN for Semantic Segmentation of Indoor Imagery. The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post-processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.
Principal Component Analysis for Gaussian Process Posteriors

This letter proposes an extension of principal component analysis for gaussian process (GP) posteriors, denoted by GP-PCA. Since GP-PCA estimates a low-dimensional space of GP posteriors, it can be used for metalearning, a framework for improving the performance of target tasks by estimating a structure of a set of tasks. The issue is how to define a structure of a set of GPs with an infinite-dimensional parameter, such as coordinate system and a divergence. In this study, we reduce the infiniteness of GP to the finite-dimensional case under the information geometrical framework by considering a space of GP posteriors that have the same prior. In addition, we propose an approximation method of GP-PCA based on variational inference and demonstrate the effectiveness of GP-PCA as meta-learning through experiments.
Barriers to developing futures literacy in organisations

We live in an age of transition driven by technological opportunities, and the speed with which industries are disrupted accelerates. For organisations, the consequences of these changes are to replace static capabilities with dynamic capabilities, such as futures literacy. This paper explores the concept of futures literacy and identifies some of the barriers to developing futures literacy in organisations. As research related to futures literacy is still in its infant stage, no existing research has identified barriers to developing futures literacy in organisations. This article aims to make that contribution to advance this field of study and provide understanding of where best to invest to overcome such barriers. Due to the nature of futures literacy and its relation to foresight, we put forward the hypothesis that for all its merits, futures literacy may be faced with several barriers when developed in organisations, such as organisational culture, lack of experience, mental models, measurability and clarity on investment. We explore this hypothesis through a literature review on barriers to develop foresight in organisations, combined with an additional literature review on barriers to enhance learning environments in organisations, followed by a single retrospective case study to test if the identified barriers are present.
Abstractive text summarization using deep learning with a new Turkish summarization benchmark dataset

Abstract Exponential increase in the amount of textual data made available on the Internet results in new challenges in terms of accessing information accurately and quickly. Text summarization can be defined as reducing the dimensions of the expressions to be summarized without spoiling the meaning. Summarization can be performed as extractive and abstractive or using both together. In this study, we focus on abstractive summarization which can produce more human‐like summarization results. For the study we created a Turkish news summarization benchmark dataset from various news agency web portals by crawling the news title, short news, news content, and keywords for the last 5 years. The dataset is made publicly available for researchers. The deep learning network training was carried out by using the news headlines and short news contents from the prepared dataset and then the network was expected to create the news headline as the short news summary. To evaluate the performance of this study, Rouge‐1, Rouge‐2, and Rouge‐L were compared using precision, sensitivity and F1 measure scores. Performance values for the study were presented for each sentence as well as by averaging the results for 50 randomly selected sentences. The F1 Measure values are 0.4317, 0.2194, and 0.4334 for Rouge‐1, Rouge‐2, and Rouge‐L respectively. Performance results show that the approach is promising for Turkish text summarization studies and the prepared dataset will add value to the literature.
Notes on the Leonard System Classification

Abstract Around 2001 we classified the Leonard systems up to isomorphism. The proof was lengthy and involved considerable computation. In this paper we give a proof that is shorter and involves minimal computation. We also give a comprehensive description of the intersection numbers of a Leonard system.
WebEvo: taming web application evolution via detecting semantic structure changes

The development of Web technology and the beginning of the Big Data era have led to the development of technologies for extracting data from websites, such as information retrieval (IR) and robotic process automation (RPA) tools. As websites are constantly evolving, to prevent these tools from functioning improperly due to website evolution, it is important to monitor the changes in websites and report them to the developers and testers. Existing monitoring tools mainly use DOM-tree based techniques to detect changes in the new web pages. However, these monitoring tools incorrectly report content-based changes (i.e., web content refreshed every time a web page is retrieved) as the changes that will adversely affect the performance of the IR and RPA tools. This results in false warnings since the IR and RPA tools typically consider these changes as expected and retrieve dynamic data from them. Moreover, these monitoring tools cannot identify GUI widget evolution (e.g., moving a button), and thus cannot help the IR and RPA tools adapt to the evolved widgets (e.g., automatic repair of locators for the evolved widgets). To address the limitations of the existing monitoring tools, we propose an approach, WebEvo, that leverages historic pages to identify the DOM elements whose changes are content-based changes, which can be safely ignored when reporting changes in the new web pages. Furthermore, to identify refactoring changes that preserve semantics and appearances of GUI widgets, WebEvo adapts computer vision (CV) techniques to identify the mappings of the GUI widgets from the old web page to the new web page on an element-by-element basis. Empirical evaluations on 13 real-world websites from 9 popular categories demonstrate the superiority of WebEvo over the existing DOM-tree based detection or whole-page visual comparison in terms of both effectiveness and efficiency.
Promising Digital Schools: An Essential Need for an Educational Revolution

This paper endeavors to propose a clear framework to show how the Lebanese traditional schools will be transformed to digital schools to benefit from digital technology. Digitalization is prompting education at a very fast pace. Educational institutions are in vital need of technology implementations to stay digitally relevant to Net-generation learners. Though many schools have digital initiatives and plans, the implementation of these up to a well-studied framework is something many institutions still lack. This study adopts the instructional design theory, which aims to propose a clear technology implementation framework to promote schools’ digital transformation. The study emphasizes that schools are in need to deal with a well-defined framework in their establishment for digital institutions and proposes a digital framework for schools to implement educational technology tools and strategies to gain the benefits attached to technological progress. The suggested framework highlights the need for mutual interaction between schools’ visions, teachers’ professional development, some appropriate tools, learners’ beliefs, and the learning society’s support in guiding schools to attain the opportunities of digitalization in reforming education. Researchers, policymakers, and educators need to contribute to this developed framework and identify strengths, weaknesses, opportunities, and threats (SWOT analysis) of the suggested model on schools’ digitalization process. However, additional research should be conducted regarding the impact of the model on enhancing the educational revolution. Hence, a call is made for researchers to continue testing the model in different contexts with the intent of refining it.
Comparative Performance Analysis of NoSQL Cassandra and MongoDB Databases

As communication technologies evolve, increasing convergences between them, it is essential that there are flexible and scalable infrastructures capable of storing and processing the millions of data that are produced daily through the massive use of mobile applications and social networks. NoSQL databases are becoming an alternative to store those data. In this paper two experiments that aim to compare the performance of the Cassandra and MongoDB databases are presented. In the first experiment, we tested the performance using the YCSB tool with three workloads and found that Cassandra had the best performance. While in the second experiment, we simulate operations to a database of investors from the Tesouro Nacional (National Treasury), to answer some questions and check the performance at run time, then the MongoDB database had the best results in most operations. The results are presented in this paper.
The Application of Geostatistical Analysis on Radiometric Mapping Data to Recognized the Uranium and Thorium Anomaly in West Sulawesi, Indonesia.

Abstract Mamuju Region in West Sulawesi, Indonesia is one of prospect area of uranium/thorium in Indonesia. High radiometric concentrations were located in Adang Volcanic Rocks. This formation is grouped as basaltic to intermediate rock and composed by lava, pyroclastic rocks, and tuff. Regional field radiometric mapping, including radiation dose rates, potassium, uranium, and thorium contents of soil and rock was conducted to identify the existence of radioactive minerals and prospect zones. Some radiometric anomalies are detected and probably related to hydrothermal alteration, leaching, and precipitation processes. Uranium anomaly (97,261 ppm) located in Mamuju River upstream, Botteng, Takandeang and Ahu area. The anomaly related to uranium leaching and precipitation processes in leucite basalt lava. Thorium anomaly (369,461 ppm) located in Pangasaan, Takandeang, Ahu, and Taan area. To expand this radiometric mapping, we characterized the reflectance of Landsat-8 imagery based on field measurement data. Based on circular structures identification, uranium/thorium anomaly are related to the existence of volcano vents on Adang Volcanic. Based on band rationing, high uranium contents distributed in the area with less ferromagnesian mineral, while thorium did not affect. These field based and remote sensing characterization results could be used as preliminary parameters to identify wide and detail prospects.
Predicting intraoperative bleeding in patients undergoing a hepatectomy using multiple machine learning and deep learning techniques

• This is the first study to employ multiple machine learning and deep learning techniques to predict and evaluate intraoperative bleeding in patients with liver cancer. • The XGB algorithm suggests a strong correlation between albumin, globulin, calcium ion and platelet count with intraoperative blood loss.
A Watermarking-Based Framework for Protecting Deep Image Classifiers Against Adversarial Attacks

Although deep learning-based models have achieved tremendous success in image-related tasks, they are known to be vulnerable to adversarial examples—inputs with imperceptible, but subtly crafted perturbation which fool the models to produce incorrect outputs. To distinguish adversarial examples from benign images, in this paper, we propose a novel watermarking-based framework for protecting deep image classifiers against adversarial attacks. The proposed framework consists of a watermark encoder, a possible adversary, and a detector followed by a deep image classifier to be protected. Specific methods of watermarking and detection are also presented. It is shown by experiment on a subset of ImageNet validation dataset that the proposed framework along with the presented methods of watermarking and detection is effective against a wide range of advanced attacks (static and adaptive), achieving a near zero (effective) false negative rate for FGSM and PGD attacks (static and adaptive) with the guaranteed zero false positive rate. In addition, for all tested deep image classifiers (ResNet50V2, MobileNetV2, InceptionV3), the impact of watermarking on classification accuracy is insignificant with, on average, 0.63% and 0.49% degradation in top 1 and top 5 accuracy, respectively.
Crafting Communication Protocols with a San Community in Namibia

Research and development collaboration projects between researchers and rural indigenous communities have the potential to substantially contribute to knowledge creation while at the same time carrying the risk of miscommunications. As such it is of importance to jointly establish communication protocols considering the vast cultural differences between University procedures and community etiquette. In this paper we present efforts undertaken towards crafting a communication protocol with a collaborating San community in Namibia. Exploring interventions within running projects contributed towards practical resolutions. It has to be noted that a communication protocol needs to continuously evolve throughout the collaboration.