Reviewerly/exHarmony huggingface dataset:
Distorted Representation Space Characterization Through Backpropagated Gradients

In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms. We demonstrate the utility of such gradients in applications including perceptual image quality assessment and out-of-distribution classification. The applications are chosen to validate the effectiveness of gradients as features when the test image distribution is distorted from the train image distribution. In both applications, the proposed gradient based features outperform activation features. In image quality assessment, the proposed approach is compared with other state of the art approaches and is generally the top performing method on TID 2013 and MULTI-LIVE databases in terms of accuracy, consistency, linearity, and monotonic behavior. Finally, we analyze the effect of regularization on gradients using CURE-TSR dataset for out-of-distribution classification.
Developing a Work-based Software Engineering Degree in Collaboration with Industry

Work-based learning has been in practice in Software Engineering for some time, but only in recent years has it been introduced as a pathway to an honours-level undergraduate degree across the UK. Through the lens of one such scheme, the Graduate Apprenticeship programme in Scotland, we have investigated what challenges work-based learning degree programmes are likely to face and took this question to 26 industry partners. Also, since we are aware of a persistent skills gap between Software Engineering graduates and entry-level industry roles, we investigated the skills that Software Development teams are looking for in Scotland. This paper details our findings concerning perceived challenges to industry, the skills and knowledge to be imparted at university and the workplace learning opportunities which can be exploited by companies.
Fault Detection and Isolation in Industrial Networks using Graph Convolutional Neural Networks

Industrial networks represent large-scale systems that consist of several interacting components. In this paper, we present a solution for Fault Detection and Isolation (FDI) in industrial networks with same type of components (electrical, mechanical, etc.) such as power grids, and water supply networks. Traditional FDI algorithms are trained to detect and isolate faults on the level of a single component by considering features from this component and sometimes from nearby components. These algorithms are sub-optimal as they are independently applied to individual components without explicitly taking into consideration the dependency between the several components that co-exist in industrial network. The interaction between the components makes fault isolation challenging. An operation change or a fault in a component can affect the neighboring components. This negatively impact the diagnosis accuracy when we design an independent diagnoser for each component. On the other hand, designing a global diagnoser without considering the network structure can lead to overfitting which can degrade the diagnosis performance significantly specially when the training data is limited. Moreover, because of the large number of components in these systems, the single fault assumption may not stand. This increases the complexity of the problem. In order to solve this problem, we first model the industrial network as a weighted undirected graph structure. The graph structure represents the connected components. The weights quantify these connections. We then apply Graph Convolutional Neural Networks (GCNN) to detect and isolate faulty components in these systems. In the case study, we apply our solution to a simulated industrial network with 100 components. The case study shows that GCNN outperforms several baseline algorithms.
Motion Saliency Based Generative Adversarial Network for Underwater Moving Object Segmentation

The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed. The proposed network comprises of both identity mapping and dense connections for underwater MOS. To the best of our knowledge, this is the first paper with the concept of GAN-based unpaired learning for MOS in underwater videos. Initially, current frame motion saliency is estimated using few initial video frames and current frame. Further, estimated motion saliency is given as input to the proposed network for foreground estimation. To examine the effectiveness of proposed network, the Fish4Knowledge [1] underwater video dataset and challenging video categories of ChangeDetection.net-2014 [2] datasets are considered. The segmentation accuracy of existing state-of-the-art methods are used for comparison with proposed approach in terms of average F-measure. From experimental results, it is evident that the proposed network shows significant improvement as compared to the existing state-of-the-art methods for MOS.
Training Object Detectors With Noisy Data

The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.
Visual Analytics for Analyzing Technological Trends from Text

The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.
P3D-CTN: Pseudo-3D Convolutional Tube Network for Spatio-Temporal Action Detection in Videos

The spatial independence and temporal continuity of video data as a whole are not fully investigated for video action detection. To tackle this issue, a deep network architecture is proposed, named Pseudo-3D Convolutional Tube Network (P3D-CTN). In particular, the proposed P3D-CTN integrates the frame-based two-dimensional convolutional module with the P3D convolutional module to balance the spatial and temporal information, and generates deeper features about human actions. Evaluations on two benchmark datasets (i.e., UCF-Sports and J-HMDB) demonstrate that the proposed P3D-CTN has superior performances in the task of action label prediction and yields state-of-the-art results for spatio-temporal action detection.
An Improved Algorithm for Digital Image Authentication and Forgery Localization Using Demosaicing Artifacts

This paper focuses on the digital image authentication and forgery localization using demosaicing artifacts.The aim is to build an algorithm allowing a bridge between the color filter array pattern and demosaicing algorithm estimation, and the statistical analysis of demosaicing artifacts in spatial domain to improve the authentication and localization performance.After analyzing the evolution of demosaicing traces in camera acquisition pipeline, a robust feature statistic characterizing demosaiced digital images is first developed on the basis of the noise residue of green channel.Such a feature statistic is less sensitive to the edges problem because only the smooth region of green channel is used in the development.Next, a single normal mixture model is proposed to describe the probability distribution of feature statistics for both original and tampered images.Therefore, normality tests can be used to authenticate automatically digital images.The authentication performance can be further improved by human interpretation of supported graphic tools.Finally, a penalized expectation-maximization algorithm is used to localize forged areas in tampered images.Numerous comparative studies on four well-known datasets show that the developed algorithm yields better performance and robustness than existing forensics algorithms of the same kind.
Applicability of Neural Network Based Models for Software Effort Estimation

Effort Estimation is a very challenging task in the software development life cycle. Inaccurate estimations may cause the client dissatisfaction and thereby, decrease the quality of the product. Considering the problem of software cost and effort prediction, it is conceivable to call attention to that the estimation procedure considers the qualities present in the data set, as well as the aspects of the environment in which the model is embedded. Existing literatures have the instances where machine learning techniques such as Linear Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) have been used to estimate the effort required to develop any software. Yet it is quite uncertain for any particular model to perform well with all the data sets. Most of the research is based on the dataset of any single organization. Consequently, the results obtained through these models cannot be generalized. So, the main objectives of this research are: i) to use different data preparation techniques such as selection, cleaning, and transformation to improve the quality of data set given to the model ii) to use other machine learning models such as Multi-Layer Perceptron Neural Network (MLPNN), Probabilistic Neural Network (PNN), and Recurrent Neural Network (RNN) to increase the performance of software effort estimation process iii) to use different optimization techniques to tune the parameters of machine learning models iv) to use ensemble methods to improve the accuracy of software effort estimation process. In this study, first, we found out the most influential attributes in the Desharnais data set, then, MLPNN has been applied on reduced data set with to improve the accuracy of software effort estimation. Then, the performance of the MLPNN model is compared with LR, SVM and KNN models in the literature to find the best model fitting this dataset. Results obtained from the study demonstrate that some of the variables are more important in comparison to others for effort estimation. Also among the various models used in this study, the best-obtained R2 value is 79 % for the MLPNN model.
Fast Bright-Pass Bilateral Filtering for Low-Light Enhancement

We consider the problem of enhancing images captured under low-light conditions. Several variational and filtering based solutions have been proposed for this problem that are based on the retinex model. The idea in retinex is to first estimate the illumination and reflectance from the observed image, enhance the illumination, and then combine it with the reflectance to get the rectified image. A variant of bilateral filtering, called bright-pass bilateral filtering (BPBF), can be used for illumination estimation. However, BPBF is computation intensive and takes up a significant amount of the processing time. Motivated by recent work, we propose a Fourier approximation of BPBF that can accelerate the filtering (by an order) without loss in visual quality. Experimental results demonstrate that our algorithm is sufficiently fast and can effectively enhance low-light images. In particular, our proposal is competitive with recent algorithms in terms of visual perception and quality metrics.
Erroneous data generation for Grammatical Error Correction

It has been demonstrated that the utilization of a monolingual corpus in neural Grammatical Error Correction (GEC) systems can significantly improve the system performance.The previous state-of-theart neural GEC system is an ensemble of four Transformer models pretrained on a large amount of Wikipedia Edits.The Singsound GEC system follows a similar approach but is equipped with a sophisticated erroneous data generating component.Our system achieved an F 0.5 of 66.61 in the BEA 2019 Shared Task: Grammatical Error Correction.With our novel erroneous data generating component, the Singsound neural GEC system yielded an M 2 of 63.2 on the CoNLL-2014 benchmark (8.4% relative improvement over the previous state-of-the-art system).
OBIS Infrastructure, Lessons Learned, and Vision for the Future

This mini-review paper analyses the achievements of the Ocean Biogeographic Information System (OBIS), as a distributed global data system and as a community of data contributors and users. We highlight some issues and challenges and identify ways OBIS is trying to address these with developing community standards, protocols and best practices, applying new innovative technologies, improving human capacity through training, and establishing beneficial partnerships. With the release of the second generation of OBIS (OBIS 2.0), we now have a more solid foundation to build improved data processing/integration workflows, new data synthesis routines that add value to OBIS data, and new types of products and applications for scientific and decision-making. The future of OBIS will be in working toward an open and inviting process of co-developing OBIS as a global networked open-source data system that will enable the community to organize, document, and contribute analytical codes that interface directly with OBIS, provide analyses, and share results. The main challenges will be in mobilizing and organizing the scientific community to publish richer and high quality data more rapidly in support of developing robust and timely indicators of status and change on Essential Ocean Variables and Essential Biodiversity Variables.
An Automated Intelligent Solar Tracking Control System With Adaptive Algorithm for Different Weather Conditions

The paper considers an intelligent automated solar tracking control system designed to increase the efficiency of solar energy production. The proposed method of detecting cloudiness allows system to adapt to various weather conditions in real time by changing the angle of the solar panel. It is known that in case of strong scattering of solar radiation in cloudy weather panels installed horizontaly are more effective, rather than trackers which precisely oriented to the Sun. Proposed solar biaxial solar tracker equipped with two additional small solar modules. One of the modules is installed in a horizontal position, the second module is also a biaxial solar tracker. The algorithm for determining the position of the solar panel is based on known trajectory of the Sun during whole year saved in memory card and on monitoring the output currents of small solar panels built into the system. When cloudiness increases, the output current of a small solar horizontal module will exceed the current of the module oriented to the Sun. Then the big solar panel goes into a horizontal position. For remote monitoring of the energy characteristics of the solar battery, wireless data transmission based on LoRa AS32 TTL modules were added into the system. Experimental results showed that energy generated in cloudy weather using proposed method exceeds energy collected by biaxial solar tracker by 18%.
Classification Model C.45 on Determining the Quality of Custumer Service in Bank BTN Pematangsiantar Branch

Abstract This study aims to classify the quality of customer service at Bank BTN Pematangsiantar Branch. Data is obtained from the results of the customer questionnaire of Bank BTN Pematangsiantar Branch. Attributes used are 7, namely Age (C1), Job (C2), Old to Customer (C3), Tangiable (C4), Reliability (C5), Assurance (C6), Responsivenenss (C7). Motede used in this research is C4.5 Algorithm and assisted by Rapidminer software to make decision trees. From the results of the study, there were 5 rules for classification in determining the quality of customer service with 3 rules with satisfaction status and 2 rules with dissatisfied status. The C.45 algorithm can be used in the case of determining customer service quality of Bank BTN Pematangsiantar Branch with an accuracy of 77.78%. With this analysis, it is expected that Bank BTN can improve the quality of service to customer satisfaction.
DeepVR: Deep Reinforcement Learning for Predictive Panoramic Video Streaming

Online panoramic video has recently gained enormous popularity. Tile-based adaptive streaming is a promising paradigm to deliver a panoramic video for the sake of bandwidth saving. Nevertheless, it is challenging to accurately predict user’s field of view (FoV) and deliver the optimal bitrate due to the dynamic user behavior and time-varying network conditions. In this paper, we propose a novel approach of deep reinforcement learning based predictive panoramic video delivering. Specifically, a carefully-devised long short-term memory (LSTM) model is used to predict the FoV in the next few seconds. Our quality adaptation policy is based on a deep reinforcement learning (DRL) agent, which is able to intelligently adapt its bitrate selection policy tailored to the dynamic environments. To validate the effectiveness we have implemented a prototype of this system. With the integrated DRL algorithm Rainbow, we have achieved a superior performance in terms of the quality of experience (QoE) score, which outperforms existing panoramic video streaming frameworks.
Blockchain-Based Fair Payment Protocol for Deduplication Cloud Storage System

Today more and more enterprises and individuals are outsourcing their data to cloud storage system. Data deduplication is one of the important technologies to reduce the storage cost of cloud storage system. In a cloud storage system with deduplication technology, the client can outsource the data files to the cloud storage server and pay for them. Fair payment is one of key issues in the cloud deduplication storage system. At present, a variety of secure deduplication encryption schemes have been designed to protect the privacy of client data. However, most existing fair payment solutions use traditional electronic cash systems to generate payment tokens, which requires a trusted authority to prevent double-spending. Trusted authorities will become bottlenecks in the payment system. Faced with this problem, in this paper, we propose a new decentralized fair payment protocol for cloud deduplication storage system by utilizing ethereum blockchain technology. The new protocol takes advantage of the decentralization of blockchain technology, allowing direct transactions without the participation of trusted third parties. In the new protocol, if a malicious situation occurs, the system can guarantee fair payment by pre-storing penalty money in the smart contract. Safety analysis and experimental analysis show that our new protocol is feasible.
JPEG Pleno light field coding technologies

JPEG Pleno provides a standard framework to facilitate the capture, representation, and exchange of light field, point cloud and holographic imaging modalities. JPEG Pleno Part 2 addresses coding of light field data. Two coding modes are supported for this modality. The first mode exploits the redundancy in this 4D data by utilizing a 4D transform technique, the second mode is based on 4D prediction. Both techniques are outlined in this paper as well as the file format that encapsulates the resulting codestreams.
MDMS: Efficient and Privacy-Preserving Multidimension and Multisubset Data Collection for AMI Networks

Advanced metering infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers' activities. Privacy-preserving data aggregation techniques have been used to preserve consumers' privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multidimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multisubset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this article, we propose an efficient and privacy-preserving multidimensional and multisubset data collection scheme, named “MDMS. ” In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks' gateways using the encrypted readings and following the dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers' readings.
Implementation of Enhanced Secure Hash Algorithm Towards a Secured Web Portal

In this paper, the application of the enhanced secure hash algorithm-512 is implemented on web applications specifically in password hashing. In addition to the enhancement of hash function, hill cipher is included for the salt generation to increase the complexity of generating hash tables that may be used as an attack on the algorithm. The testing of same passwords saved on the database is used to create hash collisions that will result to salt generation to produce a new hash message. The matrix encryption key provides five matrices to be selected upon based on the length of concatenated username, password, and concatenated characters from the username. In this process, same password will result to a different hash message that will to make it more secured from future attacks.
Constructing and Visualizing High-Quality Classifier Decision Boundary Maps

Visualizing decision boundaries of machine learning classifiers can help in classifier design, testing and fine-tuning. Decision maps are visualization techniques that overcome the key sparsity-related limitation of scatterplots for this task. To increase the trustworthiness of decision map use, we perform an extensive evaluation considering the dimensionality-reduction (DR) projection techniques underlying decision map construction. We extend the visual accuracy of decision maps by proposing additional techniques to suppress errors caused by projection distortions. Additionally, we propose ways to estimate and visually encode the distance-to-decision-boundary in decision maps, thereby enriching the conveyed information. We demonstrate our improvements and the insights that decision maps convey on several real-world datasets.