Reviewerly/exHarmony huggingface dataset:
Component attributes and their importance in decisions and component selection

Component-based software engineering is a common approach in the development and evolution of contemporary software systems. Different component sourcing options are available, such as: (1) Software developed internally (in-house), (2) Software developed outsourced, (3) Commercial off-the-shelf software, and (4) Open-Source Software. However, there is little available research on what attributes of a component are the most important ones when selecting new components. The objective of this study is to investigate what matters the most to industry practitioners when they decide to select a component. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using compositional data analysis. The results of this exploratory analysis showed that cost was clearly considered to be the most important attribute for component selection. Other important attributes for the practitioners were: support of the component, longevity prediction, and level of off-the-shelf fit to product. Moreover, several practitioners still consider in-house software development to be the sole option when adding or replacing a component. On the other hand, there is a trend to complement it with other component sourcing options and, apart from cost, different attributes factor into their decision. Furthermore, in our analysis, nonparametric tests and biplots were used to further investigate the practitioners’ inherent characteristics. It seems that smaller and larger organizations have different views on what attributes are the most important, and the most surprising finding is their contrasting views on the cost attribute: larger organizations with mature products are considerably more cost aware.
Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network

Recommender systems (RS) were created to recommend interesting items to users. There are two recommendation techniques: content-based filtering (CBF) and collaborative filtering (CF). CBF makes recommendations based on a user’s past behavior, whereas CF makes a recommendation using past neighbors’ opinions who have similar behavior to the target user. Nowadays, there are many data on social networks, including Tweets on Twitter. Thus, many researchers have studied RS based on Tweets using latent Dirichlet allocation (LDA) to extract latent data from observed data. Nevertheless, those researchers use either CBF or CF with LDA only. However, CBF provides recommendations that are too specific, whereas CF has sparsity and a cold-start problem. Therefore, this research proposes a new method of recommending Tweets based on hybrid RS with LDA (unsupervised topic modeling) and generalized matrix factorization (supervised learning-based neural network). From experimental results, the proposed method outperforms on mean absolute error and coverage.
Adaptive PID-Fuzzy Logic Controller for Brushless DC Motor

There is a increase in use of variable speed drives in all the fields like automobile, medicals and household that also increase the demand of brushless DC motors (BLDCM). As BLDC motor is superior over conventional DC motor in areas like higher efficiency, better speed-torque characteristics, noise reduction and low maintenance because there are no brushes to get wearout or produce sparking. Though most of the BLDC motors adopt PI controller and PWM scheme for the speed control of BLDCM. To make speed controlling more precise and robust an adaptive PID-fuzzy controller has been design. Dynamic changes in speed if required can be controller precisely.
Automatic Acute Lymphoblastic Leukemia Detection and Comparative Analysis from Images

In this era, surrounded by numerous technologies, medical sector has seen a lot of advancement through implementing various autonomous systems to identify different types of diseases. In this paper, a framework for identification of Acute Lymphoblastic Leukemia from the microscopic image of white blood cell is proposed. Microscopic images are at first carefully preprocessed to prepare them for classification. In addition, four different machine learning algorithms, namely, Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT) are applied and respective results are analyzed to provide a comparison between these algorithms in terms of different performance metrics. After a thorough comparison, it is observed that the SVM works well to classify and identify the Acute Lymphoblastic cell which is responsible for Leukemia cancer.
Artificial Intelligence Data Science Methodology for Earth Observation

This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images.
Customisable Control Policy Learning for Robotics

Deep reinforcement learning algorithms integrate deep neural networks with traditional reinforcement learning methodologies. These techniques have been developed and used for various applications to produce exciting results in many fields, including robotics. However, physical robots require a large amount of training episodes which can damage the robot if directed by immature policies. Training using simulations can serve as a viable alternative before a robot is deployed in the field. This study addresses a computational challenge of deep reinforcement learning by developing a hardware architecture for the Deep Deterministic Policy Gradient (DDPG) algorithm. Additionally, we identify the customisation opportunities for a full-stack development framework with reinforcement learning to discover control policies for robotic arms. Finally, we transfer policies encoded in fixed-point numbers from our FPGA DDPG implementation to a robotic arm to evaluate the feasibility of our learning platform.
EPIC-Tent: An Egocentric Video Dataset for Camping Tent Assembly

This paper presents an outdoor video dataset annotated with action labels, collected from 24 participants wearing two head-mounted cameras (GoPro and SMI eye tracker) while assembling a camping tent. In total, this is 5.4 hours of recordings. Tent assembly includes manual interactions with non-rigid objects such as spreading the tent, securing guylines, reading instructions, and opening a tent bag. An interesting aspect of the dataset is that it reflects participants' proficiency in completing or understanding the task. This leads to participant differences in action sequences and action durations. Our dataset, called EPIC-Tent, also has several new types of annotations for two synchronised egocentric videos. These include task errors, self-rated uncertainty and gaze position, in addition to the task action labels. We present baseline results on the EPIC-Tent dataset using a state-of-the-art method for offline and online action recognition and detection.
Accelerating DNN Inference with GraphBLAS and the GPU

This work addresses the 2019 Sparse Deep Neural Network Graph Challenge with an implementation of this challenge using the GraphBLAS programming model. We demonstrate our solution to this challenge with GraphBLAST, a GraphBLAS implementation on the GPU, and compare it to SuiteSparse, a GraphBLAS implementation on the CPU. The GraphBLAST implementation is $1.94 \times $ faster than Suite-Sparse; the primary opportunity to increase performance on the GPU is a higher-performance sparse-matrix-times-sparse-matrix (SpGEMM) kernel.
Person re-identification by integrating metric learning and support vector machine

Person re-identification (PRID) refers to a technology of matching person across non-overlapping camera views. Metric learning is one of the most commonly used methods in PRID. However, most of them do not explore the label information carried by the labeled training samples, which limits the improvement of recognition performance. To this end, we propose a joint learning method by integrating the discriminative metric learning, the support vector machine (SVM) and the identity discriminator into one model, so as to realize joint construction of metric learning and identity discriminator. In this process, the label information carried by the training samples is fully exploited and the latent identify space of pedestrians is constructed by predicting the person’s identity. To mitigate the appearance ambiguity caused by the variations in camera views, body poses, illumination and occlusion, we develop an extreme distance regularization term and introduce it into the joint learning framework to refine the solution spaces of the metric learning and discriminator. Finally, we present a similarity measure method by combining the advantages of the metric learning and the identity discriminator. Experimental results on several benchmark datasets show that the proposed method significantly outperforms some state-of-the-art methods.
Temporal Safety for Stack Allocated Memory on Capability Machines

Memory capabilities as supported in capability machines are very similar to fat pointers, and hence are very useful for the efficient enforcement of spatial memory safety. Enforcing temporal memory safety however, is more challenging. This paper investigates an approach to enforce temporal memory safety for stack-allocated memory in C-like languages by extending capabilities with a simple dynamic mechanism. This mechanism ensures that capabilities with a certain lifetime can only be stored in memory that has a longer lifetime. Our mechanism prevents temporal memory safety violations, yet is sufficiently permissive to allow typical C coding idioms where addresses of local variables are passed up the call stack. We formalize the desired behavior of a simple C-like language as a dependently typed operational semantics, and we show that existing compilers to capability machines do not simulate this desired behavior: they either have to break temporal safety, or they have to defensively rule out allowed behaviors. Finally, we show that with our proposed dynamic mechanism, our compiler is fully abstract.
Image Classification and Text Extraction using Machine Learning

Machine Learning is a branch of Artificial Intelligence in which a system is capable of learning by itself without explicit programming or human assistance based on its prior knowledge and experience. It is used to predict or make decisions to perform certain task based on the training set that is provided. In the proposed system, image classification is implemented using Convolutional Neural Network (CNN). The text is then extracted from the classified image using Tesseract, which has implemented a Long Short-Term Memory (LSTM) based recognition engine. The LSTM networks are the units of Recurrent Neural Network. The CNN performs better on very large datasets, by overcoming the problem of overfitting. Also, single line text extraction is replaced by multiple line text extraction. Thus, the accuracy of this system can be improved by incorporating a large dataset and increasing the number of epochs. In addition, a trial-and-error methodology is used to determine the number of convolution and pooling layers with the number of nodes in each layer. Finally, CNNs use relatively few preprocessing compared to other image classification algorithms.
Performance Analysis for Image Security using SVM and ANN Classification Techniques

Image Security has been mainly concerned about the classification extemporized in numerous structures and utilizing distinctive systems just as innovations. The upgrades will continue by including the quickest security that refreshes the system framework. This proposes a representation for verifying the video framework alongside the system and upgrades it more by related AI methods, SVM (Support Vector Machine) and ANN (Artificial Neural Network). Both the methods are utilized together for training and testing the classification in order to produce results that are fitting for investigation reason and subsequently, turn out to be the achievement for security.
Experimental study on fluid migration mechanism related to Pb–Zn super-enrichment: Implications for mineralisation mechanisms of the Pb–Zn deposits in the Sichuan–Yunnan–Guizhou, SW China

This study focuses on the problem of Pb–Zn super-enrichment, and is based on a new conceptualisation of fluid–rock interactions → change in fluid properties → Pb–Zn hydrolysis → complex instability. Non-magmatic, epigenetic, hydrothermal Pb–Zn deposits in carbonates were used to perform Pb–Zn hydrolysis experiments under normal pressure. Lead–zinc fluid and carbonate interaction experiments, as well as Pb–Zn hydrolysis kinetics experiments during fluid–rock interaction, were performed analyse the roles of pH and fluid–rock interactions in Pb–Zn transport and precipitation, and to investigate the main reasons for the grade disparity between Mississippi Valley-type (MVT) deposits and Pb–Zn deposits found in the Sichuan–Yunnan–Guizhou region of China. Our results show that a pH = 4 is the threshold value for the large-scale hydrolysis of Pb and Zn. When reactions between acidic solutions containing Pb and Zn (pH = 1.0–6.0) and carbonate rocks (i.e., the surrounding rock) reach equilibrium, the final pH of the solution stabilises at 5.2–6.2, which is not associated with the lithology of the surrounding carbonate rocks (dolomite or limestone), crystallite size or the initial pH of the solution, and is negatively correlated with rock particle size. Fluid–rock interactions cause pH to increase, Pb and Zn to partially hydrolysis, and minerals to precipitate, with the maximum precipitation rate reaching ~20%. When hydroxide precipitates are mixed with fluids containing reduced S, the precipitates are converted into sulphides. Thus, mineralisation fluid pH, the driving force of fluid migration, Pb–Zn hydrolysis, and fluid–rock interactions combine to restrict the formation of Pb–Zn deposits in carbonates. Among these factors, differences in Pb–Zn hydrolysis are the main causes of disparities among deposit grades. This study not only provides an in-depth consideration of the geochemical behaviour of Pb and Zn in a carbonate-hosted hydrothermal system, but also serves to clarify the mineralisation mechanisms of Pb–Zn deposits in the Sichuan–Yunnan–Guizhou region.
Towards Computational Persuasion via Natural Language Argumentation Dialogues

Computational persuasion aims to capture the human ability to persuade through argumentation for applications such as behaviour change in healthcare (e.g. persuading people to take more exercise or eat more healthily). In this paper, we review research in computational persuasion that incorporates domain modelling (capturing arguments and counterarguments that can appear in a persuasion dialogues), user modelling (capturing the beliefs and concerns of the persuadee), and dialogue strategies (choosing the best moves for the persuader to maximize the chances that the persuadee is persuaded). We discuss evaluation of prototype systems that get the user's counterarguments by allowing them to select them from a menu. Then we consider how this work might be enhanced by incorporating a natural language interface in the form of an argumentative chatbot.
Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.
Dynamic ARMA-Based Background Subtraction for Moving Objects Detection

Background subtraction is a prevailing method for moving object detection in videos with stationary backgrounds. However, accurate and real-time moving object detection is challenging in the presence of complex dynamic scenes. This paper presents a novel technique for background subtraction based on the dynamic autoregressive moving average (ARMA) model. Specifically, we utilize the temporal and spatial correlation of images in a video sequence to model each pixel to accurately model the background image's dynamic characteristics. In addition, we apply an adaptive least mean square (LMS) scheme to update the parameters of the background model to offset the dramatically dynamic characteristic of the background. The proposed algorithm is evaluated on two publicly available benchmark datasets with complex dynamic backgrounds. The experimental results show that this technique is robust and effective for background subtraction in complex dynamic backgrounds and is a promising moving object detection scheme for real-time visual surveillance.
Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters

Abstract Disaster is a series of events that threaten and disrupt human life caused by natural factors, non-natural factors and human factors themselves. Therefore, disasters cause casualties, environmental damage, property losses, and psychological impacts. In this study will be discussed about the prediction of the number of victims affected by the disaster, either died, lost, injured, suffered or displaced. Data sources were obtained by the National Disaster Management Agency and the Indonesian Central Statistics Agency. The method used to predict is the Incremental Sequential Order method. This method is one part of the Artificial Neural Network method. With this method, network architecture patterns will be established to predict the number of victims affected by the disaster for years to come. The network architecture models used are 4-5-1, 4-10-1, 4-5-10-1, 4-10-20-1 and 4-15-30-1. Of the five models, the best models will be obtained, namely 4-15-30-1 with an accuracy rate of 80%. With this architectural model, predictions will be made on the number of victims affected by the disaster for years to come.
Breakdown Resilience of Key Exchange Protocols: NewHope, TLS 1.3, and Hybrids

Broken cryptographic algorithms and hardness assumptions are a constant threat to real-world protocols. Prominent examples are hash functions for which collisions become known, or number-theoretic assumptions which are threatened by advances in quantum computing. Especially when it comes to key exchange protocols, the switch to quantum-resistant primitives has begun and aims to protect today’s secrets against future developments, moving from common Diffie–Hellman-based solutions to Learning-With-Errors-based approaches, often via intermediate hybrid designs. To this date there exists no security notion for key exchange protocols that could capture the scenario of breakdowns of arbitrary cryptographic primitives to argue security of prior or even ongoing and future sessions. In this work we extend the common Bellare–Rogaway model to capture breakdown resilience of key exchange protocols. Our extended model allows us to study security of a protocol even in case of unexpected failure of employed primitives, may it be number-theoretic assumptions, hash functions, signature schemes, key derivation functions, etc. We then apply our security model to analyze two real-world protocols, showing that breakdown resilience for certain primitives is achieved by both an authenticated variant of the post-quantum secure key encapsulation mechanism $$\textsc {NewHope} $$ (Alkim et al.) which is a second round candidate in the Post Quantum Cryptography standardization process by NIST, as well as by TLS 1.3, which has recently been standardized as RFC 8446 by the Internet Engineering Task Force.
An Enhanced Recommendation Algorithm Based on Modified User-Based Collaborative Filtering

With the huge amount of information available on the Internet, recommendation systems gained popularity over the years. Traditional recommendation algorithm usually uses collaborative filtering to determine user and item similarity. However, data sparsity and overfitting affects the accuracy of the recommendation systems that lead to poor recommendation quality. This paper presents an enhanced recommendation algorithm based on modified user-based collaborative filtering to overcome the problem and improve the recommendation quality. The enhanced algorithm was compared to the traditional algorithm using the MovieLens dataset and evaluates its accuracy and performance using the Root Mean Square Error (RSME), Precision and Recall. The experimental results show that the enhanced algorithm outperforms the traditional algorithm and improves the accuracy of the recommendation.