Reviewerly/exHarmony huggingface dataset:
Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis

The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, search, and rescue operations among others. The current technologies are using fixed base stations to operate on-site and off-site in the fixed position with its associated problems like poor connectivity. This opens gates for the UAVs technology to be used as a mobile alternative to increase accessibility with a fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general, and provides detailed steps that are involved in the multi-armed bandit approach in solving UAV biomedical engineering technologies devices and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal medical engineered devices' performance and efficiency. The simulated results depicted that a multi-armed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technologies systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.
Why Developers Refactor Source Code: A Mining-based Study

Refactoring aims at improving code non-functional attributes without modifying its external behavior. Previous studies investigated the motivations behind refactoring by surveying developers. With the aim of generalizing and complementing their findings, we present a large-scale study quantitatively and qualitatively investigating why developers perform refactoring in open source projects. First, we mine 287,813 refactoring operations performed in the history of 150 systems. Using this dataset, we investigate the interplay between refactoring operations and process (e.g., previous changes/fixes) and product (e.g., quality metrics) metrics. Then, we manually analyze 551 merged pull requests implementing refactoring operations and classify the motivations behind the implemented refactorings (e.g., removal of code duplication). Our results led to (i) quantitative evidence of the relationship existing between certain process/product metrics and refactoring operations and (ii) a detailed taxonomy, generalizing and complementing the ones existing in the literature, of motivations pushing developers to refactor source code.
DirtNet: Visual Dirt Detection for Autonomous Cleaning Robots

Visual dirt detection is becoming an important capability of modern professional cleaning robots both for optimizing their wet cleaning results and for facilitating demand-oriented daily vacuum cleaning. This paper presents a robust, fast, and reliable dirt and office item detection system for these tasks based on an adapted YOLOv3 framework. Its superiority over state-of-the-art dirt detection systems is demonstrated in several experiments. The paper furthermore features a dataset generator for creating any number of realistic training images from a small set of real scene, dirt, and object examples.
Tx-ray: Quantifying and explaining model-knowledge transfer in (un-) supervised NLP

While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-) supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle of ‘visualizing preferred inputs of neurons’ to make it usable for both NLP and for transfer analysis. This allows one to analyze, track and quantify how self-or supervised NLP models first build knowledge abstractions in pretraining (1), andthen transfer abstractions to a new domain (2), or adapt them during supervised finetuning (3)–see Fig. 1. TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. We find that, similar to Lottery Ticket based pruning, TX-Ray based pruning can improve test set generalization and that it can reveal how early stages of self-supervision automatically learn linguistic abstractions like parts-of-speech.
Pathology Trainees’ Experience and Attitudes on Use of Digital Whole Slide Images

Digital whole slide images are Food and Drug Administration approved for clinical diagnostic use in pathology; however, integration is nascent. Trainees from 9 pathology training programs completed an online survey to ascertain attitudes toward and experiences with whole slide images for pathological interpretations. Respondents (n = 76) reported attending 63 unique medical schools (45 United States, 18 international). While 63% reported medical school exposure to whole slide images, most reported ≤ 5 hours. Those who began training more recently were more likely to report at least some exposure to digital whole slide image training in medical school compared to those who began training earlier: 75% of respondents beginning training in 2017 or 2018 reported exposure to whole slide images compared to 54% for trainees beginning earlier. Trainees exposed to whole slide images in medical school were more likely to agree they were comfortable using whole slide images for interpretation compared to those not exposed (29% vs 12%; P = .06). Most trainees agreed that accurate diagnoses can be made using whole slide images for primary diagnosis (92%; 95% CI: 86-98) and that whole slide images are useful for obtaining second opinions (93%; 95% CI: 88-99). Trainees reporting whole slide image experience during training, compared to those with no experience, were more likely to agree they would use whole slide images in 5 years for primary diagnosis (64% vs 50%; P = .3) and second opinions (86% vs 76%; P = .4). In conclusion, although exposure to whole slide images in medical school has increased, overall exposure is limited. Positive attitudes toward future whole slide image diagnostic use were associated with exposure to this technology during medical training. Curricular integration may promote adoption.
Automatically predicting bug severity early in the development process

Bug severity is an important factor in prioritizing which bugs to fix first. The process of triaging bug reports and assigning a severity requires developer expertise and knowledge of the underlying software. Methods to automate the assignment of bug severity have been developed to reduce the developer cost, however, many of these methods require 70-90% of the project's bug reports as training data and delay their use until later in the development process. Not being able to automatically predict a bug report's severity early in a project can greatly reduce the benefits of automation. We have developed a new bug report severity prediction method that leverages how bug reports are written rather than what the bug reports contain. Our method allows for the prediction of bug severity at the beginning of the project by using an organization's historical data, in the form of bug reports from past projects, to train the prediction classifier. In validating our approach, we conducted over 1000 experiments on a dataset of five NASA robotic mission software projects. Our results demonstrate that our method was not only able to predict the severity of bugs earlier in development, but it was also able to outperform an existing keyword-based classifier for a majority of the NASA projects.
Retrieval Guided Unsupervised Multi-domain Image to Image Translation

Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data.
Mapless Navigation Based on 2D LIDAR in Complex Unknown Environments

This paper presents a novel approach for navigation in complex and unknown environments. At present, existing local path planners whose control output is the mapping of current sensor data have been widely studied. However, these methods cannot really solve the problem of being trapped by obstacles. We analyzed the reasons and made improvements, and finally our approach can avoid being trapped in complex environments. The proposed method is based on 2D LIDAR. A central part of the approach is finding out gaps in the environment by analyzing sensor data. Then, we choose one of the gaps we find as the sub-goal. Linear and angular velocities are provided by the approach considering nonholonomic mobile robots. The method does not rely on global planners and environment maps. Therefore, it has the advantages of low computational complexity and fast response, which is of great significance to robots with low computing power; it will also help to reduce the manufacturing cost of robots. In addition, simulations and real tests were performed using the Turtlebot2 robotic platform. Successful results are achieved in both simulations and experimental tests.
Proof of Experience: empowering Proof of Work protocol with miner previous work

Even after 12 years of services, Bitcoin still remains the first e-cash system and most influential player in the cryptocurrency market. For each block, the underlying distributed Proof of Work consensus where much computing power (named miners) is required to solve a hard mathematical problem, rewards the first one solving and sharing the solution with the rest of the network. Two main issues regarding this consensus can be easily pointed out i.e. the computation power spend by miners is a waste since only one of them will be rewarded, and miners with much smaller computing capacity can almost never solve the mathematical problem, hence cannot earn any reward. This paper proposes a substantial amendment to the existing Bitcoin consensus by using the wasted computation to adjust the difficulty of each miner that did provide computation effort overtime. Therefore, anytime a miner justifies of its work toward the consensus hence the network, without reaching the solution, then its difficulty will decrease accordingly for next blocks.
Reading habits of generation Z students in Pakistan: Is it time to re-examine school library services?

Quality academic achievement is based on the development of good reading skills. Generally, reading is important for students to cope up with the evolving world of knowledge. Information and Communication Technologies (ICTs) have vastly influenced the reading practices of students. The younger generation of the information age spends most of their time in front of screens. Due to these drastic changes in the fabric of reading, generation Z is more likely to adopt e-reading. Therefore, an empirical investigation was carried out to explore the reading habits of generation Z students. For the study, a survey-based quantitative research design was applied. Data was collected through a structured questionnaire. The population included the students of two schools - one semi-government and one private higher secondary school of the Lahore district. The sample was selected purposively. Findings revealed that generation Z students prefer to read in paper form and in the English language. On average they read for up to four hours daily, preferably at home and mostly for academic purposes. They prefer print format, however, the advantages associated with e-reading motivate them to use electronic contents. One of the noteworthy findings is that libraries (academic/public) were their least preferred place for reading and obtaining reading material. The findings of the study would help parents, educators, and publishers to take necessary measures for promoting reading habits among students. The research findings would serve as a guideline for educationists to plan education policies according to the need and preferences of the future generation and for libraries to develop user-centered library services.
Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

The viewing time of media content per week through TV is still dominant. Users are exposed to numerous advertisements, such as commercials, electronic home shopping, product placement (PPL), and T-Commerce while watching TV programs. Most of the advertisement systems provide a good overview of products. However, traditional advertising services do not consider user preferences, meaning it is difficult to expect anything more than mere exposure to them. We can adopt a recommendation system to predict the preference. However, existing recommendation systems find it difficult to satisfy the real-time requirements of online broadcasting because of the large overhead incurred in preference prediction processes. In this paper, we propose a real-time recommendation system to provide personalized advertisements. The proposed system generates tree models based on user historical data. To reduce the overhead of preference prediction, we introduce a sorted HashMap that enables fast tree searches. For sophisticated preference prediction, the proposed system normalizes the users' preferences by considering the characteristics of their tree model. Finally, we conduct experiments to evaluate the performance of the proposed tree-based recommendation system.
Contrast Enhancement of MRI Images using Histogram Equalization Techniques

MRI images provide a new pathway in the medical field for clinical analysis and diagnose process. As MRI images suffers from contrast degradations, these contrast degradations in MRI images can be solved by application of image contrast enhancement techniques making it more suitable for medical applications. The main objective of image enhancement is to obtain a high quality image after improving the characteristics of the input image. Contrast enhancement techniques are used in images to enhance the visibility quality of internal human body texture in magnetic resonance imaging (MRI) images. In this paper we have applied the Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE) in order to improve the contrast of MRI medical images. Experimental results have shown good efficiency to improve the contrast of MRI medical images with very high quality. Comparison of various image quality measures like MSE, PSNR and SNR are taken into account for evaluation the quality of enhanced MRI medical images.
DDLPF: A Practical Decentralized Deep Learning Paradigm for Internet-of-Things Applications

In recent years, it has been observed the exponential growth of the Internet of Things (IoT) in different application fields, such as manufacturing and energy industry. To effectively fuse and process the tremendous amount of IoT sensing data timely, there is an urgent need to shift from a conventional centralized computing to a decentralized computing. However, there remain some essential technical challenges to develop effective decentralized computing methods in the context of IoT applications, including 1) the timely response, sufficient privacy preservation, and high security are normally required in IoT-related applications and 2) the biases and non-independent identically distributed (IID) properties potentially presented in the IoT sensing data. To address these challenges, in this article, we propose a decentralized deep learning paradigm with privacy-preservation and fast few-shot learning (DDLPF) by exploiting federated learning, metalearning, and blockchain techniques. In the simulation section, we evaluate the performance of our proposed DDLPF paradigm in different scenarios and compare it with other existing techniques.
“Anyone Can Print”: Supporting Collaborations with 3D Printing Services to Empower Broader Participation in Personal Fabrication

Broader participation in 3D printing may be facilitated through printing services that insulate clients from the costs and detailed technical knowledge necessary to operate and maintain printers. However, newcomers to 3D printing encounter barriers and challenges even before gaining access to printing facilities. This paper explores the challenges and barriers newcomers encounter when identifying printing opportunities and when learning how to specify 3D printing ideas through observations of stakeholders (n=20) in two university 3D printing shops, and through a focused lab study investigating how to introduce newcomers individually to 3D printing (n=21). We adopt Olsons and Olson’s framework for remote collaborations, proposed in “Distance Matters”, to analyze the sociotechnical requirements for initiating collaborations with 3D printing services. We found that newcomers often require prior guidance towards 3D printing procedures and websites before establishing what to print in collaboration with 3D printing services. Finally, we discuss how future printing processes and computational systems may empower a future where Anyone Can Print.
Fraudulent financial reporting and data analytics: an explanatory study from Ireland

Purpose This paper aims to explore the effectiveness of fraud prevention and detection techniques, including data analytics, machine learning and data mining, and to understand how widespread the use of data analytics is across different sectors and to identify and understand the potential barriers to implementing these techniques to detect and prevent fraud. Design/methodology/approach A survey was administered to 73 Irish businesses to determine to what extent traditional approach, data mining or text mining are being used to prevent or detect fraudulent financial reporting, and to determine the perception level of their effectiveness. Findings The study suggests that whilst data analytics is widely used by businesses in Ireland there is an under-utilisation of data analytics as an effective tool in the fight against fraud. The study suggests there are barriers that may be preventing companies from implementing advanced data analytics to detect financial statement fraud and identifies how those barriers may be overcome. Originality/value In contrast to the majority of literature on big data analytics and auditing, which lacks empirical insight into the diffusion, effectiveness and obstacles of data analytics, this explanatory study contributes by providing useful insights from the field on big data analytics. While the extant auditing literature generally addresses the avenues of big data utilisation in auditing domain, our study explores particularly the use big data analytics as a fraud prevention and detection techniques.
Contextual Explanation Networks

Modern learning algorithms excel at producing accurate but complex models of the data. However, deploying such models in the real-world requires extra care: we must ensure their reliability, robustness, and absence of undesired biases. This motivates the development of models that are equally accurate but can be also easily inspected and assessed beyond their predictive performance. To this end, we introduce contextual explanation networks (CEN)---a class of architectures that learn to predict by generating and utilizing intermediate, simplified probabilistic models. Specifically, CENs generate parameters for intermediate graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain simultaneously. Our approach offers two major advantages: (i) for each prediction valid, instance-specific explanation is generated with no computational overhead and (ii) prediction via explanation acts as a regularizer and boosts performance in data-scarce settings. We analyze the proposed framework theoretically and experimentally. Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support. We also show that while post-hoc methods may produce misleading explanations in certain cases, CENs are consistent and allow to detect such cases systematically.
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty

Various adversarial audio attacks have recently been developed to fool automatic speech recognition (ASR) systems. We here propose a defense against such attacks based on the uncertainty introduced by dropout in neural networks. We show that our defense is able to detect attacks created through optimized perturbations and frequency masking on a state-of-the-art end-to-end ASR system. Furthermore, the defense can be made robust against attacks that are immune to noise reduction. We test our defense on Mozilla's CommonVoice dataset, the UrbanSound dataset, and an excerpt of the LibriSpeech dataset, showing that it achieves high detection accuracy in a wide range of scenarios.
Analysis of Music Online Teaching Curriculum Arrangement Based on BP Neural Network Model

Abstract Music online teaching course is a course based on Internet human-computer interaction, which has the feasibility of technology, concept and conditions. However, the rationality of online music teaching curriculum arrangement will directly and objectively reflect the actual teaching quality of the course. By reducing the interference factors in the process of online course arrangement, we can improve the applicability of the evaluation method based on BP neural network model. Music online teaching course can enhance students’ comprehensive artistic quality, which will better cultivate high-quality, compound music art talents. The lack of normal characteristics in online music teaching will lead to the imbalance of curriculum structure. Therefore, the value of online music courses may not be reflected. At the same time, the single content or maladjustment of curriculum content will result in disconnection with basic education, which will ignore the "irrational" phenomenon of music students’ professional advantages. Therefore, this paper puts forward some suggestions, which will be more reasonable to set up music online teaching courses.