Reviewerly/exHarmony huggingface dataset:
Interpretable clustering: an optimization approach

State-of-the-art clustering algorithms provide little insight into the rationale for cluster membership, limiting their interpretability. In complex real-world applications, the latter poses a barrier to machine learning adoption when experts are asked to provide detailed explanations of their algorithms’ recommendations. We present a new unsupervised learning method that leverages Mixed Integer Optimization techniques to generate interpretable tree-based clustering models. Utilizing a flexible optimization-driven framework, our algorithm approximates the globally optimal solution leading to high quality partitions of the feature space. We propose a novel method which can optimize for various clustering internal validation metrics and naturally determines the optimal number of clusters. It successfully addresses the challenge of mixed numerical and categorical data and achieves comparable or superior performance to other clustering methods on both synthetic and real-world datasets while offering significantly higher interpretability.
Integrating encryption techniques for secure data storage in the cloud

Abstract Cloud computing has emerged as one of the most groundbreaking technologies to have redefined the bounds of conventional computing techniques. It has ushered in a paradigm shift and pushed the frontiers of how computing assets, inclusive of infrastructure resources, software, and applications can be used, adopted, and purchased. The economic benefits or rather the fundamental economic shift offered by cloud computing in reducing capital expenditure and converting it to operational expenditure has been a primary motivating factor for early adopters. However, despite its inherent advantages that include better access and control, there exist several reservations around cloud computing that have impeded its growth. The control, elasticity, and ease of use that cloud computing is associated with also engender many security issues. Security is considered to be the topmost hurdle out of the nine identified challenges of cloud computing as underlined by the study conducted by the International Data Corporation. It therefore follows that an exceedingly secure system is essential for the safeguarding of an organizational entity, its resources, and assets. In this article, it is our endeavor to offer insights into the implementation of a novel architecture that can deliver an enhanced degree of security for outsourcing information in a cloud computing environment while involving numerous independent cloud providers. The framework comprises of dual encryption and data fragmentation techniques that envision the secure distribution of information in a multicloud environment. The various concerns surrounding this area, specifically, the challenges of integrity, security, confidentiality, and authentication have been addressed. All simulations and scrutiny have been accomplished on an Oracle virtual machine Virtual‐Box and a Fog environment on an Ubuntu 16.04 platform. Extensive safety measures and performance analysis that take into account diverse parameters, especially execution time, integrity, throughput, entropy, transfer rate, and delay demonstrate that our projected proposal is vastly proficient and satisfies the security prerequisites of secure data sharing and can efficiently withstand security attacks.
An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review

Software developers require effective effort estimation models to facilitate project planning. Although Usman et al. systematically reviewed and synthesized the effort estimation models and practices for Agile Software Development (ASD) in 2014, new evidence may provide new perspectives for researchers and practitioners. This article presents a systematic literature review that updates the Usman et al. study from 2014 to 2020 by analyzing the data extracted from 73 new papers. This analysis allowed us to identify six agile methods: Scrum, Xtreme Programming and four others, in all of which expert-based estimation methods continue to play an important role. This is particularly the case of Planning Poker, which is very closely related to the most frequently used size metric (story points) and the way in which software requirements are specified in ASD. There is also a remarkable trend toward studying techniques based on the intensive use of data. In this respect, although most of the data originate from single-company datasets, there is a significant increase in the use of cross-company data. With regard to cost factors, we applied the thematic analysis method. The use of team and project factors appears to be more frequent than the consideration of more technical factors, in accordance with agile principles. Finally, although accuracy is still a challenge, we identified that improvements have been made. On the one hand, an increasing number of papers showed acceptable accuracy values, although many continued to report inadequate results. On the other, almost 29% of the papers that reported the accuracy metric used reflected aspects concerning the validation of the models and 18% reported the effect size when comparing models.
Internet adoption and financial development in sub-Saharan Africa

Information and communications technology (ICT) adoption, and its penetration and use, in sub-Saharan Africa (SSA) have steadily grown since the turn of the century. Subsequently, studies have sought to investigate several of its benefits, particularly for economic growth and entrepreneurial activities. However, little attention has been paid to SSA, human capital development and economic freedom within studies on ICT and financial development. Using data from the Global Financial Development and the World Bank Development Indicator databases for the period 2000–2016 on 42 countries in SSA, we found evidence of the positive impact of internet use on different measures of financial development. Further analysis reveals that sub-samples of SSA countries differ on their levels of human capital development and economic freedom. Our results are robust to different estimations and specifications that account for heteroscedasticity in our sample and potential endogeneity. Implications of the study are also highlighted.
A Human Study of Comprehension and Code Summarization

Software developers spend a great deal of time reading and understanding code that is poorly-documented, written by other developers, or developed using differing styles. During the past decade, researchers have investigated techniques for automatically documenting code to improve comprehensibility. In particular, recent advances in deep learning have led to sophisticated summary generation techniques that convert functions or methods to simple English strings that succinctly describe that code's behavior. However, automatic summarization techniques are assessed using internal metrics such as BLEU scores, which measure natural language properties in translational models, or ROUGE scores, which measure overlap with human-written text. Unfortunately, these metrics do not necessarily capture how machine-generated code summaries actually affect human comprehension or developer productivity.
A Financial Fraud Detection Model Based on LSTM Deep Learning Technique

As the use of the internet is growing exponentially, more and more businesses such as the financial sector are operationalizing their services online. Consequently, financial frauds are increasing in number and forms around the world, which results in tremendous financial losses which make financial fraud a major problem. Unauthorized access and irregular attacks are examples of threats that should be detected by means of financial fraud detection systems. Machine learning and data mining techniques have been widely used to tackle this issue over the past few years. However, these methods still need to be improved in terms of speed computation, dealing with big data, and identify the unknown attack patterns. Therefore, in this paper, a deep learning-based method is proposed for the detection of financial fraud based on the Long Short-Term Memory (LSTM) technique. This model is aimed at enhancing the current detection techniques as well as enhancing the detection accuracy in the light of big data. To evaluate the proposed model, a real dataset of credit card frauds is utilized and the results are compared with an existing deep learning model named Auto-encoder model and some other machine learning techniques. The experimental results illustrated a prefect performance of LSTM where it achieved 99.95% of accuracy in less than a minute.
Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau

Wild animal surveys play a critical role in wild animal conservation and ecosystem management. Unmanned aircraft systems (UASs), with advantages in safety, convenience and inexpensiveness, have been increasingly used in wild animal surveys. However, manually reviewing wild animals from thousands of images generated by UASs is tedious and inefficient. To support wild animal detection in UAS images, researchers have developed various automatic and semiautomatic algorithms. Among these algorithms, deep learning techniques achieve outstanding performances in wild animal detection, but have some practical issues (e.g., limited animal pixels and sparse animal samples). Based on a typical deep learning pipeline, faster region based convolutional neural networks (Faster R-CNN), this study adopted several tactics, including feature stride shortening, anchor size optimization, and hard negative class, to overcome the practical issues in wild animal detection in UAS images. In this study, a kiang survey was conducted in UAS datasets (23,748 images) obtained by 14 flight campaigns in the eastern Tibetan Plateau. The validation experiments of our adopted tactics revealed the following: (1) feature stride shortening and anchor size optimization improved small animal detection performance in the animal patch set, increasing the F1 score from 0.84 to 0.86 and from 0.86 to 0.92, respectively; and (2) the hard negative class significantly suppressed false positives in the full UAS image set, increasing the F1 score from 0.44 to 0.86. The test results in the full UAS image set showed that the modified model with the adopted tactics can be applied to either a semiautomatic survey to accelerate manual verification by 25 times or an automatic survey with an F1 score of approximately 0.90. This study demonstrates that the combination of UAS and deep learning techniques can enable automatic/semiautomatic, accurate, inexpensive, and efficient wild animal surveys.
Hyperspectral Image Restoration: Where Does the Low-Rank Property Exist

Hyperspectral image (HSI) restoration is to recover the clean image from degraded version, such as the noisy, blurred, or damaged. Recent low-rank tensor-based recovery methods have been widely explored in HSIs restoration. Most of previous methods, however, neglect an inconspicuous but important phenomenon that the physical meaning and dimension along the spatial, spectral, and nonlocal mode are markedly different. In this work, we discover the low-rank property discrepancy along spatial, spectral, and nonlocal self-similarity mode in the HSIs, and argue that the intrinsic low-rank correlations along each mode contribute different to the final restoration results. Consequently, we figure out that the combination of the spectral and nonlocal-induced low-rank is most beneficial for HSIs modeling, and propose an optimal low-rank tensor (OLRT) model for HSIs restoration. Furthermore, we not only explore the low-rank property in the image component, but also in the sparse error component (stripe noise in HSIs). Thus, we extend OLRT to the OLRT-robust principal component analysis (RPCA) with low-rank tensor priors for both the HSIs and sparse error. Besides, previous methods are usually designed for one specific HSI task, which is less robust to various tasks. We prove that the proposed optimal low-rank prior is very flexible for various HSI restoration problems including denoising, deblurring, inpainting, and destriping. The proposed methods have been extensively evaluated on several benchmarks and tasks, and greatly outperform state-of-the-art (STOA). We show the simple yet effective OLRT strategy is also beneficial to STOA.
Survey of pedestrian detection with occlusion

Abstract Pedestrian detection is widely applied in surveillance, autonomous robotic navigation, and automotive safety. However, there are many occlusion problems in real life. This paper summarizes the research progress of pedestrian detection technology with occlusion. First, according to different occlusion, it can be divided into two categories: inter-class occlusion and intra-class occlusion. Second, it summarizes the traditional method and deep learning method to deal with occlusion. Furthermore, the main ideas and core problems of each method model are analyzed and discussed. Finally, the paper gives an outlook on the problems to be solved in the future development of pedestrian detection technology with occlusion.
Artificial Intelligence in Screening Mammography: A Population Survey of Women’s Preferences

ObjectiveTo investigate the general population’s view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms.MethodsDutch women aged 16 to 75 years were surveyed using the Longitudinal Internet Studies for the Social sciences panel, representative for the Dutch population. Attitude toward AI in mammography screening was measured by means of five items: necessity of a human check; AI as a selector for second reading; AI as a second reader; developer is responsible for error; and radiologist is responsible for error.ResultsOf the 922 participants included, 77.8% agreed with the necessity of a human check, whereas the item AI as a selector for a second reading was more heterogeneously answered, with 41.7% disagreement, 31.5% agreement, and 26.9% responding with “neither agree nor disagree.” The item AI as a second reader was mostly responded with “neither agree nor disagree” (37.1%) and “agree” (37.6%), whereas the two last items on developer’s and radiologist’ responsibilities were mostly answered with “neither agree nor disagree” (44.6% and 39.2%, respectively).DiscussionDespite recent breakthroughs in the diagnostic performance of AI algorithms for the interpretation of screening mammograms, the general population currently does not support a fully independent use of such systems without involving a radiologist. The combination of a radiologist as a first reader and an AI system as a second reader in a breast cancer screening program finds most support at present. Accountability in case of AI-related diagnostic errors in screening mammography is still an unresolved conundrum.
Remote health diagnosis and monitoring in the time of COVID-19

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.
No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions

Recently, the visual quality evaluation of screen content images (SCIs) has become an important and timely emerging research theme. This article presents an effective and novel blind quality evaluation metric for SCIs by using stacked autoencoders (SAE) based on pictorial and textual regions. Since the SCI consists of not only the pictorial area but also the textual area, the human visual system (HVS) is not equally sensitive to their different distortion types. First, the textual and pictorial regions can be obtained by dividing an input SCI via an SCI segmentation metric. Next, we extract quality-aware features from the textual region and pictorial region, respectively. Then, two different SAEs are trained via an unsupervised approach for quality-aware features that are extracted from these two regions. After the training procedure of the SAEs, the quality-aware features can evolve into more discriminative and meaningful features. Subsequently, the evolved features and their corresponding subjective scores are input into two regressors for training. Each regressor can obtain one output predictive score. Finally, the final perceptual quality score of a test SCI is computed by these two predicted scores via a weighted model. Experimental results on two public SCI-oriented databases have revealed that the proposed scheme can compare favorably with the existing blind image quality assessment metrics.
Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems

Recommender Systems have been playing essential roles in e-commerce portals. Existing recommendation algorithms usually learn the ranking scores of items by optimizing a single task (e.g. Click-through rate prediction) based on users' historical click sequences, but they generally pay few attention to simultaneously modeling users' multiple types of behaviors or jointly optimize multiple objectives (e.g. both Click-through rate and Conversion rate), which are both vital for e-commerce sites. In this paper, we argue that it is crucial to formulate users' different interests based on multiple types of behaviors and perform multi-task learning for significant improvement in multiple objectives simultaneously. We propose Deep Multifaceted Transformers (DMT), a novel framework that can model users' multiple types of behavior sequences simultaneously with multiple Transformers. It utilizes Multi-gate Mixture-of-Experts to optimize multiple objectives. Besides, it exploits unbiased learning to reduce the selection bias in the training data. Experiments on JD real production dataset demonstrate the effectiveness of DMT, which significantly outperforms state-of-art methods. DMT has been successfully deployed to serve the main traffic in the commercial Recommender System in JD.com. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/CIKM2020_DMT.
Adoption of telemedicine applications among Saudi citizens during COVID-19 pandemic: An alternative health delivery system

This study aims to investigate individual behavior towards adoption of telemedicine application based on wireless sensor network applications (WSN) during COVID-19 pandemic. Therefore, the research model extends the unified theory of acceptance and use of technology with the task technology fit model, awareness and self-efficacy to determine user intention to adopt wireless sensor network application for medical assistance. A research survey was conducted towards Saudi citizens in voluntary setting. In response, 348 valid responses were retrieved. Sample size was computed with prior-power analysis using G-power software. The research model is empirically tested with latest statistical approach namely structural equation modeling (SEM). Findings of the structural model indicate that wireless sensor network application was jointly predicted by performance expectancy, social influence, effort expectancy, facilitating condition, task technology fit, awareness and self-efficacy and explained substantial variance R2 79.5% in individual behavior to adopt wireless sensor network application. The effect size analysis (f2) revealed that task technology fit and facilitating condition were the most important factors to determine user intention to adopt wireless sensor network application. The current study contributes to theory by extending the unified theory of acceptance and use of technology with task technology fit model, self-efficacy and awareness and enrich information system literature in the context of user intention to adopt wireless sensor network application. Practically, this study suggest that health consultant should focus on technology fitness including wireless devices and the number of facilities to operate wireless sensor network which in turn boost user confidence towards adoption of wireless sensor network application for health consultation. This study develops a unique research model that inspects user behavior towards adoption of wireless sensor network technology for better health care system. The findings of this study answer how to monitor COVID-19 while using of wireless sensor network applications. Moreover, this study is useful for clinical management through virtual environment and guides health advisors, software developers and physicians to design user friendly telemedicine application by using wireless sensor network technology.
Medical image based breast cancer diagnosis: State of the art and future directions

The intervention of medical imaging has significantly improved early diagnosis of breast cancer. Different radiological and microscopic imaging modalities are frequently utilized by medical practitioners for identification and categorization of different breast abnormalities by manual scrutiny. The meticulous classification of different breast abnormalities is challenging, because of ambiguous imaging data and due to indistinguishable characteristics of benign and malignant breast lesions. However, with the advent in applications of Artificial Intelligence (AI) in healthcare, researchers have turned their focus towards designing of efficient intelligent computer aided detection and diagnosis systems for prognosis of this catastrophic disease using image processing and computer vision (CV) techniques. An abundance of work could be found in literature on classification of different breast abnormalities, where majority of them has dealt with binary classification (i.e. benign and malignant). In current study, a comprehensive review has been presented to analyze and evaluate state of the art proposed methodologies for breast cancer diagnosis based over commonly used breast screening imaging modalities. The studies under consideration are mainly categorized into statistical machine learning based and deep learning based classifier, where deep classifiers further sub-categorized into models built from scratch and transfer learning based models. A number of factors have been taken to compare the performance of these classification models, on the basis of which some recommendations are provided for researcher to precede this work in future.