Reviewerly/exHarmony huggingface dataset:
Improving the search efficiency of differential evolution algorithm by population diversity analysis and adaptation of mutation step sizes

The aim of this research work is to improve the efficiency of differential evolution (DE) algorithm, at the cases of its unsuccessful searches. Initially, this work discusses and compares different methods to measure the population diversity of DE algorithm implemented for DE/rand/1/bin variant for a set of benchmarking functions. A method which well demonstrates difference in population diversity evolution at successful and unsuccessful cases of DE search is identified based on comparison. This work is then extended to detect unsuccessful searches in advance using the evolution of population diversity measured by the identified method. On detecting a search as unsuccessful, a parameter adaptation strategy to adapt the mutation step size (F) is added to DE algorithm to recover from it. The improved DE algorithm, which comprises of the logic of adapting F value based on the population diversity, is compared with its classical version and found outperforming. The comparison results are reported in this paper.
Confirmation bias and trust: Human factors that influence teachers' attitudes towards AI-based educational technology

Evidence from various domains underlines the key role that human factors, and especially, trust, play in the adoption of AI-based technology by professionals. As AI-based educational technology is increasingly entering K-12 education, it is expected that issues of trust would influence the acceptance of such technology by educators as well, but little is known about this matter. In this work, we bring the opinions and attitudes of science teachers that interacted with several types of AI-based technology for K-12. Among other things, our findings indicate that teachers are reluctant to accept AI-based recommendations when it contradicts their previous knowledge about their students and that they expect AI to be absolutely correct even in situations that absolute truth may not exist (e.g., grading open-ended questions). The purpose of this paper is to provide initial findings and start mapping the terrain of this aspect of teacher-AI interaction, which is critical for the wide and effective deployment of AIED technologies in K-12 education.
Do Androids Dream of Electric Copyright?

Abstract The advent of sophisticated artificial neural networks has opened new artistic opportunities, but also a variety of new legal challenges. Computer programs such as Google’s Deep Dream can take an image and process it in manners that resemble biological networks, producing artwork that is both unique and unpredictable. The law is not unfamiliar with the challenges of artificial intelligence (AI), in the past academics and policymakers have had to deal with the legal implications of autonomous agents in contract formation, to name just one area of interest. However, for the most part the implementation of smart systems has been limited in its reach and scope, and in many instances autonomous agents required quite a lot of direction from the programmer, following a very stringent set of rules. This meant that for the most part all rights, responsibilities, and liabilities arising from artificial agents fell squarely on the program creator. Neural networks are different, these systems have the potential to generate works in which human interaction is minimal. Modern copyright law has been drafted to consider originality as an embodiment of the author’s personality, and originality is one of the main requirements for the subsistence of copyright. So, what happens when you remove personality from the equation? Are machine-created works devoid of copyright? Do we need to change copyright law to accommodate autonomous artists? This chapter will explore this and other questions.
https://www.mocoda2.de: a database and web-based editing environment for collecting and refining a corpus of mobile messaging interactions

Abstract This paper reports on findings from the MoCoDa2 project which is creating a corpus of private CMC interactions from smartphone apps based on donations by their users. Different from other projects in the field, the project involves users not only as donators but also as editors of their data: In a web-based editing environment which provides users with access to their raw data, they are supported in pseudonymising their data and enhancing them with rich metadata on the interactional context, meta-data on the interlocutors and their relations, and on embedded media files. The resulting corpus will be a useful resource not only for quantitative but also for qualitative CMC research. For representation and annotation of the data the project builds on best practices from previous projects in the field and cooperates with a language technology partner.
‘1m fingerprint’ data leak raises doubts over biometric security

The discovery last month of up to 1 million fingerprint records plus facial images exposed on an open database has raised fears that biometric data is as vulnerable to hackers as traditional ID data if it is not stored securely.
Prediction of Breast Cancer Disease using Machine Learning Algorithms

Background/Aim: Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate among women. The early strategies for estimating the breast cancer sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered breast cancer data set from kaggle and we have done pre-processing tasks for missing values .We have no missing data values from the considered data set .The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a breast cancer disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting breast cancer disease. Results: The machine learning algorithms under study were able to predict breast cancer disease in patients with accuracy between 52.63% and 98.24%. Conclusions: It was shown that Random Forest has better Accuracy (98.24 %) when compared to different Machine-learning Algorithms.
GARLSched: Generative adversarial deep reinforcement learning task scheduling optimization for large-scale high performance computing systems

Efficient task scheduling has become increasingly complex as the number and type of tasks proliferate and the size of computing resource grows in large-scale distributed high-performance computing (HPC) systems. At present, deep reinforcement learning (DRL) methods have achieved certain success in scheduling problems. However, due to the exogeneity of the task and the sparsity of the reward, the learning of the DRL control policy requires a significant amount of training time and data and cannot guarantee effective convergence. Meanwhile, based on the understanding of HPC system characteristics, various scheduling policies with acceptable performance for different optimization goals have been developed by the experts. But these heuristic methods cannot adapt to environmental changes and optimize for specific workloads. Therefore, the generative adversarial reinforcement learning scheduling (GARLSched) algorithm is proposed to effectively guide the learning of DRL in large-scale dynamic scheduling issues based on the optimal policy in the expert pool. In addition, the task embedding-based discriminator network effectively improves and stabilizes the learning process. Experiments show that compared with heuristic and DRL scheduling algorithms, GARLSched can learn high-quality scheduling policies for various workloads and optimization objects. Furthermore, the learned models can perform stably even when applied to invisible workloads, making them more practical in HPC systems.
The bias hunter

Itiel Dror is determined to reveal the role of bias in forensics, even if it sparks outrage.
BestCyte® Cell Sorter Imaging System: Primary and adjudicative whole slide image rescreening review times of 500 ThinPrep Pap test thin-layers - An intra-observer, time-surrogate analysis of diagnostic confidence potentialities

The novel Artificial Intelligence-driven BestCyte® Cell Sorter Imaging System (BestCyte) enables hybrid digital screening through classification and sorting of tiles depicting cells in 8 galleries or whole slide image (WSI) reviews.(1) Analyze expenditures of time (minutes) for primary BestCyte cell sorter screening and adjudicative WSI rescreening of 500 blinded, randomized ThinPrep thin-layers to determine review times per Bethesda nomenclature; (2) Analyze review times for NILM qualifier diagnoses reflecting increasing interpretive complexity (i.e., Inflammation, Reactive/Repair, Bacterial cytolysis, Bacterial vaginosis, Atrophy, and Atrophic vaginitis); (3) Challenge accuracy of primary diagnoses (Downgraded, Upheld, and Upgraded) following adjudicative WSI rescreening to assess correlated review times as surrogate indicators of diagnostic confidence in BestCyte functionality (i.e., learning curve); and (4) Correlate primary and adjudicative diagnoses to calculate intra-observer reproducibility Kappa coefficients per Bethesda nomenclature.Of 500 thin-layers, the mean [primary/adjudicative rescreening review times (minutes)] were: Overall study [1.38/3.94], NILM [1.23/3.02], ASCUS [1.18/2.53], ASC-H [1.73/4.86], AGUS [1.84/6.34], LSIL [1.49/4.16], HSIL [1.52/4.10], CA [0.65/2.57]. Of 500 primary Bethesda diagnoses: 2 (0.40%) downgraded; 483 (96.6%) upheld; 15 (3.00%) upgraded after adjudicative WSI rescreening. Of 354 NILM diagnoses: 0 downgraded; 344 (97.2%) upheld; 10 (2.82%) upgraded. Of 34 ASCUS diagnoses: 2 (5.88%) downgraded; 28 (82.4%) upheld; 4 (11.8%) upgraded. Of 17 ASC-H diagnoses: 0 downgraded; 16 (94.1%) upheld; 1 (5.88%) upgraded. Of AGUS (n=1), LSIL (n=24), HSIL (n=52), CA (n=1), UNSAT (n=17): 100% upheld. Kappa coefficients with 95% (Confidence Intervals): Overall study 0.9305 (0.8983-0.9627), NILM 0.9429 (0.9110-0.9748), ASCUS 0.8378 (0.7393-0.9363), ASC-H 0.9112 (0.8113-0.9999), AGUS 1.0 (1.0-1.0), LSIL 0.9189 (0.8400-0.9978), HSIL 0.9894 (0.9685-0.9999), CA 1.0 (1.0-1.0), UNSAT 1.0 (1.0-1.0). Primary BestCyte cell image review time trends for NILM, ASCUS, LSIL, and HSIL, revealed plateaus relative to decreasing respective adjudicative WSI rescreening times.Given innovative robustness, BestCyte accommodates interpretive fundamentals, enabling shorter ThinPrep thin-layer review times with optimal intra-observer concordance per Bethesda nomenclature through classifying, ranking, sorting, and displaying clinically relevant cells efficiently in galleries. BestCyte fosters continuously optimizing diagnostic confidence learning curves; may supplant manual microscopy for primary screening.
A survey on ethereum smart contract vulnerability detection using machine learning

Blockchain applications go far beyond cryptocurrency. As an essential blockchain tool, smart contracts are executable programs that establish an agreement between two parties. Millions of dollars of transactions attract hackers at a hastened pace, and cyber-attacks have caused large economic losses in the past. Due to this, the industry is seeking robust and effective methods to detect vulnerabilities in smart contracts to ultimately provide a remedy. The industry has been utilizing static analysis tools to reveal security gaps, which requires an understanding and insight over all possible execution paths to identify known contract vulnerabilities. Yet, the computational complexity increases as the path gets deeper. Recently, researchers have been proposing ML-driven intelligent techniques aiming to improve the efficiency and detection rate. Such solutions can provide quicker and more robust detection options than the traditionally used static analysis tools. As of this publication date, there is currently no published survey paper on smart contract vulnerability detection mechanisms using ML models. In order to set the ground for further development of ML-driven solutions, in this survey paper, we extensively reviewed and summarized a wide variety of ML-driven intelligent detection mechanism from the following databases: Google Scholar, Engineering Village, Springer, Web of Science, Academic Search Premier, and Scholars Portal Journal. In conclusion, we provided our insights on common traits, limitations and advancement of ML-driven solutions proposed for this field.
Inaccurate recording of routinely collected data items influences identification of COVID-19 patients

During the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients.The following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled 'COVID-19' if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled 'non-COVID-19' if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F1 score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences.The total dataset consisted of 402 patients: 196 'COVID-19' and 206 'non-COVID-19' patients. F1 scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F1 scores ranged between 0.73 and 0.99. F1 scores showed no clear pattern in variability between the two time periods.Our study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse.
EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection

Feature selection can efficiently improve classification accuracy and reduce the dimension of datasets. However, feature selection is a challenging and complex task that requires a high-performance optimization algorithm. In this paper, we propose an enhanced binary bat algorithm (EBBA) which is originated from the conventional binary bat algorithm (BBA) as the learning algorithm in a wrapper-based feature selection model. First, we model the feature selection problem and then transfer it as a fitness function. Then, we propose an EBBA for solving the feature selection problem. In EBBA, we introduce the Lévy flight-based global search method, population diversity boosting method and chaos-based loudness method to improve the BA and make it more applicable to feature selection problems. Finally, the simulations are conducted to evaluate the proposed EBBA and the simulation results demonstrate that the proposed EBBA outmatches other comparison benchmarks. Moreover, we also illustrate the effectiveness of the proposed improved factors by tests.
Deep Light Field Spatial Super-Resolution Using Heterogeneous Imaging

Light field (LF) imaging expands traditional imaging techniques by simultaneously capturing the intensity and direction information of light rays, and promotes many visual applications. However, owing to the inherent trade-off between the spatial and angular dimensions, LF images acquired by LF cameras usually suffer from low spatial resolution. Many current approaches increase the spatial resolution by exploring the four-dimensional (4D) structure of the LF images, but they have difficulties in recovering fine textures at a large upscaling factor. To address this challenge, this paper proposes a new deep learning-based LF spatial super-resolution method using heterogeneous imaging (LFSSR-HI). The designed heterogeneous imaging system uses an extra high-resolution (HR) traditional camera to capture the abundant spatial information in addition to the LF camera imaging, where the auxiliary information from the HR camera is utilized to super-resolve the LF image. Specifically, an LF feature alignment module is constructed to learn the correspondence between the 4D LF image and the 2D HR image to realize information alignment. Subsequently, a multi-level spatial-angular feature enhancement module is designed to gradually embed the aligned HR information into the rough LF features. Finally, the enhanced LF features are reconstructed into a super-resolved LF image using a simple feature decoder. To improve the flexibility of the proposed method, a pyramid reconstruction strategy is leveraged to generate multi-scale super-resolution results in one forward inference. The experimental results show that the proposed LFSSR-HI method achieves significant advantages over the state-of-the-art methods in both qualitative and quantitative comparisons. Furthermore, the proposed method preserves more accurate angular consistency.
Ontology-based Meta AutoML

Automated machine learning (AutoML) supports ML engineers and data scientist by automating single tasks like model selection and hyperparameter optimization, automatically generating entire ML pipelines. This article presents a survey of 20 state-of-the-art AutoML solutions, open source and commercial. There is a wide range of functionalities, targeted user groups, support for ML libraries, and degrees of maturity. Depending on the AutoML solution, a user may be locked into one specific ML library technology or one product ecosystem. Additionally, the user might require some expertise in data science and programming for using the AutoML solution. We propose a concept called OMA-ML (Ontology-based Meta AutoML) that combines the features of existing AutoML solutions by integrating them (Meta AutoML). OMA-ML can incorporate any AutoML solution allowing various user groups to generate ML pipelines with the ML library of choice. An ontology is the information backbone of OMA-ML. OMA-ML is being implemented as an open source solution with currently third-party 7 AutoML solutions being integrated.
Pareto Rank Surrogate Model for Hardware-aware Neural Architecture Search

Hardware-aware Neural Architecture Search (HWNAS) has recently gained much attention by automating the design of efficient deep learning models with tiny resources and reduced inference time requirements. However, HW-NAS inherits and exacerbates the expensive computational complexity of general NAS due to its significantly increased search spaces and more complex NAS evaluation component. To speed up HWNAS, existing efforts use surrogate models to predict a neural architecture’s accuracy and hardware performance on a specific platform. Thereby reducing the expensive training process and significantly reducing search time. We show that using multiple surrogate models to estimate the different objectives does not achieve the true Pareto front. Therefore, we propose HW-PRNAS, a novel Pareto Rank-preserving surrogate model. HWPR-NAS training is based on a new loss function that ranks the architectures according to their Pareto front. We evaluate our approach on seven different hardware platforms, including ASIC, FPGA, GPU and multi-cores. Our results show that we can achieve up to 2. 5x speedup while achieving better Pareto-front results than state of the art surrogate models.
Dynamic Depth-Aware Network for Endoscopy Super-Resolution

Endoscopy super-resolution (SR) plays an important role in improving diagnostic results and reducing the misdiagnosis rate. Even though recent studies have investigated the SR for endoscopy, these methods apply equal importance to the whole image and do not consider the relationship among pixels, especially the depth information, which can provide diagnosis-related information for clinicians. To address this problem, we propose a dynamic depth-aware network for endoscopy super-resolution, which represents the first effort to comprehensively integrate the depth information to the SR task for endoscopic images. It includes a depth-wise feature extracting branch (DW-B) and a depth-guided SR branch (DGSR-B). The DW-B aims to extract the representative feature for each depth level (i.e. depth matrix) further to provide auxiliary information and guide the super-resolution of texture under different depth levels. In DGSR-B, a depth-guided block (DGB) consisting of depth-focus normalization (DFN) is introduced to inject both the depth matrix and depth map into the LR image feature, so as to guide the image generation for each depth region. To adaptively super-resolve the regions under different depth levels, we devise a dynamic depth-aware loss to assign different trainable weights to each region for SR optimization. Extensive experiments have been conducted on two main publicly available datasets, i.e., the Kvasir dataset and the EndoScene dataset, and the superior performance verifies the effectiveness of our method for SR task and polyp segmentation. Source code is to be released.