Reviewerly/exHarmony huggingface dataset:
Construction of a Retrospective Cohort to Observe 10-Year Urologic Cancer Treatment Trends at the Biggest Medical Center of South Korea

Purpose: To construct a urologic cancer database using a standardized, reproducible method, and to assess preliminary characteristics of this cohort.Materials and Methods: Patients with prostate, bladder, and kidney cancers who were enrolled with diagnostic codes in the electronic medical record (EMR) at Asan Medical Center from 2007–2016 were included. Research Electronic Data Capture (REDCap) was used to design the Asan Medical Center-Urologic Cancer Database (AMC-UCD). The process included developing a data dictionary, applying branching logic, mapping clinical data warehouse structures, alpha testing, clinical record summary testing, creating “standards of procedure,” importing data, and entering data. Descriptive statistics were used to identify rates of surgeries and numbers of patients.Results: Clinical variables (n=407) were selected to develop a data dictionary from REDCap. In total, 20,198 urologic cancer patients visited our institution from 2007–2016 (bladder cancer, 4,616; kidney cancer, 5,750; prostate cancer, 10,330). The overall numbers of patients and surgeries increased over time, with robotic surgeries rapidly growing over a decade. The most common treatment for urologic cancer was surgery, followed by chemotherapy and radiation therapy.Conclusions: Using a standardized method, the AMC-UCD fosters multidisciplinary research. This constructed database provides access to clinical statistics to effectively assist research. Preliminary data should be refined through EMR chart review. The successful organization of data from 2007–2016 provides a framework for future periods of investigation and prospective models.
A high-frequency angular vibration calibration system based on phase modulated laser interferometer technique

In this paper, we proposed a high-frequency angular vibration calibration system based on phase modulated laser interferometer technique, which can provide the real-time rotation angle measurement with vibration frequency of up to 1000Hz. The optical configuration, operation principle, and phase generation carrier demodulation (PGC) technique of this calibration system are presented. Both theoretical and experimental results demonstrate that our proposed angular vibration calibration system can realize an angle measurement with an amplitude of 0.3" at angular frequency of 1000Hz.
Towards a Plug-In Architecture to Enable Self-Adaptation through Middleware

To be self-adaptive, a system must exhibit capabilities related to the monitoring, analyzing, planning, and executing adaptations. Often these activities require in-depth knowledge of the system's architecture, functionality, and requirements. Thus, the traditional design pattern for self-adaptive systems is the MAPE- K loop, which can be infeasible to implement into legacy systems. Popular approaches to incorporate self-adaptive behavior into legacy systems employ an external framework and/or middleware. These software applications require significant expertise in their choice of tools and can be unaccommodating to systems dependent on third-party vendors, i.e., enterprise systems. In this paper, we investigate the design of the Adapt! architecture as a plug-in architecture that operates as middleware to enable system self-adaptation. We discuss key challenges associated with middleware for self-adaptive system configuration, survey existing approaches, and characterize an extensible architecture that would be applicable to legacy systems. The Adapt! architecture is exemplified through two case studies that include a load balancer and a tele-assistance system.
A Novel Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm for the Travelling Counselling Problem for Utilization of Solar Energy

This article proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is proposed. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.An improved scheduling of the technical counselling process for utilization of the electricity from solar energy power stations is introduced. The scheduling aims at achieving the best utilization of the available day time for the counselling group,n this regard,a new application problem is presented, which is called a Travelling Counselling Problem (TCP).A Nonlinear Binary Model is introduced with a real application
Design and Development of Interactive Media Application Based on Android Case Study of Hydrocarbon Chemical Lesson Materials

This study aims to design interactive media builds based on mobile application android in chemistry subject matter, namely hydrocarbons. This research uses the knowledge management method which consists of several stages including (1) Knowledge Discovery (2) Knowledge Capture (3) Knowledge Sharing (4) Knowledge Application. The object of this research is interactive media based on android hydrocarbon chemistry subject matter. This study used a subject consisting of 4 experts, namely 2 media experts and 2 material experts. Instruments in data collection based on functional requirements and test results were analyzed using the User Experience Questionnaire. The research results were obtained using black box testing, based on functional testing combined with testing scenarios, obtaining 100% results to function properly. The results of the analysis were obtained by using the User Experience Questionnaire in order to obtain a Good scale.
Publicly Verifiable Vibrant Digital Medical Information Systems

Abstract As data processing advances, decentralized media has been widely recognized for its ability to store large amounts of data. By comparing a revisited content to a dispersed repository, a cloud provider may verify the document’s integrity without having to retrieve it. A reconsidered examining strategy is offered to lead the customer to reconsider the significant assessing task to third sector inspector, taking into consideration the important computing cost brought up by the checking process (TPA). TPA may be deterred by the primary revisited evaluating strategy, but the second plan gives the harmful organization the right of inspection over the readdressed data of users, which poses a significant risk to patient privacy. Human Emphasis for reconsidered inspection is presented in this work, which emphasizes that the service user can be overwhelmed by her own data. Based on user-centered design, our suggested methodology not only prevents patient’s data from leaking to TPA without depending on cryptographic algorithms, but can also avoid the use of additional free unpredictable supply that is impossible to fulfill on a daily basis. Also, we start to make our approach work with continuous changes. Our recommended scheme is both verifiably safe and essentially productive, as shown by the privacy analysis and test evaluations.
A Hierarchical Nonparametric Bayesian Model Based on Scaled Dirichlet Distribution

Data clustering is one of the principle unsupervised learning methods in various domains of science. In this paper, we propose a new clustering approach based on hierarchical Dirichlet processes of scaled Dirichlet distribution. Our motivation is flexibility of this distribution which provides a good potential to fit non-Gaussian data. Also, some issues such as the high costs of labeling medical data and sensitivity in this domain encouraged us to construct an unsupervised learning algorithm. We applied batch and online variational inference to learn our model as both methods can estimate model parameters and complexity, simultaneously. To demonstrate the capability of our proposed model, we tested our framework on two applications related to Internet of Health Things (IoHT) and computer-assisted diagnosis (CAD). Our proposed model demonstrates comparable results to similar alternatives.
Low Illumination Image Enhancement Algorithm Combining Homomorphic Filtering and Retinex

Aiming at the problems of blurred details and dim colors in images under low illumination conditions, a low illumination image enhancement algorithm combining homomorphic filtering and Retinex is proposed in this paper. Firstly, in the RGB space of the original image, the homomorphic filtering algorithm based on wavelet transform and improved Butterworth filter is used to processes the image to obtain a detail-enhanced image; Secondly, in the HSV space of the original image, the Retinex algorithm based on the improved bilateral filter function is used to processes the V channel to obtains a color-enhanced image; Finally, the detail-enhanced image and the color-enhanced image are weighted and merged. Comparative experimental data shows that the improved algorithm can effectively enhance the details and colors of the image.
Meta-Learning Based Feature Selection for Clustering

Clustering is a highly demanded task nowadays. However, it requires the attention of human experts and it might certainly benefit from automation. This paper presents a method to perform simultaneous automatic algorithm selection and hyperparameter tuning with feature selection for clustering. The algorithm also features a meta-model to predict promising algorithm configurations based on dataset properties. Experimental results are provided for a set of benchmark datasets, it is shown that the proposed method outperforms known alternatives.
Detecting Ambiguous Phishing Certificates using Machine Learning

Recent phishing attacks have started to migrate to HTTP over TLS (HTTPS), making a phishing web page appear safe to the user&#x2019;s browser despite its malicious purpose. This paper benefits from both digital certificates and domains related data features to propose machine learning-based solutions to predict digital certificates involved in HTTPS as phishing or benign certificates. In contrast to previous works that consider this a binary classification problem, we take into account that a certificate can be partially benign and phishy simultaneously. We propose a multi-class classifier and a regressor to classify these ambiguous certificates, in addition to benign and phishing certificates, where the &#x2018;phishyness&#x2019; of a certificate is expressed as a value between 0 and 1 for the regressor. We apply our method to a set of certificates obtained from certificate transparency logs and show that we can classify them with high performance. We extend our validation by evaluating the performance of the model over time, showing that our model generalizes over time on our training data set.
RASAECO: Requirements Analysis of Software for the AECO Industry

Digitalization is forging its path in the architecture, engineering, construction, operation (AECO) industry. This trend demands not only solutions for data governance but also sophisticated cyber-physical systems with a high variety of stakeholder background and very complex requirements. Existing approaches to general requirements engineering ignore the context of the AECO industry. This makes it harder for the software engineers usually lacking the knowledge of the industry context to elicit, analyze and structure the requirements and to effectively communicate with AECO professionals. To live up to that task, we present an approach and a tool for collecting AECO-specific software requirements with the aim to foster reuse and leverage domain knowledge. We introduce a common scenario space, propose a novel choice of an ubiquitous language well-suited for this particular industry and develop a systematic way to refine the scenario ontologies based on the exploration of the scenario space. The viability of our approach is demonstrated on an ontology of 20 practical scenarios from a large project aiming to develop a digital twin of a construction site.
Rényi Divergence Based Bounds on Generalization Error

Generalization error captures the degree to which the output of a learning algorithm overfits the training data. We obtain a family of bounds which generalize the bounds developed by Xu & Raginsky (2017) and Bu, Zou and Veeravalli (2019), under certain assumptions. Our bounds are based on the Rényi analogue of the Donsker-Varadhan representation of Kullback-Leibler divergence. We also obtain bounds on the probability of generalization error which recover the bounds of Esposito, Gastpar and Issa (2020). We also give a multiplicative lower bound on the expected true loss for a 0-1 loss function.
Application and practice of Artificial Neural Network in innovative experimental teaching of soil mechanics under the background of Emerging Engineering Education

The construction of Emerging Engineering Education is entering a new stage of further deepening. It is one of the important goals of the civil Engineering major to cultivate innovative civil Engineering talents who meet the connotation requirements of “Emerging Engineering Education”. This paper takes the undergraduate students of the innovation and entrepreneurship research team of civil engineering as the research object, and carries out the innovation experiment. Taking the Unsaturated Soil as the object, the students analyzed the experimental design, test and results, analyzed and derived the influencing factors of unsaturated soil shear strength parameters, optimized them and successfully proposed an artificial neural network prediction model. Through the innovative experiment teaching link, the school improves students' innovation and entrepreneurship ability, cultivate students' independent learning ability and application ability, and improve their decision-making ability and scientific research ability.
Digital quantum simulation of beam splitters and squeezing with IBM quantum computers

We present results on the digital quantum simulations of beam-splitter and squeezing interactions. The bosonic Hamiltonians are mapped to qubits and then digitalized in order to implement them in the IBM quantum devices. We use error mitigation and postselection to achieve high-fidelity digital quantum simulations of single-mode and two-mode interactions, as evinced---where possible---by full tomography of the resulting states. We achieve fidelities above 90% in the case of single-mode squeezing with low squeezing values and ranging from 60% to 90% for large squeezing and in the more complex two-mode interactions.
Theoretical Foundations of Ternary Logic Application in Logical-Probabilistic Method for Assessment of Socio-Economic System Reliability

The theoretical aspect of the application of event modeling and ternary logic by S.C. Kleene for calculating the reliability and risk of socio-economic systems is considered. For these systems, due to the specifics of their functioning, there is no strict concept of “failure” adopted for technical systems and objects. Socio-economic systems evolve in the changing external environment but the range of admissible parameters of these systems is also constantly changing in accordance with the environment parameters and the goals of management. To assess and analyze the reliability of the socio-economic system’s functioning, the state of “validity” is introduced, so, during modeling we are considering three states of the system: an operable state, an valid state, and a failure, and the operability functions are written as equations of Kleene’s ternary logic.
PowerPrep: A power management proposal for user-facing datacenter workloads

Modern data center applications are user facing/latency critical. Our work analyzes the characteristics of such applications i.e., high idleness, unpredictable CPU usage, and high sensitivity to CPU performance. In spite of such execution characteristics, datacenter operators disable sleep states to optimize performance. Deep-sleep states hurt performance mainly due to: a) high wake-latency and b) cache warm-up after exiting deep-sleep. To address these challenges, we quantify three necessary characteristics required to realize deep-sleep states in datacenter applications: a) low wake-latency, b) low resident power, and c) selective retention of cache-state. Using these observations, we show how emerging technological advances can be leveraged to improve the energy efficiency of latency-critical datacenter workloads.
Implementing the Simplex Method with Grover’s Search

The simplex method, as proposed by Dantzig in 1947, is a widely-used practical algorithm for solving Linear Programs (LPs)—systems of linear inequalities headed by a single linear objective function. We examine the difficulty of implementing quantum subroutines outlined by Nannicini in Fast quantum subroutines for the simplex method. Classically, choosing an entering variable in an LP with n optimization variables, m constraints, and at most d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> non-zero entries per column requires $O\left({d_c^{0.7}{m^{1.9}} + {m^{2 + o(1) + {d_c}n}}}\right)$ time using the fastest known algorithm for sparse matrix multiplication. By using quantum subroutines Nannicini’s approach results in $O\left({\frac{1}{\varepsilon }\kappa d\sqrt n \left({{d_c}n + dm}\right)}\right)$ time, where Õ hides polylogarithmic factors. We examine the implementation of subroutine FindColumn (A,B,ϵ) that selects a valid entering variable (if any) using quantum search and quantum phase estimation algorithms; here, A is the constraint matrix, B is the basis and ϵ is optimality tolerance. We focus on challenges presented by transforming the matrices and vectors given by an LP into quantum states and the preparation of the oracle. We discuss our implementation and encountered challenges when implementing the algorithm using IBM’s Quantum Experience.
Wall rock alteration in part of the Sarnak gold deposit, SE Bulgaria. Preliminary data

This short communication presents preliminary data on а wall rock alteration in part of the Sarnak epithermal gold deposit based on host rock samples from drill hole cores. Methods used are powder X-ray diffraction as well as optical and scanning electron microscopy. The most pronounced alterations related to the epithermal mineralization are vein silicification, adularization and pyritization.
A Tree Based Machine Learning Approach for PTB Diagnostic Dataset

Abstract The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.
Sentiment Analysis of Twitch.tv Livestream Messages using Machine Learning Methods

Livestreaming refers to the practice of broadcasting media over the internet in real-time. It has steadily grown in popularity with platforms such as Twitch.tv seeing a consistent influx of users over the past few years. Twitch provides a chat feature that allows viewers to participate by sharing their opinions in real-time. These messages can serve as rich and interesting banks of public thought that can help streamers make decisions synchronously about the content that they are producing. However, with several thousand viewers involved, the added complexity of context-specific emotes, memes and the meta-language that pervades through these platforms, summarizing and extracting useful information from them becomes a daunting task. Sentiment analysis is a possible solution to this problem. In this paper, a methodology to perform sentiment analysis with a set of machine learning-based models on livestream messages from Twitch.tv is proposed. Machine Learning models like Support Vector Classifier, Logistic Regression, Decision Tree Classifier, Random Forest Classifier and Multinomial Naïve Bayes were implemented. Among these models, the Support Vector Classifier outperformed the current state-of-the-art model and displayed a 10.3% rise in accuracy, a 9.1% rise in recall and a 7.5% rise in the F1-score.
A Convolutional Neural Network for Small Sample's Ring Structured Light Denoising

Denoising is an indispensable step when measuring depth by using omnidirectional ring structured light depth perception system. However, it is difficult to obtain uniform, clear and continuous structured light stripe by using general image denoising algorithm. To solve this problem, our paper adopts the method of combining Deep Convolutional Generative Adversarial Networks (DCGAN) and Denoising Convolutional Neural Network-Ring Structured Light (DnCNN-RSL) to denoise. We first use DCGAN network to generate more data sets, and then use DnCNN-RSL network to denoise images so that noiseless images can be obtained. DnCNN-RSL network is suitable for removing the noise of small sample data sets, which reflects in improving PNSR value of images, and SSIM value is also closer to 1. Experiments show that DnCNN-RSL network can get better denoising results than traditional image processing methods and obtain clear, continuous structured light stripe. In this way depth information obtained by denoised structured light stripe is more accurate.
Code Autocomplete Using Transformers

In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting to the user long lists of irrelevant items. Since innovations introduced by transformer-based models that have reached the state of the art performance in tasks involving natural language processing (NLP), the application of these models also in tasks involving code intelligence, such as code completion, has become a frequent object of study in recent years. In these paper, we present a transformer-based model trained on 1.2 million Java files gathered from top-starred Github repositories. Our evaluation approach was based on measuring the model’s ability to predict the completion of a line, proposing a new metric to measure the applicability of the suggestions that we consider better adapted to the practical reality of the code completion task. With a recently developed Java web project as test set, our experiments showed that in 55.9% of the test cases the model brought at least one suggestion applicable, while the best baseline model presented this in 26.5%.
An autonomous driving system utilizing image processing accelerated by FPGA

This paper presents an autonomous driving system utilizing FPGA-based image processing. We develop a robot that our system is implemented on Ultra96-V2, a board with programmable logic and processing system. We use ROS, a middleware framework for developing robots, to manage the system such as controlling hardware devices, localization and determination of the direction to go. We implement a neural network to detect road markings on the road on a programmable logic on the board. The robot with our system implemented drives autonomously along the specified route on a miniature road, recognizing edge line and road markings.