Reviewerly/exHarmony huggingface dataset:
Optimal treatment recommendations for diabetes patients using the Markov decision process along with the South Korean electronic health records

The extensive utilization of electronic health records (EHRs) and the growth of enormous open biomedical datasets has readied the area for applications of computational and machine learning techniques to reveal fundamental patterns. This study's goal is to develop a medical treatment recommendation system using Korean EHRs along with the Markov decision process (MDP). The sharing of EHRs by the National Health Insurance Sharing Service (NHISS) of Korea has made it possible to analyze Koreans' medical data which include treatments, prescriptions, and medical check-up. After considering the merits and effectiveness of such data, we analyzed patients' medical information and recommended optimal pharmaceutical prescriptions for diabetes, which is known to be the most burdensome disease for Koreans. We also proposed an MDP-based treatment recommendation system for diabetic patients to help doctors when prescribing diabetes medications. To build the model, we used the 11-year Korean NHISS database. To overcome the challenge of designing an MDP model, we carefully designed the states, actions, reward functions, and transition probability matrices, which were chosen to balance the tradeoffs between reality and the curse of dimensionality issues.
Regenerativity of Viterbi Process for Pairwise Markov Models

For hidden Markov models, one of the most popular estimates of the hidden chain is the Viterbi path—the path maximising the posterior probability. We consider a more general setting, called the pairwise Markov model (PMM), where the joint process consisting of finite-state hidden process and observation process is assumed to be a Markov chain. It has been recently proven that under some conditions the Viterbi path of the PMM can almost surely be extended to infinity, thereby defining the infinite Viterbi decoding of the observation sequence, called the Viterbi process. This was done by constructing a block of observations, called a barrier, which ensures that the Viterbi path goes through a given state whenever this block occurs in the observation sequence. In this paper, we prove that the joint process consisting of Viterbi process and PMM is regenerative. The proof involves a delicate construction of regeneration times which coincide with the occurrences of barriers. As one possible application of our theory, some results on the asymptotics of the Viterbi training algorithm are derived.
Inbodied Interaction: Introduction

column Share on Inbodied Interaction: Introduction Author: m.c. schraefel University of Southampton University of SouthamptonView Profile Authors Info & Claims InteractionsVolume 27Issue 2March - April 2020 pp 32–37https://doi.org/10.1145/3380811Published:25 February 2020Publication History 9citation1,588DownloadsMetricsTotal Citations9Total Downloads1,588Last 12 Months234Last 6 weeks24 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

Abstract Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K -nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.
Comparing quantum hybrid reinforcement learning to classical methods

Abstract In recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.
Advanced Face Mask Detection System

Pandemic COVID-19 novelly induced corona viruses that are continually spreading all over the world.In most areas of COVID-19 affect creation fallen.There is a crisis in the health sector.There have been several precautionary steps brought to minimize the propagation of this disease, with a mask.We are proposing a system that reduces COVID-19 development, identifying individuals who don't use a facial mask in a smart city system that monitors all public places with CCTV cameras.The appropriate authority is alerted by the city network whilst an individual is identified without a mask.Architecture for a data set that includes images of individuals with and without masks from various sources can train machine learning.A qualified architecture has achieved precision in identifying individuals from previously unknown testing data with and without a face mask
A new correlation-based approach for ensemble selection in random forests

Purpose Ensemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems. Despite the effectiveness of these techniques, studies have shown that ensemble methods generate a large number of hypotheses and that contain redundant classifiers in most cases. Several works proposed in the state of the art attempt to reduce all hypotheses without affecting performance. Design/methodology/approach In this work, the authors are proposing a pruning method that takes into consideration the correlation between classifiers/classes and each classifier with the rest of the set. The authors have used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by a technique inspired by the CFS (correlation feature selection) algorithm. Findings The proposed method CES (correlation-based Ensemble Selection) was evaluated on ten datasets from the UCI machine learning repository, and the performances were compared to six ensemble pruning techniques. The results showed that our proposed pruning method selects a small ensemble in a smaller amount of time while improving classification rates compared to the state-of-the-art methods. Originality/value CES is a new ordering-based method that uses the CFS algorithm. CES selects, in a short time, a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-the-art techniques used in this study.
A Framework of Quantum Strong Exponential-Time Hypotheses.

The strong exponential-time hypothesis (SETH) is a commonly used conjecture in the field of complexity theory. It essentially states that determining whether a CNF formula is satisfiable can not be done faster than exhaustive search over all possible assignments. This hypothesis and its variants gave rise to a fruitful field of research, fine-grained complexity, obtaining (mostly tight) lower bounds for many problems in P whose unconditional lower bounds are very likely beyond current techniques. In this work, we introduce an extensive framework of Quantum Strong Exponential-Time Hypotheses, as quantum analogues to what SETH is for classical computation.Using the QSETH framework, we are able to translate quantum query lower bounds on black-box problems to conditional quantum time lower bounds for many problems in P. As an example, we provide a conditional quantum time lower bound of Ω(n^1.5) for the Longest Common Subsequence and Edit Distance problems. We also show that the n² SETH-based lower bound for a recent scheme for Proofs of Useful Work carries over to the quantum setting using our framework, maintaining a quadratic gap between verifier and prover.Lastly, we show that the assumptions in our framework can not be simplified further with relativizing proof techniques, as they are false in relativized worlds.
A Survey on Curriculum Learning

Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision and natural language processing etc. In this survey article, we comprehensively review CL from various aspects including motivations, definitions, theories, and applications. We discuss works on curriculum learning within a general CL framework, elaborating on how to design a manually predefined curriculum or an automatic curriculum. In particular, we summarize existing CL designs based on the general framework of Difficulty Measurer+Training Scheduler and further categorize the methodologies for automatic CL into four groups, i.e., Self-paced Learning, Transfer Teacher, RL Teacher, and Other Automatic CL. We also analyze principles to select different CL designs that may benefit practical applications. Finally, we present our insights on the relationships connecting CL and other machine learning concepts including transfer learning, meta-learning, continual learning and active learning, etc., then point out challenges in CL as well as potential future research directions deserving further investigations.
Work-in-Progress: Development of the Discipline “Innovations in Engineering Pedagogy” as Part of an Advanced Professional Training for Educators of Engineering Schools in Higher Education Institutions

Engineering workforce fuels high-pace technology developments in all aspects of human life. A quality of engineering education today is essential to build engineers for tomorrow. This raises standards for pedagogical training for professors and other staff of engineering educational institutions. The framework of ERASMUS + project “EngineeriNg educaTors pEdagogical tRaining” (“ENTER”) provides a venue for partnership between universities to develop and enhance curriculum. This article provides results of collaboration of KNRTU with thirteen universities from Europe, Kazakhstan and Russia in an effort to develop a Multi-Level Modular Curricula “Innovative Pedagogy for Engineering HEIs’ Educators” (iPET) for the advanced professional training of the “educator of the future”. In this work, creativity is identified as a key component of professional competence of an educator. As such, main goal of the discipline is to boost creativity of educator, to practice conventional and contemporary innovative pedagogical techniques and to build confidence to implement them in professional activity. Understanding of motivation, operational conditions, creative thinking, ability to reflect and their interconnections is necessary for successful implementation of the course. Analysis of innovative pedagogical technologies was also conducted as part of this work. It was established that the most promising are mind-maps, concept maps of literature review, Thesaurus description of terminological systems, so called Thesaurus maps. Research concluded, that the best results are expected when training is continuous, but administered in sessions, alternated with periods of practicing in a workplace. This allows educators and students to adapt to new techniques and master fewer of them at a time. This article summarizes methods, tools and organizational forms that are recommended for use in the discipline “Innovations in Engineering Pedagogy”.
Focused Proof-search in the Logic of Bunched Implications

Abstract The logic of Bunched Implications (BI) freely combines additive and multiplicative connectives, including implications; however, despite its well-studied proof theory, proof-search in BI has always been a difficult problem. The focusing principle is a restriction of the proof-search space that can capture various goal-directed proof-search procedures. In this paper we show that focused proof-search is complete for BI by first reformulating the traditional bunched sequent calculus using the simpler data-structure of nested sequents, following with a polarised and focused variant that we show is sound and complete via a cut-elimination argument. This establishes an operational semantics for focused proof-search in the logic of Bunched Implications.
Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

The first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a 1280×768 image resolution is achieved which is favorably faster in comparison with other work.
Generate a system of linear equation through unimodular matrix using Python and Latex

A System of Linear Equation (SLE) that has the same number of equations as the number of variables is called the Determined SLE. But, an SLE with non-integer solutions is a problem for students. This paper can assist teachers in providing exercises on the topic of SPL with integer solutions, so students can better understand it. Moreover, a square matrix is said to be unimodular if it has a determinant value of 1 or -1. The inverse of a unimodular matrix and the product of two unimodular matrices is also unimodular. Moreover, the notion of unimodular will facilitate us in determining the solution because it does not involve fractions at all. In this article, a method is given for generating an SLE via a unimodular matrix with Python and Latex. The result is a file with pdf format that contains some Determined SLE with a unique solution in the form of integers. The conclusion is that by using the Python program, we can generate a finite Determined SLE easily with the solutions are integers.
A Parameter-Free Approach for Lossless Streaming Graph Summarization

In rapid and massive graph streams, it is often impractical to store and process the entire graph. Lossless graph summarization as a compression technique can provide a succinct graph representation without losing information. However, the problem of lossless streaming graph summarization is computationally and technically challenging. Although the state-of-the-art method performs well with respect to efficiency, its summarization quality is usually unstable and unsatisfactory. This is because it is a randomized algorithm and depends heavily on the pre-tuned parameters. In this paper, we propose a parameter-free lossless streaming graph summarization algorithm. As the graph changes over time, we incrementally maintain the summarization result, by carefully exploring the influenced subgraph, which is shown to be a bounded neighborhood of the inserted edge. To enhance the performance of our method, we further propose two optimization techniques regarding candidate supernodes refinement and destination supernode selection. The experiment results demonstrate that the proposed methods outperform the state-of-the-art by a large margin in terms of compression quality with comparable running time on the majority of datasets.
Semi-supervised time series classification method for quantum computing

In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques.
Digital image encryption algorithm through unimodular matrix and logistic map using Python

Hill Cipher is one of the methods used in Cryptography. In the Hill Cipher algorithm, the key square matrix must have an inverse modulo. One special matrix that definitely has an inverse is the unimodular matrix. The unimodular matrix can be used as a key in an encryption process. The purpose of this research is to show an alternative in securing digital image data. The type of cryptography used is symmetric cryptography. The algorithm presented by generating a unimodular matrix using the Logistic Map. First, to get a unimodular matrix, we use an identity matrix. The sequence of real number in (0,1) of Logistic Map then converted into integer number from 0 to 255. That number then occupies upper triangular entries of the unimodular matrix. We use the Elementary Row Operation to obtain the complete matrix as a key. Then the multiplication of matrix modulo is used as an encryption process. The unimodular matrix generated by the proposed algorithm guarantees a key in the Hill Cipher for all matrix sizes (n > 4) and only need two parameters (password 1 and password 2). We also tested the algorithm in python language on two digital images (grayscale and color) with different sizes. The results show that the encrypted images are very difficult to read by third parties. The time needed is based on password 1, that is the size of the key matrix. So, the unimodular matrix and logistic map work very well with Hill Cipher to encrypt a digital image.
Weighted feature fusion and attention mechanism for object detection

Recently, anchor-free methods have brought new ideas to the field of object detection that eliminate the need for anchor boxes in object detection and provide a simpler detection structure. CenterNet is the representative anchor-free method. However, this method still has the problem of obtaining high-resolution representation from low-resolution representation using upsampling, and the predicted heatmap is not accurate enough in space and does not make full use of the shallow low-level features of the network. We introduce CenterNet-HRA to solve this problem. An attention module is proposed to calibrate the high-level semantic features of the network output using the shallow low-level features from different receptive fields; HRNet is used as the backbone to maintain high-resolution feature representation through the whole process rather than using upsampling to generate high-resolution feature representation as HourglassNet. Considering that the feature representations with different resolutions have different contributions to the network but HRNet fuses them without distinction, a novel weighted feature fusion HRNet is designed to achieve higher detection precision. Our method achieves an average precision (AP) of 42.3% at 13.5 frames-per-second (FPS) (40.3% AP at 13.3 FPS for CenterNet-HG) on the MS-COCO benchmark.
Multiple Image Watermarking with Discrete Cosine Transform

Multiple image watermarking is an advanced concept of singular watermarking techniques.The existing singular digital image watermarking techniques cannot obtain all the design goals, such as imperceptibility, robustness, security, and capacity simultaneously with perfection.Hence, the multiple image watermarking technique is designed that embeds several watermarks into the same host image for conveying multiple information.This paper proposed a multiple image watermarking technique with Discrete Cosine Transform (DCT) for ensuring the low computational time for embedding, encryption, decryption as well as extraction of the watermark images.Here, DCT is used to ensure the visual quality of the host image, and a random binary matrix is used to improve the security of the digital image.We have given a basic framework for multiple image watermarking.Our experimental results show satisfactory performance.
Use of Computer Trainers for Teaching Management Manufacturing Personnel of Chemical Industries

The chapter describes the questions of developing the life cycle of intelligent computer trainers that allow teaching modern industrial equipment, training in the control of technological processes on the basis of the Industry 4.0 concept. The advantages of using computer trainers for teaching management manufacturing personnel consist of gaining experience and skills of behavior in emergency situations, a deep understanding of the cause and effect relationships in the facility, quick response to malfunctions, reducing the psychological overload, increasing confidence and self-solving problems of management. Increasing the professional level of the management manufacturing personnel of chemical enterprises allows improving the quality of products, reducing spoilage, and improving the environmental characteristics of industrial production of high-tech industries.