Reviewerly/exHarmony huggingface dataset:
Discrete Cuckoo Search Algorithm for Tour Recommendation System

This paper presents route planning for a tourist as tourist trip design problem (TTDP) using discrete cuckoo search algorithm (DCS). The DCS algorithm is based on brood parasitic nature of cuckoo bird which is characterized by levy flight. The experiment of proposed DCS is conducted on orienteering problem (OP) benchmark instances and their performance compared with existence data of iterative local search (ILS) and greedy randomized adaptive search procedure (GRASP). Result showed that proposed DCS is required less computational time as compared to ILS and GRASP. It concluded that DCS is efficient method for exploring and exploiting search space in reasonable amount of time.
Mechanical incrementalization of typing algorithms

The ever-growing size of programs and their continuous evolution require building fast and efficient analyzers. Here we focus on the static ones, in particular on type systems, for both checking and inference. Just as programs change by incrementally changing or inserting pieces of code, called diffs, also type systems should be incremental and re-type the diffs, only. An algorithmic schema is proposed that mechanically derives an incremental version of existing, standard typing algorithms. Ours is a grey-box approach: just the shape of the typing rules, that of the types and some domain-specific knowledge are needed to instantiate our schema. Here, we present the foundations of our approach and the conditions for its correctness. Our schema is applied to derive four incremental typing and inference algorithms for languages in different programming paradigms. We implemented an OCaml module that inputs a type system and outputs its incrementalized version. Experimental results show that our approach is effective, and prove its usage beneficial.
Music – Number – Word. History and Theory of Connections

The purpose of the article is to analyze the history of connections of music with the world of numbers, on the one hand, and the world of words and verbal utterance, on the other. Number and word, usually considered to be drastically opposed in relation to music, in the doctrine of the Russian philosopher of the 20th century Alexei Losev became the components of an integral system. The novelty of the article is in the conclusions made from the analyzed musical-philosophical texts. Namely, Losev’s doctrine of music as an expression of becoming of number, in integrity with his doctrine of musical myth, synthesized the centuries-old evolution of musical theory and provided an optimal methodology for studying musical meaning, thereby opening the way to understanding the latest forms of music. Inspired by philosophical texts on this issue, an interdisciplinary approach seems to be of value on the way towards identification the specifics of musical thinking. The conclusions of the article concern new ways of studying the musical meaning - both in general and in individual musical texts.
Computing Mortality for ICU Patients Using Cloud Based Data

Computing Mortality for ICU patients, who are in critical conditions and in need of extra intensive care has been a major problem. The focus of this work is to predict patient’s health mortality through health record data from ICU Mortality Prediction Challenged. Data are taken from the first 24 h to figure out the in-hospital death by using few models from machine learning. Here, in this health record-based work, personal health information particularly for ICU patients are recorded and observed by the physicians. These methods are cost-effective, reliable, easily accessible, and are maintained in a Cloud platform to increase the quality of service. We have taken 6 general descriptors recorded at the time of admission to a particular unit ward and other different time-series measurements collected during the first 24 h. This chapter focuses on predicting the mortality of ICU patients by checking their health-care data. We have used online mode that can be access by the physicians, patients, and other staff members easily. Therefore, it has the considerable potential to provide an accurate result with a simple and easily accessible mode. As there is less available research works on ICU patients with Cloud Computing. That’s why, our approach has the potential to reach the prediction of mortality for in-hospital ICU patients using machine learning.
Analysis on the Application of Big Data Technology in “Internet + Wisdom Judicial Expertise”

It can be seen from the current development process of the field of forensic expertise that there are many problems in the application of the mechanism of the information structure. These problems lead to imperfect informatization construction process and too limited identification forms, which will have a great impact on the final identification results. This article summarizes the main content of big data technology in the construction of “Internet + Wisdom Judicial Expertise”. This article discusses the specific application recommendations of big data technology in the “Internet + smart forensic appraisal”, from focusing on forensic appraisal data collection and sorting, understanding forensic appraisal big data application requirements, building forensic appraisal big data analysis platform, and strengthening big data analysis platform resource network sharing four aspects.
Fire Equipment Information Traceability System based on Blockchain

To solve the problems of centralization, tampering, incomplete storage, and privacy of fire fighting equipment traceability system, this paper proposes a fire fighting equipment information traceability system based on blockchain. The system is developed on the Fabric blockchain platform of Hyperledger. The system environment is equipped with three organizations: manufacturer, dealer, and consumers, and the query request is initiated with the traceability function of fire fighting equipment in the chaincode. Finally, through the certificate authentication user account can realize the fire fighting equipment information inquiry, and the query response time average value is 19.5 ms. The characteristics of blockchain are difficult to tamper with, timestamp and transaction traceability, which can be well applied to the traceability system of fire fighting equipment, which makes the traceability function of the system more perfect, and consumers can get all traceability information, including production information, logistics information and usage information of fire fighting equipment.
Analysis of User-Oriented Network Operation and Maintenance Management

With the improvement of information and communication infrastructure construction, the competition in the telecommunications market from Blue Ocean to Red Sea is becoming increasingly fierce. In this new era, differentiated user experience has become the focus of competition among providers. The concept of user-oriented network operation and maintenance management is thus deeply rooted in people's hearts. In this paper, the development of network operation and maintenance management concept is first summarized. The main factors affecting user satisfaction are then analyzed. Furthermore, the process of providers from perceiving user service expectations to providing corresponding services is studied, and the gap of service quality in different stages of operation and maintenance management is discussed. In the end, methods are proposed to improve user satisfaction.
Comparative Analysis of Approaches to Developing Emotions with the Infrared Camera

This paper presents a new approach to the automatic recognition of micro-expressions. The decision to recognize emotions is implemented using two cameras: an infrared camera and a web camera, as well as other add-ons to increase the accuracy of the result. Using a non-synthetic database that contains faces marked by basic facial points and emotions - "AffectNet". On the basis of it, the main ensemble model was trained. The study identifies the main three stages: highlighting the face, setting effective points, determining emotions. Our solution was tested on live photos and user videos that are publicly available on social networks.
Enhancement of VerticalThings DSL with Learnable Features

Resource-efficient ML for edge and endpoint IoT devices is a field of active research and increasing development. Libraries have been providing support for machine learning enthusiasts to run ML algorithms in the cloud. Executing ML algorithms on motes is a challenge, as resources are highly constrained. To optimally use these resources, the developer often needs to have complete knowledge of the underlying architecture. VerticalThings is a domain-specific language (DSL) developed for programming ML-based embedded applications. The language offers constructs for key platform functions such as resource management, concurrency, task isolation, and security. This enables static analysis of (a) important safety and security properties, and (b) timing and power considerations. To enhance this DSL further, we developed a DSL named FieryIce which provides intelligent learning of parameters based on sensor data. We would integrate both the DSLs within the IDE developed for VerticalThings. The learnable parameters are learnt at compile time avoiding the use of scarce memory of the embedded systems. This paper shows the capabilities of a domain-specific language (DSL) named FieryIce which is designed to help embedded developers use VerticalThings and develop ML-based embedded applications with ease. The contributions of this article are: (a) A domain-specific language (DSL) named FieryIce and it is capabilities to perform machine learning-related tasks; (b) how FieryIce helps students better their understanding of machine learning algorithms.
Efektifitas Kompres Dingin dan Teknik Relaksasi Nafas Dalam untuk Skala Nyeri Pasien Saat Kanulasi (Inlet Akses Femoral) di Unit Hemodialisa RSUD Cilacap

Hemodialysis cannulation action will respond to discomfort due to stimulation of a large needle puncture. The puncture stimulates sensory nerve fibers which cause pain. This study aims to determine the differences in the effectiveness of cold compresses and deep breath relaxation techniques to reduce patient pain during hemodialysis cannulation (femoral access inlet) in the Hemodialysis Unit of the Cilacap Hospital. This type of research is a quasy experimental study with a static group comparison design of 12 people who received cold compresses and 12 people who received deep breath relaxation techniques that were taken using the total sampling method. Data analysis using t-test. The results showed that there was a significant difference in the pain scale of patients during cannulation (inlet femoral access) hemodialysis before and after being given cold compresses (pv = 0.001, α = 0.05), there was a significant difference in the pain scale of patient during cannulation (inlet femoral access) hemodialysis before and after being a deep breathing relactation technique (pv = 0.002, α = 0.05), there was no significant difference between the patient’s pain scale during cannulation (femoral access inlet) hemodialysis after being given cold compresses and after being given deep breathing relaxation techniques (pv = 0,081, a = 0,05).
Image scene classification based on discriminative clustering and weighted fusion representation

In this paper, a new discriminative clustering method was proposed. For each traversing sliding block in the training samples, the deep features were extracted and their correlation distance matrices were obtained. The cluster was obtained by traversing the correlation distance matrix in turn from the minimum value, and the average value of each cluster was taken as the clustering center to construct the visual sub-codebook of each image category. The visual sub-codebooks of all the image categories were concatenated to construct the universal visual codebook. After the image segmentation, the local description of the image was obtained based on the universal visual codebook. The integrated image representation has richer semantics information than the single local or global description of an image. To obtain a more accurate image representation, in this paper, we weighted and fused the local information and the global information to form the final image representation. In the experiment, classifications were performed on two common scene datasets. The results showed that the image fusion representation of local and global information achieved better performance of classification.
Research of Neural Networks Efficiency in Video Generation Problem

This paper describes the research of video generation using neural networks. The main ideas of several existing approaches based on the generative adversarial networks are presented and their characteristics are compared. During this research, vid2vid approach was selected and its effectiveness was examined. The process of collecting the corresponding dataset is described. Influence of various factors, such as training and segmentation parameters, on the model's effectiveness was studied during the experiments and obtained results were analyzed. The conclusions are presented and the directions of further research are formulated.
Development of cloud recognition system for ground-based cloud images based on machine vision

Clouds are the core of weather observation and forecasting. They help promote the water cycle and the entire climate system. Accurate and automatic recognition of cloud types is one of the important applied researches to improve the accuracy of weather forecasts. At present, the recognition of the cloud shape of ground-based cloud images is a major and difficult problem in realizing the automation of ground cloud measurement instruments. Therefore, this paper builds a neural network framework training model based on wireless sensors and Python+TensorFlow, and develops a ground-based cloud image cloud recognition system. After experiments, the system can run successfully, and the experiments on the cloud map data set published by Guanyun Zhitian show that the recognition accuracy of the system can reach about 87%.
The J-Orthogonal Square-Root Fifth-Degree Cubature Kalman Filtering Method for Nonlinear Stochastic Systems

This paper addresses the issue of square-rooting in the Fifth-Degree Cubature Kalman Filtering (5D-CKF) method grounded in the Itô-Taylor approximation of order 1.5 and designed by Santos-Diaz, Haykin and Hurd in 2018. That filter is rather accurate and efficient in treating nonlinear continuous-discrete stochastic systems of practical value and shown to outperform many other algorithms in a radar tacking scenario. However, the cited authors mention “the lack of a square-root implementation” of the filter under consideration as a principle shortcoming reducing its applied potential. Here, we address the reported lack and resolve it with a hyperbolic QR factorization used for devising the filter’s J-orthogonal square-root version, which possesses an exceptional robustness to round-off and other disturbances. Our square-root implementation of the 5D-CKF technique is justified theoretically and examined and compared numerically to its non-square-root predecessor in a flight control scenario with ill-conditioned measurements.
Multi-modal Multi-scale Attention Guidance in Cyber-Physical Environments

This work proposes a new method for guiding a user's attention towards objects of interest in a cyber-physical environment (CPE). CPEs are environments that contain several computing systems that interact with each other and with the physical world. These environments contain several sensors (cameras, eye trackers, etc.) and output devices (lamps, screens, speakers, etc.). These devices can be used to first track the user's position, orientation, and focus of attention to then find the most suitable output device to guide the user's attention towards a target object. We argue that the most suitable device in this context is the one that attracts attention closest to the target and is salient enough to capture the user's attention. The method is implemented as a function which estimates the "closeness" and "salience" of each visual and auditive output device in the environment. Some parameters of this method are then evaluated through a user study in the context of a virtual reality supermarket. The results show that multi-modal guidance can lead to better guiding performance. However, this depends on the set parameters.
Social Requirements Models for Services

Social dependance relationships were used in the i* requirements modelling language to represent dependencies among social actors. We study the evolution of the notion of social dependency into that of commitment in the Azzurra specification language for business processes, and then into the notions of obligation and power in the Symboleo specification language for legal contracts. Our account focuses on the difference in the semantics of these relationships, the language used to talk about them, and how appropriate they are for capturing requirements for services.
Blockchain-Enabled Secure Electronic Voting System in India

India has always been the largest democratic country all over the world, and voting is prominent in Indian democracy. Election plays an important role in a modern democracy, and electronic voting machine (EVM) is a medium of conducting elections. EVM is the combination of software- and hardware-based program, which is controlled by an organization. Security issues, vote manipulation and voter integrity are some major issues in the current EVM-based system. It is difficult to rely on such programs when voting is concerned. Through this paper, a secure voting system is being made blockchain technology. This paper starts with the traditional voting system and then moves forward to understand the advanced system with blockchain technology in a real-case scenario with its features such as security and peer-to-peer network. The implementation of blockchain-enabled voting application with existing frameworks such as Ethereum, which improves the security of votes, makes elections transparent and maintains the integrity of voters.
Prediction of Junior High School National Exam Results Based on Academic Report Using K-Nearest Neighbor

The National Examination is a mechanism adopted by the Government of the Republic of Indonesia to evaluate the performance of the learning process at every level of education. This study sees the decline in national exam scores for junior high school students that occurred during 2016 to 2018 as a problem that needs to be resolved. The k-Nearest Neighbor method which is applied to the report card scores is used to predict the achievement of student performance in the national exam from the four subjects tested. The dataset containing 307 instances resulted from the acquisition of primary data from a state Junior High School in Malang, Indonesia. Two scenarios, one of which involved SMOTE resampling, were used in the performance comparison study. The results showed that the best performance was generated by a scenario involving k-Nearest Neighbor as the classifier, combined with SMOTE preprocessing. The best performance of national exam predictions can be seen in English, with an accuracy of 81.16%.
Research of the Efficiency of Gender and Age Recognition According to an X-ray Image Based on a Neural Network

This article examines the effectiveness of gender and age recognition using a neural network. The search and analysis of existing methods of image recognition and selection of characteristics from them are performed, and similar studies are described. The description of the used model, the principle of its operation, training parameters, and the reason for choosing this model is produced. An experiment is being conducted on the effectiveness of gender and age recognition based on chest x-rays. To conduct the experiment, a neural network model is created based on the existing one. The obtained results are analyzed. Conclusions are drawn on the experiment and the possibility of further research.
Research on Thrust Allocation Method of Semi-submersible Drilling Platform Based on Improved Cuckoo Search Algorithm

The thrust allocation problem of the dynamic positioning system for semi-submersible drilling platform can be modeled as a nonlinear optimization problem. Because this problem is non-convex and has a lot of parameters, it is suitable to be solved by using swarm intelligence algorithm. Based on the characteristics of the thrust allocation problem, this paper optimizes the "discovery" parameter of the cuckoo search algorithm and uses the improved cuckoo search algorithm to solve the thrust allocation problem of the semi-submersible drilling platform. The results show that compared with genetic algorithm and cuckoo search algorithm, the improved cuckoo search algorithm has faster convergence speed and higher convergence accuracy.
Restyling Images with the Bangladeshi Paintings Using Neural Style Transfer: A Comprehensive Experiment, Evaluation, and Human Perspective

In today's world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the artwork or painting of Bangladeshi painters. Bangladeshi painting has a long history of more than two thousand years and is still being practiced by Bangladeshi painters. This study generates NST stylized image on Bangladeshi paintings and analyzes the human point of view regarding the aesthetic preference of NST on Bangladeshi paintings. To assure our study's acceptance, we performed qualitative human evaluations on generated stylized images by 60 individual humans of different age and gender groups. We have explained how NST works for Bangladeshi paintings and assess NST algorithms, both qualitatively & quantitatively. Our study acts as a pre-requisite for the impact of NST stylized image using Bangladeshi paintings on mobile UI/GUI and material translation from the human perspective. We hope that this study will encourage new collaborations to create more NST related studies and expand the use of Bangladeshi artworks.
Brittle Features of Device Authentication

Authenticating a networked device relies on identifying its unique characteristics. Recent device fingerprinting proposals demonstrate that device activity, such as network traffic, can be used to extract features which identify devices using machine learning (ML). However, there has been little work examining how adversarial machine learning can compromise these schemes. In this work, we show two efficient attacks against three ML-based device authentication (MDA) systems. One of the attacks is an adaptation of an existing gradient-estimation-based attack to the MDA setting; the second uses a fuzzing-based approach. We find that the MDA systems use brittle features for device identification and hence, can be reliably fooled with only 30 to 80 failed authentication attempts. However, selecting features that are robust against adversarial attack is challenging, as indicators such as information gain are not reflective of the features that adversaries most profitably attack. We demonstrate that it is possible to defend MDA systems which rely on neural networks, and in the general case, offer targeted advice for designing more robust MDA systems.