Reviewerly/exHarmony huggingface dataset:
Non-commutative propositional logic with short-circuit evaluation

Short-circuit evaluation denotes the semantics of propositional connectives in which the second argument is evaluated only if the first is insufficient to determine the value of the expression. Compound statements are evaluated from left to right. Short-circuit evaluation is widely used in programming, with negation and sequential conjunction and disjunction as primitives. We study the question of which laws axiomatise short-circuit evaluation. In MSCL (memorising short-circuit logic), atoms (propositional variables) evaluate to true or false, and in the evaluation of a compound statement, the first evaluation result of each atom is memorised. Hence, MSCL is 'Non-commutative propositional logic with short-circuit evaluation' and atomic evaluations cannot cause a side effect. Next, we consider the case that atoms can also evaluate to the truth value 'undefined'. For two- and three-valued MSCL, we present evaluation trees as an intuitive semantics and provide complete independent equational axiomatisations.
Research on Virtual Face Mapping Method Based on Expression Classification

This paper proposes a virtual face mapping method based on facial expression classification. Accord to that face data collected by the camera, CNN is adopt to classify the collected faces, and the classification result is expressed in the form of probability, that is, the expression classification ratio obtain by CNN network. At the same time, 3D_max is used to reconstruct the 3D model of facial expression, and the offset matrix is weighted and summed to output the virtual face model according to the classification scale, thus constructing a face mapping model based on facial expression classification. Experimental results show that the virtual face has a strong sense of reality.
Spectrum Analysis of 2.4 GHz Band Based on Successive Signal Detection

The high cost of cellular spectrum has motivated network providers to extend LTE to unlicensed band. The 2.4 GHz band is an attractive choice since it provides higher penetration and coverage. However, operating in 2.4 GHz band inevitably suffers serious interference from heterogeneous network devices. We in this paper propose a 2.4 GHz band spectrum analysis method, which is the important first step to build reliable connections among LTE devices in the 2.4 GHz band. Our method involves two stages. It first locates and captures the interference signal on the time-frequency spectrogram with a region growing-based image segmentation method. With the extracted signal, it then identifies the type of interference technology based on Convolutional Neural Networks (CNN). The obtained information (i.e., the type and the time-frequency position of the interference on the spectrogram) provided the base for the interference avoidance and resistance methods. Experimental evaluation shows that the proposed spectrum analysis system can locate low-power (1.09 dB) signal with high accuracy and achieves a classification accuracy of 96.15%.
Classification of Plasmodium Skizon and Gametocytes Malaria Images Using Deep Learning

Identification analysis of the malaria parasite cell infection, there is a possibility of human error factor done by paramedics because of the number of samples that must be analyzed. This case is because the human eye tends to be tired while working continuously, which can lead to misclassification and treatment that is not right. Therefore, it takes a computer-based system that facilitates medical expert or laboratory technician in identifying two types of parasite cells namely Plasmodium skizon and Plasmodium gametocytes to reduce instances of human error. This research will be conducted on computer-based identification by processing the image type of plasmodium malariae consists of two types, namely Plasmodium skizon and Plasmodium gametocytes levels using convolutional neural network with VGG-16 pre-trained model using 13 layers and 2 dense layers. This study applied 5-fold cross validation for datasets and the datasets are tested using 4 level epoch nodes. The results showed the success of the classification results which have highest training accuracy 90&#x0025; as well as the results of the highest testing accuracy 100&#x0025;&#x2022; It showed the classification using CNN VGG-16 pre-trained model successfully classified the malaria type images.
On Multi-vector Education in an Educational Institution of Cooperation

The purpose of the research is to establish approaches to identifying the connection between the education of a participant in the cooperative movement and patriotic education in the conditions of the educational organization of cooperation and the economic activity of the existing cooperative. Based on the dialectical methodological approach, the authors use the empirical method of observation, the theoretical method of comparative analysis and the historical method to analyze possible problems and prospects of patriotic education and education of students as future participants of the cooperative movement, which promote and ensure the sustainable integrated development of rural areas and effective economic activity of consumer cooperation.
Cyber Warfare Integration to Conventional Combat Modeling: A Bayesian Framework

Malware propagation is capable of introducing adverse effects in military operations since today&#x2019;s high-tech military weapons and command, control, and communication systems are essentially interconnected ICT systems. Conventional warfare models have been recently upgraded to include cyber warfare elements to reflect the potential influence of cyber-attacks. In the present study, we propose and evaluate a framework for modeling the integrated effect of the spread of malware through computer networks and kinetic warfare. We employ a probabilistic learning approach, namely Bayesian Network analysis, to develop integrated combat models that characterize the spread of malware. The overall aim of the models is to predict which side will likely have the superiority at the end of the war, given the parameters that address the kinetic and cyber-effect influences of the fighting forces at the beginning.
Supporting Music Arrangement, Ringer Assignment, and Score Preparation for Musicbell Ensembles

The authors focus on arrangement and practice support of musicbell ensembles. The repertories for musicbells often have to be arranged by non-expert directors. The authors developed a support software which reduces trial-and-errors and saves time in the arrangement work. The software can also generate scores for ringers who are not familiar with music. The authors were able to make a score which musicbell ensembles could perform, with the support of the software. And it managed to be almost the same as the score arranged by an expert at which the authors aimed.
Strategy Direction of the Quality Assurance System of Metrology according to the Change of Sensor and Measurement Technology

Sensors and measurements are the most basic means of collecting physical data, and the measurement results should be trusted and utilized. The management system of the future measurement network based on the next-generation sensor has limitations in responding to the current quality assurance system due to changes in the use of sensors, an increase in observation points, and changes in the method of using measurement results. Due to this reason, the measurement strategy direction should be investigated to ensure the reliability of measurement data. The basic approach can be summarized as follows: change in the viewpoint from hardware devices to measured data themselves, openness to novel type of devices, and removal of uncertainties by the process of sharing the basis of reliability through clear and efficient information exchange. In principle, the direction should be targeted for novel sensors to remove the barriers to enter the industry and market, based on the continuity with the current measurement standard system and traceability to the International System of Units.
Azure Virtual Machine Security

Many systems use virtual machines-based solution architectures in the cloud to deploy the application workloads. Because Azure provides several ready-made virtual machine templates, such solutions in Azure are widely popular. However, deploying your application workloads into virtual machines requires you to manage security aspects by yourself to a greater extent than using PaaS services.
Evidence-Based Virtual Reality Use for Mental Health Conditions

The advancements in immersive technologies have impacted various sectors, with mental healthcare being one of them. The subsequent interaction between immersive technologies, particularly virtual reality and mental health, has created interesting effects that call for a closer look. This chapter intends to provide a comprehensive picture of mental health conditions, namely anxiety and related disorders, post-traumatic stress disorder, and major depressive disorder, as tackled by VR-based therapy. The focus is on its effectiveness and how the results compare to the traditional modes of treatment in terms of efficacy. The impact of user experience towards this approach of intervention and the importance of ethical consideration when VR intersects with the field of mental health are addressed.
Design of Blockchain Implementation for Supervision of Vaccine Distribution: Indonesia Case

Vaccination is still one of the most reliable ways to deal with pandemic outbreak. The vaccine distribution process involves a lot of data from various parties, which must be maintained its validity and security to provide a basis for making the right public health decisions. Blockchain technology can be a solution to secure data and build trust between parties because data do not only hold on one party but distributed to all parties with cryptographic security. This study aims to design an architectural model of blockchain system for supervision of vaccine distribution by taking Indonesia as the case of implementation. It uses analytical study to identify the possible problems and the stakeholders involved. As the result, it proposed the type of blockchain and the architecture that are most suitable for use in the supervision system. By making valid data readily available to policy and decision makers, it can facilitate evidence-based decisions on public health.
The System of Extracurricular Activities on the Example of Primary General Education

The chapter summarizes the experience of studying the historical stages of the formation of extracurricular activities in education, and gives its characteristics. The elements of extracurricular activities, the mechanism of their implementation on the basis of the current regulatory framework are systematized. The assessment of the importance of extracurricular activities of primary general education is given. The author's development of the program of extracurricular activities of the general cultural direction «Magic needle» is given.
An Innovative Approach for Cloud-Based Web Dev App Migration

In computing, process management is a key challenge where the process is shifted from one node to another. In the distributed computing architecture, this is an important activity as all the running processes are managed and distributed among available resources. Hence, resource utilization will be done efficiently. Like in multicore machines, process migration happens based on schedule, and it is normally done since the migration happens within a system. In this paper, we aim to implement the process migration in a distributed system using CRIU and DMTCP, and also, we compare the efficiency of process migration based on time. Process migration is a mechanism of transferring a process’s state from one system to another for the process to execute on the target system. Checkpoint and restart are multiplatform techniques for increasing performance. It is mainly used in Linux-based operating system. Using the concept of checkpoint–restart in userspace (CRIU), many running processes will be migrated. To indicate the state of the art in checkpoint–restart, we surveyed existing implementations. This paper presents the results of this survey and understanding the migration concept for our further works.
Histogram Analysis with MATLAB Software for Motorcycle Image Processing

Quality of image was differentiated via histogram of each image presented. Motorcycle image was captured in different lighting conditions. MATLAB software was used to verify number of pixels calculated and tabulated in histogram equalization. Results shows daylight conditions pixel tabulated uniformly for all zones in histogram compared to rainy daylight and night conditions. Image quality enhanced since number of pixels increased at all tonal variations.
On the Conceptual Optimization of Generalized Net Models

Andonov, Velin Poryazov, Stoyan Saranova, EmiliyaThe problem of conceptual optimization of Generalized Nets (GNs) models is discussed. An overview of some operators for complexity of GNs and relations with respect to them is presented. Some new operators and relations are defined. The proposed approach to the conceptual optimization of GN models is applied to a GN model of a queuing system with finite capacity of the buffer and server, and FIFO discipline of service of the requests.
Solution of the Generalized Dual Problem in the Network Programming Method

The network programming method is based on the ability to represent the objective function and the system of constraints in the form of a superposition of simpler problems. It is convenient to represent such a superposition in the form of a network, the final vertices of which correspond to variables, and the remaining vertices correspond to simpler problems included in the superposition. The solution to the problem at the input vertex gives either an estimate (upper or lower) for the original problem, or an exact solution if the network representation structure is a tree.The article discusses the application of the method for an integer linear programming problem. To do this, the coefficients of the objective function are divided arbitrarily into m parts (according to the number of problem constraints). We get m tasks (each with one constraint). These tasks are called evaluative tasks. According to the main theorem of the network programming method, the sum of the values of the objective functions in the optimal solutions of estimation problems gives the upper estimate for the solution of maximum problems (or lower for the solution of minimum problems) for the original problem. The problem of choosing a partition of the coefficients of the objective function of the original problem so that the upper estimate is minimal is called the generalized dual problem (GDV). Note that the Lagrange minimization problem in the Lagrange multiplier method is a special case of the GDV. I.V. Burkova proved that GDV is a convex programming problem.The article proposes a method for solving the GDV based on the sequential improvement of the initial solution. At each step of the method, the system of linear inequalities is solved.
An Edge Feature Extraction Method for Remote Sensing Image Edge Based on Generative Adversarial Strategy

Affected by changes in illumination, weather, and surface activities, multi-temporal remote sensing image data changes slowly all the time. How to extract edge feature is a key common problem that needs to be resolved in the field of remote sensing image intelligent processing. This paper proposes an edge feature extraction method for remote sensing image edges based on a generative confrontation strategy. This method first designs a CycleGAN network based on the Smooth L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss function to meet the requirements of robust feature extraction for edges with semantic information in remote sensing images. The network transfers the style of remote sensing images, and finally extracts edge feature for the images after the style transfer, which is used as the description of the robust features on the surface. Experimental results show that the edge extraction model designed in this paper improves the accuracy of robust feature extraction of remote sensing images by 8.1%. In addition, the method is less affected by environmental factors such as illumination, and can realize the intelligent extraction of robust features of the remote sensing image. The relevant code of this article has been published on the research group's home page.
Variational Induction

In this Chapter, a distinction between enumerative, eliminative and variational induction is elaborated. Enumerative induction infers causal relationships from observed regularities, i.e. from the repetition of instances. Eliminative induction proceeds by eliminating hypotheses from a given exhaustive and mutually exclusive set. Variational induction infers causal relationships from systematically varying the circumstances of the examined phenomenon and examining the corresponding impact on the phenomenon. The quintessential method of the latter is the method of difference. In part due to the general skepticism concerning inductivist approaches, variational induction has largely been neglected in twentieth century philosophy of science. However, variational induction constitutes the central methodology of phenomenological science. Also, most, if not all, data science and machine learning algorithms rely on variational induction.
$$4 \times 4$$ Recursive MDS Matrices Effective for Implementation from Reed-Solomon Code over $${\varvec{GF}}\left( {\varvec{q}} \right)$$ Field

Maximum Distance Separable (MDS) matrices have applications not only in code theory but also in the design of block ciphers and hash functions. However, MDS matrices often cause large overhead in hardware/software implementations. Recursive MDS matrices allow this problem to be solved because they can be powers of a very sparse Serial matrix, and thus can be suitable even for limited, constrained environments. In this paper, 4 × 4 recursive MDS matrices effective for implementation from Reed-Solomon code over GF(q) field will be shown. These matrices are very effective for implementation and can be applied in lightweight cryptography.
Empirical parameter tuning of task-based tiled QR factorization using quantum annealing

Task-based applications are useful for recent highly parallel architectures. A typical example of these applications is a tiled algorithm for matrix factorization. Tile size is a performance parameter in the tiled matrix factorization algorithm, and its tuning is critical to the performance. There are two types of tuning methods, model-driven approaches and empirical approaches, with the latter being successful for task-based applications. In the present paper, tile size tuning is performed using an empirical approach. The major difference from existing tuning methods is the use of the quantum annealing algorithm. The tuning results are compared with the results obtained through existing methods.
Modeling serially correlated heavy-tailed data with some missing response values using stochastic EM algorithm

The linear regression model is a popular tool used by almost all in different areas of research. The model relies mainly on the assumption of uncorrelated errors from a Gaussian distribution. However, many datasets in practice violate this basic assumption, making inference in such cases invalid. Therefore, the linear regression model with structured errors driven by heavy-tailed innovations are preferred in practice. Another issue that occur frequently with real-life data is missing values, due to some reasons such as system breakdown and labor unrest. Despite the challenge these two issues pose to practitioners, there is scarcity of literature where they have jointly been studied. Hence, this article considers these two issues jointly, for the first time, and develops an efficient parameter estimation procedure for Student’s-t autoregressive regression model for time series with missing values of the response variable. The procedure is based on a stochastic approximation expectation–maximization algorithm coupled with a Markov chain Monte Carlo technique. The procedure gives efficient closed-form expressions for the parameters of the model, which are very easy to compute. Simulations and real-life data analysis show that the method is efficient for use with incomplete time series data.
HANS ABRAHAMSEN AND A RECURRENT ‘CHILDLIKE’ RHYTHM

Abstract Hans Abrahamsen has reused the same rhythm across four pieces spanning 33 years: in his Ten Studies , for solo piano, and Six Pieces , for horn trio (both from 1984), in Schnee (2008) and in Three Pieces for Orchestra (2017). Because self-borrowing is crucial to Abrahamsen's compositional practice, this rhythm provides a case study in his compositional priorities, particularly in the role canonic techniques play in his music. Although the rhythm's formal properties lend it a marked asymmetry at the foreground, it is presented in Schnee as part of a canon with highly symmetric pitch materials. But despite this apparent conflict between symmetries and asymmetries, Abrahamsen's music freely combines different approaches to the rhythm, as long as it is linked to a high-register shimmer, suggesting that Abrahamsen's noted uses of canons are largely for textural ends.
VLSI implementation of Digital Watermarking Technique for security and authentication of Digital Data

Nowadays it’s very easy to tamper with any image and make it available to whole world. As digital image manipulation software are at readily accessible on each and every desktop. Ensuring digital image integrity and image owner safety has become a major issue. This is probably better solution (digital watermarking technique) which is available for this issue to prevent and protect from tampering of image and objects. As an advance to the existing various methods of digital watermarking techniques. In this approach ato watermark the image in the digital camera we can integrate the watermarking chip within framework, to demonstrate that this chip can embedded the watermark directly at the stage of capturing process itself which acts as a real time insertion and reduces the time consuming process of externally adding the watermark to the original image or video after capturing it. Illegal recovering and transmission of multimedia data can be protected via watermark or digital signature or copyright label that authenticates the data. This approach extended the opportunity of protecting the content after the release of content to the open environment.
A New Method for Arabic Text Detection in Natural Scene Images

Text in scene images can provide useful and vital information for content-based image analysis. Therefore, text detection and script identification in images are an important task. In this paper, we propose a new method for text detection in natural scene images, particularly for Arabic text, based on a bottom-up approach where four principal steps can be highlighted. The detection of extremely stable and homogeneous regions of interest (ROIs) is based on the Color Stability and Homogeneity Regions (CSHR) proposed technique. These regions are then labeled as textual or non-textual ROI. This identification is based on a structural approach. The textual ROIs are grouped to constitute zones according to spatial relations between them. Finally, the textual or non-textual nature of the constituted zones is refined. This last identification is based on handcrafted features and on features built from a Convolutional Neural Network (CNN) after learning. The proposed method was evaluated on the databases used for text detection in natural scene images: the competitions organized in 2017 edition of the International Conference on Document Analysis and Recognition (ICDAR2017), the Urdu-text database and our Natural Scene Image Database for Arabic Text detection (NSIDAT) database. The obtained experimental results seem to be interesting.