Reviewerly/exHarmony huggingface dataset:
A Formal Process Virtual Machine for EOS-Based Smart Contract Security Verification

With the rapid development of blockchain technology, the reliability and security of blockchain smart contracts is one of the most emerging issues of greatest interest for researchers. In this paper, the framework of formal symbolic process virtual machine, called FSPVM-EOS, is presented to certify the reliability and security of EOS-based smart contracts in Coq proof assistant. The fundamental theoretical concepts of FSPVM-EOS are abstract formal symbolic process virtual machine and execution-verification isomorphism. The current version of FSPVM-EOS is constructed on a formal virtual memory model with multiple-level table structure, a formal intermediate specification language, which is a large subset of the C ++ programming language based on generalized algebraic datatypes, and the corresponding formal definitional interpreter. This framework can automatically execute the smart contract programs of EOS blockchain and simultaneously symbolically verify their reliability and security properties using Hoare logic in Coq.
GraphRedex: Look at your research

Abstract A significant aspect of designing new programming languages is to define their operational semantics. Working with a pen and paper version of such a semantics is notoriously difficult. For this reason, tools for computer aided semantics engineering were created. Many of these tools allow programmers to execute their language's operational semantics. An executable semantics makes it easier to verify whether the execution of a program leads to the desired result. When a program exhibits unexpected behavior, the programmer can consult the reduction graph to see what went wrong. Unfortunately, visualization of these graphs is currently not well‐supported by most tools. Consequently, the comprehension of errors remains challenging. In this article, we present GraphRedex an open‐source tool that empowers language designers to interactively explore their reduction graphs, offering three main benefits. First, a global exploration mode allows users to obtain a bird's‐eye overview of the reduction graph and learn its high level workings. Second, a local exploration mode lets the programmer closely interact with the individual reduction rules. Third, our query interface allows the programmer to filter out and highlight specific regions of the reduction graph. We evaluated our tool by carrying out a user study showing that participants comprehend programs on average twice as fast while being able to answer questions more accurately. Finally, we demonstrate how GraphRedex helps to understand the semantics of two published works. Exploration of the semantics with GraphRedex unveiled an issue in one of the implementations of these works, which the author confirmed.
FORMATION OF SELF-EDUCATIONAL COMPETENCE FUTURE EDUCATORS PRESCHOOL EDUCATION INSTITUTIONS

In the article self-education as a component of professional training acts as a way to implement it. In this context, the self-education of future educators, we mean the process that ensures the continuity of raising the educational level and expanding the information field of professionals, improving the quality and raising the level of their competence and professionalism, related to personal development and participation in general and pedagogical culture. Serves to form a worldview, because the student is the subject of management of their own educational activities during professional training. Based on the analysis of different approaches to the definition of "self-educational competence" in the context of this study, we consider this term as an integrative phenomenon characterized by stable internal motivation, the formation of professionally significant personal qualities, skills and abilities to organize cognitive personality. search activities aimed at a continuous process of professional development; and is an integral part of the professional competence of the specialist and an indicator of the success of his professional activity. Given the above, it is possible to state the close connection between the concept of "self-education" and the concept of "vocational training"; self-education should be understood as a way to implement this training, which, accordingly, aims to form a skilled worker capable of developing strategies for professional development as a result of self- education. This is one of the most important prerequisites for further professional realization of him as a specialist In this regard, higher education faces the task of developing more effective learning technologies and creating conditions that would be focused on stimulating students' self- education. The purpose of the article is to theoretically substantiate the key concepts "educator", "future educator", "self-education", "competence", "formation of self- educational competence". Methods: analysis, systematization and generalization of psychological and pedagogical literature in order to clarify the state of development of the research problem and clarify the conceptual apparatus.
How Weak Reference is Used in Java Projects?

Many programming languages have a system of garbage collection to automate memory management. Problems such as unexpected memory leak still may occur due to the automation of memory management. Weak reference has been proposed as a solution to such problems. However, the use of weak reference often requires consideration of memory release timing, which is difficult for developers. In this study, we investigate the use of weak reference on open source projects. More concretely, we investigated the domain of software projects where the weak reference was used, the timing of the introduction of the weak reference, the usage method, the presence or absence of the test code for weak reference, and the case of failure to introduce weak reference in the Java language. The survey showed that weak references were used in 73 repositories, about one-third of the total 202 repositories, and that weak references were most common in test code.
Mean Gradient Descent Algorithm for Single-Shot Interferogram Analysis

We present a new optimization approach of ‘Mean Gradient Descent (MGD)’ for single-shot interferogram analysis. In this methodology, the guess solution progresses iteratively with straightforward algebraic steps. MGD provides efficient complex-valued object recovery with full pixel resolution from an interferogram of step phase object and on-axis spherical reference beam. The MGD iteration does not require any empirical tuning of free parameters and computationally expensive operation of line search.
A Study of Multiparty Interactions in Continuation Semantics

We employ the mathematical methodology of metric semantics in designing the continuation-based denotational and operational semantics for two process calculi recently investigated in the literature based on well-known Milner's CCS. We prove that our denotational models are weakly abstract with respect to their corresponding operational models.
STFNet: Image Classification Model Based on Balanced Texture and Shape Features

This paper proposes a network model that classifies objects in images by reflecting texture and shape in a balanced way. In the case of the existing image classification model, objects are classified depending on the texture of the object. Because of this characteristic, if noise occurs in the image or the texture is intentionally changed, the image classification model is likely to misclassify the object. In addition, it can be deliberately misclassified by noise generated by hostile attack, and if it is used in industrial sites closely related to humans, a big problem may occur. To solve these problems, this paper proposes a method of extracting texture and shape features from the classification image and blending them appropriately to classify objects in the image appropriately. The results of comparing and evaluating the performance with the existing model and a new network model proposed in this paper for a general image, texture converted image, and an image with hostile attack noise added are as follows: Similar performance was obtained in the general images, but the accuracy improved by 54.64% and 69.02% for the image with textured and hostile attack noised added when using the newly proposed network model.
Enabling Continuous Drone Tracking Across Translational Scene Transitions through Frame-Stitching

This paper introduces a method for enabling the continuous tracking of drones across translational scene transitions through the introduction of a stationary global frame. The main benefit of such a method is that it allows current pan-tilt-zoom (PTZ) cameras commonly used in security applications to be used in the domain of drone tracking. The first step in the process is to correct for lens distortion with the intrinsic parameters of the camera obtained through calibration. The motion of the camera frame is then estimated using the optical flow of tracked features from frame to frame. Information obtained about the translational motion of the frame is then used to construct a global frame on which object tracking is performed. Results from the comparison of object detection and tracking performed on two clips with and without pre-processing to construct the global frame show an improvement in tracking performance when the technique was applied to the video. The method was shown to successfully compensate for the relative velocity of objects in the frame relative to the frame, with no sudden changes of velocity during the scene transition. In addition, the introduction of a global frame enabled the continuous tracking of a stationary drone that was reassigned to a new track in the original video.
Comparison of the simple additive weighting saw method and multi-objective optimization by ratio analysis MOORA

Abstract The manual assessment process is very likely to be at risk of errors and sometimes there is nepotism fraud and takes a long time due to the large number of employees. Currently, the East Jakarta Administration City Water Resources Water Resources Department does not yet have a special method and application that is used for evaluation and performance appraisal. Therefore, the purpose of this research is to build a decision support system using the simple additive weighting (SAW) method and multi-objective optimization by ratio analysis (MOORA). With the hope that it will make it easier to carry out the process of evaluating and appraising employee performance and can be carried out objectively with predetermined criteria.
Designing a Greek Electronic Dictionary based on Ontology

In this paper we examine the design of a conceptual dictionary of the modern Greek language with intelligent features, which will offer possibilities of semantic integration and interoperability in an automatic and secure way, connecting heterogeneous systems and approaches in the field of engineering and technologies under the light of a standard and standardized organization and coding of data that come from structured and semi-structured information sources.
Fischer-Ladner Closure for Many-Sorted Modal Logic with Application for Operational Semantics

In prior work we have developed a many-sorted polyadic modal logic. Progressively adding different operations and binders, we expressed operational semantics, thus enabling us to certify execution. In this paper we prove standard completeness for a many-sorted hybrid modal logic with satisfaction operators and PDL-inspired axioms. In order to do this, we define the Fischer-Ladner closure for our particular system and we prove a variant of the small model property.
Sensor-based mobile application for teaching physics to regular basic education students

In this work, we present the development of a mobile application that uses sensors to teach the physics course. We expect to improve portability and reduce complexity in experiments using services and standards. The application is based on design thinking and design material to standardize interaction. We think this is an outstanding improvement to previous efforts where the creation of technological educational materials are complex at high costs. This new alternative will motivate the use of mobile educational materials. So, educators can focus their efforts on creative tasks such as content design, instead of dealing with complex developments, seeking to complement learning, proposing innovation in the presentation of content through interactions with sensors, promoting learning outside the classroom, profoundly impacting on improving the quality of the educational content.
Constructing the Code Description Of the Transformant DCT Taking into Account their Structural Features

An approach to the construction of a code description of DCT transformants, taking into account the structural features of their binary representation, is presented. It is shown that the proposed model for constructing the code has the ability to reduce the initial number of bits, and also allows you to form a code description of the transformant by a set of independent code blocks. This makes it possible to use this coding method in a video bit rate control system.
Optimized PSO-EFA Algorithm for Energy Efficient Virtual Machine Migrations

The expansion of cloud infrastructure follows with increase in number of data centers hosting number of computing nodes and then, it becomes the reason for huge amount of energy consumption across the world. However, benefits of cloud computing industry with its low-price and high productivity keep diverting the attention of organizations from environmental mess and high energy cost incurred by the data centers. Therefore, it becomes very urgent to curtail the increase in requirement of energy for cloud service providers with the provision of sufficient quality of service to end users. The best way to achieve the balance between energy usage and quality of service is workload aware energy efficient Virtual Machine (VM) consolidation. The various parameters are managed to strike the trade-off between energy consumption and cloud services. This paper presents the optimized PSO-EFA algorithm for energy efficiency with workload management in terms of number of migrations and number of systems shut down during migration process of consolidation. This study paved the way forward for energy efficient cloud environment during migration process. The simulation conducted in constrained environment indicated that workload variation has significant impact on different energy consumption allied parameters. The PSO-EFA algorithm outperformed existing base algorithm for energy consumption and other parameters. The proposed algorithm worked in sync with sustainability efforts.
Automated Hardware and Neural Network Architecture co-design of FPGA accelerators using multi-objective Neural Architecture Search

State-of-the-art Neural Network Architectures (NNAs) are challenging to design and implement efficiently in hardware. In the past couple of years, this has led to an explosion in research and development of automatic Neural Architecture Search (NAS) tools. AutoML tools are now used to achieve state of the art NNA designs and attempt to optimize for hardware usage and design. Much of the recent research in the auto-design of NNAs has focused on convolution networks and image recognition, ignoring the fact that a significant part of the workload in data centers is general-purpose deep neural networks. In this work, we develop and test a general multilayer perceptron (MLP) flow that can take arbitrary datasets as input and automatically produce optimized NNAs and hardware designs. We test the flow on six benchmarks. Our results show we exceed the performance of currently published MLP accuracy results and are competitive with non-MLP based results. We compare general and common GPU architectures with our scalable FPGA design and show we can achieve higher efficiency and higher throughput (outputs per second) for the majority of datasets. Further insights into the design space for both accurate networks and high performing hardware shows the power of co-design by correlating accuracy versus throughput, network size versus accuracy, and scaling to high-performance devices.
Comparison of Deep Learning Models for Cancer Metastases Detection: An Experimental Study

Deep Learning (DL) models have shown to achieve remarkable results for classification, segmentation and detection tasks in medical image analysis. In this study, we experiment on Autoencoders (AEs), a Deep Learning model that aims to encode features from the input images into a sparse representation by reconstructing the input images. We experiment whether the sparse features from an Autoencoder be effectively used for the detection of metastases from breast histopathology patches when compared with a Convolutional Neural Network (CNN) for the same task. Two Autoencoder models are explored in this study. In order to make fair comparison, the architecture of the CNN is made similar to the encoder part of the AE models. All the models are trained using the breast cancer lymph node histopathology patches from the PatchCamelyon dataset. The features extracted from the AEs are passed to a Random Forest classifier. Experimental results show that for the given task, the CNN has an edge over extracting the features from the autoencoder and classifying it through the Random Forest classifier.
The LECTOR Podium. An Innovative Teacher Workstation for the Intelligent Classroom of the Future

This paper explores a new approach to a teacher's workstation in the context of the intelligent classroom of the 21st century. Nowadays, the term “intelligent” is not only associated with efforts to incorporate smart/mobile devices into the learning experience (distance learning, educational games/apps, etc.), but also to equip the physical environment of the classroom with technologically enhanced objects. These technologically augmented artefacts (Student Desk, Interactive Classroom Board and Educator's Workstation) are embedded discreetly in the classroom's environment. One of the main concerns in designing and developing such artefacts is to facilitate seamless interaction between educators and students, as well as to enable unobtrusive monitoring and supervision of the students by the educators. This paper presents LECTOR Podium, a system that liberates teachers from the confinement of a desk and introduces a flexible and empowering workstation in the form of a smart arm-chair. This arm-chair assumes the role of a control center, enabling the educator to monitor and operate every feature and artefact of the intelligent classroom.
Collaborator Recommendation Based on Dynamic Attribute Network Representation Learning

Scientific collaboration plays an important role in modern academic research. Collaborations between scholars will bring about high-quality papers and improve the academic influence of scholars. However, it is more and more difficult to find a suitable collaborator due to the rapid growth of academic data. There are already some recommendation systems based on calculating the similarity between scholars. But most of them do not consider the dynamic nature of the scientific collaboration network. To this end, we propose a collaborator recommendation algorithm based on dynamic attribute network representation learning (DANRL). It takes advantage of the network topology, scholar attributes and the dynamic nature of the network to represent scholars as low-dimensional vectors. By calculating the cosine similarity between scholar vectors, we can recommend the most similar collaborators to target scholars. Moreover, at each time step of the dynamic network, our method only needs to train embedding vectors for some selected nodes instead of performing random walks and training embedding vectors for all nodes, which can significantly improve the recommendation efficiency. Experiments on two real-world datasets show that DANRL outperforms several baseline methods.
Transformation of handwritten Devnagari script into word editable form using CNN

In India, many historical documents are preserved where handwritten Devnagari script has been used. With the advancement of time, the handwritten documents is in deforming styles and shapes. The handwritten OCR software that can transform this task with different accuracy rate. In 2015, this work was done for the printed character and provided the accuracy rate for Devanagari numerals of 89% with 4.5% of confusion rate whereas the recognition rate for English set was 78.4% with 18% of confusion rate. Although many work has been done towards HOCR but accuracy rate is 94% to 97%. My research work will be towards the transformation of handwritten Devanagari script into editable word form by using Convolutional Neural Network. For choosing this area for my research is because transformation of handwritten ancient characters is important as many important historical documents like manuscripts into machine editable form which can be easily preserved and accessed. With the help of strong and powerful dataset and combination of different neural network techniques, the accuracy rate can be increased and we can be able to preserve more historical documents for our access.
On the Extension of the Haar Cascade Algorithm for Face Recognition: Case Study and Results

In this paper, we build upon existing work and suggest an extension of the Haar Cascade face recognition algorithm in order to improve its accuracy and efficiency under varying lighting conditions. For doing so, we add a phase-preserving denoising method, which leads to a faster convergence of the classifier and improved face recognition efficiency. We evaluate the suggested approach by comparing the native Haar Cascade algorithm with our suggestion in terms of efficiency and accuracy within six different options for video filters, depending on varying lighting conditions i.e. broad daylight vs. dark environments. Analysis of results revealed that the suggested approach outperformed the native implementation in most evaluation scenarios and that the best filter options in broad daylight settings would be the No and the Gray filters, whereas in dark environments, the Gray and the Redish filters would be the best options. The Gray filter achieved top ratings in both lighting condition settings yet requires a higher computational cost for facial recognition.
Scale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter

Abstract Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.