Reviewerly/exHarmony huggingface dataset:
Blockchain and Smart-Contracts in terms of Economics

In this paper the author’s interpretation of essence and applicability of smart-contracts in economics. Examples are given along with critical view on wrong statements, found in publications on examined topic. Repeating of certain theses from similar papers won’t be redundant, because it will help to observe the topic from different point of view. In general, author gives positive valuation of applicability of smart-contracts technology in economics (both, financial and real sector, despite of some obvious inherent disadvantages of current implementation in Ethereum. In author’s opinion, integration of smartcontracts and blockchain with IoT can give synergetic effect and consider real cyber-social interaction. Certain value to this paper is added by author’s practical experience of coding and adopting smart-contracts. Smart-contracts are mentioned by Bank of Russia with implementation of CBDC (Central Bank Digital Currency) concept in the form of digital rubble and receive additional significance in current trend of dematerialization and virtualization of money. In paper essential terms and author’s point to ability to change contract terms after deploy are given.
Analysis on Various Approaches to Visualize and Interpret Convolution Neural Network

The paper aims to explore and evaluate the efficacy of methods that visualizes the internal structure of convolution neural networks as well as neural networks that are inherently interpretable. The paper implements three data set, including MNIST digits, MNIST Fashion, and CUB 200–2011. Tow base structures implemented to construct the convolution neural network are VGG 19 and a two-convolution layer CNN to provide intuitions upon analyzation. The paper toke the two approaches in analyzing the various methods: post hoc interpretation, which includes visualization of convolution kernels, and interpretable models, which includes a novel convolution neural network-based model. As for the outcomes, while the post hoc interpretations only offer superficial and unfaithful explanations to the CNN behavior, the use of the ProtoPNet in real-time interpretation provides an in-depth analysis of the model's reasoning process in decision making in contrast to the inexplicability of mainstream deep learning models.
Accelerating Data-Flow Analysis with Full-Partitioning

Data-flow analysis is a classical way to deal with program optimization and program analysis issues. However, the classical iterative data-flow analysis prone to low efficiency when applied to vulnerability detection, because more exhaustive information is required. Therefore, we propose the full-partitioned interprocedural data-flow analysis. In this way, all works to a program are carried out to procedures strictly. We also introduce the novel Pointee Objects Intermediate Representation object to replace the real pointees during interprocedural pointer analysis. It aims to solve the representation of pointee objects when interprocedural pointer analysis is full-partitioned. The interprocedural data-flow analysis is realized by using the function summary. We have observed a significant increase in efficiency and a good capability to support the use-after-free detection.
Digitization of Library Resources and the Development of Institutional Repositories in Nigeria

Institutional repositories are online repositories that contain scholarly works of staff and students of an institution. The repositories provide access to a school's scholarly works and reflect present and past research works of the institution. This chapter gives a review of the concept of library digitization, status of institutional repositories in Nigeria, process of digitization, guidelines for library digitization, digitization workflow, and challenges of library digitization. It identifies the various universities in Nigeria that have established institutional repositories. The chapter also reviewed the library digitation carried out in some of these institutions. It concluded that Nigeria lags behind in the practice of library digitization and the establishment of institutional repositories. Some challenges were highlighted as factors affecting library digitization and institutional repositories in Nigeria. Some recommendations were suggested to ensure successful establishment and implementation of library digitization and institutional repositories in Nigeria.
An observable degree analysis method for nonlinear Systems with noise correlation

The observability in the modern control field describes whether a system can reflect the combination relationship of the state components of the system by the output measurement value. Observability is the question of whether observability is big or small with a certain value, which is of great significance to the analysis of the system. There are many methods to calculate the observability of linear systems. For the calculation of observability in the field of nonlinear systems, the complexity of nonlinear systems leads to the lack of observability theory of nonlinear systems. In the existing research on the observability of nonlinear systems, the analysis and research under the situation where the observation noise of the system is correlated with the process noise is still lacking. In the existing research of nonlinear observability, this paper considers the correlation between process noise and observation noise to be added to the analysis and calculation of nonlinear observability. Compare the observability relationship between the noise-related situation and the noise-uncorrelated situation, and further improve the analysis theory of the observability of the nonlinear system. The research and analysis of related fields have certain reference value.
Construction of School Quality Assurance System Based on Big Data Analysis

In order to continuously improve the quality of education and teaching and better realize the goal of talent cultivation, we should combine education and teaching with big data analysis technology, adhere to the working principle of “student-centered, achievement oriented and continuous improvement”, improve the construction of education and teaching quality assurance system, and strive to build a long-term mechanism to improve the quality of talent cultivation, Form an internal quality assurance system guided by “national and social needs” and “student learning effectiveness”, with “743” as the main content and “monitoring and evaluation” as the focus.
Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.
Using Open Information Extraction to Extract Relations: An Extended Systematic Mapping

Context: For thousands of years humans have been using natural language to register their knowledge on important information to enable its access to future generations. With internet, a large amount of textual data is produced and shared on a daily basis. So, scientists started to research techniques for efficiently process knowledge stored in textual format. In this context, Natural Language Processing (NLP) became a popular area studying linguistic phenomena and using computational methods to process texts in natural language. In particular, Open Information Extraction (Open IE) was proposed to gather information from plain text. Despite the advances in this area, it is still necessary to map details about how these approaches were proposed to support the community while creating more efficient Open IE systems. Objective: In this paper, we identify, in the literature, the main characteristics of proposed Open IE approaches. Method: First, we extended the search performed in a systematic mapping previously published by using backward snowballing and a manual search. Next, we updated the electronic database search including ACL Anthology. Finally, 159 studies proposing Open IE approaches were considered for data extraction. Results: Data analysis showed a significant increase in the number of studies published about Open IE in the last years. In addition, we provide important details about how these techniques were proposed (e.g., data sets used and output evaluation techniques). Results indicate that researchers started to adopt neural networks to perform Open IE instead of using conventional supervised learning techniques. Conclusion: Recent advances in Artificial Intelligence and neural networks techniques allowed scientists to have a new perspective on how to perform efficient textual data management. Therefore, Open IE approaches gained much attention as they can help in many contexts, especially in knowledge management tasks.
Design and Application of Digital Party Building Cloud Platform based on C# and Opencv

Party building is the core driving force to lead the work of grass-roots tax departments forward. Strengthening the party building of grass-roots tax departments plays an extremely important role in promoting the tax cause. In this form, the party's grass-roots construction is also in a major transformation stage. The government, state-owned enterprises and colleges all attach great importance to the management of the construction of grass-roots party organizations. The software development environment of this system is visual studio 2015 and opencv 3.4.2. The core code is written by C + +, and the software interface is written by C #. In this paper, many cases are tested and the test results are counted. The test results show that the system can accurately detect and track moving targets.
The Sense of Style

In this chapter, I will talk a little about why we as F# developers need a style guide and what such a guide should look like. I’ll also outline a few principles that, independent of language, great developers follow. These principles will be our guiding light in the many decisions we’ll examine in future chapters.
Efficient Identity-Based Broadcast Encryption Scheme on Lattices for the Internet of Things

In an identity-based broadcast encryption (IBBE) scheme, the ciphertext is usually appended with a set of user identities to specify intended recipients. However, as IBBE is adopted in extensive industries, the demand of anonymity for specific scenarios such as military applications is urgent and ought no more to be ignored. On the contrary, how to optimize computation and communication is an unavoidable challenge in the IBBE scheme construction, especially in the large-scaled resource-limited wireless networks such as the Internet of Things (IoT), where the cost of computation and communication should be mitigated as much as possible since other functions including connectivity and privacy should be given the top priority. Thus, we present an IBBE scheme from the lattice, in which we employ the Chinese remainder theorem and lattice basis delegation in fixed dimensions to obtain several desirable characteristics, such as constant-size public parameter, private key, and ciphertext. In addition, our encryption and decryption algorithms are more efficient than broadcast encryption (BE) schemes based on number-theoretic problems. To be noticed, our scheme can simultaneously achieve confidentiality and outsider anonymity against the chosen-plaintext attack under the hardness of the learning with error (LWE) problem.
Research on Tibetan medicine intelligent question answering system integrating confrontation training and reinforcement learning

In this study, a knowledge graph (KG) based Tibetan medicine intelligent question answering (QA) system model was proposed based on an adversarial learning generative network model, in an attempt to alleviate the scarcity of medical resources, promote the heritage and innovation of Tibetan medicine, and ease the shortage of Tibetan medical information. In this model, the simulated answers were generated via adversarial learning, and subsequently the reinforcement learning was applied for feedback-based optimization, with the ultimate aim of enhancing the accuracy rate of this model. Besides, a triple extraction method based on Tibetan features was proposed to construct a KG dialog set. Finally, this model was subjected to an experiment in Chinese and Tibetan datasets, with the results indicating that the accuracy of this intelligent QA model incorporating adversarial networks and reinforcement learning was higher than other models.
Optical Maxwell's demon phase space theory and its efficiency improvement to 90 % with local online squeezing

A Maxwell's demon is a being with the ability to extract work from information. Recently, an optical simulator of Maxwell's demon was experimentally demonstrated. Here, we formulate the power of Maxwell's demon using the phase space method, which enables a clear explanation of power optimality at 16/27. We apply our phase space method to determine if local online squeezing might improve the demon's performance. Local online squeezing will alter photon statistics, and it is difficult to derive a strict (no approximation) closed formula for efficiency in the photon number space. We observe that phase space shows improvement in the demon's efficiency from 59 percent to 90 percent with online squeezing, for approximately 51.93 percent enhancement. Our work provides a simple and deterministic method to enhance the efficiency of Maxwell's demon.
Waste Classifications Using Convolutional Neural Network

This research presents the use of automated machine learning models to manage and separate waste efficiently. The focus of this study is to use Convolutional Neural Network (CNN) with 3 different architecture designs to compare which gave the best model. Moreover, this research further investigates the model&#x2019;s performance when using RGB images and greyscale images. The dataset used was obtained from Kaggle website that contains 156,362 images belonging to two classes: biodegradables and non-biodegradables. The best model achieved adequate results were of architecture 3 using the greyscale images with accuracy 81.0&#x0025; and reported loss of 44&#x0025;. Similar results obtained with RGB image using the same architecture with accuracy of 82.7&#x0025; and loss 49&#x0025;. The greyscale images showed better performance for all model structures, where it reported loss value less than the RGB images, and similar accuracy. Moreover, the impact of FC layers in CNN for image classification was positive, it enhanced the models performance. The moderate accuracy and extreme high loss value were due to the overfitting of the low-quality images. Future study can add other hyper parameter to increase accuracy such as more dropout layers on a good quality data, and to further understand the impact of FC layers in architecture.
Gamification and Augmented Reality to Upgrade Elementary Bilingual Education Students' Health and Engagement

Sedentary life-style, obesity, and early school leaving have been identified as major causes leading to the biggest health and educational problems in developed countries. This may derive into students showing a not recommended passive role at these ages, which also leads to their lack of interest and motivation in learning and education. This chapter presents an innovation research project which aims to face sedentariness in elementary school by applying an instructional technology-based model with the use of gamification and augmented reality in a bilingual education context. The case study shows several educational advantages, including increase in physical exercise, health awareness, learning engagement, and upgrade in L2 content understanding and linguistic skills.
Small Scale Targeted Face Detection using Deep Convolutional Neural Network

Under the field of object detection, face detection is the fundamental element of computer vision technology, for a few decades it become the popular research area. However due to the limitation of some scenes image data have low resolution, occlusion, illumination, makeup and others issue that make face detection hard. Today many recent algorithms can recognize face detection well under the condition of positive and high resolution. However most of the faces were located in challenged condition and low resolution are the problem of face detection. Small Scale Face Detection is one of the challenged faces that hard to detect easily. Because of the low resolution, scale invariance and contextual reasoning, due to the lack of detail information about located face. To achieve the required goal of small scale face detection, this research work has proposed a novel Small Scale Targeted Face Detection (SSTFD) and the combination of MS-faster-RCNN to increase the accuracy of multi scale face. The proposed approach has used Residual Network (ResNet) 50, 101 and 152 as the backbone network and from Intersection over Union (IoU) regression, the Complete Intersection over Union (CIoU) and Control Distance Intersection over Union (CDIoU) are used. Also, this research work has applied various techniques like small scale targeted face detection to produce high accuracy results and good speed.
Classifier Module of Types of Movements Based on Signal Processing and Deep Learning Techniques

Human Activity Recognition (HAR) has been widely addressed by deep learning techniques. However, most prior research applied a general unique approach (signal processing and deep learning) to deal with different human activities including postures and gestures. These types of activity typically have highly diverse motion characteristics, which could be captured with wearable sensors placed on the user&rsquo;s body. Repetitive movements such as running or cycling have repetitive patterns over time and generate harmonics in the frequency domain, while postures such as sitting or lying are characterized by a fixed position, with some positional changes and gestures or non-repetitive movements being based on an isolated movement usually performed by a limb. This work proposes a classifier module to perform an initial classification among these different types of movements, which would allow for applying afterwards the most appropriate approach in terms of signal processing and deep learning techniques for each type of movement. This classifier has been evaluated using the PAMAP2 and OPPORTUNITY datasets using a subject-wise cross-validation methodology. These datasets contain recordings from inertial sensors on hands, arms, chest, hip, and ankles, collected in a non-intrusive way. In the case of PAMAP2, the baseline approach for classifying the 12 activities using 5-s windows in the frequency domain obtained an accuracy of 85.26 &plusmn; 0.25%. However, an initial classifier module could distinguish between repetitive movements and postures using 5-s windows reaching higher performances. Afterward, specific window size, signal format, and deep learning architecture were used for each type of movement module, obtaining a final accuracy of 90.09 &plusmn; 0.35% (an absolute improvement of 4.83%).
An Attention-Based Photovoltaic Forecasting Scheme Combined with LSTM Model

Abstract With the development of new energy around the world, the proportion of photovoltaic energy used as a clean energy in the distribution network is gradually rising. The forecast of photovoltaic power plants is vital to many energy providers for their marketing analysis. Thus, photovoltaic forecasting has become an important research direction at present. However, owing to the high volatility and intermittent characteristics of photovoltaic power generation, it is still a challenge to predict photovoltaic power accurately. As far as traditional photovoltaic forecasting methods are concerned, SVM and ARIMA, as machine learning methods can solve the timing prediction problem of certain scenarios, but they are often not appropriate for some time series closely related to features. To address this problem, this paper proposes a short-term photovoltaic load forecasting model based on the Attention mechanism and LSTM model. Firstly, the correlation coefficient and LASSO regression are used for feature selection to filter out redundant features. Secondly, a long short-term memory network (LSTM) is used to make predictions to solve the problem of gradient disappearance during model training. Finally, the Attention mechanism is added to better capture feature weights and further improve the prediction accuracy of the training model. The proposed method can predict the change trend well. Comparative results confirm that the proposed method with feature selection can has better effect than ARIMA, SVM, and ELM.
How Curriculum Learning Performs on AMR Parsing

Curriculum learning is a commonly used method in deep learning to improve training model efficiency. This method has been proven effective on a wide range of tasks in natural language and image processing. However, there are no studies yet fully investigating the possibility of applying this method to AMR parsing, the task of converting a sentence into an AMR, its abstract meaning representation. In this article, we experiment and investigate in detail the possibilities of applying curriculum into AMR parsing.
“Harvest” by Denis Tretyakov: modeling the image of the world through historical, metaphysical and receptive transcendence

The author turns to the synthetic (intermediate) analysis of the musical composition Harvest by Denis Tretyakov. Two interpretative and semantic layers of the song text are identified and analyzed. The socio-historical (external) level of the text is realized through a decipherable event-plot code. The second (internal) level of the text is much more implicit, since it correlates with its metaphysical semiotic component. It is here that the key to an adequate interpretation is hidden. The song Harvest is connected with the ode To Joy (Beethoven / Schiller) both musically / melodically and textually. Moreover, the correlation of different levels can be traced with individual texts of other authors, in turn, conceptually correlated with this work. These are primarily Gnostic apocrypha (The Apocrypha of Johan, The Walk of the Virgin in Torment), the involvement of which serves to a certain extent the correct analysis of the central image (Misha Gorlikov). He can be identified with both Michael the archangel and the fallen angel Samael, which allows us to speak about the ambivalence not only of the image itself, but also of the picture unfolded in the text, represented as a universal model of the world. There are at least two ways of receiving the ode To Joy by Denis Tretyakov: embodying the original idea and rethinking it. Both solutions receive voluminous, multi-valued implementations, although it is the second way (reinterpretation) that is obviously semantically representative and dynamic. Thanks to the original explication of the musical and verbal sides of the composition, a new author's concept is born, a new own text, at the same time understood as a kind of gesture of art / anti-art.