Reviewerly/exHarmony huggingface dataset:
Block-Split Array Coding Algorithm for Long-Stream Data Compression

With the advent of IR (Industrial Revolution) 4.0, the spread of sensors in IoT (Internet of Things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. Hence, the study of big data compression is valuable in the field of sensors. A problem is how to compress the long-stream data efficiently with the finite memory of a sensor. To maintain the performance, traditional techniques of compression have to treat the data streams on a small and incompetent scale, which will reduce the compression ratio. To solve this problem, this paper proposes a block-split coding algorithm named “CZ-Array algorithm,” and implements it in the shareware named “ComZip.” CZ-Array can use a relatively small data window to cover a configurable large scale, which benefits the compression ratio. It is fast with the time complexity O(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>N</mml:mi></mml:math>) and fits the big data compression. The experiment results indicate that ComZip with CZ-Array can obtain a better compression ratio than gzip, lz4, bzip2, and p7zip in the multiple stream data compression, and it also has a competent speed among these general data compression software. Besides, CZ-Array is concise and fits the hardware parallel implementation of sensors.
Creating Blockchain Driven Ecosystem Model for Enterprise Financial Management

Blockchain technology is broadly used for financial transactions at present in many fields.Technical features of blockchain technology are decentralization, trustworthiness, verifiability, and programmability.Financial management ecosystem is a system model based on natural ecosystems.Financial ecosystem model links the internal and external environment of the enterprise development and the various departments and institutions to contribute to financial management.Financial ecosystem model has a certain role in promoting the financial management level of enterprises.Applying the blockchain to the financial ecosystem is the development trend of blockchain application.The article constructs the financial management ecosystem model based on blockchain and analyzes the problems existing in the blockchain application.Suggestions for improvement to promote the application of blockchain in the financial ecosystem model.
Separating the Effects of Batch Normalization on CNN Training Speed and Stability Using Classical Adaptive Filter Theory

Batch Normalization (BatchNorm) is commonly used in Convolutional Neural Networks (CNNs) to improve training speed and stability. However, there is still limited consensus on why this technique is effective. This paper uses concepts from the traditional adaptive filter domain to provide insight into the dynamics and inner workings of BatchNorm. First, we show that the convolution weight updates have natural modes whose stability and convergence speed are tied to the eigenvalues of the input autocorrelation matrices, which are controlled by BatchNorm through the convolution layers' channel-wise structure. Furthermore, our experiments demonstrate that the speed and stability benefits are distinct effects. At low learning rates, it is BatchNorm's amplification of the smallest eigenvalues that improves convergence speed, while at high learning rates, it is BatchNorm's suppression of the largest eigenvalues that ensures stability. Lastly, we prove that in the first training step, when normalization is needed most, BatchNorm satisfies the same optimization as Normalized Least Mean Square (NLMS), while it continues to approximate this condition in subsequent steps. The analyses provided in this paper lay the groundwork for gaining further insight into the operation of modern neural network structures using adaptive filter theory.
Enhancing Itemset Tree Rules and Performance

Association mining is the process of discovering relationships between items in a data set, where a group of items forms an itemset. A problem with the typical association mining approach is a large number of the generated frequent itemsets typically do not contain any items of interest to the user. Targeted association mining solves this by only deriving itemsets that include the items specified within a user’s query. A popular targeted approach is the Itemset Tree, which consists of an index tree structure and algorithms for search and rule generation. Numerous enhancements to improve the Itemset Tree efficiency or extend its capability have been proposed. However, two major problems exist. First, the itemset generation process utilized by the Itemset Tree does not leverage the Apriori Principle, resulting in the unnecessary and costly generation of infrequent itemsets. Second, the Itemset Tree rule generation process has restrictions that prevent some rules containing the user query from being generated; as a result, the user misses useful information. In this paper, we offer a redesigned generation process resulting in faster itemset generation (milliseconds versus minutes) and expanded rulesets.
Augmenting Variational Autoencoders with Sparse Labels: A Unified Framework for Unsupervised, Semi-(un)supervised, and Supervised Learning

We present a new flavor of Variational Autoencoder (VAE) that interpolates seamlessly between unsupervised, semi-supervised and fully supervised learning domains. We show that unlabeled datapoints not only boost unsupervised tasks, but also the classification performance. Vice versa, every label not only improves classification, but also unsupervised tasks. The proposed architecture is simple: A classification layer is connected to the topmost encoder layer, and then combined with the resampled latent layer for the decoder. The usual evidence lower bound (ELBO) loss is supplemented with a supervised loss target on this classification layer that is only applied for labeled datapoints. This simplicity allows for extending any existing VAE model to our proposed semi-supervised framework with minimal effort. In the context of classification, we found that this approach even outperforms a direct supervised setup.
On the Role of Homework in the Learning Process

A complete, long-term retention of material learned during the lesson should be supplemented by independent study at home, as it is a method of reinforcing knowledge and skills; it forms independence in educational and cognitive activities in children as well as develops their thinking and self-discipline skills. This article deals with different approaches to creating a highly effective homework organization system, which allows to get complete and high-quality information about the level of studied material.
Maximizing CNN Throughput on FPGA Clusters

Field Programmable Gate Array (FPGA) platform has been a popular choice for deploying Convolutional Neural Networks (CNNs) as a result of its high parallelism and low energy consumption. Due to the limitation of on-chip resources on a single board, FPGA clusters become promising solutions to improve the throughput of CNNs. In this paper, we firstly put forward strategies to optimize the resource allocation intra and inter FPGA boards. Then we model the multi-board cluster problem and design algorithms based on knapsack problem and dynamic programming to calculate the optimal topology of the FPGA clusters. We also give a quantitative analysis of the inter-board data transmission bandwidth requirement. To make our design accommodate for more situations, we provide solutions for deploying fully connected layers and special convolution layers with large memory requirement. Experimental results show that typical well-known CNNs with the proposed topology of FPGA clusters could obtain a higher throughput per board than single-board solutions and other multi-board solutions.
Relationship between Ocular Fatigue and Use of a Virtual Reality Device

Purpose: To investigate ocular fatigue after the use of a head-mounted display (HMD)-type virtual reality device.Methods: Healthy adult volunteers were examined for ocular fatigue before and after watching videos for 10 min with an HMD-type virtual reality device.Subjective ocular fatigue was measured using a questionnaire.Objective fatigue was measured using the critical flicker fusion frequency (CFF), high frequency component of accommodative microfluctuation, and accommodation amplitude.The accommodation amplitude was measured using the push-up method and the dynamic measurement mode of the autorefractometer.Changes in the spherical equivalent were also measured. Results:The questionnaire-based subjective ocular fatigue increased (p = 0.020) after use of the HMD device.In the dominant eye, the high frequency component of accommodative microfluctuation increased (p < 0.05).The accommodation amplitude using the push-up method was decreased in the nondominant eye (p = 0.007), and temporary myopia was observed (p < 0.05).However, there was no increase in ocular fatigue in the CFF or the accommodation amplitude using the dynamic measurement mode, which showed no significant difference before and after using the HMD device (p > 0.05).Conclusions: A subjective test and some objective tests suggested that use of the HMD-type virtual reality display increased ocular fatigue.However, no increase in ocular fatigue was measured using CFF nor in the accommodation amplitude using the dynamic measurement mode which was a limitation of the study.More studies with the aim to alleviate ocular fatigue after using HMD-type virtual reality devices are therefore needed.
Analisis Kemampuan Menulis Surat Pribadi Siswa Kelas V SDN 26 Pekanbaru

This reaserch examines the ability to write a personal letter of graders V students of SDN 26 Pekanbaru.. This research is a descriptive quantitative research. This research has a population of 97 students. The test used is a story essay test. The tests instruct students to write personal letters on the topics provided and take note of parts of personal letters. The ability of the V graders students of SDN 26 Pekanbaru after being analyzed using categories, from the 6 aspects that have been determined, get the results that on the aspects of the letter date an average of 88.23 with a very good category, the aspects of the mail address category is very good with an average of 96,29, in the aspect of opening greetings the category is very good with an average of 89,00, on the aspect of letter contents an average of 51.22 with enough categories, on the aspect of closing greetings an average of 56.36 with enough categories, in the signature and name sender aspects the category is very good with an average of 88.66. From these results, an overall average of 78.29 was obtained in the very good category.
A High-Speed Method for Generating Edge-Preserving Bubble Images

We propose a non-photorealistic rendering method for generating edge-preserving bubble images from gray-scale photographic images. Bubble images are non-photorealistic images embedded in many bubbles, and edge-preserving bubble images are bubble images where edges in photographic images are preserved. The proposed method is executed by an iterative processing using absolute difference in window. The proposed method has features that processing is simple and fast. To validate the effectiveness of the proposed method, experiments using various photographic images are conducted. Results show that the proposed method can generate edge-preserving bubble images by preserving the edges of photographic images and the processing speed is high.
On the Search for Feedback in Reinforcement Learning

The problem of Reinforcement Learning (RL) in an unknown nonlinear dynamical system is equivalent to the search for an optimal feedback law utilizing the simulations/ rollouts of the dynamical system. Most RL techniques search over a complex global nonlinear feedback parametrization making them suffer from high training times as well as variance. Instead, we advocate searching over a local feedback representation consisting of an open-loop sequence, and an associated optimal linear feedback law completely determined by the open-loop. We show that this alternate approach results in highly efficient training, the answers obtained are repeatable and hence reliable, and the resulting closed performance is superior to global state-of-the-art RL techniques. Finally, if we replan, whenever required, which is feasible due to the fast and reliable local solution, it allows us to recover global optimality of the resulting feedback law.
The comparison of Faster R-CNN and Atrous Faster R-CNN in different distance and light condition

Abstract This paper presents the comparison of Faster R-CNN and Atrous Faster R-CNN, which detection model, in the different distance and light condition. Also, the dataset for model training is COCO, and the classification model is residual network. The parameter for decision the performance of the model is Mean Average Precision (mAP). The results from an object resolution at 1024x768 of Faster R-CNN at 3 meters in the evening achieved mAP 1.000. Besides, the mAP at 5 meters and 8 meters were 0.798 and 0.760, respectively. The same resolution as previous, the results of Atrous Faster R-CNN at 3 meters in the evening presented mAP 1.000. Also, the mAP at 5 meters and 8 meters were 1.000 and 0.960, respectively. In addition, Atrous Faster R-CNN had better accuracy than Faster R-CNN with appropriate range and brightness from the period of the day for real-life usage.
POSSIBILITIES OF BLENDED LEARNING TECHNOLOGY IN THE ORGANIZATION OF EDUCATIONAL ACTIVITIES OF PRIMARY SCHOOL STUDENTS

The article substantiates the relevance of using the technology of blended learning as one of the means of personalizing the education of primary school students. In modern conditions for the teacher, the formation of the ability to learn independently on the part of primary school children is becoming a leading priority. This problem is especially relevant in conditions of small schools in classes with low occupancy, in which 2-3 study groups are engaged in the same classroom with one teacher. In this regard, it becomes important to solve the problem of organizing the educational space in such a way that the younger student takes a subjective position in the learning process, actively acts in the development of educational material, is able to carry out planning and self-control, and interact in a small group. The “Change of working areas” model contributes to the solution of this problem. The use of this model in classes with low occupancy allows the teacher to realize differentiation, to work individually with the student at each moment of time, use interactive forms of work in the lesson, and organize regular group work. The aim of the study is to determine and experimentally prove the effectiveness of pedagogical conditions of use in the primary low-grade school model "Change of working areas". The tools and methods that ensure the use of this model in the education of primary school children are described. The formative experiment on the implementation of the “Change of working areas” model has shown positive results: learning using the model has a positive effect on the development of meta-subject and personal learning actions.
COMPARISON OF EDGE DETECTION TECHNIQUES FOR HUMAN FEATURE POINTS EXTRACTION

Many applications depend on extracting human feature points from 2D images. e.g. non-contact anthropometric measurements, initializing 3D human body shape and recognizing human actions recognition. This paper represented a comparison between various edge detection techniques, which applied on an automated human feature points extraction algorithm from front and side images. Firstly, detecting the body contour. Secondly, Robert, Prewitt, Sobel, Canny and Laplacian of Gaussian (LoG) edge detection techniques are applied to represent the silhouette curve of the human body. Thirdly, Freeman’s 8-connected chain codes is applied on the silhouette curve.  Finally, a series of feature points are extracted automatically based on some specified rules. Errors and quality comparison techniques, mean square error (MSE), root mean square error (RMSE) and peak signal to noise ratio (PSNR), are used to precisely compare between these different edge detection techniques. The comparison shows that LoG edge detection technique has the least MSE and the most quality.
MFF: Performance Interference-Aware VM Placement Algorithm for Reducing Energy Consumption in Data Centers

Virtualization is the main technology that powers cloud computing and has enabled the execution of multiple applications in same physical hardware using virtual machines (VM) for efficient utilization of resources and energy savings.Although virtualization successfully isolates coresident VMs from a security perspective, it does not offer a guarantee from a performance interference perspective.This means that sharing of resources results in competition, which is the cause of performance interference.Performance interference is more pronounced in homogenous workloads, where applications workloads contend to the same shared resource.In this case, application workloads run for longer times due to reduced performance and thus consume more energy.To address this problem, a VM allocation policy should ensure that VM running homogeneous workloads is not co-located.In this paper, we propose a VM allocation algorithm called Minimum Interference First Fit (MFF), which co-locates dissimilar workloads.The algorithm clusters VMs using K-means based on resources usage.Before a VM is placed into a physical machine (PM), similarity index (SI) of all the active PMs is computed, the VM is then placed in a PM with least SI.MFF has been evaluated on a simulated data center using CloudSim Plus cloud simulator on application workloads logs obtained from a production data center.Results show that MFF outperforms well-known VM allocations algorithms such as first fit (FF), worst fit (WF) and best fit (BF) from an energy consumption perspective.
Feature embedding and conditional neural processes for data imputation

This Letter addresses the missing value prediction problem in data. Existing missing value prediction studies apply their algorithms after replacing missing values with a constant value, which does not represent the missing values clearly and has disadvantages in processing data with a high missing rate. The authors propose a model that generalises a single data sample from its observations and predicts the value of a query feature. The authors' model can deal with an incomplete data sample independently of missing values. In this process, the feature embedding matrix and feature importance weights are introduced to represent each feature and learned jointly with the proposed model. Through experiments, they verify that the proposed model effectively predicts the missing values of data.
Music as Synergy and Non-Classical Model of Man

The article explores the phenomenon of music from the viewpoint of Man. In the philosophy of music, the connection with man is getting relevant, replacing the positivism, as well as the speculatism, common to music theories in the past, when formal logical methods were used. The article focuses on the connection between man’s personal experience, his or her anthropological energy, that is the one that comes from the innermost foundations and utmost underlying feelings of the person (horror, pain, happiness, delight, and self-transcendence), and specific musical energy. In this rendering, we rest on the philosophical concept of the outstanding modern Russian thinker Sergey Khoruzhy. He describes man as an energetic entity formed by a limit-experience, which is the experience of reaching the limits of existence and consciousness. The synergistic approach to man continues the non-classical philosophical tradition of building the model of man using the categories like “energy”, “self-practice”, “limit-experience”, “constitution of man” and excluding the categories like “entity”, “substance”, “subject” present in the classical vision of man in European philosophy of the 17th—19th centuries. The anthropological component of the non-classical synergistic concept of music includes the comprehension of man through a certain typification of man’s striving to reach the limits. This philosophical anthropology depicts man opening up to the ontological, ontical and virtual limits. Music, as such synergy, stands for a projection of man, thus becoming the music of ontological, ontical and virtual striving. From this point of view, not only a specific concept of music is formed, different from other concepts, such as the concepts of music as a number and as an expression, but also an approach to the entire historical musical legacy and to musical practice, including composer’s, performer’s and listener’s activities.
Notes on neighborhood semantics for logics of unknown truths and false beliefs

In this article, we study logics of unknown truths and false beliefs under neighborhood semantics. We compare the relative expressivity of the two logics. It turns out that they are incomparable over various classes of neighborhood models, and the combination of the two logics are equally expressive as standard modal logic over any class of neighborhood models. We propose morphisms for each logic, which can help us explore the frame definability problem, show a general soundness and completeness result, and generalize some results in the literature. We axiomatize the two logics over various classes of neighborhood frames. Last but not least, we extend the results to the case of public announcements, which has good applications to Moore sentences and some others.
Compressed Dynamic Range Majority and Minority Data Structures

In the range $$\alpha$$-majority query problem, we are given a sequence $$S[1\ldots n]$$ and a fixed threshold $$\alpha \in (0, 1)$$, and are asked to preprocess S such that, given a query range $$[i\ldots j]$$, we can efficiently report the symbols that occur more than $$\alpha (j-i+1)$$ times in $$S[i\ldots j]$$, which are called the range $$\alpha$$-majorities. In this article we describe the first compressed dynamic data structure for range $$\alpha$$-majority queries. It represents S in compressed space—$$nH_k+ o(n\lg \sigma )$$ bits for any $$k = o(\lg _{\sigma } n)$$, where $$\sigma$$ is the alphabet size and $$H_k \le H_0 \le \lg \sigma$$ is the kth order empirical entropy of S—and answers queries in $$O \left( \frac{\lg n}{\alpha \lg \lg n} \right)$$ time while supporting insertions and deletions in S in $$O \left( \frac{\lg n}{\alpha } \right)$$ amortized time. We then show how to modify our data structure to receive some $$\beta \ge \alpha$$ at query time and report the range $$\beta$$-majorities in $$O \left( \frac{\lg n}{\beta \lg \lg n} \right)$$ time, without increasing the asymptotic space or update-time bounds. The best previous dynamic solution has the same query and update times as ours, but it occupies O(n) words and cannot take advantage of being given a larger threshold $$\beta$$ at query time. We also design the first dynamic data structure for range $$\alpha$$-minority—i.e., find a non-$$\alpha$$-majority that occurs in a range—and obtain space and time bounds similar to those for $$\alpha$$-majorities. We extend the structure to find $$\varTheta (1/\alpha )$$$$\alpha$$-minorities at the same space and time cost. By giving up updates, we obtain static data structures with query time $$O((1 / \alpha ) \lg \lg _w \sigma )$$ for both problems, on a RAM with word size $$w = \varOmega (\lg n)$$ bits, without increasing our space bound. Static alternatives reach time $$O(1/\alpha )$$, but they compress S only to zeroth order entropy ($$H_0$$) or they only handle small values of $$\alpha$$, that is, $$\lg (1/\alpha ) = o(\lg \sigma )$$.
A Canonical Model for Constant Domain Basic First-Order Logic

I build a canonical model for constant domain basic first-order logic (BQLCD), the constant domain first-order extension of Visser's basic propositional logic, and use the canonical model to verify that BQLCD satisfies the disjunction and existence properties.