Reviewerly/exHarmony huggingface dataset:
Overcoming Memory Constraint for Improved Target Classification Performance on Embedded Deep Learning Systems

Pattern recognition applications such as face recognition, detection of broken eggs, and classification of agricultural products are all using image classification in deep neural networks to improve the quality of services. However, traditional cloud inference models suffer from several problems such as network delay fluctuations and privacy leakage. In this regard, most real-time applications currently need to be deployed on edge computing devices. Constrained by the computing power and memory limitations of edge devices, the use of an efficient memory manager for model reasoning is the key to improving the quality of service. This study firstly explored the incremental loading strategy of model weights for the model reasoning. Next, the memory space at runtime is optimized through data layout reorganization from the spatial dimension. In particular, our proposed schemes are orthogonal and transparent to the model. Experimental results demonstrate that the proposed approach reduced the memory consumption by 43.74% on average without additional reasoning time overhead.
Rapid Design Space Exploration of Near-Optimal Memory-Reduced DCNN Architecture Using Multiple Model Compression Techniques

In spite of the attractive accuracy, it is hard to use a deep convolutional neural network (DCNN) directly at the resource-limited devices due to the energy-consuming memory overheads, and thus the aggressive compression schemes are essentially utilized in practice to reduce the DCNN model size. As the recent methods have been individually developed, however, it is inevitable to exhaustively find the optimal combination of different approaches, requiring an enormous amount of search time. Given the complex baseline network, in this work, we introduce a rapid and systematic way to find the near- optimal memory-reduced DCNN option using multiple compression schemes together. We first precisely observe the accuracy-size trade-off of each method and make a novel interpolating scheme to speculate the accuracy of an arbitrary combination. We then present an iterative search algorithm to minimize the number of network evaluations for finding the memory-efficient DCNN structure satisfying the required accuracy. Experimental results reveal that our framework provides a similar compression level to the naive full-search strategy with three popular optimization methods while saving the search time by 7.35 times.
Definition of a didactically expedient continuum in informatics for future primary school teachers

The article analyzes modern preconditions that determine specific features of a future primary school teacher's Informatics training, which is necessary for teaching Informatics to younger students. An approach to determining the content of a future primary school teacher's Informatics training, focused on understanding the socio-cultural significance of modern digital technologies for sustainable development of society and sufficient for the assimilation of Informatics concepts and procedures that are directly related to the content of the school Informatics course and its methodological support is substantiates in the article. The necessity of focusing the attention of future primary school teachers on information technologies that are widespread in everyday life and significantly simplify the achievement of program learning outcomes. The necessity of mastering by teachers the skills of handling algorithmic procedures, the use of block diagrams for the visual presentation of algorithmic procedures, the use of simple categorical syllogisms and inference rules in the construction of inferences, reference to evaluative procedures and legal norms of information interaction is argued.
Evaluating the Impact of BTI on Hiding Countermeasures for DPA and DEMA Attacks

Bias Temperature Instability (BTI) is a central issue in integrated circuits' reliability, as it degrades transistors' performance. In deeply scaled technologies, an increasing variability accompanies this effect. Secure topologies for cryptographic circuits aiming to increase resilience to Side-Channel Attacks through a homogeneous power consumption may be particularly subject to the impact of an unbalance caused by an aging effect. A statistical evaluation based on Monte Carlo simulations with a time-dependent BTI model based on trap kinetics was performed in this work. The results indicate that, despite the significant variability and degradation caused by the BTI in the secure cells' performance, the homogeneity of the power consumption was not considerably affected.
Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity

The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multiclass classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Interclass sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix. The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.
Students’ Study Habit in Speaking Skill (At Eleventh Grade of Manahijussadat Islamic Boarding School Lebak Banten)

The research focuses on students’ study habit in speaking skill at Eleventh grade students of Manahijussadat Islamic Boarding School Lebak Banten. The aim of this research is to analyze how students manage their habits in learning speaking. The research method of this research used case study and qualitative approach. For data collection, researcher used interview, observation, and documentation. The results of the research are (1) students’ study habit while process in speaking skill that used by students at Manahijusadat Islamic Boarding School Lebak Banten are: Time Management; Participation; and Memorization. (2) Reading and note taking applied when students have motivation to increase their knowledge (3) Planning of subjects applied by students in order more active and understood during learning process, and (4) Preparation of examination applied by students in order to obtain good score and success in examination. It can be concluded that study habit can improve students’ achievement especially in speaking skill.
Impact of playout buffer dynamics on the QoE of wireless adaptive HTTP progressive video

The quality of experience (QoE) of video streaming is degraded by playback interruptions, which can be mitigated by the playout buffers of end users. To analyze the impact of playout buffer dynamics on the QoE of wireless adaptive hypertext transfer protocol (HTTP) progressive video, we model the playout buffer as a G/D/1 queue with an arbitrary packet arrival rate and deterministic service time. Because all video packets within a block must be available in the playout buffer before that block is decoded, playback interruption can occur even when the playout buffer is non-empty. We analyze the queue length evolution of the playout buffer using diffusion approximation. Closed-form expressions for user-perceived video quality are derived in terms of the buffering delay, playback duration, and interruption probability for an infinite buffer size, the packet loss probability and re-buffering probability for a finite buffer size. Simulation results verify our theoretical analysis and reveal that the impact of playout buffer dynamics on QoE is content dependent, which can contribute to the design of QoE-driven wireless adaptive HTTP progressive video management.
Estimation of Eye-Pupil Size During Blink by Support Vector Regression

Pupillography can be an index of mental activity and sleepiness, however, blinks prevent its measurability as an artifact. A method of estimation of pupil size from pupillary changes during blinks was developed using a support vector regression technique. Pupil responses for changes in periodic brightness were prepared, and appropriate pupil sizes for blinks were given as a set of training data. The performance of the trained estimation models was compared and an optimized model was obtained. An examination of this revealed that its estimation performance was better than that of the estimation method using MLP. This development helps in the understanding of the behavior of pupillary change and blink action.
WLeidenRDF: RDF Data Query Method based on Semantic-Enhanced Graph-Clustering Algorithm

Graph-clustering algorithms are designed to split large-scale Resource Description Framework (RDF) graphs into subgraphs to improve RDF query performance. However, triple semantics and graph structures embedded in RDF graphs are often ignored during the RDF graph partition. Accordingly, this study utilizes the Leiden algorithm for uncovering community structure to cluster RDF graphs. We propose an optimized WLeidenRDF algorithm to uncover RDF communities and cluster vertex with strong semantics. Different weights are set to predicates in RDF triples to identify semantic-relevance degree and ensure semantic connections after RDF clustering and segmentation. The experiments on WatDiv data sets demonstrate that our WLeidenRDF algorithm obtains better partitions in accordance with modularity than those of the Leiden algorithm. We implement our algorithm on Presto distributed SQL query engine. Experimental results indicate that our algorithm can substantially reduce query time by clustering RDF graphs than with other RDF query methods.
The inevitable sociality of money: the primacy of practical affirmation over conceptual consensus in the construction of Bitcoin’s economic value

Abstract Bitcoin went from being an obscure online project to a globally exchanged money valued at tens of thousands of dollars (USD) per unit. It has achieved this in spite of fundamental irreconcilabilities between the economic theories which spurred its creation and its own material basis. This article investigates how this happened using a computationally grounded analysis of 100 000s of messages from the early years of Bitcoin’s two main online communities. It will show how the continuing divergence in participants’ understandings of why Bitcoin possessed value ultimately gave way to an emerging focus on the social problem of adoption. In demonstrating this shift and accompanying promotion of activities that affirmed Bitcoin had value in practice, this article with argue that shared meaning in the form of practical affirmations of worth, rather than conceptual understandings of it, are key to communities’ ability to ‘bootstrap’ a money’s initial economic value.
Pseudo Rehearsal using non photo-realistic images

Deep neural networks forget previously learnt tasks when they are faced with learning new tasks. This is called catastrophic forgetting. Rehearsing the neural network with the training data of the previous task can protect the network from catastrophic forgetting. Since rehearsing requires the storage of entire previous data, Pseudo rehearsal was proposed, where samples belonging to the previous data are generated synthetically for rehearsal. In an image classification setting, while current techniques try to generate synthetic data that is photo-realistic, we demonstrated that Neural networks can be rehearsed on data that is not photo-realistic and still achieve good retention of the previous task. We also demonstrated that forgoing the constraint of having photo realism in the generated data can result in a significant reduction in the consumption of computational and memory resources for pseudo rehearsal.
Third generation universities: foreign experience and Russian practices

The article analyzes the third generation universities and their main features according to the methodology developed by Johan Wissema. The world experience in building third generation universities can be based, according to the authors, on the three main models described in the present study, namely, the models used by Harvard, Cambridge, and the University of Ruse. The main purpose of the present study is to analyze the main advantages of third generation universities on the example of international and Russian practices. The universities, selected as research objects, not only show high results in international rankings and the scientific community but also have strategic importance for the development of their regions. However, the most important criterion remains the commercialization of know-how and the active involvement of the educational organization in the regional economy, which is demonstrated by all three universities selected for the analysis of global practices. Each of the presented models has its peculiar characteristics and can be adapted for forming the Financial University under the Government of the Russian Federation (Financial University) into a new third generation university that can be created taking into account the capabilities and available facilities and resources, as well as the scientific and educational-methodological base.
Proposal of an Economy of Things Architecture and an Approach Comparing Cryptocurrencies

In the present computational scenario, one can perceive the emergence of cryptocurrencies and the increased utilization of IoT devices, which are pushing to new challenges, opportunities, and behavior changes. It is still not known how these technologies will impact the current business and economic models. In this regard, this study proposes an economy of things architecture and an approach comparing several cryptocurrencies. Therefore, the proposed architecture aims to use these new opportunities to enable device-to-device (D2D) interaction based on this novel paradigm, called the Economy of Things (EoT). An experimental environment was conducted to compare characteristics of the cryptocurrencies Ripple, Iota, and Ethereum. The initial results show several interesting differences related to transaction costs, errors, speeds, and threads.
Efficient Verification of Optimized Code

Code that is highly optimized poses a problem for program-level verification: programmers can employ various clever tricks that are non-trivial to reason about. For cryptography on low-power devices, it is nonetheless crucial that implementations be functionally correct, secure, and efficient. These are usually crafted in hand-optimized machine code that eschew conventional control flow as much as possible.We have formally verified such code: a library which implements elliptic curve cryptography on 8-bit AVR microcontrollers. The chosen implementation is the most efficient currently known for this microarchitecture. It consists of over 3000 lines of assembly instructions.Building on earlier work, we use the Why3 platform to model the code and prove verification conditions, using automated provers.We expect the approach to be re-usable and adaptable, and it allows for validation. Furthermore, an error in the original implementation was found and corrected, at the same time reducing its memory footprint.This shows that practical verification of cutting-edge code is not only possible, but can in fact add to its efficiency—and is clearly necessary.
A Comprehensive overview of AR/VR by Writing in Air

The research is all about how often people could identify alphabets and numbers written in the open air. A leap motion captures motion trajectory information and plots it as a continuous stream of points. Lines would be combined and major slopes identified from the major points. Significant slopes are converted into directions by the use of geometry. Slope orientation is another name for this. Slope Alignment Sequence is a list of slope orientations that are used to individually depict Alphanumeric Letters. In this experiment, Dependency injection OpenCV is used to sketch on the camera with a virtual pen, i.e. any marker may be used to draw using the contour detection technique centered on the mask of the desired cultured reference marker. If you're a beginner who wants to enthuse your fellow members with a visual demonstration, the application user will be identified by the system until the produced slope direction sequence matches the system's comparison slope alignment sequence. In the algorithm's matching process, the sets and subsets process is used. The researchers suggested a new approach for classifying Alphabetic Chars written in the air in this article. The outcome of the experiment shows that the suggested approach is reliable.
Experience of using 1С cloud services and technologies to organize the learning process in the IT directions of training

The article discusses the organizational and methodological aspects of the use of 1С cloud services and technologies in the educational process or students of IT directions of training. The use of 1C cloud services allows you to gain knowledge, skills and abilities associated with the use of typical configurations (1C:Accounting, 1C:Trade Management, 1С:Payroll &amp; HR Management, etc.). At the same time, the use of 1C services provides many advantages related to the convenience of work, the availability of up-to-date versions of programs and teaching materials, as well as the wide capabilities of organizing the educational process.The use of cloud services and technologies in industrial, research, engineering, educational and other activities leads to an increase in the efficiency of using computing resources, reducing the cost of purchasing licensed software and maintaining virtual infrastructure. The article substantiates that the study of the capabilities of 1C cloud services and technologies is of particular importance for students, since in the context of the digitalization of the Russian economy, the labor market requires specialists who have the skills to work with the 1C:Enterprise system in user mode, as well as have knowledge of development of own applied solutions.The experience of organizing training for students of the Orenburg State University in the framework of the formation of professional competencies of students in the directions of training bachelors “Applied informatics” (profile “Applied informatics in economics”) and “Business informatics” (profile “Information systems in economics”) using the service 1C:Enterprise via the Internet for educational institutions is described in the article. The use of 1C cloud services allows to increase the cognitive interest in disciplines, improve the academic performance of students and ensure the convenience of working in both full-time and distance learning formats.
WorkTrue: An Efficient and Secure Cloud-based Workflow Management System

This paper proposes an e-document workflow system called “WorkTrue” that supports secure, fine-grained access control and multiple digital signings for e-document workflow management implemented on cloud. We apply a ciphertext policy attribute-based encryption (CP-ABE) to enable the access control with data privacy-preserving for all signed documents stored in the cloud server. In addition, we present a dynamic workflow scheduling algorithm to dynamically schedule the workflow tasks to be run by VM instances (VMIs) from a single VM in a cloud. In our experiment, we developed a WorkTrue prototype as a proof of concept for validating its functionality. We also conducted performance evaluation to substantiate that our WorkTrue is efficient in practice.
Security Analysis and Improvements on a Remote Integrity Checking Scheme for Regenerating-Coding-Based Distributed Storage

Enabling remote data integrity checking with failure recovery becomes exceedingly critical in distributed cloud systems. With the properties of a lower repair bandwidth while preserving fault tolerance, regenerating coding and network coding (NC) have received much attention in the coding-based storage field. Recently, an outstanding outsourced auditing scheme named NC-Audit was proposed for regenerating-coding-based distributed storage. The scheme claimed that it can effectively achieve lightweight privacy-preserving data verification remotely for these networked distributed systems. However, our algebraic analysis shows that NC-Audit can be easily broken due to a potential defect existing in its schematic design. That is, an adversarial cloud server can forge some illegal blocks to cheat the auditor with a high probability when the coding field is large. From the perspective of algebraic security, we propose a remote data integrity checking scheme RNC-Audit by resorting to hiding partial critical information to the server without compromising system performance. Our evaluation shows that the proposed scheme has significantly lower overhead compared to the state-of-the-art schemes for distributed remote data auditing.
The sharing method of computerized accounting information resources based on Data Fusion Technology

In order to overcome the problems of low security and efficiency existing in traditional computerized accounting information resource fusion methods, this paper proposes a new method of computerized accounting information resource sharing based on data fusion technology. According to the function of data fusion, the mapping relationship of knowledge elements of computerized accounting information resources is constructed by using data fusion technology, and the computerized accounting information resources are fully integrated. Based on the fused computerized accounting information resources, the Lagrange interpolation method is used to calculate the secure sharing key of computerized accounting information resources, and the secure sharing of computerized accounting information resources is completed. The experimental results show that the proposed resource sharing method can effectively ensure the security of computerized accounting information resources, and effectively improve the transmission efficiency of computerized accounting information resources.
Classification Problem Using Imbalanced Ratio Based Random Forest Method

Abstract In recent decades, as machine learning is getting more and more popular in many fields, multiple classification methods have been proposed and applied to various applications for intelligent decision making. Most of the classification methods are just work on a shallow machine learning classifier. And many of these algorithms are already existed in the machine learning package. In this study, a new Imbalanced Ratio Based Random Forest(IR-RF) method was proposed to make classification and prediction. We focused on dealing with the imbalanced data. So we use several imbalanced datasets and put processed features into our IR-RF model. Three datasets we used are from the UCI machine learning database where RF and IR-RF are compared in the final results. The former RF method indicates the traditional random forest algorithm and the latter IR-RF method is our newly written one. Comparing to other classification model, IR-RF is an algorithm-level method, which is not limited by existing parameters. We can generate more new parameters within the algorithm and train our own classifier. Results show that proposed method has higher accuracy than traditional random forest method after our ratio compared and it can take better account of imbalanced condition. So proposed method not only performs well in high accuracy but also can be applied to especially imbalanced condition which is a new window for practice.