Reviewerly/exHarmony huggingface dataset:
Research of object detection method based on DCGAN data-set enhancement technique

With the rise of the new generation of artificial intelligence technology, the object detection method based on deep learning has achieved remarkable results. In this paper, the detection accuracy of three popular object detection algorithms such as You Only Look Once (YOLO V3), Region-CNN (Faster R-CNN) and Single Shot MultiBox Detector (SSD) has been compared. Aiming at the actual detection problems of building block parts with irregular shape and different sizes, a method that combines deep convolutional generative adversarial networks (DCGAN) with deep learning based object detection algorithm is proposed to solve the problems of over fitting or weak generalization ability in the case of limited datasets, and to improve the detection accuracy of object detection algorithm. Experimental results show that: 1. Using public datasets, when the training data is reduced, the mean average precision (mAP) values of the above three algorithms are reduced respectively. Among those, SSD algorithm has the smallest change, which decreases 7.81%. 2. The control variable method is used to train the building block parts. In the case of insufficient training data, the detection accuracy of three object detection algorithms is low. 3. After combining SSD algorithm with DCGAN algorithm and applying it into the detection task of building block parts, the mAP value is improved from 79.63% to 83.32%, and the detection accuracy is obviously improved.
Random placement algorithm of Military game based on Fisher-Yates

Computer game <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> is a research field, an important research direction in the field of artificial intelligence, and an important scientific research foundation in the field of machine intelligence, wargame deduction, intelligent decision system and other artificial intelligence. Machine game is considered to be one of the most challenging research directions in the field of artificial intelligence. As a member of computer game, military game is a game with incomplete and asymmetric information. One of the important links is that both sides need to play according to their own wishes in advance. It can be said that in the computer Military game, the layout affects the outcome of the whole game to a certain extent. In order to solve the problem of layout choice in practice, this paper proposes a random layout algorithm based on Fisher Yates shuffle algorithm, which can be used to generate absolutely random military chess layout, test the existing layout and program strength, and find the most suitable layout for the determined combat strategy and style. Compared with the previous method of generating a large amount of test data randomly, it can solve the problem. The new algorithm optimizes the original 25! times to 50 times, and optimizes the algorithm from O (n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of Fisher algorithm to O(n), so researchers can have more energy to find the best way for different strategies, which objectively strengthens the combat capability of the program. And it weakens the influence of human factors in the test and reduces the dependence on human intelligence in computer game.
Mobile Application to Distribute Water Quality and Weather Information in Rural Nicaragua

The ability to access accurate information about water quality and weather is something that many people take for granted, but in small, rural communities in northern Nicaragua this information is not easily accessible to everyone. Currently, weekly in-person meetings are held to distribute information such as water quality, weather conditions, and other important agricultural updates to the communities. However, technological limitations still prevent Nicaraguan farmers from getting updates in real-time. In this paper, we describe an Android app that allows the delivery of this kind of information to farmers in a real-time way. This mobile application has the potential to improve the lives of many local farmers and communities in northern rural Nicaragua.
Design and Implementation of an Information Management System for Exhibitions Oriented to the Internet of Things

research-article Share on Design and Implementation of an Information Management System for Exhibitions Oriented to the Internet of Things Authors: Yi Wei Guilin Tourism University, China Guilin Tourism University, ChinaView Profile , Chunhong Li Guilin Tourism University, China Guilin Tourism University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2225–2230https://doi.org/10.1145/3482632.3484134Online:22 November 2021Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Separating Map Variables in a Logic-Based Intermediate Verification Language

In SMT solver based verification, the program to be verified is often given in an intermediate verification language such as Boogie. We present a program transformation that aims at splitting mathematical arrays (i.e., maps, which are typically used to model arrays and specifically the heap) into different partitions, so that the resulting verification conditions are easier to solve (due to the need of fewer case splits when analysing the effect of reads and writes over the same array). Our method takes the similar role of classical preprocessing steps based on alias analysis; the difference is that it works on any (mathematical) map, as opposed to a data structure that is known to present a chunk of memory managed by some compiler. Having to forfeit the benefits of general assumptions about memory (e.g., allocate-before-use), we need to deal with additional difficulties but obtain a more general technique. In particular, our technique can be applied to arbitrary programs in the intermediate verification language, including programs that are not directly derived from a program in a production-type programming language, like C or Java. We have implemented a prototypical version of the program transformation in order to demonstrate that it can lead to up to exponential reductions in execution time for the Ultimate software verification tool, despite the cost of performing the initial static analysis.
Lempel-Ziv Factorization in Linear-Time O(1)-Workspace for Constant Alphabets

Computing the Lempel-Ziv Factorization (LZ77) of a string is one of the most important problems in computer science. Nowadays, it has been widely used in many applications such as data compression, text indexing and pattern discovery, and already become the heart of many file compressors like gzip and 7zip. In this paper, we show a linear time algorithm called Xone for computing the LZ77, which has the same space requirement with the previous best space requirement for linear time LZ77 factorization called BGone. Xone greatly improves the efficiency of BGone. Experiments show that the two versions of Xone: XoneT and XoneSA are about 27% and 31% faster than BGoneT and BGoneSA, respectively.
Black-box tests for algorithmic stability

Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g. removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm's stability properties is often useful for many downstream applications-for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish these properties through an empirical exploration of the algorithm's behaviour on various datasets. In this work, we lay out a formal statistical framework for this kind of black-box testing without any assumptions on the algorithm or the data distribution, and establish fundamental bounds on the ability of any black-box test to identify algorithmic stability.
Relationship between Social Network Characteristics, Flow in Class, Communication Skills, and Problem-solving Skills of Nursing Students in Simulation Nursing Education: Focusing on Social Network Analysis

Purpose: The purpose of this study was to investigate the relationship between social network characteristics, flow in class, communication skills, and problem-solving skills of nursing students in simulation.Methods: For this study a descriptive survey design was used. Participants were 100 nursing students who attended in 2 university and completed the self-report questionnaire. Data were collected from October 14 to December 6, 2019 and were analyzed with SPSS 26.0, AMOS 21.0 and Netminer 4 evaluation version.Results: It was confirmed that the task advice network centrality of nursing students fully mediates their communication skills and affects their problemsolving skills and that friendship network centrality completely mediates flow in class and communication skills, and affects problem-solving skills. Task advice network centrality, friendship network centrality, flow in class, and communication skills were found to explain problem solving skills by 51.8%.Conclusion: In order to increase the problem-solving skills of nursing students in simulation nursing education, a strategy to improve their flow in class and communication skills is essential and it suggests the need to make it part of the curriculum. In addition, in order to increase the communication skills of nursing students, it is necessary to be careful when making a team to minimize the team members isolated within the team so that smooth interaction can occur.
Semantic and Content-Based Medical Image Retrieval with Proven Pathology for Lung Cancer Diagnosis

In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. Due to lack of ground truth and semantic information, lung CAD systems are not progressing in the manner these are supposed to. In this study, we have explored Lung Image Database Consortium (LIDC) database containing annotated pulmonary computed tomography (CT) scans, and we have used semantic and content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. We evaluated the method by various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems while tested on PGIMER data. Also a little knowledge of biopsy confirmed cases can also assist the physician’s as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies
Trends in Blockchain Technologies

The aim of this work is to investigate emerging research trends and propose an evidence-based roadmap for encouraging further research into the management of blockchain technology. A bibliometric analysis is proposed, with a focus on intellectual property (IP) issues, in the field of blockchain technology. Then, the study highlights the main benefits that blockchain provides as well as the main difficulties, barriers, and challenges that emerge from the literature. The present study provides a reference for scientific communities to understand the current state of blockchain technology, thereby contributing to future research in the area. Moreover, it offers industrial implications and recommendations for entrepreneurs, managers, and practitioners.
Stereo phase unwrapping using deep learning for single-shot absolute 3D shape measurement

In fringe projection proﬁlometry (FPP), eﬃciently recovering the absolute phase has always been a great chal-lenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional patterns, which maximizes the eﬃciency of the retrieval of abso-lute phase. Inspired by recent successes of deep learning for phase analysis, we demonstrate that deep learning can be an eﬀective tool that organically uniﬁes phase retrieval, geometric constraints, and phase unwrapping into a comprehensive framework. Driven by extensive training dataset, the properly trained neural network can achieve high-quality phase retrieval and robust phase ambiguity removal from only single-frame projection. Experimental results demonstrate that compared with conventional SPU, our deep-learning-based approach can more eﬃciently and robustly unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views.
Research on Automated Reinforcement Learning: based on Tree-structured Parzen Estimators and Median Pruning

Tuning the hyper parameters of machine learning algorithm is regarded as a boring and difficult challenge to the researcher in the artificial intelligence domain. However, with the rapidly development of computer clusters and GPU processors, the automated machine learning algorithm have been proposed to solve this problem. In this paper, an automated reinforcement learning method has been proposed by focusing on the automated hyper parameters tuning of the reinforcement learning, a relatively new branch of machine learning which is more suitable to the motion control. For reducing the amount of calculations and finding the optimization solution more quickly, an open automated optimization framework have been proposed by combing the tree-structured Parzen estimators based Bayesian optimization and median pruning algorithm. A specific simulation has been given based on the deep deterministic policy gradient case to show the effectiveness and practicability of the proposed method.
A Forensic Approach on Data Retrieval from IC/eMMC of Damaged Windows Mobile Phone using Easy JTagPlus Box tool and Magnet Forensic Axiom

The digital world is dominated and ruled by the IOS and Android operating systems on mobile as well as tablet platforms. Together they share a whopping 99% (till 2021) of the share market. The Windows Phones (WP) are occupants of that less than 1% platform that they share with other subsidiary operating systems. Due to the lack of commercial popularity and production of such Windows devices, the process of data extraction and analysis of such devices is unique and challenging for forensic experts. The standard forensic data retrieving software and hardware do not support advanced requisition techniques except the direct extraction. Thus, in cases with locked WP devices, the software/ hardware is unable to support the device’s physical extraction or lock bypass facility. As observed in several digital cases, these portable devices contain details of an individual’s most private life including communications, contacts, browsing history, and location specifics at any given time. Although the operating systems of Microsoft and Windows mobile devices are similar in certain ways, specialized skill sets and tools are required while dealing with location, examination, and interpretation of the digital evidence on these systems. In this research paper, the authors are discussing the reliability and success of data extraction of a Windows mobile device from IC/eMMC using specialized hardware/software with the Windows device.
Fault-tolerant Structures of Digital Devices Based on Boolean Complement with the Calculations Checking by Sum Codes

The article considers the construction of fault-tolerant digital devices and computing systems that does not use the principles of introducing modular redundancy. To correct the signals, a special distorted signal fixation unit, concurrent error-detection by the pre-selected redundant code circuit, as well as a signal correction block are used. The distorted signal fixation unit is implemented by the Boolean complement method, which makes it possible to design a large number of such blocks with different indicators of technical implementation complexity. When synthesizing a fault-tolerant device according to the proposed method, it is possible to organize a concurrent error-detection circuit for both the source device and the Boolean complement block in the structure of the distorted signal fixation unit. This makes it possible to choose among the variety of ways to implement fault-tolerant devices according to the proposed method, one that gives a device with the least structural redundancy. Various redundant codes can be used to organize concurrent error-detection circuits, including classical and modified sum codes. The author provides algorithms for the synthesis of distorted signal fixation unit and the Boolean complement block. The results of experimental researches with combinational benchmarks devices from the well-known LG’91 and MCNC Benchmarks sets are highlighted. The article presents the possibilities of the considered method for the organization of faulttolerant digital devices and computing systems.
READABILITY OF READING MATERIALS IN KEMENDIKBUD ENGLISH TEXTBOOK REVISED 2016 FOR THE TENTH GRADE STUDENTS OF COMPUTER AND NETWORK ENGINEERING PROGRAM AT SMKN 1 DOLOK MASIHULREADABILITY OF READING MATERIALS IN KEMENDIKBUD ENGLISH TEXTBOOK REVISED 2016 FOR THE TENTH GRADE STUDENTS OF COMPUTER AND NETWORK ENGINEERING PROGRAM AT SMKN 1 DOLOK MASIHUL

This study was aimed to describe the readability level of the reading materials in Kemendikbud English textbook revised 2016 for the tenth grade students of computer and network engineering program written by Utami Widiati, et al. The design of this research is used descriptive qualitative research. The subject of this research is Kemendikbud English textbook revised 2016 for the tenth grade students of computer and network engineering and an English teacher at SMKN 1 Dolok Masihul. The instruments of the data collection in this study were interview and Flesch Reading Ease Formula. The researcher analyse the data to get judgment from professional teacher by interview and analyse the reading materials in textbook used Flesch Reading Ease Formula to determine the level of readability. Therefore, the researcher takes a conclusion that the result of interviewing English teacher was the students ability in comprehend the reading texts still low and the readability of reading materials in the textbook fairly difficult to comprehend by the students and the result of the data analysis by using Flesch Reading Ease formula indicates that the texts can be categorized; fairly easy, fairly difficult and difficult.
The History of the DReaM Group

I describe the history of the DReaM Group (Discovery and Reasoning in Mathematics), which I created after my arrival at the University of Edinburgh in 1971. The group has been characterised by its diversity of approaches to the representation of and reasoning with knowledge, including: deduction; meta-level reasoning; learning, especially of new reasoning methods; representation creation and change; as well as applications to problems as diverse as formal verification, analogical blending and computational creativity. From 1982, we have been supported first by a series of EPSRC rolling grants and then, when this funding mechanism ceased, platform grants. Now that the latter mechanism has also ceased, we felt it was time to take stock, celebrate our achievements, assess our strengths and plan our future research. This history lays the bedrock for that self-analysis. Inevitably, space restrictions have forced me to be highly selective in what research I cover. I apologise to those whose excellent research I have had to omit or only hint at. My selection has been mainly influenced by my desire to illustrate our methodological and application diversity. I hope that the other chapters in this book will fill some of those gaps.
Foreword to the Special Section on the 2021 Symposium on Virtual and Augmented Reality (SVR 2021)

• A model of groups with flexible structures, relationships, and personalities. • Real-time automated stereoscopic camera control for depth-authored virtual scenes. • Assessment of displays and interaction techniques on perception and learning.
About long period sequences

This paper presents estimation results of the ratio of input to output sequence periods for combination schemes and the ratio of input to internal sequence periods for schemes with a memory element. A decrease in the length of output sequence period, which is possible in the case with circuits with a memory element, is not discussed. The following results are given: (1) for any $${\varvec{m}}$$ sequences with period lengths of $${{\varvec{T}}}_{1}$$ ,…, $${{\varvec{T}}}_{{\varvec{m}}}$$ , $${\varvec{m}}>1$$ , a lower bound of their modification period length is given using the combination function $$\varvec{\upvarphi }({{\varvec{y}}}_{1},\dots ,{{\varvec{y}}}_{{\varvec{m}}})$$ that is bijective for some variables; (2) the modification period length for $${\varvec{m}}>1$$ sequences generated by maximum-length linear feedback shift registers is proved to be maximal, using the combination function $$\varvec{\upvarphi }({{\varvec{y}}}_{1},\dots ,{{\varvec{y}}}_{{\varvec{m}}})$$ , that depends essentially on all variables, and the number of ones per period of the output sequence is estimated; (3) for a certain class of non-autonomous automata, it is shown that the ratio of the period lengths of the internal sequence to the input sequence can be large, in particular, reach the number of automaton states. The conditions under which the period lengths of resulting sequences are maximum are specified.
FlexOS

Modern operating systems are tightly coupled to a specific isolation approach and safety mechanism. At design time, the isolation strategy is set in stone and rarely revisited later, due to prohibitive costs. This lack of flexibility hurts specialization, makes it hard to leverage new software/hardware isolation technologies, and makes the OS less resilient to attacks targeting the isolation mechanism. To address these issues we have developed FlexOS, a novel libOS approach that decouples isolation properties from the OS design. Depending on the configuration, the same FlexOS code can mimic a microkernel with multiple address-spaces, a single-address-space OS with Intel MPK compartments, or many other OS isolation approaches.
A Simplistic Overview of Machine Learning

While dealing with machine learning, a computer learns first to perform a roles/task by learning a set of training examples. The computer performs then the same task along with data it hasn't found before. This paper presents a brief overview of machine-learning types along with instances. The paper also covers differences between supervised and unsupervised learning.