Reviewerly/exHarmony huggingface dataset:
The Application of AutoCAD in the Drawing of Archaeological Artifacts—Line Plot Surveying and Mapping of Standard and Complete Artifacts

In the process of archaeological excavation and artifact sorting, the direct use of computers to draw artifact maps has not yet reached popularization. Compared with tomb maps, it is more difficult to master. It requires mappers to be skilled in engineering drawing software, archaeological typology and also the basic judgment on the characteristics of artifacts’ shape and structure. This article focuses on analyzing the advantages of CAD drawing artifact maps, and taking the Jomon pottery amphora as an example, introduces the line plot operation process of relatively standard and complete artifacts by using AutoCAD software, summarizes the methods and key points of line drawing surveying and mapping, and discusses its operating principles and precautions.
Parallel Implementation of Swarm Intelligent Algorithms in a Spark-Based Cloud Computing Environment

Swarm intelligent algorithms can effectively tackle optimization problems that are difficult to solve by using traditional optimization algorithms. However, with the huge increase in the time and space cost for solving optimization problems, the use of swarm intelligent algorithms suffer from the limitation of overly long computation time. Based on Spark, which is the most popular open-source distributed computing framework, this paper studies specifically using swarm intelligent algorithms to solve combinatorial optimization problems. Based on the characteristics of typical swarm intelligent algorithms, we develop Spark-based parallel implementation of these algorithms to accelerate the population updating and parameter tuning procedures involved in swarm intelligence. Specifically, we first initialize the swarm and generate the initial solution, then perform the distributed iterative evolution procedure, and finally obtain the optimal solution. In addition, in order to improve solution quality, we rely on the Spark platform to perform distributed parameter tuning. The tuning strategy first generates different parameter combinations according to a given parameter list, then execute swarm intelligent algorithms with different parameter combinations in a distributed and parallel manner, and finally determine the optimal parameter combination by comparing the solutions of all algorithms. Experimental results on benchmark datasets show that the distributed algorithms can significantly enhance the computational efficiency without affecting the solution quality.
Best of both worlds: a model combining online and mixed reality learning environments based on cognitive affordances

The purpose of this study is to evaluate cognitive affordances of two course designs: a Moodle-based online course and a mixed reality (MR) learning environment. The evaluation is done with a semi-structured qualitative survey based on Cognitive affordances of technology-framework. This study helps understand how different features of these environments support student learning. The study also proposes a model on how different learning environments can be combined to create more effective virtual learning environment designs. The results imply that the MR environment works well in experiential learning, discursive learning and supportive learning, which are, on the other hand, weaknesses of the online environment. The strength of the online environment is that it promotes critical thinking, conceptual change and self-regulated learning, which are weaknesses of the MR environment. Learning by doing is supported in both environments. The analysis shows that the two environments are complementary in terms of cognitive affordances and their parallel use in course design is therefore encouraged.
Accelerating Convolutional Neural Networks in FPGA-based SoCs using a Soft-Core GPU

Field-Programmable Gate Arrays (FPGAs) have increased in complexity over the last few years. Now available in the form of Systems-on-Chip (SoCs) such as Xilinx Zynq or Intel Stratix, users are offered significant flexibility in deciding the best approach to execute their Deep Learning (DL) model: a) in a fixed, hardwired general-purpose processor, or b) using the programmable logic to implement application-specific processing cores. While the latter choice offers the best performance and energy efficiency, the programmable logic’s limited size requires advanced strategies for mapping large models onto hardware. In this work, we investigate using a soft-core Graphics Processing Unit (GPU), implemented in the FPGA, to execute different Convolutional Neural Networks (CNNs). We evaluate the performance, area, and energy tradeoffs of running each layer in a) an ARM Cortex-A9 with Neon extensions and in b) the soft-core GPU, and find that the GPU overlay can provide a mean acceleration of 5.9 $$\times $$ and 1.8 $$\times $$ in convolution and max-pooling layers respectively compared to the ARM core. Finally, we show the potential of the collaborative execution of CNNs using these two platforms together, with an average speedup of 2 $$\times $$ and 4.8 $$\times $$ compared to using only the ARM core or the soft GPU, respectively.
Argus: Efficient Job Scheduling in RDMA-assisted Big Data Processing

Efficient job scheduling is an important and challenging issue in big data processing systems. Traditional designs commonly give priority to data locality during scheduling and follow a network-optimized principle to avoid costly data moving across the network. The emergence of the high-performance Remote Direct Memory Access (RDMA) network brings new opportunities for big data processing systems. However, the existing RDMA-assisted designs ignore the dependency among stages during scheduling and this can result in unsatisfied system efficiency. In this work, we propose Argus, a novel RDMA-assisted job scheduler which achieves high resource utilization by fully exploiting the structure feature of stage dependency. Argus prioritizes the stages whose completion can enable more schedulable stages. We implement Argus on top of RDMA-Spark, and conduct comprehensive experiments to evaluate the performance using large-scale traces collected from real-world systems. Results show that compared to state-of-the-art designs, Argus reduces the job completion time and makespan by 38% and 31%, respectively.
Implementation of the Magic Book Math Media Based on Augmented Reality in UPGRIS Senior High School

This study aims to implement the Magic Book Math media based on Augmented Reality in UPGRIS Senior High School. The research model used the ADDIE learning design model. This model, as the name implies, consists of five main phases, namely (A) analysis, (D) design, (D) development, (I) implementation, and (E) valuation. The stages of analysis, design and development have been conducted in previous studies resulting in the magic book math media based on augmented reality, which was a valid product and worthy of use. In this study, the stages carried out were the implementation and evaluation stages. Due to data collection during the Covid-19 pandemic, data collection and learning processes were carried out online using the Zoom platform. Questionnaire student responses used Google Form. The results showed that implementing learning using the Magic Book Math media based on Augmented Reality in UPGRIS Senior High School met the learning objectives. This was indicated by the difference in learning achievement between the control and experimental classes, where the average value of the experimental class was x1 = 85.83, and the control class average was x2 = 64.43. The percentage of student responses about the feasibility of Magic Book Math based on Augmented Reality (AR) was 87.00%.
A High-Throughput Detection Circuit based on 2<sup>q</sup>+1-Valued Deep Neural Networks

The demands of applications using a high-speed deep learning models at data centers are rapidly increasing. However, most of these accelerators depend on many memory accesses and DSP blocks, which cause performance bottleneck. We present a lookup table (LUT) mapping to directly map convolutional layers, mainly used in modern deep learning models. To reduce the number of LUTs, we develop a training method for a sparse local convolution (SLC), which trains sparse convolutional layers with unshared weight kernels with 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sup> + 1-valued representation to eliminate a zero weight edge. Compared with conventional sparse CNN training methods, 88% of multiply-accumulate operations are reduced by SLC training while maintaining the same accuracy. We implement an LUT-based convolutional layer circuit with 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> LUTs, accommodated by data center FPGAs and operating at a high-speed at 500 MHz (500 MFPS).
Robust Indian Currency Recognition Using Deep Learning

The applications of currency counting are vast in real-time. From the ATM to small entrepreneur business applications includes currency recognition. In contrary, currency recognition is one of the HCI applications that can be taken for any human-computer application. This technology limits the manpower and human supervision. The emerging technologies such as AI and Machine Learning were now part of every application. In this regard, the proposed methodology is a robust methodology built on deep learning technique using relatives less number of images in the database with high noise, including rotated notes, tilted notes, held by hand note images. The proposed methodology extracts denomination in 90 s. It uses a Convolution Neural Network (Inception using transfer learning) to extract currency denomination. We acquired the accuracy of 83% in predicting the denomination.
ENHANCING GEOMETRIC EDGE DETAILS IN MVS RECONSTRUCTION

Abstract. Mesh models generated by multi view stereo (MVS) algorithms often fail to represent in an adequate manner the sharp, natural edge details of the scene. The harsh depth discontinuities of edge regions are eventually a challenging task for dense reconstruction, while vertex displacement during mesh refinement frequently leads to smoothed edges that do not coincide with the fine details of the scene. Meanwhile, 3D edges have been used for scene representation, particularly man-made built environments, which are dominated by regular planar and linear structures. Indeed, 3D edge detection and matching are commonly exploited either to constrain camera pose estimation, or to generate an abstract representation of the most salient parts of the scene, and even to support mesh reconstruction. In this work, we attempt to jointly use 3D edge extraction and MVS mesh generation to promote edge detail preservation in the final result. Salient 3D edges of the scene are reconstructed with state-of-the-art algorithms and integrated in the dense point cloud to be further used in order to support the mesh triangulation step. Experimental results on benchmark dataset sequences using metric and appearance-based measures are performed in order to evaluate our hypothesis.
An improved moving object tracking algorithm based on CAMSHIFT

Abstract There is a problem that Camshift algorithm relies too much on color as a criterion, in order to solve it, this paper presented an improved moving object tracking algorithm which combined Kalman filter predictor with Camshift algorithm. Firstly, the object model was built by Camshift Algorithm, and the centroid coordinates of the moving objectwere extracted, then the position of the object in the next frame was predicted by using Kalman filter, and finally, the value was used as the centroid coordinates of the object searched by Camshift algorithm in the next frame, proceeding with target match. The simulation results showed that this method can effectively solve the problem of target loss caused by the similar color between the target and the back. The new algorithm can effectively reduce the tracking error accumulation in the tracking process, greatly reduce the template drift, shorten the tracking time, and can have a better practicability and real-time performance.
Controllers Design Based on Affine T-S Fuzzy Model Using S-Procedure

Controllers based on the affine T-S fuzzy model are considered. Tak- ing advantage of an inequality on the system state when which is away from the origin, S-procedure is used to help us make the resulting LMIs more feasible.
Is Entropy enough for measuring Privacy?

Anonymization is critical to privacy. It helps protect the identity and sensitive information of individuals from their profile data. Knowing the degree of anonymity attained is an important step to advance privacy and anonymization techniques. However, little research has focused on articulating a measure to quantify the quality of anonymization. On the other hand, many have used popular Shannon's entropy, a well-established measure from information theory, as a way to measure anonymity. In this paper, we take a closer look at the meaning, the distinction and the relationship between anonymity and entropy with respect to privacy. We argue that, even though information entropy is used amply as a metric for anonymity, it is not a befitting measure. Furthermore, although parts of the entropy's information theory are relevant, they alone are not adequate to be a proper measure for anonymity. This paper presents a simple, intuitive, and theoretically grounded measure for anonymity. We provide a comparison analysis between our measure with other entropy-based metrics along with experiments to show the effectiveness of our proposed measure.
Fall Detection Using Half Circle Ultrasonic Array Sensors for Indoor Environment

In indoor environment that elderlies live, ultrasonic array sensors which are believed to protect their privacy, are hidden in the walls for collecting distance signals of all things in the place. The distance signals in term of array which are assumed as distance images are then needed to process for detecting elderly fall. This paper discusses how to process these distance images for fall detection by using machine learning. First of all, moving objects in the indoor environment are detected by subtraction between neighboring frames, all continuing frames of moving objects are classified by machine learning, and classification results of all frames are finalized to recognize elderly fall. The experiments with some samples and constructed prototype based on the fall standard are performed, and the results reveal good performance in accuracy.
Perspective and Experiences of Decision Support Systems (DSS) and AI in Prevention and Care for Older Adults

This chapter describes the experience with the NESTORE DSS, a software conceived to be the brain of the system, and the main collector of information, which objective is to recommend personalised and targeted activities to be performed by older adults who are willing to prevent decline. It reports on the lessons learnt and important methods of the whole process from the design to the implementation.
Automatic Summarizing the News from Inform.kz by Using Natural Language Processing Tools

The rapid rise of the information on the web brought up new problems of data access and processing. Therefore there is a need for tools that will help to overcome the problem of management and handling the Big Data in a quick manner. The primary goal of this work is to propose an efficient method for automatic text summarization by using Natural Language Processing (NLP) and Machine Learning (ML) techniques. This research introduces an abrupt, easily understandable and uncomplicated implementation of this method via overusing Python programming language. Efficient performance is necessary in web search tasks where an enormous of unstructured data need to be summarized very quickly. The novelty of the work is that text summarization is implemented on Kazakh texts. Extractive summarization uses new, keywords focused, approach. Contribution of the work is manually created stop words used for text summarization specifically for Kazakh language and dataset constructed by scraping news from country's largest international news portal www.inform.kz. State-of-the-art results of the work show that it is possible to implement automatic text summarization for Kazakh language.
Application of Analytic Network Process in Power Grid Development-Diagnosis Management

Current power grid development-diagnosis management doesn't analyze the power grid cooperation-scale, development-speed, business-quality, operation-effectiveness and internal policy from comprehensive view. In order to improve management efficiency, this paper modified the evaluation method, built the evaluation index system based on Analytic Network Process (ANP), which took the connection and restriction among index into full consideration. And then took 5 aspect namely Scale, Structure, Safety, Effectiveness and Policy of 220 kV Grid from one province into calculation, obtained the provided the index score. Finally, concluded the advantages and disadvantages of this province 220 kV grid and provided objective and pointed suggestions to the high-quality development of power grid management as an example to other provinces.
Sequence-Based Word Embeddings for Effective Text Classification

In this work we present DiVe (Distance-based Vector Embedding), a new word embedding technique based on the Logistic Markov Embedding (LME). First, we generalize LME to consider different distance metrics and address existing scalability issues using negative sampling, thus making DiVe scalable for large datasets. In order to evaluate the quality of word embeddings produced by DiVe, we used them to train standard machine learning classifiers, with the goal of performing different Natural Language Processing (NLP) tasks. Our experiments demonstrated that DiVe is able to outperform existing (more complex) machine learning approaches, while preserving simplicity and scalability.
AirCollect

Apple's file-sharing service AirDrop leaks phone numbers and email addresses by exchanging vulnerable hash values of the user's own contact identifiers during the authentication handshake with nearby devices. In a paper presented at USENIX Security'21, we theoretically describe two attacks to exploit these vulnerabilities and propose "PrivateDrop" as a privacy-preserving drop-in replacement for Apple's AirDrop protocol based on private set intersection.
Analysis of C4.5 Algorithm of Water Quality Dataset

Abstract The C4.5 algorithm still has weaknesses in predicting or classifying data if a large number of classes are used which can lead to increased decision-making time. So an approach is needed to improve the performance of the C4.5 algorithm with the selected split attributes that use the application of the average gain value to help predictions. The C4.5 algorithm is one of the Decision Tree methods in the classification process using the information entropy concept. The C4.5 algorithm uses the split criteria from ID3, the Gain Ratio is a modification of the method. The ID3 algorithm uses Information Gain (IG) for the split attribute criteria, while the C4.5 algorithm with Gain Ratio (GR), where the root value comes from high gain. The conclusion of the tests that have been carried out using the Water Quality dataset in the C4.5 method has an accuracy rate of 91.30%, with a classification error rate of 8.70%. Successful implementation using the C4.5 method in predicting the Water Quality dataset.
Towards a More Structured Peer Review Process with Empirical Standards

Context. Empirical research consistently demonstrates that that scholarly peer review is ineffective, unreliable, and prejudiced. In principle, the solution is to move from contemporary, unstructured, essay-like reviewing to more structured, checklist-like reviewing. The Task Force created models—called “empirical standards”—of the software engineering community’s expectations for different popular methodologies. Objective. This paper presents a tool for facilitating more structured reviewing by generating review checklists from the empirical standards. Design. A tool that generates pre-submission and review forms using the empirical standards for software engineering research was designed and implemented. The pre-submission and review forms can be used by authors and reviewers, respectively, to determine whether a manuscript meets the software engineering community’s expectations for the particular kind of research conducted. Evaluation. The proposed tool can be empirically evaluated using lab or field randomized experiments as well as qualitative research. Huge, impractical studies involving splitting a conference program committee are not necessary to establish the effectiveness of the standards, checklists and structured review. Conclusions. The checklist generator enables more structured peer reviews, which in turn should improve review quality, reliability, thoroughness, and readability. Empirical research is needed to assess the effectiveness of the tool and the standards.
Correcting Erasures with Topological Subsystem Color Codes

Qubit loss is one of the forms of noise encountered in some quantum technologies. Such noise is modeled using the quantum erasure channel. Unlike the depolarizing noise, it is much more tractable, yet the performance of many quantum codes over the erasure channel has not been studied as extensively. In this paper, we study the performance of topological subsystem color codes (TSCCs) over the quantum erasure channel. It is the first such study of TSCCs over the erasure channel. We propose multiple decoding algorithms for TSCC and obtain the highest threshold of about 9.7% for the subsystem color code derived from the square octagon lattice.
Cluster-based Generative Adversarial Network Imbalanced Data Generation Method

Data imbalance is widespread in data mining. However, the imbalance of data makes the minority recognition rate low but the cost of misclassification is high. In order to improve the minority recognition rate, this paper proposed a data generation method for balance data set. First, the minority data is clustered and divided into sub-categories, next it built a GAN model to generate each sub-category data separately to improve the quality of data generation and balance the number of majority and minority categories, and also alleviate the imbalance within the minority category. Then, this paper built a cost-sensitive convolutional neural network model for classification. Finally, experiments on the UCI public data set show that the method has a good classification performance.