Reviewerly/exHarmony huggingface dataset:
Observability for 3-D Range-only Positioning System

Three-dimensional range-only positioning system (3-DRPS) is characteristic of nonlinearity and measurement incompleteness and so on, and involved in observability. In order to attain the observable conditions for 3-DRPS an analytic expression of Gram matrix determinant of a linear system (LS) derived from the 3-DRPS is precisely deduced. After the observable conditions of the LS are attained, we prove a sufficient and necessary condition of the observability for the 3-DRPS which are simply expressed by the algebraical expression of observation point coordinates and measurements. This may be prone to being used as observability criteria for 3-DRPS. It is geometrically showed that the 3-DRPS is observable if and only if there are two observation points that are collinear with target, or there are three non-collinear observation points that are coplanar with target, or there are four non-coplanar observation points. The results have special reference to the application to control of observer points and choice of beacon position and satellite.
Statistical Education as Part of the Digital Transformation of Statistical Offices

National Statistical Institutes agencies are faced with growing opportunities and challenges from the ongoing digital revolution. This chapter considers the cooperation among academic, public, and private sector entities to further develop statistical skills to make effective use of the newly available data. Such cooperation offers opportunities for better understanding and learning from each other about best practices. This chapter specifically focuses on the European Master of Official Statistics (EMOS), a degree program to prepare students for roles in data analysis and also a knowledge network of official data producers and universities working together to develop best practices and skilled analysts. It also describes the approaches and experiences of the Federal Statistical Office of Germany (Destatis).
Rapid Temporal Information Identification in Paper Worksheets Using Light-Weight Convolutional Neural Networks

Even current OCR technology has made significant progress with the help of deep learning, rapid identification algorithms for specific information in documents still need elaborate design. In this paper, a lightweight operating framework based on deep neural network is established to deal with the extraction of date and time information from paper worksheets used in transformer station. In the framework, YOLOv4-Tiny is employed to detect the locations of the dates and the time in document images which are mixed with printing and handwritten fonts. Since the handwritten dates in pictures are difficult to segment, a novel lightweight CNN named MobileDateNet is proposed that can recognize the date serially without segmentation. The experimental results show that the identification accuracy of the whole framework reaches 92.1% at the average running speed of 36.5 FPS on an NVIDIA 3090 GPU, ensuring the recognition accuracy and the operating efficiency.
Colluding through smart technologies: Understanding agreements in the age of algorithms

By affecting business strategies and consumers’ behavior, the wide-scale use of algorithms, prediction machines and blockchain technology is currently challenging the suitability of several legal rules and notions which have been designed to deal with human intervention. In the specific sector of antitrust law, the question is arising on the adequacy of the traditional doctrines sanctioning anticompetitive cartels to tackle coordinated practices which, in the absence of an explicit “meeting of the minds” of their participants, may be facilitated by algorithmic processes adopted, and eventually shared, by market actors. The main concern in these cases, discussed both at regulatory and academic level, derives from the general observation that while the traditional concept of collusive agreement requires some form of mutual understanding among parties, nowadays decision-making of firms is increasingly transferred to digitalized tools. Moving on from these premises, the paper investigates the impact that the rules applicable to the conclusion of (smart) contracts may have, from an antitrust law perspective, in the detection and regulation of anticompetitive practices.
Task scheduling and resource allocation of seasonal requests of users in cloud using NMKA and CM-GA techniques

Purpose The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC). Design/methodology/approach Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall. Findings The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding P rc (Precision), R ca (Recall), F ms (f measure), Acr (Accuracy)and C t (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for P rc , R ca , F ms and A cr , takes the lowest time (C t ) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to R t (Response Time), P t (Process Time), A wt (Average Waiting Time), A tat (Average Turnaround Time), L cy (Latency) and T p (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for R t , P t , A wt , A tat and L cy and also provides the highest values for T p . So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud. Originality/value The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.
USING OF I-EXAM.RU INFORMATION PORTAL FOR TEACHING MATHEMATICAL DISCIPLINES

The paper considers the possibilities of the information portal i-exam.ru, used to organize teaching Mathematics and other disciplines, including activities that can be carried out with computer testing.
ExecutionFlow: a tool to compute test paths of Java methods and constructors

Calculating the code coverage of a test suite depends on the identification of the executed test paths. Available code coverage tools are limited to a few basic tracing strategies and do not provide this information as proper test paths, precluding its use in further analyses. We present ExecutionFlow, a solution to trace executed test paths in Java code. The tool is based on the use of a debugger, aspect-oriented programming, and a testing framework. Through empirical validation we show that it is possible to obtain the test path from several methods and constructors, including high complexity ones. Video demonstration: https://youtu.be/2W5BjVuc6fw
Fingertip blood sampling robot navigation system based on binocular vision

Abstract In the fingertip intelligent blood collection robot, we need to move the blood collection needle to the designated position quickly and accurately, but it is difficult to meet the two indicators of speed and accuracy at the same time. This paper proposes a fast navigation method based on binocular vision. This method obtains the coordinates of the fingertip blood sampling point through three-dimensional reconstruction and contour extraction, and guides the robotic arm to quickly move the blood sampling needle to a position near the blood sampling point according to the coordinate position. The method in this paper is used for the rough navigation of blood collection robots, and it is verified by experiments that this method can meet the needs of blood collection robots.
ACKERMANN’S FUNCTION IN ITERATIVE FORM: A PROOF ASSISTANT EXPERIMENT

Ackermann's function can be expressed using an iterative algorithm, which essentially takes the form of a term rewriting system. Although the termination of this algorithm is far from obvious, its equivalence to the traditional recursive formulation--and therefore its totality--has a simple proof in Isabelle/HOL. This is a small example of formalising mathematics using a proof assistant, with a focus on the treatment of difficult recursions.
MIT Full STEAM Ahead: Bringing Project-Based, Collaborative Learning to Remote Learning Environments

Abstract With schools and educational centers around the country moving from in-person to emergency remote learning due to the COVID-19 pandemic, education faces an unprecedented crisis (Hodges et al., Educause Review 27, 2020). This case study presents the efforts and impact of Full STEAM Ahead (FSA) launched by the Massachusetts Institute of Technology (MIT) in response to the pandemic to support remote collaborative learning for K-12 learners, parents, and educators. We present two FSA initiatives: (1) weekly themed packages with developmentally appropriate activities for K-12 remote learning and (2) Full STEAM Ahead Into Summer (FSAIS), an online summer program for middle school Massachusetts students, specifically targeting students who are at risk for “COVID Slide.” (Institute-wide Task Force on the Future of MIT Education-Final Report: http://web.mit.edu/future-report/TaskForceFinal_July28.pdf ?) Our operative theory of change is that we can improve K-12 remote collaborative learning experiences through developing and sharing a curriculum that exemplifies the minds-on and hands-on approach advocated by MIT, strategically leveraging existing structures and projects within MIT, and establishing partnerships with the local and international community. We gauge the effect of these efforts on contributing members of the MIT community and targeted learners by analyzing data gathered through participant surveys and artifacts such as the website, packages, modules, and student projects created during the summer programs. Our findings indicate that existing structures and resources – with community building – facilitated the achievement of our goal to develop and distribute problem-based learning activities and that interaction and community building were central in meeting those goals. This work contributes to the knowledge base regarding emergency online learning and the development of effective university outreach efforts.
Towards free-space quantum key distribution with a 2D single-photon sensor

Applications of quantum key distribution are becoming more diverse due to the increase in interest in the secure key sharing protocol. Transmission through free-space channels has risen in popularity in recent years, primarily due to global coverage using satellite platforms. However, free-space channels come with challenges that need to be addressed, such as; diffraction loss, background noise, pointing-and-tracking, and atmospheric aberration. Novel design and use of state-of-the-art detector technologies in quantum receivers can help alleviate these difficulties. This paper presents and discusses the implementation of 2D single-photon sensor technology for free-space quantum key distribution. We present an experimental method that utilizes independent single-photon avalanche diode pixel read-out to reduce background noise contributions while simultaneously increasing optical field-of-view. Finally, we show and discuss single-photon level beaconing capabilities for pointing and tracking.
Phase Space Reconstruction Network for Lane Intrusion Action Recognition

In a complex road traffic scene, illegal lane intrusion of pedestrians or cyclists constitutes one of the main safety challenges in autonomous driving application. In this paper, we propose a novel object-level phase space reconstruction network (PSRNet) for motion time series classification, aiming to recognize lane intrusion actions that occur 150m ahead through a monocular camera fixed on moving vehicle. In the PSRNet, the movement of pedestrians and cyclists, specifically viewed as an observable object-level dynamic process, can be reconstructed as trajectories of state vectors in a latent phase space and further characterized by a learnable Lyapunov exponent-like classifier that indicates discrimination in terms of average exponential divergence of state trajectories. Additionally, in order to first transform video inputs into one-dimensional motion time series of each object, a lane width normalization based on visual object tracking-by-detection is presented. Extensive experiments are conducted on the THU-IntrudBehavior dataset collected from real urban roads. The results show that our PSRNet could reach the best accuracy of 98.0%, which remarkably exceeds existing action recognition approaches by more than 30%.
An Analysis of an Augmented Reality Application to Support Service Staff in Industrial Maintenance

Augmented Reality is an emerging technology and gains in importance. With AR, the physical world can be extended with virtual content. Especially AR-applications for industrial maintenance are experiencing an upswing. AR-technologies for maintenance aim to improve human performance by providing relevant information for a specific task. This information can be provided by sensor data from machines, software systems, or human experts. Currently, there are not many industry-ready AR-applications on the market. This lack raises the question if AR-applications can effectively support service staff in the field. An experimental investigation was carried out to explore this field. Two industrial maintenance cases with different levels of difficulty were simulated, whereby service staff had to solve these real-life problems. Support from an expert was provided via an AR-application. In addition, the Affinity for Technology Interaction score was assessed in order to identify the target group for AR-applications. The results clearly show that AR-applications can effectively support service staff in the field. The research also reveals that a high Affinity for Technology Interaction score of the service technician leads to a higher acceptance of the AR-application. The application offers many potentials, but there are still some limitations concerning the user interface and technological aspects.
Tweet Contextualization Approach Using a Semantic Query Expansion

Twitter is a communication medium and a collaboration system that allows broadcasting short messages called tweets. In contrast to traditional blogs, media-sharing and social networks services, microblogs (tweets) are textual messages submitted in real-time to report an idea, an actual interest, or an opinion. The size of these messages may be limited by a maximum number of characters. This constraint, related to the size of message, causes the use of a particular vocabulary. The aim is to exchange a maximum of information in as little characters as possible. In this respect, we will focus on the Tweet Contextualization task. The purpose of this task is to allow the reader a better understanding of the tweet. This paper deals with a new Tweet Contextualization approach using a semantic query expansion. The main idea of our proposed method is to enhance queries (tweets) in order to produce more informative contexts. The effectiveness of our method is proved through an experimental study conducted on the INEX 2014 collection.
TextRank Keyword Extraction Method Based on Multi-feature Fusion

Keyword extraction technology is widely used in text mining tasks such as text classification, text clustering, and abstract extraction. There are many ways to extract keywords, but the efficiency of keyword extraction still needs to be improved. In response to this issue, we proposed a multi-feature fusion TextRank (MFFTR). After constructing the text into a word network graph, the frequency features and position features of the words were merged as the initial weights of word nodes, and the dual-core network node contribution calculation method was used to assign weights to edges. By doing so, a new node weight calculation method based on the traditional TextRank was introduced. Experiments were conducted on two different datasets with significant differences in average text lengths. The results showed that the multi-feature fusion TextRank proposed in this paper was significantly better than the five keyword extraction methods. We concluded that the use of multi-feature fusion can improve the efficiency of automatic keyword extraction.
A Similarity Measure of Gaussian Process Predictive Distributions

Some scenarios require the computation of a predictive distribution of a new value evaluated on an objective function conditioned on previous observations. We are interested on using a model that makes valid assumptions on the objective function whose values we are trying to predict. Some of these assumptions may be smoothness or stationarity. Gaussian process (GPs) are probabilistic models that can be interpreted as flexible distributions over functions. They encode the assumptions through covariance functions, making hypotheses about new data through a predictive distribution by being fitted to old observations. We can face the case where several GPs are used to model different objective functions. GPs are non-parametric models whose complexity is cubic on the number of observations. A measure that represents how similar is one GP predictive distribution with respect to another would be useful to stop using one GP when they are modelling functions of the same input space. We are really inferring that two objective functions are correlated, so one GP is enough to model both of them by performing a transformation of the prediction of the other function in case of inverse correlation. We show empirical evidence in a set of synthetic and benchmark experiments that GPs predictive distributions can be compared and that one of them is enough to predict two correlated functions in the same input space. This similarity metric could be extremely useful used to discard objectives in Bayesian many-objective optimization.
Study on image quality Control Method based on Gaussian Noise

Abstract At present, various image quality evaluation algorithms emerge in endlessly. The establishment of these algorithms, the performance evaluation of the system and the accuracy of evaluation results all need to be based on images of different quality as reference standards. This paper selects the peak signal-to-noise ratio as the characteristic parameter to measure whether the image quality changes, and uses the control image quality parameter to control the image quality. The experimental results show that the model can quickly and accurately obtain a large number of images with different distortion degrees, and control the image quality.
Gamifying Flipped Classes: An Experience Report in Software Engineering Remote Teaching

Context. In the COVID-19 pandemic, higher education was one of the most affected in adapting the teaching-learning process to comply with social distance measures. In this context, this research reports how we combined two active methodologies in Software Engineering Emergency Remote Teaching. Objective. Therefore, this study aims to report the experiences obtained from the application of gamified study guides in Software Engineering (SE) between November 2020 and April 2021. Method. We combined flipped classroom, an active methodology already known by them, with Gamification to improve students’ acceptance and engagement. We created six gamified study guides for the SE flipped classes. 51 students participated in the classes, 12 students from the Computer Technician course and 39 students from the Computer Science Bachelor course. Each study guide had a group of checkpoints to organize the content in levels. Results. At the end of the application, we observed 83.6% of the participating students maintained their engagement. According to self-declaration, 85% felt motivated by the approach adopted, with 75.9% considering that the class format presented enhanced their learning. We also observed a positive impact on learning gain in an average of 18.3% in their grades. Final Considerations. We concluded that using Gamification and Flipped Classroom has positive impacts on learning and acceptance, although the COVID-19 pandemic and the ERT negatively affect students’ motivation.
Lightweight convolutional neural network of YOLO v3- Tiny algorithm on FPGA for target detection

With the rapid development of artificial intelligence, the convolution of the neural network has been deploying across a range of industries, and has certain applications in national defense security, transportation monitoring and medical research. However, due to the constraints of speed and the power consumption, convolutional neural networks are still limited to a great extent in the implementation of the edge computing and mobile devices, etc. For this reason, we design a lightweight convolutional neural network based on FPGA. In this paper, we use the YOLOv3- Tiny algorithm, which is fast in execution, small in computation and small in size, is suitable for deployments on embedded devices such as FPGA. This paper uses 16-bit fixed-point quantization, special data storage and hardware circuit for convolutional computation written in verilog - hardware description language, consumes 512 DSPs, consumes 37.037 ms to recognize a single image frame, and consumes 9.611W. The system basically achieves the design goal of target detection.
Method for Estimating the Total Distributed Photovoltaic Generation Power in a Load Region

In the simulation analysis of the power grid, the load region is usually equivalent to the load model. With the widespread access to distributed photovoltaic power generation (DPVG), the dynamic characteristics of the load region change. Therefore, when modeling the load region, the DPVG should be considered. To take the DPVG into account in load modeling, obtaining the total DPVG power in the load region is required. However, the DPVG systems connected to 380-V voltage level are only equipped with watthour meter nowadays, it is difficult to obtain the accurate total generation power or accurate total installed capacity of the DPVG systems in operation. To this end, a method to estimate the total DPVG power in the load region is proposed. First, a DPVG station with real-time power measurement in the load region will be chosen as an observation station; then, the ratio between the generation power of the DPVG observation station and the total DPVG power in the load region will be determined through the similarity of the load power in two similar days; last, the total DPVG power in the load region can be obtained by multiplying the generation power of the DPVG observation station by the estimated power ratio. Because the proposed method does not need the data of light intensity or ambient temperature, it is easy to implement. The effectiveness of the proposed method is verified by field measurement.