Reviewerly/exHarmony huggingface dataset:
Median algorithm for estimating the variance of intrinsic noise when detecting a signal with an unknown initial phase

The article presents the finding of the dispersion of intrinsic noise distributed according to Rayleigh's law to find a signal with an unknown initial phase according to the Neumann- Pearson criterion. This algorithm is used for primary signal processing. The practical significance of the work is the study of the basics of programming in C and Python, comparison and identification of the advantages of this algorithm in comparison with the evaluation by the average value method. It also shows the finding of the threshold at which a decision is made to detect a useful signal. In this work, the integrated programming environment Visual studio was used – a popular environment for writing, debugging and compiling code. It also contains most of the libraries used, which simplify the writing of code. And Anaconda is a distribution for the Python programming language, which includes a set of free libraries, which is also freely available. Using the C language, the task of calculating data and filling it into an array was performed, Python was used for modeling, constructing histograms and comparing the results obtained with theoretical ones. This work can be used for teaching students of higher educational institutions for the purpose of familiarization. The optimal method is considered. Familiarization and study of these programming languages are conducted within the walls of one of the leading engineering universities of the Russian Federation, the Bauman Moscow State Technical University.
Comparison of techniques for distillation of entanglement over a lossy channel

We analyze three quantum communication protocols that have been proposed in the literature, and compare how well they communicate single-rail entanglement. We use specific metrics for output state purity and probability of success and include the presence of imperfect photon source and detection components. We find that a distributed noiseless linear amplification (NLA) protocol with a relay point placed half-way between Alice and Bob outperforms NLA at Bob's end and a recently proposed purification protocol under most conditions, unless the distance is very small or the photon source component is very good.
Template pool updating and feature combination object tracking

In the target tracking task, most Siamese network target tracking algorithms only use the template information of the first frame and do not make good use of the feature information of the subsequent frames, which shows poor robustness and target drift in dealing with target occlusion and appearance change in complex scenes. In this regard, an online template pool update module is established based on the SiamFC++ algorithm to store the information of reliable template frames, and an accuracy and necessity judgment mechanism of template update is proposed to improve the reliability of the final template. In addition, a feature combination module is proposed to capture the information of different scale regions and obtain more scale information through an asymmetric dilated convolution layer. Experiments and analysis are conducted on OTB2015 and VOT2018 datasets, and the proposed algorithm improves the accuracy and success rate by 1.7% and 1.7%, respectively, on OTB2015 compared with the SiamFC++ algorithm; the accuracy and average overlap rate improve by 0.8% and 6.1%, respectively, on VOT2018, and the robustness decreases from 0.169 to 0.140, and the tracker more stable.
Fractal Structure Characteristics and Prospecting Direction of Dispersed Metals in the Eastern Guizhou Pb–Zn Metallogenic Belt, SW China

The eastern Guizhou Pb–Zn metallogenic belt (EGMB) is an important source of Pb–Zn resources and other critical minerals (including dispersed metals, such as Ge, Cd and Ga) in China. In order to ensure the continuous resource supply of Pb–Zn and associated dispersed metals, it is urgent to explore the direction of further prospecting for them. Fractal theory can realize the fractal structure characterization of fault structures and the spatial distribution of mineral deposits, which is helpful for mineral exploration. However, the fault fractal research and prospecting application are still seldom covered in the EGMB. We used fractal theory to determine fine-scale fractal structure characteristics of fault structures and ore deposits in the EGMB, and Fry analysis to delineate favorable metallogenic areas. The results show that within a scale range of 3.670–58.716 km, the integrated faults capacity dimension (CPD) is 1.5095, the information dimension (IND) is 1.5391, and the correlation dimension (CRD) is 1.5436, indicating fault structures with high maturity, which are conducive to the migration and accumulation of ore-forming fluids. The multi-fractal spectrum width and height are 0.3203 and 1.5355, respectively, implying a significant metallogenic potential. The spatial distribution fractal dimensions (SDD) of Pb–Zn specifically and metal deposits in general are 1.0193 and 1.0709, respectively; the quantity distribution fractal dimensions (QDD) are 1.4225 and 1.4716, respectively, and the density distribution fractal dimensions (DDD) are 1.422 and 1.472, respectively, indicating strong clustering. Hence, the favorable metallogenic regions can be divided into four grades, among which grade I region is continuously distributed in space and has the greatest prospecting potential.
Report on the 4th Global Summit of Research Institutes for Disaster Risk Reduction (4thGSRIDRR2019)

The 4th Global Summit of Research Institutes for Disaster Risk Reduction (4thGSRIDRR2019) under the theme of Increasing the Effectiveness and Relevance of our Institutes was organized by the Global Alliance of Disaster Research Institutes (GADRI) and sponsored by the Kyoto University and the Disaster Prevention Research Institute (DPRI), Kyoto University. The summit was held at the DPRI, Uji Campus, Kyoto, Japan, from 13 to 15th of March 2019.
Henry Thoreau's Perpetual Grief and Unquenchable Life

Abstract This review essay offers an enthusiastically positive review of Branka Arsić's Bird Relics: Grief and Vitalism in Thoreau (2016). Arsić gives us a Thoreau who is a pondside Pythagoras, learned and disciplined, with roots deep in Greek and Ionian and Persian and Hindu thought. Her Thoreau is a prophet with a freshly thought-out message about how perpetual mourning drives the perpetual renewal of life, about the importance of disindividualizing, and about the persistence of life at its most basic and elemental level. Arsić shows how, once we learn to see and hear and walk and sit without filters, without metaphors, and without other preconceived containers for pure experience, we can come to see, with Thoreau, that at the most important level, there is no death.
The effect of Chinese foreign direct investment on Africa's industrialization process

Purpose This study empirically investigates the long-run and interactive effect of Chinese foreign direct investment (CFDI) on Africa's industrialization process. Design/methodology/approach The authors employed industry and manufacturing value-added (% GDP) as the dependent variables and applied the two-step GMM and panel-corrected standard errors' (PCSE) techniques involving a panel of 49 African countries from 2003 to 2020. Findings The industry value-added (% GDP) results show that the presence of CFDI propels industrial productivity by contributing to value-addition in the short and long run. Moreover, the study shows that the magnitude of the CFDI effect on industrialization is pronounced in the short-run when it is associated with labor and natural resources. This result reveals efficiency-seeking behavior of CFDI and the CFDI-Africa industrialization nexus is not primarily resource-driven. More importantly, the authors found human capital, electricity and political stability, as primary factors that magnify CFDI's effect on industrialization in the short and long run. Originality/value This study is the first to use macro-level data to empirically investigate and find the significant effect of CFDI on Africa's industrialization in the long run. More importantly, the authors investigated channels through which CFDI magnifies industrialization in Africa in the short and long run.
Fine-grained Vehicle Classification Based on Feature Augmentation

Abstract Fine-grained vehicle classification is a hot research topic in the field of computer vision, which aims to recognize different models of vehicles belonging to the same brand. Limited by the large appearance differences of the same vehicle (due to different attitudes and perspectives) and small appearance differences between different vehicles, the vehicle’s reorganization performance is not high and can’t meet the real application requirements. In this paper, a Fine-grained Vehicle Classification is proposed based on Feature Augmentation (FAFCC), which focuses on mining the key discriminative parts of the object. Specifically, the FAFCC first improves the weakly supervised data augmentation network (WSDAN) by using multi-layer feature extraction and fusion instead of single-layer features. Further, the FAFCC assigns local feature weights to select key features, which can solve the problem that the discriminant features in the channel are not prominent enough and realize the high-quality compact fine-grained vehicle recognition task. The performance of the FAFCC on the fine-grained image classification tasks for vehicles has been evaluated through algorithm comparison experiments and ablation experiments. Through visual analysis, it can be seen that the introduction of each new module has optimized the loss function value and convergence speed of the model. In general, our FAFCC algorithm has a certain improvement effect compared with the original algorithm.
Chatbots as a gamification element for increasing involvement of students in the educational process

The article describes the problem of increasing involvement of students in the educational process, presents the types of involvement, as well as ways to increase it. One of the ways is connected with the addition of game elements to the educational process, using game elements and game technologies in non-game activities (gamification). As a practical example of increasing involvement and the use of gaming technologies, the use of chatbots in the learning process is considered. The article presents some theoretical aspects of chatbot technology: the concept of a chatbot, types of chatbots, areas of application of chatbots in education. To create a chatbot, which can later be used in lessons, it is proposed to use the free BorisBot service. The article provides an algorithm for creating didactic materials in this service.
Programming graphics and animation in the informatics course of basic school

The article analyzes new opportunities in the study of the thematic section "Algorithms and Programming" of the informatics course of basic school, which appeared during the transition to updated Federal State Educational Standards. The planning of lessons of the theme "Computer graphics and animation" is given, invariant with respect to the graph and turtle modules of the Python programming language ("Introduction to the module", "Rectangle, circle. Building images from graphic primitives", "Using loops to create images", "Simple shape closed area hatching", "Procedures", "Nested loops", "Animation", "Controlled animation"). The main features of the turtle module are described. A detailed description of each of the eight lessons of the theme is presented, including an example of solving a basic problem and its analysis, as well as tasks for modifying the proposed solution and developing solutions to other problems based on it. The solution by means of turtle graphics of the problem of the Unified State Exam in informatics is presented.
A Transformer Based Complex-YOLOv4-Trans for 3D Point Cloud Object Detection on Embedded Device

Abstract 3D object detection is widely used in the field of autonomous driving. The embedded devices have few resources and low power consumption, so the existing 3D object detection algorithms are difficult to complete the detection task in the required time under some conditions. In this paper, the Complex-YOLOv4-Trans model is proposed to use point cloud data for 3D object detection. Firstly, a transformer encoder block is introduced to improve the structure of Backbone, which makes full use of its context information to improve detection accuracy. Secondly, the general convolution in Backbone is replaced with a depth-wise separable convolution, which effectively reduces the computational load of the model. Finally, Complete-IoU (CIoU) is used as the loss function, and then 8-bit model quantization is performed to speed up the inference. We select the KITTI dataset and deploy the model on NVIDIA Jetson Xavier NX by using TensorRT acceleration tools to test the methods. The mAP of Bird’s Eye View for Car is 71.79%, the mAP for 3D Detection is 15.82%, and the FPS on the NX device is 42 frames. Experimental results show that our Complex-YOLOv4-Trans can perform real-time 3D object detection tasks on low-energy embedded devices.
Reservoir computing with nonlinear optical media

Abstract Reservoir computing is a versatile approach for implementing physically Recurrent Neural networks which take advantage of a reservoir, consisting of a set of interconnected neurons with temporal dynamics, whose weights and biases are fixed and do not need to be optimized. Instead, the training takes place only at the output layer towards a specific task. One important requirement for these systems to work is nonlinearity, which in optical setups is usually obtained via the saturation of the detection device. In this work, we explore a distinct approach using a photorefractive crystal as the source of the nonlinearity in the reservoir. Furthermore, by leveraging on the time response of the photorefractive media, one can also have the temporal interaction required for such architecture. If we space out in time the propagation of different states, the temporal interaction is lost, and the system can work as an extreme learning machine. This corresponds to a physical implementation of a Feed-Forward Neural Network with a single hidden layer and fixed random weights and biases. Some preliminary results are presented and discussed.
The 2CG® Poetry Machine―a Hybrid Approach to Human Capability Cultivation with Disruptive Artistic Impulses

Human capability cultivation has been neglected in educational and professional contexts in the previous decades. The focus has clearly been on tasks and efficiency, on planning, scaling, and then executing according to plan. Professionals, students and pupils have become used to getting clear directions, simplified instructions and exact predictions and managers have continually looked for measurable output. Unrealistic goals imposed on students and workforce have increasingly led to frustration, drop-out and even burnout. Digitalization has added to this development. Cutting out all redundancy from our lives has prevented us from staying open to surprise, exploring unkown territory and embracing difference and diversity, whereby diversity does not only refer to color, race and sexual orientation but to different opinions and perspectives. As we live in a complex world and face increasingly complex challenges, however, we need to train these human capabilities, also referred to as 21st century skills. The current paper aims to introduce an educational approach that fosters human capability cultivation. Practice examples shall demonstrate how the 2CG® Poetry Machine, a disruptive educational model that combines artistic impulses with collaborative and social learning processes in communities of practice, enables learners across disciplines, cultures and hierarchies to practice their human skills, leave their mental models and tap into their full creative potential. The multi-method approach has been applied in in-person, online, live virtual and hybrid settings over the past 15 years. Evaluation has mostly been based on qualitative data collected through observation, customer surveys, peer feedback and expert opinion and analyzed by means of a customized value creation framework.
Emergency Remote Teaching: A Case Study

During the first quarter of 2020, nearly every country declared a COVID-19 pandemic emergency, and schools suddenly had to migrate to remote teaching modalities. As a result, 1.29 billion learners in 151 countries changed their learning models. Every school reacted differently, with varying degrees of success. One success story is the Tecnologico de Monterrey university system in Mexico. The school’s previous extensive experience with e-learning and a recent emergency remote teaching event (ERT) enabled Tecnologico de Monterrey to preempt the emergency declaration by going online two weeks before any other school in Mexico. More than 90,000 students attend 55,000 class sessions per week on its 33 campuses throughout Mexico. Students’ acceptance rate of the models implemented was high. The case study results of the university’s success highlight four factors that may simplify a school’s transition to ERT: the school’s previous experience with emergencies, experience with distance learning and course design, availability of technology, and commitment from the students, teachers, school administrators, and parents. The analysis also identifies five distinct stages in an ERT event: before the ERT event, the ERT declaration, the first week of ERT, the following weeks of ERT, and the return to normal operations. Despite the tragic events of the COVID-19 pandemic, we must still try to learn something from it and work to build a better world. The lessons learned in the last two years might help bring a better response in a (hopefully distant) future emergency.
Experiences on Creating Personal Study Plans with Chatbots

With the ever-increasing number of the college students, universities face a growing need for different kinds of counselling services. To fill this demand, the higher education institutions have tested and applied many sophisticated solutions including chatbots, which are Artificial Intelligence-based (AI-based) computer programs designed to simulate the discussion with the human users. This paper analyzes on the suitability of chatbots or virtual advisers in personal study plan creation and course selection. The empiric part of the study reports the observations of the experiment conducted in a Finnish university of applied sciences. In the study, post-graduate engineering students (n = 53) used a tailor-made chatbot while creating their personal study plans. During the task, the students were able to request suggestion for optional study modules, using different criteria, such as personal interest, learning needs, and course popularity. The findings of the study indicated that chatbots could, to some extent, improve student counselling, and the main advantages of the chatbots were scalability and unlimited service hours. However, students did not see that AI could at least now fully remove the need for human counselling. The main reported shortcomings of the chatbot were the minor significance of the individuality, lack of inspiring effect, and general attitude towards automated services.
Parallel-mode-channel approach to multimode Gaussian state estimation

The imaging process and its corresponding state estimation are treated as parallel mode channels and two estimation subproblems, respectively. In the two subproblems, the displacement vector and covariance matrix of the object field are estimated independently from the measured ones of the aperture field by minimizing their respective cost functions defined quantum mechanically. The measurement scheme is also briefly discussed. As an application, the location estimation of a point feature of a disk object is studied analytically and numerically. The results show that the estimation that uses only the displacement vector does not enhance the resolution of the amplitude image to a large extent. On the contrary, the quantum Cram\'er-Rao bound of spatial resolution can be pushed down toward the uncertainty relation. Furthermore, the high quantum fidelities verify the effectiveness of the estimation and the broad applicability to arbitrary multimode Gaussian states.
Toward stronger coupling between technical infrastructures and institutional processes in data-intensive science

ABSTRACT The techniques and approaches within geoinformatics and data science rely on the effective coupling of supporting infrastructures and institutions. Without underlying infrastructures for data discovery, analysis, management, distribution, and preservation, new computational techniques wither on the vine for lack of input or remain isolated as niche tools that miss broader potential audiences. Likewise, without supporting institutions that enable governance of policies and finances, coordination of stakeholders, and validation of new knowledge and tools, technological advances become detached from the people and organizations that operate and use them. This paper centers on a case study of work within the National Center for Atmospheric Research (NCAR) and University Corporation for Atmospheric Research (UCAR) to develop effective systems and processes for research data curation, access, discovery, and preservation. By emphasizing iterative alignment of institutional work (policies, intermediaries, governance processes, routines, and financial instruments) and infrastructural work (data storage systems, repositories, tools, and interfaces), balanced progress has been made toward developing solutions to gaps in organizational data services.
Synthetic training datasets generating for fringe projection profilometry based on deep learning

Although Deep learning-based Fringe projection profilometry (FPP ) has some success in in three-dimensional(3D) shape measurement,it still exists some difficult problems,especially, collecting a huge of training datasets is particularly troublesome and inconvenient.We introduce Blender software to simulate virtual scanning in 3D shape measurement, present a synthetic training datasets generating method.The proposed method can produce an effective training datasets that does not need real-world 3D scanning procedure.It can automatically produce deformed fringe patterns of the tested object in a very short time when a group of phase-shifting fringe pattern is projected onto the tested object in virtual scanning way,then calculate the wrapped phase using phase-shifting demodulation algorithm and obtain the 3D shape information from the continuous phase after the measurement system is calibrated.To verify the effectiveness of our method, we did simulations and real experiments. The results show that our method is effective for the measuring the complex object’s shape. Compared the training datasets obtained with real-world scanning,the network model trained by training datasets obtained from our proposed method has the similar accuracy and generalization ability, but our method is simple and fast in preparing training datasets for network.