Reviewerly/exHarmony huggingface dataset:
On Baer cones in $$\mathrm {PG}(3, q)$$

Abstract Recently, in Innamorati and Zuanni (J. Geom 111:45, 2020. 10.1007/s00022-020-00557-0 ) the authors give a characterization of a Baer cone of $$\mathrm {PG}(3, q^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>PG</mml:mi> <mml:mo>(</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , q a prime power, as a subset of points of the projective space intersected by any line in at least one point and by every plane in $$q^2+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> , $$q^2+q+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:mi>q</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> or $$q^3+q^2+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> points. In this paper, we show that a similar characterization holds even without assuming that the order of the projective space is a square, and weakening the assumptions on the three intersection numbers with respect to the planes.
System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor

First posted October 21, 2021 For additional information, contact: Director, Earth Resources Observation and Science Center U.S. Geological Survey47914 252nd Street Sioux Falls, SD 57198Contact Pubs Warehouse This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.108 to 0.104 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 2.52–2.57 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.018–0.028.
APF

While widely adopted in practical applications, face recognition has been disputed on the malicious use of face images and potential privacy issues. Online photo sharing services accidentally act as the main approach for the malicious crawlers to exploit face recognition to access portrait privacy. In this demo, we propose an adversarial privacy-preserving filter, which can preserve face image from malicious face recognition algorithms. This filter is generated by an end-cloud collaborated adversarial attack framework consisting of three modules: (1) Image-specific gradient generation module, to extract image-specific gradient in the user end; (2) Adversarial gradient transfer module, to fine-tune the image-specific gradient in the server; and (3) Universal adversarial perturbation enhancement module, to append image-independent perturbation to derive the final adversarial perturbation. A short video about our system is available at https://github.com/Anonymity-for-submission/3247.
Parameter Adaptive Multi-robot Formation Based on Fuzzy Theory

To solve the problem of complex parameter adjustment and low formation efficiency in traditional leader-follower method, the formation control law is improved, and a parameter adaptive multi-robot formation method based on fuzzy theory is proposed in this paper. Firstly, the kinematics model of the robot is established by introducing the virtual leader, the formation problem of multi-robots is transformed into the tracking control problem between robots. Then the tracking control law is designed, and it is theoretically proved that the designed control law can make the robot complete formation. Moreover, Using fuzzy control theory, a fuzzy controller is designed according to the pose errors between robots, and the parameters in the control law are burred so that the parameters can be adjusted adaptively. Finally, simulations are provided to verify the efficacy and superiority of the method.
Reinforcement-Learning-Based Quantum Adiabatic Algorithm Design for Integer Programming

Adiabatic quantum computing (AQC) is a computation protocol to solve difficult problems exploiting quantum advantage, directly applicable to optimization problems. In performing the AQC, different configurations of the Hamiltonian path could lead to dramatic differences in the computation efficiency. It is thus crucial to configure the Hamiltonian path to optimize the computation performance of AQC. Here we apply a reinforcement learning approach to configure AQC for integer programming, where we find the learning process automatically converges to a quantum algorithm that exhibits scaling advantage over the trivial AQC using a linear Hamiltonian path. This reinforcement-learning-based approach for quantum adiabatic algorithm design for integer programming can well be adapted to the quantum resources in different quantum computation devices, due to its built-in flexibility.
Hate Speech on Social Media

Based on the content that users prefer to follow, they are presented with similar content, which they may like. Thus, they can interact with individuals whose mindsets are similar to theirs. The users who cannot socialize in real life due to the COVID-19 pandemic and thus consider social media as an escape point think that they can produce unlimited and uncontrolled hatred in a synergistic environment consisting of people who produce similar contents to what they produce. This study focuses on hate speech on social media and analyzes the hate, discriminative, and derogatory speech regarding men within the “dunyaerkeklergunu” hashtag on Twitter based on symbolic interaction theory using the content analysis method. Within the scope of the “dunyaerkeklergunu” hashtag, 500 shares were analyzed. The study aims to raise awareness of the fact that hate speech can be made even on international men's day and that hate speech is not only directed towards women but is also made regarding men.
Analysis and improvement of verifiable blind quantum computation

In blind quantum computation (BQC), a client with weak quantum computation capabilities is allowed to delegate its quantum computation tasks to a server with powerful quantum computation capabilities, and the inputs, algorithms and outputs of the quantum computation are confidential to the server. Verifiability refers to the ability of the client to verify with a certain probability whether the server has executed the protocol correctly and can be realized by introducing trap qubits into the computation graph state to detect server deception. The existing verifiable universal BQC protocols are analyzed and compared in detail. The XTH protocol (proposed by Xu Q S, Tan X Q, Huang R in 2020), a recent improvement protocol of verifiable universal BQC, uses a sandglass-like graph state to further decrease resource expenditure and enhance verification capability. However, the XTH protocol has two shortcomings: limitations in the coloring scheme and a high probability of accepting an incorrect computation result. In this paper, we present an improved version of the XTH protocol, which revises the limitations of the original coloring scheme and further improves the verification ability. The analysis demonstrates that the resource expenditure is the same as for the XTH protocol, while the probability of accepting the wrong computation result is reduced from the original minimum (0.866) d * to (0.819) d * , where d * is the number of repeated executions of the protocol.
Sky Computing: The Advancement of Cloud Computing

When we think of the future of cloud computing and ask how the technology of cloud can evolve then there is a more coherent service, we call sky computing which comes into picture. Sometimes, a single cloud isn’t enough. Sometimes, you need the whole sky. That’s why a number of researchers are developing tools to federate clouds, an architectural concept dubbed “sky computing”. The Sky Computing model allows the creation of large-scale infrastructure using resources from multiple cloud providers. These infrastructures are able to run embarrassingly parallel computation with high performance. The big challenge here is managing the combined resources of available Clouds, public or private and interoperating between them without an excessive efficiency loss. There is the need for an effective model that can deliver a proficient deployment, possible with Sky Computing. In this paper I have talked about sky computing and differences between grid, cloud and sky computing and various mechanics that might lead to sky computing over multiple clouds, and I have also covered the architecture of sky computing and the characteristic of sky computing.
Quantum-Inspired Recommendation System with Threshold Proportion Interception

Modern recommendation systems leverage historical behavior information to generate precise recommendation results for users. However, when the data scale of users and items is large, it is difficult to generate recommendation results in time. Tang proposed a quantum-inspired recommendation algorithm, which could solve the recommendation problem in constant time complexity. However, Tang’s approach is based on a set of assumptions which rely heavily on some empirical parameters. The time complexity for calculating parameters is high. Thus, this approach cannot be directly applied in industrial applications. In this paper, we propose a method, namely, Quantum-inspired Recommendation system with threshold Proportion Interception (QRPI), which is based on the quantum-inspired recommendation system and more suitable for industrial environments. Compared with the existing widely used recommendation algorithms, we show through numerical experiments that our solution can achieve almost the same performance with better efficiency.
Potential Quantum Advantages

In this last chapter, we summarise some of the results and efforts to understand the potential power of quantum machine learning algorithms. We will first have a closer look at different criteria for a quantum advantage, and then consider data mining on coherent or “quantum data”. We finally summarise some future perspectives for quantum machine learning research as a concluding outlook.
Uniform Mixture Convergence of Continuously Transformed Fuzzy Systems

The probability mixture structure of additive fuzzy systems allows uniform convergence of the generalized probability mixtures that represent the if-then rules of one system or of many combined systems. A new theorem extends this result and shows that it still holds uniformly for any continuous function of such fuzzy systems if the underlying functions are bounded. This allows fuzzy rule-based systems to approximate a far wider range of nonlinear behaviors for a given set of sample data and still produce an explainable probability mixture that governs the rule-based proxy system.
Enterprise Blockchain-Based Privacy Sharing on Internet of Things Devices

Nowadays, the Internet of things systems became integrated into our lives. Data produced by the sensors on these systems can be considered personal data that is private. Legal regulations such as the general data protection regulation GDPR secure the storage and sharing of these personal data. It is not easy to automatically control these systems with legal regulations. In this study, we present a new privacy-sharing method of providing privacy for personal data sharing. This method ensures that using blockchain technology secures all privacy sharing steps. We used Quorum as the blockchain infrastructure that is an enterprise blockchain, making data transparently available to the peers in a private and permissioned network.
Towards decidable and expressive DOT

While decidability of type systems may be a reasonable sacrifice to make in exchange for greatly increased expressivity in some cases, having a type checker that is guaranteed to terminate while maintaining a high degree of expressivity can be beneficial. Over the last few years there have been several papers and theses written describing decidable variants of DOT, however, these variants all present similar challenges in limitations on expressivity. We aim to increase the expressivity of these variants without sacrificing decidability.
Research on Application of Transfer Learning in Equipment Fault Diagnosis

Abstract In view of the complex and changeable operating conditions of equipment, and the different distribution of measured data, it is difficult to promote the application of fault diagnosis technology. Many scholars have introduced transfer learning to overcome the lack of training data for deep learning by transferring existing data and models in similar domains into the target domain, it has expanded the application of fault diagnosis technology. This paper introduces the related concepts of transfer learning, summarizes the current applications and existing problems of transfer learning in the field of fault diagnosis, and finally puts forward further research directions and suggestions in general detection, real-time detection, etc. The purpose is to better improve equipment support capabilities.
COVID-19, the Ubiquitous National Security Threat: Lessons Learned Around the Globe

Today is October 1, 2020. In this moment, over 34 million bodies—that we know of—have hosted a deadly virus and more than one million of those bodies have succumbed to the strain of their unwelcome, malignant guest. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—genetically related to, yet different from the coronavirus responsible for the 2003 SARS outbreak—is the virus that causes coronavirus disease 2019 (COVID-19). The virus first materialized in Wuhan, Hubei province, China as early as November 17, 2019, and quickly escalated from a small cluster of cases of what appeared to be pneumonia to a global pandemic by March 11, 2020.
Research on higher vocational education training reform based on BP neural network

In order to meet the needs of the economic market for high-level talents and solve the deficiencies in education and training, this paper carries out the exploration of higher vocational education and training reform based on BP neural network, analyzes the advantages and disadvantages of the application of BP neural network in education and training reform, and locates the deficiencies and reasons in Higher Vocational Education and training. Based on the cloud platform, improve the reform measures of higher vocational education training curriculum system, and deeply integrate the training contents obtained from multiple channels, so as to improve the adaptability of education and training contents to industry policies. The BP neural network is introduced, the sigmoid function is used to activate the BP neural network nodes, analyze the higher vocational education training data, and calculate the higher vocational education training evaluation error. According to the education and training evaluation mechanism, BP neural network is used to construct the higher vocational education training evaluation model to evaluate the teaching results in different stages online. The experimental results show that the evaluation accuracy of this method is 91.65% and the evaluation time is 2.09 s. Based on the evaluation results, the macro adjustment of teaching and training progress and objectives is carried out to ensure that the adjusted training objectives and tasks can meet the basic needs of the educated groups.
Meningkatkan hasil belajar materi pecahan dengan model Discovery learning pada Siswa Kelas IV Sekolah Dasar

This research is a Classroom Action Research (CAR). The research was conducted in two cycles with each cycle consisting of planning, implementing actions, observing and reflecting. The research subjects were the fourth grade students of SD Negeri 2 Tirtomoyo totaling 22 students. Meanwhile, the data sources were taken from (1) observations, (2) places, events, and behaviors, and (3) documents related to the research problem. The techniques used to collect the above data include observation, as well as document review. Data collection analysis techniques, namely using comparative descriptive analysis followed by reflection. At least students achieve 80% completeness of the desired indicators. The procedures used are (1) planning, (2) implementation, (3) observation, and (4) analysis and reflection. The results showed that the process of implementing learning with theModel Discovery Learning  to improve learning outcomes of Class IV Fraction Materials at SDN 2 Tirtomoyo Tirtomoyo District, Baturetno Regency for the 2020/2021 Academic Year in accordance with the RPP that had been prepared, was more effective and increased from the poor category to good.
Auto-calibration of line-laser structured-light seafloor mapping systems

The sub-centimetre resolution of line-laser generated maps exceeds acoustic bathymetry and provides detailed information needed for inspection and ecological research, but makes the maps more sensitive to errors stemming from (navigation and) mapping sensor calibration. The high resolution is achieved using a large relative baseline between the camera and laser apparatus. Their extrinsic parameters are often incidentally changed between missions when the vehicle they are attached to undergoes maintenance, making it desirable to have a means of calibrating these parameters in-situ and using natural scenes. Existing methods require the use of a second camera, which increases the complexity of the mapping hardware and often requires dedicated calibration manoeuvres to be performed. In this paper we present a novel in-situ calibration method using natural scenes that does not require the use of a second camera, but instead uses the ordinary data captured during a survey mission to perform a non-linear least squares optimisation that reduces inconsistencies between overlapping measurements. We take advantage of a dual line-laser setup to generate continuously overlapping measurements for calibration that have inherently small uncertainty in their relative pose estimates and demonstrate reduced inconsistency between overlapping data using our optimised calibration parameters over that of CAD-derived values.
Generic Object Recognition Using Both Illustration Images and Real-Object Images by CNN

In recent years, the development of robots has been carried out for making human life more convenient and more comfortable along with the development of artificial intelligence. It is necessary for the robot to recognize the surrounding environment. However, in the surrounding environment there are objects other than real objects such as illustrations and paintings. When recognizing an image showing an illustration image with the current object recognition system which learned using real-object images, the recognition rate is very low (about 65%). In this research, we aim to recognize both illustration images and real-object images, and we verified whether the pseudo illustrated image which processed contour processing and the color reduction processing to the real image is effective for the recognition of the illustrated image.
Event Logic Graph Construction for Event Mining

Event Logic Graph is a directed and cyclic graph, the nodes in the graph represent events, and the edges represent the logical relationship between events. In essence, Event Logic Graph is a knowledge base of event logic, in order to reveal the evolution law and development mode of the event, we research a method of constructing Event Logic Graph, which describes the logical structural relationship between events by adopting knowledge graph structure. At the same time, in order to describe the event more clearly, we also describe the multi-dimensional attributes of the event. We propose an architecture for constructing Event Logic Graph, which includes text corpus collection, event relationship template construction, event extraction and structured representation, event similarity calculation and fusion, event trigger word extraction and argument extraction model construction, event relationship pair construction, graph database storage, and the architecture is used to construct Serial Event Logic Graph, Causal Event Logic Graph, Conditional Event Logic Graph, Transitional Event Logic Graph, and Concurrent Event Logic Graph.