Reviewerly/exHarmony huggingface dataset:
Design and Implementation of W-shaped Steel Band Hole Image Recognition System Based on OpenCV-Python

Abstract The mine Roofbolter is widely used in drilling and supporting of underground roof and side wall, which greatly alleviates the imbalance of driving anchor. However, with the progress of sensor technology, electrical and electronic technology, electro-hydraulic proportional control technology and the continuous improvement of users’ requirements for the safety and efficiency of bolt support, domestic and foreign mining equipment manufacturers have carried out the development of automatic bolt and anchor cable support equipment, especially to improve the performance requirements of control system and develop intelligent visualization technology. At present, the hole location in the process of drilling still needs to be identified and located by human eyes, which is inefficient and the working environment is a great threat to the physical and mental health of personnel, so it is urgent to introduce computer technology and artificial intelligence technology. In this paper, with the help of image recognition technology, machine vision recognition replaces human eye recognition of hole location, and puts forward an image recognition technology of W-shaped steel belt hole recognition system based on OpenCV to assist the scheme, so as to ensure the safety and efficiency of roadway support. This system uses pychar, opencv and python language to realize the recognition and positioning of W-shaped steel strip hole. The scheme has the advantages of small amount of calculation, high accuracy and fast recognition speed. This research conclusion provides a reference for the further spatial positioning analysis of W-shaped steel belt hole, and improves the efficiency of underground support operation and personnel safety.
Features of the Use of E-learning in the Communicative Space of a Higher Educational Institution

The era of globalization and digitalization brings many transformations to the communicative environment of higher education institutions, both positive and negative. New communication channels are emerging, prospects for interaction between the subjects of both global communication and the global educational process are being built, classical ways of obtaining scientific knowledge are being transformed. In this regard, there is a need for a new - modular - approach to the organization of the modern educational process, the organization of which is considered in detail by the authors.
Bypass Network for Semantics Driven Image Paragraph Captioning

Image paragraph captioning aims to describe a given image with a sequence of coherent sentences. Most existing methods model the coherence through the topic transition that dynamically infers a topic vector from preceding sentences. However, these methods still suffer from immediate or delayed repetitions in generated paragraphs because (i) the entanglement of syntax and semantics distracts the topic vector from attending pertinent visual regions; (ii) there are few constraints or rewards for learning long-range transitions. In this paper, we propose a bypass network that separately models semantics and linguistic syntax of preceding sentences. Specifically, the proposed model consists of two main modules, i.e. a topic transition module and a sentence generation module. The former takes previous semantic vectors as queries and applies attention mechanism on regional features to acquire the next topic vector, which reduces immediate repetition by eliminating linguistics. The latter decodes the topic vector and the preceding syntax state to produce the following sentence. To further reduce delayed repetition in generated paragraphs, we devise a replacement-based reward for the REINFORCE training. Comprehensive experiments on the widely used benchmark demonstrate the superiority of the proposed model over the state of the art for coherence while maintaining high accuracy.
The Analysis of Noto Serif Balinese Font to Support Computer-assisted Transliteration to Balinese Script

This study is aimed to preserve the endangered Balinese local language knowledge through technology by analyzing Noto Serif Balinese (NSeB) font to support computer-assisted Balinese script transliteration. This research has never been done before and contributes to the future improvement of that font development for wide use on computer systems, including the smartphone. The analysis is done on Noto Balinese as the first computer-assisted transliteration to Balinese script using NSeB font that complies with Balinese Unicode. The testing result shows that NSeB font support should be enhanced by providing wider glyph for a certain appended form or gantungan, providing higher glyph for tedung, repositioning glyphs for certain clusters of vowel-sign and consonant-sign to avoid overlap displaying, and providing correction for a certain combination of syllable and long vowel. In the future, the accompanying algorithm of this study should be improved by taking care of the line breaking mechanism and by enriching its special words repository.
Capacity Laws for Steganography in a Crowd

A steganographer is not only hiding a payload inside their cover, they are also hiding themselves amongst the non-steganographers. In this paper we study asymptotic rates of growth for steganographic data -- analogous to the classical Square-Root Law -- in the context of a 'crowd' of K actors, one of whom is a steganographer. This converts steganalysis from a binary to a K-class classification problem, and requires some new information-theoretic tools. Intuition suggests that larger K should enable the steganographer to hide a larger payload, since their stego signal is mixed in with larger amounts of cover noise from the other actors. We show that this is indeed the case, in a simple independent-pixel model, with payload growing at O(√(log K)) times the classical Square-Root capacity in the case of homogeneous actors. Further, examining the effects of heterogeneity reveals a subtle dependence on the detector's knowledge about the payload size, and the need for them to use negative as well as positive information to identify the steganographer.
Machine and Deep Learning Techniques for Daytime Fog Detection in Real Time with In-Vehicle Vision Systems Using the SHRP 2 Naturalistic Driving Study Data

The main focus of this study is to develop a system that can accurately detect the presence of fog in real time at a trajectory level. This study leveraged video data from the SHRP 2 Naturalistic Driving Study (NDS). Extensive data reduction steps were taken to classify various levels of foggy weather conditions from the video data to form two unique image data sets. Afterward, features based on the gray level co-occurrence matrix (GLCM) were extracted from the images and used as classification parameters for training support vector machine (SVM) and K-nearest neighbor (K-NN) algorithms. In addition, a convolutional neural network (CNN) was also examined to improve the detection performance. Although the analysis was done initially on a data set consisting of two weather conditions, clear and fog, it has been extended to include different levels of fog, that is, near fog and distant fog. While the accuracy of the first analysis with two categories was approximately 92% and 91% for SVM and K-NN classifiers, respectively, the CNN produced much greater accuracy of 99%. As expected, the accuracy of the second analysis, with more refined weather categories, was relatively lower than the first analysis where CNN, SVM, and K-NN models produced an accuracy of about 98%, 89%, and 88%, respectively. With the rapid advances in connectivity and affordable cameras, the proposed detection models could be integrated into the smartphones of regular road users, creating an effective way to collect real-time road weather information that could be used to improve weather-based variable speed limit (VSL) systems.
Expert Fuzzy System Determining Dengue, Zika, Chikungunya and Yellow Fever Infection

This article presents a fuzzy system for determining the level of infection of four viral diseases: dengue, zika, chikungunya, and yellow fever. Medical diagnosis is a complicated task because it is full of uncertainty and imprecision, which many times causes a misdiagnosis. However, this problem can be treated through a fuzzy system with the ability to work with uncertainty by providing the opportunity to model conditions that are imprecisely defined. Dengue, yellow fever, zika, and chikungunya are diseases that in their early stages are difficult to distinguish since they have great similarities in their symptoms, making diagnosis even more difficult. Based on research, the six most relevant symptoms that allow a better diagnosis have been selected. The six input fields are pain, temperature, bleeding, poor appetite, muscle weakness, and shortness of breath. The four output fields refer to diseases and each one is made up of several fuzzy sets that make it possible to identify the level of infection in each disease. The fuzzy system implementation has been done in MATLAB, using fuzzy rules developed with the help of a human expert. The system is not a substitute for expert medical practitioners, but rather an aid in the diagnosis, which is based on the symptoms experienced by the patient. The tests were carried out in 15 cases with viral diseases and the results show that the diagnosis using the fuzzy system has an accuracy of 86.6%, displaying its helpfulness in the early diagnosis of the disease.
Learned Buffer Replacement for Database Systems

Most current database buffering schemes adopt an empirical design, which cannot adapt to the change of workloads. In this paper, we show how we can use machine learning to help design a new buffer replacement policy for database systems. We name the new policy LBR (Learned Buffer Replacement). The key idea of LBR is to use machine learning models to periodically learn the access pattern from historical requests to make the buffer replacement adaptive to the workload change. Particularly, we present two ways to learn the access pattern. One is a classifier named LBR-c, which can distinguish hot pages from cold ones based on the training on historical requests; the other is a regressor called LBR-r, which can predict the future replacement behavior according to historical accesses. We implement the proposed LBR-c and LBR-r and compare them to a number of existing schemes, including the theoretically optimal Belady's algorithm, three traditional algorithms (LRU, 2Q, and ARC), and LeCaR, which is a recently-proposed adaptive buffer scheme. The results show that our algorithms achieve a higher hit ratio than LRU, ARC, 2Q, and LeCaR. In addition, both LBR-c and LBR-r can adapt to workload changes, which is better than LRU, 2Q, ARC, and LeCaR. Overall, our proposal achieves comparable performance with the optimal buffer replacement algorithm, advancing the state-of-the-art in the well-studied area of buffer management in DBMSs.
Web-Based Ranking and Link Analysis of NAAC Accredited General Degree Colleges in West Bengal: A Webometric Study

This study examines the websites of 36 NAAC-accredited general degree colleges in West Bengal using Webometric indicators. Webometric data is acquired on the Google search engine using special query syntax and calculated different WIFs and WISER value of websites, the relationship between the ranks of all websites as defined by WISER indexing value and in-link impact value are also examined. According to the findings, Panihati Mahavidyalaya's website under West Bengal State University ranked first in Simple Web Impact Factor (SWIF) with 1887.912%, and Banipur Mahila Mahavidyalaya's websites (Sl. No. 5) ranked first in self-link WIF, INLWIF, and EWIF with 0.679803 SLWIF, 0.729064 ELWIFs, and 0.315271 RWIFs, respectively. Darjeeling Govt. College is ranked first, followed by Panihati Mahavidyalaya and Mugberia Gangadhar Mahavidyalaya, according to the WISER index value. This study may aid college administrators and webmasters in better comprehending the importance of these websites in making them user-friendly and informative websites that meet the needs of their users.
Nonlocal quantum state interferography

Quantum state interferography has recently been proposed as an interference-based technique for the measurement of the general qubit and pure qudit states [Phys. Rev. Lett. 125, 123601 (2020)]. This interferometric method exploits the relationship between the off-diagonal elements of the density matrix and the expectation values of non-Hermitian spin ladder operators. The extension of this method to a composite system is however nontrivial, as it involves the measurement of both single-photon and nonlocal multiphoton interferences. In this work, we present the theoretical analysis of the two-photon quantum state interferography of an arbitrary quantum state. We show that complete state information could in principle be obtained with two measurement settings and by recording six interferograms, using a single-photon sensitive imaging device. We then experimentally demonstrate two-photon nonlocal quantum state interferography of polarization-entangled photon pairs generated with type-I spontaneous parametric down conversion. Quantum state interferography of all four Bell states is performed. An upper bound on the separability of the bipartite states is established in terms of the visibility, phase average intensity, and phase shift of the interference patterns related to the antidiagonal elements of the matrix. Each of the four Bell states can be distinguished by the violation of these bounds uniquely.
Tracing Origin of Social Media Posts

This problem statement should result in a solution that can identify perpetrators who first posted sexually explicit abuse content on social media platforms. Images and videos of individuals of a sexually explicit nature are quite often posted online intentionally by perpetrators on various social media platforms with the sole intent of causing harassment, humiliation and distress to the victim. Social media platforms are constantly trying to create and improve automatic mechanisms to identify and reject such content at the time of upload itself. However, there are times when such content containing nudity and of a sexually explicit nature, fails to be detected at the time of upload and gets published. Such content usually has a tendency to instantly become viral, and others thereafter share it, or download and post and it again from their social media accounts. Social media platforms take down such posts when reported but by the time this is done, several copies have already been made and go in circulation. It quickly becomes ambiguous as to who posted it first, and taking advantage of this ambiguity, the original perpetrator evades detection. It is therefore critical to identify the person who first posted such distressing content online. Given a piece of text, image or video snippet as input, build a solution that can identify the person who was the first one to post it online on a particular social media platform. Please bear in mind that people could have copied it and made minor modifications before re-posting it from their accounts. Participants are expected to obtain suitable data required to work on this problem statement on their own.
Note: Image-based Prediction of House Attributes with Deep Learning

We present an image dataset and a deep learning model that enable the prediction of attributes such as floor area for low-rise buildings (i.e., houses). The dataset consists of 34,600 images of 16,403 buildings in the city of Toronto, Canada, each of which is associated with floor area. The ability to predict such an attribute can facilitate accurate, automated city-scale analysis of the built environment, which can then serve as a basis for policy evaluation and recommendation. A deep convolutional neural network is devised for the task, achieving normalized root mean square error (NRMSE) of 34.24% for interior floor area.
Performance Appraisal Model for Optimizing Teacher Incentive and Constraint Mechanisms by Random Matrix

At present, colleges and universities attach great importance to the assessment of teachers, strengthen the assessment of college teachers, and create a team of teachers with high quality, professionalism, and vitality, which can not only fully develop and allocate human resources in colleges and universities, but also promote colleges and universities. The reform of the personnel system will continuously optimize the proportion and structural allocation of teachers’ resources in colleges and universities, improve the quality and efficiency of running schools, enhance the competitiveness and attractiveness of schools, and better achieve the school’s school-running goals. In order to obtain higher signal reconstruction accuracy and satisfy random matrix with a relatively small compression ratio, this paper combines QR decomposition, which is used to increase the column independence of random matrix, and gradient descent method, which can reduce the cross-correlation between random matrix and sparse matrix. Based on the actual situation of a certain university, this paper studies and designs a set of perfect and reasonable teacher performance appraisal system that meets the requirements of the college, analyzes and researches the current situation of teacher performance appraisal in the college, and finds out the problem. Combining the advanced concept of being good at performance appraisal with the actual situation of the college, it analyzes the problems in the teacher performance appraisal system, summarizes the reasons for the formation of the problem, further redesigns the teacher performance appraisal index system, and gives the relevant implementation of the system. The basic characteristics presented in the organization of colleges and universities will not change. With the continuous adjustment of the organization of colleges and universities, the goals of colleges and universities are also being adjusted. Colleges and universities should continuously improve the performance appraisal system of teachers to make the goals of teachers consistent with the development goals of colleges and universities, so as to continuously improve the basic quality and realization of college teachers.
Towards Intellectual Property Rights Protection in Big Data

Big Data applications can revolutionize any platform by facilitating the analysis of large amounts of information. However, the biggest challenge associated with Big Data is overcoming the intellectual property barriers associated with the use of this data, especially in cross-database applications. Although intellectual property provisions have been formulated to limit inappropriate use and manage access to Big Data, it is difficult to make this trade-off and overcome the challenges of Big Data. This paper explores the limits of intellectual property rights in Big Data applications. The advent of Big Data requires an alternative conceptual framework along with security policies and regulations. The profound issues of copyright on cross-database platforms are highlighted in this paper, as well as the paradigm shift from ownership to control of access to and use of Big Data, especially on cross-database platforms. We also present a real-world case study of the underlying technologies of cross-data analytics (xdata.nict.jp). The xData platform aims to coordinate competing social, personal, and industrial interests in data to ensure fair access while minimizing legal and ethical threats. Finally, we discuss the idea of using blockchain-enabled smart contracts to protect intellectual property rights on cross-data platforms and highlight some important aspects of copyright issues, highlighting key issues and current open challenges.
Observing Cloud-Driven Surface Irradiance Patterns

&amp;lt;p&amp;gt;Clouds cast shadows and locally enhance solar irradiance through absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface. &amp;amp;#160;&amp;lt;br&amp;gt;The resulting spatiotemporal variability poses a challenge for solar energy production amidst increasing need for reliable renewable energy. It furthermore influences biological processes and the exchange of water and energy. Yet, no numerical weather prediction model is able to reproduce the observed local properties of irradiance, due to the complexity of radiative transfer and its dependence on accurately resolved cloud fields. Improving the radiative transfer models, whether it involves running full Monte Carlo raytracing in academic setups or simplified paramerizations, &amp;amp;#160;ultimately requires observations for validation. However, dense spatial observation of irradiance on the scale of cloud shadows are rare. Even single 1D time series are rarely available at high enough temporal resolution to capture irradiance variability.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In ongoing work, we provide those missing observations using a dense network of our custom, low-cost radiometers that we deployed at two field campaigns in summer 2021, FESSTVaL (Germany) and LIAISE (Spain). I will present our gathering and analyses of these new and detailed observations of surface irradiance to address knowledge gaps in our physical understanding and provide validation datasets for models. The instruments, which sample at 10 Hz, are able to closely match expensive conventional instruments, and combined with skyview imagery, the spatial observations are directly linked to observed clouds. Information about atmospheric water content can be retrieved using the information from water vapour absorption bands.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To complement these short term spatial data, long-term statistics of irradiance variability are derived from a 10-year 1 Hz resolution dataset from the Baseline Surface Radiation Network station in Cabauw, the Netherlands. Distributions and typical spatio-temporal scales of cloud shadows and irradiance peaks can be related to cloud type and meteorological conditions. The gathering and study of these datasets will lead to a better understanding of the physics. E.g., whether the dominant mechanism driving irradiance peaks is either forward scattering in transparent parts of clouds or 'reflections' from cloud sides. Furthermore, these datasets will help validate models, and ultimately improve our ability to accurately forecast irradiance variability at the small scales.&amp;lt;/p&amp;gt;