Reviewerly/exHarmony huggingface dataset:
Demonstration of a loop-based single-mode photonic quantum processor for general-purpose applications

We develop a loop-based single-mode photonic quantum processor and demonstrate its gate operations. Showing its programmability, scalability, and potential universality, the results indicate our processor is suitable for general-purpose applications.
Modeling of the radiation induced electromagnetic field in finely-disperse media

Algorithms for supercomputer modeling of the radiation electromagnetic field in heterogeneous materials of a complex finely-dispersed structure are constructed. A geometric model of a heterogeneous medium is created using Stilinger-Lubachevsky algorithms for multimodal structures. The model includes a system of detectors for statistical evaluation of functionals on the space of solutions of the photon-electron cascade transport equations. Algorithms for the three-dimensional approximation of the results of modeling the radiation transport in a fine-dispersed medium to an electrodynamic difference grid are developed. The approximation methods based on the technology of neural networks. The method of numerical solution of the complete system of Maxwell's equations for calculating the electromagnetic field in a fine-dispersed medium is worked out. The results of demonstration calculations of the electromagnetic field are presented. The results of the calculations show that the spatial distribution of the radiation electromagnetic field has a sharply inhomogeneous structure caused by the presence of boundaries of materials with different radiation properties.
Unsupervised Deep Plane-Aware Multi-homography Learning for Image Alignment

Due to its feature representation capabilities, deep learning has been applied to homography estimation in the field of image alignment. Most deep homography learning methods focus on estimating a single global homography, and cannot deal with the problem of parallax when the scene contains multiple different planes, and the translation of the camera’s optical center is not negligible. In this paper, we propose an unsupervised multi-homography learning method with a plane-perception trait to mitigate this parallax problem. In our model, the problem of multi-homography learning and plane perception are jointly considered, which can benefit from each other. To make the learning process stable under unsupervised setting, we design a special attention mechanism to bootstrap the collaboration between multi-homography learning and plane perception. We construct a new dataset that is captured in real scenes, having many challenges such as multiple planes, large parallax, etc. Quantitative and qualitative results show that our proposed method can better align images with large parallax and multiple planes.
Fractional Multi-view Hashing with Semantic Correlation Maximization

Hashing has been extensively concerned in multimedia research due to its low storage cost and high retrieval efficiency. The purpose of multi-view hashing is to project heterogeneous data from different views in high dimensional space into compact and discrete binary codes in Hamming space and meanwhile maintains the similarity of the original data. In this paper, we propose a Fractional Multi-view Hashing with Semantic Correlation Maximization (FMH-SCM), where the learning objective is to seek the maximum semantic correlation from various views. The proposed method uses labels to learn multi-view hash codes and fractional-order embedding to reduce the negative effect of noise. Moreover, a sequential optimization is used to solve the hash functions for improving the performance of this method. FMH-SCM is compared with related algorithms on two image retrieval datasets. Extensive experiments verify the effectiveness of the proposed method.
A Temporal Convolutional Network for Weakly Supervised Action Segmentation

The task of video action segmentation in weakly supervised learning is one of the key points of video content understanding. The ground truth only provides a set of actions but not frame level features. A popular type uses a neural network framework to train the prediction model. Our key contribution is a new Hidden Markov Model (HMM) grounded on a Temporal Convolutional Network (TCN) to label video frames, and thus generate a pseudo-ground truth for the subsequent pseudo-supervised training. In testing, we use Viterbi algorithm to generate the time action sequence to be selected, and finally get the largest posteriori sequence. We evaluate the performance of action segmentation task on breakfast dataset. The research experiments on this dataset show that our model gets efficient performance.
Twitter Sentiment Analysis on Government Law Using Real Time Data

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.
Use of Regtech in Compliance Practices – A Technological Innovation

‘Fin-Tech’, ‘Reg-Tech’, ‘Insure-Tech’, are the new buzzwords in the financial industry. They refer to the use of technology in the fields of finance, regulation & insurance respectively. A strong co-relation is being seen between the advancement in the field of Fin tech and the development of RegTech. The advancement of FinTech has posed a new challenge for the regulators. The number of regulations that a business enterprise has to deal with is already quite substantial and with the advent of new technologies, more laws are or likely to be in place for data security. To deal with the multitude of compliance regulations, businesses are spending a lot of money on lawyers, consultants and regulatory advisors, and facing a spiral rise in their legal costs. Technology is now coming to the aid of the business enterprises, as a more economical option. This aspect is the subject of research here. The development of RegTech or use of technology in regulation is still in the early stages of development, but is being widely discussed and also applied to streamline financial regulations and supervision. The current paper introduces the reader to the world of RegTech. It traces the genesis, the important features, elements and the various categories of RegTech services available along with the advantages it offers to businesses as well as the regulators i.e. the governing bodies.
Gesture Controlled Robot-Car Using Raspberry PI

The device handling complexity increases by integration of more functionality into Human–Machine Interface. The use of different sensory channels of human simplifies the interaction with in-car devices. Robots are playing a big role in our daily lives. The existing techniques to control these robots are by using a keyboard, joystick, or pre-programmed commands. This paper introduces a new way to control a robot by using the gestures of a human being. A robot-car is developed which is controlled by the gesture commands provided to the Raspberry Pi controller. The model has two components a car and a control station. The control station is computer that has gesture recognition hardware so that it can detect the commands and send them to the car. The control station is the microcomputer Raspberry Pi model 3B. The data from the hand movements acquired from the accelerometer sensor are fed to the motor driver L293D through the Raspberry Pi, which converts electrical energy to mechanical energy to drive the robot-car.
Adaptive Heuristic Histogram Equalisation Approach Combined With Gamma Transformation For Image Contrast Enhancement

In image processing applications, histogram equali-sation (HE) is an extensively used contrast enhancement method. The algorithm is simple to implement. However, HE has a num-ber of flaws, such as large brightness change, unnatural effects, and over-enhancement, that make it inappropriate for many applications. To evade these issues, a new adaptive heuristic HE combined with gamma transformation approach is implemented. First, gamma transform is applied on input image histogram. The transformed histogram is then split into under-exposed and over-exposed sub-histograms. Probability distribution function (PDF) of the sub-histograms are calculated. Then adaptive parameters are calculated by taking the ratio of maximum to mean values of PDFs of sub-histograms. Thereafter, PDFs and cumulative distribution functions (CDF) are modified by imposing threshold limit to that adaptive parameters. Finally, second adaptive parameter of sub-histograms are calculated by using their respective modified CDFs and new CDFs are obtained by using these second adaptive parameters. Traditional HE is then applied on sub-histograms separately with the new CDFs to get the enhanced output image. The implemented method outperforms the traditional HE method in both subjective and objective tests for most of the images. The method improves image contrast while retaining the original properties of the input images, with no over-enhancement or undesirable effects in the output images.
Breast Histopathological Image Classification Using Deep Learning

Breast histopathological image analysis for cancer diagnosis using computer tools have gained much attention in the past decade due to the development in computation power. In particular, deep learning-based algorithms which uses deep features are popularly explored for analysing breast histopathological images. However, there exists several challenges in developing computer tools such as heterogeneous characteristic of cancerous cells, illumination variation, color variation etc. Moreover, deep learning models are dependent on large annotated datasets. However, limited benchmark breast histopathological image datasets restricts the application of deep learning models. In this regard, the present paper aims at classification of breast histopathological images at 100x magnification into benign and malignant using deep learning models. Further, this paper demonstrates that data augmentation can improve the accuracy of deep learning models for classification of breast histopathological images. This paper also demonstrates that transferring the features of deep learning models learnt on general object class to and fine tuning it to classify breast histopathological images gives competitive results.
Network Crawler Technology Based on Python Under Information

With the advent of the information age, the data that people need to use in their lives is becoming larger and larger. Therefore, it is possible to collect data by using Python web crawler technology. This paper uses Python web crawler technology, taking “air quality index” and “Internet company” as keywords respectively, collects basic data through web crawler of data information of Baidu map and 58 same city websites, and analyzes the accuracy and practicability of Python through formula on the basis of each experimental analysis. This paper proposes the practicability of Python web crawler technology under the background of big data informatization in the future and constructs the path and framework of Python web crawler technology innovation.
Quantifying Blockchain Immutability Over Time

Immutability is a term used a lot in blockchain and cryptocurrency development when attemptingto analyze the economic interpretation of a network. Designs like the 21 million Bitcoin supply in BTC or incidents like the DAO fork bailout that split the Ethereum community into ETH and ETC are major examples of immutability values determining the ethos of a chain. This research sets out tocategorize different features of blockchain immutability and analyze them in three different networks: Bitcoin (BTC), Ethereum (ETH), and Ethereum Classic (ETC). Further in the paper, the Finney Ratio is introduced to help understand how blockchain immutability can be measured over time. The paper then visualizes overall blockchain immutability over time using radar charts as an immutability map. The Szabo Score is then introduced as a tool to score overall mutability over time for each blockchain.
&lt;i&gt;Stop and Think About p-Value Statistics: Fisher, Neyman, and E. Pearson Revisited&lt;/i&gt;

Good statistical practice is an integral part of modern science. Null hypothesis significance testing (NHST) is the most widely used statistical method. Nevertheless, misuse and misinterpretation of NHST are widespread, and severe criticisms have been levelled against NHST. In this study, I revisit the primary documents of Fisher, Neyman, and E. Pearson relating to statistical testing. I compare Fisher’s significance testing with Neyman-Pearson hypothesis testing and clarify their thoughts on statistical testing. I hope this study will guide researchers in stopping and thinking about p-value statistics before abandoning it.
Sliced $\mathcal{L}_{2}$ Distance for Colour Grading

We propose a new method with L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> distance that maps one N-dimensional distribution to another, taking into account available information about correspondences. We solve the high-dimensional problem in 1D space using an iterative projection approach. To show the potentials of this mapping, we apply it to colour transfer between two images that exhibit overlapped scenes. Experiments show quantitative and qualitative competitive results as compared with the state of the art colour transfer methods.
Semantic Perception Swarm Policy with Deep Reinforcement Learning

Swarm systems with simple, homogeneous and autonomous individuals can efficiently accomplish specified complex tasks. Recent works have shown the power of deep reinforcement learning (DRL) methods to learn cooperative policies for swarm systems. However, most of them show poor adaptability when applied to new environments or tasks. In this paper, we propose a novel semantic perception swarm policy with DRL for distributed swarm systems. This policy implements innovative semantic perception, which enables agents to understand their observation information, yielding semantic information, to promote agents' adaptability. In particular, semantic disentangled representation with posterior distribution and semantic mixture representation with network mapping are realized to represent semantic information of agents' observations. Moreover, in the semantic representation, heterogeneous graph attention network is adopted to effectively model individual-level and group-level relational information. The distributed and transferable swarm policy can perceive the information of uncertain number of agents in swarm environments. Various simulations and real-world experiments on several challenging tasks, i.e., sheep food collection and wolves predation, demonstrate the superior effectiveness and adaptability performance of our method compared with existing methods.
Contrast Correction Using Hybrid Statistical Enhancement on Weld Defect Images

Luminosity and contrast variation problems are among the most challenging tasks in the image processing field, significantly improving image quality. Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance. Recently, numerous methods had been proposed to normalise the luminosity and contrast variation. A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement (HSE) is presented in this study. The HSE method uses the mean and standard deviation of a local and global neighbourhood and classified the pixel into three groups; the foreground, border, and problematic region (contrast & luminosity). The datasets, namely weld defect images, were utilised to demonstrate the effectiveness of the HSE method. The results from the visual and objective aspects showed that the HSE method could normalise the luminosity and enhance the contrast variation problem effectively. The proposed method was compared to the two (2) populor enhancement methods which is Homomorphic Filter (HF) and Difference of Gaussian (DoG). To prove the HSE effectiveness, a few image quality assessments were presented, and the results were discussed. The HSE method achieved a better result compared to the other methods, which are Signal Noise Ratio (8.920), Standard Deviation (18.588) and Absolute Mean Brightness Error (9.356). In conclusion, implementing the HSE method has produced an effective and efficient result for background correction and quality images improvement.
Part-of-speech tagging of Kazakh text via LSTM network with a bidirectional modifier

In this paper, part-of-speech tagging on Kazakh text has been implemented using an LSTM neural network with a bidirectional modifier. A quite simple and fast tagger has been built, tested and evaluated on the self-collected dataset of Kazakh sentences with an accuracy of 94 %. There was addressed basically the problem of tagging having a small number of training samples (around 100 Kazakh sentences). It has been shown that quite good accuracy can be achieved in this situation.
An Algorithm of Layered Defogging for Remote Sensing Image Based on Meteorological Load Factor

Abstract For the serious degradation of outdoor images in dense fog weather, a layered defogging algorithm based on meteorological load factor was proposed. According to the concept of fog density factor, the relationship between visibility and fog concentration factor was established with the help of the atmospheric extinction coefficient based on the guided filtering method. The fog density factor was obtained by estimating the visibility value of dense fog image, and the atmospheric light value was estimated by the guided filtering of gray image. On this basis, the atmospheric scattering model was used to compensate the saturation of remote sensing image. Moreover, the depth of field layering algorithm of fog remote sensing image based on human vision system theory was used to obtain the fog-free image with vibrant color. The results show that the proposed algorithm can restore the whole remote sensing image accurately and obtain the restored remote sensing image with better brightness and color. Meanwhile, this algorithm has the characteristics of significant effect, thorough defogging, high saturation and contrast, and short defogging time.
Statistical Analysis of Atmospheric Parameters, Noise Temperature and Digital Terrestrial Television Signal Strength Over JOS Metropolis, Plateau State, Nigeria

The study of the relationship between the Received Signal Strengths(RSS) fromUnity TV (786MHz)located at Rayfield (9.84° North 8.89° East), Jos, PlateauState and some atmospheric parameters(rainfall, wind speed, relative humidity, pressure and temperature) as well as noise temperature has been carried out using two years (April 2019 to December 2020)half-hourly data. The measurement was continuously carried between the hours of 5 am and 9 pm daily. The analysis was carried out using the regression, co-integration and Granger causality models. The results show that the RSS is higher in the morning hours (5.00 am – 9.00 am) late in the evening (6.30 pm – 9.00 pm) but lower between 9.00am and 6.30 pm. This might be due to the 'dusty' atmosphere and low reflection of ionosphere in the day time or when there is much radiation from the sun which causes signal attenuation within the day time or sunny periods. The correlation coefficients from the coefficient of determination (R squared) of 0.88, 0.004, 0.838. 0.25, 0.867, and 0.112, are -0.94, -0.07, 0.92, -0.50, -0.93, -0.334 for atmospheric temperature, pressure, relative humidity, wind speed, noise temperature and rainfall respectively. This shows that atmospheric temperature, pressure, wind speed, noise temperature and rainfall are inversely proportional with the RSS, while relative humidity is directly proportional with the RSS. In addition, the higher R squared values of 0.88, 0.838 and 0.867 for atmospheric temperature, relative humidity and noise temperature respectively, indicates higher correlation between RSS and these parameters. It can be inferred that the models are suitable for predicting Unity TV (UTV) signal strength and that of other TV stations in Jos and environs, using atmospheric temperature, relative humidity and noise temperature as inputs. The results also showed that there is a co-integration between the variables; hence there is a long-run relationship between Unity TV signal strength and the atmospheric parameters. It was also observed that the parameters do not have a long-run Granger causality effect on the UTV RSS. There was a short-run Granger causality relationship between the parameters as there was a joint Granger Causal effect of all the parameters on UTV RSS as well as the individual Granger causal relationship between rainfall and atmospheric pressure with UTV RSS respectively. The result will find applications to the Nigeria Broadcasting Commission, Radio Engineers and Scientists, Satellite link designers, and mobile communication experts.
Features of educational and professional activities of future social workers in legal competence formation

We consider the features of the educational and professional activities of future social workers in the legal competence formation. The components of legal competence are analyzed: motivational-value, cognitive, communicative and activity-reflexive. The formation of these components takes place through the involvement of future social workers at all levels of professional education in social and legal activities by creating pedagogical conditions: the formation of a value attitude to the legal aspects of professional activity among future social workers; purposeful and active self-knowledge of the student, mastering the legal aspects of professional activity by solving different levels of complexity of educational and professional tasks; acquaintance of future social workers with the professional community through internships in institutions, the implementation of volunteer activities on the basis of various organizations, participation in scientific conferences, seminars, round tables, etc.; orientation of students to active legal education and reflection. Considering the stages of student personal development (stage of holistic personal development, stage of specialized personal development, stage of strengthening professional preparedness) in the university continuing legal education of future social workers is necessary. All types of activities at each of the stages of personal development of the introduction of legal education and upbringing are necessary.
Actual Machine Learning of Self-Body Image for Object Manipulation and Posture Generation with Redundancy in Musculoskeletal Humanoid Shoulder Complex

In this paper, we propose an inverse kinematic posture generation using redundancy in the musculoskeletal humanoid shoulder complex, and actual learning of the self-body image for object manipulation, and confirm its effectiveness by realizing the steering wheel operation of a car.