Reviewerly/exHarmony huggingface dataset:
Generalized interference of fermions and bosons

Using tools from representation theory, we derive expressions for the coincidence rate of partially-distinguishable particles in an interferometry experiment. Our expressions are valid for either bosons or fermions, and for any number of particles. In an experiment with $n$ particles the expressions we derive contain a term for each partition of the integer $n$; Gamas's theorem is used to determine which of these terms are automatically zero based on the pairwise level of distinguishability between particles. Most sampling schemes (such as Boson Sampling) are limited to completely indistinguishable particles; our work aids in the understanding of systems where an arbitrary level of distinguishability is permitted. As an application of our work we introduce a sampling scheme with partially-distinguishable fermions, which we call Generalized Fermion Sampling.
Towards Unification of Statistical Reasoning, OLAP and Association Rule Mining: Semantics and Pragmatics

Over the last decades, various decision support technologies have gained massive ground in practice and theory. Out of these technologies, statistical reasoning was used widely to elucidate insights from data. Later, we have seen the emergence of online analytical processing (OLAP) and association rule mining, which both come with specific rationales and objectives. Unfortunately, both OLAP and association rule mining have been introduced with their own specific formalizations and terminologies. This made and makes it always hard to reuse results from one domain in another. In particular, it is not always easy to see the potential of statistical results in OLAP and association rule mining application scenarios. This paper aims to bridge the artificial gaps between the three decision support techniques, i.e., statistical reasoning, OLAP, and association rule mining and contribute by elaborating the semantic correspondences between their foundations, i.e., probability theory, relational algebra, and the itemset apparatus. Based on the semantic correspondences, we provide that the unification of these techniques can serve as a foundation for designing next-generation multi-paradigm data mining tools.
Prevention of SQL Injection Attacks Using Cryptography and Pattern Matching

The internet is rapidly expanding that allow easy access to information, thus attackers develop different methodologies to access it and hence the security related to it becomes priority for all. SQL injection attack (SQLIA) has consistently posed serious threat since its existence. SQLIA is a web security vulnerability through which attackers can give specifically designed input to steal or manipulate sensitive information by interacting with the database. The objective of the research is to provide a defensive mechanism to protect a particular web application against such attacks. The paper acknowledged some existing models and give special attention to models based on encryption and pattern matching techniques. Encryption based models have proven themselves to be very effective against SQLIA by preventing attackers from authentication access. But such model will undermine the integrity of the tables if used in places other than the authentication form. Thus, we employ an additional layer of security based on pattern matching techniques. Our idea differs in a way that it compares a temporary structure generated from the user’s query with all defined benign structures created from the benign queries that are usually expected by the web application. The proposed model uses Blowfish algorithm in authentication form which upon simulation is preventing all kind of SQLIA from authentication access and upon the implementation of Knuth-Morris-Pratt pattern matching technique, the model will ensure the prevention of any new and existing kind of SQLIA. The model is under development and is believed to provide a robust environment in preventing all kind of SQLI attacks with overall reduced complexity.
SapientML

Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However, the huge combinatorial search space of candidate pipelines means that current AutoML techniques, generate sub-optimal pipelines, or none at all, especially on large, complex datasets. In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset. To combat the search space explosion of AutoML, SapientML employs a novel divide-and-conquer strategy realized as a three-stage program synthesis approach, that reasons on successively smaller search spaces. The first stage uses a machine-learned model to predict a set of plausible ML components to constitute a pipeline. In the second stage, this is then refined into a small pool of viable concrete pipelines using syntactic constraints derived from the corpus and the machine-learned model. Dynamically evaluating these few pipelines, in the third stage, provides the best solution. We instantiate SapientML as part of a fully automated tool-chain that creates a cleaned, labeled learning corpus by mining Kaggle, learns from it, and uses the learned models to then synthesize pipelines for new predictive tasks. We have created a training corpus of 1094 pipelines spanning 170 datasets, and evaluated SapientML on a set of 41 benchmark datasets, including 10 new, large, real-world datasets from Kaggle, and against 3 state-of-the-art AutoML tools and 2 baselines. Our evaluation shows that SapientML produces the best or comparable accuracy on 27 of the benchmarks while the second best tool fails to even produce a pipeline on 9 of the instances.
Performance Assessment of Internet of Things Implementation in Fleet Management Project—Schedule of Rate: An Oil Marketing Company Case Study

This paper describes the IoT implementation in the ABC schedule of rate project. ABC is an Oil Marketing Company (OMC) and has around 15,000 fuel filling stations in India. The prices of fuel are decided by OMCs and need to be updated on all the filling stations on daily basis. This change of price took place by establishing the connection in-between all the fuel filling stations with the Host of Server (HoS) of ABC. The term Retail Sale Price (RSP), i.e., the price change, is the key parameter that needs to achieve 100% on daily basis. This could not achieve, and the research drives more into the investigation of the factors affecting the RSP, which were ultimately a hindrance in IoT implementation. After daily observation over 85 days in between 6th January 2021 and 12th May 2021, the factors were found and classified under intermediate connectivity, no connectivity, hardware operational issues. Regression analysis was used to calculate and find the dominance of the factor affecting RSP not happening the intermediate connectivity was the most prominent factor affecting the RSP. And this problem was taken care upgrading the fuel filling station by incorporating new wireless automation by XYZ company to increase the RSP of ABC OMC.
Classifying Dynamic Motor Imagery with the Locals-Balanced Extreme Learning Machine

The dynamic motor imagery (dMI) provides additional benefits as compared to the traditional motor imagery (MI) in training and neurorehabilitation field. The objective of this work is to develop a brain-computer interface (BCI) for dMI electroencephalograph (EEG). We propose a novel method by combining synchronization likelihood (SL) based functional brain network (FBN) and modified extreme learning machine (ELM) to interpret EEG. The proposed method 1) uses L1-norm and adaptive threshold instead of L2-norm and empirical threshold in SL method; 2) improves the procedure of FBN calculations (namely, SL matrix binarization) by combining threshold and MST methods; 3) detects two defects in standard ELM in fusion, and proposes the locals-balanced ELM (LBELM); 4) employs the special version of leave-one-out cross validation (LOO) approach improved the computational complexity of optimal threshold for binarization, the most effective fusion of two complementary features, and the optimal regularization parameters for regularized LBELM. As compared with the empirical threshold, the recognition rate of the optimal threshold increases by 1.39% ~ 11.11%. Similarly, as compared with the regularized ELM, the recognition rate of LBELM increases by 2.78% ~ 9.73%.
Maxwell-Boltzmann Distribution Mathematica Based Practitioner’s Approach

Utilizing Mathematica this report shows how from a practitioner's point of view useful quantities some known, and some unknown and fresh properties about the Maxwell-Boltzmann distribution are calculated.We shortcut circling the usage of antiquated incomplete tabulated error functions given in the textbooks and professional literature replacing them with efficient upgrades.And, utilizing the animation features of Mathematica displaying the temperature-dependence of the distribution function assists in visualizing the character of the distribution.
Integrating quantum processor device and control optimization in a gradient-based framework

Abstract In a quantum processor, the device design and external controls together contribute to the quality of the target quantum operations. As we continuously seek better alternative qubit platforms, we explore the increasingly large device and control design space. Thus, optimization becomes more and more challenging. In this work, we demonstrate that the figure of merit reflecting a design goal can be made differentiable with respect to the device and control parameters. In addition, we can compute the gradient of the design objective efficiently in a similar manner to the back-propagation algorithm and then utilize the gradient to optimize the device and the control parameters jointly and efficiently. Therefore, our work extends the scope of the quantum optimal control to device design and provides an efficient optimization method. We also demonstrate the viability of gradient-based joint optimization over the device and control parameters through a few examples based on the superconducting qubits.
Analysis of the Execution Time of the Jason BDI Reasoning Cycle

This paper takes a look at the execution time of the Jason reasoning cycle combining an examination of general algorithms and code analysis to determine the timing complexity of its parts. It also experimentally confirms the analysis through the use of profiling tools and agents specifically designed to exploit factors found to affect the execution time. It is found that the reasoning cycle executes within a reasonable time frame give that increasing the number of beliefs or plans leads to a linear increase of execution time (less than 0.1 s at 10 000 beliefs/plans in our setup), but increasing the number of percepts results in a polynomial increase in execution time (approximately one second at 10 000 percepts in our setup).
An Efficient Storage Architecture Based on Blockchain and Distributed Database for Public Security Big Data

For the massive multi-source and heterogeneous public security big data, a distributed, secure and large-scale storage platform is urgently needed. As one of the most popular distributed ledger technologies, blockchain satisfies the needs of distribution and security, but there are still deficiencies in the storage of massive data. This paper provides an in-depth analysis of the advantages and disadvantages of blockchain and distributed databases in various aspects, and combines the characteristics of distributed databases to improve the performance of blockchain in terms of storage and query. HBase is proposed to extend the data storage layer of Hyperledger Fabric. The hash of the bulk data is first calculated and the complete data is stored into HBase by calling chaincode to store only the hash onto the chain. A hash-based rowkey construction has also been designed in an attempt to improve the efficiency of reading data in the blockchain. Finally, this architecture is applied to realize the storage of massive public security big data and the results demonstrate an ideal case of a 6% increase in throughput, 5% reduction in block size, and 13% reduction in transaction latency.
Personalized recommendation system of blended English teaching resources based on deep learning

In order to improve the quality of blended English teaching, a personalized recommendation system of blended English teaching resources based on deep learning is proposed. Analyze the functional architecture of the system, and design user registration module, crawler module and personalized recommendation module of teaching resources. It is used for user registration, crawling of blended English teaching resources and personalized recommendation of teaching resources. The system software part calculates the user interest similarity, and uses the neural network in deep learning to calculate the interest preference data, so as to obtain the recommendation results of teaching resources. The experimental results show that compared with the traditional teaching resource recommendation system, this system can accurately calculate the user interest similarity, so as to improve the recommendation accuracy, so it obtains higher user satisfaction.
Automatic Quality Assessment of Speech-Driven Synthesized Gestures

The automatic synthesis of realistic gestures has the ability to change the fields of animation, avatars, and communication agents. Although speech-driven synthetic gesture generation methods have been proposed and optimized, the evaluation system of synthetic gestures is still lacking. The current evaluation method still needs manual participation, but it is inefficient in the industry of synthetic gestures and has the interference of human factors. So we need a model that can construct an automatic and objective quantitative quality assessment of the synthesized gesture video. We noticed that recurrent neural networks (RNN) have advantages in modeling advanced spatiotemporal feature sequences, which are very suitable for use in the processing of synthetic gesture video data. Therefore, to build an automatic quality assessment system, we propose in our work a model based on Bi-LSTM and make a little adjustment on the attention mechanism in it. Also, the evaluation method is proposed and experiments are designed to prove that the improved model of the algorithm can complete the quantitative evaluation of synthetic gestures. At the same time, in terms of performance, the model has an improvement of about 20% compared to before the algorithm adjustment.
Systematizing the choreography of Ningyo Joruri by an Association Model from the Perspective of a Service Robot

How can service robots attract people and communicate more smoothly? On the theme of utilizing expressions that have been inherited through the times, the authors independently developed our own design method with reference to Ningyo Joruri, developed a robot OSONO with physicality, and evaluated as ``attractive''. In this paper, we propose a method of analysis and systematization on the choreography of Ningyo Joruri targeting the service robot, such as OSONO.
PErrCas: Process Error Cascade Mining in Trace Streams

Abstract Efficient and quick detection of problems is an essential task in online process monitoring. Many anomaly detection approaches excel in finding local deviations. We propose a novel approach that tracks local deviations over multiple process instances and visualizes correlations of deviation points. PErrCas provides knowledge about current cascades of deviations to give process analysts a starting point for rational root-cause analysis if processes leave their in-control parameters. PErrCas monitors deviations online and maintains cascades of varying timespans. Hence, our approach avoids defining an observation window beforehand, which is a significant advantage due to its impracticability to predefine expected cascade properties in exploratory scenarios.
Online Prediction of Aggregated Retailer Consumer Behaviour

Abstract Predicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
Multi-Stage Image Compression-Decompression System Using PCA/IPCA to Enhance Wireless Transmission Security

The goal of this paper is to propose a fast and secure multi-stage image compression-decompression system by using a wireless network between two Personal Computers (PCs).In this paper, the Principal Component Analysis (PCA) technique is used for multi-stage image compression and Inverse Principal Component Analysis (IPCA) for multi-stage image decompression.The first step of the proposed system is to select the input image, the second step is to perform PCA up to 9 times on the input image, this compression, and after multi-stage compression process then the third step begins by transforming across wireless Ad hoc Network (WANET) to the second computing device, forth step start with multi-stage decompression process up 9 times.The proposed system for different images is transferred over the wireless network using Transmission Control Protocol/Internet Protocol (TCP/IP), which is programmed using the network role property of the MATLAB program.The proposed system implements 25 different images correctly (100%).The main contribution of this paper is that we are dealing with the black image at the end of the compressed process ad start with a black image at the start of the decompressed process of this proposed system.In this work, the compressed and uncompressed images are compared with each other in their size and transmission time.This system can be very useful in networks because they provide a high level of protection to the transmitted data from hackers because they cannot guess how much the image has been compressed or what kind of information the image represents.
Graph-Based Depth Denoising &amp; Dequantization for Point Cloud Enhancement

A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption. To improve quality, previous works denoise a point cloud a posteriori after projecting the imperfect depth data onto 3D space. Instead, we enhance depth measurements directly on the sensed images a priori, before synthesizing a 3D point cloud. By enhancing near the physical sensing process, we tailor our optimization to our depth formation model before subsequent processing steps that obscure measurement errors. Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization. The designed model is validated (with parameters fitted) using collected empirical data from a representative depth sensor. To enhance each pixel row in a depth image, we first encode intra-view similarities between available row pixels as edge weights via feature graph learning. We next establish inter-view similarities with another rectified depth image via viewpoint mapping and sparse linear interpolation. This leads to a maximum a posteriori (MAP) graph filtering objective that is convex and differentiable. We minimize the objective efficiently using accelerated gradient descent (AGD), where the optimal step size is approximated via Gershgorin circle theorem (GCT). Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes in two established point cloud quality metrics.
Optical Character Recognition of Telugu Text Using Inception Model

Optical character recognition (OCR) is the process to recognise text in the image which might be handwritten or printed and convert it into a text which is in editable format. OCR for Telugu language is difficult as consonants, and vowels of Telugu language can be combined in many ways to form different sets of words. The two major problems with Telugu OCR are character-level segmentation and training of compound character. To deal with character segmentation problems for handwritten styles, we have proposed an algorithm to segment characters in such a way that most of the features in a character are preserved. To deal with training problems, characters are converted into a numerical value so that problems which occur due to training of compound characters by using Telugu characters Unicode is eliminated, as it considers a compound character as 2 or more characters. For feature extraction, we have used Inception, which captures the small variances in the character, thereby increasing the recognition rates. We have achieved character-level recognition rates of 83% and word-level recognition rates of 70%.
Recommending Books Using RNN

Many recommendation algorithms have been developed using collaborative filtering such as latent matrix factorization. But most of these algorithms are based on other similar users rating of the books and have certain limitations. Reviews given to books provide a valuable source of information for recommender systems. Algorithms used in area of natural language processing are based on similarity between word vectors in the two texts or documents. Often context between these words are missing leading to poor recommendation as it requires semantics to be analyzed between texts from the given reviews. Deep learning has been widely used in natural language processing and has obtained a great progress in this field. In this paper, we use Recurrent Neural Networks (RNN) as a deep learning approach for recommending books. RNN is an improvement over the existing models as instead of each input unit being independent as in logistic regression and other neural networks each neuron can use its internal memory to maintain information about the previous units. Hence, the context between reviews is maintained by memory units and hence a more accurate classification can be made that leads to better recommendation.
Development of teaching programs of artificial intelligence methods in aerospace education

As a part of the National Strategy on Artificial Intelligence development in the Russian Federation, the basic concepts and definitions related to artificial intelligence (AI) and the necessity for the preparation of training programs were analyzed. The analysis of Federal state educational standards (FSES) and professional standards for enlarged groups of specialties and directions (EGSD) 24.00.00 AEROSPACE ENGINEERING was conducted and the perspective programs on the foundations of artificial intelligence and intelligent data analysis were proposed.