Reviewerly/exHarmony huggingface dataset:
A Novel Method for Invariant Image Reconstruction

Abstract In this paper we propose a novel method for invariant image reconstruction with the properly selected degree of symmetry. We make use of Zernike radial moments to represent an image due to their invariance properties to isometry transformations and the ability to uniquely represent the salient features of the image. The regularized ridge regression estimation strategy under symmetry constraints for estimating Zernike moments is proposed. This extended regularization problem allows us to enforces the bilateral symmetry in the reconstructed object. This is achieved by the proper choice of two regularization parameters controlling the level of reconstruction accuracy and the acceptable degree of symmetry. As a byproduct of our studies we propose an algorithm for estimating an angle of the symmetry axis which in turn is used to determine the possible asymmetry present in the image. The proposed image recovery under the symmetry constraints model is tested in a number of experiments involving image reconstruction and symmetry estimation.
Proposal for room-temperature quantum repeaters with nitrogen-vacancy centers and optomechanics

We propose a quantum repeater architecture that can operate under ambient conditions. Our proposal builds on recent progress towards non-cryogenic spin-photon interfaces based on nitrogen-vacancy centers, which have excellent spin coherence times even at room temperature, and optomechanics, which allows to avoid phonon-related decoherence and also allows the emitted photons to be in the telecom band. We apply the photon number decomposition method to quantify the fidelity and the efficiency of entanglement established between two remote electron spins. We describe how the entanglement can be stored in nuclear spins and extended to long distances via quasi-deterministic entanglement swapping operations involving the electron and nuclear spins. We furthermore propose schemes to achieve high-fidelity readout of the spin states at room temperature using the spin-optomechanics interface. Our work shows that long-distance quantum networks made of solid-state components that operate at room temperature are within reach of current technological capabilities.
DIJKSTRA ALGORITHM USING UAV PATH PLANNING

The use of unmanned aerial vehicles (UAV) is increasing today. UAVs can be divided into two parts, which are remote controlled and can travel automatically due to a certain battery problem. Recent research has also focused on the development and application of new algorithms to autonomously control these vehicles and determine the shortest flight paths. Together with these researches, UAVs are used in many civil activities such as weather forecasts, environmental studies and traffic control. Three-dimensional (3D) path planning is an important issue for autonomously moving UAVs. The shortest path for Unmanned Aerial Vehicles (UAV) is determined by using two-dimensional (2D) path planning algorithms using the obstacles in the environment, and allows UAVs to perform their environmental tasks as soon as possible. The purpose of this study is to determine the shortest path to the target point and avoiding obstacles for UAVs using the Dijkstra algorithm. It was developed to evaluate the arrival time of the UAVs in the path planning algorithm with the simulation performed in the MATLAB program. In this study, the obstacles were defined for the purpose of the building with different heights and different widths and 2D and 3D models were carried out, assuming that the UAV flies at certain heights. In addition, the flight of the UAVs in the route planning determined in the real applications was carried out and the data such as battery consumption, amount of battery spent, speed, amount of travel were examined.
Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics

In text summarization, evaluating the efficacy of automatic metrics without human judgments has become recently popular.One exemplar work (Peyrard, 2019) concludes that automatic metrics strongly disagree when ranking high-scoring summaries.In this paper, we revisit their experiments and find that their observations stem from the fact that metrics disagree in ranking summaries from any narrow scoring range.We hypothesize that this may be because summaries are similar to each other in a narrow scoring range and are thus, difficult to rank.Apart from the width of the scoring range of summaries, we analyze three other properties that impact inter-metric agreement -Ease of Summarization, Abstractiveness, and Coverage.To encourage reproducible research, we make all our analysis code and data publicly available.
PANDA

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This article continues along with the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive and, hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We first provide a new security definition, entitled partitioned data security , for guaranteeing that the joint processing of non-sensitive data (in cleartext) and sensitive data (in encrypted form) does not lead to any leakage. Then, this article proposes a new secure approach, entitled query binning (QB), that allows secure execution of queries over non-sensitive and sensitive parts of the data. QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. In particular, we propose secure algorithms for selection, range, and join queries to be executed over encrypted sensitive and cleartext non-sensitive datasets. Interestingly, in addition to improving performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.
A Comparative Analysis of Biometrics Types: Literature Review

This study provides a thorough analysis of the various biometric types, including their advantages and disadvantages.It compares the different types and provides details about false acceptance and false rejection rates, along with their equations.Biometric screening systems are used to test and identify persons using their physiological or behavioral characteristics.Using only one recognition device is not suitable for identification systems.Multi-factor authentication can improve system security by using two or more kinds of security types, such as passwords and cards, but this is not an ideal security scheme.Passwords may be forgotten or inputted incorrectly, or the identification card may be stolen.To verify and classify people using their physiological attributes, biometric devices are used.These technologies can be classified as either behavioral or physiological biometrics.The former has many shortcomings, such as noisy data, inter-class similarity, intra-class variability, spoofing and universality, which reduce the system's accuracy.The success rate of recognition and verification is, however, substantially improved by multimodal biometric sensing and processing systems, which leverage the detection or processing of two or more behavioral or physiological traits.
Retrieval-Augmented Controllable Review Generation

In this paper, we study review generation given a set of attribute identifiers which are user ID, product ID and rating.This is a difficult subtask of natural language generation since models are limited to the given identifiers, without any specific descriptive information regarding the inputs, when generating the text.The capacity of these models is thus confined and dependent to how well the models can capture vector representations of attributes.We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes.With these references, we can now pose the problem as an instance of text-to-text generation, which makes the task easier since texts that are syntactically, semantically similar to the output text are provided as inputs.Using this framework, we address issues such as selecting references from a large candidate set without textual context and improving the model complexity for generation.Our experiments show that our models improve over previous approaches on both automatic and human evaluation metrics.
Optimizing the Resource Consumption of Blockchain Technology in Business Systems

Abstract Background Blockchain technology has gained a great public interest due to the appearance of cryptocurrencies, a digital asset used for exchanging funds. Although blockchain calculations offer the benefits of security and reduced costs, blockchain is still strongly criticised for its lack of usefulness and resource-heavy consumption. Objectives The aim of this research is to provide different insights into blockchain technology and to propose NP-complete problems as a suitable alternative to the current consensus algorithm. Methods/approach This research discusses the current state of proposed alternatives, projects such as distributed volunteering for scientific purposes and different consensus algorithms within cryptocurrencies but focusing on incorporating NP-complete problems as a secondary, more useful option. Results Using the properties of NP-complete problems, it is possible to solve various problems in different areas, such as science, biology, medicine and finance, but also to improve business processes, optimize markets, payments and supply chains while decreasing environmental costs. Conclusions This paper shows that the alternative mechanisms are being developed and used to substitute an existing Blockchain algorithm with a more efficient one. It also suggests further investigation in this area because the alternatives greatly improve blockchain’s usability and efficiency.
C-Norm: a neural approach to few-shot entity normalization

Abstract Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics. Results Our approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules. Conclusions Our results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems.
Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.
Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler

Due to the intractable partition function, training energy-based models (EBMs) by maximum likelihood requires Markov chain Monte Carlo (MCMC) sampling to approximate the gradient of the Kullback-Leibler divergence between data and model distributions. However, it is non-trivial to sample from an EBM because of the difficulty of mixing between modes. In this paper, we propose to learn a variational auto-encoder (VAE) to initialize the finite-step MCMC, such as Langevin dynamics that is derived from the energy function, for efficient amortized sampling of the EBM. With these amortized MCMC samples, the EBM can be trained by maximum likelihood, which follows an "analysis by synthesis" scheme; while the VAE learns from these MCMC samples via variational Bayes. We call this joint training algorithm the variational MCMC teaching, in which the VAE chases the EBM toward data distribution. We interpret the learning algorithm as a dynamic alternating projection in the context of information geometry. Our proposed models can generate samples comparable to GANs and EBMs. Additionally, we demonstrate that our model can learn effective probabilistic distribution toward supervised conditional learning tasks.
The Problem of a Rational Relationship of the General Transport Infrastructure

The study analyzes the existing problem of a rational relationship of modes of transport, their interrelated development and use. This problem is especially acute at the “junctions” of interaction between modes of transport, where there are large economic losses, enterprises do not have the freedom to choose the most rational schemes for transporting goods and passengers. The market situation causes a constant change in the addressing of transport links (some of them cease to exist, others reappear, forming new traffic flows to replace the lost ones). All this takes place within the framework of a single transport system of the state. Systems analysis, logical and mathematical modeling, systems theory, economic-visual modeling, research methods of operations, as well as methods of operations research and economic and mathematical methods are used in the paper. The previously developed scientific and methodological foundations and practical recommendations for mobilization training and increasing the survivability of transport communications as a single transport system for the national security of the state do not sufficiently take into account the new realities of the market economy and require certain adjustments. The study was carried out in relation to the unified transport system of the country and transport subsystems of the regions of the Russian Federation. Models and methods for assessing the economic and military-economic efficiency of using the country’s unified transport system in the interests of ensuring the national security of the state have been substantiated.
Contextual Argument Component Classification for Class Discussions

Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction.However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models.In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions.We find that both context types can improve performance, although the improvements are dependent on context size and position.
Subpixel sampling moiré method for in-plane displacement measurement considering the symmetric errors induced by interpolation

Sampling moiré method (SMM) is a highly accurate vision-based deformation measurement method, whose measurement error is minimized when an integer that is closest to the grating pitch is selected as the sampling pitch. We propose a subpixel SMM by applying subpixel interpolation operation before the downsampling in traditional SMM; the sampling pitch was selected as a noninteger nearest to the grating pitch in the subpixel resolution image. Meanwhile, the average filter method was used to eliminate the symmetric error cause by interpolation. As a result, the period of moiré fringe was enlarged greatly, and the measurement accuracy was also increased. To investigate the efficiency of the subpixel SMM, a computer simulation was applied to analyze the accuracy of the subpixel SMM. Then a simple tensile experiment was conducted to validate the efficiency of this method, and the result of the subpixel SMM accorded well with the fiber Bragg grating. In summary, the proposed novel subpixel SMM is capable of being used for high accuracy in-plane deformation measuring.
Information Extraction Tasks based on BERT and SpaCy on Tourism Domain

In this paper, we present two methodologies to extract particular information based on the full text returned from the search engine to facilitate the users. The approaches are based three tasks: name entity recognition (NER), text classiﬁcation and text summarization. The ﬁrst step is the building training data and data cleansing. We consider tourism domain such as restaurant, hotels, shopping and tourism data set crawling from the websites. First, the tourism data are gathered and the vocabularies are built. Several minor steps include sentence extraction, relation and name entity extraction for tagging purpose. These steps are needed for creating proper training data. Then, the recognition model of a given entity type can be built. From the experiments, given review texts, we demonstrate to build the model to extract the desired entity,i.e, name, location, facility as well as relation type, classify the reviews or summarize the reviews. Two tools, SpaCy and BERT, are used to compare the performance of these tasks.
An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.
An Improved Algorithm of K-means Based on Evolutionary Computation

K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a l... | Find, read and cite all the research you need on Tech Science Press
Transformer-based Arabic Dialect Identification

This paper presents a dialect identification (DID) system based on the transformer neural network architecture. The conventional convolutional neural network (CNN)-based systems use the shorter receptive fields. We believe that long range information is equally important for language and DID, and self-attention mechanism in transformer captures the long range dependencies. In addition, to reduce the computational complexity, self-attention with downsampling is used to process the acoustic features. This process extracts sparse, yet informative features. Our experimental results show that transformer outperforms CNN-based networks on the Arabic dialect identification (ADI) dataset. We also report that the score-level fusion of CNN and transformer-based systems obtains an overall accuracy of 86.29% on the ADI17 database.
RETRACTED: Human tracking of track and field athletes based on FPGA and computer vision

An essential issue that sports science faces in understanding the game's flow is to analyze the game's situation. The use of information technology can help to achieve this goal. Computing speed, the size of the system, consider the accuracy of the complex data analysis. From the practical application of views, technical problems can be divided into three significant respects. This article aims to accelerate image recognition and object tracking; propose a one-dimensional data pipeline architecture on a Field-Programmable Gate Array (FPGA). It is met by a small circuit considering calculation and the spatiotemporal data dependency between two high-velocity flows. The volleyball game has been selected as the target application. FPGA design, pre-processing, color filtering, digitization, noise reduction, including such as template matching. Design and implementation by training stations of the Spartan-6 FPGA Xilinx Atlys of the Spartan-6 FPGA LX45 'were evaluated. Computing power will reach per 100 frames at a resolution of SVGA 800 × 600 pixels. Design, excellent with the scalability, can more easily improve the performance in the case of using a large FPGA. The proposed system consists of the Atlys substrate and Atlys VmodCAM stereo camera board. The average accuracy rate before the game situation and the game with 87.1 percent and 65.7 percent. Since the streaming input data, you can improve the precision by taking into account the previous and the next frame. And 90.4 percent will be able to improve to 72.2 percent by using a moving average filter and template matching.