Reviewerly/exHarmony huggingface dataset:
Transformations and Paraphrases for Quechua Sentiment Predicates

In this paper, I present a study on the automatic generation of paraphrases of Quechua sentiment predicates. Using the transformational NooJ engine, I first build the rules corresponding to the elementary transformations for the Quechua language. I then describe in detail grammars performing pronominalization, reduction, and permutation of the arguments, passivation, and some others. Then, I show how they can combine with one another, respecting certain syntactic constraints, in order to obtain complex transformations. I present the electronic dictionary of Quechua sentiment verbs that I have built. Finally, I construct a particular subclass of transformations that will automatically generate paraphrases of a Quechua sentiment predicate.
Extracting Related Images from E-commerce Utilizing Supervised Learning

In this article for the sequence to catch the concept of ocular affinity, we suggest a deep convolutional neural network to know the embedding of images. We show the deep architecture of Siamese that learns embedding which correctly resembles objects' classification in visual similarity while trained on positive and negative picture combinations. We often introduce a novel system of loss calculation employing angular loss metrics based on the problem's requirements. The combined description of the low or top-level embeddings was its final embedding of the image. We also used the fractional distance matrix to calculate the distance in the n-dimensional space between the studied embeddings. Finally, we compare our architecture with many other deep current architectures and continue to prove our approach's superiority in terms of image recovery by image recovery. Architecture research on four datasets. We often illustrate how our proposed network is stronger than other conventional deep CNNs used by learning optimal embedding to capture fine-grained picture comparisons.
Human Skeleton Graph Attention Convolutional for Video Action Recognition

Action recognition based on human skeleton information is a hot topic in the field of computer vision, how to represent the human skeleton graph structure is the key of the method. Graph convolutional network is widely used to extract spatial features of human skeleton. However, the graph convolutional network shares the same weight for neighborhood of each node. In this paper, we propose Human Skeleton Graph Attention Convolutional Neural Network, which introduces graph attention convolution mechanism to extract the spatial features of human skeleton. The model improves the spatial feature extraction of skeleton graph based on the feature relationship of node neighborhood. The experimental results on Kinetics and NTU-RGB+D datasets show that the model can obtain better representation of spatial features, and can achieve better accuracy.
Application of high-resolution remote sensing technology for the iron ore deposits of the West Kunlun Mountains in China

This study focuses on the iron ore of Taxkorgan and Heiqia in the West Kunlun mountains as a case study, for the application of WorldView−2 and IKONOS remote sensing images as major data sources in the fabrication of a standard image map and in the adoption of image enhancement methods to extract information on the ore-controlling factors and mineralization, to interpret remote sensing for the mineral resources in these areas. ASTER, WorldView−2, and IKONOS data were applied for the extraction of alteration anomaly information. With an appropriate amount of field sampling and verification tests, this was used to establish a remote sensing geology prospecting model, that would provide the basis for future remote sensing of metallogenic belts in West Kunlun in the hope of discovering similar minerals. Survey results showed four additional iron ore mineralization belts could be delineated in the Taxkorgan area. A comparative analysis conducted for part of the field confirmation and the known mineral deposits indicated good reliability. In Heiqia, a siderite-haematite mineralization zone was observed with copperlead- zinc formation, 60-km in length and 200–500 m wide, which includes several mineralized bodies. The ore bodies, appear as stratoid, lenticular, or podiform morphologies and were located in the transition site from clastic to carbonate rocks of the D segment in the Wenquangou Group. The ore bodies generally occur within 40°–50° strike and 68°–81° dip, in accordance to the strata. The length of the single body varies from several hundred metres to more than 9500 m. Its exposed thickness on the surface ranges from 2–50 m, and the general thickness was approximately 15 m. The surface ore minerals were mainly haematite and limonite, with a small amount of siderite. Therefore, high-resolution remote sensing technology is suitable for iron ore geological and mineral remote sensing surveying. It is advantageous in both high-ground resolution of optical characteristics and a certain spectral recognition capability, and is effective not only for information extraction from a large area, but also for recognition of local mineralization outcrops. Therefore, high-resolution remote sensing technology is valuable for popularization.
Image Steganography: An Inevitable Need for Data Security

With the advent of growing terrorism in the world and the availability of the internet, data integrity is being endangered. Hence ensuring the security of the messages is paramount. Steganography is used—the process of hiding information in image, video, etc. Steganography is not any novel technique, it had been used too earlier where they use to shave the head and embed the message on to it and then wait for the hair to grow in ancient Greece; it was also used in war times. The methodology carried out here is of least significant bit technique and random pixel technique. A brief analysis of various algorithms is done and a histogram is also plotted. The image steganography methodology is performed using MATLAB/Simulink. It is also vindicated as to where to hide the message in the image so that, it is barely noticed by any observer, keeping in view the quality, size, and resolution of the image.
Frequent Itemsets Mining Algorithm for Uncertain Data Streams Based on Triangular Matrix

Aiming at the problem of frequent itemsets mining in uncertain data flows, this paper proposes a botm-mine for frequent itemsets mining in uncertain data flows. In this algorithm, trigonometric matrix, queue and frequent item set tree are used to construct the profile structure to store the relevant data flow information of transactions. The support degree of items 1_ and 2_ itemsets is efficiently stored in the matrix through matrix. Compared with the transaction matrix, it not only saves space, but also reduces the complexity of computing each support degree, and at the same time has better space-time efficiency.
Knowledge distillation application technology for Chinese NLP

At this stage, the popular deep neural network models often encounter problems of high latency, difficult deployment and high hardware requirements in practical applications. Knowledge distillation is a good approach to solve these problems. We adopted an innovative knowledge distillation approach and formulated data augmentation strategies for the tasks, and obtained a lightweight model with 6.7× acceleration ratio and 13.6× compression ratio compared to the baseline model BERT-base, and the average performance of the lightweight model reached 95% of BERT-base for each task. We continue to conduct in-depth research to investigate some of the issues that remain in the knowledge distillation phase. To address the problems in distillation model selection and model fine-tuning, we propose a teacher model and student model selection strategy and a two-stage model fine-tuning strategy before and after the knowledge distillation stage. These two strategies further improve the average performance of the models to 98% of BERT-base. Finally, we developed a lightweight model evaluation scheme based on different types of downstream tasks, which provides a reference for subsequent practical applications when encountering similar tasks.
Quantum Network Discrimination

Discrimination between objects, in particular quantum states, is one of the most fundamental tasks in (quantum) information theory. Recent years have seen significant progress towards extending the framework to point-to-point quantum channels. However, with technological progress the focus of the field is shifting to more complex structures: Quantum networks. In contrast to channels, networks allow for intermediate access points where information can be received, processed and reintroduced into the network. In this work we study the discrimination of quantum networks and its fundamental limitations. In particular when multiple uses of the network are at hand, the rooster of available strategies becomes increasingly complex. The simplest quantum network that capturers the structure of the problem is given by a quantum superchannel. We discuss the available classes of strategies when considering <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> copies of a superchannel and give fundamental bounds on the asymptotically achievable rates in an asymmetric discrimination setting. Furthermore, we discuss achievability, symmetric network discrimination, the strong converse exponent, generalization to arbitrary quantum networks and finally an application to an active version of the quantum illumination problem.
A Geophone Based Surveillance System Using Neural Networks and IoT

Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
Prediction based Load Balancing and VM Migration in Big Data Cloud Environment

In Big Data Cloud atmosphere, the cloud service provider (CSP) offers amenities to the customer with the accessible virtual cloud sources. Investigators have been provided more consideration towards the harmonizing of the load, as it has a complete impact on the system act. In this paper, Prediction based Load Balancing and Virtual Machine (VM) Migration (PLBVM) algorithm is designed for Big data cloud environments. In this algorithm, the future loads of each server are estimated. If the estimated future load is greater than an upper bound or less than a lower bound, then it indicates unbalanced load, so that VM migration is triggered. In VM migration, the VMs with minimum migration time and sufficient resources are selected. Then the task execution continues in the migrated VMs. By experimental results, it is shown that PLBVM achieves lesser response delay and execution time, among the other approaches.
Assessment of Remote Biometric Authentication Systems: Another Take on the Quest to Replace Passwords

Passwords are often criticized due to being prone to misuses such as bad password creation and management practices. Experts usually advise using other forms of authentication. While there are plenty of alternative authentication methods available, an overall assessment often proves to be challenging. This is because of aspects such as differences in security techniques, different applicability of the system, or varying difficulties of implementation. To tackle the issue of comparing different authentication systems, unified criteria are needed. Bonneau et al. proposed a framework for comparing authentication schemes in their “The Quest to Replace Passwords”. We contribute to the quest by providing information and assessment on the previously unassessed Remote Biometric Authentication Systems, thus increasing the variety of analyzed systems. We achieve this by analyzing six exemplary implementations. To enable proper evaluation of the details of that new category of authentication schemes, this work furthermore expands the framework by the two aspects Resilient-to-Biometric-Loss and No-Trusted-Execution-Environments.
SOT: An Application-based Research for Translate Natural Language from Image

This chapter proposes a new and innovative method of smart optical character recognition (OCR) for translation (SOT) from an English image to Bangla and Hindi language. For translation, this OCR system can take any picture from any documents which contain printed English sentences. SOT can translate the image into text within two languages. The translated text from SOT can also be copied and kept for further use. SOT can convert the full image in a text format and then can translate some of the portions of an image by cropping. Google translator has been used here for the OCR. This API can translate the image into a printed text or a machine-encoded text. But the translated text cannot be kept for further use which is an immense disparity between SOT and Google translator. Moreover, many APIs also deploy, which can translate the image into a text format but cannot ferry the limit of copying text which is collected after translating text from the image. SOT can be beneficiary among all the OCR as it struck the limit of copying text.
Kalman Filter Based Point Target Tracking

The detection and tracking of point targets in cluttered environment has always played a significant role in surveillance and navigation applications especially for aerial targets. Most of the contemporary MWS (Missile Warning Systems) and IRST (Infra Red Search and Track) systems use captured IR radiations from the scene for detection and tracking of such point targets. The unintended IR (Infra Red) radiations emitted from targets make the signatures more prominent than background and clutter, this contrast is commonly used as a clue for detection of targets. The present approach also relies on this contrast based clue. On the basis of statistical analysis of image content of row and column vectors and Kalman filtering, the location of the point target is explored in the considered frame of the video sequence. A dataset of synthetic video sequences having point target with variable SNR is generated to validate the proposed method. The results obtained demonstrate effectiveness and robustness of the approach.
On the convergence of a Block-Coordinate Incremental Gradient method

Abstract In this paper, we study the convergence of a block-coordinate incremental gradient method. Under some specific assumptions on the objective function, we prove that the block-coordinate incremental gradient method can be seen as a gradient method with errors and convergence can be proved by showing the error at each iteration satisfies some standard conditions. Thus, we can prove convergence towards stationary points when the block incremental gradient method is coupled with a diminishing stepsize and towards an $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math> -approximate solution when a bounded away from zero stepsize is employed.
Multi-UAV Mission Efficiency: First Results in an Agent-Based Simulation

To assess the mission effectiveness and efficiency of future airborne systems-of-systems with autonomous components, appropriate performance models and metrics are required. In a first attempt, such metrics were derived systematically by subject-matter experts (SMEs) decomposing a specific mission and its tasks hierarchically, until measurable criteria were reached. Weights of the criteria were obtained through the Fuzzy Analytic Hierarchy Process (FAHP), using linguistic variables for the pairwise comparison of criteria on all decomposition levels. This work demonstrates determination of such metrics in a multi-agent 2D simulation. The results are aggregated according to their respective weight, generating the mission run’s total evaluation. An air-to-ground operation by multiple unmanned aerial vehicles (UAVs) with pop-up threats present was chosen as an example mission. Deploying different force packages, their UAV models having different characteristics, in the simulation yields distinct aggregated evaluation outcomes. These signify differences in efficiency and thus suitability for the selected mission. For investigation of result robustness local sensitivity analysis is used. For validation it will be required to have the SMEs compare their assessments of the conducted missions to the generated results.
End-to-end relation extraction based on part of speech syntax tree

Recently, deep learning has achieved richer research results on entity relationship extraction tasks. Existing methods mainly focus on the character features of the sequence input, without considering that the input structural features may learn meaningless subsequences. In this paper, we propose an end-to-end joint extraction model based on syntactic tree structure, which can learn character features and sentence structure features at the same time. In the decoding process, we use a tree structure to learn sentence structure features based on character parts of speech. We test our models in two public datasets and our model outperforms the baseline method significantly.
Multidimensional Classification for Systematization of Fish Processing Equipment

Abstract The paper dwells upon an approach to classifying fish processing machinery by means of cluster analysis. The classification presented herein helps optimize equipment upgrades and troubleshooting. The goal hereof is to analyze the fish processing machinery present in the market by means of cluster analysis. The paper covers fish dressing machines present in the fish processing market. The research methods include hierarchical cluster analysis, Euclidean distance, intergroup relations, and comparative analysis. SPSS is used to run calculations. The research has produced fish processing machinery clusters distinguished by performance, processable raw materials, and engine power; the research team has also tested a method for multidimensional classification for fish processing machinery analysis. These results are of use in a broad range of production tasks; they will be of interest for fish processing market analysts and machinery designers. Multidimensional classifications can be applied to arrange the production process, including selecting the most optimal equipment from a list of similar units.
Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm

Aerial images are large-scale and susceptible to light. Traditional image feature point matching algorithms cannot achieve satisfactory matching accuracy for aerial images. This paper proposes a recursive diffusion algorithm, which is scale-invariant and can be used to extract symmetrical areas of different images. This narrows the matching range of feature points by extracting high-density areas of the image and improving the matching accuracy through correlation analysis of high-density areas. Through experimental comparison, it can be found that the recursive diffusion algorithm has more advantages compared to the correlation coefficient method and the mean shift algorithm when matching accuracy of aerial images, especially when the light of aerial images changes greatly.
Recognition of Partial Handwritten MODI Characters Using Zoning

Complete pattern of input character image was initial need of any handwritten optical character recognition (HOCR) system. This research work proposes a robust technique to deal with the problem of partial, broken or incomplete characters impressed in the document image. On other hand machine interaction for image acquisition. Preprocessing and segmentation, backbone for any HOCR produces incomplete patterns. Principal interest of this work was to propose Partial Character Recognition (PCR) for MODI script and evaluate efficiency and performance of proposed system by applying different classifiers. Partial area of character (PAC) was the percentage of partialness in character image. PAC was extracted by using various planar geometrical shapes including circle, square, triangle and rhombus. These shapes were applied to discover patterns, finds area and length of PAC which helps to estimate incompleteness of the ideal character. Proposed framework analyzes experimental results using Ensemble bagging with decision tree, Decision Tree, k-NN, LDA and QDA, Naïve Byase and SVM as compare to Euclidian distance classifier. Manuscript shows highest 94.92% confidence in recognition rate (CRR) using Zernike moments and 97.68% for Partial MODI handwritten character using soft computing techniques, ensemble bagging with integrated approach.
Automated Assessment and Evaluation of Contribution of Collaborative Software Engineering Development Process

In the context of New Generation of Information Technology (NGIT) and Emerging Engineering Education (3E) in China, it is a new research hot topic to in evaluating students involvements and skills in engineering practice. Many automated assessment systems were developed specifically for the use case of grading student work. Sometimes, it is subjective evaluation and not always correct. These systems usually emphasize the training results, but neglects the process tracking od the result. In this paper, our automated assessment and evaluation of the contribution of collaborative software engineering training is proposed to evaluate student involving in software engineering training against a rubric feedback. Contributions of our method are automated assessment of laboratory environment and collaborative software development process, and cooperative development contribution model. Finally, the results of automated assessment of workload and its balance is analyzed to illustrate the effect of our software engineering training.
End-to-end Physical Layer Communication using Bi-directional GRUs for ISI Channels

This paper presents a deep learning autoencoder for end-to-end physical-layer communications, in the presence of intersymbol interference (ISI) and additive white Gaussian noise (AWGN). Both the proposed transmitter and receiver employ the bi-directional gated recurrent unit (Bi-GRU) layers, and they are trained jointly with binary cross-entropy loss. By doing so, the transmitter learns tailored modulation constellations considering the channel impulse response while the receiver performs equalization and demodulation simultaneously. Experiments conducted over representative ISI channels reveal that the proposed autoencoder outperforms the Viterbi-based maximum-likelihood sequence estimation algorithm with perfect channel state information, when E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> takes low to medium values, i.e., less than 13 dB.