Reviewerly/exHarmony huggingface dataset:
Text Generation and Enhanced Evaluation of Metric for Machine Translation

Here the power of a recurrent neural network (RNN) has been exhibited for generating grammatically correct new text from given input text and translation of the new text to the Hindi language with modified bilingual evaluation understudy (BLEU) metric score. Our system aims to generate a grammatically correct new text from given input sentences or paragraphs and translate generated text to Hindi with high translation score. To accomplish a grammatically correct sentence, natural language toolkit (NLTK) is used for grammar correction at the end of text generation. RNN is not very useful for text generation of a gated connection decided to be used for this purpose. The generated text is transferred to machine translation (MT) module. For MT since evaluation is done by humans is a time-consuming task and results differ from evaluator to another evaluator. Hence, the need for assessment of translation system is emerged. The synonym issue is not considered by the BLEU metric. A synonym is treated as a separate word. A modified BLEU (M-BLEU) has been developed as evaluation metrics. It includes several features such as replacing synonym and shallow modules of parsing. The final score of translation is given by BLEU metric scores. Finally, two outputs are there: one is generated text (English) and second is translated text with an improved translation score (Hindi).
Playing Mega Man II with Neuroevolution

The problem of developing Game-Playing Agents provides a controlled environment with varying levels of difficulty in order to test different Artificial Intelligence algorithms. A recently proposed framework for testing such algorithms is called EvoMan and was created based on a classic and challenging game called MegaMan II. In this framework, the agent must defeat a number of different enemies equipped with a diverse set of weapons with different behaviors. This paper follows up the Evoman: Game-playing Competition hosted at the World Conference on Computational Intelligence in 2020 with the objective of finding a general strategy capable of defeating all of the bosses training only on a subset of those. Our approach is composed of manually crafted inputs based on the available sensors fed into a Neuroevolution algorithm composed of a Genetic Algorithm evolving the weights of a Multilayer Perceptron. Our results obtained the first place on the competition and was capable of defeating the entire set of enemies.
Blockchain Adoption Time of Shipowners: A Game Theoretic Analysis

With continuous overcapacity in vessel tonnage and slower growth of global trade, shipowners are facing fierce competition and are pushed to look for innovative ways to improve their performance and reduce costs. Blockchain is one of the emerging technologies that has the potential to help shipowners to achieve these goals. This study aims to analyse if and when shipowners would adopt blockchain when facing a request from a big shipper. A game theory model is developed to analyse the optimal adoption time of shipowners. The analytical solutions of the optimal adoption time of each shipowner are obtained in different situations. The numerical analysis reveals that the higher shipping quantity carried by the shipowners, the earlier they would join the blockchain network. Shipowners with very small shipping quantities would be unlikely to adopt blockchain until more than a decade later. It is suggested that blockchain initiators should start with large shipowners when promoting blockchain.
JROTM: Jointly reinforced object tracking with temporal content reference and motion guidance

Although the existing visual object tracking methods have made noticeable progress, many chokepoints remain unsettled. One of the most significant performance bottlenecks is the drastic change of the target, such as fast motion, scale variation, appearance changes, etc. Focusing on this major barrier, we propose our object tracking method, JROTM. It adopts the two-stage tracking-by-detection strategy, and it is reinforced by the temporal content reference (TCR) module and the motion guidance (MG) module. TCR precisely provides auxiliary frame-wise information of two consecutive frames of a video. With this information, the rapid change of the object’s appearance and position is retarded. MG module accurately offers more target position hints at the current frame. Our proposed JROTM is evaluated in multiple popular tracking benchmark, including OTB-100, TempleColor-128, and VOT-2016. It outperforms the state-of-the-art tracking methods by a non-neglectable margin.
A Comparative Analysis of Various Filters for Noise Reduction in Cardiac Ultrasound Images

Cardiac ultrasound (US) images are used to obtain qualitative information about the outline of the organ and type of tissue, as well as quantitative information on the thickness and movement of the internal walls and layers of the heart. Speckle noise generally masks the fine details of the US image and makes its interpretation difficult for medical diagnosis. This paper presents various filters for noise reduction in cardiac US images, which have been discussed and compared.
Multi-Rotor Path-following Performance using Vector Field Guidance and Velocity Control

Autonomous aerial vehicle path following requires robust guidance commands for successful mission completion. Discrete waypoints are typically used, but only allow for straight line path following. Following smooth trajectories is desired so ground features or tight turns can be highly controlled which can not be achieved while using waypoint guidance. Vector field guidance providing heading commands representing the desired path can instead be used to provide smooth path following for fixed wing aircraft, but requires the use of carrot chasing for multi-rotor vehicles using position control where a point is placed in front of the vehicle along the commanded heading. The carrot chasing method can create lagged areas causing velocity oscillation and drift from the planned path on curved trajectories resulting in poor path fol-lowing performance without explicit velocity control. Components from vector field guidance field can be used to generate velocity control commands instead of position commands which rely on carrot chasing for multi-rotor aircraft. The present work investigates velocity control for vector field guidance enabling smooth path following for multi-rotor autonomous vehicles so carrot chasing with position control can be replaced. Comparisons between vector field velocity control and carrot chasing position control path following performance using cross track error are made to identify improvements and deteriorations. Results may be used to obtain smoother path following using vector field guidance and velocity control with multi-rotor autonomous systems resulting in shorter flight times and less energy consumption compared to carrot chasing methods.
Instantaneous Velocity Estimation for 360° Perception with Multiple High-Quality Radars: An Experimental Validation Study

This paper describes two deterministic algorithms that instantaneously estimate the velocity of an observed vehicle (OV) from radar data. The low-complex approaches designed here use the algebraic properties of the Doppler-Azimuth profile to choose a pair of reflection points from the whole set of associated measurements. The selected points are utilized in a linear regression system to estimate the velocity instantaneously. This velocity is then fed into the state estimator. The novelty of this approach is in the algebraic driven choice of a non-singular pair of reflection points. This lends it a lower computation cost than other approaches in the field hence making it a feasible implementation on micro-controllers with limited computation power. The performance of the proposed approaches is evaluated using real-world data collected by the ZF Automated Driving Prototype vehicle. The results successfully validate the ability of this approach to estimate the velocity of the OV in a single frame.
Weakly-supervised Object Representation Learning for Few-shot Semantic Segmentation

Training a semantic segmentation model requires large densely-annotated image datasets that are costly to obtain. Once the training is done, it is also difficult to add new object categories to such segmentation models. In this paper, we tackle the few-shot semantic segmentation problem, which aims to perform image segmentation task on unseen object categories merely based on one or a few support example(s). The key to solving this few-shot segmentation problem lies in effectively utilizing object information from support examples to separate target objects from the background in a query image. While existing methods typically generate object-level representations by averaging local features in support images, we demonstrate that such object representations are typically noisy and less distinguishing. To solve this problem, we design an object representation generator (ORG) module which can effectively aggregate local object features from support im- age(s) and produce better object-level representation. The ORG module can be embedded into the network and trained end-to-end in a weakly-supervised fashion without extra human annotation. We incorporate this design into a modified encoder-decoder network to present a powerful and efficient framework for few-shot semantic segmentation. Experimental results on the Pascal-VOC and MS-COCO datasets show that our approach achieves better performance compared to existing methods under both one-shot and five-shot settings.
Research on Innovation of Urban Combat Equipment Support Model Based on Smart City and Artificial Intelligence

Abstract With the remarkable achievements of smart city construction and the evolution of war form to intelligence, the future urban operations will be intelligent operations supported by smart city. This paper analyses the requirements of artificial intelligence on urban operations equipment support, and clarifies the supporting condition provided by smart city construction on equipment support from the equipment status perception, equipment support command and control, equipment supply support and equipment maintenance support four aspects, focusing on winning urban operations under intelligent warfare. On this basis, a new support model of urban operations equipment support is proposed.
A Sentiment Driven Approach to Detect an Offline Event on Social Media Platform

A sentiment based approach to detect an event and predict a trend on social media platforms is proposed in the presented work. The trends are analyzed and events are detected against Twitter dataset on Sabarimala dispute. The dataset is collected over three months using Twitter API and then preprocessed and cleaned data is used for further analysis. The proposed approach uses numerical attributes and textual attributes with a blend of clustering and topic modelling techniques for analyzing trends and detecting an event. The proposed model successfully detects an event up to 80% of accuracy. The comparison of a detected event and real time event statistics drawn from social media and news channels highlights the performance and accuracy of the proposed model.
Assuring Learning-Enabled Components in Small Unmanned Aircraft Systems

Aviation has a remarkable safety record ensured by strict processes, rules, certifications, and regulations, in which formal methods have played a role in large companies developing commercial aerospace vehicles and related cyber-physical systems (CPS). This has not been the case for small Unmanned Aircraft Systems (UAS) that are still largely unregulated, uncertified, and not fully integrated into the national airspace. However, emerging UAS missions interact closely with the environment and utilize learning-enabled components (LECs), such as neural networks (NNs) for many tasks. Applying formal methods in this context will enable improved safety and ease the immersion of UASs into the national airspace. We develop UAS that interact closely with the environment, interact with human users, and require precise plans, navigation, and controllers. They also generally leverage LECs for perception and data collection. However, the impact of ML-based LECs on UAS performance is still an area of research. We have developed an advanced simulator incorporating ML-based perception in highly dynamic situations requiring advanced control strategies to study the impacts of ML-based perception on holistic UAS performance. In other work, we have developed a WebGME-based software framework called the Assurance-based Learning-enabled CPS (ALC) toolchain for designing CPS that incorporate LECs, including the Neural Network Verification (NNV) formal verification tool. In this paper, we present two key developments: 1) a quantification of the impact of ML-based perception on holistic (physical and cyber) UAS performance, and 2) a discussion of challenges in applying these methods in this environment to guarantee UAS performance under various Neural Net (NN) strategies, executed at various computational rates, and with vehicles moving at various speeds. We demonstrate that vehicle dynamics, rate of perception execution, the design of the controller, and the design of the NN all contributed to total vehicle performance.
Keyword-based Uyghur Document Image Retrieval

In order to solve the problem of printed Uyghur document image retrieval, a two-level matching method from coarse to fine is proposed. Without word recognition, the character of word image is directly used for fast and accurate document retrieval. Firstly, 100 Uyghur document images are segmented into word sets by using the improved morphological gradient algorithm, and then the contour matching of 8 keyword images is performed by using the contour moment feature to obtain the query result set. Finally, MB-LBP and other features are extracted from the word images in the set. The trained OCSVM classifier is used to classify the feature data, and the result is returned to the user. The experimental results of 8 keyword images in 100 document images show that the average accuracy is 64% and the average recall rate is 49%. The experimental results show that efficiency of the keyword-based retrieval method for Uyghur document image.
Jam-Guard: Low-Cost, Hand-held Device for First Responders to Detect and Localize Jammers

Intentional and unintentional interferences collectively referred to as Radio Frequency Interference (RFI) result in severe security threat to the public safety, first responder emergency rescue and military missions. Such RFI if not detected and localized care disrupt the wireless communication which forms the backbone of first responder and military operations. The prime objective of this work was to design and prototype a RFI detection and localization device, Jam-Guard that significantly outperforms traditional approaches in real-life deployment yet be computationally feasible to be developed as a low SWaC (size, weight, and cost) device. The proposed device employs a unique combination of robust parallel detection algorithms based on Kurtosis and FRactional Fourier Transform (FRFT) with Golden Section Search algorithm to rapidly detect RFI that affects critical communication signals. The localization scheme is designed to leverage the FRFT output from the detection phase to ease the computational load. We give a detailed account of the device prototyping and evaluation. To demonstrate the efficacy of the proposed detection approach as opposed to conventional energy detection (employed in several commercial interference detectors), we compare the two schemes on test and target platforms. The proposed scheme depicted significant improvement (~40 dB) in detection probability both iii preliminary implementation and target prototype experiments.
Compatibility Analysis of Virtual Reference Services in ABCD Software-Based Website

Reference service of any type of library can be offered over the Internet in real-time that we meant here Virtual Reference Service or VRS, in short. Virtual reference service is an online and interactive text-based communication service through which it is possible to provide the reference service of a library or a group of libraries to their distant users using a set of modern communication-based technologies. This paper is based on the results of experimental research. It mainly focuses on the customization process of Zoho chat in the ABCD site module to provide a virtual reference service from the library website. There is very little research across the globe that addresses the implementation and customization process of chat-based widget embedded into web pages, which is a key-focusing area of the current study.
Perpetuating ableist constructions of the “real world” through complaints about new communication technologies

Abstract Complaints about the use of new communication technologies are frequent in public discourse and work within a broader assemblage of discourses that promote selective ideologies. What is it that people are doing when they produce these complaints, and how might acts of complaining promote equity in our daily lives? We analyse complaints taken from 16 hours of video recorded dialogues and argue that the complaint discourse about the relationship of new communication technologies to people’s expected embodied functioning and idealized social participation reconstitutes and perpetuates broader ableist discourses about preferred engagement in the “real world.” By identifying intertextuality between two different topical discourses, we expand understanding about the reification of cross-cutting ableist discourses and promote more inclusive language use.
Lower Bounds for Policy Iteration on Multi-action MDPs

Policy Iteration (PI) is a classical family of algorithms to compute an optimal policy for any given Markov Decision Problem (MDP). The basic idea in PI is to begin with some initial policy and to repeatedly update the policy to one from an improving set, until an optimal policy is reached. Different variants of PI result from the (switching) rule used for improvement. An important theoretical question is how many iterations a specified PI variant will take to terminate as a function of the number of states n and the number of actions k in the input MDP. While there has been considerable progress towards upper-bounding this number, there are fewer results on lower bounds. In particular, existing lower bounds primarily focus on the special case of k = 2 actions. We devise lower bounds for k ≥ 3. Our main result is that a particular variant of PI can take Ω(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> /2) iterations to terminate. We also generalise existing constructions on 2-action MDPs to scale lower bounds by a factor of k for some common deterministic variants of PI, and by log(k) for corresponding randomised variants.
The Parallel Quantum Algorithm for the Class of Optimization

For the given n numbers without any other prior information, how to obtain the minimum norm of them only by assigning their signs before them? Moreover, how to know one number is the multiplication of which ones in the given n numbers? In classical solutions, enumeration is the only way via trying one by one, whose complexity is about [Formula: see text] and this is a NP problem. In this paper, the parallel quantum algorithm is proposed to solve the two questions shown in above. Through the quantum design of linear expressions of angles in parallel circuits, only [Formula: see text] time’s quantum operations and about [Formula: see text] times’ quantum measurements in the average will give the correct answer in the successful probability of 0.97 instead of the traditional [Formula: see text] times. The example and theoretical analysis demonstrate the efficiency of the proposed method.
SiamCenter

Object tracking had absorbed many beneficial practices from object detection. Recently, the anchor-free object detection algorithms have received more and more attention. Siamese network is the mainstream architecture for single object tracking because of its great balance on efficiency and effectiveness. In this paper, a new object tracking model is proposed which combining Siamese network and anchor-free object detection algorithm, named SiamCenter. It directly predicts center point location and size of object. Hourglass network is carefully selected as the feature extraction backbone network of Siamese network, and intermediate supervision is employed to improve the model's performance. Experiments have proved that SiamCenter is effective in solving object tracking problem on VOT and OTB datasets.
Application of fuzzy recognition method in mineral resources allocation

In order to provide a basis for the development of mineral resources and the implementation of spatial zoning management, the application of fuzzy recognition method in mineral resources allocation is studied and analyzed in this study to promote the optimal allocation of mineral resources and establish a reasonable regional division of labor and cooperation relationship. The results show that, taking the suitability of mineral resources spatial development as the evaluation objective, on the basis of the analysis of influencing factors and mechanism of mineral resources development, the index system, index calculation method, regionalization discriminant system and matrix discriminant model for the functional regionalization of mineral resources development are constructed, which has important reference value for promoting the system of optimum allocation of mineral resources. From the comprehensive perspective of quantity, quality, and concentration of resources, a set of comprehensive evaluation indexes and calculation methods reflecting the basic conditions of regional mineral resources (resource abundance) are proposed. Through empirical research, the index system and method of functional zoning of mineral resources development are verified, and the development function zoning scheme of main solid mineral resources in China is put forward. The results can provide macro decision-making reference for guiding mineral resources exploration, development, utilization and protection according to local conditions, zoning planning, and classification. This suggests that the limited and relatively scarce mineral resources are rationally allocated in order to produce the most suitable commodities and services and obtain the best benefits with the least resource consumption.
Learnify: An Augmented Reality-Based Application for Learning

Over the past few years, technology has seen a lot of advancements which are becoming an integral part of a person’s life. Augmented reality (AR) is one of these technologies which has emerged out and seems to have a lot of potential. This paper tries to explore that potential of AR especially in the field of education. We devise a way to improve learning experiences by incorporating more senses so that the user can easily remember things. In addition, we try to find out how effective and adaptive this way of learning is for users so that changes can be adopted for an even better system.