Reviewerly/exHarmony huggingface dataset:
Detection of Overlapping and Small-Scale Targets in Indoor Environment Based on Improved Faster-RCNN

This paper proposes an improved Faster-RCNN network to detect small-scale and overlap targets in indoor environment. In the improved Faster-RCNN model, ResNet-50 was used to replace VGG16 as the backbone network to extract multi-layer features of images, and then shallow feature images with rich detailed information were fused with the deep feature images with abstract information. In addition, Soft-NMS is used to replace NMS to improve the performance of overlapping target detection. In self-made dataset, the mAP of the improved model was improved by 1.45% relative to the unimproved model.
System of Government Audit Information Integration Platform Based on Block Chain Technology

The government audit, due to the technical ability, audit tools and process of the restrictions, the lack of a relatively unified collaboration platform between the audit team, there are highly centralized data collection and processing, data update slowly, lack of data reliability verification mechanism, and a series of problems, greatly reduces the efficiency of government audit. Since its birth, block chain technology has been widely used due to its characteristics of decentralization and tamper-proof. With these advantages of blockchain, both efficiency and security can be taken into account to solve the technical problems faced by the government audit. Therefore, the combination of the two has practical value. Based on the nature and characteristics of block chain and government audit, this paper analyzes the necessity of government audit to introduce block chain technology, builds the system framework of government audit information integration platform based on block chain technology, and provides solutions to some problems in traditional government audit.
Gender Voiceprint Identification using Machine Learning Algorithms

Voiceprint identification is a popular and highly develop potential technology that is used to identify characteristic of a person or subject. However, selection of classification algorithms behind the voiceprint identification system have to be optimized in order to maximize the performance and capability of the model. In this study, five classification algorithms were presented which are Logistic Regression (LR), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF). The performances of these algorithms were evaluated before and after the application of feature extraction technique. The result showed that SVM have the best performance in term of accuracy and AUC either with or without PCA. However, NB was identified as the fastest algorithm in term of computational time. The results also showed that feature extraction techniques have insignificant improvement over the classification of gender voiceprint dataset.
UUV multi-task route planning under obstacles constrait

Aiming at the path planning problem of UUV under obstacles constrait, this paper proposes a simple and feasible algorithm. The optimal path plan is obtained through the tangent theory based on the principle of minimum turning radius, and a detailed mathematical proof is given. The UUV multi-task route planning problem is transformed into a TSP model solution through the continuous Hopfield neural net-work. The simulation results show that the method can quickly and accurately solve UUV multi tasking route planning problem.
The problem of distance learning in higher educational institutions during quarantine

The article is devoted to the problem of distance education, namely training and educating under conditions of the pandemic. The issue of the coronavirus epidemic, which forced the urgent restructuring of all spheres of social life of people around the world, is addressed in the article. As a result, the problem of the global transition to distance learning under severe quarantine is becoming urgent. The author analyzes domestic and foreign pedagogical experience of distance education. Among domestic authors this problem was studied by Gorbatyuk R. M, Turanov Y. O., Rak V. I., Vdovenko I. S., Bodnar S. V., Dekusar G. G., Kuznetsova O. V., Opanasenko Ya. Foreign researchers (Amanda Morin, Berman Grace, and Dubinsky Alison) are more likely to point out that distance learning is associated with many difficulties. The aim of our study was to highlight the problem of distance learning in the quarantine period, its advantages and disadvantages over traditional learning, as well as a comparative analysis of the traditional and distance forms of educational activities. In the process of solving problems, theoretical research methods were used: analysis, systematization, classification of educational and methodological literature to substantiate the theoretical and methodological foundations of research, surveys, analysis of the products of the students’ pedagogical activities. The article presents data from the study of the groups consisting of first- and fifth-year students who studied in different periods: in the first half of 2019, when the study was in classrooms, and in the first half of 2020 during the quarantine, when the study was carried out using distance education. The data according to three criteria: academic success, students’ attendance and students’ satisfaction with the learning process were analyzed by the author. A comparative analysis of the traditional and distance learning is shown in the article. Some conclusions about the ambiguity of the impact of distance learning on different contingents of students have been made.
Empowering Adolescents Through Instilling Information Security Culture in Zimbabwean Schools

There is an excellent opportunity to ensure that information security culture (ISU) is embedded in school children before they are employed in the industry. However, for the majority of time spent in primary and secondary school education, pupils are alienated from supervised use of technology, making it difficult to teach pupils proper use of technology. Thus, there is no deliberate effort to empower and impart ISC to school pupils in Zimbabwe. The purpose of this study is to develop a framework for instilling information security culture in secondary school pupils. Based on the literature, the first version of the framework was developed and subjected to a focus group for review. Data from this focus group was analysed, resulting in a second improved version of the framework. Consequently, it was shown that the framework was relevant, useful, and applicable within Zimbabwean settings.
A novel top‐<i>k</i> key node query problem in subgraph matching and its greedy strategy

Abstract Top‐ k node selection in graph data is an essential problem in computer science and applications. In view of an important issue in the field of graph data, subgraph matching issue, we define the problem and propose its method for the top‐ k key node query w.r.t. the subgraph matching. Unlike the general top‐ k query problem, we aim to find out k nodes that make the matching subgraphs in data graph G that are covered by the k nodes as more as possible. This is a problem of the maximum coverage of subgraph matching, which belongs to the NP‐hard problem. We study the problem based on a greedy algorithm and give an intuitive solution. Considering the characteristics of the top‐ k problem, we propose an improved and more efficient greedy algorithm. Experiments on real social network graph data set (Twitter) show that the related results represent the key nodes that can better reveal the essential characteristics of the query graph in the data graph G . The key node query problem in subgraph matching proposed in this article may have extensive applications in reality, such as the assessment of the influence of specific group members in social network, the detection of abnormal communication in a computer communication network, the road traffic evaluation and load balance problem in a road traffic network, and so on.
Research on Equipment Joint Scheduling for Airport Bomb Disposal and Emergency Repair

In view of the missile discharging, emergency repair, obstacle clearing and transportation tasks in the airport runway after the attack. This article analyzes the complexity of the bomb ejection and repair task under the set conditions, and uses the principle of optimal timeliness to quickly complete the airport runway. For bomb discharge and emergency repair tasks, an ant colony algorithm-based equipment joint scheduling method for airport bomb discharge and emergency repair is proposed. This method divides airport equipment scheduling into task scheduling and path planning, and establishes a mathematical model for task scheduling. Ant colony algorithm for task scheduling path planning, and finally performs real-time simulation verification. Experiments show that the method is highly feasible after the airport is attacked, and it can meet the needs of rapid blasting and repairing of the airport in wartime.
Deep Architecture Compression with Automatic Clustering of Similar Neurons

The more complex the deep neural networks (DNNs) are, the more diverse the learning tasks they can be applied to. However, for complex DNNs, it is difficult to deploy them on to the edge devices, which have limited computation and storage resources. In this paper, we propose an automatic neurons clustering (ANC) approach for deep architecture compression, it can reduce the computation and storage consumption without degrading the model performance. Specifically, an automatic clustering algorithm is used to discover similar neurons in each layer of the deep architecture, then the similar neurons and the corresponding connections are merged based on the results of automatic clustering. After fine-tuning, a more compact and less storage space occupied neural network is obtained, with no performance degradation compared to the original deep architecture. This compression method is fully applicable to fully connected layer and convolutional layer, both of which are common modules of popular DNNs. The analysis of neuron redundancy in DNNs is performed on a deep belief network (DBN), and it is verified that there is great redundancy among neurons in DNNs. To verify the effectiveness of the proposed ANC, we conducted experiments on DBN and VGGNet-16 using MNIST, CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate that our method can effectively perform deep architecture compression without losing network performance. After fine-tuning, it can even obtain higher accuracy than the original network. In addition, the superiority of ANC is further demonstrated by comparing it with related network compression methods.
A TV regularisation sparse model for light field reconstruction

We present an alternating direction method of multipliers algorithm(ADMM) based on total variation(TV) regularisation for the light field reconstruction from the focal stack data. Experiments show the proposed method is effective using refocusing measure.
On the moment absolute deviation of order statistics from uniform distribution

Assume that n mobile sensors with identical sensing range r = 1 2 n are distributed independently at random on the unit interval with the uniform distribution. We study the maximum sensor’s displacements to the power a > 0 for full coverage of the unit interval in the sense that every point in the unit interval is within the range of a sensor. We derive the asymptotic result about the maximum of moments absolute deviation around the mean for order statistics from uniform distribution.
A supervisory and governance mechanism for power master-slave chain architecture

Under the power master-slave chain architecture, due to the autonomous operation and non-tampering characteristics of the blockchain, there is currently a lack of credible and efficient reliable supervision and governance mechanisms for the slave chain business. This article proposes a solution to this problem. The cross-chain supervision mechanism of the master and slave chains with the concept of “Governing the chain by chain ”, realizes the precise conversion of blockchain supervision and governance from system, theory to technology, and effectively prevents the operating risks of related power blockchain businesses.
GGP

Existing data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generation in the commercial scenario, these methods may generate illogical and uncontrollable texts. To address these aforementioned issues, we propose a graph-based grouping planner~(GGP) following the idea of first-plan-then-generate. Specifically, given a collection of key phrases, GGP firstly encodes these phrases into a instance-level sequential representation and a corpus-level graph-based representation separately. With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text. We conduct our experiments on three long text generation datasets and the experimental results reveal that GGP significantly outperforms baselines, which proves that GGP can control the long text generation with knowing how to say and in what order.
Adaptive aggregation stereo matching algorithm based on edge preservation

In this paper, an adaptive aggregation stereo matching algorithm based on edge preservation is proposed. The proposed algorithm uses the edge space information of the image to construct a weight matrix, highlights the role of the straight line in the edge weight matrix. Furthermore, the algorithm introduces the idea of cross aggregation, and utilizes the edge weight value to improve the window arm length determination function. Experiments are carried out on Middlebury stereo matching platform, and the results show that the proposed algorithm can distinguish the cost of pixels in different regions more effectively, and finally can improve the matching accuracy of the images in all regions.
Learning to plan people-aware trajectories for robot navigation: A genetic algorithm

Nowadays, one of the emergent challenges in mobile robotics consists of navigating safely and efficiently in dynamic environments populated by people. This paper focuses on the robot’s motion planning by proposing a learning-based method to adjust the robot’s trajectories to people’s movements by respecting the proxemics rules. With this purpose, we design a genetic algorithm to train the navigation stack of ROS during the goal-based navigation while the robot is disturbed by people. We also present a simulation environment based on Gazebo that extends the animated model for emulating a more natural human’s walking. Preliminary results show that our approach is able to plan people-aware robot’s trajectories respecting proxemics limits without worsening the performance in navigation.
Routing Based Context Selection for Document-Level Neural Machine Translation

Most of the existing methods of document-level neural machine translation (NMT) integrate more textual information by extending the scope of sentence encoding. Usually, the sentence-level representation is incorporated (via attention or gate mechanism) in these methods, which makes them straightforward but rough, and it is difficult to distinguish useful contextual information from noises. Furthermore, the longer the encoding length is, the more difficult it is for the model to grasp the inter-dependency between sentences. In this paper, a document-level NMT method based on a routing algorithm is presented, which can automatically select context information. The routing mechanism endows the current source sentence with the ability to decide which words can become its context. This leads the method to merge the inter-sentence dependencies in a more flexible and elegant way, and model local structure information more effectively. At the same time, this structured information selection mechanism will also alleviate the possible problems caused by long-distance encoding. Experimental results show that our method is 2.91 BLEU higher than the Transformer model on the public dataset of ZH-EN, and is superior to most of the state-of-the-art document-level NMT models.
Undergraduate Thesis System Information Service Evaluation Using UTAUT – 5 Factors Based Chatbot

Covid-19 pandemic impacts to any aspect of human life, including work activity. For any job which require interview, it will be a very difficult task because any individual must be stay at home and make a physical distancing. An online form can be used to solve the problem, but it seems less interactive and can make interviewees are bored. Chatbot technology can be exploited to make an interactive survey activity during this COVID-19 pandemic situation. Our chatbot is developed to help an acceptance analysis of user perspective to bachelor thesis information system service that is implemented in our department. The questionnaire items is available in our chatbot, also its analysis process is performed in this chatbot system. UTAUT model is implemented for the data analysis, it shows that this system is already accepted by user for positive value in H1, H3, and H4. No difficulties for user while using the system, it is proved by negative value in H2 and H5. The acceptance value is 0.656, it means that the sustainability of bachelor thesis information system service has been confirmed by user.
Lifting the Veil of Frequency in Joint Segmentation and Depth Estimation

Joint learning of scene parsing and depth estimation remains a challenging task due to the rivalry between the two tasks. In this paper, we revisit the mutual enhancement for joint semantic segmentation and depth estimation. Inspired by the observation that the competition and cooperation could be reflected in the feature frequency components of different tasks, we propose a Frequency Aware Feature Enhancement (FAFE) network that can effectively enhance the reciprocal relationship whereas avoiding the competition. In FAFE, a frequency disentanglement module is proposed to fetch the favorable frequency component sets for each task and resolve the discordance between the two tasks. For task cooperation, we introduce a re-calibration unit to aggregate features of the two tasks, so as to complement task information with each other. Accordingly, the learning of each task can be boosted by the complementary task appropriately. Besides, a novel local-aware consistency loss function is proposed to impose on the predicted segmentation and depth so as to strengthen the cooperation. With the FAFE network and new local-aware consistency loss encapsulated into the multi-task learning network, the proposed approach achieves superior performance over previous state-of-the-art methods. Extensive experiments and ablation studies on multi-task datasets demonstrate the effectiveness of our proposed approach.
Hand Gestures Recognition using Inertial Sensors Through Deep Learning

Gestures provide intuitive ways to facilitate interaction between humans and computational systems. Current gesture recognition methods use multi-step processes wherein the data stream goes through multiple processes like pre-processing, segmentation, decision-making, etc. We propose an end to end gesture recognition system using DeepLearning \& TransferLearning models where the stream of data from Microelectromechanical systems(MEMS) consisting of a three-axis accelerometer data stream is used to recognize the gestures in real-time. Gestures are communicated wirelessly to Personal Computers using Bluetooth for control purposes. Users can incorporate their gestures into a collection of pre-existing gestures to make a truly customizable system. To test the setup, a general-purpose desktop application(Action Mapping Interface) is implemented which simulates the keyboard and mouse events \& runs complex commands using gestures identified by the neural network.
SYNTAX PARSER ON KANNADA TEXTS WITH CYK ALGORITHM AND PROBABILISTIC CONTEXT FREE GRAMMAR

Limited work has been done on several Indian Languages related to Syntax and Semantic Parsing by adopting recent Natural Language Processing (NLP) techniques. In this paper we present a framework for statistical Syntax Parser developed on Kannada language which is one of the south Indian languages. As a preliminary step, we generated Kannada Treebank dataset from 1000 annotated sentences (tagged with Parts of Speech labels) by applying Cocke-Younger-Kasami (CYK) parsing algorithm. CYK algorithm works only if the input grammar is specified in CNF (Chomsky Normal form). Hence in our proposed method, the Context Free Grammar (CFG) written for Kannada language is transformed into a CNF grammar.  Basically Treebank dataset is a collection of parsed or syntactically structured texts. The Treebank dataset which is generated in the preliminary step contains 1000 syntactically structured sentences and it is given as a training data to build syntax parser model. The size of the dataset which we have taken for testing the model is 150 unannotated sentences. While training the parser and extracting grammar from a Treebank dataset, PCFG (Probabilistic Context Free Grammar) is also incorporated. The developed syntax parser model takes Kannada raw sentences as input, makes use of CNF grammar which is derived from the training Treebank dataset, applies CYK parsing algorithm on input texts and finally produces the most probable parse tree for every sentence as the output. The model has been tested and evaluated with golden Treebank dataset. We obtained considerable and encouraging results from the prepared parser model. The overall precision, recall and F1-score achieved are 74.2%, 79.4% and 75.3% respectively.
Research on News Recommendation System Based on Deep Network and Personalized Needs

In order to solve the problems of poor performance of the recommendation system caused by not considering the needs of users in the process of news recommendation, a news recommendation system based on deep network and personalized needs is proposed. Firstly, it analyzes the news needs of users, which is the basis of designing the system. The functions of the system module mainly include the network function module, database module, user management module, and news recommendation module. Among them, the user management module uses the deep network to set the user news interest model, inputs the news data into the model, completes the personalized needs of the news, and realizes the design of the news recommendation system. The experimental results show that the proposed system has good effect and certain advantages.
Learning a Deep ResNet for SAR Image Super-Resolution

To improve the performance of the traditional SAR image super resolution model, we propose a novel SAR super-resolution model based on the improved SRResnet. In the proposed framework, the batch normalization layer is removed. It can improve the details of image restoration. ReLU function is added to maintain the numerical properties. In addition, the pixel shuffler block is designed to upsampling the SAR images, which can reduce artifacts. We use a residual learning strategy to address the issue of vanishing gradient with the increasing of network depth. Compared with existing super resolution algorithms in the SAR images area, the proposed model achieves a good performance on both quantitative and visual assessments.
R-Calculi for Propositional Logic

AGM postulates[, , ] are a set of requirements a revision operator should satisfy, and R-calculus is a revision operator which satisfies the AGM postulates.