Reviewerly/exHarmony huggingface dataset:
Depth-embedded instance segmentation network for urban scene parsing

The ubiquitous availability of cost-effective cameras has rendered large scale collection of street view data a straightforward endeavour. Yet, the effective use of these data to assist autonomous driving remains a challenge, especially lack of exploration and exploitation of stereo images with abundant perceptible depth. In this paper, we propose a novel Depth-embedded Instance Segmentation Network (DISNet) which can effectively improve the performance of instance segmentation by incorporating the depth information of stereo images. The proposed network takes binocular images as input to observe the displacement of the object and estimate the corresponding depth perception without additional supervisions. Furthermore, we introduce a new module for computing the depth cost-volume, which can be integrated with the colour cost-volume to jointly capture useful disparities of stereo images. The shared-weights structure of Siamese Network is applied to learn the intrinsic information of stereo images while reducing the computational burden. Extensive experiments have been carried out on publicly available datasets (i.e., Cityscapes and KITTI), and the obtained results clearly demonstrate the superiority in segmenting instances with different depths.
DRF-FTS: A Dynamic Replication Factor Replication Scheme Based on Fault-Tolerant Set

Cloud storage systems use data replicas for fault tolerance, however, this approach does not provide enough protection for data when a correlated failure occurs within the cluster (e.g., a power outage that causes some machines to stop working) and it is almost let to lose data. The traditional random replication strategy cannot provide high data reliability, and the traditional FTS approach does not consider the impact of data heat on the whole system. In this paper we propose a dynamic replication factor replication scheme based on fault-tolerant set named DRF-FTS. In our approach we classify data into different types based on data popularity and use fault-tolerant sets generated by the algorithm to restrict the placement for different types of data. Experimental results show that DRF-FTS has high data reliability (much higher than the widely used random replication strategy) and improves read performance by 14% compared with the traditional FTS approach.
Digital Archive Management Based on Lucene Full-text Search Engine

Abstract With the rapid development of society, people have an increasing higher demand for information. As one of the vital information resources, archives are facing the significant impact of the global information tide. The original manual management method for archives can no longer meet the needs of people for their efficient management and utilization. How to transform archive management from manual to digital mode based on advanced information technology has become a hot issue of concern and research. Based on the digital application of archive management, supported by the advanced computer, communication, network, database, and multimedia technologies, we designed and developed a general digital system of archive management. The topological structure of the client/browser/server is used in the system, with the cross-platform characteristics.
Accuracy and Fairness in Pupil Detection Algorithm

Despite being highly accurate, widely used multimedia analysis algorithms can suffer from bias, affecting users’ trust in them. For instance, algorithms for face recognition, pedestrian detection, and image search have recently been reported to be biased. In this paper, we move the discussion on algorithmic fairness to a new domain, namely, pupil detection. We audit a widely used OpenCV algorithm for pupil detection from the perspective of fairness and accuracy. The algorithm is audited using two different datasets: a single-person image dataset (CelebA), and a group image dataset (Images of Groups). In both datasets, we found the OpenCV pupil detection algorithm to provide reasonably high accuracy but also yield statistically significant bias with respect to gender. The results provide the first empirical evidence for the existence of gender bias in pupil detection algorithms in both single person as well as group image settings. These results could have a deleterious impact on downstream multimedia applications such as identity authentication, attention assessment, and e-learning. Finally, we discuss the process of appropriately choosing the pupil detection algorithm parameters that may help reduce bias while maintaining high accuracy in various multimedia applications.
Deep Learning Approach for Wireless Signal and Modulation Classification

This paper aims to classify signal and modulation classes of a given wireless signal with high accuracy using a model having a low number of parameters. We propose an end-to-end method to classify a wireless signal based on its signal and modulation type using a CNN-based architecture. The proposed architecture is similar to that of the LeNet-5. Firstly, we implement signal and modulation classification using a decision tree, followed by a random forest algorithm, classic examples of machine learning(ML) based algorithms. Since our dataset is a time series, we also implement using RNN-LSTM based model for the classification. The proposed model has fewer parameters than that of the CNN-based, RNN-LSTM based architectures. Moreover, it achieves better accuracy for a wide range of signal-to-noise ratios than a decision tree, random forest, RNN-LSTM based classifiers.
A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.
Derivation and Formal Proof of Floyd-Warshall Algorithm

Graph algorithms are always complex and difficult to deduce and prove. In this paper, the Floyd-Warshall algorithm is deduced and formally proved. Firstly, the problem specification is described, and the loop invariant is detected and expressed by the recursive definition technology of loop invariant. On this basis, the Apla abstract algorithm program is obtained, and the formal proof of the algorithm program is carried out. Finally, through the Apla to C++ automatic generation system, the validated algorithm program described by Apla is automatically generated into C++ executable program. Based on the technology of developing loop invariant provided in this paper, the validity of derivation and proof of the graph structure problem are guaranteed. The correctness of the program and its development efficiency are improved. The successful experiment of this case shows that this method can not only know what the algorithm is to solve the graph structure problem, but also know how the algorithm is obtained.
Document Classification Based on Metadata and Keywords Extraction

We present a model for automatic extraction of metadata and keywords to be used in the classification of scientific documents. The model mainly consists of metadata extraction, keywords extraction and documents classification. At the metadata extraction stage, various metadata items are extracted from research documents in the domain of commerce such title of the thesis/research article, author/s, advisor/s, year, publisher, type, and abstract. At the keywords extraction stage, Latent Semantic Indexing (LSI) is used to extract the underlying topics from these documents. At the classification stage which depends on the metadata and keywords extraction stages, three classification algorithms are used which are Stochastic Gradient Descent (SGD), Linear Support Vector (LSVC) and K-Nearest Neighbor (KNN). SGD has achieved the highest classification accuracy (80.5%) compared to LSVC and KNN when applied to Arabic document corpus. LSVC has achieved the highest classification accuracy (81.5%) compared to SGD and KNN when applied to the English document corpus.
Two-stage Multi-frame Cooperative Quality Enhancement on Compressed Video

With the great success of deep learning network, compressed video quality enhancement methods based on deep learning are mushrooming. Most of these methods ignore the correlation between frames and do not make full use of the information of adjacent frames. We propose a two-stage multi-frame cooperative quality enhancement network. Our method consist of two main modules: motion compensation network and quality enhancement network. We use a two-stage enhanced structure to make full use of high-quality frames information and realize the multi-frame cooperative enhancement of a Group of Pictures(GOP), fully considering the correlation between frames. The experimental results on the HEVC standard test sequences show that the proposed method is improved by about 10% compared with MFQE2.0.
Classification and enhancement of invasive ductal carcinoma samples using convolutional neural networks

In the medical area, the detection of invasive ductal carcinoma is the most common sub-type of all breast cancers; about 80% of all breast cancers are invasive ductal carcinomas. Detection of this type of cancer shows a great challenge for specialist doctors since the digital images of the sample must be analyzed by sections because the spatial dimensions of this kind of image are above 50k × 50k pixels; doing this operation manually takes long time to determine if the patient suffers this type of cancer. Time is essential for the patient because this cancer can invade quickly other parts of the body. Its name reaffirms this characteristic, with the term "invasive" forming part of its name. With the purpose of solving this task, we propose an automatic methodology consisting in improving the performance of a convolutional neural network that classifies images containing invasive ductal carcinoma cells by highlighting cancer cells using several preprocessing methods such as histogram stretching and contrast enhancement. In this way, characteristics of the sub-images are extracted from the panoramic sample and it is possible to learn to classify them in a better way.
Review on Blockchain and Access Control Systems

Blockchain is an emerging technology that can demonstrate its usefulness through its characteristics (e.g., decentralization, distribution, peer-to-peer transmission, immutability, transparency …) in different area such as security in general, data management and access control in particular. Access control systems (ACSs) play a considerable role in the process of granting rights to resources access and assets management. However, existing distributed ledger-based access control still has challenging privacy issues, such as fine-grained access control, confidentiality, integrity and availability. The goal of this paper is to introduce blockchain and briefly access control systems and to present some works realized in the realm of blockchain and access control systems. A discussion is engaged about a comparative table which concerns some results previously introduced. Interests are expressed on access control in healthcare emergency cases, and ways are suggested for more deeply research. In the same section, future works are also announced for further investigations.
Maelzel, the Metronome, and the Modern Mechanics of Musical Time

Abstract Johann Nepomuk Maelzel, despite being most recognized today for inventing the clockwork metronome, was one of the most famous automata showmen of the nineteenth century. This chapter begins by offering a reception history of Maelzel, the metronome, and his automata, and exploring the cultural significances underlying his clockwork creations across the Industrial Age. As numerous accounts maintain, Maelzel’s automata projected decidedly inhuman performance practices. His automata emblematized a machine culture that ran in direct opposition to the subjective ‘artistry’ championed by many skilled performers and composers over the century. This study subsequently addresses the discord between Maelzel’s age and ours regarding the values of musical time and performance practices: those metronomic qualities largely rejected by Maelzel’s musical contemporaries are often vehemently endorsed today by many professional musicians and educators who apply mechanically precise tempos and rhythms to all musical repertoires. This history ultimately confronts the veiled ‘metronome mentality’ found throughout contemporary performance culture, which neglects many musical-temporal aesthetics and rhythmic qualities from a pre-industrial, pre-metronomic past.
Research on Operation Strategies for TikTok Accounts of China’s Libraries

With the rapid development of new technology, short videos have quickly become the Internet sensation. Libraries are known to be the central avenues for the acquisition, processing and dissemination of knowledge. In order to explore the operation strategies for TikTok accounts of China’s libraries and improve the communication capacity, this paper, taking the posts released by the TikTok accounts of two typical provincial public libraries as the research objects, analyzes the current situation of the development of TikTok accounts of China’s libraries and proposes optimization strategies for enhancing the influence of TikTok accounts of libraries for reference.
Hiding Leader’s Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning

Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission critical information (e.g. goal location) that the followers lack, and this makes the leader crucial for the mission’s success. However, this also makes the leader a vulnerability -an external adversary who wishes to sabotage the robot team’s mission can simply harm the leader and the whole robot team’s mission would be compromised. Since robot motion generated by traditional leader-follower navigation algorithms can reveal the identity of the leader, we propose a defense mechanism of hiding the leader’s identity by ensuring the leader moves in a way that behaviorally camouflages it with the followers, making it difficult for an adversary to identify the leader. To achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural Networks and adversarial training. Our approach enables the multi-robot team to optimize the primary task performance with leader motion similar to follower motion, behaviorally camouflaging it with the followers. Our algorithm outperforms existing work that tries to hide the leader’s identity in a multi-robot team by tuning traditional leader-follower control parameters with Classical Genetic Algorithms. We also evaluated human performance in inferring the leader’s identity and found that humans had lower accuracy when the robot team used our proposed navigation algorithm.
Analysis of confluence angle of drainages and its relation to morphometric properties of drainage basins in the Zagros Simply Folded Belt, Iran

Quantitative analysis of drainage network properties plays an important role in the identification of active tectonics and their spatial variations. The purpose of this study is to analyze the drainage's confluence angle (α) in relation to tectonic activity and morphometric characteristics of drainage basins. A total of 23 drainage basins in the Zagros Simply Folded Belt were selected and then the values of α were calculated for 7829 drainage junctions. The relationship between α and other morphometric indexes such as basin area, perimeter, slope, asymmetry factor, shape, circularity, drainage density, ratio of the 1st-order streams to the total number of streams of all orders, and density of drainage's hierarchical anomaly were analyzed. Results show that mean of α increases as the order of stream junctions increases. Basins with higher values of elongation, the ratio of the 1st-order streams to the total number of streams of all orders, and density of drainage's hierarchical anomaly have higher α values. Results also show that the lateral and vertical growth of anticlines, axes of anticlines, trends of faults and drainage patterns influenced by folds growth strongly control the confluence angles of drainage channels in the studied basins.
Named Entity Recognition Problem for Long Entities in English Texts

This paper is related to the problem of natural language processing (NLP), namely the named entity recognition (NER). This paper reveals the features of named entities recognition in English texts using deep learning (DL). The peculiarity of the study was the rather long length of the presented named entities: many of them could include a rather large number of words. The main problem was the amount of text that had to be recognized as a single entity. The results of the research are described here show the effectiveness of using deep neural network architectures for the task of recognizing long named entities in texts in English.
Regression in Estimation of Software Attributes: A Systematic Literature Review

Software estimation is a fundamental activity in the Software development process, since it is possible to predict the number of defects, size, effort, among other attributes. With this, it is possible to improve the quality of the product and process. To predict quantitative values, it is common to use Regression Model mechanisms, although each model adjusts to a specific behavior of the data. In this work, a Systematic Literature Review is carried out based on the Kitchenham and Charters guide, to know the different types of Regression that have been used in the Software estimates. In addition, it seeks to know those attributes that are estimated and those that function as independent variables. Simple Linear Regression, Multiple Linear Regression and Logistic Regression were the most used, although other types of regression were found that can be further explored. Finally, the attributes that work as predictor variables were categorized, where the attributes of effort, lines of code and use cases were the most frequent.
Contrastive learning for a single historical painting’s blind super-resolution

Most of the existing blind super-resolution(SR) methods explicitly estimate the kernel in pixel space, which usually has a large deviation and results in poor SR performance. As a seminal work, DASR learns abstract representations to distinguish various degradations in the feature space, which effectively reduces degradation estimation bias. Therefore, we also employ the feature space to extract degradation representations for an ancient painting. However, most of the blind SR mehods, including DASR, are committed to removing degradations introduced by kernels, downsampling and additive noise. Among them, downsampling degradation is often accompanied by unpleasant artifacts. To address this issue, the paper designs a high-resolution(HR) representation encoder E H R based on contrastive learning to distinguish artifacts introduced by downsampling. Moreover, to optimize the ill-posed nature of blind SR, we propose a contrastive regularization ( C R ) to minimize the contrastive loss based on VGG-19. With the help of C R , the SR images are pulled closer to the HR images and pushed far away from bicubic LR observations. Benefiting from these improvements, our method consistently achieves higher quantitative performance and better visual quality with more natural textures than state-of-the-art approaches on a specialized painting dataset.
Augmenting television narratives using interactive home appliances

This study focuses on the use of everyday objects as new media for enriching digital storytelling experiences, especially as they pertain to viewing television narratives in living room environments. Through a design ideation workshop, we develop a strategy for augmenting television narratives using interactive home appliances. The ideas obtained through the workshop revealed that interactive home appliances connected to television systems can serve as both input and output channels capable of delivering narrative elements as additional output and can support active viewer engagement in narrative manipulation. Another strategy involved mirroring the environments of television narratives with the physical world to enable spatial immersion into such narratives. Based on the strategies, we discuss the criteria valued by the designers when it comes to designating interactions between home appliances and television narratives and the critical aspects that should be considered in further configurations and applications of such strategies.
Blockchain-based Reward System: a Means for Providing Incentive to Students for Teaching Feedback

We have been witnessing a rapid growth of blockchain usage in different sectors in our society, including cryptocurrency, and healthcare. In this paper, we propose a blockchain-based reward system to encourage students to submit the teaching feedback. We developed a proof-of-concept application in order to demonstrate how our proposed reward system works. Additionally, a smart contract experimentation is carried out based on the automation framework we introduced as a part of the solution. We demonstrate in this paper that the proof of concept and the automation framework we developed facilitate transaction integrity and immutability.
A Modified Cuckoo Search Algorithm for Data Clustering

Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Particle Swarm Optimization with Age Group topology (PSOAG) and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.