Reviewerly/exHarmony huggingface dataset:
A Transformer-based Approach for Translating Natural Language to Bash Commands

This paper explores the translation of natural language into Bash Commands, which developers commonly use to accomplish command-line tasks in a terminal. In our approach a terminal takes a command as a sentence in plain English and translates it into the corresponding string of Bash Commands. The paper analyzes the performance of several architectures on this translation problem using the data from the NLC2CMD competition at the NeurIPS 2020 conference. The approach presented in this paper is the best performing architecture on this problem to date and improves the current state-of-the-art accuracy on this translation task from 13.8% to 53.2%.
Analysis and Simulation of Quantum Scattering Characteristics of Target Based on Spatial Correlation

In the traditional radar, radar cross section (RCS) is used to represent the echo intensity that the target scattered. The modulation signals are used in the traditional radar systems to detect the targets based on principle of induced radiation, which makes the targets with geometric shape stealth design hard to detect. Quantum radar systems employ photons to detect targets. The processes of absorption and emission of photons follows the rules of quantum electrodynamics. The quantum scattering is not sensitive to the shape design of the targets, which can be used to enhance detection capability of radar system. Quantum radar cross section (QRCS) is used to analyze the performance of quantum radars. A computation method of QRCS based on spatial correlation is proposed in this paper to improve the performance of the quantum radar system. Multiple detectors are used to collect the quantum scattering signals of the target. The spatial correlation characteristics of scattered signals are extracted to represent the quantum scattering characteristics of the target. The randomness of atom-photon scattering process is efficiently used in this method, which can enhance detection capability of radar system. The method proposed in this paper is verified by numerical simulation. In addition, the multiphoton scattering of a typical three-dimensional target is studied in this paper. The results show that quantum radar with multiphoton as transmitting signal may have better detection capability. These results are of great significance to further study the quantum scattering characteristics of complex targets. The research of quantum radar system based on spatial correlation detection is a potential research direction especially for the stealth target detection.
COCLUBERT: Clustering Machine Learning Source Code

Nowadays, we can find machine learning (ML) applications in nearly every aspect of modern life, and we see that more developers are engaged in the field than ever. In order to facilitate the development of new ML applications, it would be beneficial to provide services that enable developers to share, access, and search for source code easily. A step towards making such a service is to cluster source code by functionality. In this work, we present COCLUBERT, a BERT-based model for source code embedding based on their functionality and clustering them accordingly. We build COCLUBERT using CuBERT, a variant of BERT pre-trained on source code, and present three ways to fine-tune it for the clustering task. In the experiments, we compare COCLUBERT with a baseline model, where we cluster source code using CuBERT embedding without fine-tuning. We show that COCLUBERT significantly outperforms the baseline model by increasing the Dunn Index metric by a factor of 141, the Silhouette Score metric by a factor of two, and the Adjusted Rand Index metric by a factor of 11.
SInGER: A Recommendation System based on Social-Influence-aware Graph Embedding Approach

Due to the massive explosion of social network data in the past few decades, recent research has shown an increasing trend of exploring user influence in social media. The role of the users and their collaborative activities towards the others are worthy of study to model the overall influential behaviours of the users in social networks. In this paper, we propose an unsupervised measure of Influence Score to estimate how influential a user is to the other users so that his/her rating given to an item can affect the other users’ perception of the item. Typically, the influence score is estimated with consideration to the trustworthiness/reliability of the user in reviewing the items under each specific category. Later, we utilize the users’ Influence score to predict the missing ratings by employing a social recommender system based on graph neural network. Our proposed social influence-aware graph embedding-based recommendation (SInGER) is evaluated compared to four state-of-the-art models, considering two benchmark datasets, namely CIAO and EPINION. Experimental results show that the proposed influence score can improve the average recommendation accuracy by 19%.
Academic Collaborator Recommendation Based on Attributed Network Embedding

Abstract Purpose Based on real-world academic data, this study aims to use network embedding technology to mining academic relationships, and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks. Design/methodology/approach We propose an academic collaborator recommendation model based on attributed network embedding (ACR-ANE), which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes. The non-local neighbors for scholars are defined to capture strong relationships among scholars. A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space. Findings 1. The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors. 2. It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously. Research limitations The designed method works for static networks, without taking account of the network dynamics. Practical implications The designed model is embedded in academic collaboration network structure and scholarly attributes, which can be used to help scholars recommend potential collaborators. Originality/value Experiments on two real-world scholarly datasets, Aminer and APS, show that our proposed method performs better than other baselines.
Document Clustering vs Topic Models: A Case Study

Document collections can be characterised in a variety of ways. Two key approaches are clustering, which partitions collections into subcollections with the expectation that the contents will be thematically linked, and topic models, which describe the contents in terms of weighted lists of words that are expected to represent different themes. In this paper, we report experiments on the observed relationship between clusters and topic models in a preliminary study of a large text collection. Both produce results that appear cohesive in their own right, but surprisingly – given the very different ways in which they are formed – the descriptions of the collections that they generate are strongly similar. This unexpected mutual reinforcement creates confidence in both approaches as tools for annotating and describing the contents of document collections.
Graph Convolutional Networks for Categorizing Online Harassment on Twitter

Twitter is one of the social media platforms that people express themselves freely. Harassment is one consequence of these such platforms, which is hard to obstruct. Text categorization and classification is a task that aims to solve this problem. Several studies applied classical machine learning methods and recent deep neural networks to categorize the text. However, only a few studies have explored graph convolutional neural networks while using classical approaches to categorize harassment Tweets. In this work, we propose using graph convolutional networks (GCN) for tweet categorization. Second, we explore this categorization task using classical machine learning approaches and compare the results with the GCN model. Third, we show the effectiveness of the GCN model on this problem by the other evaluation of the model on fewer sample datasets. In addition, we used different embedding approaches to find the best representation for the dataset in each of the models and represent the best embedding approach to use in this problem.
Ontology-based NSD Modeling for NFV Service Management

Recently, research on methods for reducing CAPEX and OPEX by the use of automation technology in the NFV environment has been conducted actively. In particular, technologies for the standardization and automatic generation of description formats, which describe information on VNF and network service, are actively investigated. This paper conducted data modeling for VNFs management automation and automatic network service generation, using NSD(Network Service Description) data and ontology, and similar VNF reasoning by the use of ontology reasoning technologies was carried out to define and test relationships between ontology nodes
Performance Evaluation of HDR Image Reconstruction Techniques on Light Field Images

The reconstruction of high dynamic range (HDR) images from low dynamic range (LDR) images is a challenging task.Multiple algorithms are implemented to perform the reconstruction process for HDR images and videos.These techniques include, but are not limited to reverse tone mapping, computational photography and convolutional neural networks (CNNs).From the aforementioned techniques, CNNs have proven to be the most efficient when tested against conventional 2D images and videos.However, at the time of this paper, applying such CNNs to light field contents have not yet been performed.Light field images impose more challenges and difficulties to the proposed CNNs, as there are multiple images for the creation of a single light field scene.In this paper, we test some of the existing HDR CNNs (ExpandNet, HDR-DeepCNN and DeepHDRVideo) on the Teddy light field image dataset and evaluate their performance using PSNR, SSIM and HDR-VDP 2.2.1.Our work addresses both image and video reconstruction techniques in the context of light field imaging.The results indicate that further modifications to the state-of-the-art reconstruction techniques are required to efficiently accommodate the spatial coherence in light field images.
V2X-based COVID-19 Pandemic Severity Reduction in Smart Cities

In a jiffy after the outbreak of the 2019 novel coron-avirus, also called COVID-19 or SARS-CoV-2, the World Health Organization (WHO) considered it as a pandemic that threatens the demise of humanity. This quick decision was in conjunction with a real situation of biological inability to find a vaccine that can eliminate the virus or at least limit its spread. For that reason, technological intervention and cooperation are needed more than ever to face this pandemic. In this same context, we propose a novel system that deploys Vehicle-to-everything (V2X) technology and Blockchain in collaboration to face such a pandemic. Our proposal is centered on a triple-stage processing i) zone identification and classification based on vehicles&#x0027; thermal cameras detection, ii) Blockchain-based information storage for enhanced patient medical information privacy, and iii) drones-based zone neutralization processes. Simulation results show that, thanks to the use of Blockchain technology, the network-related performance remain almost unchanged and hence, all Intelligent Transportation System (ITS) including inter-vehicles and inter-drones functionalities are not affected. In addition the additional overhead is very acceptable and does not exceed the 5 Kb in the worst case.
Fast Unsupervised Feature Selection with Bipartite Graph and l2,0-Norm Constraint

Since obtaining data labels is a time-consuming and laborious task, unsupervised feature selection has become a popular feature selection technique. However, the current unsupervised feature selection methods are facing three challenges: (1) they rely on a fixed similarity matrix derived from the original data, which will affect their performance; (2) due to the limitation of sparsity, they can only obtain sub-optimal solutions; (3) they have high computational complexity and cannot handle large-scale data. To solve this dilemma, we propose a fast unsupervised feature selection algorithm with bipartite graph and 2;0-norm constraint (BGCFS). We use the original data and the selected anchors to construct an adaptive bipartite graph in the subspace, and apply the l2,0-norm constraint to the projection matrix for feature selection. In this way, we can update the adaptive bipartite graph and the projection matrix simultaneously, and we can get the feature subset directly, without sorting the features. In addition, we propose an iterative algorithm that can solve the proposed problem globally to obtain a closed-form solution, and we provide a strict proof of convergence for it. Experiments on eight real data sets with different scales show that our method can select more valuable feature subsets more quickly
An Improved Deep Pairwise Supervised Hashing Algorithm for Fast Image Retrieval

In recent years, hashing algorithm has been widely researched and has made considerable progress in large-scale image retrieval tasks due to its advantages of convenient storage and fast calculation efficiency. Nowadays most researchers use deep convolutional neural networks (CNNs) to perform feature learning and hash coding learning at the same time for image retrieval and the deep hashing methods based on deep CNNs perform much better than the traditional manual feature hashing methods. But most methods are designed to handle simple binary similarity and decrease quantization error, ignoring that the features of similar images and hashing codes generated are not compact enough. In order to enhance the performance of CNNs-based hashing algorithms for large scale image retrieval, this paper proposes a new deep-supervised hashing algorithm in which a novel channel attention mechanism is added and the loss function is elaborately redesigned to generate compact binary codes. It experimentally proves that, compared with the existing hashing methods, this method has better performance on two large scale image datasets CIFAR-10 and NUS-WIDE.
IOGOD: An interpretable outlier generation-based outlier detector for categorical databases

With the property of interpretability, applications can provide valuable information on the functioning of machine learning models. Interpretability is important in applications where explanations are a must, as in loan rejections, and it allows feedback to executives and analysts on what a models’ results are based on. However, the best performing outlier detectors are not interpretable, and therefore, their results cannot be taken advantage of effectively, or used for relevant applications. Hence, we propose IOGOD, an interpretable outlier generation-based outlier detector, which uses an outlier generation stage to train a two-class outlier detection to achieve comparable performance to state-of-the-art black-box algorithms. IOGOD uses decision trees within an autoencoder to learn the structure of the genuine data and generate synthetic outlier samples, which are then used to train an interpretable two-class contrast pattern-based classifier. Our experimental results show that IOGOD outperforms other interpretable outlier detectors and performs comparably to black-box outlier detectors in comparisons against 14 outlier detector algorithms on 39 categorical databases taken from the KEEL machine learning repository. • An Interpretable Outlier Generation-based Outlier Detector for Categorical Databases. • Our proposal outperforms other interpretable outlier detectors. • Our proposal obtains better results than 14 algorithms on 39 categorical databases.
Federated Fuzzy Learning with Imbalanced Data

Federated learning (FL) is an emerging and privacy-preserving machine learning technique that is shown to be increasingly important in the digital age. The two challenging issues for FL are: (1) communication overhead between clients and the server, and (2) volatile distribution of training data such as class imbalance. The paper aims to tackle these two challenges with the proposal of a federated fuzzy learning algorithm (FFLA) that can be used for data-based construction of fuzzy classification models in a distributed setting. The proposed learning algorithm is fast and highly cheap in communication by requiring only two rounds of interplay between the server and clients. Moreover, FFLA is empowered with an an imbalance adaptation mechanism so that it remains robust against heterogeneous distributions of data and class imbalance. The efficacy of the proposed learning method has been verified by the simulation tests made on a set of balanced and imbalanced benchmark data sets.
Blockchain-based secure Handover for IoT using Zero-Knowledge Proof protocol

Currently, the Internet of Things (IoT) is widely used by the emerging of internet-integrated wireless devices in daily life. In order to communicate and exchange data with each other, IoT devices must pass by a gateway that belongs to the same network. But, considering the mobility of such devices and their constraints, it becomes difficult to trust and to connect from a gateway to another. Therefore, this paper introduces a blockchain-based secure handover protocol. It ensures an anonymous mutual authentication solution between a mobile IoT device and a visited gateway by using Zero-Knowledge Proof (ZKP) protocol. To do, simulations have been performed thanks to a discrete events simulator. In addition to a good security level, compared with the most used protocol (DTLS), our simulation results lead to better performance in terms of temporal complexity, energy consumption, and communication cost.
The Study of Subjective Content and Conditions for Generating the Professional Development Path in University Environment

In modern Russia, the choice of young people’s professional development path is becoming a determinant factor in economic development. The methodology of activities at educational institutions for choosing the professional activity was developed in the second half of the twentieth century. Studies show that 36% of Russian university graduates do not work within their degree field, 30% do not plan to work in it, and 60% are dissatisfied with their employment. It is obvious that in the current environment of innovative economy development, the measures to choose a profession do not achieve their intended goals. Today’s employer assumes the intellectual activity of an individual, his readiness to introduce innovations and quick adaptation to work in changing conditions. The active life position, responsibility and freedom to choose one’s own career path is formed in the educational environment, based on the competencies formed at the university. A theoretically based model of professional development path in the overall structure of socio-economic relations contributes to deeper and more harmonious transformations, the emergence of new professional communities, and affects the level of human capital development in the country. Education should involve forming relevant competencies in students and teaching them to build a successful path of their professional development.
Insulator Infrared Image Segmentation Algorithm Based on Dynamic Mask and Box Annotation

Aiming at the problems of inaccurate positioning, low recognition efficiency, and difficult segmentation of insulator image in complex background, an insulator infrared image segmentation algorithm based on the dynamic mask and box annotation is proposed. In the conditional convolution instance segmentation network framework, the single-shot and high-performance box-supervised methods are introduced, and the mask-level loss is used to replace the pixel-level loss. By marking the insulators in the infrared image with a rectangular frame, the algorithm can realize the overall segmentation of the insulator string. The experimental results show that the algorithm uses the ResNet-101-FPN backbone and 3x training plan to train a large number of infrared images, which can achieve accurate identification and high-precision segmentation of insulators, and demonstrate excellent segmentation performance.
Instance Segmentation of Transmission Line Images Based on an Improved D-SOLO Network

Under the background of unmanned aerial vehicle inspection of transmission lines, in order to solve the problems of overlapping detection boxes and label adhesion in the mainstream object detection and semantic segmentation methods, in this paper, we propose a transmission line instance segmentation dataset and an optimized instance segmentation network, based on the characteristics of transmission line pictures. It is derived from the decoupled-SOLO (D-SOLO). The network combines the transformer attention module and deformable ConvNets v2 (DCNv2) module to get the Transformer and DCNv2 (TAD) modules according to the residual method. Extensive experiments on our dataset show the superiority of the proposed transformer and DCNv2 decoupled-SOLO (TAD-D-SOLO) network over four representative methods, and thereby prove the effectiveness of the proposed TAD components.
NHS Big Data Intelligence on Blockchain Applications

With the exponentially increasing number of AI (Artificial intelligence) applications on Big Data being developed, AI cyber security defense becomes ever required. Blockchain technology invented in 2008 with BitCoin could be benefited alongside the customer of Big Data and so on. Following a rapid progress in its advance, this subject has recently become a hot discussion topic in the ICT (Information and communications technology) world. In this chapter, Big Data security is discussed from the beginning to the impact which could benefit IT engineers, ICT students and CS academic researchers. As a case study, because medical record is personally identifiable privacy information, it needs strictly access control security. Blockchain technology has the features of trustworthy cyber security, anti-fake, anti-alteration, integrity, immutability and transaction accounting transparency reputation, these made it a good candidate for being applied to NHS medical records. Currently Blockchain technology, as one of the most important smart technologies, had been very widely used in smart applications that could influence the world. An analysis on such topic is provided in this chapter.
Exact Decentralized Receding Horizon Planning for Multiple Aerial Vehicles

This article presents a decentralized approach to motion planning for a group of aerial vehicles that combines deliberate and reactive planning strategies to ensure multivehicle interception of a moving target and coordinated tracking while keeping a desired formation. A new on-board receding horizon planning and control architecture realizes each vehicle’s motion plan by generating and tracking dynamically feasible trajectories, while also incorporating a reactive collision avoidance feature that allow them to avoid collisions with each other and with a priori unknown obstacles. The ability of the architecture to safely steer the vehicles to their intercepting formation time-varying positions is demonstrated in realistic Gazebo simulations.