Reviewerly/exHarmony huggingface dataset:
Abstractive Web News Summarization Using Knowledge Graphs

The rapid progresses in digital data acquisition techniques have led huge volume of news data available in the news websites. Most of such digital news collections lack summaries. Due to that, online newspaper readers are overloaded with lengthy text documents. Also, it is a tedious task for human beings to generate an abstract for a news event manually since it requires a rigorous analysis of the news documents. An achievable solution to this problem is condensing the digital news collections and take out only the essence in the form of an automatically generated summary which allows readers to make effective decision in less time. The graph based algorithms for text summarization have been proven to be very successful over other methods for producing multi document summaries. The summary generated from knowledge graphs is more in line with human reading habits and possesses the logic of human reasoning. Due to the fast growing need of retrieving information in abstract form, we are proposing a novel approach for abstractive news summarization using the knowledge graphs to fulfill the need of having more accurate automatic abstractive text summarization.
Towards Artifacts-Free Image Defogging

In this paper we present a novel defogging technique, named CurL-Defog, aimed at minimizing the creation of unwanted artifacts during the defogging process. The majority of learning based defogging approaches rely on paired data (i.e., the same images with and without fog), where fog is artificially added to clear images: this often provides good results on mildly fogged images but does not generalize well to real difficult cases. On the other hand, the models trained with real unpaired data (e.g. CycleGAN) can provide visually impressive results but they often produce unwanted artifacts. In this paper we propose a curriculum learning strategy coupled with an enhanced CycleGAN model in order to reduce the number of produced artifacts, while maintaining state-of-the-art performance in terms of contrast enhancement and image reconstruction. We also introduce a new metric, called HArD (Hazy Artifact Detector) to numerically quantify the amount of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.
HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features

In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such as elderly care and mental health monitoring. Preliminary results show that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this dataset public, researchers can test different machine learning algorithms for activity recognition, especially, sensor data fusion methods.
Advanced distributed filtering

In this chapter, we provide the theory and analytical development of advanced distributed filtering based on various classes of methods. In one class, the weighted average, based on the Bayesian point of view, yields the best possible estimate of the state at a particular time instant which can be computed from the conditional probability distribution of the state given all past measurements up to the particular time. In another class, a distributed estimation approach is developed using an information matrix filter on a distributed tracking system in which multiple sensors are tracking the same target. The information matrix filter version is derived from covariance intersection, weighted covariance, and Kalman-like particle filter, respectively. The steady performance of these filters is evaluated with different feedback strategies.
Compressed-Transformer

Recently, Transformer has achieved state-of-the-art performance in neural machine translation. However, the number of parameters in Transformer is so large that it needs to be compressed before deployed and executed on resource-restricted devices. In this paper, we propose a compressed version of Transformer called Compressed-Transformer. We introduce two techniques, factorizing parameters and block reduction, to compress Transformer model. Consequently, the number of parameters can be reduced by more than 50%. We exploit a stage-wise distillation strategy with the temperature dynamically adjusted in knowledge distillation practice to transfer knowledge from base Transformer (teacher) to Compressed-Transformer (student). A Chinese-to-English (Zh~En) dataset of United Nations Parallel Corpus and a German-to-English (De~En) dataset of Multi30K are used, and the experimental results show that our compressed model achieves BLEU score only slightly lower than uncompressed teacher model. Specially, when the number of parameters is reduced by 59.3%, the student model can achieve BLEU score of 40.69, only 1.64 lower than that of the teacher model, and the inference speed is improved by 17% on Zh~En dataset. The experiments on De~En dataset also achieve the similar results.
Network Module of Generative Adversarial Network for Low-Light Image Enhancement

With the application of deep learning technology into the field of image enhancement, combined with traditional enhancement algorithms, the performance of enhancement algorithms has become more excellent. However, the enhancement of low-light images has not been completely solved by applying deep learning techniques. Enhancement network module is introduced to generative adversarial networks which aim to form a network module that can effectively solve the problem of low-light image enhancement. Through experiments, the performance of proposed algorithms has been proven to be outstanding in the target field.
Improved query model for rapidly query based on distributed hash index

Aiming at the low efficiency of the Big data query of the internet of Things, the research based on the smart urban management platform, and on the premise of retaining the original platform data integration framework. The query model and process are redesigned and a new data improved query model is proposed by introducing hash coding, distributed index and data dictionary. Compared with the one by one comparison query method, the speed of the improved query model is increased by 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> times when processing millions of data, which significantly improves the query efficiency of big data. The research results can help improve the data processing and analysis ability of Chongqing Liangjiang Smart Urban Management Platform in China.
The Fusion of Secure Function Evaluation and Logic Synthesis

Designing custom secure function evaluation compilers has been an active research area. However, intelligent adaptation of the integrated circuit synthesis tools outperforms these compilers. It is time for the custom compilers to embrace this trend.
Stability Region of Trapezoidal Rule for Dynamic Software Dependability Assessment Model for Open-Source Cloud Computing

This paper presents a simple way to find influential papers to research the future development on the topic stability region of trapezoidal rule for dynamic software dependability assessment model of open-source cloud computing using citation network analysis. Using co-citation network analysis, influential papers are also identified for three subdomains of applications; finding influential papers will save the time of active researchers and will avoid repetition of research works. Also, the stability region of a trapezoidal rule on applying to a dynamic software dependability assessment model is obtained and noticed that there is a gap in the neighborhood of (2, 0). So the trapezoidal rule is not A-stable. To design a trapezoidal rule, a new approach is suggested. The influential papers direct, to get higher-order methods, not only the order of convergence but also one must improve the stability region of the numerical solution of the numerical method. Experimental results are provided to show the applicability and performance of citation network analysis and trapezoidal rule.
Free-viewpoint Motion Recognition Using Deep Alternative Learning

In this study, we aim to develop a robust motion recognition system for an intelligent video surveillance system, that can be used for security, sports and rehabilitation by using extended alternative learning. A robust motion recognition system is necessary for the automated detection of security incidents by using a machine learning approach. However, to avoid the difficulty of collecting a huge training dataset, we propose an alternative learning approach that trains a deep neural network with a 3D-CG dataset to recognize several motions. We present our experimental results on motion recognition from free-viewpoint videos by using deep learning and alternative learning. The trained deep neural network (DNN) is evaluated using actual videos by classifying the different actions performed by real humans in these videos.
Cryptanalysis of Smart TV using Discrete Cosine Transforms

The Smart TV, an IoT device is gaining a lot of popularity as an intelligent sensor device It has substituted the heirloom television system by bestowing competence in network and data connectivity as well as processing. This ultramodern technology has attracted viewers worldwide. With advancing technology, smart TV poses several challenges and security threats to consumer privacy. This vulnerability can have detrimental effects on the end user. To ensure video security in smart environment, a novel solution is proposed by using cryptanalysis with help of cosine transforms. Two dimensional discrete cosine transform (DCT) is used to compress and encode the video images which upon authentication by authorized user can be decompressed or decoded using inverse discrete transform (IDCT). Thereby, real-time security is provided to video applications in IoT like the Smart TV.
Study on Implementation of ITK Campus Drainage Master Plan (Case Study: Faculty of Mathematics and Natural Sciences)

Two main building Kalimantan Institute of Technology was established on an area of 3500 m 2 on October 6, 2014 which serves as the infrastructure for teaching and learning activities of students. The rapid increase in the number of students each year results in an increase in the number of buildings as facilities for teaching and learning. This is the background for the development of the Kalimantan Institute of Technology area by adding five more lecture buildings to support teaching and learning activities for 3500 students. The expansion area for five more lecture buildings is currently under construction in the area of ​​the Faculty of Mathematics and Natural Sciences. The function of the land area has been change due to building construction resulted in an increase in runoff discharge. This condition certainly affects the region if not handled properly. Increased runoff discharge will affect inundation or flooding in the area if it is not equipped with a drainage system as needed. This research is one alternative solution given. This research is in the form of a study on the implementation of a drainage master plan that aims to obtain drainage dimensions such as drainage width, drainage length and drainage depth by observing runoff due to rainwater using the concept of environmentally friendly drainage. The research obtained from the dimensions of tertiary canals with dimensions of 0.10-0.30 meters, secondary channels 0.30-0.45 meters and primary channels 0.35-0.70 meters with the discharge area of ​​the Faculty of Mathematics and Natural Sciences ITK is 1.18 m 3 / sec.
Work with what you’ve got

Quantum computing combines great promise with daunting challenges — the road to devices that solve real-world problems is still long. Now, an implementation of a quantum algorithm maps the problems we want to solve to the devices we already have.
Research on Soft Self-Destruction Program of Secrecy Module of Electronic Equipment on Seepage

In order to improve the security performance of electronic equipment on board, the self-destruction scheme of secrecy module is studied. Taking the missile-borne reconnaissance equipment as an example to analyze its composition and confidentiality requirements, the soft self-destruction scheme is studied according to the characteristics of soft self-destruction and hard self-destruction, which can provide a reference for the development of subsequent missile-borne reconnaissance equipment.
Grammar Based Computing: A New Paradigm for Solving Real Life Problems

A computer is very useful device in solving real life problems. However due to limited storage and processing power, it can not handle problems operating on big data values. Many cryptographic problems involve operations on big data values and these operations are very important in deciding the strength of security algorithm. Some problems belong to bioinformatics and require processing of large genomes. In current scenario, no method can be designed for solving such problems in reasonable time. In fact for some problems no solution exists. In proposed work, we define a new strategy which can be applied in those problems where input structure has some repetitive pattern. To solve such problems, we will create training data by using a new method which is called as grammar based method. We will study input and output of related problems and use it to predict answer of complex problem. This is a completely new method and can be used in solving many problems. This method is very fast and easy. The performance of proposed method is very fast as compared to other methods. Currently, the work is in primary phase and has been used in many problems. Later on it will be applied on other problems.
Research on object recognition algorithm based on color histogram

Based on the concept of color histogram, a new definition of color histogram is proposed in this paper, which is introduced into the target recognition algorithm. This algorithm is more robust to mutation and can overcome the interference introduced by occlusion to a certain extent. Physical simulation proves that the algorithm can satisfy the real-time requirement while ensuring the accuracy of recognition.
On the adaptive spectral approximation of functions using redundant sets and frames

Abstract The approximation of smooth functions with a spectral basis typically leads to rapidly decaying coefficients, where the rate of decay depends on the smoothness of the function and vice versa. The optimal number of degrees of freedom in the approximation can be determined with relative ease by truncating the coefficients once a threshold is reached. Recent approximation schemes based on redundant sets and frames extend the applicability of spectral approximations to functions defined on irregular geometries and to certain nonsmooth functions. However, due to their inherent redundancy, the expansion coefficients in frame approximations do not necessarily decay even for very smooth functions. In this paper, we highlight this lack of equivalence between smoothness and coefficient decay, and we explore approaches to determine an optimal number of degrees of freedom for such redundant approximations.
Effectual Cloud Storage Management based on De-duplication Techniques: A Survey

The expeditious growth of computer technology leads to data eruption which requires more storage space. Conventional backup takes much time to store the data and it does not have the capability to avert duplication. To enhance cloud storage efficiency, various solutions have been developed. Data de-duplication is one of the powerful approaches that play a crucial role in managing the storage space. De-duplication removes unnecessary data and reduces the cost of storage. This article reviews the different existing methods for eliminating repeated data using de-duplication.
Spatial-aware Network using Wasserstein Distance for Unsupervised Domain Adaptation

In a general scenario, the purpose of Unsupervised Domain Adaptation (UDA) is to classify unlabeled target domain data as much as possible, but the source domain data has a large number of labels. To address this situation, this paper introduces the optimal transport theory into the transfer learning, and proposes a deep adaptation network based on the second-order Wasserstein distance, which can measure the discrepancy between the two distributions. In addition, in order to retain the spatial structure information of features, the network is combined with convolutional auto-encoder. Experiments show that our method has achieved good results.
A Study of the Stability Characteristics of the Intelligent Robotic Vehicle Movement

Unmanned vehicles have been developed at a rapid pace worldwide. However, despite the significant sophistication of these technologies, there arises a broad list of problems related to the IRV (intelligent robotic vehicle) movement. One of the most important problems is to ensure the stability characteristics of the IRV movement. To this end, we developed a methodology for calculating the angular parameters, the radius of the trajectory curvature, and the coordinates of the IRV position during its movement. We created a 3-D model of the IRV, which allowed us to provide the most complete insight into its movement, placement of new sensors, monitoring, and calibration. The experimental studies have shown that the minimum deviation of the trajectory is observed at the IRV movement speed of 7 km/h.
Systematically Encoded Polynomial Codes to Detect and Mitigate High-Status-Number Attacks in Inter-Substation GOOSE Communications

Inter-substation Generic Object Oriented Substation Events (GOOSE) communications that are used for critical protection functions have several cyber-security vulnerabilities. GOOSE messages are directly mapped to the Layer 2 Ethernet without network and transport layer headers that provide data encapsulation. The high-status-number attack is a malicious attack on GOOSE messages that allows hackers to completely take over intelligent electronic devices (IEDs) subscribing to GOOSE communications. The status-number parameter of GOOSE messages, stNum is tampered with in these attacks. Given the strict delivery time requirement of 3 ms for GOOSE messaging, it is infeasible to encrypt the GOOSE payload. This work proposes to secure the sensitive stNum parameter of the GOOSE payload using systematically encoded polynomial codes. Exploiting linear codes allows for the security features to be encoded in linear time, in contrast to complex hashing algorithms. At the subscribing IED, the security feature is used to verify that the stNum parameter has not been tampered with during transmission in the insecure medium. The decoding and verification using syndrome computation at the subscriber IED is also accomplished in linear time.
Fuzzy Logic based QoS Management and Monitoring System for Cloud Computing

Owing to the flexibility and cost-effectiveness of cloud computing, several large and medium types of business organizations are using cloud service for their business purpose. Guaranteeing Quality-of-Service (QoS) to the cloud's clients is a significant and unavoidable problem, especially for cloud media services like live streaming and cloud video conferencing. The main intention of this proposed paper is to develop a more reliable cloud client-side QoS monitoring and management system and thereby provide high-quality media services to the cloud customer. Fuzzy rules have developed this cloud client-side QoS monitoring and management system. Its experiment is done with real-time cloud architecture. The experimental analysis makes it clear that this method has many benefits for cloud service providers and cloud customers. Moreover, the results show the remarkable improvements of the client-side QoS attributes: throughput, latency and response time.
CMT: A Comprehensive Clone Management Solution

Copying existing code for reuse is a common practice during software development and maintenance. This leads to duplication of code in software systems, also called code clones. Clones can be spotted using any available clone detection tool. A common activity followed by detection is to refactor and remove clones. However, for pragmatic reasons, it is not always possible to remove clones and hence clones should be managed effectively to avoid any negative impacts on software maintenance. We present CMT, an Eclipse IDE integrated tool that provides a comprehensive clone management mechanism including clone detection, clone tracking, clone analysis, clone visualization, meta-level clone unification, and controlled clone generation for code-fragment level simple clones and higher level structural clones.
An Efficient Privacy Preserving Algorithm for Cloud Users

Now a day’s cloud-based instant message services are becoming popular. It can be an efficient way of communication even for events that require a certain level of security like banking, e-commerce applications, e-transactions etc. Cloud can provide a distributed notification of messages along with the queuing them to facilitate the transfer of messages between various applications and connected systems. Cloud message services can be as simple as a pay-as-you-go service, which does not require significant investment and protect its customers, providing a hassle-free and maintenance-free services platform. This research paper proposes a privacy-preserving approach to users utilizing cloud-based services. A cloud-based model is created and simulated. The proposed method is based on a multi-method approach where a virtual system is acting as an agent and is interconnected with other virtual systems. Inside a virtual system, a discrete event model of job processing and privacy preserving is defined as shown in the process flowchart. The proposed model is scalable. To preserve the privacy, Blowfish and Honey Encryption algorithm is used. This research aims to make the current messaging framework more secure, resilient and effective by the application of symmetric-key algorithm amalgamated with Honey key encryption. The performance of the proposed algorithm evaluated and found satisfactory results.
Design of Status Analysis System for Brick Machines Based on Industrial Internet of Things

Status analysis of machines is important for the factories to reduce the breakdown of machines and improve the quality of the products. Most existing solutions are limited to manual data collection and analysis, which has poor real-time performance and low data utilization. To solve this problem, this paper proposes a health status analysis for brick machines based on industrial internet of things. We firstly introduce the framework of industrial internet of things to obtain the operation data of brick machines. Then the data are analyzed to derive the features that presents the status of the brick machine. The preprocessed data are further evaluated based on the clustering algorithm. Based on the clustering results, the status of brick machines can be classified as shutdown, standby, abnormal, voltage loss and normal. The experimental results are provided to prove the effectiveness of the proposed system.