Reviewerly/exHarmony huggingface dataset:
A Predictive Control Model for Master Slave Robotic Manipulator with RBF Neural Network

In recent years, manipulator control has been widely concerned, and its uncertainty is one of the focuses. As we all know, the manipulator is a MIMO nonlinear system, which has the characteristics of severe variable coupling, large time-varying amplitude of parameters and high degree of nonlinearity. Therefore, a lot of uncertain factors must be considered when designing the control algorithm of manipulator system. The predictive control algorithm adopts online rolling optimization, and in the process of optimization, feedback correction is carried out by the difference between the actual output and the reference output. It can iterate the predictive model and suppress the influence of some uncertain disturbances to a certain extent. Therefore, the design of predictive controller for robot is not only of theoretical significance, but also of great practical significance. The trajectory tracking problem is proposed in this paper, and a predictive control method for master slave robotic manipulator with sliding mode controller is designed. In addition, when external disturbances occurred, the approximation errors are compensated by the proposed control method. Finally, The results demonstrate that the stability of the controllers can be improved for the trajectory tracking errors.
Funcons for HGMP: the fundamental constructs of homogeneous generative meta-programming (short paper)

The PLanCompS project proposes a component-based approach to programming-language development in which fundamental constructs (funcons) are reused across language definitions. Homogeneous Generative Meta-Programming (HGMP) enables writing programs that generate code as data, at run-time or compile-time, for manipulation and staged evaluation. Building on existing formalisations of HGMP, this paper introduces funcons for HGMP and demonstrates their usage in component-based semantics.
Virtual Carer

In view of the rapidly progressive increase in the average population age, “Ambient Assisted Living” (AAL) defines the actions and policies needed to promote the improvement of living conditions within domestic spaces to foster autonomy, safety, and social inclusion for the elderly or disabled. The idea is to design an innovative and comprehensive information system for AAL, an ICT-based “Virtual Caregiver,” which is informed, intelligent and friendly, and which constantly monitors the health warning, informing and advising the elderly while controlling the environment and then asking for help when needed. The system will have the ability to establish interactive communication with the person but also extend it automatically outside the house in times of need. Virtual Caregiver will be able to enable the software protocols that activate the emergency phone calls to the family, medics or even first aid in emergencies.
An Effective Approach to Secure Web Application by Identifying and Removing Programming Code Vulnerabilities through Script

with the rapid growth of internet users, possible attacks on websites are increasing very fast as a result more cyber crimes are happening. Now a day’s almost everyone is using internet, for different purposes. Some users use for online transaction, some use for sending and receiving mails etc. generally people are not so much aware what an attacker can do with their personal or confidential data whatever they use on internet. Here the focus is on some possible vulnerabilities, which can be exploited by an attacker and he can get benefits from it.The proposed approach is based on that as an input website folder name will be asked, and then a list of vulnerabilities present in the website will be displayed and also the patches will be provided to these vulnerabilities. The best part of this approach is it provides list of vulnerabilities category wise and also show the vulnerable page name of the website. In this research work the focus is on how to detect vulnerabilities present in a website and how we can patch these vulnerabilities by running scripts only without any manual investigation.
Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.
A privacy-preserving revocable framework in the deep-learning-as-a-service platform system based on non software as a service

With the gradual maturity of deep learning, it is possible to make deep learning models as a service on the cloud. However, privacy issues are the key problem of making deep learning models as cloud services. According to the service mode of cloud computing, the service mode of deep learning is divided into software-as-a-service-based systems and non-software-as-a -service- based systems. In non -software-as-a-service-based systems, the cloud computing provider can hold the deep learning model, but the deep learning model can be revoked by the model owner. Many solutions have been proposed to protect privacy in a cloud server that makes deep learning models as cloud services. However, there is no study on how to revoke the cloud server's authority to use the deep learning model. To solve the privacy issues and support the revocability of the model in non-software-as-a-service-based systems, we propose a privacy-preserving revocable framework based on fully homomorphic encryption and digital signature. This framework not only protects the privacy of input data, output results, and model parameters but also allows the model owner to revoke the cloud's right to use the model. Then, the framework is proved to conform to our security definition through security analysis. Next, an instance based on the existing fully homomorphic encryption scheme and the digital signature scheme is constructed. At last, experiments are used to prove that the framework is feasible in practice and estimate the computing time cost of the framework.
Human Subject Distance Estimation Using the Pupillary Distance and Head Orientation

In this article we propose a method to estimate the distance to the camera of a human subject from a monocular image without restricting the orientation of the head. The inter pupillary distance of the subject is used as anchor to estimate the size of pixels in the plane of the subject and the distance to the camera is recovered from this measurement. The inter pupillary distance in the image is estimated by using a learning-based approach that detects the head of the subject and estimates the orientation of the head with respect to the optical axis of the camera. It also proposes a method to estimate the pupillary distance from 2 images. The proposed approaches are validated by comparing the recovered distance to the real distance obtained with an OptiTrack motion-capture system. It is demonstrated that using the proposed algorithm allows a more robust estimation of the distance regardless of the head rotation.
Report on the CHIIR 2021 third workshop on evaluation of personalisation in information retrieval (WEPIR 2021)

The Third Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2021) was held in conjunction with the ACM SIGIR Conference on Human Information Interaction &amp; Retrieval (CHIIR 2021) in Canberra, Australia, as a virtual event. WEPIR 2021 followed on from the first and second WEPIRs held at CHIIR 2018 and 2019. The purpose of the workshop was again to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focused on further development of a common understanding of the challenges, requirements and practical limitations of personalisation in information retrieval and its evaluation.
IHE XDS

IHE XDS (cross-enterprise document sharing) can be used to share documents between different organizations using a common portal. The XDS registry holds document metadata, which can be searched to retrieve appropriate documents stored in XDS repositories. Local specifications for metadata are defined in an affinity domain. The metadata includes information about each document, the patient, author, event and technical data. A number of related specifications are defined as extensions.
iOS App and Architecture of Convolutional Neural Networks

Deep convolutional neural networks (CNN) have attracted many attentions of researchers in the field of artificial intelligence. Based on several well-known architectures, more researchers and designers have joined the field of applying deep learning and devising a large number of CNNs for processing datasets of interesting. Equipped with modern audio, video, screen-touching components, and other sensors for online pattern recognition, the iOS mobile devices provide developers and users friendly testing and powerful computing environments. This chapter introduces the trend of developing pattern recognition CNN Apps on iOS devices and the neural organization of convolutional neural networks. Deep learning in Matlab and executing CNN models on iOS devices are introduced following the motivation of combining mathematical modelling and computation with neural architectures for developing pattern recognition iOS apps. This chapter also gives contexts of discussing typical hidden layers in the CNN architecture.
Interval Type-2 Fuzzy Decision Making Based on LINMAP

It is noted that all these previous decision methods of supplier selection can not solve the MAGDM problems with incomplete decision preference information. In fact, there always exists such situation that the decision maker (DM) feels comfortable to express his/her decision in terms of preference relation instead offering numerical quantification. This problem can be solved by using the LINMAP method.
Blockchain and the Protection of Patient Information in Line with HIPAA

Blockchains used for the protection of patient information can serve as a way for hospitals and other medical facilities to secure patient records. With more information threats targeting hospitals and other medical facilities, it is becoming more important for these organizations to improve the information systems that are charged with protecting their patient records. HIPAA regulations are in place to try and protect patient records and information, but the hospitals do not allocate enough funds to information systems so they are put at risk of attack. Blockchains can offer the type of protection that these medical facilities need without them having to worry about upgrading the technology every year. The Blockchain is not without its faults, and that can be seen in the attacks that take place against Bitcoin. Even with these attacks, the use of Blockchain would be beneficial to the hospitals and medical facilities.
A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, the authors propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. The authors first validate the relevance of their approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. The authors also demonstrate its superiority to related methods.
Line of the Sight Cone Artificial Repulsion Force Method for Autonomous Obstacle Avoidance in Space under the Model Predictive and Inversive Control Guidance

Aiming at the obstacle avoidance problem in the process of the space rendezvous, docking and relative guidance, analysis and compare with the main methods and ways of the autonomous path planning and the obstacle avoidance in motion. Based on the image profile of the obstacle under the spacecraft relative navigation field of view, building dynamically the space obstacle line of sight cone model in real time; Under the condition of the spacecraft’s velocity vector point to the inside of the line of the sight cone, we design an artificial exclusion potential field which is perpendicular to the velocity vector and push the velocity vector away from the line of the sight cone. Combined with the model predictive and inversive control guidance, we propose a method which is combined with the model predictive and inversive control guidance and the line of the sight cone artificial repulsion field. The simulation result indicates that the method we proposed can avoid the traditional artificial repulsion field method ’s problem, which include the crossing obstacle space, bounces and oscillates around the obstacle space and fly around the distance. This method can better avoid the spacecraft fall into a local stabilization point, effective implementation autonomous avoidance of the space obstacles.
Multimedia Text Summary Generator for Visually Impaired

Banerjee, Shreya Sirigeri, Prerana Karennavar, Rachana B. Jayashree, R.With the advancing methodologies in the field of NLP, text summarization has become an important application and is still under research.Taking into consideration today’s busy world, time is a most important factor for everyone and people do not bother to make time to listen to long audio news or read long news articles, and as the visually impaired are an important part of our society, it becomes still difficult for them to read although there is braille, but it is inconvenient for them every time to read through braille. The aim is to make use of advancing technology to make their lives easier. This gave us inspiration that led to the idea of generating concise and short summaries for the visually impaired. Our system makes use of various APIs like speechrecognition, pyspeech, Google Cloud Speech API, etc., to extract text and then use summarisation techniques to present the most accurate summary and convert it back to audio format so that the news is more accessible for the visually impaired.
Continuation Semantics for Interaction and Concurrency

We present an operational semantics designed in continuation-passing style for an abstract concurrent language providing a general mechanism of multiparty interactions. Using the classic notion of strong bisimulation, we show that the basic laws of concurrent systems are satisfied in this semantics. By customizing the behavior of continuations, we obtain semantic models for formalisms based on Milner’s CCS extended with constructions for multiparty interactions.
A Yolov3-Based Multi-target Detection System for Complex Scenes

With the rise of deep learning technology in recent years, many deep learning techniques have been applied to several aspects, and a deep learning-based target detection system is one of them. In fact, traditional target detection for complex scenes usually faces many problems, including spatial occlusion, small target and multi-target detection, and real-time detection efficiency. In response to this phenomenon, this paper adopts the YOLOv3 algorithm and uses the Pascal VOC2007 dataset for model training to build a multi-target detection system. The experimental results show that YOLOv3 can still detect objects in complex scenes with classical dataset training, and mitigate the effect of spatial occlusion on target detection compared with traditional target detection algorithms, which has a certain application value for complex scenes.
SpeedDeblur: A Framework to speed up CNN-based Deblurring for HEVC compressed video

This paper proposes a speedup convolutional neural network (CNN)-based deblurring framework (SpeedDeblur) for reconstructed blurry videos. First, we extract the coding information and the reconstructed video from the compressed data. Second, a CNN-based algorithm is used for deblurring the first reconstructed frame. Pixels of the deblurred frames are transferred to the subsequent frames guided by HEVC decoded data. The transferring process is simple and faster than applying a deblurring algorithm on all frames. However, passing pixels throughout a long video propagates accumulated errors and reduces the deblurring performance. To bridge this gap, we design an adaptive reset strategy for deciding which frame needs CNN-based deblur during the transfering process. Besides, a data generation strategy simulating blurry real-world factors such as camera shake and fast movement is proposed. Compared to frame-by-frame deblurring approaches, our framework can retain the same comparable results and boost the deblurring processing by up to 4.0 × and 99.4 × on GPU and CPU, respectively.
Eye-Tracking: Measurements and Applications

Eye-tracking technology has been around now for more than 150 years helping us to understand the cognitive mechanisms that allow us to navigate the visual environment in a seemingly effortless way. In this article, I begin by briefly reviewing the history of this technology and delve into its signals and the measurements that can be extracted from it. I then review several contexts in which eye-tracking has been successfully applied, ranging from reading behavior to real-world environments, and showcase the versatility of this technology in the study of human vision.
Cloud Crime and Fraud

Changing trends in IT industry are opening new avenues. With the scalability, flexibility, and economic advantage offered by cloud computing, more and more organizations are moving towards cloud for their applications. With all the benefits of cloud computing, it poses a danger of digital crime and security breaches. These challenges are compounded by the fact that cybercrime and the transgressors transcend geographical boundaries while the law enforcement does not. This paper tries to focus on how cloud computing is rising to the challenges thrown in from cyber space and recent developments to avoid and mitigate cloud fraud and abuse. Taking counter measures at organizational level, will alleviate and up to an extent eliminate security breaches. With current knowledge on policy and standards adopted by developed nations, the policy makers and law enforcement agencies in developing countries can work towards formulating standards and guidelines for awareness on threats, vulnerabilities and effectiveness of security controls to respond to risk.
HPC inspires at SC18

We asked Ralph A. McEldowney, Associate Director for Networking for the DoD High Performance Computing Modernization Program (HPCMP) and this year's General Chair, to highlight things to watch at SC18.
Research on the Application of Mathematical Thought in Algorithm Optimization

In order to do a good job of algorithm optimization through mathematical ideas in computer programming, this paper will carry out relevant research, mainly on the basic concepts of algorithm optimization, then introduce the relationship between mathematical ideas and algorithm optimization, and finally put forward the application of mathematical ideas in algorithm optimization. Through the analysis of this paper, the mathematical thought can help the programmer to do the algorithm optimization work, help the programmer to form the algorithm optimization consciousness, and improve the quality of the program algorithm.
An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.
ACHC: Associative Classifier Based on Hierarchical Clustering

The size of collected data is increasing and the number of rules generated on those datasets is getting bigger. Producing compact and accurate models is being the most important task of data mining.
Conclusion

The discussion of answer set programming in this book is incomplete in three ways. First, it says almost nothing about the algorithms that answer set solvers use to find stable models, about what happens “under the hood.” Section 3.4 is, in fact, the only place where the operation of answer set solvers is discussed in any detail. The algorithms implemented in Smodels are described in Chaps. 3 and 4 of the doctoral dissertation of one of its designers (Simons, Extending and implementing the stable model semantics. Ph.D. Thesis, Helsinki University of Technology, 2000). You can learn about the operation of clingo from Chaps. 4 and 6 of the book (Gebser et al. Answer set solving in practice. Synthesis lectures on artificial intelligence and machine learning. Morgan and Claypool Publishers, 2012) written by members of the Potassco team.
Software Module Clustering Using Bio-Inspired Algorithms

Nature has always been a source of inspiration for human beings. Large numbers of complex optimization problems have been solved by the techniques inspired by nature. Software modularization is one of such complex problems that have been encountered by software engineers. It is the process of organizing modules of a software system into optimal clusters. In this chapter, some bio-inspired algorithms such as bat, artificial bee colony, black hole and firefly algorithm have been proposed for the cause of software modularization. The hybrid of these algorithms with crossover and mutation operators of the genetic algorithm has also been proposed. All the algorithms along with their hybrids are tested on seven benchmark open source software systems. It has been evaluated from the results thus obtained that the hybrid of these algorithms proved to optimize better than the existing genetic and hill-climbing approaches.