Reviewerly/exHarmony huggingface dataset:
Protection for artificial intelligence in personalised medicine – the patenttrade secret tradeoff

This chapter explores patent and trade secret (TS) tradeoffs in European intellectual property law. It first examines the current status of AI uses in personalized medicine (PM), identifies three areas where hurdles to deliver technological promises appear and explores if patent or TS protection would be better suited to deal with the problems faced by AI uses in PM. The comparison reveals that patent and TS may overlap over the same subject matters of AI algorithm and data, and the restriction in one right may affect access created by the other right. The chapter then revisits three policy points – technology maturity, data quality and transparency. It argues that the confluence of restrictive patent protection, strong personal data protection and expansive TS protection may result in both underuse and overprotection. Based on these findings, the author considers additional regulatory measures to require disclosure of AI algorithms in the public interest.
Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic rol... | Find, read and cite all the research you need on Tech Science Press
Deep Learning- Based Surveillance System using Face Recognition

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.
How to Securely Prune Bitcoin’s Blockchain

Bitcoin was the first successful decentralized cryptocurrency and remains the most popular of its kind to this day. Despite the benefits of its blockchain, Bitcoin still faces serious scalability issues, most importantly its ever-increasing blockchain size. While alternative designs introduced schemes to periodically create snapshots and thereafter prune older blocks, already-deployed systems such as Bitcoin are often considered incapable of adopting corresponding approaches. In this work, we revise this popular belief and present CoinPrune, a snapshot-based pruning scheme that is fully compatible with Bitcoin. CoinPrune can be deployed through an opt-in velvet fork, i.e., without impeding the established Bitcoin network. By requiring miners to publicly announce and jointly reaffirm recent snapshots on the blockchain, CoinPrune establishes trust into the snapshots’ correctness even in the presence of powerful adversaries. Our evaluation shows that CoinPrune reduces the storage requirements of Bitcoin already by two orders of magnitude today, with further relative savings as the blockchain grows. In our experiments, nodes only have to fetch and process 5GiB instead of 230GiB of data when joining the network, reducing the synchronization time on powerful devices from currently 5h to 46min, with even more savings for less powerful devices.
Elementary School-university Collaboration towards Extracurricular STEM Education

Through the author's two-semester extra-curricular STEM education activities for elementary school pupils, this article introduces the preliminary preparation content, the development process, and the summary and experience of this activity. Through a detailed introduction to one of the activities about the production of hydraulic excavators, the application of STEM in the activities was explained. It shows that pupils participating in extra-curricular STEM activities not only improve themselves in terms of confidence, interest, ability, and discipline, but also help their parents balance work and family.
Self-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation.

Segmentation is a critical step in medical image analysis. Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models achieving state-of-the art results in various medical image datasets. Network architectures are usually designed manually for a specific segmentation task so applying them to other medical datasets requires extensive expertise and time. Moreover, the segmentation requires handling large volumetric data that results in big and complex architectures. Recently, methods that automatically design neural networks for medical image segmentation have been presented; however, most approaches either do not fully consider volumetric information or do not optimize the size of the network. In this paper, we propose a novel self-adaptive 2D-3D ensemble of FCNs for 3D medical image segmentation that incorporates volumetric information and optimizes both the models performance and size. The model is composed of an ensemble of a 2D FCN that extracts intra-slice and long-range information, and a 3D FCN that exploits inter-slice information. The architectures of the 2D and 3D FCNs are automatically adapted to a medical image dataset using a multiobjective evolutionary based algorithm that minimizes both the expected segmentation error and number of parameters in the network. The proposed 2D-3D FCN ensemble was tested on the task of prostate segmentation on the image dataset from the PROMISE12 Grand Challenge. The resulting network is ranked in the top 10 submissions, surpassing the performance of other automatically-designed architectures while having 13.3x fewer parameters.
Differential evolution with explicit control of diversity for constrained optimization

Evolutionary Algorithms (EAs) have been quite successful both in constrained and unconstrained single-objective optimization. However, in the constrained case little attention has been paid to controlling the diversity explicitly with the aim of avoiding premature convergence. This paper proposes Differential Evolution with Clustering-based Diversity (DECD). DECD uses a novel replacement strategy that controls the diversity explicitly. Particularly, the replacement strategy forces the selection of some distant individuals but it simultaneously allows the survival of some other individuals that form clusters to promote intensification. The experimental validation shows the important benefits provided by the explicit control of diversity.
Radial model of differential evolution dynamics

Despite extensive research into the state-of-the-art global optimization technique of Differential Evolution (DE) its theoretical foundations still need development. This paper provides a dynamics model of a population exploiting a radial (central-symmetric), unimodal function. Derivations are based on the analysis of moments of the population distribution and lead to formulae describing them in consecutive iterations. This allows for simulating the execution of DE for radial functions without running this algorithm. Analytical insights include an explicit, approximate formula linking the convergence speed of DE population with a generalized scaling factor, crossover probability and the search space dimension.
Venue Suggestion Using Social-Centric Scores

User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues’ features with the users’ preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues’ content.
Design principles for matrix adaptation evolution strategies

tutorial Design principles for matrix adaptation evolution strategies Share on Author: Hans-Georg Beyer Vorarlberg University of Applied Sciences, Dornbirn, Austria Vorarlberg University of Applied Sciences, Dornbirn, AustriaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 682–700https://doi.org/10.1145/3377929.3389870Online:08 July 2020Publication History 0citation57DownloadsMetricsTotal Citations0Total Downloads57Last 12 Months22Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Formation and Conical Obstacle Avoidance Control of UAS Based on Two-hop Network

Aiming at the formation control problem of Unmanned Aerial System (UAS) with fixed communication network topology, we present a fast formation and obstacle avoidance control algorithm for UAS with conical obstacles in this paper. On the basis of traditional consensus protocol, the concept of two-hop network is introduced in this paper, which enhances the communication network connectivity of UAS and realizes rapid formation of UAS. At the same time, the conization of hill obstacles is processed to calculate the shortest path from the β -agent at the intersection of plane of UAV and conical obstacle to the farthest generatrix, so as to guide UAV to avoid obstacles. And in order to adapt to the real performance parameters of UAVs, a new coefficient function is designed on the basis of the original artificial potential function. Combining with the two-hop network, the rapid formation and obstacle avoidance of UAS in three-dimensional case are realized. Finally, simulation results are provided to demonstrate the effectiveness of our proposed methods.
Image processing: why quantum?

Quantum Image Processing has exploded in recent years with dozens of papers trying to take advantage of quantum parallelism in order to offer a better alternative to how current computers are dealing with digital images. The vast majority of these papers define or make use of quantum representations based on very large superposition states spanning as many terms as there are pixels in the image they try to represent. While such a representation may apparently offer an advantage in terms of space (number of qubits used) and speed of processing (due to quantum parallelism), it also harbors a fundamental flaw: only one pixel can be recovered from the quantum representation of the entire image, and even that one is obtained non-deterministically through a measurement operation applied on the superposition state. We investigate in detail this measurement bottleneck problem by looking at the number of copies of the quantum representation that are necessary in order to recover various fractions of the original image. The results clearly show that any potential advantage a quantum representation might bring with respect to a classical one is paid for dearly with the huge amount of resources (space and time) required by a quantum approach to image processing.
Q-plate based dual image asymmetric polarization encryption

With the rapid development of computer network technology, information security has drawn considerable attention in recent years. Owing to the characteristics of multi-dimensional operation and parallel processing capability, optical image encryption techniques are arousing great interest in many exciting fields. Since the pioneering work on optical image encryption using double random phase encoding technique, a large number of algorithms and architectures have been proposed and realized. However, with the further analysis of the securities of these schemes, most of them have been verified to be vulnerable to different types of attack algorithms. Recently, optical encryption schemes based on the polarization properties of light wave have been extensively studied, for an additional flexibility in the encryption key design is provided, which can achieve high robustness against brute force attack by a combination of multiple private keys. Nevertheless, optical encryption schemes based on the polarization properties of light wave could still be vulnerable to known- and chosen- plaintext attacks. Therefore, in this paper, a novel asymmetric polarization encryption method is implemented for dual images, and combined with interference-based optical image encryption method and a Q-plate. First, the information about the two images to be encrypted is separated into two pure phase plates by means of interference optical image encryption, which will be further encoded into two mutually orthogonally polarized light beams. Next, the Q-plate and pixelated polarizer are used for realizing different polarization distributions of the two light beam. Ultimately, the output intensity distribution is recorded by a charge coupled device (CCD) which will be treated as the final ciphertext. For actualizing the asymmetric encryption, one of the pure phase plates acts as a decryption key, which is different from the encryption key. We can control the polarization state of each pixel according to the parameter &lt;i&gt;q&lt;/i&gt;, causing the Q-plate to be electrically controllable and the optic-axis orientation of each pixel to differ from one another. It should be emphasized that the value of &lt;i&gt;q&lt;/i&gt; and the polarization angle of the pixelated polarizer play the role of two encryption keys, which improves the security of the algorithm extremely, due to their high sensitiveness. Theoretical analyses and numerical simulations verify the feasibility and effectiveness of the proposed encryption scheme.
HealthStack–A Decentralized Medical Record Storage Application

The aim of this study is to design and develop a blockchain-based web app called Health Stack to maintain accurate and complete medical records of patients, to help doctors to fetch previous medical history of the patients, to assist user to find out the disease he or she is suffering from and much more. For medical services, secure data storage is one of major concern for people. This problem can be resolved by developing an app using a blockchain technology having the features of decentralization and verifiability. Development of this app doesn’t involve any kind of dependency on third-party.
Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

Abstract Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground-truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB + LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules in the KITTI depth completion benchmark.
Unconventional photon blockade in a three-mode system with double second-order nonlinear coupling*

The unconventional photon blockade (UPB) for low-frequency mode is investigated in a three-mode system with double second-order nonlinearity. By analyzing the Hamiltonian of the system, the optimal analytic condition of UPB in low-frequency mode is obtained. The numerical results are calculated by solving the master equation in a truncated Fock space, which agrees well with the analytic conditions. Through the numerical analysis of the system, it is found that the weak driving strength is favorable for the system to realize the UPB effect, and the system is insensitive to the changes of attenuation rate and environmental temperature. The comparison with the two-mode system and another similar three-mode system shows that, under similar system parameters, the UPB effect of this double two-order nonlinear system is more obvious.
Use of Virtual Reality in the Nursing School’s Toco-Surgery Teaching Process

At this work a virtual reality environment is created, in order to simulate a medical training room. This aims to help in nursing teaching process focuses on Toco-surgery, a medical area which match Labor and Delivery. At first, a virtual scene, which replicates the real area were medical students are trained, was designed. Then interaction aimed to show students both procedure and tools they needed to know at medical area. Finally, tests were made with a group of people who already knew procedures and another that were in the learning process.
A Framework for the Assessment of Information Security Risk, the Reduction of Information Security Cost and the Sustainability of Information Security Culture

Information security data breaches are becoming larger and more frequent. Incorporating information security into the culture of the information technology (IT) staff members that support these technologies is a key function that must be considered in parallel to improved security technology. The framework proposed in this paper considers focusing on cost reducing products, services and structures while building the correct behaviour and values in IT staff members, and strengthening their ability to improve information security assessment capabilities in the organization to better support information security management.
Within-class multimodal classification

Abstract In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is within-class multimodal. One example is face recognition where a face (i.e. a class) may be presented in frontal view or side view, corresponding to different modalities. This issue has been largely ignored in the literature or at least under studied. How to address the within-class multimodality issue is still an unsolved problem. In this paper, we present an extensive study of within-class multimodality classification. This study is guided by a number of research questions, and conducted through experimentation on artificial data and real data. In addition, we establish a case for within-class multimodal classification that is characterised by the concurrent maximisation of between-class separation, between-subclass separation and within-class compactness. Extensive experimental results show that within-class multimodal classification consistently leads to significant performance gains when within-class multimodality is present in data. Furthermore, it has been found that within-class multimodal classification offers a competitive solution to face recognition under different lighting and face pose conditions. It is our opinion that the case for within-class multimodal classification is established, therefore there is a milestone to be achieved in some machine learning algorithms (e.g. Gaussian mixture model) when within-class multimodal classification, or part of it, is pursued.
Comparison of Open Source Data Mining Tools: Naive Bayes Algorithm Example

Data Mining is a set of processes that use many disciplines together in the process of analyzing large data. It is the concept of data mining that combines computer technologies, statistical analysis techniques, database technologies and many disciplines. There are many commercial and open source programs to implement Data Mining applications. In this study, open source data mining programs WEKA, Orange, Knime is described. A sample is analyzed with the classification algorithm in all of these programs. In this study, it was aimed to determine the difference between these 3 open source Data Mining programs with Naive Bayes classification algorithm by taking Bay iris, breast cancer, wine, monk, balance es data sets from UCI Machine Learning Repository database. With this study, there are suggestions for making comparisons according to the outputs from the programs.
Sentiment Analysis in Twitter: Impact of Morphological Characteristics

This paper presents a series of experiments aimed at the sentiment analysis on texts posted in Twitter. In particular, several morphological characteristics are studied for the representation of texts in order to determine those that provide the best performance when detecting the emotional charge contained in the Tweets.
Rancang Bangun Sistem Pakar dengan Metode Forward Chaining untuk Rekomendasi Pariwisata di Labuan Bajo Menggunakan iOS Platform

Nowadays many tourists like to travel. When on vacation the user is confused about where to go and whether it's good or not. When tourists want to take a vacation and the user must choose to do a tour and it turns out that it is not good in terms of good or bad attractions. So there is encouragement to help tourists to provide recommendations for good tourist places to visit. But now with so many online media to buy tickets, but the local guide is still rampant in maintaining tourism. This location with the presence of local guides who do not understand the price and when they are in Labuan Bajo the price of the user is still not right then it makes tourists confused. The existence of a problem raises the urge to make an application of an expert system that recommends tourist attractions in Labuan Bajo by using the Forward Chaining method. In making this application uses a rule based to process data on applications on iOS. And using mcgoo is used to create and process data taken from experts. This application can help tourists to find excellent places to travel. Because the purpose of the user to travel is to have fun if the user is confused to determine the existing location then this application will help the user determine the desired tourist attractions. This application also provides several tourist options such as tours on the sea, cities, or hills/mountains. The target of visitors is always less because tourists lack the desire to travel in Labuan Bajo because of the lack of recommendations on Labuan Bajo tourism. The results of research conducted by more foreign tourists who prefer to visit Labuan Bajo because local tourists prefer tourist destinations that are still crowded, such as Bali and Lombok. So that makes tourism in Labuan Bajo not yet an attraction for local tourists.