Reviewerly/exHarmony huggingface dataset:
Learning to Mutate for Differential Evolution

Adaptive parameter control and mutation operator selection are two important research avenues in differential evolution (DE). Existing works consider the two avenues independently. In this paper, we propose to unify the two modules and develop a unified parameterized mutation operator. With different settings of the parameters, different mutation operators can be retrieved. Further, the settings of the parameters closely relate to the control parameters of the DE. By determining the parameters we can achieve adaptive parameter control and mutation operator selection simultaneously. We propose to use a neural network to output the parameters and learn the network parameter by the natural evolution strategies algorithm under the consideration of modeling the evolution process as a Markov Decision Process. Experimental results on the CEC 2018 test suite show that the proposed method performs significantly better than traditional DEs with different operators and an advanced adaptive DE. We further analyze the time complexity and population diversity of the proposed method. The analysis shows that our method can achieve a balanced exploration and exploitation with a properly learned network.
Quantum Satellite Backbone Networks Design and Performance Evaluation

In the recent year, quantum computing and communications (QCC) represented two revolutionary technologies that are rapidly developing and integrating to interconnect heterogeneous quantum devices within the so-called Quantum Internet (QI). Despite several investigations and developments have been performed on terrestrial quantum communications over typical optical fiber links, there are some limitations in terms of extremely high losses that can be faced by deploying several repeaters, which in turns involves impractical costs for network management, especially in terms of forwarding procedures. Quantum Satellite Networks (QSNs) is capable to overcome the constraints of terrestrial optical networks, i.e., the remarkable attenuations over long distances which makes the intercontinental communications unaffordable. The recent technological developments in terms of quantum satellite communications motivated our investigation on an ad hoc quantum satellite backbone interconnecting quantum on Earth Servers in order to achieve an unprecedented computational capacity. Our aim is to investigate existing constellations, spanning from Low Earth Orbit (LEO) up to micro-satellites, to properly derive some guidelines for designing an efficient backbone. Specifically, the focus is on evaluating the impact of forwarding approaches on the end-to-end capacity (i.e., the entanglement generation rate) to achieve a trade-off between performance and cost.
Synthesis and Implementation of Reconfigurable Reversible Generalized Fredkin Gate

The paper represents synthesis of the reconfigurable reversible fault-tolerant gate in the basis of generalized Fredkin gates. The gate is designed for an FPGA (Field Programmable Gate Array). Additionally, a genetic algorithm was used to optimize the characteristics of the circuit such as number of gates, quantum cost, delay, number of auxiliary inputs (garbage outputs). The model of the gate was created and verified in Active-HDL environment. Consequently, comparative analysis showed that the proposed design is fault-tolerant and it has more efficient quantum cost, gate count, and garbage outputs lines in contrast to the results of other authors.
Development of a lunar-phase learning system based on holographic projection technology

The purposes of this study were to, first, design a holographic projection system to assist students in learning the concept of lunar phases. This study also aimed to evaluate university students' learning achievements and self-efficacy after using this holographic projection system. A holographic projection system with three modes was developed. The system allows users to switch the modes and operate the system through the keyboard. 30 Taiwanese university students participated in a holographic projection learning activity. The participants' knowledge acquisition and self-efficacy regarding lunar phases were evaluated through a pre-test and a post-test comparison. The results indicated that the participants showed significantly better learning achievements and higher self-efficacy after using the system. The findings of this study were expected to provide significant insights into how to adequately accommodate this novel technology in education.
Improved Collision Detection Of MD5 Using Sufficient Condition Combination

Abstract Counter-cryptanalysis uses cryptanalytic techniques to detect cryptanalytic attacks. It was introduced by Stevens with a collision detection algorithm that detects whether a message is one of a colliding message pair constructed using a collision attack. Later, Stevens and Shumow improved the collision detection against SHA-1 by using unavoidable conditions. However, there are no results improving collision detection against MD5 due to its weak diffusion properties. In this paper, an improved collision detection algorithm against MD5 is proposed by using the 14-bit sufficient condition combinations. This leads to the dividing the 223 classes into four sets. Each element, belonging to the first two sets, holds the same sufficient condition combination. Our new algorithm can classify 126 classes efficiently. The runtime is 28.6% of the previous collision detection method.
Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning

In this paper, based on the heterogeneous information network, we propose a cross-domain recommendation model by integrating adversarial learning (Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning, CDR-HA). Using information from other domains to alleviate the target data sparseness of the domain can improve the accuracy and performance of recommendations. In this paper, we focus on the cross-domain recommendation. Firstly, due to the differences in the feature distributions of the same users in different domains, we use the HIN2Vec algorithm to extract the user's feature distribution in the network based on the heterogeneous information network. Secondly, we propose a multi-domain feature filtering method, which maximizes the difference in the distribution of different domains based on Wasserstein Distance to preserve the differences in the feature distributions of users in different domains. Then, separately establish a classifier for each domain, we consider the results of the two classifiers comprehensively, and take the best as the final result. We apply the proposed model to two datasets and experimental results demonstrate that our approach outperforms state-of-the-art recommender baselines.
Determine the clustering of cities in Indonesia for disaster management using K-Means by excel and RapidMiner

Abstract The impact of disasters can disrupt people’s lives, both natural and non-natural, resulting in human casualties, environmental damage, property loss, and psychological impact. Besides that, disasters that occur can also cause damage to health facilities, worship, education, and damage to homes, both severely, moderately, and lightly. The impact of disasters is so large, so a logistics warehouse is needed to handle the disaster. One of the countries prone to disasters, Indonesia which has the fourth largest population in the world with 34 provinces and 502 regions or cities. The purpose of this research is to determine the clustering of areas in Indonesia with a very high-risk, high-risk, moderate risk, low risk, and very low risk of disaster based on disaster data in Indonesian National Agency for Disaster Managementin 2010-2019 using K-Means calculations by Excel and the RapidMiner application. The results of both clustering methods are 6 cities that have a very high-risk index, 79 cities that have a high-risk index, 29 cities that have a medium risk index, 19 cities that have a low-risk index, and 369 cities have a very low-risk index. Thisresult can be considered for the construction of logistics warehouses for disaster management and K-Means method also can be used to know the clustering risk.
Virtual Reality Live Theatre on No Budget

Jettison, along with the entire OnBoardXR festival, is produced and performed live entirely in virtual reality using the open-source Mozilla Hubs platform. These shows took place in a virtual representation of a theater, leveraging the shared conventions of live theatrical performance to simplify the challenge of onboarding the audience: knowing how to behave in a real world theater translates intuitively to behavior in a virtual theater. At the same time, our production process mimicked that of real world theater, with very close parallels in the application of traditional theatrical skills and practices – with the main difference being that our cast and crew were all fully remote. Finally, the Hubs platform allows audiences to view the show on a range of hardware from VR HMDs to tablets and phones. Put together, our process represents a model by which independent theater companies can create productions using familiar techniques and skills, and present it for distributed audiences who don't have dedicated hardware. Ours was an approach of utilizing the latest virtualization technology to allow access to the widest range of audience possible, all from a bare-bones production budget. Unlike other approaches to virtual or online theater, the work of Jettison and its OnBoardXR is an attempt to, as closely as possible, replicate the experience of attending a show in a real world theater – an experience unavailable over the past year. In this talk we will describe our process and learnings.
Modal Logic S5 Satisfiability in Answer Set Programming

Abstract Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducing copies of propositional atoms for each set of interpretations (possible worlds). This approach is simple, but often results into large formulas that are too difficult to process, and therefore more parsimonious constructions are required. In this work, we propose to use Answer Set Programming for implementing such constructions, and in particular for identifying the propositional atoms that are relevant in every world by means of a reachability relation. The proposed encodings are designed to take advantage of other properties such as entailment relations of subformulas rooted by modal operators. An empirical assessment of the proposed encodings shows that the reachability relation is very effective and leads to comparable performance to a state-of-the-art S5 solver based on SAT, while entailment relations are possibly too expensive to reason about and may result in overhead.
Realizing Neural Decoder at the Edge With Ensembled BNN

We propose extreme compression techniques like binarization, ternarization for Turbo code based Neural Decoders such as TurboAE. These methods reduce memory and computation by a factor of 64 and perform better than the quantized (with 1-bit or 2-bits) Neural Decoders. However, because of the limited representation capability of the Binary and Ternary networks, the performance is not as good as the real-valued decoder. To fill this gap, we further propose to ensemble 4 such weak performers to deploy in the edge to achieve a performance similar to the real-valued network. These ensemble decoders give a saving of 16 and 64 times in memory and computation respectively and help achieve performance the same as real-valued TurboAE.
A text classification framework for depression tendency detection over online social platform

As social networks become more and more prevalent, people like to share their feelings on the Internet, which provides a new research idea for the detection of depression patients. In this paper, we propose to detect the depression tendency of microblog texts based on LSTM model. First, we embed the preprocessed data set into word vectors, then put them into the model, LSTM taking the word vector layer as the first hidden layer, building several hidden layers, the output layer outputting the labels of prediction data and the detection of depression tendency on the batch micro blog data. Through many experiments, we constantly adjust the super parameters of the model, and in the end, we achieved an accuracy of 93.68%, which is superior to the general machine learning model, and makes the research on depression in online social platform have practical value.
The Prospects for Using Distributed Ledger Technology in the Russian Insurance Sector

Insurance is an essential financial instrument of a risk management system that helps to reduce uncertainty about future financial performance and mitigate consequences of various undesired events. However, the low level of financial literacy in general, and the insurance culture in particular, severely limits insurance market development in Russia. The development of new insurance products that would be understandable and transparent to a consumer is the most relevant issue in this area. The development of the insurance market in the short term is oriented towards the application of new technologies in companies seeking to establish themselves as leaders in the market. Distributed ledger technology (the so-called blockchain technology) is among them. Blockchain technology has characteristics such as stability and equity of participants, the ability to draw up “smart” contracts, which enables its use in the Russian insurance sector. The paper analyzes the possibility and the prospects for the use of the blockchain technology in the insurance sector, and presents available cases of development of insurance products based on technology referred to above. Such products may have a decisive superiority in the market since they reduce time and financial expenditures for the consumer. The insurance sector also has a number of advantages of using blockchain in the form of reduced organizational costs and simplified internal and external interaction with contracting parties. The paper contains an authors’ study of the risks that accompany the use of the blockchain technology in the insurance sector. The risks identified on the basis of expert opinion have been identified and ranked, their forms of appearance and vulnerable actors have been presented. In accordance with research findings, the authors conclude that there is a need to develop methods to manage and mitigate new risks which accompany the introduction of digital technologies.
Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks

Abstract Information fusion, i.e., the combination of expert systems, has a huge potential to improve the accuracy of pattern recognition systems. During the last decades, various application fields started to use different fusion concepts extensively. The forensic sciences are still hesitant if it comes to blindly applying information fusion. Here, a potentially negative impact on the classification accuracy, if wrongly used or parameterized, as well as the increased complexity (and the inherently higher costs for plausibility validation) of fusion is in conflict with the fundamental requirements for forensics. The goals of this paper are to explain the reasons for this reluctance to accept such a potentially very beneficial technique and to illustrate the practical issues arising when applying fusion. For those practical discussions the exemplary application scenario of morphing attack detection (MAD) is selected with the goal to facilitate the understanding between the media forensics community and forensic practitioners. As general contributions, it is illustrated why the naive assumption that fusion would make the detection more reliable can fail in practice, i.e., why fusion behaves in a field application sometimes differently than in the lab. As a result, the constraints and limitations of the application of fusion are discussed and its impact to (media) forensics is reflected upon. As technical contributions, the current state of the art of MAD is expanded by: The introduction of the likelihood-based fusion and an fusion ensemble composition experiment to extend the set of methods (majority voting, sum-rule, and Dempster-Shafer Theory of evidence) used previously The direct comparison of the two evaluation scenarios “MAD in document issuing” and “MAD in identity verification” using a realistic and some less restrictive evaluation setups A thorough analysis and discussion of the detection performance issues and the reasons why fusion in a majority of the test cases discussed here leads to worse classification accuracy than the best individual classifier
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network

Building an interpretable AI diagnosis system for breast cancer is an important embodiment of AI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning are easy to explain, but the accuracy is very low. Deep neural network greatly improves the accuracy of diagnosis, but the black box model does not provide transparency and interpretation. In this work, we propose a semantic embedding self-explanatory Breast Diagnostic Capsules Network (BDCN). This model is the first to combine the capsule network with semantic embedding for the AI diagnosis of breast tumors, using capsules to simulate semantics. We pre-trained the extraction word vector by embedding the semantic tree into the BERT and used the capsule network to improve the semantic representation of multiple heads of attention to construct the extraction feature, the capsule network was extended from the computer vision classification task to the text classification task. Simultaneously, both the back propagation principle and dynamic routing algorithm are used to realize the local interpretability of the diagnostic model. The experimental results show that this breast diagnosis model improves the model performance and has good interpretability, which is more suitable for clinical situations.
Impact Analysis of Hypervisors on the Performance of Virtualized Resources

Virtualization offers several benefits including reduction in hardware expenses, accessories, power consumption, security and resistance on the failure. Additionally, it improves the availability and simplicity in system management. It abstracts hardware and software resources and generates a virtual copy of the same. This paper presents the performance analysis of various hypervisors like VMware’s vSphere and Workstation, KVM, XenServer and Oracle’s VirtualBox that are based on different classification of virtualization technology like Type-1 and Type-2. VM performance is evaluated for different hypervisors with reference to virtualized CPU performance, impact on virtualized memory performance, I/O performance of virtual disk (secondary storage) as well as virtual network download and upload bandwidth. Performance has been observed after deployment of each hypervisor on multiple hardware. Different features of every hardware are also considered. The performance testing and analysis are done with real-world workload traces. Mean of the outcome is utilized for performance comparison.
Software Defined Ambit of Data Integrity for the Internet of Things

On the Internet of Things (IoT), devices do not have the required computational power and storage capacity; and as a result, a variety of IoT devices may be required to outsource sensed or generated data to multiple heterogeneous cloud servers. We posit that it is the Data Owner's responsibility to verify whether the stored data remain unchanged when the owner or some trusted third party further requires accessing this data. However, the "level" of this verification may be different under different contexts based on the application need. We propose four methods of integrity verification (which we call the ambit of data integrity - ADI) that considers the "toll" in terms of time, storage and communication by enlisting typically disparate integrity approaches under a single orbit. We adapt the notion of contextual integrity, previously used for assessing privacy grants, to extract important parameters required to decide on a suitable data integrity verification process. We propose a secure architecture using an integration of software defined perimeter (SDP) and software defined network (SDN) to perform authentication and gather each partition's context information for an SDN application to decide the proper integrity verification method that addresses the context requirements. To the best of our knowledge, this is the first time that the scope of integrity (or the data context) is used to determine the required layer of integrity verification in IoT.
Data infrastructure and information design of e-learning in primary school

Abstract The Industrial Revolution 4.0 presents technologies that make it easier for humans to help their daily activities, one of them is exchange of information through the internet. Information accessed using internet makes easy because can be accessed anytime, anywhere, and there are no limited on the device used. The software that is often used to access this information is a browser which is a website-based tool. It can also be used as a learning media by academics and students are called electronic learning or e-learning. Academics and students can easily get the information they need. Information on e-learning creates new sources of wealth. However, large data have problems is information overload that results in information that is related or not related to needs. Therefore, it is necessary to call information according to either the provider or recipient of information in an e-learning container stored in a database based on the information content using the Data Manipulation Language (DML) in the Structure Query Language (SQL). This research boundary to the database design for the development of e-learning for elementary school environments, starting from analysis, designing relationships between data using Entity Relationship Diagrams (ERD), Context Diagrams and Data Flow Diagram (DFD).
A Holistic System Software Integration of Disaggregated Memory for Next-Generation Cloud Infrastructures

Modern cloud computing workloads are becoming day by day more demanding, in terms of computational resources, as they feature multiple complex components, utilize heterogeneous hardware, and require tremendous amounts of memory. Such attributes of the emerging workloads disrupt the traditional design of cloud infrastructures, which are bound to decisions at the design time of the infrastructure, and mandate the dynamic composability of the next-generation cloud infrastructures.Scaling beyond the physical boundaries of the server trays, and minimizing over-provisioning of the nodes by disaggregating computational resources, such as CPUs, memory, etc., is a timely research problem. However, the prior line of research focuses on either NVM technologies or other disk-related approaches that try to relax the resource pressure induced by over-provisioning.In this work, we design, implement, and evaluate all necessary changes in the system software stack to support the dynamic allocation of disaggregated memory, depending on the needs of the workloads at hand. As a result, we can transparently increase the available memory of a system and achieve on average 57% better performance, compared to Swap, the de-facto mechanism used today for over-provisioning. Furthermore, we present a memory balancing policy that autonomously migrates memory pages across local and disaggregated memory. Our experimental results show that, even with a limited amount of local memory, memory balancing improves the performance by 41% on average, compared with using only disaggregated memory.