Reviewerly/exHarmony huggingface dataset:
Quantum random number generators with entanglement for public randomness testing

Abstract We discuss a simple idealistic quantum entanglement based protocol for quantum random number generation allowing a trusted third party to publicly perform arbitrarily complex tests of randomness without any violation of the secrecy of the generated bit sequences. The protocol diminishes also an average time of the randomness testing (thus enabling arbitrary shortening of this time with increasing number of entangled qubits).
Abnormal Behavior Detection for Elderly People Living Alone Leveraging IoT Sensors

E-health home based solutions reduce healthcare costs and allow aging population to continue their daily life independently. Our objective, is to combine simple IoT sensors and machine learning techniques, in order to provide a home based solution that is able to detect behavioral changes of elderly people who live alone. For this purpose, we introduce a non-intrusive, spatio-temporal abnormal behavior detection approach. In this approach, motion and door sensor signals are elaborated to produce contextual metrics, which are filtered from any deviant observations, after performing a silhouette analysis on five outlier detection algorithms. Next, the combination of a classification and a regression based approach is proposed for detecting abnormalities in the metrics, both in the contexts of space and time. IoT sensor data from ten elderly people houses have been collected and seven different machine learning algorithms have been analyzed in order to evaluate the performance of the individual as well as the combined approach.
Online Path Generation and Navigation for Swarms of UAVs

With the growing popularity of unmanned aerial vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation cannot be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the UAVs and of the successfully detected, static, and moving obstacles to predict and avoid the following: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-to-moving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and complex event processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid predicted collisions in order to ensure safety of the entire swarm. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments and compare our results with the results of four existing approaches. The results show that the proposed system successfully predicts and avoids all three kinds of collisions in an online manner. Moreover, it generates safe and efficient UAV routes, efficiently scales to large-sized problem instances, and is suitable for cluttered flying zones and for scenarios involving high risks of UAV collisions.
A machine learning approach to software model refactoring

Good software quality is a consequence of good design. Model refactoring counteracts erosion of the software design at an early stage in the software development project complying with the model-driven engineering paradigm. Traditional model refactoring approaches work at the surface level by using threshold values of model metrics as indicators of suboptimal design and carry out localized corrections. Through this paper, it is proposed that identifying design flaws at a higher level of granularity will save from the vicious cycle of small refactoring operations and their cascaded side-effects. The notion of functional decomposition, as an anomalous design tendency and a dominant cause of design, smells in object-oriented software, is introduced. It is suggested that refactoring operations targeted at signs of functional decomposition instead of atomic smells achieve substantial improvement in design within a concise quality assurance procedure. The idea is realized using a deep neural network that learns to recognize the presence of functional decomposition in UML models of object-oriented software. The presented approach uses data science methods to gain insight into multidimensional software design features and uses the experience gained to generalize subtle relationships among architectural components.
Task Scheduling Based on Hybrid Algorithm for Cloud Computing

Transmission of data via internet is one of the great purposes of cloud computing is popular for transferring and storing data via internet. The user can make use of cloud services through broker. But since the job initialization is dynamic the concepts like scheduling of jobs and task management is of crucial importance. The main reason is that the users can request the use of same resources at a time. Thus, managing the resources over the requests is important so that all users can access the resources. Although generic scheduling techniques like FCFS or priority scheduling are available in many ways they fall short in they’re purposeless, due to their own disadvantages. The algorithms have higher waiting time and high turnaround time, which is not that efficient when the number of jobs in cloud environments is very large. Here a hybrid of shortest job first and priority based Scheduling is used and implemented in a cloud environment and analyzed. The conclusions are noted down and they are promising enough. The average waiting time and turnaround time are greatly reduced and highly increased the efficiency of cloud management of resources. Cloud computing includes an online exchange of data, resources, and information, where the users are in constant need of resources. This causes congestion of network, starvation or at worst case a deadlock. To counter these problems occurring a new methodology of hybrid algorithm has been proposed by implementing two techniques namely, shortest job first and priority-based scheduling.
Towards cloud-native simulations – lessons learned from the front-line of cloud computing

Cloud computing can be a game-changer for computationally intensive tasks like simulations. The computational power of Amazon, Google, or Microsoft is even available to a single researcher. However, the pay-as-you-go cost model of cloud computing influences how cloud-native systems are being built. We transfer these insights to the simulation domain. The major contributions of this paper are twofold: (A) we propose a cloud-native simulation stack and (B) derive expectable software engineering trends for cloud-native simulation services. Our insights are based on systematic mapping studies on cloud-native applications, a review of cloud standards, action research activities with cloud engineering practitioners, and corresponding software prototyping activities. Two major trends have dominated cloud computing over the last 10 years. The size of deployment units has been minimized and corresponding architectural styles prefer more fine-grained service decompositions of independently deployable and horizontally scalable services. We forecast similar trends for cloud-native simulation architectures. These similar trends should make cloud-native simulation services more microservice-like, which are composable but just “simulate one thing well.” However, merely transferring existing simulation models to the cloud can result in significantly higher costs. One critical insight of our (and other) research is that cloud-native systems should follow cloud-native architecture principles to leverage the most out of the pay-as-you-go cost model.
An Improved Grasshopper Optimization Algorithm for Solving Numerical Optimization Problems

This paper proposes a modified and improved grasshopper optimization algorithm (IGOA) for solving complex and challenging optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed bio-inspired swarm optimization algorithm which is based on the swarm nature of grasshoppers. It mainly relies upon the social interaction forces to find global optimum values of an optimization problem. However, it has a strong tendency to move towards the current optimal and hence may get trapped in local optimal points. This behaviour is strongly related to a factor, known as c factor, in GOA which varies linearly throughout the iterations from a maximum to minimum value. This work proposes two modifications in conventional GOA to avoid premature convergence of GOA. These modifications include a novel c factor variation scheme and inclusion of random walks between grasshoppers to attain global optimum points. The performance of IGOA is tested on 19 benchmark test functions for 20 independent trial runs. For all the cases, it was observed that performance of IGOA was superior to GOA and it outperformed the GOA in terms of accuracy, speed, and repeatability for all the considered test functions.
Olfactory-Based Augmented Reality Support for Industrial Maintenance

Augmented reality (AR) applications have opened innovative ways for performance improvement in the IoT industry.It can enhance user perception of the real-world by providing valuable information about an industrial environment and provide visual virtual information onto a head-mounted device (HMD).Such information is important for maintainers to quickly detect abnormalities, reduce nugatory routines and facilitate preventive maintenance activities.Since odors are made up of volatile compounds at low concentration, they can be used for olfactory-based identification.The article outlines the development of an olfactory-based AR system to help with the identification of maintenance issues using smell.The prototype comprises of three components: an electronic nose, a database and an AR application integrated with Microsoft HoloLens.After diagnosing an odor, the data is sent wirelessly through a local network to the HMD worn by the user.To validate the technology, four odors have been used, including engine oil, sun lotion, medical alcohol and perfume, to record behaviors and demonstrate the repeatability of the process.The presented technology incorporates sampling methods, cleaning processes and statistical analysis that can be further scrutinized to allow better smell augmentation for diagnosis.
A modified Gilbert-Varshamov bound for self-dual quasi-twisted codes of index four

In this work, we study a family of self-dual four circulant codes and a family of self-dual four negacirculant codes, and the exact counting formula is derived for these families of codes. In addition, we prove that asymptotically good self-dual four circulant codes and negacirculant codes over finite fields exist, and both of them satisfy a modified Gilbert-Varshamov bound on the relative minimum distance with asymptotic rate 12.
Head-Mounted, Display-Based Immersive Virtual Reality Marine-Engine Training System: A Fully Immersive and Interactive Virtual Reality Environment

Simulation technology has been used to train marine engineers for decades. However, traditional marine-engine training systems (METSs) possess low immersion levels that seriously limit their effect. To overcome this problem, we developed a head-mounted, display-based immersive virtual reality (IVR) METS based on HTC Vive Pro hardware. Like the traditional METS, it has training and automatic evaluation functions. Moreover, it can reproduce, with high degrees of immersion, all of the details of a real ship engine room and vividly simulate emergency scenes, such as a fire, so that trainers can perform various operations as though they were in the real environment. Thirty students majoring in marine engineering at Dalian Maritime University, China, were selected to test the system. The results showed that the headmounted, display-based immersive VR METS had many advantages compared to the desktop VR METS.
Exploring Covert Channels in Bitcoin Transactions

We explore the existence of covert communication channels in the Bitcoin system. Our attention is paid to the channels whose usage is hard or impossible to detect. Seven covert channels are considered. Some of them have already been known: there are two low-rate channels obtained by manipulation with parameters of cryptographic primitives used to construct a transaction, a channel created by the use of unspendable outputs, and digital signature algorithm (DSA) channel that hides a message in the cofactor used in the signature creation. The new channels that, to the best of our knowledge, have not been considered in the literature, are based on permutations of transaction inputs and outputs, and on distribution of payments. We describe the construction of all channels and characterize their capacity to transmit hidden data. Almost all channels can be used simultaneously and independently and provide wide opportunities for undetectable covert communications over the Bitcoin blockchain.
Analysis on the Higher Education Sustainability in China Based on the Comparison between Universities in China and America

To find and solve the problems existing in the development of higher education in China, the input–output, scale of higher education, students’ tuition and teachers’ income of Chinese and American universities are compared. The results show that the investment in top universities in China is similar to that in the United States, but the average student budget is much less, and the output is not comparable to that of American universities. The scale of higher education is much larger than that of the United States, and the growth rate is far more than demand. College tuition should be increased, with the absolute tuition only 5.93% of income, and relative tuition is 20.21% of that in the United States. College teachers are underpaid, earning only approximately 20% of what their peers earn in the United States. Therefore, for higher education sustainability, the paper puts forward the development direction of higher education in China, which is to control the expansion scale of colleges and universities, and to increase students’ tuition and teachers’ salary.
Generic Source Parameter Determination and Ground-Motion Prediction for Earthquake Early Warning

ABSTRACT Currently available earthquake early warning systems employ region-specific empirical relations for magnitude determination and ground-motion prediction. Consequently, the setting up of such systems requires lengthy calibration and parameter tuning. This situation is most problematic in low seismicity and/or poorly instrumented regions, where the data available for inferring those empirical relations are scarce. To address this issue, a generic approach for real-time magnitude, stress drop, and ground-motion prediction is introduced that is based on the omega-squared model. This approach leads to the following approximate expressions for seismic moment: M0∝RT0.5Drms1.5/Vrms0.5, and stress drop: Δτ∝RT0.5Arms3/Vrms2, in which R is the hypocentral distance; T is the data interval; and Drms, Vrms, and Arms are the displacement, velocity, and acceleration root mean squares, respectively, which may be calculated in the time domain. The potential of these relations for early warning applications is demonstrated using a large composite data set that includes the two 2019 Ridgecrest earthquakes. A quality parameter is introduced that identifies inconsistent earthquake magnitude and stress-drop estimates. Once initial estimates of the seismic moment and stress drop become available, the peak ground velocity and acceleration may be estimated in real time using the generic ground-motion prediction equation of Lior and Ziv (2018). The use of stress drop for ground-motion prediction is shown to be critical for strong ground accelerations. The main advantages of the generic approach with respect to the empirical approach are that it is readily implementable in any seismic region, allows for the easy update of magnitude, stress drop, and shaking intensity with time, and uses source parameter determination and peak ground motion predictions that are subject to the same model assumptions, thus constituting a self-consistent early warning method.
Semantic Domain-specific Languages

A rationale for the introduction of semantic domain-specific languages (sDSL) is discussed from the point of view of problems faced by IT industry. Goals and technological approaches for the development of sDSL are described. As well as mathematical model and foundation for such a concept is presented.
Benchmarking Deep Trackers on Aerial Videos

In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.
Is Any Room Really OK? The Effect of Room Size and Furniture on Presence, Narrative Engagement, and Usability During a Space-Adaptive Augmented Reality Game

One of the main challenges in creating narrative-driven Augmented Reality (AR) content for Head Mounted Displays (HMDs) is to make them equally accessible and enjoyable in different types of indoor environments. However, little has been studied in regards to whether such content can indeed provide similar, if not the same, levels of experience across different spaces. To gain more understanding towards this issue, we examine the effect of room size and furniture on the player experience of Fragments, a space-adaptive, indoor AR crime-solving game created for the Microsoft HoloLens. The study compares factors of player experience in four types of spatial conditions: (1) Large Room - Fully Furnished; (2) Large Room - Scarcely Furnished; (3) Small Room - Fully Furnished; and (4) Small Room - Scarcely Furnished. Our results show that while large spaces facilitate a higher sense of presence and narrative engagement, fully-furnished rooms raise perceived workload. Based on our findings, we propose design suggestions that can support narrative-driven, space-adaptive indoor HMD-based AR content in delivering optimal experiences for various types of rooms.
Hierarchical Deep Embedding for Aurora Image Retrieval

Retrieving informative images from the large-scale aurora data is of great significance in the field of space physics. In this article, we propose a hierarchical deep embedding (HDE) model to assist scientists for their aurora image retrieval. Other than conventional bag-of-words (BoW) models employing local cues individually, HDE performs visual matching in a hierarchical way, that is, only keypoints which are similar on local, regional, and global simultaneously can be treated as a true match. The added contextual evidences can effectively alleviate the occurrence of false matches and improve the precision of visual matching. Specifically, to complement the local SIFT feature, the convolutional neural network (CNN) is refined with a polar region pooling (PRP) layer to extract features from regional patches and global image, forming a group of hierarchical deep features with strong discriminative power. Also, an improved polar meshing (IPM) scheme is presented to determine the positions of keypoints, which is more suitable for images captured by circular fisheye lens and capable of reflecting the physical information in aurora images. Extensive experiments are conducted on the big aurora data, which indicate that the proposed HDE model greatly promotes the retrieval accuracy with acceptable memory cost and efficiency. In addition, the effectiveness of the IPM scheme and the superiority of the hierarchical deep feature integration are separately demonstrated.
Deepbot: A Deep Neural Network based approach for Detecting Twitter Bots

Abstract Social networks have played a very critical role in very aspect of our daily life. However, a wide variety of bots have been found which are designed for some malicious purposes such as spreading spam mes- sages and faking news. Although various techniques have been proposed, this task is still challenging if we want to judge whether the tweets are posted by a bot or not merely based on the textual information. For this challenge, the Deepbot is designed which adopts the Bi-LSTM model to analyze tweets and a Web interface is provided for public access which is developed using Web service. From our empirical studies, this system can achieve better classification accuracy.
Reversible Image Watermarking Algorithm Based on Quadratic Difference Expansion

To improve the visual quality and embedding rate of existing reversible image watermarking algorithms, a reversible image watermarking algorithm based on quadratic difference expansion is proposed. First, the pixel points with grayscale values 0 and 255 in the original image are removed, and then, the half-scrambled watermark information is embedded into the original image using linear difference expansion. Finally, the remaining half of the watermark information is embedded into the previously generated watermarked image by the quadratic difference expansion, meanwhile the removed pixel points with grayscale values 0 and 255 in the image are merged, and the final watermarked image is generated accordingly. The experimental results show that the algorithm has both a high embedding rate and a high visual quality, which can completely recover the original image. Compared with other difference expansion watermarking algorithms, it has certain advantages without having to consider the smoothness of the embedded image region.
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We construct a class of ZprZps-additive cyclic codes generated by pairs of polynomials, where p is a prime number. Based on probabilistic arguments, we determine the asymptotic rates and relative distances of this class of codes: the asymptotic Gilbert-Varshamov bound at 1+ps−r2δ is greater than 12 and the relative distance of the code is convergent to δ, while the rate is convergent to 11+ps−r for 0<δ<11+ps−r and 1≤r<s. As a consequence, we prove that there exist numerous asymptotically good ZprZps-additive cyclic codes.