Reviewerly/exHarmony huggingface dataset:
Weakly invasive metrology: quantum advantage and physical implementations

We consider the estimation of a Hamiltonian parameter of a set of highly photosensitive samples, which are damaged after a few photons <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>N</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">b</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:mrow></mml:msub></mml:math> are absorbed, for a total time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math>. The samples are modelled as a two mode photonic system, where photons simultaneously acquire information on the unknown parameter and are absorbed at a fixed rate. We show that arbitrarily intense coherent states can obtain information at a rate that scales at most linearly with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>N</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">b</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:mrow></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math>, whereas quantum states with finite intensity can overcome this bound. We characterise the quantum advantage as a function of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>N</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">b</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:mrow></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math>, as well as its robustness to imperfections (non-ideal detectors, finite preparation and measurement rates for quantum photonic states). We discuss an implementation in cavity QED, where Fock states are both prepared and measured by coupling atomic ensembles to the cavities. We show that superradiance, arising due to a collective coupling between the cavities and the atoms, can be exploited for improving the speed and efficiency of the measurement.
Hardware Software Co-Simulation of an AES-128 based Data Encryption in Image Processing Systems for the Internet of Things Environment

Security of the data is a major challenge in the deployment and management of the Internet of Things (IoT) systems in the heterogeneous environment, where data gets transferred very widely and rapidly in multiple modes across the globe. Concomitant with the advancement of the technology, the number of devices and systems which are involved in transferring the image data has increased in the heterogeneous IoT environment. At present, image processing devices have become an integral part of the IoT edge devices which can collect the images and automatically share with the network. Because of the multifaceted usage of images in multimedia applications in the IoT environment, it is essential to protect the data from unauthorized access. The Advanced Encryption Standard (AES) is a well-established cryptography algorithm used in numerous applications to protect digital data. In this paper, we present a Hardware-Software Co-Simulation with Xilinx System Generator of an AES-128 bit encryption and decryption for IoT Edge devices. Hardware Co-Simulation makes it possible to integrate a design running in an FPGA directly into Simulink simulation. AES-128 bit algorithm has been implemented on the SPARTAN-6 (XC6SLX45-CSG324) FPGA board with XILINXISE software. Authors can get the successful results for image encryption and decryption both while implementing on grey and colored images. For Hardware Co-Simulation of AES-128 bit algorithm with Xilinx system generator, a point-to-point Ethernet approach is used. This implementation is done using Xilinx ISE14.2 and MATLAB 2011a for both grey image and colored image of size 448*298.
AMSASeg: An Attention-Based Multi-Scale Atrous Convolutional Neural Network for Real-Time Object Segmentation From 3D Point Cloud

Extracting meaningful information on objects varying scale and shape is a challenging task while obtaining distinctive features on small to large size objects to enhance overall object segmentation accuracy from 3D point cloud. To handle this challenge, we propose an attention-based multi-scale atrous convolutional neural network (AMSASeg) for object segmentation from 3D point cloud. Specifically, a backbone network consists of three modules: distinctive atrous spatial pyramid pooling (DASPP), FireModule, and FireDeconv. The DASPP utilizes average pooling operations and atrous convolutions with different sizes to aggregate distinctive information on objects at multiple scales. The FireModule and FireDeconv are responsible to efficiently extract general features. Meanwhile, a spatial attention module (SAM) and channel attention module (CAM) aggregate spatial and semantic information on smaller objects from low-level and high-level layers, respectively. Our network enables to encode multi-scale information and extract distinct feature on overall objects to enhance segmentation performance. We evaluate our method on KITTI dataset. Experimental results demonstrate that the proposed network is effective to improve segmentation performance on small to large objects at real-time speed.
Design and Evaluation of a Free-Hand VR-based Authoring Environment for Automated Vehicle Testing

Virtual Reality is increasingly used for safe evaluation and validation of autonomous vehicles by automotive engineers. However, the design and creation of virtual testing environments is a cumbersome process. Engineers are bound to utilize desktop-based authoring tools, and a high level of expertise is necessary. By performing scene authoring entirely inside VR, faster design iterations become possible. To this end, we propose a VR authoring environment that enables engineers to design road networks and traffic scenarios for automated vehicle testing based on free-hand interaction. We present a 3D interaction technique for the efficient placement and selection of virtual objects that is employed on a 2D panel. We conducted a comparative user study in which our interaction technique outperformed existing approaches regarding precision and task completion time. Furthermore, we demonstrate the effectiveness of the system by a qualitative user study with domain experts.
Consensus mechanism design based on structured directed acyclic graphs

Capacity limit is a bottleneck for broader applications of blockchain systems. Scaling up capacity while preserving security and decentralization are major challenges in blockchain infrastructure design. In this paper, we design a proof of work-based mechanism by endowing directed acyclic graphs (DAG) with a novel structure so that peers can reach consensus at a large scale. At a high level, we break large blocks into smaller ones to improve utilization of broadcast network and embed a Nakamoto chain inside the DAG in a decent way to ensure security. We further exploit the DAG structure and design a mempool transaction assignment method. The method reduces the probability that a transaction is processed by multiple miners and hence improves processing efficiency. Without sacrificing security and decentralization, our design significant scales up capacity and also addresses important issues such as high latency and mining power concentration in existing blockchain systems.
Integrating virtual reality technology into architecture education: the case of architectural history courses

Purpose Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern Architecture course. The participants who were students of Modern Architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction; however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining other ways of teaching history of architecture courses and its effect on the students’ knowledge gain and performance.
Towards an automatic model-based Scrum Methodology

Software systems evolve continuously and must be developed quickly to fit user requirements and new advances in technology. This has led the software engineering to propose several methods and approaches to overcome the development and maintenance of these software systems. In this regard, Agile Methodologies and Model-Driven Engineering (MDE) are two main approaches that have emerged in recent years and suggest a solution to some of the issues associated with Software systems developments. MDE focuses on software reuse through models and on generative approaches based on separation of concerns whereas Agile Methods promote the use of simpler models and best practices for programming to achieve quick feedback from clients within a development process. However, these two approaches have evolved separately and there are only a few works related to their combination. This paper presents a customized V development life cycle based on models which combines the two MDE variants: The MDA approach in the V left branch with the MBT approach to generate tests of the V right branch. In addition, we integrate this customized V life cycle in the agile Scrum methodology to facilitate the management of each Scrum sprint.
LogoNet: Layer-Aggregated Attention CenterNet for Logo Detection

Logo detection is a key function for many applications. However, locating logos in complex scenarios is challenging and difficult due to the large variation of types and appearance of logos. In this paper, we propose a new deep learning logo detection algorithm called, LogoNet, the architecture of which includes hourglass like feature extraction backbone, a spatial attention module and a detection head same as CenterNet. The proposed LogoNet increases focus on logo objects more precisely within an image comparing with conventional algorithms. Our experiment results show approximately 1.5% improvement in performance compared to state-of-the-art anchor-free detection network on FlickrLogos-32 dataset.
Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation

The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.
Orchestrated sandboxed containers, unikernels, and virtual machines for isolation‐enhanced multitenant workloads and serverless computing in cloud

Abstract Containers emerge as the prevalent virtualization technology in cloud computing. Containers are more light‐weight and agile compared to traditional virtual machines (VMs), since they provide virtualization at the operating system level. There are specific factors driving the container adoption in cloud, however, the main disadvantage of container‐based virtualization technologies is poor isolation. To address isolation and security‐related issues, new container runtimes appeared. In this study we present and evaluate the most common security‐oriented runtimes, Kata, gVisor and Nabla, running with Docker, Containerd, and CRI‐O. We deploy containers for all the aforementioned container solutions, as well as the default Kubernetes runtime runc, on a Kubernetes cluster and additionally on a Docker node. Moreover, in a similar way to containers, we deploy and evaluate unikernels and security‐oriented lightweight Linux‐based VMs running on Kubernetes cluster. To evaluate container runtimes, we take under consideration Firecracker microVM too. To automate the deployment of high isolated containers and VMs on Kubernetes clusters, we have developed our own tool. Finally, we recognize the increasing interest on Function‐as‐a‐Service and other serverless architectures. In the same direction with these emerging cloud computing services, we have extended the Kubeless serverless framework to support security‐oriented container runtimes.
Numerical and experimental study of air containment systems in legacy data centers focusing on thermal performance and air leakage

With the increasing awareness of data center cooling and its impact on energy costs, hot-aisle containment (HAC) and cold-aisle containment (CAC) are considered vital techniques for IT environment control. Common data center cooling problems, which include air bypass and recirculation, are crucial to airtightness. The air leakage in an air containment is typically 10% or less. However, in practice, containment systems cannot guarantee complete airtightness, and do not function in the intended manner. In this study, both numerical and experimental studies were conducted to investigate the thermal performance of two systems. We found that there are operational differences that are significant factors influencing the amount of air leakage within the air containment systems. With an air leakage rate of 15%, the numerical simulation results show that selecting HAC, over CAC, can improve the thermal performance by 24.9%, corresponding to a 5.2% reduction in the return temperature index. The field measurements also show that HAC is more effective in cooling IT equipment. The supply air temperature of HAC can be raised by 2.0 °C; however, the IT operation environment will be similar to that of CAC. With an operational condition of high air leakage rate, HAC exhibits better thermal performance than CAC.
Neural Networks for Fashion Image Classification and Visual Search

We discuss two potentially challenging problems faced by the ecommerce industry. One relates to the problem faced by sellers while uploading pictures of products on the platform for sale and the consequent manual tagging involved. It gives rise to misclassifications leading to its absence from search results. The other problem concerns with the potential bottleneck in placing orders when a customer may not know the right keywords but has a visual impression of an image. An image based search algorithm can unleash the true potential of ecommerce by enabling customers to click a picture of an object and search for similar products without the need for typing. In this paper, we explore machine learning algorithms which can help us solve both these problems.
Cognitive Inefficiency of School Education in the Context of Digitalization

The article is devoted to the problem of cognitive inefficiency of modern school education under the conditions of total digitalization. The purpose of the article is to show how digitalization makes school education inefficient. The tasks of the research are to indicate the existence of the problem of cognitive ineffi-ciency in learning; to identify the factors of such inefficiency; to assess the level of the prob-lem understanding by secondary school teachers; to find possible ways of improvement the cognitive effectiveness of learning at school. A focus group study was conducted to assess the extent to which secondary school teachers understood the problem and to find out their opinions on related issues. In total, 6 focus groups were held, each involving 5 respondents. The respondents were secondary school teachers from Moscow and the Moscow Region. The study demonstrated that teachers confirm the problem of cognitive inefficiency of modern school education in the aspects of learning memory and learning attention under the conditions of digitalization, they understand that the existence of the problem and its importance. As a result of the study, possible ways to increase the cognitive effectiveness of the educational process are identified. These are regular systematic repetition, active use of the principle of enlarging the studied, the introduc-tion of intelligent systems for educational attention and stress control, special training of the cognitive sphere of schoolchildren, personification and personalization of learning by developing individual educational trajectories and implementing methods for adapting educational content, etc. The significance of the work is determined by low efficiency of the modern school educa-tion, the obvious need to take into account and analyze the cognitive factors that affect its effectiveness and are associated with digitalization. The article describes the reasons for insuf-ficient levels of learning memory and attention in the existing system of secondary school education, the relationship of educational cognitive phenomena with digitalization, possible ways of positive changes, and assesses the degree of the problem research. The results of a focus group study showed that teachers understand the existence and relevance of the prob-lem, and the necessity to develop a comprehensive model of cognitive-effective learning in the context of digitalization.
Pedagogical bases of formation of readiness of students to work

The article describes the pedagogical foundations for the formation of readiness of students to work. The concept of labor activity or readiness to any activity is widely interpreted in pedagogy. The formation of students of employees is one of the most pressing issues of today. The article features ways to attract students to useful labor and the formation of students for employment.
CYRA: A Model-Driven CYber Range Assurance Platform

Digital technologies are facilitating our daily activities, and thus leading to the social transformation with the upcoming 5G communications and the Internet of Things. However, mainstream and sophisticated attacks are remaining a threat, both for individuals and organisations. Cyber Range emerges as a promising solution to effectively train people in cybersecurity aspects. A Training Programme is considered adequate only if it can adapt to the scope of the attacks they cover and if the trainees apply the learning material to the operational system. Therefore, this study introduces the model-driven CYber Range Assurance platform (CYRA). The solution allows a trainee to be trained for known and new cyber-attacks by adapting to the continuously evolving threat landscape and examines if the trainees transfer the acquired knowledge to the working environment. Furthermore, this paper presents a use case on an operational backend ICT system, showing how the CYRA platform was utilised to increase the security posture of the organisation.
Leukocyte Classification based on Transfer Learning of VGG16 Features by K-Nearest Neighbor Classifier

White blood cells (WBCs) are also called as leukocyte which is a significant component of blood that covers 1% of the total blood, protect us from numerous types of illness and other diseases. The automated classification of different types of leukocytes is very significant since each component have some designated functions in the human body and also the manual classification by skilled medical professionals is a tedious and erroneous task. In this work an automated approach based on transfer learning methodology is used for the detection and classification of leukocytes into four types such as Lymphocyte, Monocyte, Eosinophil and Neutrophil since there are limited numbers of images in the dataset. The methodology adopted in this work is a combination of deep learning and machine learning in which the features are extracted from the segmented nucleus of leukocyte by VGG16 deep learning model which is trained and evaluated using K-Nearest Neighbor (KNN) machine learning algorithm which provided an accuracy of 82.35% which is better when compared to Naive Bayes Classifier.
The Benchmark Lottery.

The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods. This paper proposes the notion of benchmark that describes the overall fragility of the ML benchmarking process. The benchmark lottery postulates that many factors, other than fundamental algorithmic superiority, may lead to a method being perceived as superior. On multiple benchmark setups that are prevalent in the ML community, we show that the relative performance of algorithms may be altered significantly simply by choosing different benchmark tasks, highlighting the fragility of the current paradigms and potential fallacious interpretation derived from benchmarking ML methods. Given that every benchmark makes a statement about what it perceives to be important, we argue that this might lead to biased progress in the community. We discuss the implications of the observed phenomena and provide recommendations on mitigating them using multiple machine learning domains and communities as use cases, including natural language processing, computer vision, information retrieval, recommender systems, and reinforcement learning.