Reviewerly/exHarmony huggingface dataset:
Short Paper: Highly Compatible Fast Container Startup with Lazy Layer Pull

Containers are an attractive platform for developing and deploying applications. Unfortunately, in order to start up a new or updated container, the entire container image must first be pulled from a registry, increasing the waiting time for containers to actually become available. Various methods have been proposed to accelerate container startup with lazy pull of container images. However, these methods have compatibility problems with existing container ecosystems, which is a major obstacle to their deployment in production environments. In this paper, we propose a fast container startup system with lazy layer pull that is highly compatible with existing container ecosystems. Our approach is to add a boot layer with only the minimum files required for booting on top of the existing container image, and to introduce a carefully designed overlay file system called LlpFS that allows for lazy pull of layers while conforming to the specifications and de facto standards of the container ecosystem. Evaluation in practical applications showed that our system made container startup about 4.9× faster under 1 Gbps bandwidth with little performance overhead.
Revisiting the Security of COMET Authenticated Encryption Scheme

COMETv1, by Gueron, Jha and Nandi, is a mode of operation for nonce-based authenticated encryption with associated data functionality. It was one of the second round candidates in the ongoing NIST Lightweight Cryptography Standardization Process. In this paper, we study a generalized version of COMETv1, that we call \(\mathsf {gCOMET}\), from provable security perspective. First, we present a comprehensive and complete security proof for \(\mathsf {gCOMET}\) in the ideal cipher model. Second, we view COMET, the underlying mode of operation in COMETv1, as an instantiation of \(\mathsf {gCOMET}\), and derive its concrete security bounds. Finally, we propose another instantiation of \(\mathsf {gCOMET}\), dubbed COMETv2, and show that this version achieves better security guarantees as well as memory-efficient implementations as compared to COMETv1.
Braess paradox in a quantum network

Braess, while working on traffic modeling, noticed that traffic flow in a network can be worsened by adding extra edges to an existing network. This seemingly counterintuitive phenomenon is known as the Braess paradox. We consider a quantum network, where edges represent shared entangled states between spatially separated parties (nodes). The goal is to entangle two previously uncorrelated nodes using entanglement swappings. The amount of entanglement between the distant nodes is quantified by the average concurrence of the states established, as a result of the entanglement swappings. We then introduce an additional edge of maximally entangled Bell states in the network. We show that the introduction of the additional maximally entangled states to this network leads to lower concurrence between the two previously uncorrelated nodes. Thus we demonstrate the occurrence of a phenomenon in a quantum network that is analogous to the Braess paradox in traffic networks.
An Annotated Turkish Aspect Based Sentiment Analysis Corpus for Smart Tourism

In the processing of data produced by users, aspect based sentiment analysis (ABSA) studies are carried out today instead of classical sentiment analysis approaches. ABSA enables the determination of detailed feelings and thoughts for each component of the product or service in a user post. Using ABSA, it is possible to determine the weak and strong aspects of the tourism centers in line with the visitor comments about the tourist places. The concept of smart tourism has started to attract a lot of attention in recent years. Smart tourism is an artificial intelligence-based solution that offers the necessary systems for comfortable travel to visitors. Developing artificial intelligence-based solutions such as ABSA for tourism centers in Turkey, tourism centers can be turned into attractions. One of the most important tasks of ABSA is the extraction of aspect terms, that is, the extraction of tourism center features/aspects. The lack of a large-scale corpus labeled for this task in the Turkish language is one of the obstacles for researchers in this field. In this study, a new corpus has been created for smart tourism and ABSA. Moreover, visitor reviews to important tourism centers in Turkey were collected and labeled manually by seven different annotators as aspect term-sentiment pairs for ABSA. Latent Dirichlet Allocation (LDA) algorithm was used as a baseline approach for extracting aspect terms from visitor reviews. The created dataset has been uploaded to GitHub for all researchers.
Towards low-effort development of advanced IoT applications

Building customised IoT applications is often labor intensive. While several frameworks support developers in building sophisticated applications, they mainly focus on the communication with the underlying devices and their automation. Modular programming may further improve the reuse of components in other applications. However, integrating automatic device configuration, connection management, and user support for solving connectivity issues, remains a real endeavor for application developers. Likewise, integrating and enforcing policies in an IoT application is complex, and the dynamic nature of IoT systems makes it even more difficult to develop applications that properly handle changes in the environment.
Making the Invisible Visible: Risks and Benefits of Disclosing Metadata in Visualization

Accompanying a data visualization with metadata may benefit readers by facilitating content understanding, strengthening trust, and providing accountability. However, providing this kind of information may also have negative, unintended consequences, such as biasing readers’ interpretations, a loss of trust as a result of too much transparency, and the possibility of opening visualization creators with minoritized identities up to undeserved critique. To help future visualization researchers and practitioners decide what kinds of metadata to include, we discuss some of the potential benefits and risks of disclosing five kinds of metadata: metadata about the source of the underlying data; the cleaning and processing conducted; the marks, channels, and other design elements used; the people who directly created the visualization; and the people for whom the visualization was created. We conclude by proposing a few open research questions related to how to communicate metadata about visualizations.
Survey on improving the performance of MapReduce in Hadoop

Hadoop has become the most popular and the most used platform in distributed data processing, Hadoop is also an open-source software that implements the MapReduce model for processing big data, it has taken a large part in scientific research in the field of big data, a lot of research has addressed allocation and scheduling in Hadoop system, in this paper we will present the main research done in improving the performance of the MapReduce model of Hadoop platform. The Most previous surveys only focused on Hadoop MapReduce scheduling and how to ameliorate it, but this paper tries to give an overview of the important work that aim to improve the performance of Hadoop MapReduce from different sides (energy, budget, scheduling, makespan …).
How Genuino Applies Blockchain Technology in the Collectibles Industry

This chapter analyses the blockchain implemented by Genuino in the collectibles industry. Since collectibles are subject to high risk of counterfeiting and fake replications, blockchain technology can help substantially to ensure transparency, originality, ownership, and data security and protection. Two methods to use blockchain in the industry seem to be the most widespread today: creating digital native collectibles on blockchain and transposing physical objects into the blockchain by creating digital twins. By using blockchain, new consumer clusters emerge, highlighting the need to modernize both the traditional economies and the current business models.
Efficient <scp>Non‐Compression Auto‐Encoder</scp> for Driving Noise‐Based Road Surface Anomaly Detection

Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low‐friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real‐time is essential. Thus, we propose a convolutional auto‐encoder‐based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non‐compression method rather than a conventional bottleneck structured auto‐encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared with the conventional models, and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting‐edge algorithm for real‐time anomaly detection. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Factors Influencing the Degree of Penetration of Blockchain Technology in the Bulgarian Entrepreneurial Ecosystem

Blockchain technology is gaining popularity in developed economies, but there is a tendency to enter developing countries. Bulgaria is among the examples in which the development of technology is discussed at every business event and scientific forum. More and more companies are looking at technology in an attempt to achieve significant competitive advantages, which in turn will help them position themselves in global value chains. The perception of the technology within the company is associated with many challenges, greatly limiting the mass distribution in the Bulgarian entrepreneurial ecosystem. The main reasons leading to the decision to use innovation are the need to digitize business processes, but the final decision to include in Blockchain's information infrastructure is influenced by external and internal stimulating and limiting factors.
Capturing Users Requirements Using a Data Mining Approach

In success or failure of software project, software requirements plays a critical role. Requirement engineering is a human intensive process so the chances of errors, and failure to elicit and specify the right requirements, are more. The software practitioners have to be cautious about what is actually required by the stakeholders and elicit only those features and functionalities rather than some features that may look good but are impractical, and may lead to project failure or timeline delays, without adding much value to the project. In this paper, we introduced a data mining approach to capture user’s requirement for a generic software project, which in our case, was a campus management system (CMS). Initially, we started with a set of Software Requirement Specification (SRS) documents for similar type of projects done before and we extracted the most frequently appearing requirements which would potentially be the ‘Needs’ of the stakeholders, using text mining technique. After that, the low frequency requirements were extracted which could potentially be the ‘Wants’ of some particular stakeholder. Later on, we applied association rule mining to find out links between multiple requirements that led us to the requirement that frequently appeared together, as such links can be useful in identifying requirement dependencies and specification of further requirements. In order to validate the extracted requirements, elicitation sessions in the form of interviews and questionnaires were held with the potential stakeholders of the project and the results of the comparison between the extracted and elicited requirements are discussed at the end of this paper.
COVID-19 Face Mask Detection using Deep Convolutional Neural Networks &amp; Computer Vision

Objectives: To propose a model which could classify in real-time if an individual is wearing a face mask or not wearing a face mask.A lightweight system that could be easily deployed and assist in surveillance.Methods/Statistical analysis: Analysis of the proposed model shows a limited number of research studies with regards to facial localizations.Several state-of-the-art methods were taken into considerations out of which the CNN architectural approach is analyzed in this study.Taking into consideration the use-case of deployments and structuring, a new Keras-based model is proposed that surpasses the achievement results of MobileNet-V2 and VGG-16 standard architectures.Effective facial localization is tackled with the MTCNN approach.Findings: The system has achieved a confidence score of 0.9914, an average weighted F1-score of 0.98, a precision value of 0.99.The proposed model has been compared with standard architectures of VGG16 and MobileNetV2 with regard to the accuracy, support values, precision, recall, and F1-score metrics.The proposed model performs better w.r.t traditional architectures.The average latency involved in prediction is 0.034 seconds making the average FPS 30 Frames per second.The compact architecture makes the model best for deployment in real-time scenarios.The system incorporates the concept of image localization with Multi-Task Cascaded Convolutional Neural Network (MTCNN) architecture.The analysis shows MTCNN is performing much better than Haar-Cascade in real-time facial prediction scenarios.Novelty/Applications: This compact architecture with minimal layers is easily deployable in edge devices.It can be used for mass screening at public places like railway stops, bus stops, streets, malls, entrances, schools, and many service-oriented business verticals requiring users to access the services as long as the mask has been worn correctly.
THE ORGANIZATION OF MULTIGRADE TEACHING IN PRIMARY SCHOOL. CHALLENGES AND CHANCES

Multigrade classes characterize a geographically and demographically disadvantaged area: sparsely populated areas, isolated areas, difficult to access in terms of transport, communities and infrastructure. The number of students is small, which has led to the organization of multigrade classes. More precisely, it means the coupling of two, three, four or even five grades from the primary cycle and the organization of the educational process, under the responsibility of a single teacher.The present research starts from the fact that the National Curriculum does not provide specific contents for this type of teaching in Romania. The biggest challenge of the teacher who teaches such a class is to implement the curriculum developed for non-grade class to multigrade class.This article aims to analyze the main aspects related to the organization and management of multigrade teaching in Romania, at the primary level. From the legislative point of view, the multigrade teaching is organized based on the methodology approved by MECTS, Order no. 3062 from 19.01.2012. Currently, information shows that, from a numerical point of view, the multigrade classes are increasing, both in rural and urban areas, due to the reorganization of school networks and the subordination of small schools to coordinating schools.The research data were collected following the application of a questionnaire for teachers from multigrade classes, primary level. 140 teachers from 36 counties of our country answered this questionnaire, 70% of them being with the 1st didactic degree. We followed the collection and processing of data about the organization of multigrade classes in Romania - the grouping of classes and the criteria that stood at the basis of this grouping, the efficient management of the didactic time - the established schedule, the preoccupation of the local authorities for these classes showed by the material resources in the endowment of these schools, the meticulous planning of the didactic activities by interweaving the direct activity with the students. All the surveyed teachers stated that they carry out their classes in the morning and 59.28% of them carry out all the classes simultaneously. In their case, it is necessary to follow the way in which they combine school subjects in the schedule, but also the way in which they organize each class, from the methodological point of view. The research results show that the teaching experience of teachers and, in particular, the experience in multigrade teaching, significantly influences the way the teaching process is organized, the ways of preparing and designing training sequences, in order to train students in the skills provided in curriculum.
Augmenting Design Education for Sustainability through Field Exploration: An Experience of Learning from DIY Practices in a Rural Community

This article (visual essay) provides a glimpse of a field trip ventured by design students as part of a larger study of developing a localised version of design education for sustainability, focusing on the wants and needs of non-urban populations in vast Russian hinterlands. The central goal is to introduce would-be designers to the concepts of locally appropriate technology and sustainable/circular living by real-life examples and, eventually, teach them to recognise the sustainable potential of place-based technologies and practices of their making, using and maintaining. The primary data came from the trip to Pozhva, a village in Permskiy Krai, Russia, that gained popularity among DIY activists and users of off-road vehicles in Russia in the early 2000s because of its unique, community-centred manufacture of lightweight ATVs on low-pressure tires, nicknamed “jeeps”. This article presents the students’ journey in a comic strip portraying a composite character of technologies and their user-designers as experts in local conditions and (subconsciously) agents of circularity. The article closes with a discussion on the expedition’s discoveries and learning outcomes, correlating them with broader implications for design education.
How are framework code samples maintained and used by developers? The case of Android and Spring Boot

Modern software systems are commonly built on top of frameworks. To accelerate the learning process of features provided by frameworks, code samples are made available to assist developers. However, we know little about how code samples are developed and consumed. In this paper, we aim to fill this gap by assessing the characteristics of framework code samples. We provide insights into how code samples are maintained and used by developers. We analyze over 230 code samples provided by Android and Spring Boot, and assess aspects related to their code, evolution, popularity, and client usage. We find that most code samples are small and simple, provide a working environment for the clients, and rely on automated build tools. They frequently change, for example, to adapt to new framework versions. We also detect that clients commonly fork the code samples, however, they rarely modify them. To further understand the problems faced by developers, we analyze 614 Stack Overflow questions about the code samples and 269 issues from code sample repositories. We find that developers face problems when trying to modify the code samples and the most common issue is related to improvement. Finally, we propose implications to creators and clients of code samples to improve maintenance and usage activities.
Nonmonotonic Reasoning, Expectations Orderings, and Conceptual Spaces

Abstract In Gärdenfors and Makinson (Artif Intell 65(2):197–245, 1994) and Gärdenfors (Knowledge representation and reasoning under uncertainty, Springer-Verlag, 1992) it was shown that it is possible to model nonmonotonic inference using a classical consequence relation plus an expectation-based ordering of formulas. In this article, we argue that this framework can be significantly enriched by adopting a conceptual spaces-based analysis of the role of expectations in reasoning. In particular, we show that this can solve various epistemological issues that surround nonmonotonic and default logics. We propose some formal criteria for constructing and updating expectation orderings based on conceptual spaces, and we explain how to apply them to nonmonotonic reasoning about objects and properties.
Exploring the Evolution of Exploit-Sharing Hackers: An Unsupervised Graph Embedding Approach

Cybercrime was estimated to cost the global economy $945 billion in 2020. Increasingly, law enforcement agencies are using social network analysis (SNA) to identify key hackers from Dark Web hacker forums for targeted investigations. However, past approaches have primarily focused on analyzing key hackers at a single point in time and use a hacker’s structural features only. In this study, we propose a novel Hacker Evolution Identification Framework to identify how hackers evolve within hacker forums. The proposed framework has two novelties in its design. First, the framework captures features such as user statistics, node-level metrics, lexical measures, and post style, when representing each hacker with unsupervised graph embedding methods. Second, the framework incorporates mechanisms to align embedding spaces across multiple time-spells of data to facilitate analysis of how hackers evolve over time. Two experiments were conducted to assess the performance of prevailing graph embedding algorithms and nodal feature variations in the task of graph reconstruction in five time-spells. Results of our experiments indicate that Text-Associated Deep-Walk (TADW) with all of the proposed nodal features outperforms methods without nodal features in terms of Mean Average Precision in each time-spell. We illustrate the potential practical utility of the proposed framework with a case study on an English forum with 51,612 posts. The results produced by the framework in this case study identified key hackers posting piracy assets.
A Data-Driven Approach to Air Leakage Detection in Pneumatic Systems

During the transition phase of traditional manufacturing companies towards smart factories, they are likely to experience challenges like lack of prehistoric data recordings or events on which the machine learning models need to be trained. This paper introduces a novel approach of artificially induced anomalies for data labelling. Moreover, for newly installed systems or a setup, which has not seen any kind of malfunction yet, the combination of artificially induced anomalies by experiments and machine learning model help to proactively prepare for any kind of future hindrance of the production systems. Two experiments were performed for detection of air leakage. The first one was designed to identify ‘sensitive feature’ and understand the behaviour of the sensor readings with respect to different state of the machine. The second one was performed to capture more data points pertaining to leaking state of machine on a normal production day since the first one was conducted on a maintenance break). RUSBoosted bagged trees model was built as a supervised machine-learning model, which was yielded 98.73% accuracy, 99.40% precision, recall of 99.21%, and F1 score of 99.30% on test data for detecting pneumatic leakage. As a conclusion, previously unknown hidden patterns and insights regarding temperature feature along with a standardized and systematic methodology are the important deliverables of this study.