Reviewerly/exHarmony huggingface dataset:
A survey paper on blockchain as a service platforms

Blockchain provides more security and decentralised systems. However, it is difficult for startups and small companies to build and deploy their blockchain networks. So, cloud computing providers make it easy for those to work with blockchain using their networks and cloud infrastructure through using blockchain as a service platform. There are many blockchain as a service providers and platforms. This survey provides an overview of 23 blockchain-as-a-service platforms and provides a comparison based on provider, blockchain protocol, blockchain type, cost, security, and support of smart contract.
A Mobile Interactive Robot for Social Distancing in Hospitals

We introduce the multimodal interactive mobile robot ISOLDE, intended for use in hospitals, with the primary aim of helping healthcare staff to maintain social distancing during pandemics, such as the ongoing Covid-19 pandemic. ISOLDE also addresses the growing concern related to the use of black box models in artificial intelligence, especially in situations involving high-stakes decisions. Thus, ISOLDE’s interactive capabilities have been implemented using a fully interpretable dialogue manager, making it easy to monitor and, if needed, correct the robot’s actions, even for a non-expert. A use case is presented (in a laboratory setting) in which the robot successfully interacts with healthcare staff to carry out a requested transportation and delivery task, and also measuring a patient’s temperature.
Barriers of Blockchain Technology Adoption in Viable Digital Supply Chain

In recent years, blockchain, the foundational technology of Bitcoin, has attracted substantial interest from industry and academics. Specifically, blockchain technology becomes regarded as a rapid innovation joining the global supply chain in the digital era. Adopting blockchain technology makes digital supply chain operations more transparent, safer, traceable, and resilient. Despite this rosy outlook, many barriers prevent its adoption considering the digital supply chains' viability performance. This paper aims to explore crucial barriers to the implementation of blockchain technology in viable digital supply chains. In this context. a total of 12 barriers were identified through a comprehensive literature review and discussion with team experts. This paper presents a DEMATEL-based method for investigating identified blockchain technology adoption barriers and developing a causality diagraph. The elaborated diagraph illustrates the causality relationship between barriers. This study gives stakeholders an overview of the barriers that should be prioritized and addressed to ensure Blockchain technology's successful implementation in viable digital supply chains.
ClipWidgets: 3D-printed Modular Tangible UI Extensions for Smartphones

Touchscreens provide a platform for adaptable and versatile user interfaces making them a popular choice for modern smart devices. However, touchscreens lack physicality. Existing solutions to add tangible user interfaces to smart devices often require complicated assembly or occlude part of the touchscreen. We propose ClipWidgets: 3D-printed modular tangible UI extensions for smartphones. ClipWidgets uses a conical mirror and a custom phone case to redirect the field of view of the rear camera of a smartphone to the phone’s periphery. This allows the phone to optically sense input from modular passive 3D-printed widgets that are attached to the phone case. We developed three different widget types (button, dial and slider) that require minimal calibration and minimal assembly. To demonstrate the functionality of ClipWidgets we used it to prototype three different applications: a game controller, a music interface and an interactive graph tool.
Tripolye Mega-Sites: “Collective Computational Abilities” of Prehistoric Proto-Urban Societies?

In the East European region between Prut and Dnieper, proto-urban mega-sites developed ca. 4100–3600 BCE with population agglomerations of around 10000 inhabitants per site. An outline of complexity categories, based on P. Turchin et al. (2018), illustrates that “computational abilities” are first developed to make the shift from dispersed to agglomerated settlement patterns. The development of an internal decision-making system for a polity that organizes communication via public buildings on different levels, together with a site-specific track system, may be responsible for this shift (or made it possible). However, after generations, this communication pattern was not developed into further collective communication abilities (e. g., into a writing system), while at the same time a tendency toward centralizing decision processes probably destroyed the communication flow. This ultimately led to the collapse of Tripolye mega-sites.
Expanding Fix Patterns to Enable Automatic Program Repair

Automatic Program Repair (APR) has been proposed to help developers and reduce the time spent repairing programs.Recent APR tools have applied learned templates (fix patterns) to fix code using knowledge from fixes successfully applied in the past.However, there is still no general agreement on the representation of fix patterns, making their application and comparison with a baseline difficult.As a consequence, it is also difficult to expand fix patterns and further enable APR.We automatically generate fix patterns from similar fixes and compare the generated fix patterns against a state-of-the-art taxonomy.Our automated approach splits fixes into smaller, method-level chunks and calculates their similarity.A thresholdbased clustering algorithm groups similar chunks and finds matches with state-of-the-art fix patterns.In our evaluation, we present 33 clusters whose fix patterns were generated from the fixes of 835 Defects4J bugs.Of those 33 clusters, 22 matched a state-of-the-art taxonomy with good agreement.The remaining 11 clusters were thematically analysed and generated new fix patterns that expanded the taxonomy.Our new fix patterns should enable APR researchers and practitioners to expand their tools to fix a greater range of bugs in the future.Index Terms-automatic program repair, similarity metric, clustering, fix patternThe buggy code accesses a null variable and the fix consists of inserting if statements (as null checks) into two methods.APR tools [6], [11], [15], [18], [20]-[24], are unable to generate fixes similar to the human fix in Listing 1, one of the 1
Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers

Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners.Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the risk of a potential cyberattack-the requirements for threat information sharing range from simplistic sharing of documents to threat intelligence sharing.Therefore, the storage and sharing of highly sensitive threat information raises considerable concerns regarding constructing a secure, trusted threat information exchange infrastructure.Establishing a trusted ecosystem for threat sharing will promote the validity, security, anonymity, scalability, latency efficiency, and traceability of the stored information that protects it from unauthorized disclosure.This paper proposes a system that ensures the security principles mentioned above by utilizing a distributed ledger technology that provides secure decentralized operations through smart contracts and provides a privacy-preserving ecosystem for threat information storage and sharing regarding the MITRE ATT&CK framework.
Challenges of DevOps Implementation : A Case Study from Technology Companies in Indonesia

Although agile is an iterative approach that encourages collaboration and feedback from all its stakeholders, most agile projects do not extend toward operation people and create a gap between them. To solve that, the paradigm of DevOps is introduced. DevOps is considered the most effective way to improve collaboration between development and IT operations in achieving quality, stability, reliability, and team satisfaction. Due to these thrive factors, DevOps are still popular in IT project development nowadays. However, the present survey shows that organizations still face barriers and struggle in their adoption of DevOps. This study aims to identify the challenges which involved hindering DevOps adoption by conducting a literature review. The identified challenges were further validated and prioritized by IT practitioners from technology companies in Indonesia using AHP (Analytic Hierarchy Process) method. The result is ‘highly bureaucratic deployment process’, ‘insufficient communication and collaboration issues’, and ‘lack of clear definition and strategic direction’ are the most significant challenges that need to be addressed to successful DevOps implementation with value 16.1%, 14,4%, and 13.7 % respectively.
The case for Zero Trust Digital Forensics

It is imperative for all stakeholders that digital forensics investigations produce reliable results to ensure the field delivers a positive contribution to the pursuit of justice across the globe. Some aspects of these investigations are inevitably contingent on trust, however this is not always explicitly considered or critically evaluated. Erroneously treating features of the investigation as trusted can be enormously damaging to the overall reliability of an investigation's findings as well as the confidence that external stakeholders can have in it. As an example, digital crime scenes can be manipulated by tampering with the digital artefacts left on devices, yet recent studies have shown that efforts to detect occurrences of this are rare and argue that this leaves digital forensics investigations vulnerable to accusations of inaccuracy. In this paper a new approach to digital forensics is considered based on the concept of Zero Trust, an increasingly popular design in network security. Zero Trust describes the practitioner mindset and principles upon which the reliance on trust in network components is eliminated in favour of dynamic verification of network interactions. An initial Definition of Zero Trust Digital Forensics will be proposed and then a specific example considered showing how this strategy can be applied to digital forensic investigations to mitigate against the specific risk of evidence tampering. A definition of Zero Trust Digital Forensics is proposed, specifically that it is ‘a strategy adopted by investigators whereby each aspect of an investigation is assumed to be unreliable until verified’. A new principle will be introduced, namely the ‘multifaceted verification of digital artefacts’ that can be used by practitioners who wish to adopt a Zero Trust Digital Forensics strategy during their investigations. A qualitative review of existing artefact verification techniques is also conducted in order to briefly evaluate the viability of this approach based on current research efforts.
“Authentic” digital inclusion? Dis/ability performances on social media by users with concealable communicative disabilities

Despite the increased technical accessibility of social media, disabled users’ attempts to be socially included are still understudied. These users’ agency-based choices are impacted by constant able-bodied surveillance and disciplining, especially when it comes to dis/ability visibility and potential context collapse. This paper develops the concept of disability performance within social media studies, presenting a qualitative study of social media users with concealable communicative disabilities: autistics, hard-of-hearing and people who stutter. Four factors were found to shape users’ dis/ability performances: the use of social media interfaces for the compartmentalization of disabled identity; users’ continuous efforts to make their social environment inclusionary; the centrality of users’ disabled identity and intersectionality; and the design of the performance’s message. Together, these show disability performances to be deliberate expressions of agency on the part of vulnerable social media users, who perform dis/ability according to their own evaluation of authenticity to increase their social inclusion.
A Novel Automatic Query Expansion with Word Embedding for IR-based Bug Localization

Information retrieval-based bug localization (IRBL) aims at finding buggy files using a bug report as a query. IRBL performance is highly dependent on the query quality. To improve the query quality for IRBL, automatic query expansion (AQE) method has been proposed for identifying query-related terms from the first-retrieved source files. This approach inevitably depends on two determinant of post- retrieval results, the retrieval model and the initial query quality. We propose a novel word embedding-based AQE technique, WEQE, to avoid the heavy dependency of the current AQE approach. Word embedding model enables to fetch terms semantically related to a query by representing words in a vector space. Our method embeds the words from both the global corpus and project-specific-corpus. The initial query is extended by adding words semantically similar to it based on vector representations from our embedding model. We validated the effectiveness of WEQE by using 4,583 bug reports from seven projects, four IRBL models, and two em-bedding models. Our large-scale experimental results show that WEQE can improve the average precision for bug localization for at least 42% of all queries. Our expanded queries on the best IRBL model achieve a 6% higher mean average precision for bug localization than the initial query.
What shape grammars do that CAD should: the 14 cases of shape embedding

Abstract Shape queries based on shape embedding under a given Euclidean, affine, or linear transformation are absent from current CAD systems. The only systems that have attempted to implement shape embedding are the shape grammar interpreters albeit with promising but inconclusive results. The work here identifies all possible 14 cases of shape embedding with respect to the number of available registration points, four for determinate cases and ten for indeterminate ones, and an approach is sketched to take on the complexities underlying the indeterminate cases. All visual calculations are done with shapes consisting of straight lines in the Euclidean plane within the algebra U ij for i = 1 the dimension of lines and j = 2 the dimension of space in which the lines are defined, transformed and combined. Aspects of interface design and integration to current work design workflows are deliberately left aside.
Human-Robot Interaction using Mixed Reality

All along the appearance of the fourth industrial revolution many research and engineering fields has been facing a significant growing notably in robots. These interests evolve from the development of robots for structured industrial environments to collaborative and autonomous development operating in hybrid environments. Several problems have arisen with the growing number of collaborative robots; they are not user-friendly to work in hybrid teams and they also require high engineering and computational capabilities to program. Industry 4.0 has endorsed the combination of diverse new technologies that build-up a more efficient and user-friendly human-robot interaction such as Augmented and Mixed Reality, System integration, Internet of Things and 3D simulation. In this paper, we propose our approach which is a Mixed Reality-based human-robot interaction method for an human-friendly and easy cobot programming. The presented method is a marker-based algorithm that allows users to easily interact with cobot.
Applied Cryptography in Network Systems Security for Cyberattack Prevention

Application of cryptography and how various encryption algorithms methods are used to encrypt and decrypt data that traverse the network is relevant in securing information flows. Implementing cryptography in a secure network environment requires the application of secret keys, public keys, and hash functions to ensure data confidentiality, integrity, authentication, and non-repudiation. However, providing secure communications to prevent interception, interruption, modification, and fabrication on network systems has been challenging. Cyberattacks are deploying various methods and techniques to break into network systems to exploit digital signatures, VPNs, and others. Thus, it has become imperative to consider applying techniques to provide secure and trustworthy communication and computing using cryptography methods. The paper explores applied cryptography concepts in information and network systems security to prevent cyberattacks and improve secure communications. The contribution of the paper is threefold: First, we consider the various cyberattacks on the different cryptography algorithms in symmetric, asymmetric, and hashing functions. Secondly, we apply the various RSA methods on a network system environment to determine how the cyberattack could intercept, interrupt, modify, and fabricate information. Finally, we discuss the secure implementations methods and recommendations to improve security controls. Our results show that we could apply cryptography methods to identify vulnerabilities in the RSA algorithm in secure computing and communications networks.
Robust Detection of Tables in Documents Using Scores from Table Cell Cores

Abstract Table detection is an essential step in many document analysis systems. Tabular data are a pivotal form of information representation that can organize data in a conventional structure for comfortable and quick information retrieval and comparison. Detection of table structures in PDF files or images is a challenging task because of the variability of table layouts, and sometimes the tabular structures’ similarities with non-tabular elements like charts, plots, etc. In this work, we have presented a table detection method using a geometric analysis of the table cell cores that represents the table cell texts. The proposed method works by analyzing the text gap information, and hence it can detect the table cell cores, irrespective of the presence of the table boundary lines and cell-separating rule-lines. Experimentations have been done on various document images of complex structures from well-known datasets. The detection accuracies obtained by us corroborate the usefulness of the proposed method.
Exploring scientific literature by textual and image content using DRIFT

Digital libraries represent the most valuable resource for storing, querying, and retrieving scientific literature. Traditionally, the reader/analyst aims to compose a set of articles based on keywords, according to his/her preferences, and manually inspect the resulting list of documents. Except for the articles which share citations or common keywords, the results retrieved will be limited to those which fulfill a syntactic match. Besides, if instead of having an article as a reference, the user has an image, the process of finding and exploring articles with similar content becomes infeasible. This paper proposes a visual analytic methodology for exploring and analyzing scientific document collections that consider both textual and image content. The proposed technique relies on combining multiple Content-Based Image Retrieval (CBIR) components and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Moreover, we extend its analytical capabilities with visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We evidence the effectiveness of our methodology through three case studies and a user evaluation, which attest to its usefulness during the process of scientific collections exploration.
Feature Pyramid Network With Level-Aware Attention for Meningioma Segmentation

Meningiomas are the most common primary intracranial tumors in adults, and they are dangerous and even lethal when they grow and oppress vital organs. In clinical, microsurgical resection is the most widely used treatment for most meningiomas. And tumor segmentation is an essential primary step before applying any therapy. However, due to the various meningioma locations and complicated intracranial structures, it is still challenging to segment the tumor accurately in both boundaries and contour automatically. In this work, a novel method is proposed for the automatic segmentation of meningioma. In general, the proposed method follows a coarse-to-fine strategy. Specifically, the feature pyramid structure is employed to extract multi-level features. Further, a Level-aware Attention is proposed to refine multi-level features by naturally utilizing the complementary features of different levels, thus significantly improving the segmentation performance. Moreover, to validate the proposed method, a realistic dataset of meningioma with fine labels is constructed, and experimental results on the dataset demonstrate the effectiveness of the proposed method.
Securing Remote User Authentication in Industrial Internet of Things

The Industrial Internet of Things (IIoT) enhances the benefit of the Internet of Things (IoT) to a higher level, especially in industries where human error can lead to catastrophic effects. However, security is a major concern in IIoT as hackers can gain access to connected systems, thus potentially subjecting operations to a shutdown. Besides, the outbreak of the COVID-19 pandemic changed the operations style of organizations into a remote work model. Consequently, there has been a significant increase in cyber-attacks leveraging vulnerabilities of IoT devices connected to the Internet. Considering the above factors, we propose a method of remote user authentication combining Photo Response Non-Uniformity (PRNU) with fingerprint bio-metric, which can prevent attacks. PRNU uniquely identifies the scanner, thereby authenticates the device of the user. To prove the effectiveness of PRNU, we collect fingerprint images from various scanners prototyped using Raspberry Pi and evaluate the performance. Our performance evaluation with a set of 10 fingerprint scanners shows promising results. Moreover, our analysis shows that the proposed scheme achieves a classification accuracy of 99%.
Enabling efficient privacy-preserving subgraph isomorphic query over graphs

Benefiting from the cloud computing, graph owners tend to store their data graphs on the cloud server to release the local computation and storage burden. The complex subgraph isomorphic query, as an important graph query operation, is completed by the cloud server in this way. In order to realize privacy-preserving subgraph isomorphic query over graphs in cloud servers, some schemes have been proposed. However, these schemes need to mine specific subgraphs as index features, which causes high computation burden to graph owner. In addition, they need to traverse the entire index to find the data graphs that meet the requirements, which makes the query inefficient. In this paper, we focus on implementing an efficient and privacy-preserving subgraph isomorphic query over graphs, and give a practical scheme. We extract trees and cycles from the data graphs as features instead of the specific subgraphs, which reduces the precomputation overhead for the graph owner. We generate fingerprint vectors for data graphs based on the features, and construct a tree-based index for vectors. The tree-based index enables our scheme to provide faster query service by cutting off the branch structure that does not meet the requirements without traversing the whole index. Our scheme follows the "filtering-and-verification" principle and ensures the efficiency of the query process. To support graph semantic security without privacy leakage, we adopt the secure inner product computation technology to protect the privacy in the index. This technology ensures that the index and the query process will not disclose the privacy of users. The security proof and the performance evaluation show that our scheme is secure and efficient.