Reviewerly/exHarmony huggingface dataset:
Patching zero-day vulnerabilities: an empirical analysis

Abstract Zero-day vulnerabilities remain one of the major security threats that are faced by organizations. Once a vendor learns about a zero-day vulnerability, releasing a timely patch becomes a priority given the risk of zero-day exploits. However, we still lack information on the factors that affect patch release time of such vulnerabilities. The main objective of this study is to examine the impact of other as-yet unexplored factors on the patch release time of zero-day vulnerabilities. Using zero-day vulnerability dataset captured between 2010 and 2020, we employ survival analysis technique. Our model explores the impact of vulnerability attack vector, attack complexity, privileges required, user interaction, scope, confidentiality, integrity, and availability impact on patch release timing. Findings show that a zero-day vulnerability is more likely to be patched on time if the vulnerability results in a scope change and affects more vendors, products, and versions. However, a zero-day vulnerability is less likely to be patched on time if it requires privileges and impacts confidentiality. Our sub-analyses also reveal how patch release times vary across different products and vulnerability types.
Bringing a Natural Language-enabled Virtual Assistant to Industrial Mobile Robots for Learning, Training and Assistance of Manufacturing Tasks

Nowadays, industrial companies want to enhance their Industry 4.0 competencies. Therefore, they need to help employees master state-of-the-art technologies and gain the necessary knowledge to stay relevant and competitive. As a result, there is a global demand for learning and training tools that assist the employees at all levels. In this paper, we propose a natural language-enabled virtual assistant (VA) integrated with an industrial mobile manipulator to fulfill this target in manufacturing tasks. The latest Learning, Training, Assistance - Formats, Issues, Tools (LTA-FIT) model is leveraged to guide the design and development of a pilot version of the VA. To validate its performance, three manufacturing scenarios are analyzed based on the learning, training, and assistance phases, respectively. In our system, the human-robot interaction is achieved through conversation and a dashboard implemented as a web application. This intuitive interaction enables operators of all levels to control a industrial mobile manipulator easier and use it as a complementary tool for developing their competencies. The pilot experiments show that the proposed VA is able to respond to operator commands flexibly within the LTA-FIT model.
Measuring email sender validation in the wild

Email is a critical Internet application, and its security is important. The Sender Policy Framework (SPF), DomainKeys Identified Mail (DKIM), and Domain-based Message Authentication, Reporting, and Conformance (DMARC) were developed to enable mail servers to detect and reject email coming from fraudulent sources. In this paper we study the state of SPF, DKIM, and DMARC validation across a large number of mail servers, the first such study at scale that we know of. We consider two behaviors of sender-validating mail servers: behavior when an email with a valid sender is received and behavior when an email from a invalid sender is received. Our techniques allow us to elicit SPF, DKIM, and DMARC validation behavior of the servers without spam. We find that as many as 85% of mail servers are deploying SPF validation, and over half are deploying all three mechanisms: SPF, DKIM, and DMARC. We also observe there are some nuanced behaviors with regard to adherence to the SPF specification.
Price of Precision in Coded Distributed Matrix Multiplication: A Dimensional Analysis

Coded distributed matrix multiplication (CDMM) schemes, such as MatDot codes, seek efficient ways to distribute matrix multiplication task(s) to a set of N distributed servers so that the answers returned from any R servers are sufficient to recover the desired product(s). For example, to compute the product of matrices U, V, MatDot codes partition each matrix into $p\gt1$ sub-matrices to create smaller coded computation tasks that reduce the upload/storage at each server by $1 / p$, such that UV can be recovered from the answers returned by any $R=2 p-1$ servers. An important concern in CDMM is to reduce the recovery threshold R for a given storage/upload constraint. Recently, Jeong et al. introduced Approximate MatDot (AMD) codes that are shown to improve the recovery threshold by a factor of nearly 2, from $2 p-1$ to p. A key observation that motivates our work is that the storage/upload required for approximate computing depends not only on the dimensions of the (coded) sub-matrices that are assigned to each server, but also on their precision levels - a critical aspect that is not explored by Jeong et al. Our main contribution is a rudimentary asymptotic dimensional analysis of AMD codes inspired by the Generalized Degrees of Freedom (GDoF) framework previously developed for wireless networks, which indicates that for the same upload/storage, once the precision levels of the task assignments are accounted for, AMD codes are not better than a replication scheme which assigns the full computation task to every server. The dimensional analysis is supported by simple numerical experiments.
Semisupervised Fuzzy Clustering With Fuzzy Pairwise Constraints

In semi-supervised fuzzy clustering, this paper extends the traditional pairwise constraint (i.e., must-link or cannot-link) to fuzzy pairwise constraint. The fuzzy pairwise constraint allows a supervisor to provide the grade of similarity or dissimilarity between the implicit fuzzy vectors of a pair of samples. This constraint can present more complicated relationship between the pair of samples and avoid eliminating the fuzzy characteristics. We propose a fuzzy discriminant clustering model (FDC) to fuse the fuzzy pairwise constraints. The nonconvex optimization problem in our FDC is solved by a modified expectation-maximization algorithm, involving to solve several indefinite quadratic programming problems (IQPPs). Further, a diagonal block coordinate decent (DBCD) algorithm is proposed for these IQPPs, whose stationary points are guaranteed, and the global solutions can be obtained under certain conditions. To suit for different applications, the FDC is extended into various metric spaces, e.g., the Reproducing Kernel Hilbert Space. Experimental results on several benchmark datasets and facial expression database demonstrate the outperformance of our FDC compared with some state-of-the-art clustering models.
PrivStream: A privacy-preserving inference framework on IoT streaming data at the edge

Edge computing combining with artificial intelligence (AI) has enabled the timely processing and analysis of streaming data produced by IoT intelligent applications. However, it causes privacy risk due to the data exchanges between local devices and untrusted edge servers. The powerful analytical capability of AI further exacerbates the risks because it can even infer private information from insensitive data. In this paper, we propose a privacy-preserving IoT streaming data analytical framework based on edge computing, called PrivStream, to prevent the untrusted edge server from making sensitive inferences from the IoT streaming data. It utilizes a well-designed deep learning model to filter the sensitive information and combines with differential privacy to protect against the untrusted edge server. The noise is also injected into the framework in the training phase to increase the robustness of PrivStream to differential privacy noise. Taking into account the dynamic and real-time characteristics of streaming data, we realize PrivStream with two types of models to process data segment with fixed length and variable length, respectively, and implement it on a distributed streaming platform to achieve real-time streaming data transmission. We theoretically prove that Privstream satisfies ε-differential privacy and experimentally demonstrate that PrivStream has better performance than the state-of-the-art and has acceptable computation and storage overheads.
An improved exponential metric space approach for <scp>C‐mean</scp> clustering analysing

Abstract In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two‐and three‐dimensional data, including Diamond and Iris real‐world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed.
Vassago: Efficient and Authenticated Provenance Query on Multiple Blockchains

Recent successful pilot studies on blockchains showed significant benefits of data provenance towards improved visibility and authenticity, as well as a reduction of administrative costs. Traditionally, all the parties reside in a single blockchain system, which leads to various single-chain provenance query schemes. However, for the sake of convenience, it has been a trend for different parties to deploy their own blockchain systems separately, which requires cross-chain provenance queries. Unfortunately, state-of-the-art blockchain query schemes suffered from inauthentic transactions and low efficiencies when expanding to adversarial cross-chain commercial deals. To be more specific, query results may be inconsistent and incomplete over multiple blockchains due to the lack of global knowledge on cross-chain dependencies. Moreover, these schemes perform cross-chain provenance queries in sequence, leading to high query latencies. In this paper, we present Vassago, the first multi-chain system that empowers efficient and authenticated provenance queries. Vassago incorporates three innovative designs: 1) it explores the dependencies of cross-chain transactions, 2) it validates the authenticity of cross-chain provenance by recording and querying the dependencies on a shared blockchain, and 3) it improves efficiency by parallelizing query processes. Our experimental results show that Vassago can shorten the query latency by 85.9&#x0025; and reduce the storage consumption by up to 85.7&#x0025;, with negligible overhead.
Quantifier-free epistemic term-modal logic with assignment operator

In standard epistemic logic, the names and the existence of agents are usually assumed to be common knowledge implicitly. This is unreasonable for various applications in computer science and philosophy. Inspired by term-modal logic and assignment operators in dynamic logic, we introduce a lightweight modal predicate logic where names can be non-rigid, and the existence of agents can be uncertain. The language can handle various de dicto/de re distinctions in a natural way. We characterize the expressive power of our language, obtain complete axiomatisations of the logics over several classes of varying-domain/constant-domain epistemic models, and show their (un)decidability.
The Effects of Classes using Virtual Reality Simulations of the Hospital Environment on Knowledge of the Hospital Environment, Academic Self-efficacy, Learning Flow, Educational Satisfaction and Academic Achievement in Nursing Students

Purpose: This study examined the effects of virtual reality (VR)-based fundamental nursing education on nursing students’ knowledge of the hospital environment, academic self-efficacy, learning flow, educational satisfaction, and academic achievement.Methods: In total, 137 nursing students participated in this non-equivalent control group pretest-posttest study, with 69 and 68 in the experimental and control groups, respectively. Differences in the dependent variables between the two groups before and after the intervention were compared using the independent-sample t-test and Mann-Whitney U test.Results: The VR intervention group showed significantly higher knowledge of the hospital environment (t=3.88, p&lt;.001), educational satisfaction (z=-3.82, p&lt;.001), and academic achievement (z=-2.54, p=.011) than the control group.Conclusion: These findings indicate that VR-based education is an effective intervention for improving knowledge of the hospital environment, educational satisfaction, and academic achievement in nursing education.
A Survey on Role-Oriented Network Embedding

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in various graph mining tasks including link prediction and node clustering and classification. A wide variety of NE methods focus on the proximity of networks, they learn community-oriented embedding for each node, where the corresponding representations are similar if two nodes are closer to each other in the network. Meanwhile, there is another type of structural similarity, i.e., role-based similarity, which is completely different from and complementary to the proximity. In order to preserve the role-based structural similarity, the problem of role-oriented NE is raised. However, compared to the community-oriented NE, there are only a few role-oriented embedding approaches proposed recently. Although less explored, considering the importance of roles in analyzing networks and many applications that role-oriented NE can shed light on, it is necessary and timely to provide a comprehensive overview of existing role-oriented NE methods. In this review, we first clarify the differences between community-oriented and role-oriented network embedding. Afterward, we propose a general framework for understanding role-oriented NE and a two-level categorization to better classify existing methods. Then, we select some representative methods according to the proposed categorization and briefly introduce them by discussing their motivation, development, and differences. Moreover, we conduct comprehensive experiments to empirically evaluate these methods on a variety of role-related tasks including node classification and clustering (role discovery), top-k similarity search, and visualization using some widely used synthetic and real-world datasets. Finally, we further discuss the research trend of role-oriented NE from the perspective of applications and point out some potential future directions.
A two-stage location-sensitive and user preference-aware recommendation system

Nowadays, cloud customers use cloud services increasingly to satisfy their demands. Usually, a significant number of customers are immature and inexpert and cannot express their needs accurately and numerically. They usually express their needs verbally and in the form of linguistic terms. On the other hand, the experienced customers express their needs numerically and accurately. In this situation, a recommendation system can be considered as one of the most useful ideas to support all type of customers. However, current recommendation systems (e.g., collaborative filtering based recommendations) meet customer requests that are accurately and numerically expressed. To support all types of customers, the construction of a strong recommendation system to analysis the demands expressed by customers (experienced and inexperienced) and to recommend suitable services is vital. As another important matter, cloud customers and services have been geographically distributed. Identifying the location of customers and services has a significant effect on the quality of services offered to customers. Therefore, the recommendation system should consider the location of customers and services in order to provide better services. In this paper, we introduce an efficient method to construct a powerful recommendation system which can provide suitable services considering the preferences of the customer and their location. The proposed recommendation system comprises two algorithms. The first algorithm is a fuzzy clustering algorithm, named FCA, that can well classify the location of customers and services. The second algorithm is an iterative adaptive neural-fuzzy algorithm, named IANFRA, which receives the preferences of the customer along with their location and identifies suitable services based on the locations clustered by FCA and the demands of customers (experienced and inexperienced). Finally, the feasibility of the proposed method has validated in terms of accuracy and scalability through conducting extensive experiments on a real distributed service quality dataset WS-DREAM. The evaluation results illustrate that both the service recommendation accuracy in the prediction of quality of services and the scalability, when the volume of the dataset is huge, have been improved.
On the Role of User Feedback in Software Evolution: a Practitioners’ Perspective

User feedback is indispensable in software evolution. Previous work has proposed ways for automatically extracting requirements, bug reports and other valuable information from feedback. However, little is actually known about how user feedback— especially the one available through newer channels, such as social media—is incorporated in development processes. To date, only a few case studies discuss the matter and the results are not always consistent. We carried out a mixed methods study to understand the current state of practice of harnessing user feedback in software development. Qualitatively, we performed interviews with 18 software practitioners to get a deeper understanding of the role of user feedback in software evolution. Quantitatively, we surveyed 101 software practitioners to cross-validate the interview findings and improve the generalizability of the results. We found that feedback is captured to (1) identify bugs, features and usability issues, (2) get a better understanding of the user, and (3) prioritize requirements. Our results indicate that analyzing feedback is time-consuming and has a number of challenges. Among them, feedback is typically analyzed manually and is spread over a wide range of channels and company departments. Our findings stress the current importance for cross-department cooperation and call for the exploration of tools that can centralize user feedback.
Duplicate Bug Report Detection by Using Sentence Embedding and Fine-tuning

Industrial software maintenance devotes much time and effort to find duplicate bug reports. In this paper, we propose an automated duplicate bug report detection system to improve software maintenance efficiency. Our system detects duplicate reports by vectorizing the contents of each report item by deep-learning-based sentence embedding and calculating the similarity of the whole report from those of the item vectors. The Sentence-BERT fine-tuned with report texts is used for sentence embedding. Finally, we verify that the combination of processing separately by item and Sentence-BERT fine-tuned with reports effectively detects duplicate bug reports in industrial experiments that compare the performance of existing methods.
Exploring Tourism Students’ Engagement through Telepresence, Pleasantness of the Experience and Memory: A Virtual Reality Approach

This article focuses on a virtual reality (VR) experience aiming to extend the S-O-R framework by portraying the influence of VR experience on tourism students’ engagement. A sample of tourism management college students are exposed to a visualization of an immersive VR experience – visualization of a short 360° video with a touristic experience – through VR headset equipment in a controlled environment (room and equipment fully dedicated to the experiment). Telepresence, pleasantness of the experience, and memory are considered mediators between VR experience and tourism students’ engagement. Memory is a mediator between telepresence and tourism students’ engagement.
Migrating from a Centralized Data Warehouse to a Decentralized Data Platform Architecture

To an increasing degree, data is a driving force for digitization, and hence also a key asset for numerous companies. In many businesses, various sources of data exist, which are isolated from one another in different domains, across a heterogeneous application landscape. Well-known centralized solution technologies, such as data warehouses and data lakes, exist to integrate data into one system, but they do not always scale well. Therefore, robust and decentralized ways to manage data can provide the companies with better value give companies a competitive edge over a single central repository. In this paper, we address why and when a monolithic data storage should be decentralized for improved scalability, and how to perform the decentralization. The paper is based on industrial experiences and the findings show empirically the potential of a distributed system as well as pinpoint the core pieces that are needed for its central management.
Predicting Energy Consumption of Ground Robots on Uneven Terrains

Optimizing energy consumption for robot navigation in fields requires energy-cost maps. However, obtaining such a map is still challenging, especially for large, uneven terrains. Physics-based energy models work for uniform, flat surfaces but do not generalize well to these terrains. Furthermore, slopes make the energy consumption at every location directional and add to the complexity of data collection and energy prediction. In this letter, we address these challenges in a data-driven manner. We consider a function which takes terrain geometry and robot motion direction as input and outputs expected energy consumption. The function is represented as a ResNet-based neural network whose parameters are learned from field-collected data. The prediction accuracy of our method is within 12% of the ground truth in our test environments that are unseen during training. We compare our method to a baseline method in the literature: a method using a basic physics-based model. We demonstrate that our method significantly outperforms it by more than 10% measured by the prediction error. More importantly, our method generalizes better when applied to test data from new environments with various slope angles and navigation directions.
Non-locality and geometric potential provide the phenomenology of the double-hole single massive particle and light interference

Abstract In spite of its accurate prediction of the experimental outcomes of double-hole single particle interference, quantum mechanics does not provide a phenomenological description of the individual realizations of the experiment. By defining a non-locality function and considering the non-paraxial solution of the time-independent Schrödinger equation by the Green’s theorem, we introduce a geometrical potential which leads to an outstanding result. The geometric potential allows the description of spatially structured Lorentzian wells in the volume between the double-hole mask and the detector. The buildup of the interference patterns results from the confined propagation of single particles through these Lorentzian wells. The phenomenological implications of this description are discussed and illustrated by numerical examples, and its compatibility with quantum mechanical predictions is also shown. A further, non-trivial advantage of this model over the conventional formalism, is that the present quantum probability density can be exactly calculated both in the near and far field conditions.