Reviewerly/exHarmony huggingface dataset:
Simultaneous Isolation of Software Faults for Effective Fault Localization

Due to fault-to-failure complexity in the existence of multiple faults, debugging faults is extremely hard. Many studies were done to improve localization effectiveness in the existence of multiple faults. Some studies attempt to isolate faults into separate fault-focused clusters that target single faults. However, isolating failures to their causative faults is still an issue and needs improvement. In this paper, we propose the use of a network community clustering algorithm to isolate faults into separate fault-focused communities, each targeting a single fault. These fault-focused communities will be given to developers to debug the faults simultaneously in parallel. The method is evaluated on 5 well-known multiple-fault subject programs from the Siemens test suite benchmark. The experimental results show that the network community clustering algorithm is relatively effective in isolating different faults into distinct fault-focused communities with improvements in faults localization effectiveness. The result also shows improvement in terms of reducing the expense to produce a failure-free program.
Using the object ID index as an investigative approach for NTFS file systems

When investigating an incident it is important to document user activity, and to document which storage device was connected to which computer. We present a new approach to documenting user activity in computer systems using the NTFS file system by using the $ObjId Index to document user activity, and to correlate this index with the corresponding records in the MFT table. This may be the only possible approach when investigating external NTFS storage devices, and is hence a valuable addition to the storage forensics toolbox.
The mathematical model of estimation the multidimensional steganalytical methods reliability

The most effective approach in steganalysis is adaptive multidimensional models. In order to apply multidimensional steganalytical methods in computer forensic expertise, it is necessary to evaluate their reliability in terms of mathematical statistics. The article is devoted to development of a mathematical model for estimating the reliability of the multidimensional steganoanalytical methods. Wherein such parameters as sampling representativeness, significance level, and error are taking into account. The article describes the relevance of the topic, provides mathematical premises of the model development. The model based on evalution of regression. The relationship between sample representativeness and significance level is determined. An example of application of the proposed model in the computer forensics expertise is considered.
Interactive resource recommendation with optimization by tag association and significance analysis

Along with the fast growing web-based applications, the recommender system is now attracting much attention due to its core function that matches the target users’ interest with the potential resources from the massive online information. Since the recommender system is a user centric application, in this work, we propose a recommendation framework based on user interaction, so as to explore the user’s real-time interest from the instant feedback. Naturally, we utilize the tag information assigned to different resources as the medium for user interaction. During the interaction, the most effective tags will be provided for users to choose, and the chosen tag words will be considered as the personalized preference and utilized to dynamically adjust the recommendation list during the process. However, the interaction procedure may cause the problem of potential false dismissal during the candidate filtering. In this work, we propose to analyze the association between different tags, and utilize the tag co-occurrence to refine the recommendation candidate, so as to avoid false dismissal. To generate the recommendation list from the filtered candidates, we design the representation of user and resource characteristics based on tag information and user historical behavior. We distinguish the significance of each tag word for the corresponding resource item, so as to precisely describe the item feature. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can achieve more accurate predictions and higher recommendation efficiency.
Extraction of abstracted sensory data to reduce the execution time of context‐aware services in wearable computing environments

Summary Context awareness is a necessary technique for providing optimized services to users by recognizing their surrounding environment at a particular time. To provide context‐aware services, a context‐aware middleware is required to detect the changes surrounding the user as well as the processing procedure. However, when the context‐aware middleware is applied to wearable computing, the processing time increases in proportion to the increase in the number of context information due to the lack of processing capacity from the terminal devices. To access the terminal devices, a specific context representation with Resource Description Framework–based triplet is used. Since the triplet consists of three keyword elements that refer to the status of a given situation around the user, retrieving these elements takes O(n 3 ) time complexity with a linear search. To overcome this problem, a hash‐based comparison method is suggested to minimize the response time. The suggested comparison method gives a better performance without searching every single keyword element among the triplet sets. In the experiment, we applied the suggested method to the context‐aware workflow middleware and demonstrated that the proposed method improves the processing time by at least 30% compared with the linear search by enhancing the comparison module in the middleware.
A particle filter approach to estimating target location using Brownian bridges

We study the problem of modelling the trajectory of a moving object of interest, or target, given limited locational and temporal information. Because of uncertainty in information, the location of the target can be represented using a spatial distribution, or heatmap. This paper proposes a comprehensive method for constructing and updating probability heatmaps for the location of a moving object based on uncertain information. This method uses Brownian bridges to model and construct temporal probability heatmaps of target movement, and employs a particle filter to update the heatmap as new intelligence arrives. This approach allows for more complexity than simple deterministic motion models, and is computationally easier to implement than detailed models for local target movement.
Proper error bars for self-calibrating quantum tomography

Self-calibrating quantum state tomography aims at reconstructing the unknown quantum state and certain properties of the measurement devices from the same data. Since the estimates of the state and device parameters come from the same data, one should employ a joint estimation scheme, including the construction and reporting of joint state-device error regions to quantify uncertainty. We explain how to do this naturally within the framework of optimal error regions. As an illustrative example, we apply our procedure to the double-crosshair measurement of the BB84 scenario in quantum cryptography and so reconstruct the state and estimate the detection efficiencies simultaneously and reliably. We also discuss the practical situation of a satellite-based quantum key distribution scheme, for which self-calibration and proper treatment of the data are necessities.
New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements

Nowadays, the deformation measurement in metal sheets is important for industries such as the automotive and aerospace industries during its mechanical stamping processes. In this sense, Digital Image Correlation (DIC) has become the most relevant measurement technique in the field of experimental mechanics. This is mainly due to its versatility and low-cost compared with other techniques. However, traditionally, DIC global image registration implemented in software, such as MATLAB 2018, did not find the complete perspective transformation needed successfully and with high precision, because those algorithms use an image registration of the type “afine” or “similarity”, based on a 2D information. Therefore, in this paper, a DIC initialization method is presented to estimate the surface deformation of metal sheets used in the bodywork automotive industry. The method starts with the 3D points reconstruction from a stereoscopic digital camera system. Due to the problem complexity, it is first proposed that the user indicates four points, belonging to reference marks of a “Circle grid”. Following this, an automatic search is performed among the nearby marks, as far as one desires to reconstruct it. After this, the local DIC is used to verify that those are the correct marks. The results show reliability by reason of the high coincidence of marks in experimental cases. We also consider that the quality of mark stamping, lighting, and the initial conditions also contribute to trustworthy effects.
Design of compressed sensing fault-tolerant encryption scheme for key sharing in IoT Multi-cloudy environment(s)

The booming growth of the Internet of things(IoT) has improved the productivity of companies and improved people's quality of life. However, as a growing number and variety of connected devices are introduced into the IoT networks, not only are communication bandwidth and storage severely challenged, but potential security threats are escalating. Therefore, we consider using compressive sensing(CS) in the IoT system to solve this problem. The solution we designed solves three main security threats in the IoT, the perception layer, the transport layer and the application layer. In our work, the gateway node of the perception layer provides the function of anonymous identity authentication and information authentication, which can not only prevent external attackers from obtaining private information from the gateway node, but also solve security problems such as identity authentication and man-in-the-middle attack in the transmission layer. Meanwhile, our scheme handles the ciphertext integrity protection and energy leakage in existing CS encryption(CSE) schemes, protecting the security of application layer privacy data. And our scheme provides a secure key sharing method, which improves the security of the system. In addition, we designed two fault-tolerant models to ensure that the ciphertext can still be decrypted correctly when the cloud server fails in the IoT environment. The use of the replaced sparse vector instead of the random matrix method in the traditional CSE scheme improves the quality of recovery while saving a large amount of storage space. Simulation results show that our scheme improves the overall compression and recovery performance compared to other CSE schemes.
Integration Platform for Metric-Based Analysis of Web User Interfaces

We present a software tool for collecting web UI metrics from different providers and integrating them in a single database for further analysis. The platform’s architecture supports both code- and image-based UI assessment, thus allowing to combine advantages of the two approaches. The data structures are based on a web UI measurement domain ontology (OWL) that organizes the currently disperse set of metrics and services. Our platform can be of use to interface designers, researchers, and UI analysis tools developers.
Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning

Recently, world-class human players have been outperformed in a number of complex two-person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems. However, the data efficiency of the learning systems is unclear given that they appear to require far more training games to achieve such performance than any human player might experience in a lifetime. In addition, the resulting learned strategies are not in a form which can be communicated to human players. This contrasts to earlier research in Behavioural Cloning in which single-agent skills were machine learned in a symbolic language, facilitating their being taught to human beings. In this paper, we consider Machine Discovery of human-comprehensible strategies for simple two-person games (Noughts-and-Crosses and Hexapawn). One advantage of considering simple games is that there is a tractable approach to calculating minimax regret. We use these games to compare Cumulative Minimax Regret for variants of both standard and deep reinforcement learning against two variants of a new Meta-interpretive Learning system called MIGO. In our experiments, tested variants of both normal and deep reinforcement learning have consistently worse performance (higher cumulative minimax regret) than both variants of MIGO on Noughts-and-Crosses and Hexapawn. In addition, MIGO’s learned rules are relatively easy to comprehend, and are demonstrated to achieve significant transfer learning in both directions between Noughts-and-Crosses and Hexapawn.
Feature analysis using information retrieval, community detection and structural analysis methods in product line adoption

In industrial practice the clone-and-own strategy is often applied when in the pressure of high demand of customized features. The adoption of software product line (SPL) architecture is a large one time investment that affects both technical and organizational issues. The analysis of the feature structure is a crucial point in the SPL adoption process involving domain experts working at a higher level of abstraction and developers working directly on the program code. We propose automatic methods to extract feature-to-program links starting from very high level set of features provided by domain experts. For this purpose we combine call graph information with textual similarity between code and high level features. In addition, in depth understanding of the feature structure is supported by finding communities between programs and relating them to features. As features are originated from domain experts, community analysis reveals discrepancies between expert view and internal code structure. We found that communities correspond well to the high level features, with usually more than half of feature code located in specialized communities. We report experiments at two levels of features and more than 2000 Magic 4GL programs in an industrial SPL adoption project.
Applying standard independent verification and validation techniques within an agile framework: Identifying and reconciling incompatibilities

Abstract Agile methods have gained wide acceptance over the past several years. They are now a standard management and execution approach for small‐scale system development projects. While Agile methods are not generally applicable to large multiyear and mission‐critical systems, hybrid Agile approaches are being developed to exploit the productivity improvements of Agile while retaining the necessary process rigor and coordination needs of these types of projects. From the perspective of Independent Verification and Validation (IV&amp;V), however, adoption of these hybrid‐Agile frameworks is becoming problematic. Hence, we find it prudent to question the compatibility of conventional IV&amp;V techniques with hybrid‐Agile practices. This paper documents investigation of relevant literature, the modification and adoption of Agile frameworks to accommodate the development of large scale, mission‐critical systems, and the compatibility of standard IV&amp;V techniques within hybrid‐Agile development frameworks. Specific to the latter, we find that the IV&amp;V techniques employed within a hybrid‐Agile process can be divided into three groups: early lifecycle techniques that are fully compatible with the hybrid frameworks, techniques that focus on tracing requirements, test objectives, etc, which are somewhat problematic, but can be tailored with modest effort, and techniques involving an assessment requiring artifact completeness, and consequently, are not compatible with hybrid‐Agile processes. We identify seven prominent techniques in this third category and propose alternative approaches that achieve similar goals and which can be applied within an Agile development process.
Exploring the Impact of Network Impairments on Remote Collaborative Augmented Reality Applications

Our research explores the impact of network impairments on remote augmented reality (AR) collaborative tasks, and possible strategies to improve user experience in these scenarios. Using a simple AR task, under a controlled network environment, our preliminary user study highlights the impact of network outages on user workload and experience, and how user roles and learning styles play a role in this regard.
Cloud Security Auditing: Major Approaches and Existing Challenges

Cloud computing is emerging as a promising IT solution for enabling ubiquitous, convenient, and on-demand accesses to a shared pool of configurable computing resources. However, the widespread adoption of cloud is still being hindered by security and privacy concerns. Various cloud security and privacy issues have been addressed in the literature. However, the mere existence of such security mechanisms is usually insufficient to fully relieve cloud tenants from their security and privacy concerns. To increase tenants' trust in cloud, it is of paramount importance to provide adequate auditing mechanisms and tools to verify the security postures of their applications. However, there are currently many challenges in the area of cloud auditing and compliance validation. There exists a significant gap between the high-level recommendations provided in most cloud-specific standards and the low-level logging information currently available in existing cloud infrastructures. Furthermore, the unique characteristics of cloud computing may introduce additional complexity to the task, e.g., the use of heterogeneous solutions for deploying cloud systems may complicate data collection and processing and the sheer scale of cloud, together with its self-provisioning, elastic, and dynamic nature. In this paper, we conduct a survey on the existing cloud security auditing approaches. Additionally, we propose a taxonomy identifying the classifications based on auditing objectives and auditing techniques. We further devise a systematic process flow for cloud security auditing. Also, we conduct a comparative study on existing works to identify their strengths and weaknesses. Finally, we report existing challenges in cloud security auditing.
Child-Computer Interaction SIG

The global refugee crisis is a significant current challenge affecting millions of children. The process of refugee migration comes with major immediate as well as long-term risks to children's physical and mental health, education, and prospects. Despite the multiple dangers and challenges during migration, most refugee families have access to and make use of interactive technologies, prior to, during, and after migration. This SIG meeting is an opportunity to discuss novel potential roles for technologies to alleviate some of the challenges faced by child refugees.
A didactic innovation project in Higher Education through a Visual and Academic Literacy competence-based program

This research paper describes the application of a didactic innovation project in Higher Education. We present the theoretical foundation of the project. Thanks to the evolution of the Web and the potential of image to disseminate and generate knowledge, visual materials have had an increasingly po werful projection in Education, especially for the development of new methods, media and didactic materials in Higher Education. As a result of researchers interested in it, Visual Literacy has emerged as an academic field developing research and didactic effectiveness of the image, and digital competences and academic literacy as instruments to be integrated into curriculum of higher education for its excellence. We analyse the didactic innovation project by presenting how we integrated a Visual and Academic Literacy competence-based program into a course at the Carlos III University of Madrid. We show its instructional design, didactic materials, pedagogical activities, planning and evaluation. Findings suggest the need for this type of programs for academic excellence in higher education.
Cable Installation by a Humanoid Integrating Dual-arm Manipulation and Walking

In this paper, we present a four layers hierarchical framework to complete the cable installation task with a humanoid robot. By decoupling task planning layer and motion planning layer, flexibility of the framework is revealed. After symbolic relational representation and planning method is used in task planning layer, the planning result is then mapped to primitive manipulation action sequence in motion planning layer. By integrating position detection of operating points and humanoid walking, installation of a long cable on two industrial clamps with a certain distance was completed by humanoid robot HRP-2Kai.
A Survey on Medical Images for Reversible Data Hiding Techniques

In the recent trends, technology has advanced so much that most of the individuals are using internet for communication purpose. It became very easy, accurate and fast to send data with the help of internet from one end to another across the world. The major issue while sending data over the internet is the security of the data whether it is personal or confidential. Many of the techniques are there for protecting the data, but data hiding is the most protective technique in data security. Basically, Data hiding is a technique in which secret data is embedded into the cover image and at the receiver side, the host will extract the secret data. Another technique is reversible data hiding (RDH) in which the host can extract the secret data as well as the original image without any loss of data. In this paper, we represent an overview of the recent research in the field of the reversible data hiding techniques, related methods and procedures.
Context‐aware encoding for clothing parsing

Clothing parsing is a special type of semantic segmentation in which each pixel is assigned with clothing labels. Unlike general scene semantic segmentation, stylish match (e.g. skirts + blouse, jeans + T-shirt) is an important cue for recognising fine-grained categories in clothing parsing. In this Letter, the authors propose a context-aware outfit encoder (COE), as a side branch, that drives the convolutional neural network to take the stylish match into account for clothing parsing. The proposed COE provides information on matching clothes that can be utilised to improve the prediction accuracy of the base network significantly. Experimental results show that fully convolutional network and MobileNet with the COE improve the mean intersection of the union of those without the COE by 2.5 and 2.8%, respectively, on CFPD dataset.
Pairwise normalization in SimRank variants

Despite of the success in the real-world applications, SimRank and its variants, rvs-SimRank and PRank, suffer from the pairwise normalization problem (PNP) as a counter intuitive property hidden in their computation paradigm. Jac-Sim, a state-of-the-art measure, provides an effective solution to PNP; however, it considers PNP and the effectiveness of the provided solution only in SimRank. In this paper, we consider PNP in SimRank variants, generalize the solution to those measures, conduct extensive experiments with two real-world datasets to accurately perform parameter tuning, and verify the effectiveness of applying the solution to SimRank variants with both un-weighted and weighted graphs.