Reviewerly/exHarmony huggingface dataset:
Evaluating and strategizing the onboarding of software developers in large-scale globally distributed projects

The combination of scale and distribution in software projects makes the onboarding of new developers problematic. To the best of our knowledge, there is no research on the relationship between onboarding strategies and the performance evolution of newcomers in large-scale, globally distributed projects. Furthermore, there are no approaches to support the development of strategies to systematically onboard developers. In this paper, we address these gaps by means of an industrial case study. We identified that the following aspects seem to be related to the observed onboarding results: the distance to mentors, the formal training approach used, the allocation of large and distributed tasks in the early stages of the onboarding process, and team instability. We conclude that onboarding must be planned well ahead and should consider avoiding the aspects mentioned above. Based on the results of this investigation, we propose a process to strategize and evaluate onboarding. To develop the process, we used business process modeling. We conducted a static validation of the proposed process utilizing interviews with experts. The static validation of the process indicates that it can help companies to deal with the challenges associated with the onboarding of newcomers through more systematic, effective, and repeatable onboarding strategies.
Sage

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved memory-density. NVRAM systems today have an order of magnitude more NVRAM than traditional memory (DRAM). NVRAM systems could therefore potentially allow very large graph problems to be solved on a single machine, at a modest cost. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be much more expensive than NVRAM reads. In this paper, we propose an approach to parallel graph analytics using the Parallel Semi-Asymmetric Model (PSAM) , in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the PSAM approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits. To experimentally study this new setting, we develop Sage , a parallel semi-asymmetric graph engine with which we implement provably-efficient (and often work-optimal) PSAM algorithms for over a dozen fundamental graph problems. We experimentally study Sage using a 48--core machine on the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) equipped with Optane DC Persistent Memory, and show that Sage outperforms the fastest prior systems designed for NVRAM. Importantly, we also show that Sage nearly matches the fastest prior systems running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.
Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions

Ant Colony algorithm has been applied to various optimisation problems, however, most of the previous work on scaling and parallelism focuses on Travelling Salesman Problems (TSPs). Although useful for benchmarks and new idea comparison, the algorithmic dynamics do not always transfer to complex real-life problems, where additional meta-data is required during solution construction. This paper explores how the benchmark performance differs from real-world problems in the context of Ant Colony Optimization (ACO) and demonstrate that in order to generalise the findings, the algorithms have to be tested on both standard benchmarks and real-world applications. ACO and its scaling dynamics with two parallel ACO architectures - Independent Ant Colonies (IAC) and Parallel Ants (PA). Results showed that PA was able to reach a higher solution quality in fewer iterations as the number of parallel instances increased. Furthermore, speed performance was measured across three different hardware solutions - 16 core CPU, 68 core Xeon Phi and up to 4 Geforce GPUs. State of the art, ACO vectorisation techniques such as SS-Roulette were implemented using C++ and CUDA. Although excellent for routing simple TSPs, it was concluded that for complex real-world supply chain routing GPUs are not suitable due to meta-data access footprint required. Thus, our work demonstrates that the standard benchmarks are not suitable for generalised conclusions.
Generative image inpainting via edge structure and color aware fusion

Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing methods fail to effectively reconstruct vivid contents and refine structures. In order to solve this issue, in this paper, a novel two-stage generative adversarial network based on the fusion of edge structures and color aware maps is proposed. In the first-stage network, edges with missing regions are employed to train an edge structure generator. Meanwhile, the input image with missing regions is transformed into a global color feature map after the content aware fill algorithm and a large kernel size Gaussian filtering. In the second-stage network, the image fused from the edge map and the color map is used as a label to guide the network to reconstruct the refined image. Qualitative and quantitative experiments conducted on multiple public datasets demonstrate that the method proposed in this paper has superior performance.
Efficient Encrypted Data Search With Expressive Queries and Flexible Update

Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Liang <i>et al.</i> proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency, <inline-formula><tex-math notation="LaTeX">$O(n d)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the total number of system files and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the size of query formula. This article, for the first time, leverages the “oblivious cross search” technology in public key searchable encryption context to reduce the search complexity to <i>only</i> <inline-formula><tex-math notation="LaTeX">$O(nf(w))$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$f(w)$</tex-math></inline-formula> is the number of files embedded with the “least frequent keyword” <inline-formula><tex-math notation="LaTeX">$w$</tex-math></inline-formula> . The new scheme maintains efficient encrypted data share and keyword field update as well. This article further revisits the security models for payload security, keyword privacy and search token privacy (i.e., search pattern privacy) and meanwhile, presents security and efficiency analysis for the new scheme.
(AIAM2019) Artificial Intelligence in Software Engineering and inverse: Review

(2020). (AIAM2019) Artificial Intelligence in Software Engineering and inverse: Review. International Journal of Computer Integrated Manufacturing: Vol. 33, Application of Artificial Intelligence in Advanced Manufacturing, pp. 1129-1144.
A research-based approach for evaluating resources for transitioning to teaching online

Purpose This paper provides a research-based approach for evaluating resources for transitioning to teaching online. Design/methodology/approach This paper uses Davies’ (2011) discussion of technological literacy; Koehler and Mishra’s (2009) Technology, Pedagogy and Content Knowledge (TPACK); Leacock and Nesbit’s (2011) Learning Object Review Instrument; and Reynolds and Leeder’s (2018) expanded notion of “technology stewardship” to underpin an approach that educators can use to evaluate educational resources for transitioning to teaching online. Findings This paper introduces and applies an approach focused on evaluating the source of a given educational resource, as well as how it can be implemented. Research limitations/implications This paper synthesizes frameworks relating to qualities of educational technologies and frameworks relating to qualities of educators, and introduces two criteria for evaluating resources for transitioning to distance learning. Practical implications This paper provides readily applicable criteria for evaluating resources in a time of emergency distance learning. Social implications This approach enables educators to evaluate resources in a time of emergency distance learning. Originality/value The synthesis of four approaches to evaluating educational technologies, and applying the approach to four resources that have emerged to address COVID-19-related instructional needs.
Ethogram-based automatic wild animal monitoring through inertial sensors and GPS data

Direct monitoring of wild animals' behavior is challenging and data tampering. Instrument the animals with collars that embeds sensors, such as tri-axial accelerometer and GPS, allows obtaining sufficient information for remotely classifying the performed activities. In this work is presented an accurate and human intelligible framework, designed leveraging the authors' skills in machine-learning and data analysis. The system covers all the steps required to accurately map the raw signals to the activities carried out, grouped in pre- processing, features extraction and selection, and classification phases. A case of study consists of a dataset collected by the Crofoot Lab at the Mpala Centre, in Kenya, instrumenting free-ranging Olive Baboons. This dataset provides both sensors time-series paired with respective activity labels and unlabeled ones. Labeled data was used to tune the parameters of the framework phases and to train and test the employed boosted- trees classifiers, while unlabeled ones were used for further system validations. The average accuracy obtained on a single activity is 94.5%. At best of the authors' knowledge, this is the first work that aims to solve the problem of direct human monitoring with such high accuracy, outperforming the state of the art by a lift about 10%. The main contribution of the proposed systems consists of the attention paid to the ethologist's needs. Together with the predictions, the framework also returns a ranking for the features considered, based on their importance in the decision-making process of the classifier. Therefore, the extracted features are consistent with the logical human path that the ethologist follows in performing direct monitoring. The produced framework has also been designed consistently with the ethogram structure to be easily interpretable and to allow activities classification at different aggregation levels.
No-reference image quality assessment of authentically distorted images with global and local statistics

Abstract The development of digital image processing techniques requires reliable image quality assessment (IQA) methods. Since images acquired by a camera often contain various distortions and their non-distorted versions are not available, a no-reference IQA (NR-IQA) technique should be used. Many popular methods are developed to assess artificially distorted images, available in benchmark databases. In this paper, a new large benchmark database, containing naturally distorted images captured with a digital camera, is introduced along with a new NR-IQA metric. The method uses a wide spectrum of local and global image features and their statistics to address a diversity of distortions. Among 80 employed features, 56 are introduced to the IQA for the first time, while the remaining statistics are used to further improve the quality prediction performance of the method. The obtained perceptual feature vector is used to provide a quality model with support vector regression technique. The experimental comparison of the method with the state-of-the-art IQA measures on the database reveals its superiority in terms of correlation with human scores.
Fluid MPC: Secure Multiparty Computation with Dynamic Participants

Existing approaches to secure multiparty computation (MPC) require all participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities, resulting in computations spanning several hours or days. Such scenarios call for a dynamic participation model for MPC where participants have the flexibility to go offline as needed and (re)join when they have available computational resources. Such a model would also democratize access to privacy-preserving computation by facilitating an “MPC-as-a-service” paradigm—the deployment of MPC in volunteer-operated networks (such as blockchains, where dynamism is inherent) that perform computation on behalf of clients. In this work, we initiate the study of fluid MPC, where parties can dynamically join and leave the computation. The minimum commitment required from each participant is referred to as fluidity, measured in the number of rounds of communication that it must stay online. Our contributions are threefold:
Towards HPC I/O Performance Prediction through Large-scale Log Analysis

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.
Documentary narrative visualization: Features and modes of documentary film in narrative visualization

Documentary narrative visualization is a data visualization approach using the features of documentary film. Researchers in the field of visualization are searching for better methods of constructing narratives from data sets. In this article, we explore the structure and techniques of documentary film and how they apply to the practice of constructing narrative visualization with video. We review the structural aspects of documentary film with examples relevant for narrative visualization. Using six of the highest quality video-based narrative visualizations, we conducted a study of user preferences for three pairs of videos. The video pairs were specifically matched to highlight unique features available in documentary film. Using the preferences expressed by our participants, we performed an empirical study to examine the documentary features most valued by our participants. Our results provide implications about the style and features of documentary film that are most useful in the construction of narrative visualization. Overall, this work provides a clear starting point for the construction of documentary narrative visualization providing content creators with specific techniques that will improve engagement of their content.
Towards the adoption of OMG standards in the development of SOA-based IoT systems

A common feature of the Internet of Things (IoT) is the high heterogeneity, regarding network protocols, data formats, hardware and software platforms. Aiming to deal with such a degree of heterogeneity, several frameworks have applied the Model-Driven Development (MDD) to build IoT applications. On the software architecture viewpoint, the literature has shown that the Service-Oriented Architecture (SOA) is a promising style to address the interoperability of entities composing these solutions. Some features of IoT make it challenging to analyze the impact of design decisions on the SOA-based IoT applications behavior. Thus, it is a key requirement to simulate the model to verify whether the system performs as expected before its implementation. Although the literature has identified that the SOA style is suitable for addressing the interoperability, existing modeling languages do not consider SOA elements as first-class citizens when designing IoT applications. Furthermore, although existing MDD frameworks provide modeling languages comprising well-defined syntax, they lack execution semantics, thus, are not suitable for model execution and analysis. This work aims at addressing these issues by introducing IoTDraw. The framework provides a fully OMG-compliant executable modeling language for SOA-based IoT systems; thus, its specifications can be implemented by any tool implementing OMG standards.
Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging

Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical class clusters.However, DPC relies heavily on the calculation methods of the cutoff distance threshold and local density and cannot analyze complex manifold data, especially datasets with uneven density distribution and multiple peaks in the same cluster.To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC).First, we redefine the local density calculation method using the layered k-nearest neighbors.To adapt to datasets with different densities, the k-nearest neighbors are divided into multiple layers.Second, for the multiple peaks in the same cluster problem, we design a new mechanism to calculate the similarity of subclusters based on the idea of shared neighbors and Newton's law of gravitation, and a subcluster merging strategy is proposed.To prove the effectiveness of our algorithm, we compare the LKSM_DPC with K-means, DBSCAN, DPC, and DPC derivatives for 24 datasets.A large number of experiments demonstrate that our algorithm can often outperform other algorithms.
Simultaneous incremental matrix factorization for streaming recommender systems

Recommender systems are large-scale machine learning and knowledge discovery tools aimed at providing personalized recommendations to customers based on their preferences and needs. They need to handle large quantities of diverse and very sparse data in a matter of seconds. Matrix factorization techniques have proven to be useful and reliable for implementing recommender systems, while data sparsity problem can be indirectly alleviated by considering multiple heterogeneous data sources. Furthermore, utilization of data fusion can resolve in a higher predictive accuracy. For real-world applications, e.g., such with continuous user feedback, incrementally handling recommender systems upon multiple data streams remains a crucial and only partially solved problem. This paper presents one way of fusing multiple data streams through matrix factorization. Our proposed method (SIMF) models heterogeneous and asynchronous data streams and provides predictions in real time. As a result of incremental updating, the proposed method successfully adapts to changes in data concepts, while application of data fusion improves prediction accuracy and reduces effects of the cold-start problem. Using the proposed methodology, we have develop a streaming algorithm and show how prediction accuracy can be substantially increased by considering multiple data sources, while at the same time the negative effects of the cold-start can be greatly diminished. Evaluations on a large-scale real-life problem (Yelp recommendations) confirm these claims as we present a highly scalable streaming recommender system that adapts to new concepts in data and provides accurate predictions (compared to the other matrix factorization techniques) in a very sparse problem domain. Apart from a recommender system proposed in this work, the versatility of matrix factorization could further allow the presented methodology for adaptation to solve several other machine learning problems, such as dimensionality reduction, clustering and classification.
A separable reversible data hiding scheme for encrypted images based on Tromino scrambling and adaptive pixel value ordering

To further improve capacity and reduce distortion, a novel separable reversible data hiding (RDH) scheme for encrypted images based on Tromino scrambling and adaptive pixel value ordering (PVO) is proposed. An image is first partitioned into non-overlapped blocks with three pixels in L shape, and then Tromino scrambling and stream encryption are performed to the blocks in sequence. In the embedding phase, an adaptive pixel value ordering (PVO) scheme is utilized. The differences between the smallest pixel value and the rest two pixel values are calculated, and the pairwise prediction errors corresponding to each block is accumulated to form a two-dimensional prediction error histogram. Subsequently, an adaptive 2D histogram mapping is established for reversible data hiding. Experimental results and analysis show that it is separable and can completely recover the original image. Furthermore, compared with other PVO, the adaptive PVO makes better use of the features of the images after Tromino encryption, it can achieve larger capacity and better visual quality of the decrypted images compared with the existing methods.
Two-stage knowledge transfer framework for image classification

The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel Two-Stage Representation method (TSR), and convert it to a Single-Teacher Single-Student (STSS) problem in our two-stage knowledge transfer framework for image classification. Specifically, the first stage classifier is formulated as the teacher, which holds the 'gate value' to supervise the student classifier in the second stage. To transfer knowledge from the teacher classifier, we seek the nearest neighbours of the test sample to generate a set of candidate target classes in the first stage. Then, a student classifier learns from the samples belonging to these candidate classes in the second stage. Under the supervision of the teacher classifier, the teacher approves the student only if it obtains a higher score than the 'gate value'. In actuality, the proposed framework generates a stronger classifier by staging two weaker classifiers in a novel way. The experiments on several databases show that our proposed framework is effective, which outperforms multiple popular classification methods.