Reviewerly/exHarmony huggingface dataset:
Code Review Knowledge Perception: Fusing Multi-Features for Salient-Class Location

Code review is a common software engineering practice of practical importance to reduce software defects. Review today is often with the help of specialized tools, such as Gerrit. However, even in a tool-supported code review involves a significant amount of human effort to understand the code change, because the information required to inspect code changes may distribute across multiple files that reviewers are not familiar with. Code changes are often organized as commits for review. In this paper, we found that most of the commits contain a salient class(es), which is saliently modified and causes the modification of the rest classes in a commit. Our user studies confirmed that identifying the salient class in a commit can facilitate reviewers in understanding code change. Inspired by the effectiveness of machine learning techniques in the classification field, we model the salient class identification as a binary classification problem and a number of discriminative features is extracted for a commit and used to characterize the salience of a class. The experiments results show that our approach achieves an accuracy of 88 percent. A user study with industrial developers shows that our approach can really improve the efficiency of reviewers understanding code changes in a reviewing scenario without using comment.
Benefits of crowdsourcing for libraries: A case study from Africa

Many libraries in the Global South do not collect comprehensive data about themselves, which creates challenges in terms of local and international visibility. Crowdsourcing is an effective tool that engages the public to collect missing data, and it has proven to be particularly valuable in countries where governments collect little public data. Whereas crowdsourcing is often used within fields that have high levels of development funding, such as health, the authors believe that this approach would have many benefits for the library field as well. They present qualitative and quantitative evidence from 23 African countries involved in a crowdsourcing project to map libraries. The authors find benefits in terms of increased connections between stakeholders, capacity-building, and increased local visibility. These findings demonstrate the potential of crowdsourced approaches for tasks such as mapping to benefit libraries and similarly positioned institutions in the Global South in multifaceted ways.
An Ontological Approach for Creating a Brassware Craft Knowledge Base

Terengganu brassware is a local craft product with high artistic value and traditional craftsmanship that involves a lot of creativity and elaborate artistry. This cultural heritage is in danger of disappearing because the current younger generation lacks interest, creativity and artistic skills to continue the craftsmanship. Therefore, gathering detailed knowledge of the craft from ageing practitioners can preserve the heritage. This article reports the study on brassware intangible heritage knowledge base development using an ontological approach. The ontology modelling used Ontology Development 101. Content validation result by experts found that the brassware ontology is complete, consistent, concise, precise and clear. Evaluation results show that the experts agreed on content representation (mean ≥3.80), and the ontology meets all the quality criteria (mean ≥4.03). The brassware craft ontology contributes to the intangible heritage preservation effort for one of the almost extinct local culture and practices. This intangible heritage knowledge base can be applied for the development of semantic search, mobile and gaming applications to attract and disseminate heritage information to young people. Additionally, the ontological design can serve as a guide for the design and development of a knowledge base for other types of heritage.
SVP: Sinusoidal Viewport Prediction for 360-Degree Video Streaming

The rapid growth of user expectations and network technologies has proliferated the service needs of 360-degree video streaming. In the light of the unprecedented bitrates required to deliver entire 360-degree videos, tile-based streaming, which associates viewport and non-viewport tiles with different qualities, has emerged as a promising way to facilitate 360-degree video streaming in practice. Existing work on viewport prediction primarily targets prediction accuracy, which potentially gives rise to excessive computational overhead and latency. In this paper, we propose a sinusoidal viewport prediction (SVP) system for 360-degree video streaming to overcome the aforementioned issues. In particular, the SVP system leverages 1) sinusoidal values of rotation angles to predict orientation, 2) the relationship between prediction errors, prediction time window and head movement velocities to improve the prediction accuracy, and 3) the normalized viewing probabilities of tiles to further improve adaptive bitrate (ABR) streaming performance. To evaluate the performance of the SVP system, we conduct extensive simulations based on real-world datasets. Simulation results demonstrate that the SVP system outperforms state-of-the-art schemes under various buffer thresholds and bandwidth settings in terms of viewport prediction accuracy and video quality, revealing its applicability to both live and video-on-demand streaming in practical scenarios.
The technology of adversarial attacks in signal recognition

The wide application of contour stellar images has helped researchers transform signal classification problems into image classification problems to solve signal recognition based on deep learning. However, deep neural networks (DNN) are quite vulnerable to adversarial examples, thus simply evaluating the adversarial attack performance on the signal sequence recognition model cannot meet the current security requirements. From the perspective of an attacker, this study converts individual signals into stellar contour images, and then generates adversarial examples to evaluate the adversarial attack impacts. The results show that whether the current input sample is a signal sequence or a converted image, the DNN is vulnerable to the threat of adversarial examples. In the selected methods, whether it is under different perturbations or signal-to-noise ratio (SNRs), the momentum iteration method has the best performance among them, and under the perturbation of 0.01, the attack performance is more than 10% higher than the fast gradient sign method. Also, to measure the invisibility of adversarial examples, the contour stellar images before and after the attack were compared to maintain a balance between the attack success rate and the attack concealment.
Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification.However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets.In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder.We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used.More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection.Some of these algorithms use feature selection techniques to improve the accuracy of the classification.Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings.Significant improvements in the accuracy performance of the algorithms are observed while the number of features is being reduced.
Contrastive Self-Supervised Hashing With Dual Pseudo Agreement

Recently, unsupervised deep hashing has attracted increasing attention, mainly because of its potential ability to learn binary codes without identity annotations. However, because the labels are predicted by their pretext tasks, unsupervised deep hashing becomes unstable when learning with noisy labels. To mitigate this issue, we propose a simple but effective approach to self-supervised hash learning based on dual pseudo agreement. By adding a consistency constraint, our method can prevent corrupted labels and encourage generalization for effective knowledge distillation. Specifically, we use the refined pseudo labels as a stabilization constraint to train hash codes, which can implicitly encode semantic structures of the data into the learned Hamming space. Based on the stable pseudo labels, we propose a self-supervised hashing method with mutual information and noise contrastive loss. Throughout the process of hash learning, the stable pseudo labels and data distributions collaboratively work together as teachers to guide the binary codes learning process. Extensive experiments on three publicly available datasets demonstrate that the proposed method can consistently outperform state-of-the-art methods by large margins.
The Identification of a Model Victim for Social Engineering: A Qualitative Analysis

End users present a key challenge for the protection of contemporary information security systems. The manipulation of people through deceit to gain access to sensitive information and otherwise secure systems is known to hackers, information security practitioners, and other technologists as “social engineering.” To date, little research has investigated the attributes that people who engage in such deception – so-called “social engineers” – associate with vulnerable targets. To address this gap, this study engages in a grounded theory-based analysis of interviews with nonprofessional and professional social engineers. The results describe six attributes of a “model victim” for social engineers, a hypothetical person considered particularly susceptible to social engineering deceptions: (1) prized, (2) uninformed, (3) unconcerned, (4) outgoing, (5) connected, and (6) controlled. Additionally, this study describes heuristic categories described by participants to help make decisions about target vulnerability which include target socio-demographic characteristics, social roles, and organizational positions. Implications for theory, future research, and policy are considered.
Convex cones in mapping spaces between matrix algebras

We introduce the notion of one-sided mapping cones of positive linear maps between matrix algebras. These are convex cones of maps that are invariant under compositions by completely positive maps from either the left or right side. The duals of such convex cones can be characterized in terms of ampliation maps, which can also be used to characterize many notions from quantum information theory—such as separability, entanglement-breaking maps, Schmidt numbers, as well as decomposable maps and k-positive maps in functional analysis. In fact, such characterizations hold if and only if the involved cone is a one-sided mapping cone. Through this analysis, we obtain mapping properties for compositions of cones from which we also obtain several equivalent statements of the PPT (positive partial transpose) square conjecture.
E-Fraud in Nigerian Banks: Why and How?

The advent of the internet and the adoption of e-payment platforms as a convenient means of payment have increased the extent of occurrence of e-fraud and cyber-attacks in Nigerian Banks.The study, therefore, investigated why and how e-frauds are perpetrated in the Deposits Money Banks in Nigeria by employees.The survey research design was adopted.Primary data were sourced from 120 fraud investigation officers in the Banks through the administration of structured questionnaires.Data were analysed using simple percentages.Results revealed that e-frauds were perpetrated by the employees whose employment was threatened as a result of not achieving deposit targets and using either expert or legitimate power to connive with other employees to commit e-fraud against the Banks.Furthermore, findings revealed that job losses were occasioned by disruptive technologies and economic challenges which often lead to employees' disengagement without or little compensation created fear in the mind of employees to commit e-fraud through Phishing, Pharming, and breach of internal checks.The study recommended that unachievable deposits and sales targets should be discouraged in the Banks through our labour laws.Also, the human resources department of the Banks should institute whistleblowing policy that can assist employees to get a reprieve from a supervisor that may want to influence them using any form of power to commit e-fraud.Finally, it was recommended that e-fraud consciousness of the general users of e-payment channels and employees' sensitization on negative consequences of employees' e-frauds should be heightened through frequent education and continuous training.
Attribute Privacy: Framework and Mechanisms

Ensuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data. Much attention has been devoted to methods for protecting individual privacy during analyses of large datasets. However in many settings, global properties of the dataset may also be sensitive (e.g., mortality rate in a hospital rather than presence of a particular patient in the dataset). In this work, we depart from individual privacy to initiate the study of attribute privacy, where a data owner is concerned about revealing sensitive properties of a whole dataset during analysis. We propose definitions to capture attribute privacy in two relevant cases where global attributes may need to be protected: (1) properties of a specific dataset and (2) parameters of the underlying distribution from which dataset is sampled. We also provide two efficient mechanisms for specific data distributions and one general but inefficient mechanism that satisfy attribute privacy for these settings. We base our results on a novel and non-trivial use of the Pufferfish framework to account for correlations across attributes in the data, thus addressing “the challenging problem of developing Pufferfish instantiations and algorithms for general aggregate secrets” that was left open by Kifer and Machanavajjhala in 2014 [15].
Global Manifold Learning for Interactive Image Segmentation

This paper presents an interactive image segmen-tation algorithm, in which the segmentation problem is formulated as a global manifold learning process. Based on the principle that the label of each element depends on the influence of all the elements in the image, we extend the conventional local neighborhood or the long range regional relationships to the global relationships over the whole image. A probabilistic framework is established to measure the global effect of each element on all other elements. Based on two different manifold learning styles, the semi-global manifold learning (SGML) and the fully-global manifold learning (FGML) algorithms are proposed to capture the global geometry structure of the data manifold. SGML learns the intrinsic effects of each element separately, equivalent to a matrix diffusion process on an affinity graph. FGML learns the intrinsic effects of all elements together, equivalent to a label pair diffusion process on a higher-order tensor product graph. The global manifold learning helps to overcome the low contrast, weak boundary and texture problems. Extensive experiments on three public interactive segmentation datasets demonstrate the superior performance of the proposed algorithms both in accuracy and efficiency.
A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs

Image denoising is a basic problem in image processing. An important task of image denoising is to preserve the significant geometric features such as edges and textures while filtering out noise. So far, this is still a problem to be further studied. In this paper, we firstly introduce an edge detection function based on the Gaussian filtering operator and then analyze the filtering characteristic of the fractional derivative operator. On the basis, we establish the spatially adaptive fractional edge-preserving denoising model in the variational framework, discuss the existence and uniqueness of our proposed model solution and derive the nonlinear fractional Euler-Lagrange equation for solving our proposed model. This forms a fractional order extension of the first and second order variational approaches. Finally, we apply the proposed method to the synthetic images and real seismic data denoising to verify the effectiveness of our method and compare the experimental results of our method with the related state-of-the-art methods. Experimental results illustrate that our proposed method can not only improve the signal to noise ratio (SNR) but also adaptively preserve the structural information of an image compared with other contrastive methods. Our proposed method can also be applied to remote sensing imaging, medical imaging and so on.
Rumor Source Detection With Multiple Observations Under Adaptive Diffusions

Recent work, motivated by anonymous messaging platforms, has introduced adaptive diffusion protocols which can obfuscate the source of a rumor: a “snapshot adversary” with access to the subgraph of “infected” nodes can do no better than randomly guessing the entity of the source node. What happens if the adversary has access to multiple independent snapshots? We study this question when the underlying graph is the infinite d-regular tree. We show that (1) a weak form of source obfuscation is still possible in the case of two independent snapshots, but (2) already with three observations there is a simple algorithm that finds the rumor source with constant probability, regardless of the adaptive diffusion protocol. We also characterize the tradeoff between local spreading and source obfuscation for adaptive diffusion protocols (under a single snapshot). These results raise questions about the robustness of anonymity guarantees when spreading information in social networks.
Interval analysis of the HIV dynamics model solution using type-2 fuzzy sets

The objective of this study is to analyze the numerical intervals of the Human Immunodeficiency Virus (HIV) using the solution of a dynamics model at each time iteration, considering the infection rate of CD4+ T lymphocytes and the production rate of the virus as outputs of an interval type-2 Fuzzy Rule-Based System (FRBS). The mathematical model consists of a system of ordinary differential equations in the case of the HIV-seropositive individuals who are under antiretroviral treatment. The input variables of FRBS are adherence to treatment and medication potency, which are important factors in the treatment of HIV-seropositive individuals. If the infection rate of CD4+ T lymphocytes and the production rate of the virus are output variables of a type-1 FRBS, then we obtain curves for the solution of the HIV dynamic model. When the rates are outputs obtained through the interval type-2 FRBS, we can obtain ranges. This means that a type-2 FRBS provides more information about the behavior of the uninfected and infected CD4+ T lymphocytes, free virus particles, and virus-specific cytotoxic T lymphocytes (CTL) that attack infected cells in the bloodstream of a HIV-seropositive individual. The theory of the single level constraint interval arithmetic allows us to obtain a single interval that containing the intervals of all iterations for uninfected and infected CD4+ T lymphocytes, free virus particles, and CTL. Thus, the novel contribution of this research is twofold. Firstly, the use of interval type-2 fuzzy rule-base analysis to obtain more informative and robust results not possible with standard approaches is new. Secondly, the use of the fuzzy constraint interval representation for fuzzy type-2 intervals in the context of the type-2 fuzzy rule base is new. Moreover, constraint intervals unify the theory and its use in single-level constraint representations simplifies the calculations, though this aspect is not pursued in detail here.
Generalized multi-output Gaussian process censored regression

When modelling censored observations (i.e. data in which the value of a measurement or observation is un-observable beyond a given threshold), a typical approach in current regression methods is to use a censored-Gaussian (i.e. Tobit) model to describe the conditional output distribution. In this paper, as in the case of missing data, we argue that exploiting correlations between multiple outputs can enable models to better address the bias introduced by censored data. To do so, we introduce a heteroscedastic multi-output Gaussian process model which combines the non-parametric flexibility of GPs with the ability to leverage information from correlated outputs under input-dependent noise conditions. To address the resulting inference intractability, we further devise a variational bound to the marginal log-likelihood suitable for stochastic optimization. We empirically evaluate our model against other generative models for censored data on both synthetic and real world tasks and further show how it can be generalized to deal with arbitrary likelihood functions. Results show how the added flexibility allows our model to better estimate the underlying non-censored (i.e. true) process under potentially complex censoring dynamics.
New Variants of Global-Local Partial Least Squares Discriminant Analysis for Appearance-Based Face Recognition

We propose new appearance-based face recognition methods based on global-local structurepreserving partial least squares discriminant analysis.Two variants of the method are described in this article: the neighbourhood-preserving partial least squares discriminant analysis (NPPLS-DA) and the uncorrelated NPPLS-DA (UNNPPLS-DA).In contrast to standard partial least squares discriminant analysis (PLS-DA), which effectively only recognizes the global Euclidean structure of the face space, both NPPLS-DA and UNNPPLS-DA are designed to find an embedding that preserves both the global and local neighbourhood information and obtain a face subspace that best detects the essential manifold structure of the face space.Unlike global-local features extracted using other methods, the global-local PLS-DA features are obtained by maximizing covariance between data matrix and a response matrix which is coded with the class structure of the data.Furthermore, in UNPPLS-DA, an uncorrelated constraint is introduced into the objective function of NPPLS-DA to extract uncorrelated features that are important in many pattern recognition problems.We compare the proposed NPPLS-DA and UNPPLS-DA methods with several competing methods on six different face databases.The experimental results show that the proposed NPPLS-DA and UNPPLS-DA methods provide better representation and consistently achieve higher recognition rates in face recognition than the other competing methods.