Reviewerly/exHarmony huggingface dataset:
Who Is Smart with Their Smartphones? Determinants of Smartphone Security Behavior

Smartphones have recently become a major target for cybercriminals due to large amounts of sensitive data and credentials being stored on the devices. To protect themselves against cyberthreats, users can employ a range of security behaviors. Although research has largely focused on computer security, relatively little is known about personal smartphone security behavior. The goal of our study was to evaluate determinants of smartphone security behavior based on the combination of the Health Belief Model and Protection Motivation Theory. We extended the models by including the construct of general security orientation. We also developed a smartphone security behavior scale that measured various aspects of this behavior. The sample included 331 Czech Android smartphone users aged between 26 and 81 years who were not IT specialists by employment or education. Path analysis showed that individuals who perceived a potential smartphone security threat as more severe, had higher smartphone self-efficacy, and general orientation and interest in digital security, and less personal experience with a digital security incident reported more secure behavior on their smartphones. Perceived susceptibility to security threats and family and friends' previous experience with digital security incidents did not predict smartphone security behavior. General security orientation affected smartphone security behavior also indirectly through perceived severity. These findings have theoretical implications for the models and also emphasize the importance of general digital security awareness as well as smartphone training to increase smartphone security behavior.
Understanding Credibility of Adversarial Examples against Smart Grid

Stability assessment is an important task for maintaining reliable operations of power grids. With increased system complexity, deep learning-based stability assessment approaches are promising to address the shortfalls of the traditional time-domain simulation-based approaches. However, in the field of computer vision, the deep learning models are shown vulnerable to adversarial examples. Although this vulnerability has been noticed by the energy informatics research, the domain-specific analysis on the requirements imposed for implementing effective adversarial examples is still lacking. These attack requirements, albeit reasonable in computer vision tasks, can be too stringent in the context of power grids. In this paper, we systematically investigate the requirements and discuss the credibility of six representative adversarial example attacks for a case study of voltage stability assessment for the New England 10-machine 39-bus system. We show that (1) compromising the voltage traces of half of transmission system buses is a rule of thumb requirement; (2) the universal adversarial perturbations that are independent of the original clean voltage trajectory have the same credibility as the widely studied false data injection attacks on power grid state estimation, while other adversarial example attacks are less credible; (3) the universal perturbations can be effectively defended with strong adversarial training.
Integration of Open Educational Resources Using Semantic Platform

Universities around the world have been taking advantage of open educational resources (OERs) for publishing educational material.The teachers and students can access, the published material for teaching, learning and research through institutional repositories.This article proposes the design, construction and validation of the semantic platform (MIIDAS), considering two case studies of higher-level educational institutions (civil and military schools).The MIIDAS Platform integrates educational resources in institutional repositories related to the areas of Computing and Electronics engineering through the incorporation of Semantic Web technologies.The descriptions of resources consider metadata of the OAI-PMH protocol and also add educational metadata.The platform uses RDF resource descriptions, ontologies and the SPARQL query language to improve resource retrieval, in addition to proposing guidelines for the management of educational resources.The results of validation of the platform are presented through the development of evaluation instruments, which suggest possible ways to improve the platform.The article provides the basis for developing platforms that help in spreading open educational resources and allow the construction of semantically enrich data sets.
Enhanced Separable Convolution Network for Lightweight JPEG Compression Artifacts Reduction

JPEG images are usually corrupted by various undesirable compression artifacts resulted from block-wise coarse quantization on discrete cosine transform coefficients. In recent years, deep convolutional neural networks (CNNs) have made spectacular achievements in compression artifacts reduction. However, most deep CNNs are difficult to be implemented on mobile devices due to their large number of parameters and operations. In this letter, we propose a novel deep CNN called ESCNet for lightweight JPEG compression artifacts reduction, in which enhanced separable convolution (ESConv) is carefully designed to make full use of image multi-scale information for better dense pixel value predictions. Specifically, ESConv consists of a grouped multi-scale dual depth-wise convolution (GMDDConv) and a wide-activated dual point-wise convolution (WDPConv). GMDDConv is dedicated to efficiently extracting abundant image multi-scale spatial features, which will be sent to WDPConv for effective non-linear feature fusion. The experimental results on benchmark datasets show that compared with state-of-the-art methods, our ESCNet not only achieves better performance in both objective indices and subjective quality but also greatly reduces network parameters and operations.
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images

Humans perceive and construct the surrounding world as an arrangement of simple parametric models. In particular, man-made environments commonly consist of volumetric primitives such as cuboids or cylinders. Inferring these primitives is an important step to attain high-level, abstract scene descriptions. Previous approaches directly estimate shape parameters from a 2D or 3D input, and are only able to reproduce simple objects, yet unable to accurately parse more complex 3D scenes. In contrast, we propose a robust estimator for primitive fitting, which can meaningfully abstract real-world environments using cuboids. A RANSAC estimator guided by a neural network fits these primitives to 3D features, such as a depth map. We condition the network on previously detected parts of the scene, thus parsing it one-by-one. To obtain 3D features from a single RGB image, we additionally optimise a feature extraction CNN in an end-to-end manner. However, naively minimising point-to-primitive distances leads to large or spurious cuboids occluding parts of the scene behind. We thus propose an occlusion-aware distance metric correctly handling opaque scenes. The proposed algorithm does not require labour-intensive labels, such as cuboid annotations, for training. Results on the challenging NYU Depth v2 dataset demonstrate that the proposed algorithm successfully abstracts cluttered real-world 3D scene layouts.
Verifiable Privacy-Preserving Scheme Based on Vertical Federated Random Forest

Presently, under the condition of privacy preserving, vertical federated learning (VFL) has played an important role in training the machine learning (ML) models in the application scenarios, such as medical prediction, fraud detection, in which the data is distributed vertically. Random forest (RF) is one of the most widely used ML methods in VFL, which has the advantages of strong predictive performance, availability, and the ability to parallel tasks. However, current research on privacy preserving of vertical federated RF is limited, and none of them can achieve the application level of security, that is, in a system where users are dynamically changing, not only privacy can be preserved, but also data integrity can be verified. Therefore, we propose a verifiable privacy-preserving scheme (VPRF) based on vertical federated RF, in which the users are dynamic change. First, we design homomorphic comparison and voting statistics algorithms based on multikey homomorphic encryption for privacy preservation. Then, we propose a multiclient delegated computing verification algorithm to make up for the disadvantage that the above algorithms cannot verify data integrity. Finally, we used the data sets in UCI ML warehouse to evaluate the proposed scheme. The experiment results indicate that our scheme is more efficient than the existing ones that can achieve the same security level.
Buying time in software development: how estimates become commitments?

Despite years of research for improving accuracy, software practitioners still face software estimation difficulties. Expert judgment has been the prevalent method used in industry, and researchers' focus on raising realism in estimates when using it seems not to be enough for the much-expected improvements. Instead of focusing on the estimation process's technicalities, we investigated the interaction of the establishment of commitments with customers and software estimation. By observing estimation sessions and interviewing software professionals from companies in varying contexts, we found that defensible estimates and padding of software estimates are crucial in converting estimates into commitments. Our findings show that software professionals use padding for three different reasons: contingency buffer, completing other tasks, or improving the overall quality of the product. The reasons to pad have a common theme: buying time to balance short- and long-term software development commitments, including the repayment of technical debt. Such a theme emerged from the human aspects of the interaction of estimation and the establishment of commitments: pressures and customers' conflicting short and longterm needs play silent and unrevealed roles in-between the technical activities. Therefore, our study contributes to untangling the underlying phenomena, showing how the practices used by software practitioners help to deal with the human and social context in which estimation is embedded.
Building a Fuzzy Cognitive Map from stakeholder knowledge: An Episodic, asynchronous approach

Participatory modelling (PM) processes involve stakeholders in developing a simplified representation of reality based on stakeholders' knowledge, perceptions, values and assumptions about a system in which they live and/or work. There has been an increase in the need for structured methods for the implementation of PM processes, to elicit knowledge from stakeholders and to represent this knowledge in a model. This paper presents a method to support the participatory component of modelling processes without the need for face-to-face interactions. The method, which we term Episodic and Asynchronous (EAsy) was applied to construct a Fuzzy Cognitive Map of the Nigerian rice agri-food system. The stakeholder determined Fuzzy Cognitive Map was further applied to develop scenarios and identify leverage points for intervention in the system. The results demonstrate that the EAsy approach is an effective way for co-production to be achieved. The EAsy approach can thus be considered valid to construct a representation of a complex social-ecological system. Using the results and analysis of our process, we discuss the limitations and benefits of this PM method.
Pooling Attention-based Encoder–Decoder Network for semantic segmentation

Aiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an Encoder–Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the Encoder to extract discriminative features from input images, and the Decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed Encoder–Decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN.
Worst Smells and Their Worst Reasons

Code bad smells are symptoms of poor design and implementation. There are several well-known smell types, such as large classes (aka God classes), code clones, etc. and they have been shown to lead to technical debt and hence to decrease code maintainability. Quality gates are a recent technology that prevents the automatic acceptance of push requests of code commits that have been identified as containing certain smells. However, it is a challenging activity to decide which smells should be included in the quality gate, as developers may choose to optimize short term benefits like time to market over long term benefits like maintainability. But some smells appear to provide no benefit to developers whatsoever and hence such smells should always be avoided. The aims of this paper are: 1) to identify "worst smells", i.e., bad smells that never have a good reason to exist, 2) to determine the frequency, change-proneness, and severity associated with worst smells, and 3) to identify the "worst reasons", i.e., the reasons for introducing these worst smells in the first place. To achieve these aims we ran a survey with 71 developers. We learned that 80 out of 314 catalogued code smells are "worst"; that is, developers agreed that these 80 smells should never exist in any code base. We then checked the frequency and change-proneness of these worst smells on 27 large Apache open- source projects. Our results show insignificant differences, in both frequency and change proneness, between worst and non-worst smells. That is to say, these smells are just as damaging as other smells, but there is never any justifiable reason to introduce them. Finally, in follow-up phone interviews with five developers we confirmed that these smells are indeed worst, and the interviewees proposed seven reasons for why they may be introduced in the first place. By explicitly identifying these seven reasons, project stakeholders can, through quality gates or reviews, ensure that such smells are never accepted in a code base, thus improving quality without compromising other goals such as agility or time to market.
Mimicking Individual Media Quality Perception with Neural Network based Artificial Observers

The media quality assessment research community has traditionally been focusing on developing objective algorithms to predict the result of a typical subjective experiment in terms of Mean Opinion Score (MOS) value. However, the MOS, being a single value, is insufficient to model the complexity and diversity of human opinions encountered in an actual subjective experiment. In this work we propose a complementary approach for objective media quality assessment that attempts to more closely model what happens in a subjective experiment in terms of single observers and, at the same time, we perform a qualitative analysis of the proposed approach while highlighting its suitability. More precisely, we propose to model, using neural networks (NNs) , the way single observers perceive media quality. Once trained, these NNs, one for each observer, are expected to mimic the corresponding observer in terms of quality perception. Then, similarly to a subjective experiment, such NNs can be used to simulate the users’ single opinions, which can be later aggregated by means of different statistical indicators such as average, standard deviation, quantiles, etc. Unlike previous approaches that consider subjective experiments as a black box providing reliable ground truth data for training, the proposed approach is able to consider human factors by analyzing and weighting individual observers. Such a model may therefore implicitly account for users’ expectations and tendencies, that have been shown in many studies to significantly correlate with visual quality perception. Furthermore, our proposal also introduces and investigates an index measuring how much inconsistency there would be if an observer was asked to rate many times the same stimulus. Simulation experiments conducted on several datasets demonstrate that the proposed approach can be effectively implemented in practice and thus yielding a more complete objective assessment of end users’ quality of experience.
An Area-Efficient SPHINCS<sup>+</sup> Post-Quantum Signature Coprocessor

The significant advances in the area of quantum computing of the past decade leave no doubt about the fact that quantum computers are an actual threat to cryptography. For this reason, a lot of efforts have been made lately in designing so-called post-quantum cryptographic primitives. The adoption of these schemes depends on the future capability of post-quantum cryptographic schemes to offer performances and functionalities similar to their classical counterparts. In particular, a milestone towards standardization is the implementation on FPGA of cryptographic primitives which leads to an efficient execution. We contribute in this respect by providing an area-efficient FPGA implementation of SPHINCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , a post-quantum signature scheme which guarantees very high security, allowing its deployment into embedded systems such as hardware security modules, IoT devices or nanosatellites.
Synthesizing data structure refinements from integrity constraints

Implementations of many data structures use several correlated fields to improve their performance; however, inconsistencies between these fields can be a source of serious program errors. To address this problem, we propose a new technique for automatically refining data structures from integrity constraints. In particular, consider a data structure D with fields F and methods M, as well as a new set of auxiliary fields F′ that should be added to D. Given this input and an integrity constraint Φ relating F and F′, our method automatically generates a refinement of D that satisfies the provided integrity constraint. Our method is based on a modular instantiation of the CEGIS paradigm and uses a novel inductive synthesizer that augments top-down search with three key ideas. First, it computes necessary preconditions of partial programs to dramatically prune its search space. Second, it augments the grammar with promising new productions by leveraging the computed preconditions. Third, it guides top-down search using a probabilistic context-free grammar obtained by statically analyzing the integrity checking function and the original code base. We evaluated our method on 25 data structures from popular Java projects and show that our method can successfully refine 23 of them. We also compare our method against two state-of-the-art synthesis tools and perform an ablation study to justify our design choices. Our evaluation shows that (1) our method is successful at refining many data structure implementations in the wild, (2) it advances the state-of-the-art in synthesis, and (3) our proposed ideas are crucial for making this technique practical.
Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits

Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5–466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.
Wasserstein distance feature alignment learning for 2D image-based 3D model retrieval

2D image-based 3D model retrieval has become a hotspot topic in recent years. However, the current existing methods are limited by two aspects. Firstly, they are mostly based on the supervised learning, which limits their application because of the high time and cost consuming of manual annotation. Secondly, the mainstream methods narrow the discrepancy between 2D and 3D domains mainly by the image-level alignment, which may bring the additional noise during the image transformation and influence cross-domain effect. Consequently, we propose a Wasserstein distance feature alignment learning (WDFAL) for this retrieval task. First of all, we describe 3D models through a series of virtual views and use CNNs to extract features. Secondly, we design a domain critic network based on the Wasserstein distance to narrow the discrepancy between two domains. Compared to the image-level alignment, we reduce the domain gap by the feature-level distribution alignment to avoid introducing additional noise. Finally, we extract the visual features from 2D and 3D domains, and calculate their similarity by utilizing Euclidean distance. The extensive experiments can validate the superiority of the WDFAL method.