Reviewerly/exHarmony huggingface dataset:
A Cyber-Security Culture Framework for Assessing Organization Readiness

This paper presents a cyber-security culture framework for assessing and evaluating the current security readiness of an organization’s workforce. Having conducted a thorough review of the most commonly used security frameworks, we identify core security human-related elements and classify them by constructing a domain agnostic security model. We then proceed by presenting in detail each component of our model and attempt to quantify them in order to achieve a feasible assessment methodology. The paper thereafter presents the application of this methodology for the design and development of a security culture evaluation tool, that offers recommendations and alternative approaches to workforce training programs and techniques. The model has been designed to easily adapt on various application domains while focusing on their unique characteristics. The paper concludes on applications of our instrument on security-critical domains, and its contribution to current research by providing deeper insights regarding the human factor in cybersecurity.
HoughNet: Integrating Near and Long-Range Evidence for Bottom-Up Object Detection

This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presence of an object at a certain location by the sum of the votes cast on that location. Votes are collected from both near and long-distance locations based on a log-polar vote field. Thanks to this voting mechanism, HoughNet is able to integrate both near and long-range, class-conditional evidence for visual recognition, thereby generalizing and enhancing current object detection methodology, which typically relies on only local evidence. On the COCO dataset, HoughNet’s best model achieves 46.4 AP (and 65.1 $$AP_{50}$$ ), performing on par with the state-of-the-art in bottom-up object detection and outperforming most major one-stage and two-stage methods. We further validate the effectiveness of our proposal in another task, namely, “labels to photo” image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases. Code is available at https://github.com/nerminsamet/houghnet .
Defect Prediction With Semantics and Context Features of Codes Based on Graph Representation Learning

To optimize the process of software testing and to improve software quality and reliability, many attempts have been made to develop more effective methods for predicting software defects. Previous work on defect prediction has used machine learning and artificial software metrics. Unfortunately, artificial metrics are unable to represent the features of syntactic, semantic, and context information of defective modules. In this article, therefore, we propose a practical approach for identifying software defect patterns via the combination of semantics and context information using abstract syntax tree representation learning. Graph neural networks are also leveraged to capture the latent defect information of defective subtrees, which are pruned based on a fix-inducing change. To validate the proposed approach for predicting defects, we define mining rules based on the GitHub workflow and collect 6052 defects from 307 projects. The experiments indicate that the proposed approach performs better than the state-of-the-art approach and five traditional machine learning baselines. An ablation study shows that the information about code concepts leads to a significant increase in accuracy.
Towards a Blockchain-Based Supply Chain Management for E-Agro Business System

Considering the overall situations of cultivator's economic degradation, food safety chain issue, and whole food supply chain, newer technologies are emerging in the field of agriculture over time. If we look at third world countries, these traditional food supply chains still play the main bearish down prediction. For the result, if any customer deceived by adulterated foods, then in most cases, he/she could not get the original source of the food where it was produced or processed. And in this case, also the administration cannot make this possible to identify and punish the fraudsters. This is where the fraudsters get the opportunity to mix adulteration in foods, put new expiration on expired foods, and release them in the market. Again because of the lack of efficient distribution facilities between cultivators and industrial or last stage vendors, most of the cases cultivators could not get fair pay. And to solve this problem and set explicit or trustworthy communications, the civilization needed immutable and trustworthy technology so that all these problems can be solved. And in that stage, blockchain comes to the picture. In this research paper, we propose an effective, efficient, and satisfactory model or system and service solution to agro traders and also a food traceability system based on blockchain and the help of IoT to make their business more smart and rich. And through the blockchain, smart contract, and the help of IoT sensors, we tried to do maximum effort to reduce human intervention.
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition

This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too.
Exploiting optical chaos for double images encryption with compressive sensing and double random phase encoding

Images as a common information carrier are widely disseminated and used at present, meanwhile, image security and privacy protection are also facing severe tests. To enhance the security of images, an asymmetric double color images cryptosystem introduced optical chaos technology is proposed based on compression sensing (CS) and double random phase encoding (DRPE). It is the first time that the combination of CS, DRPE and optical chaos technology is demonstrated to the best of our knowledge. By means of the optical chaos keys generated by a constructed optical chaos model, the two color images are first decomposed into their respective Red–Green–Blue (RGB) channels, CS based on discrete wavelet transform is then performed for the first encryption. Afterwards, the second encrypion of DRPE in fractional Fourier transform domain (FrFT) with optical chaos technology is also used, and the asymmetric encryption by carrying out equal modulus decomposition (EMD) with optical chaos keys further enhance the security finally. Simulation results and security analysis confirm that the cryptosystem owns multi-layer protection and adequate resistance to brute-force attack, statistical attack, differential attack, noise attack and chosen-plaintext attack, which is efficient and capable of guaranteeing the security of images and can provide an alternative solution for image security and privacy protection.
Optical Convolutional Neural Network With WDM-Based Optical Patching and Microring Weighting Banks

We propose an optical convolutional neural network (OCNN) architecture for high-speed and energy-efficient deep learning accelerators. The WDM-based optical patching scheme (WDM-OPS) is adopted as the data-feeding structure for its superior energy efficiency and the microring banks are used for the large-scale weighting and summing (the computing core). We thoroughly investigate the performance (including prediction accuracy, speed, and energy efficiency) of this architecture in different system defects. The results indicate that, the prediction accuracy of OCNN can reach 97% in the MNIST dataset with a computing speed of over 100 TMAC/s on condition of achievable low insertion loss. It is also observed that the WDM-OPS notably reduces the energy consumption of the electro-optic modulation and thus the OCNN becomes an exceptionally energy-efficient architecture among several well-known optical architectures. In the evaluations, instead of merely considering the computing core, we take the holistic optical system including lasers, electro-optic modulators, data preprocessing, photodetection and transimpedance amplification into consideration. Therefore, this work provides a potential guide for the systematic implementation of the OCNN architecture.
A nearest-neighbor search model for distance metric learning

Distance metric learning aims to deal with the data distribution by learning a suitable distance metric from the training instances. For distance metric learning, the optimization constraints can be constructed based on the similar and dissimilar instance pairs. The instance pairs are generated by selecting the nearest-neighbors for each training instance. However, most methods select the same and fixed nearest-neighbor number for different training instances, which may limit performance for learning distance metric. In this paper, we propose a nearest-neighbor search model for distance metric learning (NNS-DML), which is capable of constructing the metric optimization constraints by searching different optimal nearest-neighbor numbers for different training instances. Specifically, we formulate a nearest-neighbor search matrix to contain the nearest-neighbor correlations of all training instances. Using the search matrix, we can construct and weight the metric optimization constraints of each training instance, such that the influence of its irrelevant features for its corresponding similar and dissimilar instance pairs can be reduced. Moreover, we develop a k-free nearest-neighbor model for classification problems via the SVM solver, which can ignore the setting of k. Extensive experiments show that the proposed NNS-DML method outperforms the state-of-the-art distance metric learning methods.
An In-Depth Exploration of the Effect of 2D/3D Views and Controller Types on First Person Shooter Games in Virtual Reality

The amount of interest in Virtual Reality (VR) research has significantly increased over the past few years, both in academia and industry. The release of commercial VR Head-Mounted Displays (HMDs) has been a major contributing factor. However, there is still much to be learned, especially how views and input techniques, as well as their interaction, affect the VR experience. There is little work done on First-Person Shooter (FPS) games in VR, and those few studies have focused on a single aspect of VR FPS. They either focused on the view, e.g., comparing VR to a typical 2D display or on the controller types. To the best of our knowledge, there are no studies investigating variations of 2D/3D views in HMDs, controller types, and their interactions. As such, it is challenging to distinguish findings related to the controller type from those related to the view. If a study does not control for the input method and finds that 2D displays lead to higher performance than VR, we cannot generalize the results because of the confounding variables. To understand their interaction, we propose to analyze in more depth, whether it is the view (2D vs. 3D) or the way it is controlled that gives the platforms their respective advantages. To study the effects of the 2D/3D views, we created a 2D visual technique, PlaneFrame, that was applied inside the VR headset. Our results show that the controller type can have a significant positive impact on performance, immersion, and simulator sickness when associated with a 2D view. They further our understanding of the interactions that controllers and views have and demonstrate that comparisons are highly dependent on how both factors go together. Further, through a series of three experiments, we developed a technique that can lead to a substantial performance, a good level of immersion, and can minimize the level of simulator sickness.
Distributed Auxiliary Particle Filtering With Diffusion Strategy for Target Tracking: A Dynamic Event-Triggered Approach

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors.
Otherware

column Otherware: how to best interact with autonomous systems Share on Authors: Marc Hassenzahl University of Siegen University of SiegenView Profile , Jan Borchers RWTH Aachen University RWTH Aachen UniversityView Profile , Susanne Boll University of Oldenburg University of OldenburgView Profile , Astrid Rosenthal-von der Pütten RWTH Aachen University RWTH Aachen UniversityView Profile , Volker Wulf University of Siegen University of SiegenView Profile Authors Info & Affiliations InteractionsVolume 28Issue 1January - February 2021 pp 54–57https://doi.org/10.1145/3436942Published:23 December 2020 3citation1,392DownloadsMetricsTotal Citations3Total Downloads1,392Last 12 Months1,392Last 6 weeks55 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Understanding Physical Practices and the Role of Technology in Manual Self-Tracking

Self-tracking practices enable users to record and analyze their personal data. In recent years, non-digital forms of manual self-tracking, such as bullet journaling, have gained popularity. We conduct a survey (N = 404) and follow-up interviews (N = 18) to better understand users' motivations for physical tracking, the challenges they face with their current practices, and their perceptions of both digital and physical tracking tools. We find that for some users, physical practices are a structured and constructive creative outlet and a form of artistic expression. While the resulting physical artifacts may not easily enable retrospective reflection over long-term data, they preserve personal traces in a unique and tangible form that is meaningful to users. Moreover, the reflective power of physical tracking stems from the interaction with the physical materiality, the slow pace of these practices, the creative exploration they facilitate, and the associated digital disconnect. We conclude with design implications for future technologies, including ways digital tools might extend current physical practices and support richly reflective self-tracking.
Quantum advantage of two-level batteries in the self-discharging process

Devices that use quantum advantages for storing energy in the degree of freedom of quantum systems have drawn attention due to their properties of working as quantum batteries (QBs). However, one can identify a number of problems that need to be adequately solved before the start of a real manufacturing process of these devices. In particular, it is important to pay attention to the ability of quantum batteries in storing energy when no consumption center is connected to them. In this paper, by considering quantum batteries disconnected from external charging fields and consumption center, we study the dissipative effects that lead to charge leakage to the surrounding environment. We identify this phenomena as a self-discharging of QBs, in analogy to the inherent decay of the stored charge of conventional classical batteries in a open-circuit configuration. The performance of QBs compared to the classical counterpart is highlighted for single- and multicell quantum batteries.
High-sensitivity image encryption algorithm with random cross diffusion based on dynamically random coupled map lattice model

This paper proposes a novel high-sensitivity image encryption algorithm with random cross diffusion based on dynamically random coupled map lattice model implemented by piecewise linear chaotic map (PDRCML). Through theoretical analysis and experimental results analysis, it can be demonstrated that the system has the larger parameter space and the state of each lattice is more effective compared with traditional coupled logistic map lattices (CML). The system is appropriate for chaotic encryption and secure communications. Moreover, the proposed image encryption algorithm combines S-box, fast disturbance handling and random cross diffusion to realize non-linear diffusion, which breaks traditional diffusion methods based on permutation. By analyzing simulation performance of encryption, it can be easily determined that the algorithm has a high degree of security, highly sensitive, good statistical performance. And it further demonstrates the excellent features of the PDRCML model.
Counteracting dynamical degradation of a class of digital chaotic systems via Unscented Kalman Filter and perturbation

Theoretically, any chaotic system or chaotic map has ideal complex dynamics. However, because of the finite precision of simulation software and digital devices during implementation, chaotic systems often undergo dynamical degradation, which hinders the further application of digital chaotic systems in many fields. Therefore in this paper, the method based on the perturbation and Unscented Kalman Filter (UKF) theory is designed to counteract the dynamical degradation of digital chaotic systems. Specifically, the UKF algorithm is employed to reinstate the original dynamic performance of the chaotic system, and then perturbation feedback technology is used to cause the chaotic system to obtain strong dynamic performance to resist attacks. The experimental and simulation results demonstrate that this method has good effect on improving the dynamic degradation of digital chaotic map. In addition, the corresponding pseudorandom number generator (PRNG) is constructed via this method, and its randomness is evaluated using the National Institute of Standards and Technology (NIST) SP800-22 and TestU01 test suites. By comparing with other schemes, it can be seen that this PRNG has better performance which illustrates the proposed scheme can be applied in the chaos-based cryptography and utilized in other potential applications.
Deeper Look at Image Salient Object Detection: Bi-Stream Network With a Small Training Dataset

Compared with the conventional hand-crafted approaches, the deep learning based ISOD (image salient object detection) models have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets. However, do we really need large-scale training set for ISOD? In this article, we provide a deeper insight into the interrelationship between the ISOD performance and the training data. To alleviate the conventional demands for large-scale training data, we provide a feasible way to construct a novel small-scale training set, which only contains 4 K images. To take full advantage of this new set, we propose a novel bi-stream network consisting of two different feature backbones. Benefit from the proposed gate control unit, this bi-stream network is able to achieve complementary fusion status for its subbranches. To our best knowledge, this is the first attempt to use a small-scale training set to compete with other large-scale ones; nevertheless, our method can still achieve the leading SOTA performance on all tested benchmark datasets. Both the code and dataset are publicly available at <uri>https://github.com/wuzhenyubuaa/TSNet</uri>.
MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos

In this paper, we study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires, through aerial video analysis. We make two main contributions. The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks. This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos. The second contribution is a new model, namely MSNet, which contains novel region proposal network designs and an unsupervised score refinement network for confidence score calibration in both bounding box and mask branches. We show that our model achieves state-of-the-art results compared to previous methods in our dataset.