Reviewerly/exHarmony huggingface dataset:
An empirical study of ransomware attacks on organizations: an assessment of severity and salient factors affecting vulnerability

Abstract This study looks at the experiences of organizations that have fallen victim to ransomware attacks. Using quantitative and qualitative data of 55 ransomware cases drawn from 50 organizations in the UK and North America, we assessed the severity of the crypto-ransomware attacks experienced and looked at various factors to test if they had an influence on the degree of severity. An organization’s size was found to have no effect on the degree of severity of the attack, but the sector was found to be relevant, with private sector organizations feeling the pain much more severely than those in the public sector. Moreover, an organization’s security posture influences the degree of severity of a ransomware attack. We did not find that the attack target (i.e. human or machine) or the crypto-ransomware propagation class had any significant bearing on the severity of the outcome, but attacks that were purposefully directed at specific victims wreaked more damage than opportunistic ones.
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles

Autonomous vehicles rely heavily on sensors such as camera and LiDAR, which provide real-time information about their surroundings for the tasks of perception, planning and control. Typically, a LiDAR can only provide sparse point cloud owing to a limited number of scanning lines. By employing depth completion, a dense depth map can be generated by assigning each camera pixel a corresponding depth value. However, the existing depth completion convolutional neural networks are very complex that requires high-end GPUs for processing, and thus they are not applicable to real-time autonomous driving. In this article, a light-weight network is proposed for the task of LiDAR point cloud depth completion. With an astonishing 96.2% reduction in the number of parameters, it still achieves comparable performance (9.3% better in MAE but 3.9% worse in RMSE) to the state-of-the-art network. For real-time embedded platforms, depthwise separable technique is applied to both convolution and deconvolution operations and the number of parameters decreases further by a factor of 7.3, with only a small percentage increase in error performance. Moreover, a system-on-chip architecture for depth completion is developed on a PYNQ-based FPGA platform that achieves real-time processing for HDL-64E LiDAR at the speed 11.1 frame per second.
Blockchain-Driven Trusted Data Sharing With Privacy Protection in IoT Sensor Network

The development of the Internet of Things (IoT) technology has achieved remarkable results in recent years. A large number of sensors and machinery are connected, and combined with the Internet, to achieve intelligent management and operation. However, the traditional centralized IoT data management solution inevitably encounters challenges of data trust, security, sustainability, and user privacy is difficult to guarantee. This paper takes the intelligent transportation sensor network as an example and proposes a blockchain-based Internet of Vehicles (IoV) data secure sharing scheme (called IoVChain), which implements automatic registration, rapid authentication, and reliable sharing method of IoV data through smart contract. The smart contract performs homomorphic encryption and zero-knowledge proof processing on the sensitive part of the data, and this part exists in the form of ciphertext on the blockchain. We adopt PBFT consensus mechanism to ensure the consistency of the entire network ledger, and all the IoV data processing and usage procedure is stored in the Merkle-tree-based block, which cannot be tampered with. Compared with the centralized IoV data management solution, the IoVChain scheme avoids the risk of single point of failure, and keeps the IoV data credible, available, and tamper-resistant under the premise of privacy and security, so that the data can be traced when needed. We have implemented the IoVChain scheme based on the consortium blockchain for trusted and secure sharing of IoV data. Finally, analysis and evaluation show that the proposed IoVChain scheme is feasible, safe, and extensible for the secure sharing of IoV data.
Depth quality-aware selective saliency fusion for RGB-D image salient object detection

Previous RGB-D salient object detection (SOD) methods have widely adopted the deep learning tools to automatically strike a trade-off between RGB and depth (D). The key rationale is to take full advantage of the complementary nature between RGB and D, aiming for a much-improved SOD performance than that of using either of them solely. However, because to the D quality itself usually varies from scene to scene, such fully automatic fusion schemes may not always be helpful for the SOD task. Moreover, as an objective factor, the D quality has long been overlooked by previous work. Thus, this paper proposes a simple yet effective scheme to measure D quality in advance. The key idea is to devise a series of features in accordance with the common attributes of the high-quality D regions. To be more concrete, we advocate to conduct D quality assessments following a multi-scale methodology, which includes low-level edge consistency, mid-level regional uncertainty and high-level model variance. All these components will be computed independently and later be combined with RGB and D saliency cues to guide the selective RGBD fusion. Compared with the SOTA fusion schemes, our method can achieve better fusion result between RGB and D. Specifically, the proposed D quality measurement method is able to achieve steady performance improvements for almost 2.0% averagely.
On the benefits of structural equation modeling for corpus linguists

Abstract The present article aims to introduce structural equation modeling, in particular measured variable path models, and discuss their great potential for corpus linguists. Compared to other techniques commonly employed in the field such as multiple regression, path models are highly flexible and enable testing a priori hypotheses about causal relations between multiple independent and dependent variables. In addition to increased methodological versatility, this technique encourages big-picture, model-based reasoning, thus allowing corpus linguists to move away from the, at times, somewhat overly simplified mindset brought about by the more narrow null-hypothesis significance testing paradigm. The article also includes commentary on corpus linguistics and its trajectory, arguing in favor of increased cumulative knowledge building.
Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably

<h2>Abstract</h2><h3>Objective</h3> The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records. <h3>Study Design and Setting</h3> We analyzed national hospital records and official death records for patients with myocardial infarction (<i>n</i> = 200,119), hip fracture (<i>n</i> = 169,646), or colorectal cancer surgery (<i>n</i> = 56,515) in England in 2015–2017. One-year mortality was predicted from patient age, sex, and socioeconomic status, and 202 to 257 International Classification of Diseases 10th Revision codes recorded in the preceding year or not (binary predictors). Performance measures included the <i>c</i>-statistic, scaled Brier score, and several measures of calibration. <h3>Results</h3> One-year mortality was 17.2% (34,520) after myocardial infarction, 27.2% (46,115) after hip fracture, and 9.3% (5,273) after colorectal surgery. Optimism-adjusted <i>c</i>-statistics for the logistic regression models were 0.884 (95% confidence interval [CI]: 0.882, 0.886), 0.798 (0.796, 0.800), and 0.811 (0.805, 0.817). The equivalent <i>c</i>-statistics for the boosted tree models were 0.891 (95% CI: 0.889, 0.892), 0.804 (0.802, 0.806), and 0.803 (0.797, 0.809). Model performance was also similar when measured using scaled Brier scores. All models were well calibrated overall. <h3>Conclusion</h3> In large datasets of electronic healthcare records, logistic regression and boosted tree models of numerous diagnosis codes predicted patient mortality comparably.
Low-light image joint enhancement optimization algorithm based on frame accumulation and multi-scale Retinex

It is acknowledged that images taken under low-light conditions are easily affected by low visible light and noise, which can cause important image information loss, low signal-to-noise ratio, blurred edges, and poor subjective vision. Related researchers have targeted some solutions are proposed for the above problems, such as histogram equalization and gamma correction, but all have problems such as edge loss and color distortion. Based on the above problems, this paper proposes a low-light image enhancement optimization algorithm based on frame accumulation and multi-scale Retinex joint processing. First, single-channel image frame accumulation filtering is performed on the low-light image, and then the image is jointly enhanced with the optimized multi-scale Retinex algorithm. The experimental results show that the peak signal-to-noise ratio of the image processed by the joint enhancement optimization algorithm used in this article is increased to 51.2041 dB, which is 15.2633 dB higher than the original image and 1.799 dB higher than the image processed by the traditional MSRCR algorithm, structure similarity increased by 0.12, the enhanced image has higher grayscale resolution and signal-to-noise ratio, while retaining more image edges and detailed texture, reducing color distortion to a certain extent, and the generation of aperture artifacts is weakened. It has a high structural similarity to the original image. The overall quality of the image has been improved to a certain extent, and the subjective and objective evaluations are better than traditional algorithms. Finally, the comparison experiment verifies the effectiveness and practicability of the joint enhancement optimization algorithm in this paper to improve the low-light image quality, Which provides a new pre-processing method for future intelligent target detection, and has important research value.
Bright Telecom-Wavelength Single Photons Based on a Tapered Nanobeam

Telecom-wavelength single photons are essential components for long-distance quantum networks. However, bright and pure single photon sources at telecom wavelengths remain challenging to achieve. Here, we demonstrate a bright telecom-wavelength single photon source based on a tapered nanobeam containing InAs/InP quantum dots. The tapered nanobeam enables directional and Gaussian-like far-field emission of the quantum dots. As a result, using above-band excitation we obtain an end-to-end brightness of 4.1 ± 0.1% and first-lens brightness of 27.0 ± 0.1% at the ∼1300 nm wavelength. Furthermore, we adopt quasi-resonant excitation to reduce both multiphoton emission and decoherence from unwanted charge carriers. As a result, we achieve a coherence time of 523 ± 16 ps and postselected Hong–Ou–Mandel visibility of 0.91 ± 0.09 along with a comparable first-lens brightness of 21.0 ± 0.1%. These results represent a major step toward a practical fiber-based single photon source at telecom wavelengths for long-distance quantum networks.
SG-Net: Syntax Guided Transformer for Language Representation

Understanding human language is one of the key themes of artificial intelligence. For language representation, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy texts and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanisms for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. The proposed SG-Net is applied to typical Transformer encoders. Extensive experiments on popular benchmark tasks, including machine reading comprehension, natural language inference, and neural machine translation show the effectiveness of the proposed SG-Net design.
Generalized string-net models: A thorough exposition

We describe how to construct generalized string-net models, a class of exactly solvable lattice models that realize a large family of two-dimensional topologically ordered phases of matter. The ground states of these models can be thought of as superpositions of different ``string-net configurations,'' where each string-net configuration is a trivalent graph with labeled edges, drawn in the $xy$ plane. What makes this construction more general than the original string-net construction is that, unlike the original construction, tetrahedral reflection symmetry is not assumed, nor is it assumed that the ground-state wave function $\mathrm{\ensuremath{\Phi}}$ is ``isotropic'': i.e., in the generalized setup, two string-net configurations ${X}_{1},{X}_{2}$ that can be continuously deformed into one another can have different ground-state amplitudes $\mathrm{\ensuremath{\Phi}}({X}_{1})\ensuremath{\ne}\mathrm{\ensuremath{\Phi}}({X}_{2})$. As a result, generalized string-net models can realize topological phases that are inaccessible to the original construction. In this paper, we provide a more detailed discussion of ground-state wave functions, Hamiltonians, and minimal self-consistency conditions for generalized string-net models than what exists in the previous literature. We also show how to construct string operators that create anyon excitations in these models, and we show how to compute the braiding statistics of these excitations. Finally, we derive necessary and sufficient conditions for generalized string-net models to have isotropic ground-state wave functions on the plane or the sphere, a property that may be useful in some applications.
Shifting Consumption towards Diverse Content on Music Streaming Platforms

Algorithmic recommendations shape music consumption at scale, and understanding the impact of various algorithmic models on how content is consumed is a central question for music streaming platforms. The ability to shift consumption towards less popular content and towards content different from user's typical historic tastes not only affords the platform ways of handling issues such as filter bubbles and popularity bias, but also contributes to maintaining a healthy and sustainable consumption patterns necessary for overall platform success.
Presence in Digital Spaces. A Phenomenological Concept of Presence in Mediatized Communication

Abstract Theories of face-to-face interaction employ a concept of spatial presence and view communication via digital technologies as an inferior version of interaction, often with pathological implications. Current studies of mediatized communication challenge this notion with empirical evidence of “telepresence”, suggesting that users of such technologies experience their interactions as immediate. We argue that the phenomenological concepts of the lived body and mediated immediacy (Helmuth Plessner) combined with the concept of embodied space (Hermann Schmitz) can help overcome the pathologizing of digital communication in social theory and enable descriptions which are truer to the experience of using said technology. From this perspective it appears as an ethnocentric premise to restrict interaction to human actors being present in local space. This restricted understanding of interaction does not allow for an appropriate empirical analysis of the emerging structures of digital communication.
Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.
DistBlockSDN: A Distributed Secure Blockchain Based SDN-IoT Architecture with NFV Implementation for Smart Cities

Blockchain, Software Defined Networking (SDN) and the Internet of Things (IoT) are the leading emerging technology all over the world. Blockchain is the main contributor to Bitcoin which aims to ignore the third party among the proceedings. On the contrary, the aims of the IoT network is connected with a massive number of objects over the internet. For smart cities, a distributed secure Blockchain-based SDN-IoT architecture has been proposed in this research paper with Network Function Virtualization (NFV) implementation. We have also revolved around the blockchain network which is a highly secured technology that provides good results in network performances, adaptability, privacy, security, accessibility, versatility, confidentiality, and efficiency. Moreover, we have presented a cluster head selection algorithm which aim is to select the desired head of the clusters. In addition, an approach has been also developed that is more efficient for generating a cluster head utilizing SDN-IoT. SDN-IoT with the NFV conception bring in benefits to the associated areas in terms of energy preservation and load equilibrating. In the projected architecture, the Blockchain network allows more prominent security of each network layer relative to the schematic network.
Reversible data hiding based on multiple histograms modification and deep neural networks

In the previous multiple histograms modification (MHM) based reversible data hiding (RDH) method, the prediction-error histograms are generated by a fixed manner, which may constrain the performance owing to the lack of adaptivity. In order to compensate this, we propose a deep neural networks (DNN) based method for dynamical multiple histograms generation. Through learning the prior knowledge, DNN is able to establish the histograms with different sizes for a better redundancy exploitation. For each histogram, two optimal expansion bins will be determined to minimize the distortion caused by the modification. Besides, the strategy consisted of the memo technique and the entropy measurement are applied to accelerate the parameter optimization. Experimental results show that the proposed method outperforms some of state-of-the-art RDH methods.
Towards Anomaly Detection in Dashcam Videos

Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their way into these devices. Unfortunately, the long-tailed distribution of road hazards means that these hand-crafted pipelines are inadequate for driver safety systems. We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. Unfortunately, there exists almost no literature applying anomaly understanding to moving cameras, and correspondingly there is also a lack of relevant datasets. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets. We introduce formulations for modeling object interactions in this context as priors. Our experiments indicate that our dataset is indeed more challenging than standard anomaly detection datasets, and previous anomaly detection methods do not perform well here out-of-the-box. In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data.
Solar power plants site selection for sustainable ecological development in Nigde, Turkey

Abstract Turkey's population is constantly increasing, and thus, the energy consumption is also increasing. Wind turbines, nuclear power plants, and boron and uranium resources are used for energy needs. Turkey meets its energy needs using these resources. Sun which is a natural and unlimited resource among these resources is one of the most important natural energy resources. The most important issue to consider in order to utilize solar energy in the most efficient way and to obtain advantages is the selection of the suitable place for solar plants. The aim of this study is to select the most suitable location for solar energy plants and provide to build solar power plants in suitable places. Eleven data layers (sunshine duration, solar radiation, slope, aspect, road, water sources, residential areas, earthquake fault line, mine areas, power line and transformers) that were prepared using analytical hierarchy process (AHP) method in GIS were used and analysed. Based on the results of the analysis, the location of the areas where solar energy could be built was selected. As a result of the study, it was determined that in particular the north-east of the Nigde is the most suitable for solar plants. Ulukisla district has the most suitable feature for solar plants among the Nigde districts. 80% of the existing solar plants are located in the detected areas. It has been determined that the other 20% of the solar plant is also in the suitable areas, but their location is not suitable for the most efficiency.