Reviewerly/exHarmony huggingface dataset:
Security, Cybercrime and Digital Forensics for IoT

The Internet of Things (IoT) connects almost all the environment objects whether physical or virtual over the Internet to produce new digitized services that improve people’s lifestyle. Currently, several IoT applications have a direct impact on our daily life activities including smart agriculture, wearables, connected healthcare, connected vehicles, and others. Despite the countless benefits provided by the IoT system, it introduces several security challenges. Resolving these challenges should be one of the highest priorities for IoT manufacturers to continue the successful deployment of IoT applications. The owners of IoT devices should guarantee that effective security measures are built in their devices. With the developments of the Internet, the number of security attacks and cybercrimes has increased significantly. In addition, with poor security measures implemented in IoT devices, the IoT system creates more opportunities for cybercrimes to attack various application and services of the IoT system resulting in a direct impact on users. One of the approaches that tackle the increasing number of cybercrimes is digital forensics. Cybercrimes with the power of the IoT technology can cross the virtual space to threaten human life, therefore, IoT forensics is required to investigate and mitigate against such attacks. This chapter presents a review of IoT security and forensics. It started with reviewing the IoT system by discussing building blocks of an IoT device, essential characteristic, communication technologies and challenges of the IoT. Then, IoT security by highlighting threats and solutions regarding IoT architecture layers are discussed. Digital forensics is also discussed by presenting the main steps of the investigation process. In the end, IoT forensics is discussed by reviewing related IoT forensics frameworks, discussing the need for adopting real-time approaches and showing various IoT forensics.
Reduced Reference Stereoscopic Image Quality Assessment Using Sparse Representation and Natural Scene Statistics

An ideal quality assessment model should simulate the properties of the visual brain to be consistent with human evaluation. The visual brain appears to have both evolved to seek an efficient, decorrelated representation of image information and to “match” the statistics of the natural image. On one hand, the theoretical studies suggest that sparse representation resembles the strategy in the primary visual cortex of brain for representing natural images. On the other hand, the natural scene statistics have driven the evolution of human visual system and have also inspired the understanding and simulating of visual perception. Inspired by these observations, in this paper, we propose a novel reduced-reference stereoscopic image quality assessment metric using sparse representation and natural scene statistics to simulate the visual perception of the brain. Specifically, the distribution statistics of the classified visual primitives extracted by sparse representation are used to measure the visual information, which is closely related to the hierarchical progressive process of human visual perception. Particularly, the mutual information of classified primitives between two view images is derived as a binocular cue to simulate the binocular fusion process. The maximum mechanism that is applied to select the visual information is a pooling mechanism with which complex cells use the maximal stimuli from a group of simple cells during the transfer process in the primary visual cortex. The natural scene statistics of locally normalized luminance coefficients are used to evaluate the natural losses due to the presence of distortions. The differences of the visual information and the natural scene statistics between the original and distorted images are used to compute the quality score by a prediction function which is trained using support vector regression. Experimental results show that the proposed metric outperforms the state-of-the-art stereoscopic image quality assessment metrics on LIVE 3D IQA database and NBU-MDSID Phase-II database, and delivers competitive performance on Waterloo IVC 3D database.
Generative adversarial networks based on Wasserstein distance for knowledge graph embeddings

Knowledge graph embedding aims to project entities and relations into low-dimensional and continuous semantic feature spaces, which has captured more attention in recent years. Most of the existing models roughly construct negative samples via a uniformly random mode, by which these corrupted samples are practically trivial for training the embedding model. Inspired by generative adversarial networks (GANs), the generator can be employed to sample more plausible negative triplets, that boosts the discriminator to improve its embedding performance further. However, vanishing gradient on discrete data is an inherent problem in traditional GANs. In this paper, we propose a generative adversarial network based knowledge graph representation learning model by introducing the Wasserstein distance to replace traditional divergence for settling this issue. Moreover, the additional weak supervision information is also absorbed to refine the performance of embedding model since these textual information contains detailed semantic description and offers abundant semantic relevance. In the experiments, we evaluate our method on the tasks of link prediction and triplet classification. The experimental results indicate that the Wasserstein distance is capable of solving the problem of vanishing gradient on discrete data and accelerating the convergence, additional weak supervision information also can significantly improve the performance of the model.
Certificate Transparency in the Wild

To detect fraudulent TLS server certificates and improve the accountability of certification authorities (CAs), certificate transparency (CT) is proposed to record certificates in publicly-visible logs, from which the monitors fetch all certificates and watch for suspicious ones. However, if the monitors, either domain owners themselves or third-party services, fail to return a complete set of certificates issued for a domain of interest, potentially fraudulent certificates may not be detected and then the CT framework becomes less reliable. This paper presents the first systematic study on CT monitors. We analyze the data in 88 public logs and the services of 5 active third-party monitors regarding 3,000,431 certificates of 6,000 selected Alexa Top-1M websites. We find that although CT allows ordinary domain owners to act as monitors, it is impractical for them to perform reliable processing by themselves, due to the rapidly increasing volume of certificates in public logs (e.g., on average 5 million records or 28.29 GB daily for the minimal set of logs that need to be monitored). Moreover, our study discloses that (a) none of the third-party monitors guarantees to return the complete set of certificates for a domain, and (b) for some domains, even the union of the certificates returned by the five third-party monitors can probably be incomplete. As a result, the certificates accepted by CT-enabled browsers are not absolutely visible to the claimed domain owners, even when CT is adopted with well-functioning logs. The risk of invisible fraudulent certificates in public logs raises doubts on the reliability of CT in practice.
The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms

Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, function-as-a-service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage complex applications without the need for system-level expertise. Victim of its own success, and partially due to proprietary technology, currently the community has a limited overview of these platforms. To address this, we propose a reference architecture and ecosystem for FaaS platforms. Based on a year-long survey of real-world platforms conducted within the SPEC-RG Cloud Group, we highlight specific components and identify common operational patterns.
SkyScapes ­ Fine-Grained Semantic Understanding of Aerial Scenes

Understanding the complex urban infrastructure with centimeter-level accuracy is essential for many applications from autonomous driving to mapping, infrastructure monitoring, and urban management. Aerial images provide valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of aerial scenes at the level of granularity required by real-world applications. To address this, we introduce SkyScapes, an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling. SkyScapes provides annotations for 31 semantic categories ranging from large structures, such as buildings, roads and vegetation, to fine details, such as 12 (sub-)categories of lane markings. We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction. We carry out extensive experiments to evaluate state-of-the-art segmentation methods on SkyScapes. Existing methods struggle to deal with the wide range of classes, object sizes, scales, and fine details present. We therefore propose a novel multi-task model, which incorporates semantic edge detection and is better tuned for feature extraction from a wide range of scales. This model achieves notable improvements over the baselines in region outlines and level of detail on both tasks.
Multi-Stage Pathological Image Classification Using Semantic Segmentation

Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigapixel resolution considering the available memory capacity. Most of the previous works divide high resolution WSIs into small image patches and separately input them into the model to classify it as a tumor or a normal tissue. However, patch-based classification uses only patch-scale local information but ignores the relationship between neighboring patches. If we consider the relationship of neighboring patches and global features, we can improve the classification performance. In this paper, we propose a new model structure combining the patch-based classification model and whole slide-scale segmentation model in order to improve the prediction performance of automatic pathological diagnosis. We extract patch features from the classification model and input them into the segmentation model to obtain a whole slide tumor probability heatmap. The classification model considers patch-scale local features, and the segmentation model can take global information into account. We also propose a new optimization method that retains gradient information and trains the model partially for end-to-end learning with limited GPU memory capacity. We apply our method to the tumor/normal prediction on WSIs and the classification performance is improved compared with the conventional patch-based method.
Efficient inception V2 based deep convolutional neural network for real‐time hand action recognition

The most effective and accurate deep convolutional neural network (faster region-based convolutional neural network (Faster R-CNN) Inception V2 model, single shot detector (SSD) Inception V2 model) based architectures for real-time hand gesture recognition is proposed. The proposed models are tested on standard data sets (NUS hand posture data set-II, Senz-3D) and custom-developed (MITI hand data set (MITI-HD)) data set. The performance metrics are analysed for intersection over union (IoU) ranges between 0.5 and 0.95. IoU value of 0.5 resulted in higher precision compared to other IoU values considered (0.5:0.95, 0.75). It is observed that the Faster R-CNN Inception V2 model resulted in higher precision (0.990 for APall, IoU = 0.5) compared to SSD Inception V2 model (0.984 for all) for MITI-HD 160. The computation time of Faster R-CNN Inception V2 is higher compared to SSD Inception V2 model and also resulted in less number of mispredictions. Increasing the size of samples (MITI-HD 300) resulted in improvement of APall = 0.991. Improvement in large (APlarge) and medium (APmedium) size detections are not significant when compared to small (APsmall) detections. It is concluded that the Faster R-CNN Inception V2 model is highly suitable for real-time hand gesture recognition system under unconstrained environments.
Reducing transaction costs of tradable permit schemes using Blockchain smart contracts

Abstract Tradable permit schemes (TPS) are market‐based policy instruments, which are claimed to be more statically and dynamically efficient in achieving their policy objectives compared to the traditional regulatory instruments. However, some researchers argue that the high level of transaction costs associated with these policy instruments might undermine their efficiency. This paper addresses this issue through exploring the use of Blockchain technology in order to lower transaction costs that arise from TPS transactions. More specifically, it identifies the benefits of using Blockchain smart contracts in implementing TPS that include: increasing the amount of relevant information available to interested actors thus reducing uncertainties; reducing the amount of irrelevant information available to interested actors thus decreasing complexities; correcting information asymmetries among the actors involved thus limiting opportunistic behaviours; lowering the need for the involvement of intermediaries thus decreasing direct monetary costs; and facilitating the linkage between buyers and sellers thus improving trading quality. These potential benefits can increase the efficiency of TPS by decreasing policy‐related transaction costs.
PPCPP: A Predator–Prey-Based Approach to Adaptive Coverage Path Planning

Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons.
Design and results of the Second International Competition on Computational Models of Argumentation

Argumentation is a major topic in the study of Artificial Intelligence. Since the first edition in 2015, advancements in solving (abstract) argumentation frameworks are assessed in competition events, similar to other closely related problem solving technologies. In this paper, we report about the design and results of the Second International Competition on Computational Models of Argumentation, which has been jointly organized by TU Dresden (Germany), TU Wien (Austria), and the University of Genova (Italy), in affiliation with the 2017 International Workshop on Theory and Applications of Formal Argumentation. This second edition maintains some of the design choices made in the first event, e.g. the I/O formats, the basic reasoning problems, and the organization into tasks and tracks. At the same time, it introduces significant novelties, e.g. three additional prominent semantics, and an instance selection stage for classifying instances according to their empirical hardness.
Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise

Enhancing low light videos, which consists of denoising and brightness adjustment, is an intriguing but knotty problem. Under low light condition, due to high sensitivity camera setting, commonly negligible noises become obvious and severely deteriorate the captured videos. To recover high quality videos, a mass of image/video denoising/enhancing algorithms are proposed, most of which follow a set of simple assumptions about the statistic characters of camera noise, e.g., independent and identically distributed(i.i.d.), white, additive, Gaussian, Poisson or mixture noises. However, the practical noise under high sensitivity setting in real captured videos is complex and inaccurate to model with these assumptions. In this paper, we explore the physical origins of the practical high sensitivity noise in digital cameras, model them mathematically, and propose to enhance the low light videos based on the noise model by using an LSTM-based neural network. Specifically, we generate the training data with the proposed noise model and train the network with the dark noisy video as input and clear-bright video as output. Extensive comparisons on both synthetic and real captured low light videos with the state-of-the-art methods are conducted to demonstrate the effectiveness of the proposed method.
Toward New Retail: A Benchmark Dataset for Smart Unmanned Vending Machines

Deep learning is a popular direction in computer vision and digital image processing. It is widely utilized in many fields, such as robot navigation, intelligent video surveillance, industrial inspection, and aerospace. With the extensive use of deep learning techniques, classification and object detection algorithms have been rapidly developed. In recent years, with the introduction of the concept of &#x201C;unmanned retail,&#x201D; object detection, and image classification play a central role in unmanned retail applications. However, open-source datasets of traditional classification and object detection have not yet been optimized for application scenarios of unmanned retail. Currently, classification and object detection datasets do not exist that focus on unmanned retail solely. Therefore, in order to promote unmanned retail applications by using deep learning-based classification and object detection, in this article we collected more than 30&#x2009;000 images of unmanned retail containers using a refrigerator affixed with different cameras under both static and dynamic recognition environments. These images were categorized into ten kinds of beverages. After manual labeling, images in our constructed dataset contained 155&#x2009;153 instances, each of which was annotated with a bounding box. We performed extensive experiments on this dataset using ten state-of-the-art deep learning-based models. Experimental results indicate great potential of using these deep learning-based models for real-world smart unmanned vending machines.
Authoring Data Stories in a Media Makerspace: Adolescents Developing Critical Data Literacies

This article centers sociocultural and sociopolitical considerations of how young people understand, represent, and use data by presenting findings from a social design research study about how students in a public urban high school authored “data stories” using personal data they curated, collected, and visualized. The study contributes to theoretical understandings of critical data literacies by considering the experiences and practices of adolescents enrolled in a required media arts class as they produced data visualizations drawn from their everyday lives. Findings center on two aspects of critical data literacies youth developed—understanding themselves as people capable of using data for multiple purposes and understanding data as socially situated resources for meaning-making. This study foregrounds the importance of positioning youth as authors and architects of data, making central youth perspectives in understanding the role of data in young people’s digitally connected lives and highlighting the importance of expanding what “counts” as data. It also suggests the importance of creating infrastructure to support the development of culturally relevant data practices that highlight the social, cultural, and political uses of data and its racialized dimensions.
A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment

The ability of four models to provide short-term (up to 6 h ahead) GHI and DNI forecasts in the Iberian Peninsula is assessed based on two years of data collected at four stations. The models follow (mostly) independent approaches: one pure statistical model (Smart Persistence), one model based on CMV derived from satellite images (Satellite), one NWP model (WRF-Solar) and a hybrid satellite-NWP model (CIADCast). Overall, results show Smart Persistence to be the best at the first lead steps, advective models (Satellite and CIADCast) at intermediate ones and the WRF-Solar at the end of the forecasting period. The break-even point between the advective models and WRF-Solar varies between 1 and 3 h for GHI and 3 and 5 h for DNI. Nevertheless, a detailed analysis shows enormous differences between models performance related to 1) the local geographic and topographic conditions of the evaluation stations; 2) the evaluated variable (GHI vs. DNI); and 3) the sky and synoptic weather conditions over the study area. Depending on the station and lead time, rRMSE values range from 25% to 70% for GHI and from 35% to 100% for DNI. For the same stations and leading time, rRMSE values for DNI are between 50% and 100% higher than the corresponding GHI counterparts. Depending on the synoptic pattern, rRMSE values are about 10/20% for GHI/DNI (3 h lead time, during high pressure conditions) to about 80/180% for GHI/DNI (during low pressure conditions). All models show a poor performance at a coastal station, attributed to a lack of ability to forecast clouds associated with sea-land breezes. To conclude, no single model proves to be the best performing model and, therefore, results show that the four models are, somehow, complementary. The advantages attained by this complementarity are further explored in a companion paper (Part II).
How Diversity Affects Deep-Learning Side-Channel Attacks

Deep learning side-channel attacks are an emerging threat to the security of implementations of cryptographic algorithms. The attacker first trains a model on a large set of side-channel traces captured from a chip with a known key. The trained model is then used to recover the unknown key from a few traces captured from a victim chip. The first successful attacks have been demonstrated recently. However, they typically train and test on power traces captured from the same device. In this paper, we show that it is important to train and test on traces captured from different boards. Otherwise, it is easy to overestimate the classification accuracy. For example, if we train and test an MLP model on power traces captured from the same board, we can recover all key byte values with 88.5% accuracy from a single trace. However, the single-trace attack accuracy drops to 13.7% if we test on traces captured from a board different from the one we used for training, even if both boards carry identical chips.