Reviewerly/exHarmony huggingface dataset:
GAN-Generated Image Detection With Self-Attention Mechanism Against GAN Generator Defect

With Generative adversarial networks (GAN) achieving realistic image generation, fake image detection research has become an imminent need. In this paper, a novel detection algorithm is designed to exploit the structural defect in GAN, taking advantage of the most vulnerable link in GAN generators - the up-sampling process conducted by the Transposed Convolution operation. The Transposed Convolution in the process will cause the lack of global information in the generated images. Therefore, the Self-Attention mechanism is adopted correspondingly, equipping the algorithm with a much better comprehension of the global information than the other current work adopting pure CNN network, which is reflected in the significant increase in the detection accuracy. With the thorough comparison to the current work and corresponding careful analysis, it is verified that our proposed algorithm outperforms other current works in the field. Also, with experiments conducted on other image-generation categories and images undergone usual real-life post-processing methods, our proposed algorithm shows decent robustness for various categories of images under different reality circumstances, rather than restricted by image types and pure laboratory situation.
A Collaborative Auditing Blockchain for Trustworthy Data Integrity in Cloud Storage System

Cloud storage system provides data owners with remote storage service, which allows them to outsource data without local storage burden. Nevertheless, the cloud storage service is not fully trustworthy since it may not be honest and remote data would be corrupted. One way to ensure trustworthy preservation of cloud data is the remote data auditing method, through which data owners can check storage reliability of cloud system on demand and avoid potential data corruption in time. However, private auditing methods fail to promise the mutual trust in auditing results. Thus, public auditing methods are introduced, in which traditionally a third party auditor is delegated to interact with cloud service providers for auditing tasks. Although the third party auditor serves as a medium to exchange trust, a centralized third party is hard to stay neutral, which exposes the remote data auditing to some threats such as collusion attacks. To address the trust problem between data owners and cloud service providers, we propose a collaborative auditing blockchain framework for cloud data storage. In this framework, all consensus nodes substitute for the single third party auditor to execute auditing delegations and record them permanently, thereby preventing entities from deceiving each other. Security analysis shows that the proposed framework has advantage of preserving remote data integrity from various attacks. Performance analysis demonstrates that the framework is more functional and resource-friendly than existing schemes.
Forecast modeling and performance assessment of solar PV systems

Electricity is converted directly from incident solar energy through photovoltaic systems. This latter is considered an elegant mean of exploiting renewable energy. It contributes significantly to the energy transition by reducing fossil fuel usage. Several studies about photovoltaic (PV) technology have focused on the design of photovoltaic systems or the theory behind them. On the contrary, this paper presents an evaluation of PV systems performance; it investigates the impact of diverse factors on the performance of these PV technologies. The aim of this work is to assess and compare the performance of different PV types including monocrystalline, polycrystalline, and amorphous silicon (Si) systems. Various indices are utilized in this evaluation such as system efficiency, performance ratio, energy output, and capacity factor. The performance analysis was evaluated using real measured data for a period of 5 years. It has shown that polycrystalline-Si technologies have higher performance compared to both monocrystalline-Si and amorphous-Si. This latter has lower conversion efficiency and capacity factor as compared to its counterparts. The performed economic analysis indicates that the most cost-effective system in the investigated Case study is polycrystalline -Si as it has the lowest levelized cost of energy (LCOE) of 0.10 USD/kWh. In addition, a dynamic simulator based on physical resources has been developed using Python to predict the power production of PV systems over one week. The forecast model is based on cloud cover predictions obtained from Dark Sky API. The simulation results are compared to the measured ones. The obtained results demonstrate that the proposed prediction model has high accuracy. The root mean square errors of polycrystalline-Si, monocrystalline-Si, and amorphous-Si are 17.9%, 17%, and 20.4%, respectively. In addition, the mean absolute percentage errors of the aforementioned systems are 5.05%, 4.57%, and 4.35%, respectively. Finally, a detailed analysis comparing the performance of PV systems installed in various regions of Morocco is presented in this paper. The compared systems have the same brand and power capacity.
Introduction to the special issue – Digital business models: A multi-disciplinary and multi-stakeholder perspective

The advent and enormous growth of digital technologies, and associated data, force firms to respond to novel digital challenges and increasingly lead them to transform their existing business models. Importantly, given that digital transformation has a strong impact on multiple disciplines, such as logistics, marketing, and strategy, and involves multiple stakeholders, such as service providers, platforms, employees and end-users, it also requires researchers and businesses to adopt a multi-disciplinary, multi-stakeholder perspective, in which multiple research and business fields cooperate in order to create collaborative solutions. In this editorial to the special issue, we aim to bring together insights from multiple research fields to account for the multi-faceted nature of digital transformation. We discuss the relevance of this multi-disciplinary, multi-stakeholder perspective, propose an overarching research framework, and highlight future avenues of research.
On Classification of BMD Images Using Machine Learning (ANN) Algorithm

Osteoporosis is a disease which has affected major part of human body that is bones. It trends to reduce the mass of the bones and hence degrade the micro architecture of tissues of bones. There are multiple imaging technologies which have been used from the decades in diagnosis and the micro architecture of distressed as well as affected bones in order to find the bone density deficiency. Image processing methods are widely used like filtering, segmentation, classification, image enhancement as well as other pre-processing technique to diagnosis the affected bone structure easily. It will help to extract the vital information about deformed micro architecture pattern. In this paper we have collected the basic information of tissues such as osteoblast as well as osteoclast and also have presented a comparative study of various diagnosis technique based on the image processing for the osteoporosis. The main aim of this work is to assess the prevalence of osteoporosis and changes in bone mass with increasing age, compare bone health status of apparently healthy men, premenopausal and postmenopausal women. In this work, we have used ethical database of 260 subject 130 men, 80 women (premenopausal) and 50 women (postmenopausal). Bone mineral density (BMD) has been measured through dual energy X-ray absorptiometry at femoral neck.
High Accuracy Phishing Detection Based on Convolutional Neural Networks

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this paper compares favourably to the state-of-the art in deep learning based phishing website detection.
It Is Your Turn: Collaborative Ideation With a Co-Creative Robot through Sketch

Co-creative systems have been widely explored in the field of computational creativity. However, existing AI partners of these systems are mostly virtual agents. As sketching on paper with embodied robots could be more engaging for designers' early-stage ideation and collaborative practices, we envision the possibility of Cobbie, a mobile robot that ideates iteratively with designers by generating creative and diverse sketches. To evaluate the differences in co-creativity and user experience between the co-creative robots and virtual agents, we conducted a comparative experiment and analyzed the data collected from quantitative scales, observation, and semi-structured interview. The results reveal that Cobbie is more satisfying in motivating exploration, provoking unexpected ideas and engaging designers in the collaborative ideation process. Based on these findings, we discussed the prospects of co-creative robots for future developments of human-AI collaborative systems.
MIRIS: Fast Object Track Queries in Video

Video databases that enable queries with object-track predicates are useful in many applications. Such queries include selecting objects that move from one region of the camera frame to another (e.g., finding cars that turn right through a junction) and selecting objects with certain speeds (e.g., finding animals that stop to drink water from a lake). Processing such predicates efficiently is challenging because they involve the movement of an object over several video frames. We propose a novel query-driven tracking approach that integrates query processing with object tracking to efficiently process object track queries and address the computational complexity of object detection methods. By processing video at low framerates when possible, but increasing the framerate when needed to ensure high-accuracy on a query, our approach substantially speeds up query execution. We have implemented query-driven tracking in MIRIS, a video query processor, and compare MIRIS against four baselines on a diverse dataset consisting of five sources of video and nine distinct queries. We find that, at the same accuracy, MIRIS accelerates video query processing by 9x on average over the IOU tracker, an overlap-based tracking-by-detection method used in existing video database systems.
The Care Work of Access

Current approaches to AI and Assistive Technology (AT) often foreground task completion over other encounters such as expressions of care. Our paper challenges and complements such task-completion approaches by attending to the care work of access-the continual affective and emotional adjustments that people make by noticing and attending to one another. We explore how this work impacts encounters among people with and without vision impairments who complete tasks together. We find that bound up in attempts to get things done are concerns for one another and how well people are doing together. Reading this work through emerging disability studies and feminist STS scholarship, we account for two important forms of work that give rise to access: (1) mundane attunements and (2) non-innocent authorizations. Together these processes work as sensitizing concepts to help HCI scholars account for the ways that intelligent ATs both produce access while sometimes subverting people with disabilities.
Parallel Programming of an Arbitrary Feedforward Photonic Network

Reconfigurable photonic mesh networks of tunable beamsplitter nodes can linearly transform $N$-dimensional vectors representing input modal amplitudes of light for applications such as energy-efficient machine learning hardware, quantum information processing, and mode demultiplexing. Such photonic meshes are typically programmed and/or calibrated by tuning or characterizing each beam splitter one-by-one, which can be time-consuming and can limit scaling to larger meshes. Here we introduce a graph-topological approach that defines the general class of feedforward networks and identifies columns of non-interacting nodes that can be adjusted simultaneously. By virtue of this approach, we can calculate the necessary input vectors to program entire columns of nodes in parallel by simultaneously nullifying the power in one output of each node via optoelectronic feedback onto adjustable phase shifters or couplers. This parallel nullification approach is robust to fabrication errors, requiring no prior knowledge or calibration of node parameters and reducing programming time by a factor of order $N$ to being proportional to the optical depth (number of node columns). As a demonstration, we simulate our programming protocol on a feedforward optical neural network model trained to classify handwritten digit images from the MNIST dataset with up to 98% validation accuracy.
Probabilistic Knowledge Transfer for Lightweight Deep Representation Learning

Knowledge-transfer (KT) methods allow for transferring the knowledge contained in a large deep learning model into a more lightweight and faster model. However, the vast majority of existing KT approaches are designed to handle mainly classification and detection tasks. This limits their performance on other tasks, such as representation/metric learning. To overcome this limitation, a novel probabilistic KT (PKT) method is proposed in this article. PKT is capable of transferring the knowledge into a smaller student model by keeping as much information as possible, as expressed through the teacher model. The ability of the proposed method to use different kernels for estimating the probability distribution of the teacher and student models, along with the different divergence metrics that can be used for transferring the knowledge, allows for easily adapting the proposed method to different applications. PKT outperforms several existing state-of-the-art KT techniques, while it is capable of providing new insights into KT by enabling several novel applications, as it is demonstrated through extensive experiments on several challenging data sets.
A Framework Based on Distributed Ledger Technologies for Data Management and Services in Intelligent Transportation Systems

Data are becoming the cornerstone of many businesses and entire systems infrastructure. Intelligent Transportation Systems (ITS) are no different. The ability of intelligent vehicles and devices to acquire and share environmental measurements in the form of data is leading to the creation of smart services for the benefit of individuals. In this paper, we present a system architecture to promote the development of ITS using distributed ledgers and related technologies. Thanks to these, it becomes possible to create, store and share data generated by users through the sensors on their devices or vehicles, while on the move. We propose an architecture based on Distributed Ledger Technologies (DLTs) to offer features such as immutability, traceability and verifiability of data. IOTA, a promising DLT for IoT, is used together with Decentralized File Storages (DFSes) to store and certify data (and their related metadata) coming from vehicles or by the users' devices themselves (smartphones). Ethereum is then exploited as the smart contract platform that coordinates the data sharing through access control mechanisms. Privacy guarantees are provided by the usage of distributed key management systems and Zero Knowledge Proof. We provide experimental results of a testbed based on real traces, in order to understand if DLT and DFS technologies are ready to support complex services, such as those that pertain to ITS. Results clearly show that, while the viability of the proposal cannot be rejected, further work is needed on the responsiveness of DLT infrastructures.
Automated Anomaly Detection in Large Sequences

Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, current approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose NorM, a novel approach, suitable for domain-agnostic anomaly detection. NorM is based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior. The experimental results on several real datasets demonstrate that the proposed approach outperforms by a large margin the current state-of-the art algorithms in terms of accuracy, while being orders of magnitude faster.
Inferring Point Cloud Quality via Graph Similarity

Objective quality estimation of media content plays a vital role in a wide range of applications. Though numerous metrics exist for 2D images and videos, similar metrics are missing for 3D point clouds with unstructured and non-uniformly distributed points. In this paper, we propose [Formula: see text]-a metric to accurately and quantitatively predict the human perception of point cloud with superimposed geometry and color impairments. Human vision system is more sensitive to the high spatial-frequency components (e.g., contours and edges), and weighs local structural variations more than individual point intensities. Motivated by this fact, we use graph signal gradient as a quality index to evaluate point cloud distortions. Specifically, we first extract geometric keypoints by resampling the reference point cloud geometry information to form an object skeleton. Then, we construct local graphs centered at these keypoints for both reference and distorted point clouds. Next, we compute three moments of color gradients between centered keypoint and all other points in the same local graph for local significance similarity feature. Finally, we obtain similarity index by pooling the local graph significance across all color channels and averaging across all graphs. We evaluate [Formula: see text] on two large and independent point cloud assessment datasets that involve a wide range of impairments (e.g., re-sampling, compression, and additive noise). [Formula: see text] provides state-of-the-art performance for all distortions with noticeable gains in predicting the subjective mean opinion score (MOS) in comparison with point-wise distance-based metrics adopted in standardized reference software. Ablation studies further show that [Formula: see text] can be generalized to various scenarios with consistent performance by adjusting its key modules and parameters. Models and associated materials will be made available at https://njuvision.github.io/GraphSIM or http://smt.sjtu.edu.cn/papers/GraphSIM.
Establishing a Zero Trust Strategy in Cloud Computing Environment

The increased use of cloud services and its various security and privacy challenges such as identity theft, data breach, data integrity and data confidentiality has made trust management, which is one of the most multifaceted aspect in cloud computing, inevitable. The growing reputation of cloud computing technology makes it immensely important to be acquainted with the meaning of trust in the cloud, as well as identify how the customer and the cloud service providers establish that trust. The traditional trust management mechanisms represent a static trust relationship which falls deficit while meeting up the dynamic requirement of cloud services. In this paper, a conceptual zero trust strategy for the cloud environment has been proposed. The model offers a conceptual typology of perceptions and philosophies for establishing trust in cloud services. Further, importance of trust establishment and challenges of trust in cloud computing have also been explored and discussed.
Reversible Image Secret Sharing

In reversible image secret sharing (RISS), the cover image can be recovered to some degree, and a share can be comprehensible rather than noise-like. Reversible cover images play an important role in law enforcement and medical diagnosis. The comprehensible share can not only reduce the suspicion of attackers but also improve the management efficiency of shares. In this paper, we first provide a formal definition of RISS. Then, we propose an RISS algorithm for a (k, n)-threshold based on the principle of the Chinese remainder theorem-based ISS (CRTISS). In the proposed RISS, the secret image is losslessly decoded by a modular operation, and the original cover image is recovered by a binarization operation, both of which are just simple operations. Theoretical analyses and experiments are provided to validate the proposed definition and algorithm.
Towards Robust Image Classification Using Sequential Attention Models

In this paper we propose to augment a modern neuralnetwork architecture with an attention model inspired by human perception. Specifically, we adversarially train and analyze a neural model incorporating a human inspired, visual attention component that is guided by a recurrent top-down sequential process. Our experimental evaluation uncovers several notable findings about the robustness and behavior of this new model. First, introducing attention to the model significantly improves adversarial robustness resulting in state-of-the-art ImageNet accuracies under a wide range of random targeted attack strengths. Second, we show that by varying the number of attention steps (glances/fixations) for which the model is unrolled, we are able to make its defense capabilities stronger, even in light of stronger attacks - resulting in a “computational race” between the attacker and the defender. Finally, we show that some of the adversarial examples generated by attacking our model are quite different from conventional adversarial examples - they contain global, salient and spatially coherent structures coming from the target class that would be recognizable even to a human, and work by distracting the attention of the model away from the main object in the original image.