Reviewerly/exHarmony huggingface dataset:
Fully Dynamic Inference with Deep Neural Networks

Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction. Their cross-domain success, however, is often achieved at the expense of computational cost, high memory bandwidth, and long inference latency, which prevents their deployment in resource-constrained and time-sensitive scenarios, such as edge-side inference and self-driving cars. While recently developed methods for creating efficient deep neural networks are making their real-world deployment more feasible by reducing model size, they do not fully exploit input properties on a per-instance basis to maximize computational efficiency and task accuracy. In particular, most existing methods typically use a one-size-fits-all approach that identically processes all inputs. Motivated by the fact that different images require different feature embeddings to be accurately classified, we propose a fully dynamic paradigm that imparts deep convolutional neural networks with hierarchical inference dynamics at the level of layers and individual convolutional filters/channels. Two compact networks, called Layer-Net (L-Net) and Channel-Net (C-Net), predict on a per-instance basis which layers or filters/channels are redundant and therefore should be skipped. L-Net and C-Net also learn how to scale retained computation outputs to maximize task accuracy. By integrating L-Net and C-Net into a joint design framework, called LC-Net, we consistently outperform state-of-the-art dynamic frameworks with respect to both efficiency and classification accuracy. On the CIFAR-10 dataset, LC-Net results in up to <inline-formula><tex-math notation="LaTeX">$11.9\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>11</mml:mn><mml:mo>.</mml:mo><mml:mn>9</mml:mn><mml:mo>&#x00D7;</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq1-3056031.gif"/></alternatives></inline-formula> fewer floating-point operations (FLOPs) and up to 3.3 percent higher accuracy compared to other dynamic inference methods. On the ImageNet dataset, LC-Net achieves up to <inline-formula><tex-math notation="LaTeX">$1.4\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>4</mml:mn><mml:mo>&#x00D7;</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-3056031.gif"/></alternatives></inline-formula> fewer FLOPs and up to 4.6 percent higher Top-1 accuracy than the other methods.
GridNet: Image-Agnostic Conditional Anomaly Detection for Indoor Surveillance

We present a deep autoencoder-based anomaly detection method (GridNet) for indoor surveillance. Unlike similar studies, GridNet is image-agnostic by taking a specific representation of a scene as inputs instead of the raw image itself. Its input is grid representations of scene images, which indicate spatial layouts of objects in a scene. This approach allows us to isolate the anomaly detection problem from any vision-related issues, such as illumination variations. In addition to grid representations, GridNet takes a location vector of a scene as input to learn the normalities of each scene conditioned on its location. We also propose a novel loss function that increases the model's reconstruction capability for grid representations. It enables the network to increase its precision and recall throughout the reconstruction. In our experiments, we compare our method with the existing studies on simulated and real-world data. The experimental results show the superiority of our method compared to the baseline methods. The code, data, and simulation environments will be available at https://bozcani.github.io/gridnet.
Multi-Party Private Set Intersection: An Information-Theoretic Approach

We investigate the problem of multi-party private set intersection (MP-PSI). In MP-PSI, there are M parties, each storing a data set <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> over N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> replicated and non-colluding databases, and we want to calculate the intersection of the data sets ∩ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> without leaking any information beyond the set intersection to any of the parties. We consider a specific communication protocol where one of the parties, called the leader party, initiates the MP-PSI protocol by sending queries to the remaining parties which are called client parties. The client parties are not allowed to communicate with each other. We propose an information-theoretic scheme that privately calculates the intersection ∩ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> with a download cost of D = min <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t ∈ {1,...</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">, M}</sub> Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i ∈ {1,..., M}\t</sub> ⌈[| <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> |N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ]/[N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> -1]⌉. Similar to the 2-party PSI problem, our scheme builds on the connection between the PSI problem and the multi-message symmetric private information retrieval (MM-SPIR) problem. Our scheme is a non-trivial generalization of the 2-party PSI scheme as it needs an intricate design of the shared common randomness. Interestingly, in terms of the download cost, our scheme does not incur any penalty due to the more stringent privacy constraints in the MP-PSI problem compared to the 2-party PSI problem.
A Blockchain Based Technique for Storing Vaccination Records

Maintenance of immutable vaccination records and provision of accessing the records in order to prove immunity has been the need of the hour. The recent spread of Covid19 and related uncertainty over vaccinations and immunity have made the search for a secure trustable system for reporting vaccination data more essential. Multiple digital, as well as paper-based solutions have been tested but none has been reported successful enough. In this paper, a technique has been proposed to solve the problem by introducing blockchain-based solution to maintain records of vaccination and proof of immunity for individuals. The purpose has been to present a safe and efficient solution to the problem and hence the model proposed is based on concepts of smart contracts and built over Ethereum blockchain. The paper goes on to give a detailed study of the technique based on discussions of its various aspects like design, development and feasibility.
On the Capacity Region of Bipartite and Tripartite Entanglement Switching

We study a quantum switch serving a set of users in a star topology. The function of the switch is to create bipartite or tripartite entangled state among users at the highest possible rates at a fixed ratio. We model a set of randomized switching policies. Discovering that some are better than others, we present analytical results for the case where the switch stores one qubit per user, and find that the best policies outperform a time division multiplexing (TDM) policy for sharing the switch between bipartite and tripartite state generation. This performance improvement decreases as the number of users grows. The model is easily augmented to study the capacity region in the presence of qubit decoherence, obtaining similar results. Moreover, decoherence appears to have little effect on capacity. We also study a smaller class of policies when the switch stores two qubits per user.
Cross-Silo Model-Based Secure Federated Transfer Learning for Flow-Based Traffic Classification

Traffic classification is crucial for autonomous network management. Deep learning-based traffic classification methods are in demand because of their ability to accurately classify even encrypted traffic. Federated learning is a way to collaboratively train learning models with privacy-preservation. Transfer learning allows learning models to share knowledge between tasks from different but related domains. Federated Transfer Learning allows collaborative training of privacy-preserving models with knowledge sharing from source to target domains. In this paper, we did secure federated transfer learning for improvising the training-time and accuracy of the targetfederated-model for traffic classification. The target-federatedmodel outperforms the baseline-federated-model trained from scratch. We implemented a simple cross-silo secure aggregation protocol for security.
TollsOnly Please—Homomorphic Encryption for Toll Transponder Privacy in Internet of Vehicles

Cities have circumvented privacy norms and deployed sensors to track vehicles via toll transponders (like E-Zpass tags). The ethical problems regarding these practices have been highlighted by various privacy advocacy groups. The industry, however, has yet to implement a standard privacy protection regime to protect users’ data. Furthermore, existing risk management models do not adequately address user-controlled data-sharing requirements. In this article, we consider the challenges of protecting private data in the Internet of Vehicles (IoV) and mobile edge networks. Specifically, we present a privacy risk reduction model for electronic toll transponder data. We seek to preserve driver privacy while contributing to intelligent transportation infrastructure congestion automation schemes. We thus propose TollsOnly, a fully homomorphic encryption protocol. TollsOnly is expected to be a postquantum privacy preservation scheme. It enables users to share specific data with smart cities via blockchain technology. TollsOnly protects driver privacy in compliance with the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act.
An approach for applying blockchain technology in centralized electricity markets

The electricity sector has recently begun to undergo a series of changes to improve the generation, transmission, and energy distribution. New technologies have been testing focused on reducing fossil fuels and implementing new tools to create a more efficient energy sector. Blockchain has emerged as one of the most promising tools due to its wide range of utilities, primarily supporting renewable energy, electric mobility, purchase, and energy sale. This technology has been revolutionary, contributing to the significant renewal of the electricity market model. In electricity markets that are not very mature, you can find some centralized activities and competition—creating a commercial monopoly in centralized activities in favor of a specific group. This article aims to analyze the shortcomings of previous research on blockchain in the electricity sector and, through a case study, propose an approach for applying blockchain in centralized electricity markets. Blockchain technology has valuable points, including the excellent security in the transactions carried out in the purchase and sale of energy through a peer-to-peer network stimulating the creation of a decentralized energy trading system.
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction

We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker is SuperDiMP which combines the bounding-box regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP improves SuperDiMP in the following three aspects: (1) Uncertainty reduction using random erasing: To make our model robust, we exploit an agreement from multiple images after erasing random small rectangular areas as a certainty. And then, we correct the tracking state of our model accordingly. (2) Random search with spatio-temporal constraints: we propose a robust random search method with a score penalty applied to prevent the problem of sudden detection at a distance. (3) Background augmentation for more discriminative feature learning: We augment various backgrounds that are not included in the search area to train a more robust model in the background clutter. In experiments on the VOT-LT2020 benchmark dataset, the proposed method achieves comparable performance to the state-of-the-art long-term trackers. The source code is available at: https://github.com/bismex/RLT-DIMP .
How are attributes expressed in face DCNNs?

As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize identities, they also contain face related information such as sex, age, and pose of the face even when the networks are not trained to learn these attributes. We introduce expressivity as a measure of how much a feature vector informs us about an attribute, where a feature vector can be from internal or final layers of a network. Expressivity is computed by a second neural network whose inputs are features and attributes. The output of the second neural network approximates the mutual information between feature vectors and an attribute. We investigate the expressivity for two different deep convolutional neural network (DCNN) architectures: a Resnet-101 and an Inception Resnet v2. In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw. Additionally, we studied the changes in the encoding of facial attributes over training iterations. We found that as training progresses, expressivities of yaw, sex, and age decrease. Our technique can be a tool for investigating the sources of bias in a network and a step towards explaining the network's identity decisions.
Few-shot prototype alignment regularization network for document image layout segementation

Despite the great performance in layout analysis tasks made by semantic segmentation, they usually need a large number of annotated images for training and are difficult to learn a new category which is absent in the training categories. Meta-learning and few-shot segmentation have been developed to solve the above two difficulties. In this paper, we propose a novel method dubbed Few-Shot Prototype Alignment Regularization Network (FS-PARN). The FS-PARN method is inspired by recent studies in both metric learning and few-shot segmentation, which just need a few annotated images to solve the above two difficulties. Our FS-PARN method can make better use of the information of the support set by metric learning and have a better effect on image segmentation. It learns classification prototype within an embedding space and then completes pixel classification by matching each pixel on the query image with the learned prototype. In addition to obtaining high-quality prototypes through metric learning methods, our FS-PARN method also introduces prototype alignment regularization between support and query sets to make segmentation better. Notably, our FS-PARN model achieves the mean-IoU score of 28.8% and 31.7% on the practical document image datasets, i.e. PASCAL-5i, DSSE-200, and Layout Analysis Dataset, for 1-shot and 5-shot settings respectively.
Hybrid Deep Model for Human Behavior Understanding on Industrial Internet of Video Things

Human behavior understanding is playing more and more important role in human-centered Industrial Internet of Video Things (IIoVT) system with the deep combination of artificial intelligence and video-based industrial Internet of Things. However, it requires expensively computational resources, including high-performance computing units and large memory, to train a deep computation model with a large number of parameters, which limits its effectiveness and efficiency for IIoVT applications. In this article, a tensor-train mechanism based deep model is presented for video human behavior understanding to meet the requirement of IIoVT applications. It can get competitive performance in accuracy and training efficiency with potentiality for combination of artificial intelligence and prefront IIoVT system. On the one hand, to achieve desirable accuracy, we improved the conventional CNN and adopted the recurrent neural network mechanism to enhance the video representation over time, which takes the correlation between consecutive deep feature into consideration. On the other hand, to enhance the inference capacity between the spatial and temporal features, we carry out the self-critical reinforcement learning mechanism in parameter learning stage. Meanwhile, to further reduce parameter storage size to meet requirement for the deployment of deep neural network and edge device, the tensor-train mechanism is used, which transforms the parameter matrix to a tensor space and carry out tensor decomposition mechanism to decrease the number of parameter generated in parameter training. Finally, we conduct extensive experiments to evaluate our scheme, and the results demonstrate that our method can improve the training efficiency and save the memory space for the deep computation model with better accuracy.
Color correction and restoration based on multi-scale recursive network for underwater optical image

Underwater image processing has played an important role in various fields such as submarine terrain scanning, submarine communication cable laying, underwater vehicles, underwater search and rescue. However, there are many difficulties in the process of acquiring underwater images. Specifically, the water body will selectively absorb part of the light when light travels through the water, resulting in color degradation of underwater images. At the same time, due to the influence of floating substances in the water, the light has a certain degree of scattering, which will bring serious problems such as blurred details and low contrast to underwater images. Therefore, using image processing technology to restore the real appearance of underwater images has a high practical value. In order to solve the above problems, we combine the color correction method with the deblurring network to improve the quality of underwater images in this paper. Firstly, aiming at the problem of insufficient number and diversity of underwater image samples, a network combined with depth image reconstruction and underwater image generation is proposed to simulate underwater images based on the style transfer method. Secondly, for the problem of color distortion, we propose a dynamic threshold color correction method based on image global information combined with the loss law of light propagation in water. Finally, in order to solve the problem of image blurring caused by scattering and further improve the overall image clarity, the color-corrected image is reconstructed by a multi-scale recursive convolutional neural network. Experiment results show that we can obtain images closer to underwater style with shorter training time. Compared with several latest underwater image processing methods, the proposed method has obvious advantages in multiple underwater scenes. Simultaneously, we can restore the color information, remove blurring and boost detail for underwater images.
The Market for Fake Reviews

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Users and Bots behaviour analysis in Blockchain Social Media

Today, Online Social Networks (OSNs) represent an important communication channel in the daily life of many people in the world. OSNs are platforms where people are encouraged to interact, consume, generate, and share content with other users by establishing connections. However, they have several important issues, such as privacy and fake news, which have been the input to exploit new decentralized solution. Blockchain-based Online Social Media are decentralized Social Media where the Blockchain technology is used to reward users for their social activity. Several BOSMs have been proposed, however, their real benefits are still unknown, as is the behaviour of malicious users, such as bots and trolls. This paper proposes an analysis of the social behaviour of users and bots in Blockchain-based Online Social Media in order to highlight features that can be used in bots detection techniques. We consider Steemit as a case study because it is the most representative platform of its kind. Results show that bots are on average much more active than human users, thus suggesting that bots can be outlined using publicly available features.
Quality Meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity perspectives.