Reviewerly/exHarmony huggingface dataset:
Oriented Ship Detection Based on Intersecting Circle and Deformable RoI in Remote Sensing Images

Ship detection is an important topic in the task of understanding remote sensing images. One of the challenges for ship detection is the large length–width ratio of ships, which may weaken the feature extraction ability. Simultaneously, ships inclining in any direction is also a challenge for ship detection in remote sensing images. In this paper, a novel Oriented Ship detection method is proposed based on an intersecting Circle and Deformable region of interest (OSCD-Net), which aims at describing the characteristics of a large length–width ratio and arbitrary direction. OSCD-Net is composed of two modules: an intersecting circle rotated detection head (ICR-head) and a deformable region of interest (DRoI). The ICR-head detects a horizontal bounding box and an intersecting circle to obtain an oriented bounding box. DRoI performs three RoIAlign with different pooled sizes for each feature candidate region. In addition, the DRoI module uses transformation and deformation operations to pay attention to ship feature information and align feature shapes. OSCD-Net shows promising performance on public remote sensing image datasets.
Sentiment Analysis and Topic Modeling on News Headlines

Sentiment analysis and topic modeling has wide range of applications from medical to entertainment industry, corporates, politics and so on. News media play vital role in shaping the views of public towards any product or people. The dataset used for this work is news headlines dataset of one of the leading new portals of India i.e., Times of India. This research aims to perform comparative study of both supervised and unsupervised learning for text analysis and use the best performing models in both the category for prediction of sentiment and topic classification of news headlines. For sentiment analysis, supervised techniques like Machine learning ensemble model and Bi-LSTM have used. Similarly, unsupervised techniques like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) have been for topic modeling.
Hybrid lightweight cryptography with attribute-based encryption standard for secure and scalable IoT system

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Acoustic Modelling From Raw Source and Filter Components for Dysarthric Speech Recognition

Acoustic modelling for automatic dysarthric speech recognition (ADSR) is a challenging task. Data deficiency is a major problem and substantial differences between typical and dysarthric speech complicate the transfer learning. In this paper, we aim at building acoustic models using the raw magnitude spectra of the source and filter components for ADSR. The proposed multi-stream models consist of convolutional, recurrent and fully-connected layers allowing for pre-processing various information streams and fusing them at an optimal level of abstraction. We demonstrate that such a multi-stream processing leverages information encoded in the vocal tract and excitation components and leads to normalising nuisance factors such as speaker attributes and speaking style. This leads to a better handling of dysarthric speech that exhibits large inter- and intra-speaker variabilities and results in a notable performance gain. Furthermore, we analyse the learned convolutional filters and visualise the outputs of different layers after dimensionality reduction to demonstrate how the speaker-related attributes are normalised along the pipeline. We also compare the proposed multi-stream model with various systems based on MFCC, FBank, raw waveform and i-vector, and, study the training dynamics as well as usefulness of the feature normalisation and data augmentation via speed perturbation. On the widely used TORGO and UASpeech dysarthric speech corpora, the proposed approach leads to a competitive performance of up to 35.3% and 30.3% WERs for dysarthric speech, respectively.
A Navigation System for Visual Impaired People Based on Object Detection

Object detection technology has a great development in recent years. Many object recognition algorithms which can realize real-time object detection are proposed and implemented. Such technology is wildly used in various fields. In this paper, we discuss how to apply the object detection technology to support the outdoor movements of visual impaired people. Our proposed system will detect the users’ environment when the users are moving. According to the detecting result, our system will help users to avoiding potential dangers on their paths. In this paper, we focus on discussing the possible problems when we design such a system. Such kind of system will integrate the necessary technologies to realize the assistant function. In this paper, we use YOLOv5, RICOH R Development Kit (360-degree camera), Google map to compose our system.
Identification and prediction of attacks to industrial control systems using temporal point processes

Abstract The task of identifying malicious activities in logs and predicting threats is crucial nowadays in industrial sector. In this paper, we focus on the identification of past malicious activities and in the prediction of future threats by proposing a novel technique based on the combination of Marked Temporal Point Processes ( MTTP ) and Neural Networks. Differently from the traditional formulation of Temporal Point Processes, our method does not make any prior assumptions on the functional form of the conditional intensity function and on the distribution of the events. Our approach is based the adoption of Neural Networks with the goal of improving the capabilities of learning arbitrary and unknown event distributions by taking advantage of the Deep Learning theory. We conduct a series of experiments using industrial data coming from gas pipelines, showing that our framework is able to represent in a convenient way the information gathered from the logs and predict future menaces in an unsupervised way, as well as classifying the past ones. The results of the experimental evaluation, showing outstanding values for precision and recall, confirm the effectiveness of our approach.
Efficiently Masking Polynomial Inversion at Arbitrary Order

Physical side-channel analysis poses a huge threat to post-quantum cryptographic schemes implemented on embedded devices. Still, secure implementations are missing for many schemes. In this paper, we present an efficient solution for masked polynomial inversion, a main component of the key generation of multiple post-quantum Key Encapsulation Mechanisms (KEMs). For this, we introduce a polynomial-multiplicative masking scheme with efficient arbitrary order conversions from and to additive masking. Furthermore, we show how to integrate polynomial inversion and multiplication into the masking schemes to reduce costs considerably. We demonstrate the performance of our algorithms for two different post-quantum cryptographic schemes on the Cortex-M4. For NTRU, we measure an overhead of 35% for the first-order masked inversion compared to the unmasked inversion while for BIKE the overhead is as little as 11%. Lastly, we verify the security of our algorithms for the first masking order by measuring and performing a TVLA based side-channel analysis.
A Secure Two-Factor Authentication Scheme From Password-Protected Hardware Tokens

We investigate existing “password+hardware token”-based authentication schemes deployed in real-world applications and observe that they are vulnerable to critical threats. Specifically, a compromised manufacturer may issue a backdoored hardware token to a user and later recover the user’s secret, which is well known as backdoor attacks. Additionally, an authentication credential in these schemes consists of two parts: the one is derived from the password, the other one is derived from the hardware token. However, since the two parts are independent of each other, if an adversary can physically access the hardware token of a victim, he is able to break security of these schemes by performing dictionary-guessing attacks (DGA), which is called mislaying-then-DGA. In this paper, we design a non-interactively re-randomizable reverse firewall signature mechanism for securing hardware tokens, such that the user’s secret is well protected even if a backdoor is embedded. We also utilize a servers-aided password-based encryption mechanism to harden hardware tokens, so as to “seamlessly” integrate the two factors into one credential. Based on the above mechanisms, we develop a secure two-factor authentication scheme, dubbed ATTACH. We evaluate ATTACH in terms of security and efficiency to demonstrate it achieves a strong security guarantee with high efficiency.
Event Detection, Localization, and Classification Based on Semi-Supervised Learning in Power Grids

Real-time situational awareness and event analysis are crucial to the security of the modern power grid, which is a complicated nonlinear system and hard to be completely modeled. Massive data is collected but the information hasn't been sufficiently leveraged. To effectively extract the event features, this paper proposes a framework for event detection, localization, and classification in power grids based on semi-supervised learning. Specifically, event detection is realized by invertible neural network (INN), hence to learn complex distributions of real-world measurements in a flexible way. Abundant normal measurements are learned by INN and explicit log-likelihoods then serve as the indicator to distinguish events with adequate sensitivity. Moreover, risks induced by events are assessed and spatial locations are determined. Since the majority of power system events are recorded without labels in practice, a pseudo label (PL) technique is leveraged to classify events with limited labels. The PL-based approach has an enhanced separating capability for events and outperforms other approaches under a low labeling rate. Case studies with simulated data in the IEEE 39-bus system and real-world measurements verify the effectiveness of the proposed framework.
HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular Data

Tabular visualization techniques integrate visual representations with tabular data to avoid additional cognitive load caused by splitting users' attention. However, most of the existing studies focus on simple flat tables instead of hierarchical tables, whose complex structure limits the expressiveness of visualization results and affects users' efficiency in visualization construction. We present HiTailor, a technique for presenting and exploring hierarchical tables. HiTailor constructs an abstract model, which defines row/column headings as biclustering and hierarchical structures. Based on our abstract model, we identify three pairs of operators, Swap/Transpose, ToStacked/ToLinear, Fold/Unfold, for transformations of hierarchical tables to support users' comprehensive explorations. After transformation, users can specify a cell or block of interest in hierarchical tables as a TableUnit for visualization, and HiTailor recommends other related TableUnits according to the abstract model using different mechanisms. We demonstrate the usability of the HiTailor system through a comparative study and a case study with domain experts, showing that HiTailor can present and explore hierarchical tables from different viewpoints. HiTailor is available at https://github.com/bitvis2021/HiTailor.
CHE: Channel-Wise Homomorphic Encryption for Ciphertext Inference in Convolutional Neural Network

Privacy-preserving deep learning (PPDL), which leverages the Homomorphic Encryption (HE), has attracted attention as a promising approach to ensure the privacy of deep learning applications' data.While recent studies have developed and evaluated the HE-based PPDL algorithms, the achieved performances, such as accuracy and latency, need improvement to make the applications practical.This work aims to improve the performance of the image classification of HE-based PPDL by combining two approaches -Channel-wise Homomorphic Encryption (CHE) and Batch Normalization (BN) with coefficient merging.Although these are commonly used schemes, their detailed algorithms and formulations have not been clearly described.The main contribution of current study is to provide complete and reproducible descriptions of these schemes.We evaluate our CHE and BN implementation by targeting the Cheon-Kim-Kim-Song scheme as an HE scheme and Convolution Neural Network (CNN) as a machine learning scheme while using the MNIST and CIFAR-10 as the datasets.In addition, we compare the results with the five state-of-the-art neural network architectures.Our experiments demonstrate that the CHE can serve as a tool for empirically achieving shorter latency (the shortest 7.76 seconds) and higher accuracy (the highest 99.32%) compared with the previous studies that aimed to establish the classification of the encrypted MNIST data with CNN.Our approach can aid in designing a more robust and flexible PPDL.
Externally-Triggered Spiking and Spiking Cluster Oscillation in Broadband Optoelectronic Oscillator

An approach to generating periodic spiking and spiking cluster in a broadband nonlinear opto-electronic oscillator (OEO) is proposed and demonstrated. Through injecting a long-duration square-wave pulse train with a repetition frequency equal to an integral multiple of the free spectral range into the OEO with a high net gain, controllable spiking and spiking cluster oscillation can be achieved when the dual-drive Mach-Zehnder modulator (DDMZM) in the OEO is biased far away from its quadrature point. The proposed scheme is verified by both numerical simulation and experiment. The results indicate that, when the DDMZM in the OEO cavity is biased near its minimum transmission point, positive spiking train with nanosecond-level pulse width is excited by the rising edge of the injected square-wave pulse train. Through further enhancing the injection strength, uniformly-spaced positive spiking fills the high-level voltage duration of each injected square-wave pulse. Hence, spiking cluster oscillation is achieved. Benefiting from its controllability, this approach can be applied in ultra-wideband (UWB) communications, electronic countermeasures, spiking coding and high-speed neuromorphic computing.
Test-Measured Rényi Divergences

One possibility of defining a quantum R\'enyi $\alpha$-divergence of two quantum states is to optimize the classical R\'enyi $\alpha$-divergence of their post-measurement probability distributions over all possible measurements (measured R\'enyi divergence), and maybe regularize these quantities over multiple copies of the two states (regularized measured R\'enyi $\alpha$-divergence). A key observation behind the theorem for the strong converse exponent of asymptotic binary quantum state discrimination is that the regularized measured R\'enyi $\alpha$-divergence coincides with the sandwiched R\'enyi $\alpha$-divergence when $\alpha>1$. Moreover, it also follows from the same theorem that to achieve this, it is sufficient to consider $2$-outcome measurements (tests) for any number of copies (this is somewhat surprising, as achieving the measured R\'enyi $\alpha$-divergence for $n$ copies might require a number of measurement outcomes that diverges in $n$, in general). In view of this, it seems natural to expect the same when $\alpha<1$; however, we show that this is not the case. In fact, we show that even for commuting states (classical case) the regularized quantity attainable using $2$-outcome measurements is in general strictly smaller than the R\'enyi $\alpha$-divergence (which is unique in the classical case). In the general quantum case this shows that the above "regularized test-measured" R\'enyi $\alpha$-divergence is not even a quantum extension of the classical R\'enyi divergence when $\alpha<1$, in sharp contrast to the $\alpha>1$ case.
Learning Spatially Varying Pixel Exposures for Motion Deblurring

Computationally removing the motion blur introduced by camera shake or object motion in a captured image remains a challenging task in computational photography. Deblurring methods are often limited by the fixed global exposure time of the image capture process. The post-processing algorithm either must deblur a longer exposure that contains relatively little noise or denoise a short exposure that intentionally removes the opportunity for blur at the cost of increased noise. We present a novel approach of leveraging spatially varying pixel exposures for motion deblurring using next-generation focal-plane sensor-processors along with an end-to-end design of these exposures and a machine learning-based motion-deblurring framework. We demonstrate in simulation and a physical prototype that learned spatially varying pixel exposures (L-SVPE) can successfully deblur scenes while recovering high frequency detail. Our work illustrates the promising role that focal-plane sensor-processors can play in the future of computational imaging.
Effect of Artificial intelligence in the field of games on humanity

Beginning of modern Artificial intelligent started back, when AlexNet created Artificial intelligent that uses Deep learning to recognize objects in ImageNet competition. The result was AlexNet destroyed all the competitors and after this point the world realized that deep learning will revolutionize humanity life. The aim of this paper is to prove, how deep learning will be able to revolutionize humanity life using games fields. Based on the literatures, researches and articles, also after studying the experiences of using the deep learning by some of the largest companies in the world, in this paper we will discuss the deep learning effect on the humanity. The company Deep mind owned by google has used the deep learning, and they could use it to perform many tasks successfully, another company used the deep learning is OpenAI, which had wonderful tasks accomplished by deep learning, deep learning also can create 3D assets for the gaming, modeling structures, and many more advantages. After studying, it has been concluded that the deep learning proved successfully that the computer can learn similarly like human brain and perform many tasks that is done by human currently. The use of deep learning will improve humanity life by lowering the cost and the time required to perform some basic tasks that is done by human currently.
Image Scaling Effects on Deep Learning Based Applications

The sophistication and high accuracy of Deep Neural Networks have gotten significant attention in recent years, with a wide range of applications making use of their capabilities. However, the deployment of such networks still faces limitations due to the high volume of data to be processed and the high computational requirements. In this article we focus on the effects that data volume reduction, due to image compression and scaling down the image resolution, will have on the detection accuracy for the design case of a powered wheelchair guidance system. Throughout our analysis we show that the reduction in image resolution to a factor of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16\times$</tex> in image area alongside with JPEG compression provides a detection accuracy of over 0.93 in mAP, while the additional error in the position estimation of the caregiver is less than 0.5 cm. By reducing the data volume we inherently reduce the communication energy consumption, which is reduced by more than one order of magnitude. These results prove that we can overcome the complexity of high data volume for the deployment of DNNs in resource constrained IoT applications by interlacing the effects of image compression and resolution reduction, maintaining the accuracy and reducing the node energy consumption.
The Rise of Fully Homomorphic Encryption

Once commercial FHE is achieved, data access will become completely separated from unrestricted data processing, and provably secure storage and computation on untrusted platforms will become both relatively inexpensive and widely accessible. In ways similar to the impact of the database, cloud computing, PKE, and AI, FHE will invoke a sea change in how confidential information is protected, processed, and shared, and will fundamentally change the course of computing at a foundational level.
Once Learning for Looking and Identifying Based on YOLO-v5 Object Detection

Object detection is an essential capacity of computer vision solutions. It has gained attention over the last years by using a core component of the “Once learning” and “Few-shot learning” mechanism. This research analyzes the ability of a machine learning framework named “You Only Look Once,” to perform object localization task in a “Heuristic once learning” context. It will also study the advantages and practical limitations of YOLO by experimenting with two types of implementation: 1) the simplest one (a.k.a tiny YOLO), and 2) the first version of YOLO. The case studies are carried out in various visual data types and object contexts, such as object deformation caused by fast-forward frame, spatial distortion caused by isometric projection, and gaming images with abnormal objects. Finally, we build a dataset accounting for a new task so-called “Heuristic once learning”. Results using YOLO-v5 in such conditions showed that YOLO had difficulties to generalize simple abstractions of the characters, pointing to the necessity of new approaches to solve such challenges.
Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes

The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complications. The trajectories of patients in real-world settings are highly variable, making longitudinal data modeling challenging. In recent years, significant progress has been made in the study of deep learning models applied to time series; however, the application of these models to irregular medical time series (IMTS) remains limited. To address this issue, we developed a generic deep-learning-based framework for modeling IMTS that facilitates the comparative studies of sequential neural networks (transformers and long short-term memory) and irregular time representation techniques. A validation study to predict retinopathy complications was conducted on 1207 patients with type 1 diabetes in a French database using their historical glycosylated hemoglobin measurements, without any data aggregation or imputation. The transformer-based model combined with the soft one-hot representation of time gaps achieved the highest score: an area under the receiver operating characteristic curve of 88.65%, specificity of 85.56%, sensitivity of 83.33% and an improvement of 11.7% over the same architecture without time information. This is the first attempt to predict retinopathy complications in patients with type 1 diabetes using deep learning and longitudinal data collected from patient visits. This study highlighted the significance of modeling time gaps between medical records to improve prediction performance and the utility of a generic framework for conducting extensive comparative studies.