Reviewerly/exHarmony huggingface dataset:
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images

Forgeries have recently become more prevalent in the society as a result of recent improvements in media generation technologies. In real-time, modern technology allows for the creation of a forged version of a single image obtained from a social network. Forgery detection algorithms have been created for a variety of areas; however they quickly become obsolete as new attack types exist. This paper presents a unique image forgery detection strategy based on deep learning algorithms. The proposed approach employs a convolutional neural network (CNN) to produce histogram representations from input RGB color images, which are then utilized to detect image forgeries. With the image separation method and copy-move detection applications in mind, the proposed CNN is combined with an intelligent approach and histogram mapping. It is used to detect fake or true images at the initial stage of our proposed work. Besides, it is specially designed for performing feature extraction in image layer separation with the help of CNN model. To capture both geographical and histogram information and the likelihood of presence at the same time, we use vectors in our dynamic capsule networks to detect the forgery kernels from reference images. The proposed research work integrates the intelligence with a feature engineering approach in an efficient manner. They are well-known and efficient in the identification of forged images. The performance metrics such as accuracy, recall, precision, and half total error rate (HTER) are computed and tabulated with the graph plot.
Empowering citizens by a blockchain-Based Robinson list

A Robinson list protects phone subscribers against commercial spam calls. Its least basic functionality is to collect the denial of the subscribers to be contacted by market operators. Nowadays, Robinson lists run as centralised services, which implies that citizens should trust third parties for the management of their choices. In this paper, we show a design that allows us to realise a Robinson list as a decentralised service. Our work leverages the experience developed by Fondazione Ugo Bordoni as the manager of the Italian Robinson list. We present a general solution and a proof-of-concept (PoC) adopting the Algorand technology. We evaluate the performances of our PoC in terms of its scalability and of the latency perceived by the involved actors. We also discuss aspects related to identity management and privacy.
An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning

The detection of intracranial aneurysms from Magnetic Resonance Angiography images is a problem of rapidly growing clinical importance. In the last 3 years, the raise of deep convolutional neural networks has instigated a streak of methods that have shown promising performance. The major issue to address is the very severe class imbalance. Previous authors have focused their efforts on the network architecture and loss function. This paper tackles the data. A rough but fast annotation is considered: each aneurysm is approximated by a sphere defined by two points. Second, a small patch approach is taken so as to increase the number of samples. Third, samples are generated by a combination of data selection (negative patches are centered half on blood vessels and half on parenchyma) and data synthesis (patches containing an aneurysm are duplicated and deformed by a 3D spline transform). This strategy is applied to train a 3D U-net model, with a binary cross entropy loss, on a data set of 111 patients (155 aneurysms, mean size 3.86 mm ± 2.39 mm, min 1.23 mm, max 19.63 mm). A 5-fold cross-validation evaluation provides state of the art results (sensitivity 0.72, false positive count 0.14, as per ADAM challenge criteria). The study also reports a comparison with the focal loss, and Cohen’s Kappa coefficient is shown to be a better metric than Dice for this highly unbalanced detection problem.
New Encoder Learning for Captioning Heavy Rain Images via Semantic Visual Feature Matching

Image captioning generates text that describes scenes from input images. It has been developed for high-quality images taken in clear weather. However, in bad weather conditions, such as heavy rain, snow, and dense fog, poor visibility as a result of rain streaks, rain accumulation, and snowflakes causes a serious degradation of image quality. This hinders the extraction of useful visual features and results in deteriorated image captioning performance. To address practical issues, this study introduces a new encoder for captioning heavy rain images. The central idea is to transform output features extracted from heavy rain input images into semantic visual features associated with words and sentence context. To achieve this, a target encoder is initially trained in an encoder&#x2013;decoder framework to associate visual features with semantic words. Subsequently, the objects in a heavy rain image are rendered visible by using an initial reconstruction subnetwork (IRS) based on a heavy rain model. The IRS is then combined with another semantic visual feature matching subnetwork (SVFMS) to match the output features of the IRS with the semantic visual features of the pretrained target encoder. The proposed encoder is based on the joint learning of the IRS and SVFMS. It is trained in an end-to-end manner, and then connected to the pretrained decoder for image captioning. It is experimentally demonstrated that the proposed encoder can generate semantic visual features associated with words even from heavy rain images, thereby increasing the accuracy of the generated captions.
DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.
Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers

Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems. When these regions belong to otherwise well-represented classes, their presence and negative impact are very hard to identify. We propose an approach for the detection and mitigation of such rare subclasses in deep neural network classifiers. The new approach is underpinned by an easy-to-compute commonality metric that supports the detection of rare subclasses, and comprises methods for reducing the impact of these subclasses during both model training and model exploitation. We demonstrate our approach using two well-known datasets, MNIST's handwritten digits and Kaggle's cats/dogs, identifying rare subclasses and producing models which compensate for subclass rarity. In addition we demonstrate how our run-time approach increases the ability of users to identify samples likely to be misclassified at run-time.
A new framework based on features modeling and ensemble learning to predict query performance

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.
Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.
Different Scenarios and Query Strategies in Active Learning for Document Classification

Nowadays, machine learning and deep learning models are used in many fields and giving promising results. Large amounts of labeled data are needed to increase the performance of these models, which become more complex and growing as technology advances. Although a large amount of data is produced every day, labeling this data is a major challenge in the development of these models, as it takes a lot of time and is costly. Active learning is a semi-supervised learning method which helps us overcome this problem. The purpose of active learning is to select and label the most informative examples from unlabeled data. Therefore, same success is achieved with less labeled data. At this stage, it has been observed that query strategies greatly affect the increase in accuracy, and this fact makes us think that the accuracy may increase further if new query strategies are used. In this study, we compare the cosine similarity strategy that we propose with different scenarios, as well as classical query strategies that measure the informativeness of the data. However, higher accuracy increase comparing to classical query strategies could not be observed.
Improving Medium Frequency R-Mode Ranging with GMSK Modulation

The R-Mode terrestrial navigation system evaluates the phase of two carriers next to a modulated signal, from which a range can be derived to allow positioning using different stations. To improve the accuracy of ranging, we propose in this paper to replace the currently used Minimum Shift Keying (MSK) modulation with Gaussian Minimum Shift Keying (GMSK). By applying a Gaussian pulse shape for the bit transition to that of the MSK a higher spectral efficiency is achieved. If the data rate is kept constant, this reduces the energy of the modulated signal within the bandwidth of the phase estimator. This increases the accuracy of the added carrier estimate and achieves our goal of improved distance-estimation accuracy. In this paper, we compare the estimates for an R-Mode signal with MSK and two different GMSK modulations. The increased accuracy is verified using software and hardware in-the-loop simulations.
Quantum-enhanced interferometry by entanglement-assisted rejection of environmental noise

Sensing and measurement tasks in severely adverse conditions such as loss, noise, and dephasing can be improved by illumination with quantum states of light. Previous results have shown a modest reduction in the number of measurements necessary to achieve a given precision. Here, we compare three illumination strategies for estimating the relative phase in a noisy, lossy interferometer. When including a common phase fluctuation in the noise processes, we show that using an entangled probe achieves an advantage in parameter estimation precision that scales with the number of entangled modes. This work provides a theoretical foundation for the use of highly multimode entangled states of light for practical measurement tasks in experimentally challenging conditions.
Multi-Objective Net Architecture Pruning for Remote Sensing Classification

Remote sensing image scene classification has achieved significant breakthroughs in recent years. However, due to the high complexity and expensive computation most of CNNs used in the field of remote sensing imagery scene classification, it has become a challenging task for extracting effective features at restricted hardware conditions. To solve this problem, we present a model compression method by means of evolutionary algorithms. Specifically, we compress the model by pruning filters and transform the compression of the CNN model into a multi-objective optimization problem based on classification accuracy and compression ratio by using the adaptive-BN-based evaluation method. Furthermore, the prior knowledge of ResNet-50 on ImageNet is introduced to reduce the instability of evolutionary algorithm as a result of random population initialization. Experiments are implemented on three datasets with two evolutionary algorithms, and results demonstrate that our method can achieve state-of-the-art performances.
SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems

Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. We explore the robustness of dialogue systems to linguistic variations in schemas by designing SGD-X - a benchmark extending SGD with semantically similar yet stylistically diverse variants for every schema. We observe that two top state tracking models fail to generalize well across schema variants, measured by joint goal accuracy and a novel metric for measuring schema sensitivity. Additionally, we present a simple model-agnostic data augmentation method to improve schema robustness.
Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.
Data Orchestration for Accelerating GPU-Based Light Field Rendering Aiming at a Wide Virtual Space

Recently, research on six-degree-of-freedom virtual reality (VR) systems based on a light field (LF) has been actively conducted. The LF-based approach is photorealistic and less prone to errors than the existing three-dimensional (3D) modeling-based approach. On the other hand, because the amount of data is very large for LF-based immersive virtual reality, it is important to manage the data efficiently. This paper makes two major contributions to data management for rendering acceleration in this context. First, the GPUs commonly used for high-speed rendering require optimization of the data transfer process between the host memory and the device memory. This paper proposes a simple but effective way to maximize data reuse by finding the proper size of a transfer unit depending on the target applications and the system capability constraints. Also, view synthesis and data transfer are performed in a pipelined manner to hide the data movement time. Second, for the LF rendering of a wide space, data management in the storage-host-GPU hierarchy is attempted for the first time. As the size of the virtual space increases, the required amount of LF data grows rapidly and exceeds the allocatable host memory capacity. Because the loading of data stored in storage is a very slow process, a system-level LF rendering structure that carefully considers the characteristics of the storage-host-GPU memory hierarchy should be designed. In this paper, progressive LF updating is proposed, and it prevents rendering stalls caused by slow storage data loading. Experimental results show that a 360-degree view with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9000 \times 2048$ </tex-math></inline-formula> resolution can be rendered in an average of 2.57 ms from LF data with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4096 \times 2048$ </tex-math></inline-formula> resolution, achieving performance close to 400 frames per second.
Strong cospectrality and twin vertices in weighted graphs

We explore algebraic and spectral properties of weighted graphs containing twin vertices that are useful in quantum state transfer. We extend the notion of adjacency strong cospectrality to Hermitian matrices, with focus on the generalized adjacency matrix and the generalized normalized adjacency matrix. We then determine necessary and sufficient conditions such that a pair of twin vertices in a weighted graph exhibits strong cospectrality with respect to the above-mentioned matrices. We also determine when strong cospectrality is preserved under Cartesian and direct products of graphs. Moreover, we generalize known results about equitable and almost equitable partitions and use these to determine which joins of the form $X\vee H$, where $X$ is either the complete or empty graph, exhibit strong cospectrality.
Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm

At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.
On the Dynamics of the Tavis–Cummings Model

The purpose of this paper is to present a comprehensive study of the Tavis-Cummings model from a system-theoretic perspective. A typical form of the Tavis-Cummings model is composed of an ensemble of non-interacting two-level systems (TLSs) that are collectively coupled to a common cavity resonator. The associated quantum linear passive system is proposed, whose canonical form reveals typical features of the Tavis-Cummings model, including $\sqrt{N}$- scaling, dark states, bright states, single-excitation superradiant and subradiant states. The passivity of this linear system is related to the vacuum Rabi mode splitting phenomenon in Tavis-Cummings systems. On the basis of the linear model, an analytic form is presented for the steady-state output state of the Tavis-Cummings model driven by a single-photon state. Master equations are used to study the excitation properties of the Tavis-Cummings model in the multi-excitation scenario. Finally, in terms of the transition matrix for a linear time-varying system, a computational framework is proposed for calculating the state of the Tavis-Cummings model, which is applicable to the multi-excitation case.