Reviewerly/exHarmony huggingface dataset:
Three Fold Classification Using Shift Invariant Deep Neural Network

Due to increase in digitalization in recent years, there is huge amount of images are available with different parameters like pose, scale, lighting, and occlusion. Predicting their age, gender, as well as race is a resilient task. In past year CNN based model plays a virtuous role in prediction of age and gender. In this research we proposed a threefold classifier using shift invariant Deep Neural Network for age, gender and race classification in a same model. It will contain multiple layers like maxpooling, covlutional block. For training and testing of our model we have used UTKFace dataset that contain 23705 images with varying parameters. For age estimation, gender and race classification we have used neurons at the output layer. Output Neuron are taken 1, 2 & 5 for age, gender and race classification respectively. Loss function relu is used for age estimation, for gender and race classification sigmoid function is used. In this study we convey that a considerable upgrade in age estimation, gender and race classification be obtained by using shift invariant Deep Neural Network. The obtained result for age, gender and race are 0.08, 87.66% and 76.22% respectively. Results convey that suggested approach provide a great performance than the other modern model.
Robust Transfer Learning Based Modelling for Accelerating the Learning of Ai in the Field of NLP

People have been able to communicate with one another via the languages that have developed naturally within their societies. It is estimated that somewhere over 6,500 distinct languages are currently being used in communication around the globe. Because language is the primary means by which humans communicate with one another, it is only natural that this fundamental concept has been investigated in detail in the scholarly literature for a considerable amount of time. This investigation has taken place over a considerable amount of time. In this paper, federated learning is used as an integral feature of the transfer learning model to accelerate the process of learning in NLP. The simulation shows a reduced mean square error in training, testing and validation stages
Hay from the Haystack: Explicit Examples of Exponential Quantum Circuit Complexity

The vast majority of quantum states and unitaries have circuit complexity exponential in the number of qubits. In a similar vein, most of them also have exponential minimum description length, which makes it difficult to pinpoint examples of exponential complexity. In this work, we construct examples of constant description length but exponential circuit complexity. We provide infinite families such that each element requires an exponential number of two-qubit gates to be generated exactly from a product and where the same is true for the approximate generation of the vast majority of elements in the family. The results are based on sets of large transcendence degree and discussed for tensor networks, diagonal unitaries and maximally coherent states.
Adaptively Directed Image Restoration Using Resilient Backpropagation Neural Network

Abstract In this modern era, visual data transmission, processing, and analysis play a vital role in daily life. Image denoising is the process of approximately estimating the original version of a degraded image. The presence of unexpected noise (e.g., fixed, random, and Gaussian) is the root cause of degradation, which has been reduced to some extent by many linear and non-linear filters based on a median value. The real issue is developing a strategy that should be generalized enough to effectively restore an image corrupted with multi-nature noise. Many researchers have developed novel concepts, but their tactics must acquire the highest performance in this area. This article proposes a constrained strategy for this problem, i.e., an adaptively directed denoising filter (ADD filter) based on a neural network. It consists of three major stages: training, filtering, and enhancing. First, we train a feed-forward back-propagation neural network on noisy and noise-free pixels for effective differentiation. Second, we apply a one-pass selective filter to the noisy image. The objective of this one-pass filter is to minimize noise using an adaptive median or directional filter based on density. Finally, the iterative directional filter is applied to the pre-processed image to enhance its visual quality. The extensive experiments depict that the proposed system has achieved better subjective results and improved local (structural similarity) and global (peak signal-to-noise ratio or mean square error) statistical measures.
Comments on Quasi-Linear Support Vector Machine for Nonlinear Classification

In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.
A unit-based symbolic execution method for detecting memory corruption vulnerabilities in executable codes

Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound and complete in theory, it faces some challenges, such as path explosion, when applied to real-world complex programs. In this paper, we present a method for improving the efficiency of symbolic execution and detecting four classes of memory corruption vulnerabilities in executable codes, i.e., heap-based buffer overflow, stack-based buffer overflow, use-after-free, and double-free. We perform symbolic execution only on test units rather than the whole program to avoid path explosion. In our method, test units are considered parts of the program's code, which might contain vulnerable statements and are statically identified based on the specifications of memory corruption vulnerabilities. Then, each test unit is symbolically executed to calculate path and vulnerability constraints of each statement of the unit, which determine the conditions on unit input data for executing that statement or activating vulnerabilities in it, respectively. Solving these constraints gives us input values for the test unit, which execute the desired statements and reveal vulnerabilities in them. Finally, we use machine learning to approximate the correlation between system and unit input data. Thereby, we generate system inputs that enter the program, reach vulnerable instructions in the desired test unit, and reveal vulnerabilities in them. This method is implemented as a plugin for angr framework and evaluated using a group of benchmark programs. The experiments show its superiority over similar tools in accuracy and performance.
The system of autono‑mobility: computer vision and urban complexity—reflections on artificial intelligence at urban scale

Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI-and in particular, computer vision systems used for mapping and navigation-as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI's impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter this trend by exploring the reverse perspective: how do cities affect the development, and expose the present limits, of SDCs? The contribution of this paper is threefold. First, by comparing urban and nonurban environments and thoroughly examining the relationship between computer vision and city-specific sociality and form, it defines machine autonomy/automation as a function of the sociotechnical milieu in which an AI system operates. Second, and related, the paper problematizes the notion of SDCs as autonomous technologies and the role it plays in envisioning contending policy arrangements and technical solutions for achieving full driving automation. Finally, the article offers insight into a materialist and spatialized understanding of AI-namely, not as an abstract quality susceptible to replication within discrete machines, but rather as a distributed property emerging through embodied interactions among a multiplicity of agents (human, non-human, and technological) within/with their environments.
Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset-namely CMTD-of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.
Designing A 3d-Aware Stylenerf Encoder for Face Editing

GAN inversion has been exploited in many face manipulation tasks, but 2D GANs often fail to generate multi-view 3D consistent images. The encoders designed for 2D GANs are not able to provide sufficient 3D information for the inversion and editing. Therefore, 3D-aware GAN inversion is proposed to increase the 3D editing capability of GANs. However, the 3D-aware GAN inversion remains under-explored. To tackle this problem, we propose a 3D-aware (3Da) encoder for GAN inversion and face editing based on the powerful StyleNeRF model. Our proposed 3Da encoder combines a parametric 3D face model with a learnable detail representation model to generate geometry, texture and view direction codes. For more flexible face manipulation, we then design a dual-branch StyleFlow module to transfer the StyleNeRF codes with disentangled geometry and texture flows. Extensive experiments demonstrate that we realize 3D consistent face manipulation in both facial attribute editing and texture transfer. Furthermore, for video editing, we make the sequence of frame codes share a common canonical manifold, which improves the temporal consistency of the edited attributes.
A Content-Based Multi-Scale Network for Single Image Super-Resolution

A novel content-based multi-scale network (CMNet) is proposed in this paper for conducting single image super-resolution (SISR). Its core lies in a content-based multi-scale image representation (CMIR), which is motivated by the fact that the contents of real-world images normally have different scales. Thus, it is expected that individual treatments of these contents would yield superior SISR performance. In our CMIR, the difference curvature (DCurv) is first exploited to generate a primal sketch of the input image. Then, a filter bank is designed and used to obtain a set of coefficient matrices, and each matrix reflects the characteristics of the image content at the corresponding scale. Based on these coefficient matrices, the CMIR of the input image is formed. To conduct SISR, each scale of CMIR is processed individually in our CMNet, and the produced multi-scale outputs are then integrated to arrive at the final SISR image with a higher image quality. Extensive experiments have demonstrated that our developed CMNet can deliver superior performance compared with a number of state-of-the-art SISR methods.
Comparison of Soft and Hard Target RNN-T Distillation for Large-Scale ASR

Knowledge distillation is an effective machine learning technique to transfer knowledge from a teacher model to a smaller student model, especially with unlabeled data. In this paper, we focus on knowledge distillation for the RNN-T model, which is widely used in state-of-the-art (SoTA) automatic speech recognition (ASR). Specifically, we compared using soft and hard target distillation to train large-scale RNN-T models on the LibriSpeech/LibriLight public dataset (60k hours) and our in-house data (600k hours). We found that hard targets are more effective when the teacher and student have different architecture, such as large teacher and small streaming student. On the other hand, soft target distillation works better in self-training scenario like iterative large teacher training. For a large model with 0.6B weights, we achieve a new SoTA word error rate (WER) on LibriSpeech (8% relative improvement on dev-other) using Noisy Student Training with soft target distillation. It also allows our production teacher to adapt new data domain continuously.
SPARQL-QA-v2 system for Knowledge Base Question Answering

Accessing the large volumes of information available in public knowledge bases might be complicated for those users unfamiliar with formal languages, such as the SPARQL query language and the ontology definition languages. This issue can be overcome by providing systems able to answer questions posed in natural language on a knowledge base, a task that is called Knowledge Base Question Answering (KBQA) in the literature. More in detail, many KBQA systems aim at translating automatically questions into the corresponding SPARQL queries to be executed over the knowledge base to get the answers. Effective state-of-the-art KBQA systems are based on neural-machine translation but easily fail to recognize words that are Out Of the Vocabulary (OOV) of the training set. This is a serious issue while querying large ontologies where the list of entities is huge and easily evolves over time. In this paper, we present the SPARQL-QA-v2 system that combines in an innovative way Named Entity Linking, Named Entity Recognition, and Neural Machine Translation for addressing the problem of generating SPARQL queries from questions posed in natural language. We demonstrate empirically that SPARQL-QA-v2 is effective and resilient to OOV words and delivers state-of-the-art performance in well-known datasets for question answering over DBpedia and Wikidata knowledge bases.
Human activity recognition based on multiple inertial sensors through feature-based knowledge distillation paradigm

In recent years, numerous high accuracy methods have been developed for classifying activities using multi inertial sensors. Despite their reliability and precision, they suffer from high computational cost and which make them improper for deploying in edge devices that are limited resources. This paper addresses this drawback by employing a knowledge distillation (KD) paradigm which maps tri-axial multi signals into single axis signals, thus; it can recognize activities with fewer number of signals and consequently less computation. In this method, a big teacher model is trained in advanced with three IMU sensors each of which have tri-axial signals. Then, a small student model is trained with just one of the axes of these sensors under monitoring of teacher which reduces the number of signals. Tucker decomposition is also exploited in order to improve KD performance by separating a core tensor from feature maps that has more informative knowledge. Evaluation of our method on REALDISP dataset demonstrates that the student model could achieve accuracy of 92.90% with much less complexity making it suitable for embedded devices. Moreover, it outperforms in comparison to other state-of-the-art KD approaches.
Contrast enhancement of region of interest of backlit image for surveillance systems based on multi-illumination fusion

The surveillance images under backlit conditions is a potential and challenging research problem in image processing and computer vision. Low contrast regions of interest of backlit images form an indiscernible part for the real-time surveillance and biometrics applications. However, current state-of-the-art techniques typically offer limited dark-region contrast enhancement, are susceptible to colour distortion. This research work uses region of interest segmentation and a transfer learning-based strategy to improve the contrast of the dark intensity regions in backlit image using multi-illumination mappings, motivated by its usefulness. To generate a diversified collection of illumination-maps and enhance the brightness and contrast of the backlit image, the proposed framework applies the varied set of log and gamma transforms. The experimental findings confirm the assertion that the proposed method provides better performance than state-of-the-art methods both objectively and subjectively.
Resource Optimized Hierarchical Split Federated Learning for Wireless Networks

Federated learning (FL) uses distributed fashion of training via local models (e.g., convolutional neural network) computation at devices followed by central aggregation at the edge or cloud. Such distributed training uses a significant amount of computational resources (i.e., CPU-cycles/sec) that seem difficult to be met by Internet of Things (IoT) sensors. Addressing these challenges, split FL (SFL) was recently proposed based on computing a part of a model at devices and remaining at edge/cloud servers. Although SFL resolves devices computing resources constraints, it still suffers from fairness issues and slow convergence. To enable FL with these features, we propose a novel hierarchical SFL (HSFL) architecture that combines SFL with a hierarchical fashion of learning. To avoid a single point of failure and fairness issues, HSFL has a truly distributed nature (i.e., distributed aggregations). We also define a cost function that can be minimized relative local accuracy, transmit power, resource allocation, and association. Due to the non-convex nature, we propose a block successive upper bound minimization (BSUM) based solution. Finally, numerical results are presented.
MegaFlow2D: A Parametric Dataset for Machine Learning Super-resolution in Computational Fluid Dynamics Simulations

This paper introduces MegaFlow2D, a dataset of over 2 million snapshots of parameterized 2D fluid dynamics simulations of 3000 different external flow and internal flow configurations. It's worth noting that, simulation results on both low and high mesh resolutions are provided to facilitate the training of machine learning (ML) models for super-resolution purposes. This is the first large-scale multi-fidelity fluid dynamics dataset ever provided. We build the entire data generation and simulation workflow on open-source and efficient interfaces that can be utilized for a variety of data samples according to the user's specific needs. Finally, we provide a use case to demonstrate the potential value of the MegaFlow2D dataset in applications related to error correction.
Blockchain Strategy for Multi-level Interoperability in Public Safety Scenario

Computer-Aided Dispatch (CAD) systems optimize the management of security incidents and facilitate the call for rescue. However, these systems are often heterogeneous and designed to meet the needs of a particular city or region in isolated ways, making them challenging to integrate with CADs from others cities. This integration is necessary to share data and functionalities when there are operations with cooperation between public administration bodies at different levels (local, regional, state, or national). The more CAD systems are integrated, the more difficult this task becomes. For this integration, it is necessary to be concerned with data secrecy for each access level, besides guaranteeing data security and integrity. Blockchain can fulfill these gaps and be a trusted decentralized controller entity based on a consensus mechanism. Therefore, this work-in-progress paper proposes a blockchain strategy for multi-level interoperability in public safety scenarios.
Quantitative Verification for Neural Networks using ProbStars

Most deep neural network (DNN) verification research focuses on qualitative verification, which answers whether or not a DNN violates a safety/robustness property. This paper proposes an approach to convert qualitative verification into quantitative verification for neural networks. The resulting quantitative verification method not only can answer YES or NO questions but also can compute the probability of a property being violated. To do that, we introduce the concept of a probabilistic star (or shortly ProbStar), a new variant of the well-known star set, in which the predicate variables belong to a Gaussian distribution and propose an approach to compute the probability of a probabilistic star in high-dimensional space. Unlike existing works dealing with constrained input sets, our work considers the input set as a truncated multivariate normal (Gaussian) distribution, i.e., besides the constraints on the input variables, the input set has a probability of the constraints being satisfied. The input distribution is represented as a probabilistic star set and is propagated through a network to construct the output reachable set containing multiple ProbStars, which are used to verify the safety or robustness properties of the network. In case of a property is violated, the violation probability can be computed precisely by an exact verification algorithm or approximately by an overapproximate verification algorithm. The proposed approach is implemented in a tool named StarV and is evaluated using the well-known ACASXu networks and a rocket landing benchmark.