Reviewerly/exHarmony huggingface dataset:
GANs Spatial Control via Inference-Time Adaptive Normalization

We introduce a new approach for spatial control over the generation process of Generative Adversarial Networks (GANs). Our approach includes modifying the normalization scheme of a pre-trained GAN at test time, so as to act differently at different image regions, according to guidance from the user. This enables to achieve different generation effects at different locations across the image. In contrast to previous works that require either fine-tuning the model’s parameters or training an additional network, our approach uses the pre-trained GAN as is, without any further modifications or training phase. Our method is thus completely generic and can be easily incorporated into common GAN models. We prove our technique to be useful for solving a line of image manipulation tasks, allowing different generation effects across the image, while preserving the GAN’s high visual quality.
Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications

This study investigated whether radiologists from different countries share the same sensitivity to certain mammographic features. Retrospective data were collected from Chinese and Australian radiologists reading a high-density test set which contained 40 normal and 20 cancerous mammographic cases. Sixteen Australian radiologists, and 30 Chinese radiologists, including 18 from Nanchang and 12 from Hong Kong SAR/Shenzhen, were asked to read all images in this test set using the Royal Australian and New Zealand College of Radiologists (RANZCR) rating system and annotate the suspicious lesion(s). For each case and each radiologist group, the percentage of radiologists making the correct diagnoses was calculated. For cancer cases, we also calculated the percentage of radiologists who located the lesion correctly. Spearman correlation coefficient was used to explore the association between two radiologist groups. Data demonstrated a high correlation between Chinese and Australian radiologists in identifying cancer cases (r=0.839, p<0.0001), and locating lesions (r=0.802, p<0.0001), but no statistically significant relationship in identifying normal cases (r=0.236, p=0.142). However, between radiologists from two geographic regions of China, strong correlations were found in detecting cancer cases (r=0.686, p=0.0008), marking lesions (r=0.803, p<0.0001) and recognizing normal cases (r=0.562, p=0.0002). In conclusion, although Chinese and Australian radiologists may share the same difficulty in diagnosing and locating cancers, a difference in the challenge of identifying normal cases between them was shown. However, the performance by radiologists within China, although from different regions, remained consistent when reading high-density mammograms.
A Task Parallelism Runtime Solution for Deep Learning Applications using MPSoC on Edge Devices

AI on edge devices [1]–[4] are becoming increasing popular over the last few years. There are many research projects TVM [5] TensorFlow lite [6] that have focused on deployment and acceleration of AI/ML models on edge devices. These solutions have predominantly used data parallelism to accelerate AI/ML models on the edge device using operator fusion, nested parallelism, memory latency hiding [5] etc. to achieve best performance on the supported hardware backends. However, when the hardware supports multiple heterogenous hardware backends it becomes important to support task parallelism in addition to data parallelism to achieve optimal performance. Tasks level parallelism [7] [8] helps break down an AI/ML model into multiple tasks that can be scheduled across various heterogenous backends available in a multi-processor system on chip (MPSoC). In our proposed solution we take an AI/ML compute graph and break it into a directed acyclic graph (DAG) such that each node of the DAG represents a sub-graph of the original compute graph. The nodes of the DAG are generated using an auto-tuner to achieve optimal performance for the corresponding hardware backend. The nodes are compiled into a binary executable for the targeted hardware backend and we are extending our machine learning framework, XTA [9], to generate DAG. The XTA runtime will analyze the DAG and generate scheduling configuration. The nodes of the DAG are analyzed for dependencies and parallelized or pipelined accordingly. We are seeing a 30% improvement over the current solutions by parallelizing the execution of nodes in the DAG. The performance can be further optimized by using more hardware backend cores of the MPSoC to execute the nodes of the DAG in parallel, which is missing in the existing solutions.
Making graphs compact by lossless contraction

Abstract This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts and regular structures into supernodes. The supernodes carry a synopsis $$S_\mathcal {Q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mi>Q</mml:mi> </mml:msub> </mml:math> for each query class $$\mathcal {Q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Q</mml:mi> </mml:math> in use, to abstract key features of the contracted parts for answering queries of $$\mathcal {Q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Q</mml:mi> </mml:math> . Moreover, for various types of graphs, we identify regular structures to contract. The contraction scheme provides a compact graph representation and prioritizes up-to-date data. Better still, it is generic and lossless. We show that the same contracted graph is able to support multiple query classes at the same time, no matter whether their queries are label based or not, local or non-local. Moreover, existing algorithms for these queries can be readily adapted to compute exact answers by using the synopses when possible and decontracting the supernodes only when necessary. As a proof of concept, we show how to adapt existing algorithms for subgraph isomorphism, triangle counting, shortest distance, connected component and clique decision to contracted graphs. We also provide a bounded incremental contraction algorithm in response to updates, such that its cost is determined by the size of areas affected by the updates alone, not by the entire graphs. We experimentally verify that on average, the contraction scheme reduces graphs by 71.9% and improves the evaluation of these queries by 1.69, 1.44, 1.47, 2.24 and 1.37 times, respectively.
Effect of quantum error correction on detection-induced coherent errors

We study the performance of quantum error correction codes (QECCs) under the detection-induced coherent error due to the imperfectness of practical implementations of stabilizer measurements, after running a quantum circuit. Considering the most promising surface code, we find that the detection-induced coherent error will result in undetected error terms, which will accumulate and evolve into logical errors. However, we show that such errors will be alleviated by increasing the code size, akin to eliminating other types of errors discussed previously. We also find that with detection-induced coherent errors, the exact surface code becomes an approximate QECC.
Malaria Detection Using VGG19 and Deep Convolutional Neural Network

Malaria is caused when a person is bitten by a female Anopheles mosquito. It is a fatal disease. Lots of work has been done in the detection of this disease all over the world. Every year millions of people all over the world are affected by malaria. It is a contagious disease. Traditionally, malaria is diagnosed by distinguishing healthy red blood cells from the infected ones. It is also very time consuming and it may produce inaccurate reports due to human error. Compared with the traditional methods used to extract the engineering feature of the hand, the proposed method uses an in-depth study of end-to-end design that makes both the extraction features and direct separation from the dots separated by red blood bands. The data, used in this study, were taken from the National Institute of Health called the NIH malaria dataset. A pre-trained model and a custom model have been compared here to analyze the better model. To maximize performance, standard preprocessing methods have been used to extract various features of the data. Besides, more complex structures have been used and tested to select the most efficient model. Machine learning and deep learning have played a great role in the detection and prediction of numerous diseases all over the world. Machine learning algorithms also help people to analyze large and complex medical databases and analyze them with clinical understanding. This paper uses these advanced techniques for prediction of malaria. In this paper, convolutional neural network (CNN) has been used to build a model and to train the model to detect the parasitized from non-parasitized samples. The dataset, used here, contains stained red blood cell images. The accuracy of the custom model is 97.50%.
Visual Blockchain for Intelligent Surveillance in a Smart City

Visual blockchain takes the idea of how to output accurate intelligent surveillance data by using computational methods in a smart city. Intelligent surveillance provides location-based visualization from different cameras which are deployed in the smart city. It may result in limited tamper-resistance of the gathered data from cameras. The decentralized nature of blockchain is to integrate video frames as data output by surveillance systems over the smart city by the way of selecting the most appropriate cryptographical algorithms using blockchain prototype to create sustainable computation method of visual blockchain and make reliable solutions for the existing solutions. In this chapter, the contributions are to enrich the blockchain security shield by using the most appropriate cryptographic algorithm to achieve each unique task and create a web-based image prototype through the visual blockchain to minimize the gaps in intelligent surveillance.
A Real-Time Face Mask Detection Using SSD and MobileNetV2

After a rapid spread of Coronavirus (COVID-19) in Wuhan-China in December 2019, the World Health Organization (WHO) confirmed that this was a dangerous virus that could spread from person to person through droplets and airborne contaminants. To prevent the spread of the Covid19, people should wear a mask during the epidemic. During this pandemic, it is becoming increasingly difficult to keep track of human beings the one who wears a mask as a usual practice or not. It will not solely depend on human efforts to keep track the whole world so there is a need to build software that automatically detects whether people in public places wearing a mask or not. Many new models are developed utilizing convolutional Neural Network to build a model as accurately as possible. The method proposed in this paper uses the ResNet model to obtain multiple faces with a single (SSD - Single Shot Multibox Detector) image using a network (model) and MobileNetV2 Architecture used as face mask detectors. This proposed model has 99&#x0025; more accuracy than most other face recognition models. This mask detector model uses a dataset of hidden morphed masked images to obtain more accurate model. This system should be used in Real-time applications which require face mask discovery for protection purpose due to the sudden happening of Covid-19.
TypeNet: Towards Camera Enabled Touch Typing on Flat Surfaces through Self-Refinement

Text entry for mobile devices nowadays is an equally crucial and time-consuming task, with no practical solution available for natural typing speeds without extra hardware. In this paper, we introduce a real-time method that is a significant step towards enabling touch typing on arbitrary flat surfaces (e.g., tables). The method employs only a simple video camera, placed in front of the user on the flat surface — at an angle practical for mobile usage. To achieve this, we adopt a classification framework, based on the observation that, in touch typing, similar hand configurations imply the same typed character across users. Importantly, this approach allows the convenience of un-calibrated typing, where the hand positions, with respect to the camera and each other, are not dictated.To improve accuracy, we propose a Language Processing scheme, which corrects the typed text and is specifically designed for real-time performance and integration with the vision-based signal. To enable feasible data collection and training, we propose a self-refinement approach that allows training on unlabeled flat-surface-typing footage; A network trained on (labeled) keyboard footage labels flat-surface videos using dynamic time warping, and is trained on them, in an Expectation Maximization (EM) manner.Using these techniques, we introduce the TypingHands26 Dataset, comprising videos of 26 different users typing on a keyboard, and 10 users typing on a flat surface, labeled at the frame level. We validate our approach and present a single camera-based system with character-level accuracy of 93.5% on average for known users, and 85.7% for unknown ones, outperforming pose-estimation-based methods by a large margin, despite performing at natural typing speeds of up to 80 Words Per Minute. Our method is the first to rely on a simple camera alone, and runs in interactive speeds, while still maintaining accuracy comparable to systems employing non-commodity equipment.
Pricing social visibility service in online social networks

In online social networks (OSNs), users may want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users. Motivated by this, we propose a mechanism, where the OSN operator provides a "social visibility boosting service" to incentivize "transactions" between requesters (users who seek to enhance their social visibility via adding new "neighbors") and suppliers (users who are willing to be added as a new "neighbor" of any requester when certain "rewards" is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service, and reward the suppliers who contribute to such boosting service. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers "fairly" via a scheme based on the Shapley value. The objective of the OSN provider is to select the price and supplier set to maximize the revenue under the budget constraint of requesters. We first show that the revenue maximization problem is not simpler than an NP-hard problem. We then decompose it into two subroutines, prove the hardness of each sub-routine, and eventually design computationally efficient approximation algorithms to solve the revenue maximization problem. We conduct extensive experiments to evaluate our proposed algorithms.
Recognition of isolated characters across different input interfaces using 2D DCNN

Recognition of the characters has gained much attention due to its potential applications like document analysis, license plate detection, house number detection, virtual text entry system, etc., in pattern recognition. However, it is very challenging to recognize the characters under the variations in pattern, style, translation, scale, rotation. This work develops a computationally efficient deep learning model to recognize handwritten, printable, and gesticulated characters. For gesture, the NITS gesticulated database having 60 characters (10 digits, 26 English uppercase alphabets, 4 operations, 18 special symbols) is proposed with the variation in pattern, style, scale in this work. To evaluate the ability and robustness of the proposed model, the handwritten characters (MNIST, EMNIST), printable characters (SVHN, Chars74) databases are considered. This network achieves 94.55&#x0025;, 89.54&#x0025;, 87.33, and 93.90&#x0025; recognition accuracy on NITS gesticulated, EMNIST merge (balanced), SVHN, and Chars74 databases.
Selection of Sensors During Tracking a Submarine

In this paper, an underwater submarine tracking scenario has been considered in which sensors are spread on the sea surface, and a target (submarine) is moving under the water of the surveillance region. The sensors which are passive in nature measure azimuth and elevation angles of the target and send the measurements to the central unit for further processing. However, due to some physical limitations, only a few sensors are allowed to send the measurement data and our objective is to select those sensors. To do so, in this paper we formulate a cost functions based on $(i)$ Euclidean distance $(ii)$ Fisher information matrix. In order to select the sensors an optimization problem is solved using the local search method. The expected accuracy of the target motion analysis (TMA) with those sensors has been evaluated with the help of the Cramer-Rao lower bound (CRLB).
Composing Local Contexts

Abstract An expression’s presuppositions must be satisfied by its local context, that is, by the utterance context updated with the content of expressions that have already been evaluated. Traditional dynamic approaches track local context by remaking clause denotations into context update functions. This requires crafting for each semantic operator an update recipe that is not fully determined by its truth conditions, failing to capture how local contexts depend only on truth conditions and order of evaluation. In other theories, computing local contexts involves reasoning about the set of all possible grammatical syntactic completions, which relocates local contexts outside of the semantics. I show how to build local contexts systematically and uniformly as part of the composition of ordinary truth conditions. The result is a minimalist dynamic semantics in which the only thing that is dynamically tracked is the semantic content of what has already been said.
Lower Bounds on Stabilizer Rank

The stabilizer rank of a quantum state $\psi$ is the minimal $r$ such that $\left| \psi \right \rangle = \sum_{j=1}^r c_j \left|\varphi_j \right\rangle$ for $c_j \in \mathbb{C}$ and stabilizer states $\varphi_j$. The running time of several classical simulation methods for quantum circuits is determined by the stabilizer rank of the $n$-th tensor power of single-qubit magic states. We prove a lower bound of $\Omega(n)$ on the stabilizer rank of such states, improving a previous lower bound of $\Omega(\sqrt{n})$ of Bravyi, Smith and Smolin (arXiv:1506.01396). Further, we prove that for a sufficiently small constant $\delta$, the stabilizer rank of any state which is $\delta$-close to those states is $\Omega(\sqrt{n}/\log n)$. This is the first non-trivial lower bound for approximate stabilizer rank. Our techniques rely on the representation of stabilizer states as quadratic functions over affine subspaces of $\mathbb{F}_2^n$, and we use tools from analysis of boolean functions and complexity theory. The proof of the first result involves a careful analysis of directional derivatives of quadratic polynomials, whereas the proof of the second result uses Razborov-Smolensky low degree polynomial approximations and correlation bounds against the majority function.
Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

The analysis of high resolution whole slide tissue images is a computationally expensive task, which adversely impacts effective use of pathology imaging data in research. We propose runtime solutions to enable efficient execution of pathology image analysis applications on modern distributed memory hybrid platforms equipped with both CPUs and GPUs. Hybrid systems offer significant computation capacity, but taking advantage of this computing power is complex. An application developer may have to implement multiple versions of data processing codes targeted for different computing devices. The developer also has to tackle the challenges of efficiently distributing computational load among the nodes of a distributed memory machine and among computing devices on a node. This is particularly difficult in analysis of high resolution images because of irregular computing costs of processing different image regions. In order to address these problems, we have leveraged a high-level image processing language (Halide) and integrated it into our runtime system called Region Templates (RT). The language simplifies the application development while generating code for multiple devices, such as CPU and GPU. The integration with RT allows for efficient multiple node hybrid execution. We also developed a novel cost-aware data partitioning (CADP) strategy that considers the workload irregularity to minimize load imbalance. Our experimental evaluation shows significant performance improvements on hybrid CPU-GPU machines, as compared with using a single processor (CPU or GPU), as well as on multi-GPU systems. CADP resulted in 1.7× better performance than other workload partitioning approaches (e.g., KD-Trees) on a hybrid machine and was up to 2.24× faster in multi-node settings.
Multiple-level Alignment For Cross-domain Scene Text Detection

Deep learning based scene text detection models have achieved promising results on quite a few benchmark datasets. However, most existing methods generally assume that the training and test data are sampled from the same distribution. As a result, in real-world applications where data usually come from various distributions, those models will suffer from performance degradation. In this work, we propose a novel domain adaptation method based on adversarial training to align low-level and high-level features. Specifically, as low-level features focus on the texture information of the image, we propose a so-called Low-level Alignment Module (LAM) to align the low-level features through pixel-level prediction. Meanwhile, we design a High-level Alignment Module (HAM) to align high-level semantic features based on semantic information. We have conducted detailed experiments on two types of domain adaptation tasks in synthetic scenes and natural scenes simultaneously, and the experimental results show the effectiveness of our method.
Inductive Biases for Low Data VQA: A Data Augmentation Approach

Visual question answering (VQA) is the problem of understanding rich image contexts and answering complex natural language questions about them. VQA models have recently achieved remarkable results when training on large-scale labeled datasets. However, annotating large amounts of data is not feasible in many domains. In this paper, we address the problem of VQA in low labeled data regime, which is under-explored in the literature. We take a data augmentation approach to enlarge the initial small labeled data in order to inject proper inductive biases to the VQA model. We encode the additional inductive biases in the questions by producing new ones taking advantage of the image annotations. Our results show up to 34% accuracy improvements compared to the baselines trained on only the initial labeled data.
Splitting Algorithms for Federated Learning

Over the past few years, the federated learning (FL) community has witnessed a proliferation of new FL algorithms. However, our understating of the theory of FL is still fragmented, and a thorough, formal comparison of these algorithms remains elusive. Motivated by this gap, we show that many of the existing FL algorithms can be understood from an operator splitting point of view. This unification allows us to compare different algorithms with ease, refine previous convergence results and uncover new algorithmic variants. In particular, our analysis reveals the vital role played by the step size in FL algorithms. We perform numerical experiments on both convex and nonconvex models to validate our findings.