Reviewerly/exHarmony huggingface dataset:
Social data governance: From reflective practices to comparative synthesis

This special theme brings together reflections and deliberations regarding the design, implementation, and development of data governance. By addressing “social data governance” as the keyword of the special theme, we aim to further the discussion on a contextual understanding of both the governing foundations and effects of data, dataism, and datafication in different societies. Such a discussion reminds us to pay particular attention to—and thus account for—the social dynamics that underpin and contextualize the design, operation, and promotion of quantified governing mechanisms in which information on social behaviors is collected, datafied, manipulated, and represented. Essentially, the social dynamics of data governance have existed for a long time and in many forms, ranging from credit bureaus’ scrutiny, evaluation, and labeling of their customers to internet-enabled massive data collection and scoring systems used by governments, and to automated contact tracing techniques as a centerpiece of dataveillance and infection control amid the COVID-19 pandemic. Nevertheless, scholarly work from a wide range of disciplines like law, mathematics, and business and with diverse geographical foci has not yet been comparatively and reflectively articulated. Being rich and diverse, the special theme advances such a requisite understanding of the status and relevance of social dynamics of data governance mechanisms based on a wide range of empirical cases around the globe. To scrutinize the social dynamics helps illuminate and contrast divergent manifestations of data governance and their underlying mechanisms.
SSE: Security Service Engines to Accelerate Enclave Performance in Secure Multicore Processors

Secure processor technologies leveraging enclaves as their architectural security primitive are frequently deployed in cloud environments. However, enclave-based systems incur performance penalties due to architectural limitations arising from costly enclave exits incurred to interact with system-level software. Exitless calling aims to improve enclave-based performance by spawning additional responder threads alongside the enclave to execute system calls on its behalf, obviating costly enclave exits. However, exitless calling must operate the responder threads as truly asynchronous to the enclave for security isolation guarantees. The self governed timers induce polling stalls that lead to performance loss when enclave and responder threads saturate the available cores in the system. This paper aims to address the polling challenge by introducing security service engines (SSEs) to offload responder threads on dedicated hardware resources. The evaluation shows that SSE equipped secure multicore achieves performance scaling that is at par with a baseline system with no security primitives.
Analyzing Perceptual Picture Quality of Various Tone-Mapping Methods for Mobile Devices

With the advancement of sensor technologies, the available bit-depth of image data has increased. With these high dynamic range images, it is possible to distinguish fine details even in high contrast situations. However, display devices tend to have limited dynamic ranges compared to the image data. In this paper, we analyze the perceptual picture quality of various tone-mapping methods for mobile devices. We evaluated a number of global tone-mapping methods and performed subjective tests using mobile devices. We present the experimental results along with some analyses and observations.
A Novel and Robust Multi-Modal Remote Sensing Image Matching Method

The complicated nonlinear radiation distortion and geometric deformations between multi-modal remote sensing images pose a huge challenge. Usually, we use geographic information in remote sensing images to eliminate the influence of large geometric deformations. But geographic information itself may have errors and nonlinear radiation distortion makes precise matching difficult. In addition, the lack of large multi-modal remote sensing datasets makes it difficult to use deep learning to further improve matching results. In this paper, we find that edge features are less affected by non-linear radiometric distortions and are able to preserve the common structural properties of multi-modal images well. Therefore, we propose a hybrid method by integrating a pre-trained multi-scale edge detection network and a traditional template matching framework. We also use a coarse-to-fine strategy to reduce computational effort and improve matching accuracy. Extensive experiments show that our method achieves higher matchingaccuracy and better robustness as compared to previous methods
Expertise Computation for Automatic Reviewer Assignment

Assigning effective and accurate reviewers is a crucial step in the peer-reviewing process to ensure high-quality publications. Automatic reviewer assignment problem, which aims to assign experts for rating the submitted research, relies critically on determining the expertise of the reviewers in the topics covered in the manuscript and assigning them so that all research topics covered in the manuscript are rated by proficient reviewers. In this paper, we consider research expertise as a commixture of interest and proficiency of the reviewers in the topics that span the manuscript. We take reviewers’ recent publications and self-declaration about the areas of research interest as input. Lexical variations in the topics declared by reviewers are leveled using sentence transformers and the research publications. Similarity between the semantic content of the manuscript and the research topics is also matched using the sentence transformer framework. Subsequently, we quantify the reviewers’ interest in each topic along with their proficiency in that topic based on the number of authored publications. Reviewers are scored for expertise, which is a function of the two computed quantities. Top scoring reviewers are assigned the manuscript for review ratings. We evaluate the effectiveness of the proposed method using topic coverage and review confidence as metrics. We observe that the proposed algorithm for expertise computation and reviewer assignment performs better than the baseline method.
AR Indicators for Visually Debugging Robots

Programming robots is a challenging task exacer-bated by software bugs, faulty hardware, and environmental factors. When coding issues arise, traditional debugging techniques are not always useful for roboticists. Robots often have an array of sensors that output complex data, which can be difficult to decipher as raw text. Augmented reality (AR) provides a unique medium for conveying data to the user by displaying information directly in the scene as their corresponding visual definition. In my research, I am exploring various design approaches towards AR visualizations for expert robotics debugging support. From my initial work, I developed design guidelines to inform two future bodies of work which investigate better ways of visualizing robot sensor and state data for debugging.
Modeling and classification of deaths due to Covid 19 based on machine learning technique

Statistical classification is recently considered one of the most important and most common methods in machine learning models and consists of building models that define the target of research interest. There are many classification methods that can be used to predict the value of a response. In this article, we are interested in machine learning applications to classify the new deaths due to Covid-19. Under consideration BIC criterion, the experimental results have shown that the E (Equal variance) with four is the best mixture model. The convergence in the algorithm of expectation-maximization is satisfied after 167 iterations. The World Health Organization has presented the source of data over the period of March 2, 2020 to August 5, 2020.
Document Text Analysis and Recognition of Handwritten Telugu Scripts

Handwritten text recognition is an open problem of great interest in the area of automatic document image analysis. Handwriting recognition has been studied for a long time with only few practicable results when written on paper. The tasks related to text recognition becomes complex when it comes to Indic languages due to large size of character set. One such language is Telugu where the character list involves 150 unique characters. This paper addresses the procedure to recognize the Telugu handwritten document and convert the text into machine understandable language. The model is built on the ResNet-18, DBSCAN clustering algorithm for detecting the words in the hand written text image. The CNN, RNN and CTC. The proposed model is then evaluated using the IAM Telugu handwritten image dataset and few government school handwritten notes. The proposed model achieves an accuracy of 72.6% with a character error rate of 11.4%.
XOR-AND-XOR Logic Forms for Autosymmetric Functions and Applications to Quantum Computing

We propose a new three-level XOR-AND-XOR form for autosymmetric functions, called XORAX expression.In general, a Boolean function f over n variables is k-autosymmetric if it can be projected onto a smaller function f k , which depends on n -k variables only.We show that XORAX expressions can ease the reversible synthesis of autosymmetric functions, producing compact reversible networks, without inserting additional new input lines.Autosymmetry occurs especially for functions that exhibit a regular structure, as for instance arithmetic functions.For this reason, compact reversible networks for autosymmetric functions might be interesting for quantum computing.Experimental results validate the proposed approach.
DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment

Self-supervised (SS) learning has achieved remarkable success in learning strong representation for in-domain few-shot and semi-supervised tasks. However, when transferring such representations to downstream tasks with domain shifts, the performance degrades compared to its supervised counterpart, especially at the few-shot regime. In this paper, we proposed to boost the transferability of the self-supervised pre-trained models on cross-domain tasks via a novel self-supervised alignment step on the target domain using only unlabeled data before conducting the downstream supervised fine-tuning. A new reparameterization of the pre-trained weights is also presented to mitigate the potential catastrophic forgetting during the alignment step. It involves low-rank and sparse decomposition, that can elegantly balance between preserving the source domain knowledge without forgetting (via fixing the low-rank subspace), and the extra flexibility to absorb the new out-of-the-domain knowledge (via freeing the sparse residual). Our resultant framework, termed Decomposition-and-Alignment (DnA), significantly improves the few-shot transfer performance of the SS pre-trained model to downstream tasks with domain gaps. (The code is released at https://github.com/VITA-Group/DnA ).
Region of Interest Extraction Based on Bayesian Joint Saliency Detection for Remote Sensing Images

Saliency detection is an essential tool to extract regions of interest (ROIs) in remote sensing (RS) images. However, many methods are applied to single image and cannot detect ROIs accurately due to the ignorance of high correlation among different RS images. Thus, we propose the Bayesian joint saliency detection method to extract ROIs. Firstly, we generate the prior saliency based on global color contrast according to co-clustering, which ensures that regions with similar features have the same saliency. Secondly, we produce the likelihood saliency by constructing intensity co-occurrence histogram, which can explore the intensity distribution of multiple images. Finally, due to the complex scenes in RS images, Bayesian enhancement strategy is applied to combine the prior saliency with the likelihood saliency, and obtain ROI with less background inference. Quantitative and qualitative experiments results indicate that our method outperforms competing methods and shows good performance in ROI extraction.
Behavior-Tree Embeddings for Robot Task-Level Knowledge

Recently, the behavior tree is gaining popularity as a robotic task-level knowledge representation. Manual design of behavior trees from scratch is tedious and cumbersome. Motivated by the need for an efficient way to reuse or transfer robot task-level knowledge, we propose a vector-space embedding approach that encodes a symbolic task into a numerical form. This approach, called behavior-tree embedding, takes a behavior tree that produces a single task as input and generates a corresponding vector. By exploiting the pretrained language-embedding model and the node-aggregation mechanism, the produced embedding is capable of preserving both semantic information of task description and structural information of the hierarchical task organization. We evaluated the effectiveness and versatility of our proposed vector-space embedding approach in three different tasks.
Planning with Intermittent State Observability: Knowing When to Act Blind

Contemporary planning models and methods often rely on constant availability of free state information at each step of execution. However, autonomous systems are increasingly deployed in the open world where state information may be costly or simply unavailable in certain situations. Failing to account for sensor limitations may lead to costly behavior or even catastrophic failure. While the partially observable Markov decision process (POMDP) can be used to model this problem, solving POMDPs is often intractable. We introduce a planning model called a semi-observable Markov decision process (SOMDP) specifically designed for MDPs where state observability may be intermittent. We propose an approach for solving SOMDPs that uses memory states to proactively plan for the potential loss of sensor information while exploiting the unique structure of SOMDPs. Our theoretical analysis and empirical evaluation demonstrate the advantages of SOMDPs relative to existing planning models.
BS-pFL: Enabling Low-Cost Personalized Federated Learning by Exploring Weight Gradient Sparsity

Recent advancements in Convolution Neural Networks (CNNs) have achieved amazing success in numerous applications. The record-breaking performance of CNNs is usually at the prohibitive training costs, thus all training data are usually processed at the powerful centralized server side, which rises privacy concerns. Federated learning (FL) is a distributed machine learning method over mobile devices to train a global model while keeping decentralized data on devices to preserve the data privacy. However, there are two major limitations to deploy FL on mobile clients. Firstly, on the client side, the limited communication and computation resources on mobile devices cannot well support the full training iterations. Secondly, on the server side, conventional FL only aggregate a common output for all the clients without personalizing the model to each client, which is an important missing feature when clients have heterogeneous data distributions. In this work, we aim to enable low-cost personalized FL by focusing on the weight gradients which are the most important exchanging parameters in FL and meanwhile, dominating the computation and communication cost. We first observe that the client's calculated weight gradients have high sparsity, and the sparse pattern in weight gradients could be predicted via very simple bit-wise operations on a sequence of bits (named bit-stream) instead of conducting expensive high-precision calculations to determine them. Furthermore, a unique pattern is exhibited in each client's uploaded weight gradients according to the distribution of its local training data. Guided by this pattern, each client can get a personalized aggregated model to fit its own data. Hence, we leverage bit-streams to predict weight gradients sparsity for low-cost training on each device, and meanwhile, bit-streams are used to represent the unique sparse pattern of the weight gradient for each client which will guide the model personalization. From our experiments, our proposed framework can improve the computation efficiency by 3.5× on average (up to 4.2×) and reduce the communication cost by 23% on average (up to 41%) while still achieving the state-of-the-art personalized accuracy.
Calibrated Relighting Network for Image Light Transfer

Image relighting task aims at changing the lighting conditions while keeping the same contents in the image. Recently, image relighting has witnessed vast progress with the development of deep learning. However, most existing methods solely tackle this problem by treating it as the style transfer task, and lead to irregular illumination and shadows since they ignored complex relations of the coupled illumination and shadows. The latest research presents the decoupled framework for image relighting by decomposing the relighting process into three tasks: scene reconversion, shadow estimation and image re-rendering. In this paper, We follow the decoupled framework and propose a novel Calibrated-DConv block in recovering primary structure and estimating shadow effects by recalibrating the feature and thus enhancing feature representation. Furthermore, we design a novel re-rendering network, which adaptively repaints the image by estimating the weights of interest parts. Experimental results on the VIDIT dataset show that the proposed method performs favorably against recent state-of-the-art methods and achieves reasonable image light source transferring.
Hyper-Spectral Image Compression By Joint Spatial Spectral Dimension Reduction Using Thresholded Principal Component Analysis

Hyperspectral image analysis techniques have advanced considerably in the past decade, resulting in specialized hyperspectral applications. Because the spectral dimension of hyperspectral cubes is very broad, preserving a large number of these is a challenging task. To address this challenge, various compression methods have been employed over the past decades. Dimensional reduction methods are the most straightforward for compressing hyperspectral images, however, a significant amount of processing power is required for reasonable performance, which is reflected in long run-duration and high costs. In this work, a new method of dimensionality reduction compression based on a principal component analysis (PCA) algorithm is presented. The spectral dimension was reduced by thresholding principal components (PCs), while the spatial dimensions were reduced by average pooling/max pooling close groups of pixels. The proposed method was compared to three common dimensional reduction methods and tested on a database of 60 hyperspectral cubes. In terms of compression time to PSNR, the proposed method outperformed the other methods. The evaluation results indicate that the proposed method optimizes the tradeoff between compression performance and low complexity to obtain maximum performance with minimal runtime.
Design of Vocal Music Self-study Assistant System Based on Android Technology

As an important way of spiritual enjoyment, vocal music is of great significance to the elimination of people’s emotions, the expression of their voices, and the regulation of their body and mind. This paper aims to develop and design a vocal music self-study auxiliary system. The design principle of this system is to apply the current popular information technology to the design and development of vocal music self-study auxiliary system, and realize people’s demand for learning vocal music anytime and anywhere based on these technologies. In order to solve the practical problems of people learning vocal music and provide people with a more free, convenient and fast vocal music self-study platform, in today's environment where smart phones are widely used, this paper designs and develops a set of vocal music self-study assistant system based on Android technology. It aims to meet people's requirements for vocal music learning.
Non van-Neumann Anomaly Detection in Multi-Channel Time-Series using Charge Trap Transistor Crossbars

Many of the existing techniques to detect anomalies in multi-channel time-series lack flexibility and incur significant processing-overheads; therefore, real-time, flexible anomaly detection in resource-constrained edge devices is still an open problem. Addressing the above challenges, we present an ultra-low-power non von-Neumann framework for statistical modeling based anomaly detection in multi-channel time-series. To disruptively minimize the power dissipation for anomaly detection and enhance scalability to complex time-series statistics, we pursue a co-designed approach where the anomaly statistics are modelled using an unconventional Harmonic-Mean of Gaussian-like (HMG) functions. We show that multivariate HMG functions can be implemented simply by exploiting the short-circuit current of multi-input inverters. A non-von Neumann crossbar of charge trap inverters implements a mixture of HMG function and stores model weights. On Yahoo real time-series dataset, our anomaly detection approach achieves an f1-score higher than 0.85 even in the presence of significant process variation and consumes 181fJ/sensor sample for a three-channel time-series. Compared to baseline digital and analog approaches for anomaly detection, our framework is $\sim 40 \times$ and $\sim 6 \times$ more energy efficient, respectively, while being more scalable to time-series dimension.
Low-Light Image Enhancement Under Mixed Noise Model with Tensor Representation

Compared with the traditional Retinex model, the robust Retinex model considers the influence of additive Gaussian noise to improve the performance of low-light image enhancement with strong noise. However, the real noise model is not so simple. In order to describe the noise model more accurately, this paper considers the enhancement of low-light images under the influence of Poisson and Gaussian mixture noise, so as to better model the noise model and suppress the noise amplification. In order to smooth the noise in the reflection component, the current methods generally decompose the V-channel of the image converted to HSV color space to obtain the illuminance component and the reflection component, and then constrain the reflection component a priori such as sparse, low rank or nonlocal self-similarity based on matrix or vector operations. The problem is that the conversion of color space may lead to color distortion, and the matrix operation and vectorization operation by channel may damage the structural relationship of channel and space respectively, and may occupy a high memory. In order to better maintain the correlation between RGB image channels, the RGB image is directly represented in the form of tensor, processed in the RGB color space as a whole, and the tensor nuclear norm is used to impose a low rank constraint on the reflection component to suppress the noise. Compared with the contrast method, the proposed method achieves good visual and quantitative results in low-light image enhancement tasks.