Reviewerly/exHarmony huggingface dataset:
Soul Dancer

Body language is one of the most common ways of expressing human emotion. In this article, we make the first attempt to generate an action video with a specific emotion from a single person image. The goal of the emotion-based action generation task (EBAG) is to generate action videos expressing a specific type of emotion given a single reference image with a full human body. We divide the task into two parts and propose a two-stage framework to generate action videos with specified emotions. At the first stage, we propose an emotion-based pose sequence generation approach (EPOSE-GAN) for translating the emotion to a pose sequence. At the second stage, we generate the target video frames according to the three inputs including the source pose and the target pose as the motion information and the source image as the appearance reference by using conditional GAN model with an online training strategy. Our framework produces the pose sequence and transforms the action independently, which highlights the fundamental role that the high-level pose feature plays in generating action video with a specific emotion. The proposed method has been evaluated on the “Soul Dancer” dataset which is built for action emotion analysis and generation. The experimental results demonstrate that our framework can effectively solve the emotion-based action generation task. However, the gap in the details of the appearance between the generated action video and the real-world video still exists, which indicates that the emotion-based action generation task has great research potential.
EMF: Disaggregated GPUs in Datacenters for Efficiency, Modularity and Flexibility

Disaggregating expensive and power-hungry GPUs enable a cost-efficient and adaptive ecosystem for cloud deployment, particularly for emerging markets, wherein AI applications are some of the dominant ones using them. This paper motivates GPU disaggregation and highlights key properties useful in resource management of disaggregated resource frameworks. An evaluation of current design approaches to GPU disaggregation is made and analysis of the NVIDIA GPU stack is done to identify various abstract layers of the stack for disaggregating. Further, based on this analysis the paper proposes a rack-level, opensource based, and backward compatible GPU disaggregation system called EMF. Key design decisions of EMF and how these choices enable scalability, efficiency, and fault-tolerance are discussed. EMF design is evaluated using an analytical model derived from low-level interactions between proprietary NVIDIA host driver and NVIDIA GPUs over PCIe. The worst-case latency analysis indicate that overheads in proposed design could vary from 7.6% to 20.2% depending on the application characteristics, justifying the practicality of this design for cloud setups.
Legible Light Communications for Factory Robots

This work focuses on methods to improve mobile robot legibility in factories using lights. Implementation and evaluation were done at a robotics company that manufactures factory robots that work in human spaces. Three new sets of communicative lights were created and tested on the robots, integrated into the company's software stack and compared to the industry default lights that currently exist on the robots. All three newly designed light sets outperformed the industry default. Insights from this work have been integrated into software releases across North America.
Memory-Efficient Architecture for Contrast Enhancement and Integral Image Computation

This paper proposes a hardware architecture for contrast-limited adaptive histogram equalization (CLAHE) and integral image computation, focusing on the efficient use of memory resources. To save memory resources, the proposed architecture processes each pixel entered in real time without storing the entire image. In addition, this architecture improves resource utilization by optimizing the tile size of CLAHE and computing the integral image via an adder tree. When the proposed architecture was implemented in Xilinx's FPGA XC7Z045 FFG900-2, it used 98,945 slice LUTs, 85,600 slice registers, and 8 BRAMs for the CLAHE module, and it used 7,834 slice LUTs, 7,498 slice registers, and 19 BRAMs for the integral image module. In addition, the proposed architecture operated at a maximum frequency of 129 MHz in 512 × 512 image resolution.
Investigating translated Chinese and its variants using machine learning

Abstract Translations are generally assumed to share universal features that distinguish them from texts that are originally written in the same language. Thus, we can argue that these translations constitute their own variety of a language, often called translationese. However, translations are also influenced by their source languages and thus show different characteristics depending on the source language. Consequently, we argue that these variants constitute different “dialects” of translations into the same target language. Studies using machine learning techniques on Indo-European languages have investigated the universal characteristics of translationese and how translations from various source languages differ. However, for typologically very different languages such as Chinese, there are only few corpus studies that tap into the intricate relation between translations and the originals, as well as into the relations among translations themselves. In this contribution, we investigate the following questions: (1) What are the characteristics of Chinese translationese, both in general and with respect to different source languages? (2) Can we find differences not only at the lexical but also on the syntactic level? and (3) Based on the characteristics found in the previous questions, which of the proposed laws and universals can we corroborate based on our evidence from Chinese? We use machine learning to operationalize determining the importance of different characteristics and comparing their importance for our Chinese dataset with characteristics previously reported in studies on English. In addition, our methodology allows us to add syntactic features, which have rarely been used to study translations into Chinese. Our results show that Chinese translations as a whole can be reliably distinguished from non-translations, even based on only five features. More interestingly, typological traces from the source languages can often be found in their translations, therefore creating what we call dialects of translationese. For instance, translations from two Altaic languages exhibit more noun repetition and less frequent use of pronouns. Additionally, some characteristics that are not discriminative for English work well for Chinese, possibly because the distance between Chinese and the source languages is greater than that in English studies.
Usability heuristics for domain-specific languages (DSLs)

The usability of Domain-Specific Languages (DSLs) has been attracting considerable interest from researchers lately. In particular, our literature review found many usability studies that make use of subjective and empirical methods. However, we noted a lack of heuristic methods in the literature. In comparison, there exist several usability studies of Application Programming Interfaces (APIs) that have used heuristics with success, so we argue that this approach would be also useful for DSLs. Therefore, this paper proposes a set of usability heuristics for DSLs and illustrates the approach with a case study. We show how our heuristics helped us identify many actual usability problems, even for a simple DSL.
Multiple Batches of Motion History Images (MB-MHIs) for Multi-view Human Action Recognition

The recognition of human actions recorded in a multi-camera environment faces the challenging issue of viewpoint variation. Multi-view methods employ videos from different views to generate a compact view-invariant representation of human actions. This paper proposes a novel multi-view human action recognition approach that uses multiple low-dimensional temporal templates and a reconstruction-based encoding scheme. The proposed approach is based upon the extraction of multiple 2D motion history images (MHIs) of human action videos over non-overlapping temporal windows, constructing multiple batches of motion history images (MB-MHIs). Then, two kinds of descriptions are computed for these MHIs batches based on (1) a deep residual network (ResNet) and (2) histogram of oriented gradients (HOG) to effectively quantify a change in gradient. ResNet descriptions are average pooled at each batch. HOG descriptions are processed independently at each batch to learn a class-based dictionary using a K-spectral value decomposition algorithm. Later, the sparse codes of feature descriptions are obtained using an orthogonal matching pursuit approach. These sparse codes are average pooled to extract encoded feature vectors. Then, encoded feature vectors at each batch are fused to form a final view-invariant feature representation. Finally, a linear support vector machine classifier is trained for action recognition. Experimental results are given on three versions of a multi-view dataset: MuHAVi-8, MuHAVi-14, and MuHAVi-uncut. The proposed approach shows promising results when tested for a novel camera. Results on deep features indicate that action representation by MB-MHIs is more view-invariant than single MHIs.
Extending finite mixtures of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2625" altimg="si4.svg"><mml:mi>t</mml:mi></mml:math> linear mixed-effects models with concomitant covariates

The issue of model-based clustering of longitudinal data has attracted increasing attention in past two decades. Finite mixtures of Student's-t linear mixed-effects (FM-tLME) models have been considered for implementing this task especially when data contain extreme observations. This paper presents an extended finite mixtures of Student's-t linear mixed-effects (EFM-tLME) model, where the categorical component labels are assumed to be influenced by the observed covariates. As compared with the naive methods assuming the mixing proportions to be fixed but unknown, the proposed EFM-tLME model exploits a logistic function to link the relationship between the prior classification probabilities and the covariates of interest. To carry out maximum likelihood estimation, an alternating expectation conditional maximization (AECM) algorithm is developed under several model reduction schemes. The technique for extracting the information-based standard errors of parameter estimates is also investigated. The proposed method is illustrated using simulation experiments and real data from an AIDS clinical study.
Witchcraft: Efficient PGD Attacks with Random Step Size

State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points. Iterative FGSM-based methods without restarts trade off performance for computational efficiency because they do not adequately explore the image space and are highly sensitive to the choice of step size. We propose a variant of Projected Gradient Descent (PGD) that uses a random step size to improve performance without resorting to expensive random restarts. Our method, Wide Iterative Stochastic crafting (WITCHcraft), achieves results superior to the classical PGD attack on the CIFAR-10 and MNIST data sets but without additional computational cost. This simple modification of PGD makes crafting attacks more economical, which is important in situations like adversarial training where attacks need to be crafted in real time.
Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.
Approaching Optimal Embedding In Audio Steganography With GAN

Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography. The embedding framework consists of three parts: a U-Net based generator, an embedding simulator, and a discriminator. For practical applications, Syndrome-Trellis Coding (STC) is used to generate stego audio with the learned embedding probability. Experimental results on the UME-ERJ and WSJ speech datasets have shown that the proposed framework can automatically learn the adaptive embedding probabilities for audio steganogra- phy and has a considerable advantage in terms of resisting steganalyzers in comparison with the existing conventional method.
Ethereum Blockchain-Based Solution to Insider Threats on Perception Layer of IoT Systems

The Internet of Things (IoT) has allured so much interest since inception thanks to the amazing capabilities it offers. Consequently, it has found tremendous applications and has been employed to meet increasing automation and computerization demands of public and private organizations where it handles enormous critical information. However, the major issue surrounding the IoT is, it is exposed to various physical and cyber threats including the significantly harmful insider threat. In this work, we approach the insider threat problem to IoT from the viewpoint of examining the influence of tampering with state of the sensing environment on the overall IoT system. Our aim is to investigate how altering the environment state in perception layer of the IoT affects the integrity of the data read by sensors; and provide mechanism to preserve the integrity of the system data to ensure accurate analytics and processing. We focused on threat models where insiders compromise physical properties about which data are collected and transmitted, deceiving the sensors into reading inaccurate data. As initial solution to the problem, we developed a framework that integrates Ethereum blockchain with edge computing to perform checks and preserve integrity of incoming sensor data before being analyzed, processed and stored.
A Fast and Accurate Super-Resolution Network Using Progressive Residual Learning

Single-image super-resolution (SISR) task has witnessed great strides in the past few years with the development of deep learning. However, most existing studies concentrate on exploiting much deeper super-resolution networks, which are not friendly to the constrained computation resources. In this work, a lightweight network using progressive residual learning for SISR (PRLSR) is proposed to address this issue. Specifically, a progressive residual block (PRB) is designed to progressively downsample deep features for reducing the redundancy and obtaining refined features. Simultaneously, a high-frequency preserving module is proposed to lower the detail loss caused by resolution reduction in PRB. Furthermore, a residual learning-based architecture with learnable weights is utilized to extract multilevel features and adaptively adjust the contribution of residual mapping and identity mapping in residual structure to accelerate convergence. Experimental results on four benchmarks show that our PRLSR achieves superior performance over state-of-the-art methods with a significantly decreased computational cost.
On Distributed Stochastic Gradient Descent for Nonconvex Functions in the Presence of Byzantines

We consider the distributed stochastic optimization problem of minimizing a nonconvex function f in an adversarial setting. All the w worker nodes in the network are expected to send their stochastic gradient vectors to the fusion center (or server). However, some (at most α-fraction) of the nodes may be Byzantines, which may send arbitrary vectors instead. Vanilla implementation of distributed stochastic gradient descent (SGD) cannot handle such misbehavior from the nodes. We propose a robust variant of distributed SGD which is resilient to the presence of Byzantines. The fusion center employs a novel filtering rule that identifies and removes the Byzantine nodes. We show that T = Õ (1/wϵ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + α <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /ϵ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) iterations are needed to achieve an ϵ-approximate stationary point (x such that ∥∇f(x)∥ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≤ ϵ) for the nonconvex learning problem. Unlike other existing approaches, the proposed algorithm is independent of the problem dimension.
Prediction and Dynamic Adjustment of Resources for Latency-Sensitive Virtual Network Functions

In this paper, we propose a scheme to employ predictive multiple regression models for dynamic resource adjustment of latency-sensitive virtual network functions (VNFs). The objective is to optimize resource allocation with agile control so that both latency and resource utilization meet the target performance requirements, despite the fluctuations in the workload. As a use case of latency-sensitive VNF, we select the Internet-of-things directory service (IoT-DS). IoT-DS is capable of storing up to a billion IoT devices records and provide fast database lookup with few milliseconds of latency. Our experimental results with an operational VNF serving as the IoT-DS show that dynamic adjustment of CPU by the proposed scheme reduces CPU resource requirements by 21.9% and avoids lookup latency requirement violations by 58.2 % compared to a threshold rule-based conventional algorithm. Besides, our scheme can provide agile vertical CPU scaling within a one-second window, which is five times faster than a related machine learning-based prior work.
Arsm Gradient Estimator for Supervised Learning to Rank

We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to the multivariate categorical variables with an unbiased and low-variance gradient estimator. Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches. Although our scoring function is pointwise, the proposed framework permits flexibility over the choice of the loss function. In our new model, the loss function need not be differentiable and can either be pointwise or listwise. Our proposed method achieves better or comparable results on two datasets compared with existing pairwise and listwise methods.
On–The–Fly Feature Selection and Classification with Application to Civic Engagement Platforms

Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framework to perform joint feature selection and classification on-the-fly while relaxing the assumption on feature independence. The effectiveness of the proposed approach is showed by classifying urban issue reports on the SeeClickFix civic engagement platform. A significant reduction in the average number of features used is observed without a drop in the classification accuracy.
Integrating Persuasive Technology in Participatory Design Workshops: Prototypes for Participant Support

In participatory design, non-professional designers are also involved in the design process. Creativity and motivation play an important role, and non-professionals in particular can have problems of motivation or ability. Persuasive technologies can be used to positively influence participants’ behavior. Applying a design science research approach, this study developed four persuasive technology artifacts to support workshop participants, based on existing work on participatory design, persuasive technologies, and creativity support tools. The following prototypes were implemented: (1) tablet-based support; (2) Bluetooth beacons for location-based triggers; (3) QR codes for additional information; and (4) a humanoid robot assistant. Each prototype incorporates persuasive strategies to exert a positive influence on workshop participants’ behavior by enhancing motivation and ability and initiating triggers. In the absence of any previous concrete implementations of persuasive technologies for participatory design, the described artifacts extend the existing knowledge base by proposing in-person collaboration as a novel application field for persuasive technology.
Effective Wavenet Adaptation for Voice Conversion with Limited Data

WaveNet has shown its great potential as a direct conversion model in voice conversion. However, due to the model complexity, WaveNet always requires a large amount of training data, which has limited its applications in voice conversion, where training data is scarce. In this paper, we propose a WaveNet adaptation method that effectively reduces the need of adaptation data. We first train a speaker independent WaveNet conversion model with multi-speaker dataset. Adaptation is then applied with limited target speaker’s data. Specifically, singular value decomposition (SVD) is applied to dilated convolution layers of WaveNet to reduce the number of parameters, which makes adaptation more effective with limited data. Experiments conducted on CMU-ARCTIC and CSTR-VCTK corpus show that the proposed method outperforms baseline methods in terms of both quality and similarity.
Unsupervised Image-to-Image Translation Via Fair Representation of Gender Bias

Fairness becomes a critical issue of computer vision to reduce discriminative factors in various systems. Among computer vision tasks, Image-to-Image translation for facial attributes editing can yield discriminative results. The unexpected gender changed results can be generated instead of editing target attributes due to the dataset imbalance problem. In this work, we propose a framework of unsupervised Image-to-Image translation that learns a fair representation by separating the latent space of our model into two purposes: 1) Target Attribute Editing, 2) Gender Preserving. We evaluate the proposed framework on CelebA dataset. Both quantitive and qualitative results demonstrate that our method improves image quality and fairness than the prior Image-to-Image translation method.