Reviewerly/exHarmony huggingface dataset:
Taking A Closer Look at Synthesis: Fine-Grained Attribute Analysis for Person Re-Identification

Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. However, in pursuit of high accuracy, researchers in the academic always focus on training with large-scale datasets at a high cost of time and label expenses, while neglect to explore the potential of performing efficient training from millions of synthetic data. To facilitate development in this field, we reviewed the previously developed synthetic dataset GPR and built an improved one (GPR+) with larger number of identities and distinguished attributes. Based on it, we quantitatively analyze the influence of dataset attribute on re-ID system. To our best knowledge, we are among the first attempts to explicitly dissect person re-ID from the aspect of attribute on synthetic dataset. This research helps us have a deeper understanding of the fundamental problems in person re-ID, which also provides useful insights for dataset building and future practical usage.
Prosody and Voice Factorization for Few-Shot Speaker Adaptation in the Challenge M2voc 2021

The paper describes the CASIA speech synthesis system entry for challenge M2VoC 2021. The low similarity and naturalness of synthesized speech remains a challenging problem for speaker adaptation with few resources. Since the end-to-end acoustic model is too complex to interpret, overfitting will occur when training with few data. To prevent the model from overfitting, this paper proposes a novel speaker adaptation framework that decomposes the prosody and voice characteristics in the end-to-end model. A prosody control attention is proposed to control the phonemes’ duration of different speakers. To make the attention controlled by the prosody information, a set of phoneme-level transition tokens is auto-learned from the prosody encoder in our framework and these transition tokens can determine the duration of phonemes in the attention mechanism. Secondly, when we need to use small data set for speaker adaptation, we just need to adapt the speaker related prosody model and decoder, which can prevent the model from overfitting. Further, we use a data puring model to automatically optimize the quality of datasets. Experiments demonstrate the effectiveness of speaker adaptation based on our method, and we (team identifier is T03) get the top three results in competition M2VoC by using this framework.
Designing Telepresence Drones to Support Synchronous, Mid-air Remote Collaboration: An Exploratory Study

Drones are increasingly used to support humanitarian crises and events that involve dangerous or costly tasks. While drones have great potential for remote collaborative work and aerial telepresence, existing drone technology is limited in its support for synchronous collaboration among multiple remote users. Through three design iterations and evaluations, we prototyped Squadrone, a novel aerial telepresence platform that supports synchronous mid-air collaboration among multiple remote users. We present our design and report results from evaluating our iterations with 13 participants in 3 different collaboration configurations. Our first design iteration validates the basic functionality of the platform. Then, we establish the effectiveness of collaboration using a 360-degree shared aerial display. Finally, we simulate a type of search task in an open environment to see if collaborative telepresence impacts members' participation. The results validate some initial goals for Squadrone and are used to reflect back on a recent telepresence design framework.
Adversarial Machine Learning: Attacks From Laboratories to the Real World

Adversarial machine learning (AML) is a recent research field that investigates potential security issues related to the use of machine learning (ML) algorithms in modern artificial intelligence (AI)-based systems, along with defensive techniques to protect ML algorithms against such threats. The main threats against ML encompass a set of techniques that aim to mislead ML models through adversarial input perturbations. Unlike ML-enabled crimes, in which ML is used for malicious and offensive purposes, and ML-enabled security mechanisms, in which ML is used for securing existing systems, AML techniques exploit and specifically address the security vulnerabilities of ML algorithms.
Analyzing and Detecting Emerging Quality-Related Concerns across OSS Defect Report Summaries

Quality-related concerns are often coined with the terms non-functional requirements, architecturally significant requirements, and quality attributes. Collectively, these qualities affect non-behavioral concerns of the software system such as reliability, usability, security, or maintainability among others. As a byproduct of a long-term maintenance effort, these system qualities tend to erode over time, causing system-wide failures that emerge via quality-related bugs. Quality-related bugs can have a detrimental impact on system's sustained stability and can chiefly hinder its core functionality. Typically, for the developers, to manually examine these high-impacted quality-related bugs can become prohibitively expensive and impractical task to attain. This is often a case with bugs that are reported from medium or large-sized projects such as Eclipse. To alleviate this problem, we built a quality-based classifier to automatically detect these emerging quality-related concerns from textual descriptions of bug report summaries. Specifically, we leveraged a weighted combination of semantics, lexical, and shallow features in conjunction with the Random Forest ensemble learning method. Finally, we discuss the practical applicability of our classifier for mapping and visualizing quality-related concerns into the codebase with an example from the Derby project. To summarize, this work represents an effort and an early awareness to improve the underlying management of issue tracking systems and stakeholder requirements in open-source communities.
Ka-Boom!!! Visually Exploring Latency Measurements for XR

Latency can be detrimental for the experience of Virtual Reality. High latency can lead to loss of performance and cybersickness. There are simple approaches to measure approximate latency and more elaborated for more insight into latency behavior. Yet there are still researchers who do not measure the latency of the system they are using to conduct VR experiments.
Service recommendation driven by a matrix factorization model and time series forecasting

The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, perform a high-accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, we propose a data sparsity resilient service recommendation approach that aims to predict relevant services in a sustainable manner for end-users. Indeed, our method performs both a QoS prediction of the current time interval using a flexible matrix factorization technique and a QoS prediction of the future time interval using a time series forecasting method based on an AutoRegressive Integrated Moving Average (ARIMA) model. The service recommendation in our approach is based on a couple of criteria ensuring in a lasting way, the appropriateness of the services returned to the active user. The experiments are conducted on a real-world dataset and demonstrate the effectiveness of our method compared to the competing recommendation methods.
Interfaces for Explanations in Human-AI Interaction: Proposing a Design Evaluation Approach

Explanations in Human-AI Interaction are communicated to human decision makers through interfaces. Yet, it is not clear what consequences the exact representation of such explanations as part of decision support systems (DSS) and working on machine learning (ML) models has on human decision making. We observe a need for research methods that allow for measuring the effect different eXplainable AI (XAI) interface designs have on people’s decision making. In this paper, we argue for adopting research approaches from decision theory for HCI research on XAI interface design. We outline how we used estimation tasks in human-grounded design research in order to introduce a method and measurement for collecting evidence on XAI interface effects. To this end, we investigated representations of LIME explanations in an estimation task online study as proof-of-concept for our proposal.
Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence

The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patients using visual histopathology from the primary tumor alone.174 cases of PTC were evaluated for the presence or absence of lymph metastases. The artificial intelligence (AI) algorithm was trained and tested on its ability to discern between the two groups.The best performing AI algorithm demonstrated a sensitivity and specificity of 94% and 100%, respectively, when identifying nodal metastases.A CNN can be used to accurately predict the likelihood of nodal metastases in PTC using visual data from the primary tumor alone.
Agent-Environment Network for Temporal Action Proposal Generation

Temporal action proposal generation is an essential and challenging task that aims at localizing temporal intervals containing human actions in untrimmed videos. Most of existing approaches are unable to follow the human cognitive process of understanding the video context due to lack of attention mechanism to express the concept of an action or an agent who performs the action or the interaction between the agent and the environment. Based on the action definition that a human, known as an agent, interacts with the environment and performs an action that affects the environment, we propose a contextual Agent-Environment Network. Our proposed contextual AEN involves (i) agent pathway, operating at a local level to tell about which humans/agents are acting and (ii) environment pathway operating at a global level to tell about how the agents interact with the environment. Comprehensive evaluations on 20-action THUMOS-14 and 200-action ActivityNet-1.3 datasets with different backbone networks, i.e C3D and SlowFast, show that our method robustly exhibits outperformance against state-of-the-art methods regardless of the employed backbone network.
Learning Saliency-Aware Correlation Filters for Visual Tracking

Abstract Recently, visual object tracking has become a hot topic in computer vision community owing to its extensive applications and fundamental research significance. Many researchers have paid attention to the discriminative correlation filter (DCF) due to its excellent tracking performance. The background-aware CF (BACF) is developed to handle the inevitable boundary effects of DCFs that shows superior performance compared with other CF-based trackers. However, BACF has a poor occlusion handling ability. To overcome this defect, we propose a saliency-aware CF tracking (SACF) model. In SACF, a saliency map of the target is introduced on CFs to strengthen the ability to extract the target from a complex background. Meanwhile, to adjust to the rapid variation of the target appearance, we adaptively modify the learning rate of the filter template using adaptive learning rate. By incorporating the saliency map into the BACF objective function and adaptive learning rate adjustment, the robustness and accuracy of the tracker are boosted significantly. Extensive experiments validate that our method can efficaciously solve the occlusion problem and achieves state-of-the-art performance in terms of accuracy and speed on standard benchmarks, i.e. OTB-2013, OTB-2015 and LaSOT. Compared with other state-of-the-art CF tracking methods, the proposed method is competitive considering accuracy and success rate.
Scale-Covariant and Scale-Invariant Gaussian Derivative Networks

This paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. By sharing the learnt parameters between multiple scale channels, and by using the transformation properties of the scale-space primitives under scaling transformations, the resulting network becomes provably scale covariant. By in addition performing max pooling over the multiple scale channels, a resulting network architecture for image classification also becomes provably scale invariant. We investigate the performance of such networks on the MNISTLargeScale dataset, which contains rescaled images from original MNIST over a factor of 4 concerning training data and over a factor of 16 concerning testing data. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not present in the training data.
Business responses to positive reviews online: Face-work on TripAdvisor

The exponential growth of consumer reviews has given rise to a new online genre: responses from businesses. While previous research predominantly focused on responses to negative reviews and mainly adopted a monolingual standpoint, in this contribution we analyze business responses to positive reviews, adopting a cross-linguistic perspective. We first explore the move structure of these responses, then delve into their different linguistic realizations, paying particular attention to potential face-work strategies. Replying to positive evaluations raises specific challenges for face-work, since it creates a tension between the choice of ‘self-enhancement’ and ‘self-effacement’ (modesty). Our findings show that the professional orientation of these interactions plays a determining role in the communicative choices adopted by the response writers. Indeed, responses to positive reviews display divergent face-work practices from patterns observed in other “positive” interactional contexts such as face-to-face or Facebook compliment responses. Specifically, we observed the pervasiveness of accepting strategies and the absence of evading or rejecting strategies. From a cross-linguistic perspective, responses written in Dutch and English tend more often toward values of modesty, whereas replies written in Italian display self-enhancement of their business, adding positive descriptions of their accommodations and promotional strategies. Furthermore, responses written in English and Dutch, display a more neutral style, while responses written in Italian opt for a more involved style (e.g., including positive small talk and returning compliments to their guests).
Exposing Deepfake Videos by Tracking Eye Movements

It has recently become a major threat to the public media that fake videos are rapidly spreading over the Internet. The advent of Deepfake, a deep-learning based toolkit, has facilitated a massive abuse of improper synthesized videos, which may influence the media credibility and human rights. A worldwide alert has been set off that finding ways to detect such fake videos is not only crucial but also urgent. This paper reports a novel approach to expose deepfake videos. We found that most fake videos are markedly different from the real ones in the way the eyes move. We are thus motivated to define four features that could well capture such differences. The features are then fed to SVM for classification. It is shown to be a promising approach that without high dimensional features and complicated neural networks, we are able to achieve competitive results on several public datasets. Moreover, the proposed features could well participate with other existing methods in the confrontation with deepfakes.
STEP-GAN: A One-Class Anomaly Detection Model with Applications to Power System Security

Smart grid systems (SGSs), and in particular power systems, play a vital role in today’s urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multi-generator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task. The model only requires normal operation data to be trained, making it cheaper to deploy and robust against unseen attacks. Moreover, the model operates on the raw input data, eliminating the need for feature extraction. We show that the model reduces the well-known mode collapse problem of GAN-based systems, it has low computational complexity and considerably outperforms the baseline system (OCAN) with about 55% in terms of accuracy on a freely available cyber attack dataset.
CPA-Secure Privacy-Preserving Reversible Data Hiding for JPEG Images

The Encrypted Image Reversible Data Hiding (EIRDH) is a hot area of research in multimedia outsourcing scenarios. In such a setting, a content owner possesses confidential images that he wants to upload to an untrusted server. The server is supposed to embed a secret message in the images before sending them to potential receivers, and the receivers expect to recover the original images without any distortion. EIRDH schemes are designed to meet reversible data hiding goals while preserving the confidentiality of the host images. Confidentiality is provided by means of encryption algorithms; however, these algorithms, in turn, introduce the challenge of dealing with ciphered images in both embedding and extraction phases. The current paper tries to propose a privacy-preserving reversible data hiding scheme for JPEG images without the need for any levels of decompression. We propose an elliptic curve-based asymmetric, commutative, and separable EIRDH method, in which no entities other than the sender and the eligible receiver of the hidden message (embedded in the encrypted image) are able to recognize its existence. In particular, even the server that performs the embedding remains completely unaware of the content of the images. An important security feature of our scheme involves the security of its multiple encryptions against Chosen-Plaintext Attack (CPA), which is an attack model that assumes the adversary can obtain the encryptions of the images on his choice to achieve possible extra information to attack. Experimental results show that our scheme achieves full reversibility and a satisfactory level of capacity, while it accurately extracts all the hidden bits.
Ultrasound Image Classification Using ACGAN with Small Training Dataset

B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models require large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work, we exploit auxiliary classifier generative adversarial network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.
Understanding and Facilitating the Co-Evolution of Production and Test Code

Software products frequently evolve. When the production code undergoes major changes such as feature addition or removal, the corresponding test code typically should co-evolve. Otherwise, the outdated test may be ineffective in revealing faults or cause spurious test failures, which could confuse developers and waste QA resources. Despite its importance, maintaining such co-evolution can be time- and resource-consuming. Existing work has disclosed that, in practice, test code often fails to co-evolve with the production code. To facilitate the co-evolution of production and test code, this work explores how to automatically identify outdated tests. To gain insights into the problem, we conducted an empirical study on 975 open-source Java projects. By manually analyzing and comparing the positive cases, where the test code co-evolves with the production code, and the negative cases, where the co-evolution is not observed, we found that various factors (e.g., the different language constructs modified in the production code) can determine whether the test code should be updated. Guided by the empirical findings, we proposed a machine-learning based approach, SITAR, that holistically considers different factors to predict test changes. We evaluated SITAR on 20 popular Java projects. These results show that SITAR, under the within-project setting, can reach an average precision and recall of 81.4% and 76.1%, respectively, for identifying test code that requires update, which significantly outperforms rule-based baseline methods. SITAR can also achieve promising results under the cross-project setting and multiclass prediction, which predicts the exact change types of test code.
Azalea: Co-experience in Remote Dialog through Diminished Reality and Somaesthetic Interaction Design

We introduce Azalea: a design to enrich remote dialog by diminishing externalities, created through a process informed by somaesthetics. Azalea is a tactile cushion that envelops a smartphone running a bespoke app. A pair of Azaleas mediate an embodied co-experience between remote interlocutors via a motion-driven soundscape and audio-driven visuals. While most designs for enriching remote communication increase dimensionality and fidelity of modalities, Azalea diminishes distractions and serves an abstract medium for co-experiencing embodied information. We present the theoretical foundations and design tactics of Azalea, and characterize the experience through a qualitative empirical study. Our findings culminated in 12 qualities, supporting 5 themes with design implications that contribute to (1) a design ethos of diminished reality and (2) an expansion of somaesthetic HCI towards expression and communication.
Segmentation of the cardiac ventricle using two layer level sets with prior shape constraint

The cardiac Left Ventricle (LV) segmentation is still challengeable due to the complex anatomical structure surrounding the LV and intensity overlaps caused by intensity inhomogeneities, which widely exists in cardiac Magnetic Resonance (MR) images. To overcome these problems, a shape prior constrained two-layer level set method is proposed to simultaneously extract the endocardium and epicardium of the LV from cardiac short-axis MR images. The proposed method is validated on MR images from the LV challenge of MICCAI 2009. Quantitative and qualitative experiments and clinical metrics demonstrate that the proposed method has better performance than some representative methods in term of Dice Similarity Coefficient (DSC), Average Perpendicular Distance (APD), Ejection Fraction (EF), and LV mass. Experimental results demonstrate that the proposed method has the priority of preserving cardiac anatomical structure and improving segmentation accuracy.