Reviewerly/exHarmony huggingface dataset:
Meta Soft Label Generation for Noisy Labels

The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta Soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples. In each iteration, the base classifier is trained on estimated meta labels. MSLG is model-agnostic and can be added on top of any existing model at hand with ease. We performed extensive experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our approach outperforms other state-of-the-art methods by a large margin. Our code is available at https://github.com/gorkemalgan/MSLG_noisy_label.
Investigating the Impact of Real-World Environments on the Perception of 2D Visualizations in Augmented Reality

In this work we report on two comprehensive user studies investigating the perception of Augmented Reality (AR) visualizations influenced by real-world backgrounds. Since AR is an emerging technology, it is important to also consider productive use cases, which is why we chose an exemplary and challenging industry 4.0 environment. Our basic perceptual research focuses on both the visual complexity of backgrounds as well as the influence of a secondary task. In contrast to our expectation, data of our 34 study participants indicate that the background has far less influence on the perception of AR visualizations. Moreover, we observed a mismatch between measured and subjectively reported performance. We discuss the importance of the background and recommendations for visual real-world augmentations. Overall, our results suggest that AR can be used in many visually challenging environments without losing the ability to productively work with the visualizations shown.
Avoiding the Turing Tarpit: Learning Conversational Programming by Starting from Code’s Purpose

Conversational programmers want to learn about code primarily to communicate with technical co-workers, not to develop software. However, existing instructional materials don’t meet the needs of conversational programmers because they prioritize syntax and semantics over concepts and applications. This mismatch results in feelings of failure and low self-efficacy. To motivate conversational programmers, we propose purpose-first programming, a new approach that focuses on learning a handful of domain-specific code patterns and assembling them to create authentic and useful programs. We report on the development of a purpose-first programming prototype that teaches five patterns in the domain of web scraping. We show that learning with purpose-first programming is motivating for conversational programmers because it engenders a feeling of success and aligns with these learners’ goals. Purpose-first programming learning enabled novice conversational programmers to complete scaffolded code writing, debugging, and explaining activities after only 30 minutes of instruction.
RENDA: Resource and Network Aware Data Placement Algorithm for Periodic Workloads in Cloud

The Hadoop enabled cloud platforms are gradually becoming preferred computational environment to execute scientific big data workloads in a periodic manner. However, it is observed that the default data placement approach of such cloud platforms is not the efficient one and often ends up with significant data transfer overhead leading to degradation of the overall job completion time. In this article, a Resource and Network-aware Data Placement Algorithm (RENDA) is proposed to reduce the non-local executions and thereby reduce the overall job completion time for periodic workloads in the cloud environment. The entire job execution is modeled as a two-stage execution characterized as data distribution and data processing. The RENDA reduces the time of the stages as mentioned above by estimating the heterogeneous performance of the nodes on a real-time basis followed by careful allocation of data in several installments to participating nodes. The experimental results show that the proposed RENDA algorithm consistently outperforms over the recent state-of-the-art alternatives with as much as 28 percent reduction in data transfer overhead leading to 16 percent reduction in average job completion time with 27 percent average speedup on average job execution.
Deep Learning-Based channel estimation with SRGAN in OFDM Systems

In this paper, we propose a novel deep learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. The channel response with known pilot positions can be treated as a low-resolution image. Then, we explore a generative adversarial network (GAN) for channel super-resolution (SR) to estimate the whole channel state information (CSI). For previous deep learning-based channel estimators recovered by a single model, high-frequency details are missing and they fail to match the fidelity expected at the higher resolution. The scheme we proposed is more consistent with the real channel by adding a discriminator to recover more details of the channel. The simulation results show that our scheme is superior to other SR-based channel estimation methods and close to the linear minimum mean square error (LMMSE) performance.
Comparing Perspectives Around Human and Technology Support for Contact Tracing

Various contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context.
Communicating with Motion: A Design Space for Animated Visual Narratives in Data Videos

Data videos are a genre of narrative visualization that communicates stories by combining data visualization and motion graphics. While data videos are increasingly gaining popularity, few systematic reviews or structured analyses exist for their design. In this work, we introduce a design space for animated visual narratives in data videos. The design space combines a dimension for animation techniques that are frequently used to facilitate data communication with one for visual narrative strategies served by such animation techniques to support story presentation. We derived our design space from the analysis of 82 high-quality data videos collected from online sources. We conducted a workshop with 20 participants to evaluate the effectiveness of our design space. Qualitative and quantitative feedback suggested that our design space is inspirational and useful for designing and creating data videos.
The Multi-Speaker Multi-Style Voice Cloning Challenge 2021

The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning task. Specifically, we formulate the challenge to adapt an average TTS model to the stylistic target voice with limited data from target speaker, evaluated by speaker identity and style similarity. The challenge consists of two tracks, namely few-shot track and one-shot track, where the participants are required to clone multiple target voices with 100 and 5 samples respectively. There are also two sub-tracks in each track. For sub-track a, to fairly compare different strategies, the participants are allowed to use only the training data provided by the organizer strictly. For sub-track b, the participants are allowed to use any data publicly available. In this paper, we present a detailed explanation on the tasks and data used in the challenge, followed by a summary of submitted systems and evaluation results.
HackerRank: Identifying key hackers in underground forums

With the rapid development of the Internet, cybersecurity situation is becoming more and more complex. At present, surface web and dark web contain numerous underground forums or markets, which play an important role in cybercrime ecosystem. Therefore, cybersecurity researchers usually focus on hacker-centered research on cybercrime, trying to find key hackers and extract credible cyber threat intelligence from them. The data scale of underground forums is tremendous and key hackers only represent a small fraction of underground forum users. It takes a lot of time as well as expertise to manually analyze key hackers. Therefore, it is necessary to propose a method or tool to automatically analyze underground forums and identify key hackers involved. In this work, we present HackerRank, an automatic method for identifying key hackers. HackerRank combines the advantages of content analysis and social network analysis. First, comprehensive evaluations and topic preferences are extracted separately using content analysis. Then, it uses an improved Topic-specific PageRank to combine the results of content analysis with social network analysis. Finally, HackerRank obtains users’ ranking, with higher-ranked users being considered as key hackers. To demonstrate the validity of proposed method, we applied HackerRank to five different underground forums separately. Compared to using social network analysis and content analysis alone, HackerRank increases the coverage rate of five underground forums by 3.14% and 16.19% on average. In addition, we performed a manual analysis of identified key hackers. The results prove that the method is effective in identifying key hackers in underground forums.
Spatiotemporal chaos in multiple coupled mapping lattices with multi-dynamic coupling coefficient and its application in color image encryption

This paper proposes a new sine-multi dynamic multiple coupling mapping lattice (SMDMCML). Through comprehensive analysis, the SMDMCML system enhances the behavior of spatiotemporal dynamics and is more stable on the whole. Moreover, the states of lattices in SMDMCML are more chaotic than the coupled map lattice (CML) or even the multiple coupled mapping lattices (MCML) models. Therefore, a new two-way independent diffusion in color image encryption algorithm is proposed based on SMDMCML. Through simulation experiments analysis, R, G, and B components have lower correlation and higher sensitivity. This further proves that SMDMCML system has good chaotic performance and conforms to characteristics of cryptography.
Wake Word Detection with Streaming Transformers

Modern wake word detection systems usually rely on neural networks for acoustic modeling. Transformers has recently shown superior performance over LSTM and convolutional networks in various sequence modeling tasks with their better temporal modeling power. However it is not clear whether this advantage still holds for short-range temporal modeling like wake word detection. Besides, the vanilla Transformer is not directly applicable to the task due to its non-streaming nature and the quadratic time and space complexity. In this paper we explore the performance of several variants of chunk-wise streaming Transformers tailored for wake word detection in a recently proposed LF-MMI system, including looking-ahead to the next chunk, gradient stopping, different positional embedding methods and adding same-layer dependency between chunks. Our experiments on the Mobvoi wake word dataset demonstrate that our proposed Transformer model outperforms the baseline convolution network by 25% on average in false rejection rate at the same false alarm rate with a comparable model size, while still maintaining linear complexity w.r.t. the sequence length.
EscapeLoom: Fabricating New Affordances for Hand Weaving

Hand-weaving is a beloved craft in history, holding promise for many opportunities in making from flat sheet fabrics to smart textiles. To afford new weaving experiences, we explore how 3D printed custom weaving tools interplay with different materiality, augmenting the design space of weaving. We propose novel weaving techniques enabled by 3D printed custom tools: (1) water-soluble draft to synchronize design intention and practice, (2) flexible warps to guide complex patterns and to shape resulting object, and (3) rigid global geometry for woven artifacts in 3D. EscapeLoom as a computational design tool enables users to employ various parameters in their computational design, and showcases many creative possibilities that move away from the traditional definition of a loom to dive into what more it can be.
Isolating and enhancing single-photon emitters for 1550 nm quantum light sources using double nanohole optical tweezers

Single-photon sources are required for quantum technologies and can be created from individual atoms and atom-like defects. Erbium ions produce single photons at low-loss fiber optic wavelengths, but they have low emission rates, making them challenging to isolate reliably. Here, we tune the size of gold double nanoholes (DNHs) to enhance the emission of single erbium emitters, achieving 50× enhancement over rectangular apertures previously demonstrated. This produces enough enhancement to show emission from single nanocrystals at wavelengths not seen in our previous work, i.e., 400 and 1550 nm. We observe discrete levels of emission for nanocrystals with low numbers of emitters and demonstrate isolating single emitters. We describe how the trapping time is proportional to the enhancement factor for a given DNH structure, giving us an independent way to measure the enhancement. This shows a promising path to achieving single emitter sources at 1550 nm.
Effect of the Gas Price Surges on User Activity in the DAOs of the Ethereum Blockchain

Blockchain technology has enabled a thriving emergent ecosystem of tools and communities actively using decentralized systems. However, most blockchain infrastructure (e.g. Ethereum) requires users to pay some fees to execute their desired actions in these novel online services. To which extent an increase in the price of such fees negatively affects user activity? Would significant price surges deter users from using blockchain-enabled online services? In this work, we study the 2020 surge of transaction fee price in the Ethereum network, and analyze how that affected user activities. Our use cases are the blockchain-enabled Decentralized Autonomous Organizations (DAOs) from the platforms DAOstack and DAOhaus. Thus, we analyzed 5,580 transactions from 7,825 users grouped in 191 DAO communities, using a VAR model with a daily time series of the average fee value and the DAO operations. Our results show just a minor influence of the fee (gas) price and the activity of DAO users. The insensitivity of the activity to the fee price is an anomaly in a supposedly self-regulated market, and we consider this should be tackled in future implementations.
Invariant Continuation of Discrete Multi-Valued Functions and Their Implementation

The article discusses partially defined discrete functions and investigates the problem of logical continuation of such functions in the class of canonical normal forms. A theorem is proved on the invariant continuation of a multivalued function in the class of canonical forms, which does not depend on the adopted coding. An algorithm for constructing a set of invariant points independent of the adopted encoding and software for such an invariant continuation are constructed.
eyemR-Vis: Using Bi-Directional Gaze Behavioural Cues to Improve Mixed Reality Remote Collaboration

Gaze is one of the most important communication cues in face-to-face collaboration. However, in remote collaboration, sharing dynamic gaze information is more difficult. In this research, we investigate how sharing gaze behavioural cues can improve remote collaboration in a Mixed Reality (MR) environment. To do this, we developed eyemR-Vis, a 360 panoramic Mixed Reality remote collaboration system that shows gaze behavioural cues as bi-directional spatial virtual visualisations shared between a local host and a remote collaborator. Preliminary results from an exploratory study indicate that using virtual cues to visualise gaze behaviour has the potential to increase co-presence, improve gaze awareness, encourage collaboration, and is inclined to be less physically demanding or mentally distracting.
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning

This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks. In the same way that high-level visual information in the world changes smoothly, we believe that nearby frames in learned representations will benefit from demonstrating similar properties. Using this assumption, we train our TCE model to encode videos such that adjacent frames exist close to each other and videos are separated from one another. Using TCE we learn robust representations from large quantities of unlabeled video data. We thoroughly analyse and evaluate our self-supervised learned TCE models on a downstream task of video action recognition using multiple challenging benchmarks (Kinetics400, UCF101, HMDB51). With a simple but effective 2D-CNN backbone and only RGB stream inputs, TCE pre-trained representations outperform all previous self-supervised 2D-CNN and 3D-CNN pre-trained on UCF101. The code and pre-trained models for this paper can be downloaded at: https://github.com/csiro-robotics/TCE.
Blink-Suppressed Hand Redirection

Many interaction techniques in virtual reality break with the 1-to-1 mapping from real to virtual space. Instead, specialized techniques for 3D interaction and haptic retargeting leverage hand redirection, offsetting the virtual hand rendering from the real hand position. To achieve unnoticeable hand redirection, however, the utilization of change blindness phenomena has not been systematically explored. Inspired by recent advances in the domain of redirected walking, we present the first hand redirection technique that makes use of blink-induced visual suppression and corresponding change blindness. We introduce Blink-Suppressed Hand Redirection (BSHR) to study the feasibility and detectability of hand redirection based on blink suppression. Our technique is based on Cheng et al.'s (2017) [9] body warping algorithm and instantaneously shifts the virtual hand when the user's vision is suppressed during a blink. Additionally, it can be configured to continuously increment hand offsets when the user's eyes are opened, limited to an extent below detection thresholds. In a psychophysical experiment, we verify that unnoticeable blink-suppressed hand redirection is possible even in worst -case scenarios, and derive the corresponding conservative detection thresholds (CDTs). Moreover, our results show that the range of unnoticeable redirection can be increased by combining continuous warping and blink-suppressed instantaneous shifts. As an additional contribution, we derive the CDTs for Cheng et al.'s (2017) [9] redirection technique that does not leverage blinks.
Visual Grounding with Transformers

In this paper, we propose a transformer based approach for visual grounding. Unlike existing proposal-and-rank frameworks that rely heavily on pretrained object detectors or proposal-free frameworks that upgrade an off-the-shelf one-stage detector by fusing textual embeddings, our approach is built on top of a transformer encoder-decoder and is independent of any pretrained detectors or word embedding models. Termed as VGTR – Visual Grounding with TRansformers, our approach is designed to learn semantic-discriminative visual features under the guidance of the textual description without harming their location ability. This information flow enables our VGTR to have a strong capability in capturing context-level semantics of both vision and language modalities, rendering us to aggregate accurate visual clues implied by the description to locate the interested object instance. Experiments show that our method outperforms state-of-the-art proposal-free approaches by a considerable margin on four benchmarks.
Distribution-Aware Testing of Neural Networks Using Generative Models

The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications being deployed with DNNs. The need for reliability raises a need for rigorous testing of the safety and trustworthiness of these systems. In the last few years, there have been a number of research efforts focused on testing DNNs. However the test generation techniques proposed so far lack a check to determine whether the test inputs they are generating are valid, and thus invalid inputs are produced. To illustrate this situation, we explored three recent DNN testing techniques. Using deep generative model based input validation, we show that all the three techniques generate significant number of invalid test inputs. We further analyzed the test coverage achieved by the test inputs generated by the DNN testing techniques and showed how invalid test inputs can falsely inflate test coverage metrics. To overcome the inclusion of invalid inputs in testing, we propose a technique to incorporate the valid input space of the DNN model under test in the test generation process. Our technique uses a deep generative model-based algorithm to generate only valid inputs. Results of our empirical studies show that our technique is effective in eliminating invalid tests and boosting the number of valid test inputs generated.