Reviewerly/exHarmony huggingface dataset:
Contrastive Self-Supervised Learning for Text-Independent Speaker Verification

Current speaker verification models rely on supervised training with massive annotated data. But the collection of labeled utterances from multiple speakers is expensive and facing privacy issues. To open up an opportunity for utilizing massive unlabeled utterance data, our work exploits a contrastive self-supervised learning (CSSL) approach for text-independent speaker verification task. The core principle of CSSL lies in minimizing the distance between the embeddings of augmented segments truncated from the same utterance as well as maximizing those from different utterances. We proposed channel-invariant loss to prevent the network from encoding the undesired channel information into the speaker representation. Bearing these in mind, we conduct intensive experiments on VoxCeleb1&2 datasets. The self-supervised thin-ResNet34 fine-tuned with only 5% of the labeled data can achieve comparable performance to the fully supervised model, which is meaningful to economize lots of manual annotation.
PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond

Reducing complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this problem by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask++, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, leading to several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity. (2) We carefully design two modules (soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask++ does not depend on the bounding box prediction and thus more efficient in training. (3) PolarMask++ is fully convolutional and can be easily embedded into most off-the-shelf detectors. To further improve the accuracy of the framework, a Refined Feature Pyramid is introduced to improve the feature representation at different scales. Extensive experiments demonstrate the effectiveness of PolarMask++, which achieves competitive results on COCO dataset, and new state-of-the-art results on text detection and cell segmentation datasets. We hope polar representation can provide a new perspective for designing algorithms to solve single-shot instance segmentation. Code is released at: github.com/xieenze/PolarMask.
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition

To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeling. Different alignment label topologies are compared and word-end-based phoneme label augmentation is proposed to improve performance. Utilizing the local dependency of phonemes, we adopt a simplified neural network structure and a straightforward integration with the external word-level language model to preserve the consistency of seq-to-seq modeling. We also present a simple, stable and efficient training procedure using frame-wise cross-entropy loss. A phonetic context size of one is shown to be sufficient for the best performance. A simplified scheduled sampling approach is applied for further improvement and different decoding approaches are briefly compared. The overall performance of our best model is comparable to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard corpora.
QDNN: deep neural networks with quantum layers

Abstract In this paper, a quantum extension of classical deep neural network (DNN) is introduced, which is called QDNN and consists of quantum structured layers. It is proved that the QDNN can uniformly approximate any continuous function and has more representation power than the classical DNN. Moreover, the QDNN still keeps the advantages of the classical DNN such as the non-linear activation, the multi-layer structure, and the efficient backpropagation training algorithm. Furthermore, the QDNN uses parameterized quantum circuits (PQCs) as the basic building blocks and hence can be used on near-term noisy intermediate-scale quantum (NISQ) processors. A numerical experiment for an image classification task based on QDNN is given, where a high accuracy rate is achieved.
Blockchain-enabled government efficiency and impartiality: using blockchain for targeted poverty alleviation in a city in China

Blockchain technology has unique characteristics that make it a powerful application for transforming government processes and improving the management of public benefits. In this paper, we propose that a blockchain-enabled government can achieve higher levels of efficiency and impartiality compared to traditional and general digital government. These improvements can be achieved through blockchain’s impact on the accuracy, coordination, trust, and transparency of information-based processes. Propositions are examined using a blockchain-enabled program of targeted poverty alleviation in a major metropolitan city in China. An analysis of data from semi-structured interviews and public documents indicates that the blockchain-enabled government process, through the technology’s coordination and accuracy features, can improve the efficiency of targeted poverty alleviation work in the local government. In addition, we find that impartiality of a government program is improved through the transparency and trust that is enabled by the application of blockchain technology.
Visual memorability in the absence of semantic content

What determines how well people remember images? Most past research has explored properties of the people doing the remembering — such as their age, emotional state, or individual capacity. However, recent work has also characterized memorability — the likelihood of an image being remembered across observers. But what makes some images more memorable than others? Part of the answer must surely involve the meanings of the images, but here we ask whether this is the entire story: is there also purely visual memorability, driven not by semantic content but by perceptual features per se? We isolated visual memorability in an especially direct manner — by eliminating semantic content while retaining many visual properties. We did so by transforming a set of natural scene images using phase scrambling, and then testing memorability for both intact and scrambled images in independent samples. Across several experiments, observers saw sequences of images and responded anytime they saw a repeated image. We found reliable purely visual memorability at the temporal scales of both short-term memory (2–15 s) and longer-term memory (several minutes), and this could not be explained by the extent to which people could generate semantic labels for some scrambled images. Collectively, these results suggest that the memorability of images is a function not only of what they mean, but also of how they look in the first place.
Characteristic Analysis of 2D Lag-Complex Logistic Map and Its Application in Image Encryption

In this study, we propose a novel 2D lag-complex logistic map (LCLM). The 2D-LCLM is derived from the conventional 2D logistic map by extending its variables from real numbers to complex field. The bifurcation diagrams, Lyapunov exponents, and the evolution of chaotic attractors have been exploited for evaluating the chaotic behavior. Results show that the 2D-LCLM has extensive chaotic intervals, good ergodicity, and unpredictable trajectories. For further studying its practical significance, we then construct a color image cryptosystem based on 2D-LCLM. Bit-level permutation and one-time keys are used for speeding up the computational speed and defending chosen plaintext attack. Simulation results and corresponding analysis reveal that the cryptosystem has large key-space and good key sensitivity, and its information entropy approaches to the ideal value. Hence, the cryptosystem based on 2D-LCLM can strongly resist traditional cipher attacks.
A spatial domain‐based color image blind watermarking scheme integrating multilevel discrete Hartley transform

In this paper, a spatial domain-based blind watermarking scheme of color images integrating multilevel discrete Hartley transform (DHT) is proposed to protect color digital images' copyright under the current 5G network environment. On the basis of the unique spatial computing property of the maximum energy coefficient of DHT and the energy aggregation principle of multilevel image transform, the color watermark image scrambled by Affine transform is directly hidden into color carrier image by quantitative techniques in the spatial domain, rather than the complex DHT domain. The specific performance of the scheme in various aspects is as follows: (1) the average value of the peak signal-to-noise ratio is higher than 40 dB; (2) the average value of structural similarity index metric remains above 0.9700; (3) the average value of normalized cross-correlation under no attack is 1.0000; (4) the average value of normalized cross-correlation. under attack is above 0.9600; (5) the total execution time is 1.1856 s; (6) the maximum embedding capacity is 0.0625 bpp; and (7) the entire key space is greater than 2432. Multiple simulation results demonstrate that the proposed scheme is not merely imperceptible and robust, but also secure and efficient, which can protect the digital copyright with effect.
Overcoming cultural barriers to being agile in distributed teams

Context: Agile methods in offshored projects have become increasingly popular. Yet, many companies have found that the use of agile methods in coordination with companies located outside the regions of early agile adopters remains challenging. India has received particular attention as the leading destination of offshoring contracts due to significant cultural differences between sides of such contracts. Alarming differences are primarily rooted in the hierarchical business culture of Indian organizations and related command-and-control management behavior styles. Objective: In this study, we attempt to understand whether cultural barriers persist in distributed projects in which Indian engineers work with a more empowering Swedish management, and if so, how to overcome them. The present work is an invited extension of a conference paper. Method: We performed a multiple-case study in a mature agile company located in Sweden and a more hierarchical Indian vendor. We collected data from five group interviews with a total of 34 participants and five workshops with 96 participants in five distributed DevOps teams, including 36 Indian members, whose preferred behavior in different situations we surveyed. Results: We identified twelve cultural barriers, six of which were classified as impediments to agile software development practices, and report on the manifestation of these barriers in five DevOps teams. Finally, we put forward recommendations to overcome the identified barriers and emphasize the importance of cultural training, especially when onboarding new team members. Conclusions: Our findings confirm previously reported behaviors rooted in cultural differences that impede the adoption of agile approaches in offshore collaborations, and identify new barriers not previously reported. In contrast to the existing opinion that cultural characteristics are rigid and unchanging, we found that some barriers present at the beginning of the studied collaboration disappeared over time. Many offshore members reported behaving similarly to their onshore colleagues.
Improving the precision of optical metrology by detecting fewer photons with biased weak measurement

Abstract In optical metrological protocols to measure physical quantities, it is, in principle, always beneficial to increase photon number n to improve measurement precision. However, practical constraints prevent the arbitrary increase of n due to the imperfections of a practical detector, especially when the detector response is dominated by the saturation effect. In this work, we show that a modified weak measurement protocol, namely, biased weak measurement significantly improves the precision of optical metrology in the presence of saturation effect. This method detects an ultra-small fraction of photons while maintains a considerable amount of metrological information. The biased pre-coupling leads to an additional reduction of photons in the post-selection and generates an extinction point in the spectrum distribution, which is extremely sensitive to the estimated parameter and difficult to be saturated. Therefore, the Fisher information can be persistently enhanced by increasing the photon number. In our magnetic-sensing experiment, biased weak measurement achieves precision approximately one order of magnitude better than those of previously used methods. The proposed method can be applied in various optical measurement schemes to remarkably mitigate the detector saturation effect with low-cost apparatuses.
Federated Acoustic Modeling for Automatic Speech Recognition

Data privacy and protection is a crucial issue for any automatic speech recognition (ASR) service provider when dealing with clients. In this paper, we investigate federated acoustic modeling using data from multiple clients. A client’s data is stored on a local data server and the clients communicate only model parameters with a central server, and not their data. The communication happens infrequently to reduce the communication cost. To mitigate the non-iid issue, client adaptive federated training (CAFT) is proposed to canonicalize data across clients. The experiments are carried out on 1,150 hours of speech data from multiple domains. Hybrid LSTM acoustic models are trained via federated learning and their performance is compared to traditional centralized acoustic model training. The experimental results demonstrate the effectiveness of the proposed federated acoustic modeling strategy. We also show that CAFT can further improve the performance of the federated acoustic model.
Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training

The graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is nontrivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: 1) graph-level and 2) node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. The results show that Rubik accelerator design improves energy efficiency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$26.3\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1375.2\times $ </tex-math></inline-formula> than GPU platforms across different datasets and GCN models.
Fine-Tuning DARTS for Image Classification

Neural Architecture Search (NAS) has gained attraction due to superior classification performance. Differential Architecture Search (DARTS) is a computationally light method. To limit computational resources DARTS makes numerous approximations. These approximations result in inferior performance. We propose to fine-tune DARTS using fixed operations as they are independent of these approximations. Our method offers a good trade-off between the number of parameters and classification accuracy. Our approach improves the top-1 accuracy on Fashion-MNIST, CompCars, and MIO-TCD datasets by 0.56%, 0.50%, and 0.39%, respectively compared to the state-of-the-art approaches. Our approach performs better than DARTS, improving the accuracy by 0.28%, 1.64%, 0.34%, 4.5%, and 3.27% compared to DARTS, on CIFAR-10, CIFAR-100, Fashion-MNIST, CompCars, and MIO-TCD datasets, respectively.
A Decision Model for Choosing Patterns in Blockchain-Based Applications

Blockchains have been applied in different domains to guarantee data integrity and provide a decentralized computational infrastructure for executing smart contracts. Multiple blockchain-related patterns have been summarized by academics and industry practitioners covering different aspects, such as engineering applications on top of a blockchain, structuring smart contracts, and security. The existence of these patterns is both helpful and challenging for designers. Helpful, as the existence of these patterns means that developers do not need to recreate solutions to common problems. Challenging, as the multitude of patterns leaves a designer confused about when to adopt or adapt patterns. In this paper, we propose a decision model that assists developers and architects in selecting appropriate patterns for blockchain-based applications. The selection is based on the characteristics of the use cases and trade-offs implicit in the patterns. We evaluated the proposed decision model based on expert opinion regarding its correctness and usefulness in guiding the architecture design and understanding the rationale of various design decisions.
Bayesian Stackelberg games for cyber-security decision support

A decision support system for cyber-security is here presented. The system aims to select an optimal portfolio of security controls to counteract multi-stage attacks. The system has several components: a preventive optimisation to select controls for an initial defensive portfolio, a learning mechanism to estimate possible ongoing attacks, and an online optimisation selecting an optimal portfolio to counteract ongoing attacks. The system relies on efficient solutions of bi-level optimisations, in particular, the online optimisation is shown to be a Bayesian Stackelberg game solution. The proposed solution is shown to be more efficient than both classical solutions like Harsanyi transformation and more recent efficient solvers. Moreover, the proposed solution provides significant security improvements on mitigating ongoing attacks compared to previous approaches. The novel techniques here introduced rely on recent advances in Mixed-Integer Conic Programming (MICP), strong duality and totally unimodular matrices.
Decolonial Pathways: Our Manifesto for a Decolonizing Agenda in HCI Research and Design

As the push for intersection between decolonial and post-colonial perspectives and technology design and HCI continues to grow, the natural challenge of embracing different ways of approaching knowledge production without ’othering’ begins to emerge. In this paper, we offer what we call ’decolonial paths’, possible portals to navigate through this challenge. This collective exploration inspires five pathways for approaching decoloniality within HCI: understanding, reconsidering, changing, expanding, and reflecting. Non-prescriptive and non-definitive, these pathways offer HCI researchers a framework to investigate their own practice and the spaces of sociotechnical research and learning they inhabit.
Efficient Knowledge Distillation for RNN-Transducer Models

Knowledge Distillation is an effective method of transferring knowledge from a large model to a smaller model. Distillation can be viewed as a type of model compression, and has played an important role for on-device ASR applications. In this paper, we develop a distillation method for RNN-Transducer (RNN-T) models, a popular end-to-end neural network architecture for streaming speech recognition. Our proposed distillation loss is simple and efficient, and uses only the "y" and "blank" posterior probabilities from the RNN-T output probability lattice. We study the effectiveness of the proposed approach in improving the accuracy of sparse RNN-T models obtained by gradually pruning a larger uncompressed model, which also serves as the teacher during distillation. With distillation of 60% and 90% sparse multi-domain RNN-T models, we obtain WER reductions of 4.3% and 12.1% respectively, on a noisy FarField eval set. We also present results of experiments on LibriSpeech, where the introduction of the distillation loss yields a 4.8% relative WER reduction on the test-other dataset for a small Conformer model.
Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior

Rain is a common natural phenomenon. Taking images in the rain however often results in degraded quality of images, thus compromises the performance of many computer vision systems. Most existing de-rain algorithms use only one single input image and aim to recover a clean image. Few work has exploited stereo images. Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignore semantic information. In this paper, we present a Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information. Specifically, we develop a Semantic-Aware Deraining Module (SADM) which solves both tasks of semantic segmentation and deraining of scenes, and a Semantic-Fusion Network (SFNet) and a View-Fusion Network (VFNet) which fuse semantic information and multi-view information respectively. In addition, we also introduce an Enhanced Paired Rain Removal Network (EPRRNet) which exploits semantic prior to remove rain streaks from stereo images. We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image. Finally, a parallel stereo deraining network fuses semantic and multi-view information to restore finer results. We also propose new stereo based rainy datasets for benchmarking. Experiments on both monocular and the newly proposed stereo rainy datasets demonstrate that the proposed method achieves the state-of-the-art performance. https://github.com/HDCVLab/Stereo-Image-Deraining .
Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification

Graph is used to represent various complex relationships among objects and data entities. One of the emerging and important problems is graph classification that has tremendous impacts on various real-life applications. A good number of approaches have been proposed for graph classification using various techniques where graph embedding is one of them. Here we propose an approach for classifying graphs by mining discriminating frequent patterns from graphs to learn vector representation of the graphs. The proposed supervised embedding technique produces high-quality entire graph embedding for classification utilizing the knowledge from the labeled examples available. The experimental analyses, conducted on various real-life benchmark datasets, found that the proposed approach is significantly better in terms of accuracy in comparison to the state-of-the-art techniques.