Reviewerly/exHarmony huggingface dataset:
Dynamic sine cosine algorithm for large-scale global optimization problems

The sine cosine algorithm (SCA) is a recently proposed swarm intelligence optimization based on sine and cosine mathematical functions. It has a novel principle to process global optimization, but when solving large-scale global optimization problems, the performance of this algorithm is greatly reduced. To tackle this problem, a dynamic sine cosine algorithm (DSCA) is proposed. DSCA includes a nonlinear random convergence parameter to update equation, dynamically balancing the exploration and exploitation of SCA. Moreover, in order to avoid fall into the local optimum, a dynamic inertia weight strategy is introduced to modify the position equation of this algorithm. To evaluate the performance in solving large-scale global optimization problems, DSCA is compared with state-of-art algorithms. The 15 standard high-dimensional functions ranging from 200 to 5000 and IEEE CEC2010 functions are selected. The results show that DSCA has better convergence precision, faster convergence speed and stronger robustness when solving large-scale optimization problems. Two practical engineering problems are also applied to this algorithm, the effectiveness of the dynamic sine cosine algorithm to ensure the efficient results in real-world optimization problems.
Learning to Segment From Scribbles Using Multi-Scale Adversarial Attention Gates

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using scribble annotations in an adversarial game. With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn their correct position in the image. Central to the model's success is a novel attention gating mechanism, which we condition with adversarial signals to act as a shape prior, resulting in better object localization at multiple scales. Subject to adversarial conditioning, the segmentor learns attention maps that are semantic, suppress the noisy activations outside the objects, and reduce the vanishing gradient problem in the deeper layers of the segmentor. We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks. We also demonstrate extensions in a variety of settings: semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision). We release expert-made scribble annotations for the ACDC dataset, and the code used for the experiments, at https://vios-s.github.io/multiscale-adversarial-attention-gates
Josephson junction infrared single-photon detector

A junction for single-photon detection Josephson junctions are simple superconducting devices comprising an insulator or semiconductor separating two superconducting regions. They form the workhorse of superconducting technologies and are exquisitely sensitive to magnetic field. One long-sought proposal has been to use these devices to detect light. Walsh et al. have realized a photosensitive Josephson junction based on graphene that is capable of sensing single infrared photons. Such a photosensitive Josephson junction is expected to operate as a high-speed, low-power consumption optical interconnect for communication between superconducting-based supercomputers and quantum computers. Science , this issue p. 409
Neural RGB-D Surface Reconstruction

Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications. While current volume-based view synthesis methods that use neural radiance fields (NeRFs) show promising results in reproducing the appearance of an object or scene, they do not reconstruct an actual surface. The volumetric representation of the surface based on densities leads to artifacts when a surface is extracted using Marching Cubes, since during optimization, densities are accumulated along the ray and are not used at a single sample point in isolation. Instead of this volumetric representation of the surface, we propose to represent the surface using an implicit function (truncated signed distance function). We show how to incorporate this representation in the NeRF framework, and extend it to use depth measurements from a commodity RGB-D sensor, such as a Kinect. In addition, we propose a pose and camera re-finement technique which improves the overall reconstruction quality. In contrast to concurrent work on integrating depth priors in NeRF which concentrates on novel view synthesis, our approach is able to reconstruct high-quality, metrical 3D reconstructions.
MIPGAN—Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach's applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation poses a high threat to FRS.
Rethinking software runtimes for disaggregated memory

Disaggregated memory can address resource provisioning inefficiencies in current datacenters. Multiple software runtimes for disaggregated memory have been proposed in an attempt to make disaggregated memory practical. These systems rely on the virtual memory subsystem to transparently offer disaggregated memory to applications using a local memory abstraction. Unfortunately, using virtual memory for disaggregation has multiple limitations, including high overhead that comes from the use of page faults to identify what data to fetch and cache locally, and high dirty data amplification that comes from the use of page-granularity for tracking changes to the cached data (4KB or higher).
Self-Learning Based Computation Offloading for Internet of Vehicles: Model and Algorithm

With the fast development of Internet of Vehicles (IoV), various types of computation-intensive vehicular applications pose significant challenges to resource-constrained vehicles. The emerging Vehicular Edge Computing (VEC) and Edge Intelligence (EI) can alleviate this situation by offloading the computation tasks of vehicles to the roadside edge servers. However, with many vehicles contending for the communication and computation resources at the same time, how to quickly and efficiently make an optimal computation offloading decision for individual vehicles represents a fundamental research issue. In this paper, we propose a self-learning based distributed computation offloading scheme for IoV. Note that without any centralized controller, a fully distributed algorithm is necessary. The proposed scheme is devised based on a game-theoretic model. Specifically, through establishing an offloading framework with communication and computation for IoV, the computation offloading problem is first formulated as a distributed offloading decision-making game, in which each vehicle as a player makes its best response decision to minimize its joint cost (including latency and offloading cost). The existence of Nash Equilibrium can be proved. We then propose a self-learning based distributed computation offloading (DISCO) algorithm to reach the Nash Equilibrium, where a mutually satisfactory solution among vehicles is obtained and no vehicle is willing to change its decision. Using extensive simulations, we verify that DISCO can outperform the counterparts and achieve at least an order-of-magnitude improvement on time overhead and 88% performance gain on message overhead, only at up to 12% performance loss on joint cost over the centralized scheme.
A Deep Neural Network Model for Speaker Identification

Speaker identification is a classification task which aims to identify a subject from a given time-series sequential data. Since the speech signal is a continuous one-dimensional time series, most of the current research methods are based on convolutional neural network (CNN) or recurrent neural network (RNN). Indeed, these methods perform well in many tasks, but there is no attempt to combine these two network models to study the speaker identification task. Due to the spectrogram that a speech signal contains, the spatial features of voiceprint (which corresponds to the voice spectrum) and CNN are effective for spatial feature extraction (which corresponds to modeling spectral correlations in acoustic features). At the same time, the speech signal is in a time series, and deep RNN can better represent long utterances than shallow networks. Considering the advantage of gated recurrent unit (GRU) (compared with traditional RNN) in the segmentation of sequence data, we decide to use stacked GRU layers in our model for frame-level feature extraction. In this paper, we propose a deep neural network (DNN) model based on a two-dimensional convolutional neural network (2-D CNN) and gated recurrent unit (GRU) for speaker identification. In the network model design, the convolutional layer is used for voiceprint feature extraction and reduces dimensionality in both the time and frequency domains, allowing for faster GRU layer computation. In addition, the stacked GRU recurrent network layers can learn a speaker’s acoustic features. During this research, we tried to use various neural network structures, including 2-D CNN, deep RNN, and deep LSTM. The above network models were evaluated on the Aishell-1 speech dataset. The experimental results showed that our proposed DNN model, which we call deep GRU, achieved a high recognition accuracy of 98.96%. At the same time, the results also demonstrate the effectiveness of the proposed deep GRU network model versus other models for speaker identification. Through further optimization, this method could be applied to other research similar to the study of speaker identification.
Equivalence of quantum barren plateaus to cost concentration and narrow gorges

Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of near-term quantum computers. However, very little is known about the cost function landscape for PQCs, which hinders progress towards quantum-aware optimizers. In this work, we investigate the connection between three different landscape features that have been observed for PQCs: (1) exponentially vanishing gradients (called barren plateaus), (2) exponential cost concentration about the mean, and (3) the exponential narrowness of minina (called narrow gorges). We analytically prove that these three phenomena occur together, i.e., when one occurs then so do the other two. A key implication of this result is that one can numerically diagnose barren plateaus via cost differences rather than via the computationally more expensive gradients. More broadly, our work shows that quantum mechanics rules out certain cost landscapes (which otherwise would be mathematically possible), and hence our results are interesting from a quantum foundations perspective.
Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction

Current research on federated learning mainly focuses on joint optimization, improving efficiency and effectiveness, and protecting privacy. However, there are relatively few studies on incentive mechanisms. Most studies fail to consider the fact that if there is no profit, participants have no incentive to provide data and training models, and task requesters cannot identify and select reliable participants with high-quality data. Therefore, this paper proposes a federated learning incentive mechanism based on reputation and reverse auction theory. Participants bid for tasks, and reputation indirectly reflects their reliability and data quality. In this federated learning program, we select and reward participants by combining the reputation and bids of the participants under a limited budget. Theoretical analysis proves that the mechanism satisfies computational efficiency, individual rationality, budget feasibility, and truthfulness. The simulation results show the effectiveness of the mechanism.
A comprehensive survey of entity alignment for knowledge graphs

Knowledge Graphs (KGs), as a structured human knowledge, manage data in an ease-of-store, recognizable, and understandable way for machines and provide a rich knowledge base for different artificial intelligence applications. However, current multi-source KGs have heterogeneity and complementarity, and it is necessary to fuse heterogeneous knowledge from different data sources or different languages into a unified and consistent KG. Entity alignment aims to find equivalence relations between entities in different knowledge graphs but semantically represent the same real-world object, which is the most fundamental and essential technology in knowledge fusion. This paper investigated almost all the latest knowledge graph representations learning and entity alignment methods and summarized their core technologies and features from different aspects. Our full investigation gives a comprehensive outlook on several promising research directions for future work. We also provide an efficient and efficiency entity alignment toolkit to help researchers quickly start their own entity alignment models.
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference

As the application of deep learning continues to grow, so does the amount of data used to make predictions. While traditionally big-data deep learning was constrained by computing performance and off-chip memory bandwidth, a new constraint has emerged: privacy. One solution is homomorphic encryption (HE). Applying HE to the client-cloud model allows cloud services to perform inferences directly on clients’ encrypted data. While HE can meet privacy constraints it introduces enormous computational challenges and remains impractically slow on current systems.This paper introduces Cheetah, a set of algorithmic and hardware optimizations for server-side HE DNN inference. Cheetah proposes HE-parameter tuning and operator scheduling optimizations, which together deliver up to $79 \times$ speedup over the state-of-the-art. However, HE inference still falls short of real-time inference speeds by nearly four orders of magnitude. Cheetah further proposes an accelerator architecture to understand the degree of speedup hardware can provide and whether it can bridge HE’s real-time performance gap. We evaluate several DNNs and find that privacy-preserving HE inference for ResNet50 can approach real-time speeds with a 587mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> accelerator dissipating 30W in 5nm.
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework

Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edge-computing platforms, e.g., ASICs, FPGAs, and embedded systems, are extensively investigated. Due to the huge model size and computation amount, model compression is a critical step to deploy DNN models on edge devices. This paper focuses on weight quantization, a hardware-friendly model compression approach that is complementary to weight pruning.Unlike existing methods that use the same quantization scheme for all weights, we propose the first solution that applies different quantization schemes for different rows of the weight matrix. It is motivated by (1) the distribution of the weights in the different rows are not the same; and (2) the potential of achieving better utilization of heterogeneous FPGA hardware resources. To achieve that, we first propose a hardware-friendly quantization scheme named sum-of-power-of-2 (SP2) suitable for Gaussian-like weight distribution, in which the multiplication arithmetic can be replaced with logic shifter and adder, thereby enabling highly efficient implementations with the FPGA LUT resources. In contrast, the existing fixed-point quantization is suitable for Uniform-like weight distribution and can be implemented efficiently by DSP. Then to fully explore the resources, we propose an FPGA-centric mixed scheme quantization (MSQ) with an ensemble of the proposed SP2 and the fixed-point schemes. Combining the two schemes can maintain, or even increase accuracy due to better matching with weight distributions.For the FPGA implementations, we develop a parameterized architecture with heterogeneous Generalized Matrix Multiplication (GEMM) cores-one using LUTs for computations with SP2 quantized weights and the other utilizing DSPs for fixed-point quantized weights. Given the partition ratio among the two schemes based on resource characterization, MSQ quantization training algorithm derives an optimally quantized model for the FPGA implementation. We evaluate our FPGA-centric quantization framework across multiple application domains. With optimal SP2/fixed-point ratios on two FPGA devices, i.e., Zynq XC7Z020 and XC7Z045, we achieve performance improvement of 2.1 × -4.1 × compared to solely exploiting DSPs for all multiplication operations. In addition, the CNN implementations with the proposed MSQ scheme can achieve higher accuracy and comparable hardware utilization efficiency compared to the state-of-the-art designs.
A Close Look at Deep Learning with Small Data

In this work, we perform a wide variety of experiments with different deep learning architectures on datasets of limited size. According to our study, we show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we show that in some configurations, the state of the art can be improved using low complexity models. For instance, in problems with scarce training samples and without data augmentation, low-complexity convolutional neural networks perform comparably well or better than state-of-the-art architectures. Moreover, we show that even standard data augmentation can boost recognition performance by large margins. This result suggests the development of more complex data generation/augmentation pipelines for cases when data is limited. Finally, we show that dropout, a widely used regularization technique, maintains its role as a good regularizer even when data is scarce. Our findings are empirically validated on the sub-sampled versions of popular CIFAR-10, Fashion-MNIST and, SVHN benchmarks.
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground object (s), we consider background should be equally treated. Thus, we propose a Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. CFBI separates the feature embedding into the foreground object region and its corresponding background region, implicitly promoting them to be more contrastive and improving the segmentation results accordingly. Moreover, CFBI performs both pixel-level matching processes and instance-level attention mechanisms between the reference and the predicted sequence, making CFBI robust to various object scales. Based on CFBI, we introduce a multi-scale matching structure and propose an Atrous Matching strategy, resulting in a more robust and efficient framework, CFBI+. We conduct extensive experiments on two popular benchmarks, i.e., DAVIS and YouTube-VOS. Without applying any simulated data for pre-training, our CFBI+ achieves the performance ( J& F) of 82.9 and 82.8 percent, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.
Dynamic Coverage Control Based on <i>K</i>-Means

In this article, we propose the dynamic coverage control method based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires that the space to be covered is compact and convex, and it is difficult to realize the coverage control of high-dimensional space. Therefore, in this article, we propose a dynamic coverage planning algorithm based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means, which relaxes the requirements on the coverage objects and can calculate the optimal coverage positions of the intelligent units. In addition, we also design a dynamic coverage control law based on discrete sliding mode control to drive the intelligent units to the optimal coverage positions. Through the combination of planning algorithm and control law, the optimal coverage control performance can be achieved for the specified targets and the specified area, which can be discrete, nonconvex, and high dimensional. The stability of the planning algorithm and the control law are proved by theoretical deduction. The effectiveness and superiority of the dynamic coverage control method are verified by two examples.