Reviewerly/exHarmony huggingface dataset:
Distributed Variational Representation Learning

The problem of distributed representation learning is one in which multiple sources of information X1,…, XK are processed separately so as to learn as much information as possible about some ground truth Y. We investigate this problem from information-theoretic grounds, through a generalization of Tishby's centralized Information Bottleneck (IB) method to the distributed setting. Specifically, K encoders, K ≥ 2, compress their observations X1,…, XK separately in a manner such that, collectively, the produced representations preserve as much information as possible about Y. We study both discrete memoryless (DM) and memoryless vector Gaussian data models. For the discrete model, we establish a single-letter characterization of the optimal tradeoff between complexity (or rate) and relevance (or information) for a class of memoryless sources (the observations X1,…, XK being conditionally independent given Y). For the vector Gaussian model, we provide an explicit characterization of the optimal complexity-relevance tradeoff. Furthermore, we develop a variational bound on the complexity-relevance tradeoff which generalizes the evidence lower bound (ELBO) to the distributed setting. We also provide two algorithms that allow to compute this bound: i) a Blahut-Arimoto type iterative algorithm which enables to compute optimal complexity-relevance encoding mappings by iterating over a set of self-consistent equations, and ii) a variational inference type algorithm in which the encoding mappings are parametrized by neural networks and the bound approximated by Markov sampling and optimized with stochastic gradient descent. Numerical results on synthetic and real datasets are provided to support the efficiency of the approaches and algorithms developed in this paper.
Cybersickness during VR gaming undermines game enjoyment: A mediation model

Recent advances in virtual reality (VR) technology have ushered in a new era of VR gaming. While VR gaming experience represents a burgeoning area of research within human-computer interaction circles, the role of cybersickness, physiological repercussions of VR exposure to users characterized by a multitude of symptoms, such as nausea, lightheadedness, and dizziness, in the effect of VR gaming on game enjoyment remains understudied. In two experiments, the current study proposed and tested a causal mediation model in which the effect of VR gaming on game enjoyment was mediated by the level of cybersickness experienced during the gameplay. Results from both experiments supported the proposed model and showed that increased cybersickness levels during VR gaming led to decreases in game enjoyment, indicating that cybersickness experienced during VR gaming undermines the enjoyment of the gaming experience. Results also revealed that compared to traditional desktop gaming, VR gaming invoked greater levels of cybersickness, but VR gaming did not lead to greater levels of game enjoyment. When the effect of cybersickness on game enjoyment was statistically removed, however, VR gaming was found to be more enjoyable. The current experiments provide preliminary evidence that cybersickness may hinder the enjoyment of VR gaming experience.
Quantum Computing Circuits and Devices

Editor's note: As an emerging technology, quantum computing brings unique promises in creating a fundamentally different paradigm of information processing. This article reviews introductory concepts and building blocks-quantum logic gates and memory, i.e., qubits, for quantum computing and depicts the challenges as well as prospects associated with design, test, and fabrication of quantum devices. -Swarup Bhunia, Case Western Reserve University.
Disentangled Variational Auto-Encoder for semi-supervised learning

Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has demonstrated the benefits of semi-supervised learning. The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE. Given the limited labeled data, learning the parameters for the classifiers may not be an optimal solution for exploiting label information. Therefore, in this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via the equality constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.
A Knee-Guided Evolutionary Algorithm for Compressing Deep Neural Networks

Deep neural networks (DNNs) have been regarded as fundamental tools for many disciplines. Meanwhile, they are known for their large-scale parameters, high redundancy in weights, and extensive computing resource consumptions, which pose a tremendous challenge to the deployment in real-time applications or on resource-constrained devices. To cope with this issue, compressing DNNs for accelerating its inference has drawn extensive interest recently. The basic idea is to prune parameters with little performance degradation. However, the overparameterized nature and the conflict between parameters reduction and performance maintenance make it prohibitive to manually search the pruning parameter space. In this paper, we formally establish filter pruning as a multiobjective optimization problem, and propose a knee-guided evolutionary algorithm (KGEA) that can automatically search for the solution with quality tradeoff between the scale of parameters and performance, in which both conflicting objectives can be optimized simultaneously. In particular, by incorporating a minimum Manhattan distance approach, the search effort in the proposed KGEA is explicitly guided toward the knee area, which greatly facilitates the manual search for a good tradeoff solution. Moreover, the parameter importance is directly estimated on the criterion of performance loss, which can robustly identify the redundancy. In addition to the knee solution, a performance-improved model can also be found in a fine-tuning-free fashion. The experiments on compressing fully convolutional LeNet and VGG-19 networks validate the superiority of the proposed algorithm over the state-of-the-art competing methods.
Underwater Laser Scanner: Ray-Based Model and Calibration

Underwater 3-D perception is crucial for autonomous manipulation and mapping. Using a mirror-galvanometer system to steer a laser line projector and using triangulation with a camera it is possible to produce full 3-D perception, while avoiding moving the sensor with respect to the scene. This paper presents a ray-based model of a laser scanner and a calibration method for such a model. The model takes into account the distortion introduced to the system by the flat viewports used for both the laser and the camera. The paper also introduces the computation of elliptical cones as a way to synthesize the ray-based model and speed up the triangulation computation. The present study reports results obtained using the real sensor in a water tank to reconstruct objects and planes to assess the sensor's accuracy, as well as a comparison between the two triangulation methods (ray-based and elliptical cone).
Augmented Reality and its effect on our life

Augmented Reality is a combination of a real and a computer-generated or virtual world. It is achieved by augmenting computer-generated images on real world. It is of four types namely marker based, marker less, projection based and superimposition based augmented reality. It has many applications in the real world. AR is used in various fields such as medical, education, manufacturing, robotics and entertainment. Augmented reality comes under the field of mixed reality. It can be considered as an inverse reflection of Virtual Reality. They both have certain similarities and differences. This paper gives information about Augmented Reality and how it started. It analyses various types of augmented reality, its applications and its advantages and disadvantages. This paper also gives us knowledge regarding those major threats that augmented reality will face in the near future and about its current and future applications. It gives us a comparison between the two related topics, Augmented reality and Virtual reality. The following paper also helps us know about the effect of Augmented Reality on the human life.
Algebraic Techniques for Short(er) Exact Lattice-Based Zero-Knowledge Proofs

A key component of many lattice-based protocols is a zero-knowledge proof of knowledge of a vector $$\vec {s}$$ with small coefficients satisfying $$A\vec {s}=\vec {u}\bmod \,q$$ . While there exist fairly efficient proofs for a relaxed version of this equation which prove the knowledge of $$\vec {s}'$$ and c satisfying $$A\vec {s}'=\vec {u}c$$ where $$\Vert \vec {s}'\Vert \gg \Vert \vec {s}\Vert $$ and c is some small element in the ring over which the proof is performed, the proofs for the exact version of the equation are considerably less practical. The best such proof technique is an adaptation of Stern’s protocol (Crypto ’93), for proving knowledge of nearby codewords, to larger moduli. The scheme is a $$\varSigma $$ -protocol, each of whose iterations has soundness error $$2{/}3$$ , and thus requires over 200 repetitions to obtain soundness error of $$2^{-128}$$ , which is the main culprit behind the large size of the proofs produced. In this paper, we propose the first lattice-based proof system that significantly outperforms Stern-type proofs for proving knowledge of a short $$\vec {s}$$ satisfying $$A\vec {s}=\vec {u}\bmod \,q$$ . Unlike Stern’s proof, which is combinatorial in nature, our proof is more algebraic and uses various relaxed zero-knowledge proofs as sub-routines. The main savings in our proof system comes from the fact that each round has soundness error of $$1{/}n$$ , where n is the number of columns of A. For typical applications, n is a few thousand, and therefore our proof needs to be repeated around 10 times to achieve a soundness error of $$2^{-128}$$ . For concrete parameters, it produces proofs that are around an order of magnitude smaller than those produced using Stern’s approach.
High-Resolution Aerial Images Semantic Segmentation Using Deep Fully Convolutional Network With Channel Attention Mechanism

Semantic segmentation is one of the fundamental tasks in understanding high-resolution aerial images. Recently, convolutional neural network (CNN) and fully convolutional network (FCN) have achieved excellent performance in general images’ semantic segmentation tasks and have been introduced to the field of aerial images. In this paper, we propose a novel deep FCN with channel attention mechanism (CAM-DFCN) for high-resolution aerial images’ semantic segmentation. The CAM-DFCN architecture follows the mode of encoder–decoder. In the encoder, two identical deep residual networks are both divided into multiple levels and acted on spectral images and auxiliary data, respectively. Then, the feature map concatenation is carried out at each level. In the decoder, the channel attention mechanism (CAM) is introduced to automatically weigh the channels of feature maps to perform feature selection. On the one hand, the CAM follows the concatenated feature maps at each level to select more discriminative features for classification. On the other hand, the CAM is used to further weigh the semantic information and spatial location information in the adjacent-level concatenated feature maps for more accurate predictions. We evaluate the proposed CAM-DFCN by using two benchmarks (the Potsdam set and the Vaihingen set) provided by the International Society for Photogrammetry and Remote Sensing. Experimental results show that the proposed method has considerable improvement.
Proof of Reputation

A consensus mechanism serves as a basic and essential component of a blockchain based system (e.g. an IoT system) and constructs the decentralized trustworthiness between the decentralized nodes/things in the system network. The design of the consensus mechanism strongly impacts the blockchain system performance, including transaction capacity, scalability, and security. The Traditional consensus mechanisms (i.e., PoW and PoS) have encountered great challenges and require further investigation. In this work, a proof of reputation consensus mechanism is proposed, where the reputation of a node is constructed based on its asset, transaction activity, and consensus participation. In the proposed mechanism, a new block is generated by the leader node with the highest reputation and the new block is validated and confirmed through the reputation based voting. The rewards from generating the new block are then divided among the validators in proportion to their reputation values.
Attributes-aided part detection and refinement for person re-identification

Person attributes are often exploited as mid-level human semantic information to help promote the performance of person re-identification task. In this paper, unlike most existing methods simply taking the attribute learning as a classification problem, we perform it in a different way with the motivation that attributes are related to specific local regions, which refers to the perceptual ability of attributes. We utilize the process of attribute detection to generate corresponding attribute-part detectors, whose invariance to many influences like poses and camera views can be guaranteed. With detected local part regions, our model extracts local part features to handle the body part misalignment problem, which is another major challenge for person re-identification. The local descriptors are further refined by fused attribute information to eliminate interferences caused by detection deviation. Finally, the refined local feature works together with a holistic-level feature to constitute our final feature representation. Extensive experiments on two popular benchmarks with attribute annotations demonstrate the effectiveness of our model and competitive performance compared with state-of-the-art algorithms.
Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks

The use of 3D LiDAR, which has proven its capabilities in autonomous driving systems, is now expanding into many other fields. The sharing and transmission of point cloud data from 3D LiDAR sensors has broad application prospects in robotics. However, due to the sparseness and disorderly nature of this data, it is difficult to compress it directly into a very low volume. A potential solution is utilizing raw LiDAR data. We can rearrange the raw data from each frame losslessly in a 2D matrix, making the data compact and orderly. Due to the special structure of 3D LiDAR data, the texture of the 2D matrix is irregular, in contrast to 2D matrices of camera images. In order to compress this raw, 2D formatted LiDAR data efficiently, in this paper we propose a method which uses a recurrent neural network and residual blocks to progressively compress one frame's information from 3D LiDAR. Compared to our previous image compression based method and generic octree point cloud compression method, the proposed approach needs much less volume while giving the same decompression accuracy. Potential application scenarios for point cloud compression are also considered in this paper. We describe how decompressed point cloud data can be used with SLAM (simultaneous localization and mapping) as well as for localization using a given map, illustrating potential uses of the proposed method in real robotics applications.
Quantum Cryptanalysis in the RAM Model: Claw-Finding Attacks on SIKE

We introduce models of computation that enable direct comparisons between classical and quantum algorithms. Incorporating previous work on quantum computation and error correction, we justify the use of the gate-count and depth-times-width cost metrics for quantum circuits. We demonstrate the relevance of these models to cryptanalysis by revisiting, and increasing, the security estimates for the Supersingular Isogeny Diffie–Hellman (SIDH) and Supersingular Isogeny Key Encapsulation (SIKE) schemes. Our models, analyses, and physical justifications have applications to a number of memory intensive quantum algorithms.
Zero-Knowledge Proofs on Secret-Shared Data via Fully Linear PCPs

We introduce and study the notion of fully linear probabilistically checkable proof systems. In such a proof system, the verifier can make a small number of linear queries that apply jointly to the input and a proof vector. Our new type of proof system is motivated by applications in which the input statement is not fully available to any single verifier, but can still be efficiently accessed via linear queries. This situation arises in scenarios where the input is partitioned or secret-shared between two or more parties, or alternatively is encoded using an additively homomorphic encryption or commitment scheme. This setting appears in the context of secure messaging platforms, verifiable outsourced computation, PIR writing, private computation of aggregate statistics, and secure multiparty computation (MPC). In all these applications, there is a need for fully linear proof systems with short proofs. While several efficient constructions of fully linear proof systems are implicit in the interactive proofs literature, many questions about their complexity are open. We present several new constructions of fully linear zero-knowledge proof systems with sublinear proof size for "simple" or "structured" languages. For example, in the non-interactive setting of fully linear PCPs, we show how to prove that an input vector $$x\in {\mathbb {F}}^n$$ , for a finite field $${\mathbb {F}}$$ , satisfies a single degree-2 equation with a proof of size $$O(\sqrt{n})$$ and $$O(\sqrt{n})$$ linear queries, which we show to be optimal. More generally, for languages that can be recognized by systems of constant-degree equations, we can reduce the proof size to $$O(\log n)$$ at the cost of $$O(\log n)$$ rounds of interaction. We use our new proof systems to construct new short zero-knowledge proofs on distributed and secret-shared data. These proofs can be used to improve the performance of the example systems mentioned above. Finally, we observe that zero-knowledge proofs on distributed data provide a general-purpose tool for protecting MPC protocols against malicious parties. Applying our short fully linear PCPs to "natural" MPC protocols in the honest-majority setting, we can achieve unconditional protection against malicious parties with sublinear additive communication cost. We use this to improve the communication complexity of recent honest-majority MPC protocols. For instance, using any pseudorandom generator, we obtain a 3-party protocol for Boolean circuits in which the amortized communication cost is only one bit per AND gate per party (compared to 10 bits in the best previous protocol), matching the best known protocols for semi-honest parties.
Extended-Object or Group-Target Tracking Using Random Matrix With Nonlinear Measurements

For extended-object/group-target tracking (EOT/ GTT), the random-matrix approach assumes that measurements are linear in the state and noise with a covariance being a random matrix to represent the object extension or target group. In practice, however, most measurements are nonlinear in the state and noise. This paper proposes a random-matrix approach for EOT/GTT using nonlinear measurements. First, a matched linearization (ML) is proposed to linearize nonlinear measurements. The linearized form has two parts. The first is linear in the state, and it is optimized in the sense of minimum mean square error (MMSE). The second part is linear in the extension-related noise with a preserved second moment, which is important since extension information is contained in the covariance of this noise. The linearized measurements can be incorporated into existing random-matrix algorithms after a simple conversion under certain conditions. Second, a variational Bayesian (VB) scheme is proposed for EOT/GTT using the linearized measurements. This approach can be generally applied no matter whether the linearized measurements are converted or not. The effectiveness of the proposed ML and VB approach is demonstrated by simulation results compared with existing random-matrix algorithms.
Learning Latent Global Network for Skeleton-Based Action Prediction

Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance.
Efficient Super Resolution Using Binarized Neural Network

Deep convolutional neural networks (DCNNs) have recently demonstrated high-quality results in single-image super-resolution (SR). DCNNs often suffer from over-parametrization and large amounts of redundancy, which results in inefficient inference and high memory usage, preventing massive applications on mobile devices. As a way to significantly reduce model size and computation time, binarized neural network has only been shown to excel on semantic-level tasks such as image classification and recognition. However, little effort of network quantization has been spent on image enhancement tasks like SR, as network quantization is usually assumed to sacrifice pixel-level accuracy. In this work, we explore an network-binarization approach for SR tasks without sacrificing much reconstruction accuracy. To achieve this, we binarize the convolutional filters in only residual blocks, and adopt a learnable weight for each binary filter. We evaluate this idea on several state-of-the-art DCNN-based architectures, and show that binarized SR networks achieve comparable qualitative and quantitative results as their real-weight counterparts. Moreover, the proposed binarized strategy could help reduce model size by 80% when applying on SRResNet, and could potentially speed up inference by 5×.
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning

We introduce Deep500: the first customizable benchmarking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techniques. The key idea behind Deep500 is its modular design, where deep learning is factorized into four distinct levels: operators, network processing, training, and distributed training. Our evaluation illustrates that Deep500 is customizable (enables combining and benchmarking different deep learning codes) and fair (uses carefully selected metrics). Moreover, Deep500 is fast (incurs negligible overheads), verifiable (offers infrastructure to analyze correctness), and reproducible. Finally, as the first distributed and reproducible benchmarking system for deep learning, Deep500 provides software infrastructure to utilize the most powerful supercomputers for extreme-scale workloads.