Reviewerly/exHarmony huggingface dataset:
Few-Shot System Identification for Reinforcement Learning

Learning by interaction is the key to skill acquisition for most living organisms, which is formally called Reinforcement Learning (RL). RL is efficient in finding optimal policies for endowing complex systems with sophisticated behavior. All paradigms of RL utilize a system model for finding the optimal policy. Modeling dynamics can be done by formulating a mathematical model or system identification. Dynamic models are usually exposed to aleatoric and epistemic uncertainties that can divert the model from the one acquired and cause the RL algorithm to exhibit erroneous behavior. Accordingly, the RL process loses its generality because it is sensitive to operating conditions and changes in the model parameters. To address these problems, Intensive system identification for modeling purposes is needed for each system even if the model dynamics structure is the same, as the slight deviation in the model parameters can render the model useless in RL. The existence of an oracle that can adaptively predict the rest of the trajectory regardless of the uncertainties can help resolve the issue. The target of this work is to present a framework for facilitating the online system identification of different instances of the same dynamics class by learning a probability distribution of the dynamics conditioned on observed data with variational inference and show its reliability in robustly solving different instances of control problems without extra training in model-based RL with maximum sample efficiency.
Novel one-shot inner bounds for unassisted fully quantum channels via rate splitting

We prove the first non-trivial one-shot inner bounds for sending quantum information over an entanglement unassisted two-sender quantum multiple access channel (QMAC) and an unassisted two-sender two-receiver quantum interference channel (QIC). Previous works only studied the unassisted QMAC in the limit of many independent and identical uses of the channel also known as the asymptotic iid limit, and did not study the unassisted QIC at all. We employ two techniques, rate splitting and successive cancellation, in order to obtain our inner bound. Rate splitting was earlier used to obtain inner bounds, avoiding time sharing, for classical channels in the asymptotic iid setting. Our main technical contribution is to extend rate splitting from the classical asymptotic iid setting to the quantum one-shot setting. In the asymptotic iid limit our one-shot inner bound for QMAC approaches the rate region of Yard et al. [22]. For the QIC we get novel non-trivial rate regions in the asymptotic iid setting. All our results also extend to the case where limited entanglement assistance is provided, in both one-shot and asymptotic iid settings. The limited entanglement results for one-shot setting for both QMAC and QIC are new. For the QIC the limited entanglement results are new even in the asymptotic iid setting.
T-Distributed Stochastic Neighbor Embedding Based on Cockroach Swarm Optimization with Student Distribution Parameters

T-distributed stochastic neighbor embedding (t-SNE) is a classical dimension reduction method in manifold learning, as it solve the optimization difficulty during training process and overcrowding problem by comparing with traditional stochastic neighbor embedding (SNE). In the core projection step of t-SNE, in order to realize the similarity of probability distribution before and after projection, gradient descent method (GD) or stochastic gradient descent method (SGD) is generally used to solve the minimum parameter of objective function which is defined by Kullback-Leibler divergence (KL divergence). However, GD and SGD may often fall into the trap of local extremum, which means that their global optimization ability is limited. Till now, research on optimizing the solution process for the objective function in t-SNE can be hardly found. By considering that swarm intelligence based algorithm always have a better performance in finding the global extremum, in this paper, a t-SNE based on cockroach swarm optimization with student distribution parameters has been proposed, based on a thorough deduce and related numerical analysis, the effectiveness of the proposed algorithm in finding the global minimum of KL divergence for t-SNE has been verified.
Students Projects’ Source Code Changes Impact on Software Quality Through Static Analysis

Monitoring and examining source code and quality metrics is an essential task in software development projects. Still, it is challenging to evaluate for educational projects due to the time and effort required by instructors, and constant change during the software project evolution. In this paper, we used an automated approach to analyze source code and quality metrics’ evolution and impact in software engineering projects using static code analysis on each software change (commits and merges). We examined five undergraduate software engineering projects’ changed modules, compilability, and source code and quality metrics (size, complexity, duplication, maintainability, and security). In total, we assessed 12,103 changes from 103 students contributing to the projects. Our approach allowed us to identify students’ project trends in the impact of the source code changes, providing insights into behaviors such as technology knowledge deficiencies, issues in continuous integration practices, and software quality degradation. We believe that the early, constant feedback on student software engineering project quality can help instructors improve their courses and students enhance their development practices. Tracking of source code evolution could be done via static analysis and instructors could use the analysis results for teaching.
Force Simulation Analysis of Underground Quadrotor UAV Based on Virtual Laboratory Technology

Given the severe problems of turbulence wind field that quadrotor UAV may encounter in the complex environment of the coal mine, this paper puts forward the stress analysis of quadrotor UAV under the virtual laboratory technology, and optimizes the wing parameters through parametric design. Through the flow field numerical simulation method and simulation test, the aerodynamic characteristics of quadrotor UAV can be further understood, and the aerodynamic layout design and flight control design of UAV can be supported.
A novel computer vision-based data driven modelling approach for person specific fall detection

In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person’s normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.
Cross-modal Attention Network for RGB-T Tracking

RGB-T tracking has attracted more and more attention due to its excellent performance. However, how to make full use of the complementary advantages of visible light images and thermal infrared images in RGB-T tracking without losing this advantage in deep feature learning is still a challenge. This paper proposes a Cross-modal Attention Network, which is corrected by triple attention after each feature information is extracted to obtain richer modal feature information. Then a parallel and layer-by-layer interactive network is used to realize the feature complementarity between the two modalities and ensure that the complementary advantages are not lost in deep learning. A large number of experiments on two RGB-T benchmark datasets verify the effectiveness of this algorithm.
Binarization Strategy Using Multiple Convolutional Autoencoder Network for Old Sundanese Manuscript Images

The binarization step for old documents is still a challenging task even though many hand-engineered and deep learning algorithms have been offered. In this research work, we address foreground and background segmentation using a convolutional autoencoder network with 3 supporting components. The assessment of several hyper-parameters including the window size, the number of convolution layers, the kernel size, the number of filters as well as the number of encoder-decoder layers on the network is conducted. In addition, the skip connections approach is considered in the decoding procedure. Moreover, we evaluated the summation and concatenation function before the up-sampling process to reuse the previous low-level feature maps and to enrich the decoded representation. Based on several experiments, we determined that kernel size, the number of filters, and the number of encoder-decoder blocks have a little impact in term of binarization performance. While the window size and multiple convolutional layers are more impactful than other hyper-parameters. However, they require more storage and may increase computation costs. Moreover, a careful embedding of batch normalization and dropout layers also provides a contribution to handle overfitting in the deep learning model. Overall, the multiple convolutional autoencoder network with skip connection successfully enhances the binarization accuracy on old Sundanese palm leaf manuscripts compared to preceding state of the art methods.
Fraud Detection on Streaming Customer Behavior Data with Unsupervised Learning Methods

In today's telecom industry, fraud detection is a significant research problem. As part of this research, we propose a methodology to detect fraud cases using machine learning models with big data processing and analysis platforms based on customer data in telecommunications. The prototype implementation of the proposed methodology has been designed, developed, and applied to the usage data set of telecommunications company subscribers. We perform performance tests on the developed prototype application to understand how successful the proposed methodology is and its scalability. The obtained results demonstrate the usability of the proposed method in the telecommunications sector.
Shorting attack: Predatory, destructive short selling on Proof‐of‐Stake cryptocurrencies

Summary Bitcoin introduced blockchain which is the transparent and decentralized way of recording the lists of digital currency transactions. Bitcoin's blockchain uses Proof‐of‐Work as a Sybil control mechanism. However, PoW wastes energy since it uses hash computing competitions to find a block. Hence, various alternative mechanisms have been proposed. Among them, Proof‐of‐Stake, which is based on the deposit, has been spotlighted. As opposed to Proof‐of‐Work, Proof‐of‐Stake requires nodes to have a certain amount of tokens (stake) in order to qualify to validate blocks. The “one‐sentence philosophy” of proof of stake is not “security comes from burning energy,” but rather “security comes from putting up economic value‐at‐loss.” In this article, contrary to popular belief, we point out that this value‐at‐loss can be hedged by short selling or other financial products. We propose a “shorting attack,” which makes a profit by massive short selling and sabotage to a Proof‐of‐Stake‐based cryptocurrency. The shorting attack implies that the security of Proof‐of‐Stake‐based cryptocurrency can be vulnerable by a low stake ratio.
Causal Inference Using Linear Time-Varying Filters with Additive Noise

Causal inference using the restricted structural causal model framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms. For linear non-Gaussian noise models and nonlinear additive noise models, the asymmetry arises from non-Gaussianity or non-linearity, respectively. Despite the fact that this methodology can be adapted to stationary time series, inferring causal relationships from nonstationary time series remains a challenging task. In this work, we focus on slowly-varying nonstationary processes and propose to break the symmetry by exploiting the nonstationarity of the data. Our main theoretical result shows that the causal direction is identifiable in generic cases when cause and effect are connected via a time-varying filter. We propose a causal discovery procedure by leveraging powerful estimates of the bivariate evolutionary spectra. Both synthetic and real-world data simulations that involve high-order and non-smooth filters are provided to demonstrate the effectiveness of our proposed methodology.
A Le Cam Type Bound for Adversarial Learning and Applications

Robustness of machine learning methods is essential for modern practical applications. Given the arms race between attack and defense mechanisms, it is essential to understand the fundamental limits of any conceivable learning method used in an adversarial setting. In this work, we focus on the problem of learning from noise-injected data, where the existing literature falls short by either assuming a specific adversary model or by over-specifying the learning problem. We shed light on the information-theoretic limits of adversarial learning without assuming a particular adversary. Specifically, we derive a general Le Cam type bound for learning from noise-injected data. Finally, we apply our general bounds to a canonical set of non-trivial learning problems and provide examples of common types of noise-injected data.
Using FC-CLAHE-ADF to enhance digital mammograms for detecting breast lesions

Abstract Digital mammograms are commonly used for breast screening, especially to aid the detection of cancer. However, digital mammograms suffer with low contrast images due to the low exposure factors used. This paper presents a novel image enhancement technique for digital mammogram images known as Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization with Anisotropic Diffusion Filter (FC-CLAHE-ADF). This proposed FC-CLAHE-ADF has adopted a fuzzy-based and histogram-based image enhancement technique where it can further reduce the noise of the digital mammograms while preserving the contrast and brightness. A total of six digital mammograms were retrieved from Mammographic Image Analysis Society (MIAS) open-source database. The performance of FC-CLAHE-ADF was compared to Recursive Mean-Separate Histogram Equalization (RMSHE), Fast Discrete Curvelet Transform via Unequally Spaced Fast Fourier Transform (FDCT-USFFT), and FC-CLAHE only. In summary, this novel FC-CLAHE-ADF has provided the most superior results, among other selected enhancement techniques. The resulting images have been able to demonstrate breast lesions better.
Fast-Rate Loss Bounds via Conditional Information Measures with Applications to Neural Networks

We present a framework to derive bounds on the test loss of randomized learning algorithms for the case of bounded loss functions. Drawing from Steinke & Zakynthinou (2020), this framework leads to bounds that depend on the conditional information density between the output hypothesis and the choice of the training set, given a larger set of data samples from which the training set is formed. Furthermore, the bounds pertain to the average test loss as well as to its tail probability, both for the PAC-Bayesian and the single-draw settings. If the conditional information density is bounded uniformly in the size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> of the training set, our bounds decay as 1/n, This is in contrast with the tail bounds involving conditional information measures available in the literature, which have a less benign 1/√n dependence. We demonstrate the usefulness of our tail bounds by showing that they lead to nonvacuous estimates of the test loss achievable with some neural network architectures trained on MNIST and Fashion-MNIST.
HPOF:3D Human Pose Recovery from Monocular Video with Optical Flow

This paper introduces HPOF, a novel deep neural network to reconstruct the 3D human motion from a monocular video. Recently, model-based methods have been proposed to simplify the reconstruction task by estimating several parameters that control a deformable surface model to fit the person in the image. However, learning the parameters from a single image is a highly ill-posed problem, and the process is ultimately data-hungry. Existing 3D datasets are not sufficient, and the usage of 2D in-the-wild datasets is often susceptible to the inadequate precision of manual annotations. To address the above issues, our method yields substantial improvements in two domains. First, we leverage optical flow to supervise the 2D rendered images of predicted SMPL models to learn short-term temporal features. Besides, taking long-term temporal consistency into account, we define a novel temporal encoder based on a dilated convolutional network. The encoder decomposes the learning process of human shape and pose, first guarantees the invariance of the body shape, and then simulates a more reasonable forward kinematics process on this basis to achieve more accurate pose estimation. In addition, an adversarial learning framework is applied to supervise the reconstruction progress in a coarse-grained way. We show that HPOF not only improves the accuracy of 3D poses but ensures the realistic body structure throughout the video. We perform extensive experimentation to demonstrate the superiority of our method and analyze the effectiveness of our model, surpassing other state-of-the-arts.
Blockchain-Based Location Sharing in 5G Open RAN Infrastructure for Sustainable Communities

Current trends indicate that 5G network deployment is gaining traction around the world; this paradigm shift is expected to deliver gigabit connectivity and wide-ranging services to its subscribers. These benefits, however, are currently restricted to users in metropolitan cities. Expansion of these services to rural areas is hindered by the lack of economic incentives for providers, forcing remote populations to use legacy pre-5G technologies. The key features of 5G include small cells, low-cost radio access points, energy-efficient data centers, and low footprint dedicated networks. The impact of these features in an urban context has been widely discussed, but they also have immense potential to serve the unique and differentiated needs of rural communities, servicing quality-of-life improvements through precision agriculture, augmented reality telemedicine, and reliable disaster support. These critical services can be realized through dedicated spectrum allocation by regulating authorities, and the regionalization lowers costs, allowing for rapid deployment of 5G local networks. In this scenario, the interplay between local community networks and public mobile networks is of paramount importance. The roles of reference design, system architecture, and effective smart contracts in the creation of sustainable communication infrastructure have been outlined in this paper. Disaggregated radio access network (Open RAN) and blockchain have been integrated into the framework, allowing rapid, end-to-end secure data communication. The use of network slicing and effective co-operation between local community networks and mobile network operators, together with the elucidated framework, enable efficient, privacy-conscious networking which can be relied upon for the delivery of critical services.
Unsupervised Deep Learning of Depth, Ego-Motion, and Optical Flow from Stereo Images

Unsupervised deep learning methods have demonstrated an impressive performance for understanding the structure of 3D scene from videos. These data-based learning methods are able to learn the tasks, such as depth, ego-motion, and optical flow estimation. In this paper, we propose a novel unsupervised deep learning method to jointly estimate scene depth, camera ego-motion, and optical flow from stereo images. Consecutive stereo images are used to train the system. After training stage, the system is able to estimate dense depth map, camera 6D pose, and optical flow by using a sequence of monocular images. No labelled data set is required for training. The supervision signals for training three deep neural networks of the system come from various forms of image warping. Due to the use of optical flow, the impact caused by occlusions and moving objects on the estimation results is alleviated. Experiments on the KITTI and Cityscapes datasets show that the proposed system demonstrates a better performance in terms of accuracy in depth, ego-motion, and optical flow estimation.
On the Duality Between the BSC and Quantum PSC

In 2018, Renes [IEEE Trans. Inf. Theory, vol. 64, no. 1, pp. 577-592 (2018)] developed a general theory of channel duality for classical-input quantum-output channels. His result shows that a number of well-known duality results for linear codes on the binary erasure channel can be extended to general classical channels at the expense of using dual problems which are intrinsically quantum mechanical. One special case of this duality is a connection between coding for error correction on the quantum pure-state channel (PSC) and coding for wiretap secrecy on the classical binary symmetric channel (BSC). Similarly, coding for error correction on the BSC is related to wire-tap secrecy on the PSC. While this result has important implications for classical coding, the machinery behind the general duality result is rather challenging for researchers without a strong background in quantum information theory. In this work, we leverage prior results for linear codes on PSCs to give an alternate derivation of the aforementioned special case by computing closed-form expressions for the performance metrics. The noted prior results include the optimality of square-root measurement for linear codes on the PSC and the Fourier duality of linear codes.
A Theoretical Framework for Learning from Quantum Data

Over decades traditional information theory of source and channel coding advances toward learning and effective extraction of information from data. We propose to go one step further and offer a theoretical foundation for learning classical patterns from quantum data. However, there are several roadblocks to lay the groundwork for such a generalization. First, classical data must be replaced by a density operator over a Hilbert space. Hence, deviated from problems such as state tomography, our samples are i.i.d density operators. The second challenge is even more profound since we must realize that our only interaction with a quantum state is through a measurement which - due to no-cloning quantum postulate - loses information after measuring it. With this in mind, we present a quantum counterpart of the well-known probably approximately correct (PAC) framework. Based on that, we propose a quantum analogous of the Empirical Risk Minimization (ERM) algorithm for learning measurement hypothesis classes. Then, we establish upper bounds on the quantum sample complexity quantum concept classes.