Reviewerly/exHarmony huggingface dataset:
Modeling Image Quantization Tradeoffs for Optimal Compression

All Lossy compression algorithms employ similar compression schemes -- frequency domain transform followed by quantization and lossless encoding schemes. They target tradeoffs by quantizating high frequency data to increase compression rates which come at the cost of higher image distortion. We propose a new method of optimizing quantization tables using Deep Learning and a minimax loss function that more accurately measures the tradeoffs between rate and distortion parameters (RD) than previous methods. We design a convolutional neural network (CNN) that learns a mapping between image blocks and quantization tables in an unsupervised manner. By processing images across all channels at once, we can achieve stronger performance by also measuring tradeoffs in information loss between different channels. We initially target optimization on JPEG images but feel that this can be expanded to any lossy compressor.
A Vertical Federated Learning Method For Multi-Institutional Credit  Scoring: MICS

As more and more companies store their customers' data; various information of a person is distributed among numerous companies' databases. Different industrial sectors carry distinct features about the same customers. Also, different companies within the same industrial sector carry similar kinds of data about the customers with different data representations. Cooperation between companies from different industrial sectors, called vertical cooperation, and between the companies within the same sector, called horizontal cooperation, can lead to more accurate machine learning models and better estimations in tasks such as credit scoring. However, data privacy regulations and compatibility issues for different data representations are huge obstacles to cooperative model training. By proposing the training framework MICS and experimentation on several numerical data sets, we showed that companies would have an incentive to cooperate with other companies from their sector and with other industrial sectors to jointly train more robust and accurate global models without explicitly sharing their customers' private data.
S/MIME Secure Email

Years ago, there were a variety of incompatible schemes for attaching files to emails. The IETF created a standard for email attachments which is now universally used, not just in email, but in web pages and other places. It is called MIME. It is specified in several RFCs.
Blockwise Sequential Model Learning for Partially Observable  Reinforcement Learning

This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. Rather than compressing sequential information at every timestep as in conventional recurrent neural network-based methods, the proposed architecture generates a latent variable in each data block with a length of multiple timesteps and passes the most relevant information to the next block for policy optimization. The proposed blockwise sequential model is implemented based on self-attention, making the model capable of detailed sequential learning in partial observable settings. The proposed model builds an additional learning network to efficiently implement gradient estimation by using self-normalized importance sampling, which does not require the complex blockwise input data reconstruction in the model learning. Numerical results show that the proposed method significantly outperforms previous methods in various partially observable environments.
LDDMM meets GANs: Generative Adversarial Networks for diffeomorphic  registration

The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature for deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the state of the art within the LDDMM paradigm. We have successfully implemented two models with the stationary and the EPDiff-constrained non-stationary parameterizations of diffeomorphisms. Our unsupervised and data-hungry approach has shown a competitive performance with respect to a benchmark supervised and rich-data approach. In addition, our method has shown similar results to model-based methods with a computational time under one second.
A Dynamic Programming Algorithm to Compute Joint Distribution of Order  Statistics on Graphs

Order statistics play a fundamental role in statistical procedures such as risk estimation, outlier detection, and multiple hypothesis testing as well as in the analyses of mechanism design, queues, load balancing, and various other logistical processes involving ranks. In some of these cases, it may be desirable to compute the \textit{exact} values from the joint distribution of $d$ order statistics. While this problem is already computationally difficult even in the case of $n$ independent random variables, the random variables often have no such independence guarantees. Existing methods obtain the cumulative distribution indirectly by first computing and then aggregating over the marginal distributions. In this paper, we provide a more direct, efficient algorithm to compute cumulative joint order statistic distributions of dependent random variables that improves an existing dynamic programming solution via dimensionality reduction techniques. Our solution guarantees a $O(\frac{d^{d-1}}{n})$ and $O(d^{d})$ factor of improvement in both time and space complexity respectively over previous methods.
Optimization Techniques

To extract optimum performance from the applied frequency controllers subjected to system constraints, selection/tuning of controller settings is highly demanded. This chapter provides various recently introduced population-based meta-heuristic approaches for exploring near-optimum settings of the applied/designed frequency controllers. The inspiration of the applied algorithms following their mathematical model has been discussed in detail. The computational ability of the used meta-heuristic techniques in tuning controller parameters has been shown by performing comparative analysis. Moreover, the need for hybridized algorithms with mathematical models has been outlined in this chapter to improve the performance of optimization algorithms. Chaotic mapping and quasi-oppositional-based learning approaches are presented to study the effectiveness of hybridized algorithms.
CleanRL: High-quality Single-file Implementations of Deep Reinforcement  Learning Algorithms

CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms. It provides a simpler yet scalable developing experience by having a straightforward codebase and integrating production tools to help interact and scale experiments. In CleanRL, we put all details of an algorithm into a single file, making these performance-relevant details easier to recognize. Additionally, an experiment tracking feature is available to help log metrics, hyperparameters, videos of an agent's gameplay, dependencies, and more to the cloud. Despite succinct implementations, we have also designed tools to help scale, at one point orchestrating experiments on more than 2000 machines simultaneously via Docker and cloud providers. Finally, we have ensured the quality of the implementations by benchmarking against a variety of environments. The source code of CleanRL can be found at https://github.com/vwxyzjn/cleanrl
End-to-End Segmentation via Patch-wise Polygons Prediction

The leading segmentation methods represent the output map as a pixel grid. We study an alternative representation in which the object edges are modeled, per image patch, as a polygon with $k$ vertices that is coupled with per-patch label probabilities. The vertices are optimized by employing a differentiable neural renderer to create a raster image. The delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results: 76.26\% mIoU on the Cityscapes validation, 90.92\% IoU on the Vaihingen building segmentation benchmark, 66.82\% IoU for the MoNU microscopy dataset, and 90.91\% for the bird benchmark CUB. Our code for training and reproducing these results is attached as supplementary.
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration

Common approaches for task-agnostic exploration learn tabula-rasa --the agent assumes isolated environments and no prior knowledge or experience. However, in the real world, agents learn in many environments and always come with prior experiences as they explore new ones. Exploration is a lifelong process. In this paper, we propose a paradigm change in the formulation and evaluation of task-agnostic exploration. In this setup, the agent first learns to explore across many environments without any extrinsic goal in a task-agnostic manner. Later on, the agent effectively transfers the learned exploration policy to better explore new environments when solving tasks. In this context, we evaluate several baseline exploration strategies and present a simple yet effective approach to learning task-agnostic exploration policies. Our key idea is that there are two components of exploration: (1) an agent-centric component encouraging exploration of unseen parts of the environment based on an agent's belief; (2) an environment-centric component encouraging exploration of inherently interesting objects. We show that our formulation is effective and provides the most consistent exploration across several training-testing environment pairs. We also introduce benchmarks and metrics for evaluating task-agnostic exploration strategies. The source code is available at https://github.com/sparisi/cbet/.
Toward Learning Human-aligned Cross-domain Robust Models by Countering  Misaligned Features

Machine learning has demonstrated remarkable prediction accuracy over i.i.d data, but the accuracy often drops when tested with data from another distribution. In this paper, we aim to offer another view of this problem in a perspective assuming the reason behind this accuracy drop is the reliance of models on the features that are not aligned well with how a data annotator considers similar across these two datasets. We refer to these features as misaligned features. We extend the conventional generalization error bound to a new one for this setup with the knowledge of how the misaligned features are associated with the label. Our analysis offers a set of techniques for this problem, and these techniques are naturally linked to many previous methods in robust machine learning literature. We also compared the empirical strength of these methods demonstrated the performance when these previous techniques are combined.
Secure learning-based MPC via garbled circuit

Encrypted control seeks confidential controller evaluation in cloud-based or networked systems. Many existing approaches build on homomorphic encryption (HE) that allow simple mathematical operations to be carried out on encrypted data. Unfortunately, HE is computationally demanding and many control laws (in particular non-polynomial ones) cannot be efficiently implemented with this technology. We show in this paper that secure two-party computation using garbled circuits provides a powerful alternative to HE for encrypted control. More precisely, we present a novel scheme that allows to efficiently implement (non-polynomial) max-out neural networks with one hidden layer in a secure fashion. These networks are of special interest for control since they allow, in principle, to exactly describe piecewise affine control laws resulting from, e.g., linear model predictive control (MPC). However, exact fits require high-dimensional preactivations of the neurons. Fortunately, we illustrate that even low-dimensional learning-based approximations are sufficiently accurate for linear MPC. In addition, these approximations can be securely evaluated using garbled circuit in less than 100~ms for our numerical example. Hence, our approach opens new opportunities for applying encrypted control.
Period and intrinsic noises of permanent spin vortex system centered by  small polaron

Some fascinating phenomena have been investigated, hence spin vortex systems are a hot subarea of solid-state physics. However, simulations of these systems require some simplifications. Quantum computers have the potential to simulate these systems without them. Therefore, we simulated the time propagation of spin vortex systems centered by small polarons by the simulator of quantum computers. As a result, we revealed that these systems have much shorter periods than XXZ models and are never be relaxed. Though there is intrinsic persistent noise, this result will affect both fundamental and applied solid-state physics.
Addressing Interactive Computing Systems’ Concerns in Software Engineering Degrees

This paper arises from experience by the authors in teaching software engineering courses. It discusses the need for adequate coverage of Human-Computer Interaction topics in these courses and the challenges faced when addressing them. Three courses, at both licentiate and master’s levels, are used as triggers for the discussion. The paper argues that the lack of relevant Human-Computer Interaction concepts creates challenges when teaching and learning requirements analysis, design, and implementation of software systems. The approaches adopted to address these challenges are described.
Memory Association Networks

We introduce memory association networks(MANs) that memorize and remember any data. This neural network has two memories. One consists of a queue-structured short-term memory to solve the class imbalance problem and long-term memory to store the distribution of objects, introducing the contents of storing and generating various datasets.
Quantum phase estimation in presence of glassy disorder

We investigate the response to noise, in the form of glassy disorder present in circuit elements, in the success probability of the quantum phase estimation algorithm, a subroutine used to determine the eigenvalue - a phase - corresponding to an eigenvector of a unitary gate. We consider three types of disorder distributions: Haar-uniform with a circular finite cut-off, Haar-uniform with a squeezed cut-off, and spherical normal. We examine the behavior of the probability of estimating the correct phase in response to the inflicted disorder. There is generally a depreciation of the quenched averaged success probability in response to the disorder incorporation. Even in the presence of the disorder, increasing the number of auxiliary qubits helps to get a better precision of the phase, albeit to a lesser extent than that in the clean case. We find a concave to convex transition in the dependence of probability on the degree of disorder when the distribution at hand is Haar-uniform, and a log-log dependence is witnessed between the strength of disorder at the point of inflection and the number of auxiliary qubits used.
Facial Landmark Points Detection Using Knowledge Distillation-Based  Neural Networks

Facial landmark detection is a vital step for numerous facial image analysis applications. Although some deep learning-based methods have achieved good performances in this task, they are often not suitable for running on mobile devices. Such methods rely on networks with many parameters, which makes the training and inference time-consuming. Training lightweight neural networks such as MobileNets are often challenging, and the models might have low accuracy. Inspired by knowledge distillation (KD), this paper presents a novel loss function to train a lightweight Student network (e.g., MobileNetV2) for facial landmark detection. We use two Teacher networks, a Tolerant-Teacher and a Tough-Teacher in conjunction with the Student network. The Tolerant-Teacher is trained using Soft-landmarks created by active shape models, while the Tough-Teacher is trained using the ground truth (aka Hard-landmarks) landmark points. To utilize the facial landmark points predicted by the Teacher networks, we define an Assistive Loss (ALoss) for each Teacher network. Moreover, we define a loss function called KD-Loss that utilizes the facial landmark points predicted by the two pre-trained Teacher networks (EfficientNet-b3) to guide the lightweight Student network towards predicting the Hard-landmarks. Our experimental results on three challenging facial datasets show that the proposed architecture will result in a better-trained Student network that can extract facial landmark points with high accuracy.
Design of high quality curriculum resources recommendation system for Educational Technology Specialty

In order to overcome the problems of poor operation function and low recommendation accuracy of the traditional high-quality curriculum resource recommendation system, this paper proposes and designs a new high-quality Curriculum Resource Recommendation System for educational technology specialty. With the support of the system framework, the system hardware module is designed, mainly including user registration and login module and high-quality course resource recommendation module, so as to realize the recommendation function of the system. The system software part constructs the user&#x2019;s high-quality curriculum resources mining classification tree according to the user&#x2019;s preference attribute value of high-quality curriculum resources, and obtains the recommended basic data. On this basis, the user&#x2019;s preference for course resources is calculated, so as to complete the recommendation of high-quality course resources. The experimental results show that compared with the traditional recommendation system, the functions of the designed system have passed the test and have high recommendation accuracy.
Toward a Blockchain Healthcare Information Exchange

The introduction of healthcare information exchange (HIE) systems has been limited, although its compelling social need has been recognized. This could be due to medical institutions’ concerns about information leakage and decreasing profit. The existing research proposals suppose that the medical institutions would accept the system without incentives for medical institutions to be proactive in it. Herein, we propose the HIE system that provides incentives to both medical institutions and patients using blockchain technology. By using the system, we expect that both patients and medical institutions will proactively share medical information. We also evaluate the implementation of the proposed system and measure time required to share medical information to demonstrate that the system could function in a reasonable time. The system will help reducing concerns about information leakage and decreasing profit.
Training end-to-end speech-to-text models on mobile phones

Training the state-of-the-art speech-to-text (STT) models in mobile devices is challenging due to its limited resources relative to a server environment. In addition, these models are trained on generic datasets that are not exhaustive in capturing user-specific characteristics. Recently, on-device personalization techniques have been making strides in mitigating the problem. Although many current works have already explored the effectiveness of on-device personalization, the majority of their findings are limited to simulation settings or a specific smartphone. In this paper, we develop and provide a detailed explanation of our framework to train end-to-end models in mobile phones. To make it simple, we considered a model based on connectionist temporal classification (CTC) loss. We evaluated the framework on various mobile phones from different brands and reported the results. We provide enough evidence that fine-tuning the models and choosing the right hyperparameter values is a trade-off between the lowest WER achievable, training time on-device, and memory consumption. Hence, this is vital for a successful deployment of on-device training onto a resource-limited environment like mobile phones. We use training sets from speakers with different accents and record a 7.6% decrease in average word error rate (WER). We also report the associated computational cost measurements with respect to time, memory usage, and cpu utilization in mobile phones in real-time.
Classification of Machine Learning Algorithms

Machine learning (ML) is to make logical patterns out of various types of input data including images, texts, numbers and any other types of data. Data derived from research will be processes through machine learning algorithms and leads to a prediction that is mainly considered as the output of the machine learning algorithm. In this paper, the most popular learning algorithms have been reviewed and their specific features are discussed to help select the most appropriate algorithm through comparison in different research projects. However, there is not just one efficient method to apply to all data sets, and the appropriate algorithm may differ based on factors in a study.
TechRank: A Network-Centrality Approach for Informed  Cybersecurity-Investment

The cybersecurity technological landscape is a complex ecosystem in which entities -- such as companies and technologies -- influence each other in a non-trivial manner. Measuring the influence between entities is a tenet for informed technological investments in critical infrastructure. To study the mutual influence of companies and technologies from the cybersecurity field, we consider a bi-partite graph that links both sets of entities. Each node in this graph is weighted by applying a recursive algorithm based on the method of reflection. This endeavor helps to measure the impact of an entity on the cybersecurity market. Our results help researchers measure more precisely the magnitude of influence of each entity, and allows decision-makers to devise more informed investment strategies, according to their portfolio preferences. Finally, a research agenda is suggested, with the aim of allowing tailor-made investments by arbitrarily calibrating specific features of both types of entities.
Time Waits for No One! Analysis and Challenges of Temporal Misalignment

When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment. Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domain-specific pretraining, followed by task-specific finetuning. We establish a suite of tasks across multiple domains to study temporal misalignment in modern NLP systems. We find stronger effects of temporal misalignment on task performance than have been previously reported. We also find that, while temporal adaptation through continued pretraining can help, these gains are small compared to task-specific finetuning on data from the target time period. Our findings motivate continued research to improve temporal robustness of NLP models.
Methods of independent learning in the credit-module system

This article provides information about the credit-module system, which is the process of organizing education. The module-based teaching system uses a rating system to assess students’ knowledge, skills and abilities. Student Independent Study (IFT) is a student-specific learning activity that focuses on independent didactic assignments, motivation to learn, and knowledge in a particular subject.