Reviewerly/exHarmony huggingface dataset:
Semi-supervised cross-lingual speech emotion recognition

Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques. However, cross-lingual SER remains a challenge in real-world applications due to two main factors: the first is the big gap among the source and the target domain distributions; the second factor is the major availability of unlabeled utterances in contrast to the labeled ones for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when only few labeled examples in the target domain (i.e. the new language) are available. Our method is based on a Transformer and it adapts to the new domain by exploiting a pseudo-labeling strategy on the unlabeled utterances. In particular, the use of a hard and soft pseudo-labels approach is investigated. We thoroughly evaluate the performance of the proposed method in a speaker-independent setup on both the source and the new language and show its robustness across five languages belonging to different linguistic strains. The experimental findings indicate that the unweighted accuracy is increased by an average of 40% compared to state-of-the-art methods.
Universal Adaptive Data Augmentation

Existing automatic data augmentation (DA) methods either ignore updating DA's parameters according to the target model's state during training or adopt update strategies that are not effective enough. In this work, we design a novel data augmentation strategy called "Universal Adaptive Data Augmentation" (UADA). Different from existing methods, UADA would adaptively update DA's parameters according to the target model's gradient information during training: given a pre-defined set of DA operations, we randomly decide types and magnitudes of DA operations for every data batch during training, and adaptively update DA's parameters along the gradient direction of the loss concerning DA's parameters. In this way, UADA can increase the training loss of the target networks, and the target networks would learn features from harder samples to improve the generalization. Moreover, UADA is very general and can be utilized in numerous tasks, e.g., image classification, semantic segmentation and object detection. Extensive experiments with various models are conducted on CIFAR-10, CIFAR-100, ImageNet, tiny-ImageNet, Cityscapes, and VOC07+12 to prove the significant performance improvements brought by UADA.
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models

Self-supervised learning (SSL) is seen as a very promising approach with high performance for several speech downstream tasks. Since the parameters of SSL models are generally so large that training and inference require a lot of memory and computational cost, it is desirable to produce compact SSL models without a significant performance degradation by applying compression methods such as knowledge distillation (KD). Although the KD approach is able to shrink the depth and/or width of SSL model structures, there has been little research on how varying the depth and width impacts the internal representation of the small-footprint model. This paper provides an empirical study that addresses the question. We investigate the performance on SUPERB while varying the structure and KD methods so as to keep the number of parameters constant; this allows us to analyze the contribution of the representation introduced by varying the model architecture. Experiments demonstrate that a certain depth is essential for solving content-oriented tasks (e.g. automatic speech recognition) accurately, whereas a certain width is necessary for achieving high performance on several speaker-oriented tasks (e.g. speaker identification). Based on these observations, we identify, for SUPERB, a more compressed model with better performance than previous studies.
Parity-encoding-based quantum computing with Bayesian error tracking

Measurement-based quantum computing (MBQC) in linear optical systems is promising for near-future quantum computing architecture. However, the nondeterministic nature of entangling operations and photon losses hinder the large-scale generation of graph states and introduce logical errors. In this work, we propose a linear optical topological MBQC protocol employing multiphoton qubits based on the parity encoding, which turns out to be highly photon-loss tolerant and resource-efficient even under the effects of nonideal entangling operations that unavoidably corrupt nearby qubits. For the realistic error analysis, we introduce a Bayesian methodology, in conjunction with the stabilizer formalism, to track errors caused by such detrimental effects. We additionally suggest a graph-theoretical optimization scheme for the process of constructing an arbitrary graph state, which greatly reduces its resource overhead. Notably, we show that our protocol is advantageous over several other existing approaches in terms of fault-tolerance, resource overhead, or feasibility of basic elements.
Relighting4D: Neural Relightable Human from Videos

Human relighting is a highly desirable yet challenging task. Existing works either require expensive one-light-at-a-time (OLAT) captured data using light stage or cannot freely change the viewpoints of the rendered body. In this work, we propose a principled framework, Relighting4D, that enables free-viewpoints relighting from only human videos under unknown illuminations. Our key insight is that the space-time varying geometry and reflectance of the human body can be decomposed as a set of neural fields of normal, occlusion, diffuse, and specular maps. These neural fields are further integrated into reflectance-aware physically based rendering, where each vertex in the neural field absorbs and reflects the light from the environment. The whole framework can be learned from videos in a self-supervised manner, with physically informed priors designed for regularization. Extensive experiments on both real and synthetic datasets demonstrate that our framework is capable of relighting dynamic human actors with free-viewpoints.
PENGUJIAN PERBANDINGAN KETAHANAN BERBAGAI BUSI SEPEDA MOTOR DENGAN MENGGUNAKAN ALAT BUSI TESTER

The development of automotive technology is very fast, we can find this in today's vehicles that use a lot of very sophisticated technology, namely in terms of engines, electricity, and its newest features, especially on two-wheeled vehicles. Therefore, the user community is required to be more selective in choosing a vehicle both in terms of quality and quantity. Including in the selection of spare parts on vehicle engines must use good quality, this is useful as a motorbike power booster, especially in the ignition system, namely spark plugs as a vital tool that sprinkles sparks in the combustion chamber. Recently, many spark plugs have been found that are not good enough so that the power generated by the vehicle is reduced. The purpose of this test is as a Comparative Test of the Endurance of Various Motorcycle Spark Plugs by using a spark plug tester so that it can be seen that the spark plug is suitable or not used in the vehicle which aims to get maximum performance.
Estimating Classification Confidence Using Kernel Densities

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) as well as the classic MNIST benchmark. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually.
Near-Optimal Bounds for Testing Histogram Distributions

We investigate the problem of testing whether a discrete probability distribution over an ordered domain is a histogram on a specified number of bins. One of the most common tools for the succinct approximation of data, $k$-histograms over $[n]$, are probability distributions that are piecewise constant over a set of $k$ intervals. The histogram testing problem is the following: Given samples from an unknown distribution $\mathbf{p}$ on $[n]$, we want to distinguish between the cases that $\mathbf{p}$ is a $k$-histogram versus $\varepsilon$-far from any $k$-histogram, in total variation distance. Our main result is a sample near-optimal and computationally efficient algorithm for this testing problem, and a nearly-matching (within logarithmic factors) sample complexity lower bound. Specifically, we show that the histogram testing problem has sample complexity $\widetilde \Theta (\sqrt{nk} / \varepsilon + k / \varepsilon^2 + \sqrt{n} / \varepsilon^2)$.
DropNet: Reducing Neural Network Complexity via Iterative Pruning

Modern deep neural networks require a significant amount of computing time and power to train and deploy, which limits their usage on edge devices. Inspired by the iterative weight pruning in the Lottery Ticket Hypothesis, we propose DropNet, an iterative pruning method which prunes nodes/filters to reduce network complexity. DropNet iteratively removes nodes/filters with the lowest average post-activation value across all training samples. Empirically, we show that DropNet is robust across diverse scenarios, including MLPs and CNNs using the MNIST, CIFAR-10 and Tiny ImageNet datasets. We show that up to 90% of the nodes/filters can be removed without any significant loss of accuracy. The final pruned network performs well even with reinitialization of the weights and biases. DropNet also has similar accuracy to an oracle which greedily removes nodes/filters one at a time to minimise training loss, highlighting its effectiveness.
ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures

Simulation ensembles are a common tool in physics for understanding how a model outcome depends on input parameters. We analyze an active particle system, where each particle can use energy from its surroundings to propel itself. A multi-dimensional feature vector containing all particles' motion information can describe the whole system at each time step. The system's behavior strongly depends on input parameters like the propulsion mechanism of the particles. To understand how the time-varying behavior depends on the input parameters, it is necessary to introduce new measures to quantify the difference of the dynamics of the ensemble members. We propose a tool that supports the interactive visual analysis of time-varying feature-vector ensembles. A core component of our tool allows for the interactive definition and refinement of new measures that can then be used to understand the system's behavior and compare the ensemble members. Different visualizations support the user in finding a characteristic measure for the system. By visualizing the user-defined measure, the user can then investigate the parameter dependencies and gain insights into the relationship between input parameters and simulation output.
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning

We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree search (MCTS) is an effective heuristic search algorithm for solving episodic decision-making problems whose underlying search spaces are expansive. Leveraging a GPU-based large-scale simulator, PMBS introduces massive parallelism into MCTS for solving planning tasks through the batched execution of a large number of concurrent simulations, which allows for more efficient and accurate evaluations of the expected cost-to-go over large action spaces. When applied to the challenging manipulation tasks of object retrieval from clutter, PMBS achieves a speedup of over $30\times$ with an improved solution quality, in comparison to a serial MCTS implementation. We show that PMBS can be directly applied to real robot hardware with negligible sim-to-real differences. Supplementary material, including video, can be found at https://github.com/arc-l/pmbs.
Stochastic trust-region algorithm in random subspaces with convergence and expected complexity analyses

This work proposes a framework for large-scale stochastic derivative-free optimization (DFO) by introducing STARS, a trust-region method based on iterative minimization in random subspaces. This framework is both an algorithmic and theoretical extension of an algorithm for stochastic optimization with random models (STORM). Moreover, STARS achieves scalability by minimizing interpolation models that approximate the objective in low-dimensional affine subspaces, thus significantly reducing per-iteration costs in terms of function evaluations and yielding strong performance on large-scale stochastic DFO problems. The user-determined dimension of these subspaces, when the latter are defined, for example, by the columns of so-called Johnson--Lindenstrauss transforms, turns out to be independent of the dimension of the problem. For convergence purposes, both a particular quality of the subspace and the accuracies of random function estimates and models are required to hold with sufficiently high, but fixed, probabilities. Using martingale theory under the latter assumptions, an almost sure global convergence of STARS to a first-order stationary point is shown, and the expected number of iterations required to reach a desired first-order accuracy is proved to be similar to that of STORM and other stochastic DFO algorithms, up to constants.
Quantum Data Reduction with Application to Video Classification

We investigate a quantum video classification method using a hybrid algorithm. A quantum-classical step performs a data reduction on the video dataset and a quantum step -- which only has access to the reduced dataset -- classifies the video to one of k classes. We verify the method using sign videos and demonstrate that the reduced dataset contains enough information to successfully classify the data, using a quantum classification process. The proposed data reduction method showcases a way to alleviate the "data loading" problem of quantum computers for the problem of video classification. Data loading is a huge bottleneck, as there are no known efficient techniques to perform that task without sacrificing many of the benefits of quantum computing.
Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Specific emitter identification (SEI) is a highly potential technology for physical layer authentication that is one of the most critical supplement for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristic of hardware circuits, which difficult to counterfeit. Recently, various deep learning (DL)-based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intra-category distance and separable inter-category distance, while the latter is realized by an ensemble classifier. Simulation results show that if the number of samples per category is more than 5, the average accuracy of our proposed method is higher than 98\%. Moreover, feature visualization demonstrates the advantages of our proposed method in both discriminability and generalization. The codes of this paper can be downloaded from GitHub(https://github.com/BeechburgPieStar/Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning)
Streamlining Visualization Authoring in D3 Through User-Driven Templates

D3 is arguably the most popular tool for implementing web based visualizations. Yet D3 has a steep learning curve that may hinder its adoption and continued use. To simplify the process of programming D3 visualizations, we must first understand the space of implementation practices that D3 users engage in. We present a qualitative analysis of 2500 D3 visualizations and their corresponding implementations. We find that 5 visualization types (Bar Charts, Geomaps, Line Charts, Scatterplots, and Force Directed Graphs) account for 80% of D3 visualizations found in our corpus. While implementation styles vary slightly across designs, the underlying code structure for all visualization types remains the same; presenting an opportunity for code reuse. Using our corpus of D3 examples, we synthesize reusable code templates for eight popular D3 visualization types and share them in our open source repository. Based on our results, we discuss design considerations for leveraging users' implementation patterns to reduce visualization design effort through design templates and auto-generated code recommendations.
Forecasting Unemployment Rate in Malaysia Using Box-Jenkins Methodology during Covid-19 Outbreak

The economic dimension has revealed that weak economic growth is one of the causes of unemployment and is related to GDP.Forecasting the unemployment rate is essential and is an important determinant of monetary policy decisions and needs to be addressed.This study is conducted to identify whether novel coronavirus 2019, COVID-19 affects Malaysia's unemployment rate and forecast the rate for the next two years.The Box-Jenkins approach uses the Augmented Dickey-Fuller test to stabilize the data.After reducing the trend pattern of the partial autocorrelation coefficient, the ARIMA prediction method ARIMA (2,1,2) was selected as the best model to apply to the unemployment rate time series data.As a result, the projected unemployment rate graph showed a steady increase over the next two years.For future research, it is recommended to consider factors such as inflation, growth domestic product and employment to predict this value to improve results.
Komparasi Model 3D Jembatan dari Pemotretan Konvergen dan Normal Menggunakan Drone

CRP (Close Range Photogrammetry) is a measurement technique that can extract 3D points from an image. This study aims to compare convergent, normal, and combined shooting using drones with close-range photogrammetry methods. In the CRP technique, shooting is carried out using the DJI Phantom 4 Pro Drone with convergent and normal shooting techniques and a combination of both shots. Convergent shooting is done by positioning the drone centered on one point by forming a cone against the bridge object with an angle ranging from 40-90 degrees. While shooting normally is done by positioning the drone by facing a straight line against the object. A comparison of concurrent and normal shooting was carried out to find out better 3D Modeling results. The final result of this research is the visualization of a 3D model object that has a complete texture and resembles its original state in the field, as evidenced by the RMSE value of the 3D convergent model being 0.0940 m, for the RMSE value of the normal 3D model is 0.0882 m. The RMSE 3D value of the combination model is 0.0907 m. From each RMSE value above, a good 3D model visualization is produced using normal shooting because the resulting RMSE value is smaller than other shooting techniques.
Networked Linguistic Data and Discourse Management: The 2020 US Presidential Election

The article introduces the methodology used for analyzing networked linguistic data, regarded as a basis of global online discursive fields. The authors scrutinized the discursive fields, which emerged during 2020 US Presidential Election.The research methodology, which combines natural science methods (mathematical analysis, graph theory, network analysis and relational analysis) and modern methods of linguistic research (complex linguo-discursive analysis, and methods of network linguistics), makes it possible to analyze discursive fields as social graphs, identify narratives and discourses that form the basis of the modern global communication space, and their potential for manipulating. The empirical base of the study is comprised of bulks of networked data that include the messages published by ordinary users and D. Trump's team on Twitter platform in March – October 2020. The application of the authors' technique has resulted in discursive fields visualization, abnormal discursive activity areas identification, the interaction of discourses within the discursive field description, the mode of messages and their recurrence level determination. It is shown that the analysis of Internet communication using the developed methodology contribute to understanding the essence of socio-political and socio-economic processes and deepening the predictive analytics of their development, and can also be used for discursive management in order to strengthen constructive and neutralize destructive social practices in online space.
Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data, where the gradient of the loss function has bounded moments. Instead of the case where the loss function is Lipschitz or each coordinate of the gradient has bounded second moment studied previously, we consider a relaxed scenario where each coordinate of the gradient only has bounded (1+v)-th moment with some v∈(0, 1]. Firstly, we start from the one dimensional private mean estimation for heavy-tailed distributions. We propose a novel robust and private mean estimator which is optimal. Based on its idea, we then extend to the general d-dimensional space and study DP-SCO with general convex and strongly convex loss functions. We also provide lower bounds for these two classes of loss under our setting and show that our upper bounds are optimal up to a factor of O(Poly(d)). To address the high dimensionality issue, we also study DP-SCO with heavy-tailed gradient under some sparsity constraint (DP sparse learning). We propose a new method and show it is also optimal up to a factor of O(s*), where s* is the underlying sparsity of the constraint.
A Theoretical Perspective on Hyperdimensional Computing (Extended Abstract)

Hyperdimensional (HD) computing is a set of neurally inspired methods for computing on high-dimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. We present a novel mathematical framework that unifies analysis of HD computing architectures, and provides general, non-asymptotic, sufficient conditions under which HD information processing techniques will succeed.