Reviewerly/exHarmony huggingface dataset:
Self-Attention Bi-Rnn for Emotion Classification Based on Eeg

EEG-based emotion recognition is promising to provide real-time feedback for mental illnesses such as depression and anxiety. More and more researchers are focusing on EEG-based emotion recognition. In this paper, we propose an emotional EEG classification method called Self-Attention Bidirectional Recurrent Neural Network(SAB-RNN). One second EEG data is treated as a sample, and each sample is regarded as a sentence, and the number of channels is regarded as the embedding dimension of the word. A novel embedding method is applied to optimize features, an attention layer to visualize the embedding of raw data. The method proposed is tested with the DEAP dataset and Dreamer dataset, the dependent-subject emotion recognition rate can reach about 0.942 and 0.982, the accuracy of cross-subject emotion recognition gains 0.58 and 0.76 with few target data. Upon the attention model, we visualize the weight of attention in the time dimension and the 32-channel dimension, which can reduce channels to provide more intuitive suggestions. We also discussed the impact of different regularization methods, and frequency bandwidth. The regularization method plays an important role in classification, which improves the accuracy by 20% compared to no regularization. With the Beta frequency band, in the subject-dependent sentiment classification paradigm, the sentiment classification accuracy rate is 0.96. In the independent-subject sentiment classification paradigm, sentiment classification with transfer learning obtains 0.59.
ReAD: Reliability aware data

As transistor technology scales beyond 32 nm, the reliability of the processors and servers reduces at an exponential rate. In recent times, the qualification of reliability of servers has reduced from 5 years to 3 years. With this current trend, the lifetime of servers can be expected to shrink further. In a cloud data center, with hundreds and hundreds of servers, the ill effect of shorter lifetime reliability will be magnified. Reliability of servers influences the performance, cost and power efficiency of a cloud data center. In this paper, a cost-effective technique to continuously monitor the reliability of servers and scheduling tasks based on these reliability values is proposed. The proposed technique monitors the hardware parameters for the current executing workload to calculate the reliability metrics. The reliability metric used in ReAD is MTTF (Mean Time to Fail) and this is calculated for individual servers. The load balancer reads the MTTF of individual servers and performs the load balance, considering reliability as one of the parameters. The proposed technique is non-invasive in nature and does not require the server to go into a special test mode. This saves the testing time. The experimental analysis shows on an average 25% improvement in Server Usage Metric with the minimum improvement around 5% and the maximum is around 50%.
Locally Shifted Attention With Early Global Integration

Recent work has shown the potential of transformers for computer vision applications. An image is first partitioned into patches, which are then used as input tokens for the attention mechanism. Due to the expensive quadratic cost of the attention mechanism, either a large patch size is used, resulting in coarse-grained global interactions, or alternatively, attention is applied only on a local region of the image, at the expense of long-range interactions. In this work, we propose an approach that allows for both coarse global interactions and fine-grained local interactions already at early layers of a vision transformer. At the core of our method is the application of local and global attention layers. In the local attention layer, we apply attention to each patch and its local shifts, resulting in virtually located local patches, which are not bound to a single, specific location. These virtually located patches are then used in a global attention layer. The separation of the attention layer into local and global counterparts allows for a low computational cost in the number of patches, while still supporting data-dependent localization already at the first layer, as opposed to the static positioning in other visual transformers. Our method is shown to be superior to both convolutional and transformer-based methods for image classification on CIFAR10, CIFAR100, and ImageNet. Code is available at: https://github.com/shellysheynin/Locally-SAG-Transformer.
Knowing When You Don’t Know in Online Fashion: An Uncertainty-Aware Size Recommendation Framework

In recent years of online fashion, the availability of large-scale datasets has fueled the success of data-driven algorithmic products for supporting customers in their journey on fashion e-commerce platforms. Very often, these datasets are collected in an implicit manner, are subjective, and do not have expert annotated labels. The use of inconsistent and noisy data to train machine learning models could potentially harm their performance and generalization capabilities. In this paper, we explore uncertainty quantification metrics within the context of online size and fit recommender systems and show how they could be used to deal with noisy instances and subjective labels. We further propose an uncertainty-aware loss function based on Monte-Carlo dropout uncertainty estimation technique. Through experiments on real data at scale within the challenging domain of size and fit recommendation, we benchmark multiple uncertainty metrics and demonstrate the effectiveness of the proposed approach for training in the presence of noise.
professional ethics of a librarian

This publication outlines the main tasks of information and librarianship, formulated in the Law of the Republic of Uzbekistan «On information and library activities» in the context of the issue of library ethics, examines the activities of library specialists based on the moral values of the librarian profession, its positive personal qualities. The principles of ethical standards are revealed, as well as important components of the professional ethics of a modern librarian, thanks to which the status of an information and library institution and the prestige of the library profession are increased. The authors of the article talk about the Librarian’s Code of Ethics and its significance in shaping the professional consciousness of a librarian.
Enforcing and Discovering Structure in Machine Learning

The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm's solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm's solution: when the structure is known and when it has to be discovered.
Gated SwitchGAN for multi-domain facial image translation

Recent studies on multi-domain facial image translation have achieved impressive results. The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation. However, these methods neglect important information regarding domain distribution matching. To solve this problem, we propose a switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains. A feature-switching operation is proposed to achieve feature selection and fusion in our conditional modules. We demonstrate the effectiveness of our model. Furthermore, we also introduce a new capability of our generator that represents attribute intensity control and extracts content information without tailored training. Experiments on the Morph, RaFD and CelebA databases visually and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can achieve better translation results than StarGAN, AttGAN and STGAN. The attribute classification accuracy achieved using the trained ResNet-18 model and the FID score obtained using the ImageNet pretrained Inception-v3 model also quantitatively demonstrate the superior performance of our models.
Classes of Objects

In R, objects belong to classes as well as types and modes. Classes tell something about what the object contains. All objects belong to at least one class. The S3, S4, and Reference Class object systems differ regarding classes. In S3, there are specific classes into which an R object falls. In S4, the user defines a class for an S4 object. Classes in S3 are called informal classes, whereas classes in S4 are called formal classes. In both systems, classes are functional classes. In Reference Class, the classes are formal and referential. (For functional classes, an object is only changed when assigned a new value—locally. For referential classes, when an object changes, the value of the object changes everywhere the object is present.) Reference Class classes are usually defined for packages. This chapter covers the three kinds of classes.
Simulation fit and significance

Frequently, a decision maker will want to determine if a nonnumeric factor is important to a decision—that is, does the outcome depend upon the specific level of a given factor? This question is answered by using either an analysis of variance (ANOVA) or Chi-square statistical test. This chapter demonstrates the use of these two statistical tests and demonstrates how the results are used in decision trees to make optimal managerial decisions.
Grace Jones, Spontaneity, and Collaboration in the Moment

Songwriter, producer, and now theremin player, Bruce Woolley discusses creativity in the studio environment through key examples of working with singer songwriter Grace Jones. Woolley notes the importance of the technology in the creative ephemerality of the situation that drives the songwriting and blurs the line between studio production and songwriting.
CLIN-X: pre-trained language models and a study on cross-task transfer  for concept extraction in the clinical domain

The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models often perform sub-optimal in non-standard domains like the clinical domain where a large gap between pre-training documents and target documents is observed. In this paper, we aim at closing this gap with domain-specific training of the language model and we investigate its effect on a diverse set of downstream tasks and settings. We introduce the pre-trained CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other pre-trained transformer models by a large margin for ten clinical concept extraction tasks from two languages. In addition, we demonstrate how the transformer model can be further improved with our proposed task- and language-agnostic model architecture based on ensembles over random splits and cross-sentence context. Our studies in low-resource and transfer settings reveal stable model performance despite a lack of annotated data with improvements of up to 47 F1 points when only 250 labeled sentences are available. Our results highlight the importance of specialized language models as CLIN-X for concept extraction in non-standard domains, but also show that our task-agnostic model architecture is robust across the tested tasks and languages so that domain- or task-specific adaptations are not required.
Quantile Filtered Imitation Learning

We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning. QFIL performs policy improvement by running imitation learning on a filtered version of the offline dataset. The filtering process removes $ s,a $ pairs whose estimated Q values fall below a given quantile of the pushforward distribution over values induced by sampling actions from the behavior policy. The definitions of both the pushforward Q distribution and resulting value function quantile are key contributions of our method. We prove that QFIL gives us a safe policy improvement step with function approximation and that the choice of quantile provides a natural hyperparameter to trade off bias and variance of the improvement step. Empirically, we perform a synthetic experiment illustrating how QFIL effectively makes a bias-variance tradeoff and we see that QFIL performs well on the D4RL benchmark.
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results

We present pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events). The dataset consists of almost 1000 instances of labeled event data to encourage benchmark evaluations on phasor measurement unit (PMU) data analytics. The dataset is available online for use by any researcher or practitioner in the field. PMU data are challenging to obtain, especially those covering event periods. Nevertheless, power system problems have recently seen phenomenal advancements via data-driven machine learning solutions - solutions created by researchers who were fortunate enough to obtain such PMU data. A highly accessible standard benchmarking dataset would enable a drastic acceleration of the development of successful machine learning techniques in this field. We propose a novel learning method based on the Event Participation Decomposition of Power System Events, which makes it possible to learn a generative model of PMU data during system anomalies. The model can create highly realistic event data without compromising the differential privacy of the PMUs used to train it. The dataset is available online for any researcher to use at the pmuBAGE Github Repository - https://github.com/NanpengYu/pmuBAGE. Part I - This is part I of a two part paper. In part I, we describe a high level overview of pmuBAGE, its creation, and the experiments used to test it. Part II will discuss the exact models used in its generation in far more detail.
Radar Aided mmWave Vehicle-to-InfrastructureLink Configuration Using  Deep Learning

The high overhead of the beam training process is the main challenge when establishing mmWave communication links, especially for vehicle-to-everything (V2X) scenarios where the channels are highly dynamic. In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system. Specifically, the first DNN is built to extract the information from the radar azimuth power spectrum (APS) to reconstruct the communication APS, while the second DNN exploits the information extracted from the spatial covariance of the radar channel to realize R2C covariance prediction. The achieved data rate and the similarity between the estimated and the true communication APS are used to evaluate the prediction performance. The covariance estimation method generally provides higher similarity, as the APS predictions cannot always capture the mismatch between the radar and communication APS. Compared to the beam training method which exploits directly the radar APS without an attempt to translate it to the communication channel, our proposed deep learning (DL) aided methods remarkably reduce the beam training overhead, resulting in a 13.3% and 21.9% rate increase when using the communication APS prediction and covariance prediction, respectively.
Over-the-Air Computation with DFT-spread OFDM for Federated Edge  Learning

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the edge server (ES). The proposed scheme relies on non-coherent communication techniques for achieving distributed training by majority vote (MV). In this work, the votes, i.e., the signs of the local gradients, from the EDs are represented with the pulse-position modulation (PPM) symbols constructed with discrete Fourier transform (DFT)-spread orthogonal frequency division multiplexing (OFDM) (DFT-s-OFDM). By taking the delay spread and time-synchronization errors into account, the MV at the ES is obtained with an energy detector. Hence, the proposed scheme does not require CSI at the EDs and ES. We also prove the convergence of the distributed training when the MV is obtained with the proposed scheme under fading channel. Through simulations, we show that the proposed scheme provides a high test accuracy in fading channels while resulting in lower peak-to-mean envelope power ratio (PMEPR) symbols.
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling

Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience.
A Comparative Study of Transformers on Word Sense Disambiguation

Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized Word Embeddings (CWEs). Despite this progress, the NLP community has not witnessed any significant work performing a comparative study on the contextualization power of such architectures. This paper presents a comparative study and an extensive analysis of nine widely adopted Transformer models. These models are BERT, CTRL, DistilBERT, OpenAI-GPT, OpenAI-GPT2, Transformer-XL, XLNet, ELECTRA, and ALBERT. We evaluate their contextualization power using two lexical sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. We adopt a simple yet effective approach to WSD that uses a k-Nearest Neighbor (kNN) classification on CWEs. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks
SoK: Vehicle Orientation Representations for Deep Rotation Estimation

In recent years, there is an influx of deep learning models for 3D vehicle object detection. However, little attention was paid to orientation prediction. Existing research work proposed various vehicle orientation representation methods for deep learning, however a holistic, systematic review has not been conducted. Through our experiments, we categorize and compare the accuracy performance of various existing orientation representations using the KITTI 3D object detection dataset, and propose a new form of orientation representation: Tricosine. Among these, the 2D Cartesian-based representation, or Single Bin, achieves the highest accuracy, with additional channeled inputs (positional encoding and depth map) not boosting prediction performance. Our code is published on GitHub: https://github.com/umd-fire-coml/KITTI-orientation-learning
Features of relationship between student motivation and self-activity

The article aims to identify the link between student self-activation and satisfaction with academic performance. It has also been proven that increased motivation for learning activities is a factor influencing self-activation in a student's personality. In particular, the level of development of motivation for learning activities in the student's personality has been studied on the basis of empirical research and theoretical approaches have been put forward.
Measuring Uncertainty in Translation Quality Evaluation (TQE)

From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task. Translation service providers (TSPs) have to deliver large volumes of translations which meet customer specifications with harsh constraints of required quality level in tight time-frames and costs. MT researchers strive to make their models better, which also requires reliable quality evaluation. While automatic machine translation evaluation (MTE) metrics and quality estimation (QE) tools are widely available and easy to access, existing automated tools are not good enough, and human assessment from professional translators (HAP) are often chosen as the golden standard \cite{han-etal-2021-TQA}. Human evaluations, however, are often accused of having low reliability and agreement. Is this caused by subjectivity or statistics is at play? How to avoid the entire text to be checked and be more efficient with TQE from cost and efficiency perspectives, and what is the optimal sample size of the translated text, so as to reliably estimate the translation quality of the entire material? This work carries out such motivated research to correctly estimate the confidence intervals \cite{Brown_etal2001Interval} depending on the sample size of the translated text, e.g. the amount of words or sentences, that needs to be processed on TQE workflow step for confident and reliable evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).
Decoupling Visual-Semantic Feature Learning for Robust Scene Text  Recognition

Semantic information has been proved effective in scene text recognition. Most existing methods tend to couple both visual and semantic information in an attention-based decoder. As a result, the learning of semantic features is prone to have a bias on the limited vocabulary of the training set, which is called vocabulary reliance. In this paper, we propose a novel Visual-Semantic Decoupling Network (VSDN) to address the problem. Our VSDN contains a Visual Decoder (VD) and a Semantic Decoder (SD) to learn purer visual and semantic feature representation respectively. Besides, a Semantic Encoder (SE) is designed to match SD, which can be pre-trained together by additional inexpensive large vocabulary via a simple word correction task. Thus the semantic feature is more unbiased and precise to guide the visual feature alignment and enrich the final character representation. Experiments show that our method achieves state-of-the-art or competitive results on the standard benchmarks, and outperforms the popular baseline by a large margin under circumstances where the training set has a small size of vocabulary.