Reviewerly/exHarmony huggingface dataset:
AutoByte: Automatic Configuration for Optimal Communication Scheduling in DNN Training

ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training. The configuration of hyper-parameters (i.e., the partition size and the credit size) is critical to the effectiveness of partitioning and rearrangement. Currently, ByteScheduler adopts Bayesian Optimization (BO) to find the optimal configuration for the hyper-parameters beforehand. In practice, however, various runtime factors (e.g., worker node status and network conditions) change over time, making the statically-determined one-shot configuration result suboptimal for real-world DNN training.To address this problem, we present a real-time configuration method (called AutoByte) that automatically and timely searches the optimal hyper-parameters as the training systems dynamically change. AutoByte extends the ByteScheduler framework with a meta-network, which takes the system’s runtime statistics as its input and outputs predictions for speedups under specific configurations. Evaluation results on various DNN models show that AutoByte can dynamically tune the hyper-parameters with low resource usage, and deliver up to 33.2% higher performance than the best static configuration in ByteScheduler.
Persistent Items Tracking in Large Data Streams Based on Adaptive Sampling

We address the problem of persistent item tracking in large-scale data streams. A persistent item refers to the one that persists to occur in the stream over a long timespan. Tracking persistent items is an important and pivotal functionality for many networking and computing applications as persistent items, though not necessarily contributing significantly to the data volume, may convey valuable information on the data pattern about the stream. The state-of-the-art solutions of tracking persistent items require to know the monitoring time horizon to set the sampling rate. This limitation is further accentuated when we need to track the persistent items in recent w slots where w can be any value between 0 and T to support different monitoring granularity. Motivated by this limitation, we develop a persistent item tracking algorithm that can function without knowing the monitoring time horizon beforehand, and can thus track persistent items up to the current time t or within a certain time window at any moment. Our central technicality is adaptively reducing the sampling rate such that the total memory overhead can be limited while still meeting the target tracking accuracy. Through both theoretical and empirical analysis, we fully characterize the performance of our proposition.
Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream

Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors (e.g. the YOLO - You Only Look Once - family) use deep convolutional neural networks. However, due to their high computational complexity, they are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{AMD} \text{Xilinx\ Zynq\ UltraScale}+ \text{MPSoC}$</tex> (Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD - Ultra High Definition, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3840 \times 2160\ \text{pixels}$</tex> ) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems.
Performance Comparison of the Optimized Ensemble Model with Existing Classifier Models

The purpose of this study is to conduct an empirical investigation and comparison of the effectiveness of various classifiers and ensembles of classifiers in predicting academic performance.The study will evaluate the performance and efficiency of ensemble techniques that employ several classifiers against the performance and efficiency of a single classifier.Reducing student attrition is a serious concern for educational institutions worldwide.Educators are looking for strategies to boost student retention and graduation rates.This is only achievable if at-risk students are appropriately recognized early on.However, most commonly used predictive models are inefficient and inaccurate due to intrinsic classifier limitations and the usage of minor factors.The study contributes to the body of knowledge by proposing the development of optimized ensemble learning model that can be used for improving academic performance prediction.Overall, the findings demonstrate that the approach of employing optimized ensemble learning (OEL) model approaches is extremely efficient and accurate in terms of predicting student performance and aiding in the identification of students who are in the fear of attrition.
Exploring linguistic feature and model combination for speech recognition based automatic AD detection

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and delay progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of such specialist data leads to uncertainty in both model selection and feature learning when developing such systems. To this end, this paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders on limited data, before the resulting embedding features being fed into an ensemble of backend classifiers to produce the final AD detection decision via majority voting. Experiments conducted on the ADReSS20 Challenge dataset suggest consistent performance improvements were obtained using model and feature combination in system development. State-of-the-art AD detection accuracies of 91.67 percent and 93.75 percent were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers.
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation

We study the problem of few-shot Fine-grained Entity Typing (FET), where only a few annotated entity mentions with contexts are given for each entity type. Recently, prompt-based tuning has demonstrated superior performance to standard fine-tuning in few-shot scenarios by formulating the entity type classification task as a ''fill-in-the-blank'' problem. This allows effective utilization of the strong language modeling capability of Pre-trained Language Models (PLMs). Despite the success of current prompt-based tuning approaches, two major challenges remain: (1) the verbalizer in prompts is either manually designed or constructed from external knowledge bases, without considering the target corpus and label hierarchy information, and (2) current approaches mainly utilize the representation power of PLMs, but have not explored their generation power acquired through extensive general-domain pre-training. In this work, we propose a novel framework for few-shot FET consisting of two modules: (1) an entity type label interpretation module automatically learns to relate type labels to the vocabulary by jointly leveraging few-shot instances and the label hierarchy, and (2) a type-based contextualized instance generator produces new instances based on given instances to enlarge the training set for better generalization. On three benchmark datasets, our model outperforms existing methods by significant margins.
OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression

Recently, deep learning has been proven to be a promising approach in standard dynamic range (SDR) image compression. However, due to the wide luminance distribution of high dynamic range (HDR) images and the lack of large standard datasets, developing a deep model for HDR image compression is much more challenging. To tackle this issue, we view HDR data as distributional shifts of SDR data and the HDR image compression can be modeled as an out-of-distribution generalization (OoD) problem. Herein, we propose a novel out-of-distribution (OoD) HDR image compression framework (OoDHDR-codec). It learns the general representation across HDR and SDR environments, and allows the model to be trained effectively using a large set of SDR datases supplemented with much fewer HDR samples. Specifically, OoDHDR-codec consists of two branches to process the data from two environments. The SDR branch is a standard blackbox network. For the HDR branch, we develop a hybrid system that models luminance masking and tone mapping with white-box modules and performs content compression with black-box neural networks. To improve the generalization from SDR training data on HDR data, we introduce an invariance regularization term to learn the common representation for both SDR and HDR compression. Extensive experimental results show that the OoDHDR codec achieves strong competitive in-distribution performance and state-of-the-art OoD performance. To the best of our knowledge, our proposed approach is the first work to model HDR compression as OoD generalization problems and our OoD generalization algorithmic framework can be applied to any deep compression model in addition to the network architectural choice demonstrated in the paper. Code available at https://github.com/caolinfeng/OoDHDR-codec.
Mutual Nearest Neighbor Contrast and Hybrid Prototype Self-Training for Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain under domain shift and category shift. Without prior category overlap information, it is challenging to simultaneously align the common categories between two domains and separate their respective private categories. Additionally, previous studies utilize the source classifier's prediction to obtain various known labels and one generic "unknown" label of target samples. However, over-reliance on learned classifier knowledge is inevitably biased to source data, ignoring the intrinsic structure of target domain. Therefore, in this paper, we propose a novel two-stage UniDA framework called MATHS based on the principle of mutual nearest neighbor contrast and hybrid prototype discrimination. In the first stage, we design an efficient mutual nearest neighbor contrastive learning scheme to achieve feature alignment, which exploits the instance-level affinity relationship to uncover the intrinsic structure of two domains. We introduce a bimodality hypothesis for the maximum discriminative probability distribution to detect the possible target private samples, and present a data-based statistical approach to separate the common and private categories. In the second stage, to obtain more reliable label predictions, we propose an incremental pseudo-classifier for target data only, which is driven by the hybrid representative prototypes. A confidence-guided prototype contrastive loss is designed to optimize the category allocation uncertainty via a self-training mechanism. Extensive experiments on three benchmarks demonstrate that MATHS outperforms previous state-of-the-arts on most UniDA settings.
Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)

Training multilingual Neural Text-To-Speech (NTTS) models using only monolingual corpora has emerged as a popular way for building voice cloning based Polyglot NTTS systems. In order to train these models, it is essential to understand how the composition of the training corpora affects the quality of multilingual speech synthesis. In this context, it is common to hear questions such as "Would including more Spanish data help my Italian synthesis, given the closeness of both languages?". Unfortunately, we found existing literature on the topic lacking in completeness in this regard. In the present work, we conduct an extensive ablation study aimed at understanding how various factors of the training corpora, such as language family affiliation, gender composition, and the number of speakers, contribute to the quality of Polyglot synthesis. Our findings include the observation that female speaker data are preferred in most scenarios, and that it is not always beneficial to have more speakers from the target language variant in the training corpus. The findings herein are informative for the process of data procurement and corpora building.
Compression of Medical Images Using Wavelet Transform and Metaheuristic Algorithm for Telemedicine Applications

Medical image compression becomes necessary to efficiently handle huge number of medical images for storage and transmission purposes. Wavelet transform is one of the popular techniques widely used for medical image compression. However, these methods have some limitations like discontinuity which occurs when reducing image size employing thresholding method. To overcome this, optimization method is considered with the available compression methods. In this paper, a method is proposed for efficient compression of medical images based on integer wavelet transform and modified grasshopper optimization algorithm. Medical images are pre-processed using hybrid median filter to discard noise and then decomposed using integer wavelet transform. The proposed method employed modified grasshopper optimization algorithm to select the optimal coefficients for efficient compression and decompression. Four different imaging techniques, particularly magnetic resonance imaging, computed tomography, ultrasound, and X-ray, were used in a series of tests. The suggested method's compressing performance is proven by comparing it to well-known approaches in terms of mean square error, peak signal to noise ratio, and mean structural similarity index at various compression ratios. The findings showed that the proposed approach provided effective compression with high decompression image quality.
Knowledge Management using IoT-Blockchain Technology: State of the Art

This research investigated the database of journal articles related to knowledge management and blockchain technology. Knowledge is the most important production factor in competition. The purpose of knowledge management is to improve the ability of knowledge to create value. Blockchain is a shared distributed database, and blockchain technology, as one of the most popular computer technologies in recent years, has received great attention from all over the world. Purpose/Significance: This article aims to review and comment on the domestic and foreign literature on the use of blockchain technology for knowledge management, outline the current research status in this field, and predict its future research directions. Method/Process: This article will mainly use the literature survey method and the inductive analysis method, retrieve and screen 212 articles (2000-2021) published in the past three years, and get a total of 40+ articles directly related to the use of IOT or blockchain technology for knowledge management The literature, and then conduct a summary analysis of the literature and analyze its content. Result/Conclusion: Find out the trends and characteristics of the development and application of blockchain technology, and explore the feasibility in knowledge management based on these characteristics. The blockchain itself has the characteristics of decentralization, self-operation and sharing, which is highly compatible in knowledge management, especially in the field of enterprise knowledge sharing, and has a higher application prospect in the entire knowledge management field.
Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)

An essential design decision for multilingual Neural Text-To-Speech (NTTS) systems is how to represent input linguistic features within the model. Looking at the wide variety of approaches in the literature, two main paradigms emerge, unified and separate representations. The former uses a shared set of phonetic tokens across languages, whereas the latter uses unique phonetic tokens for each language. In this paper, we conduct a comprehensive study comparing multilingual NTTS systems models trained with both representations. Our results reveal that the unified approach consistently achieves better cross-lingual synthesis with respect to both naturalness and accent. Separate representations tend to have an order of magnitude more tokens than unified ones, which may affect model capacity. For this reason, we carry out an ablation study to understand the interaction of the representation type with the size of the token embedding. We find that the difference between the two paradigms only emerges above a certain threshold embedding size. This study provides strong evidence that unified representations should be the preferred paradigm when building multilingual NTTS systems.
Search-based diverse sampling from real-world software product lines

Real-world software product lines (SPLs) often encompass enormous valid configurations that are impossible to enumerate. To understand properties of the space formed by all valid configurations, a feasible way is to select a small and valid sample set. Even though a number of sampling strategies have been proposed, they either fail to produce diverse samples with respect to the number of selected features (an important property to characterize behaviors of configurations), or achieve diverse sampling but with limited scalability (the handleable configuration space size is limited to 1013). To resolve this dilemma, we propose a scalable diverse sampling strategy, which uses a distance metric in combination with the novelty search algorithm to produce diverse samples in an incremental way. The distance metric is carefully designed to measure similarities between configurations, and further diversity of a sample set. The novelty search incrementally improves diversity of samples through the search for novel configurations. We evaluate our sampling algorithm on 39 real-world SPLs. It is able to generate the required number of samples for all the SPLs, including those which cannot be counted by sharpSAT, a state-of-the-art model counting solver. Moreover, it performs better than or at least competitively to state-of-the-art samplers regarding diversity of the sample set. Experimental results suggest that only the proposed sampler (among all the tested ones) achieves scalable diverse sampling.
Top-down and bottom-up sources of eye-movement guidance during realistic scene search in Alzheimer’s disease.

Visual search is a crucial task in daily life, but in Alzheimer's disease (AD) it has usually been investigated using simple arrays. Here, we used scenes depicting real environments and studied the time course of attentional guidance.We analyzed eye-movement differences between mild AD patients and age-matched healthy controls during search. We examined top-down guidance, manipulating the target template (precise picture vs. word cue) and the target-scene semantic consistency (consistent vs. inconsistent), and bottom-up guidance, manipulating the perceptual salience (high vs. low) of targets and distractors.During scene scanning, AD patients had longer search times, made more fixations before the first target fixation, and showed a greater probability of distractor selection, with longer distractor fixation. AD also led to longer target fixation. In patients and controls, picture cues and highly salient targets improved all search phases, whereas consistent targets only improved search initiation (first saccade). Moreover, top-down and bottom-up guidance interacted in initiation and scanning, and this did not differ between the two groups of participants. However, AD led to a smaller picture cue benefit in shortening distractor fixation and greater bottom-up search facilitation during scanning, where a high-salience target reduced the performance gap between patients and controls.Our study shows the importance of top-down and bottom-up guidance, and their integration, in improving search in AD patients. It suggests that precise target cues and, even more, highly salient targets may act as environmental supports that enhance attentional processing and search performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Trustworthy assertion classification through prompting

Accurate identification of the presence, absence or possibility of relevant entities in clinical notes is important for healthcare professionals to quickly understand crucial clinical information. This introduces the task of assertion classification - to correctly identify the assertion status of an entity in the unstructured clinical notes. Recent rule-based and machine-learning approaches suffer from labor-intensive pattern engineering and severe class bias toward majority classes. To solve this problem, in this study, we propose a prompt-based learning approach, which treats the assertion classification task as a masked language auto-completion problem. We evaluated the model on six datasets. Our prompt-based method achieved a micro-averaged F-1 of 0.954 on the i2b2 2010 assertion dataset, with ∼1.8% improvements over previous works. In particular, our model showed excellence in detecting classes with few instances (few-shot). Evaluations on five external datasets showcase the outstanding generalizability of the prompt-based method to unseen data. To examine the rationality of our model, we further introduced two rationale faithfulness metrics: comprehensiveness and sufficiency. The results reveal that compared to the "pre-train, fine-tune" procedure, our prompt-based model has a stronger capability of identifying the comprehensive (∼63.93%) and sufficient (∼11.75%) linguistic features from free text. We further evaluated the model-agnostic explanations using LIME. The results imply a better rationale agreement between our model and human beings (∼71.93% in average F-1), which demonstrates the superior trustworthiness of our model.
Student reactions to using interactive tutorials as part of the proPIC study programme

In this chapter, we outline the use of interactive textbooks or tutorials in the form of iBooks that were developed as a major output of the proPIC project. All of these iBooks can also be retrieved on the accompanying project website. Interactive textbooks or tutorials are documents, software, or other media digitally created for the purpose of (interactively) presenting any kind of content and can further comprise a wide variety of tasks for the reader. This chapter focuses on so-called iBooks, a distinct form of e-books available exclusively for the Mac Operating System (OS – by Apple).