Reviewerly/exHarmony huggingface dataset:
Enlightening the Student in Knowledge Distillation

Knowledge distillation is a common method of model compression, which uses large models (teacher networks) to guide the training of small models (student networks). However, the student may find a hard time absorbing the knowledge from a sophisticated teacher due to the capacity and confidence gaps between them. To address this issue, a new knowledge distillation and refinement (KDrefine) framework is proposed to enlighten the student by expending and refining its network structure. In addition, a confidence refinement strategy is utilized to generate adaptive soften logits for efficient distillation. The experiments show that the proposed framework outperforms state-of-the-art methods on both CIFAR-100 and Tiny-ImageNet datasets. The code is available at https://github.com/YujieZheng99/KDrefine.
Efficient Personalized Federated Learning on Selective Model Training

Personalized Federated Learning (FL) handles the data heterogeneous problem by tailoring local models for each distributed data owner. Previous studies first train a highly-adaptable global model and then transfer it for personalization. However, the additional training aggravates burden of resource-limited end devices. Training a personalized local sub-network is a promising efficient solution. It normally prunes the global model by parameters’ scalar magnitude. In this paper, we found that the vector magnitude, i.e. the parameter stability, could further promote personalized FL. Driven by the local data characteristics, the values of some model parameters are hardly changed in their updates. But they consume the same resources as the changed ones. Thus, we propose Star-PFL, a STability-AwaRe algorithm for efficient FL Personalization. In Star-PFL, the data owner focuses on training non-stabilized parameters, and decreases the resource wastes on stabilized ones. Experimental results on two real-world biomedical datasets demonstrate that Star-PFL improves the accuracy (3.1%↑) and decreases the resource costs (communication 36.3%↓, computation 18.3%↓) than 5 typical baselines. The code is available at https://github.com/Guoyeting/Star-PFL.
PMNet: Large-Scale Channel Prediction System for ICASSP 2023 First Pathloss Radio Map Prediction Challenge

This paper describes our pathloss prediction system submitted to the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. We describe the architecture of PMNet, a neural network we specifically designed for pathloss prediction. Moreover, to enhance the prediction performance, we apply several machine learning techniques, including data augmentation, fine-tuning, and optimization of the network architecture. Our system achieves an RMSE of 0.02569 on the provided RadioMap3Dseer dataset, and 0.0383 on the challenge test set, placing it in the 1st rank of the challenge.
YOLOX-B: A Better Yolox Model for Real-Time Driver Behavior Detection

In the coal transportation scene, the object detection model proposed for the driver behavior detection task generally has the problems of inaccurate positioning and difficult detection of small objects, we propose a new model YOLOX-B, which introduces a serialized atrous spatial pyramid pooling structure (S-ASPP), obtains different sizes of receptive field information through serialized atrous convolution, solves the problem of information loss in max-pooling, and maximizes the efficiency of atrous convolution. Meanwhile, by introducing a lightweight feature reorganization module based on transposed convolution, adaptively predicting the up-sampling kernel weight, the model can better complete pixel recovery in a weighted way and improve the detection accuracy of small objects. The experimental results on the publicly available PASCAL VOC 2012 dataset and the self-built driver behavior dataset demonstrate that the YOLOX-B maintains the same inference speed as YOLOX-S, and its mean Average Precisions(mAPs) are improved by 4.4% and 0.8%, respectively.
Product Graph Learning From Multi-Attribute Graph Signals with Inter-Layer Coupling

This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions within and across graph layers. Focusing on a product graph setting with homogeneous layers, we propose a bivariate polynomial graph filter model. We then consider the topology inference problems thru adapting existing spectral methods. We propose two solutions for the required spectral estimation step: a simplified solution via unfolding the multiattribute data into matrices, and an exact solution via nearest Kro-necker product decomposition (NKD). Interestingly, we show that strong inter-layer coupling can degrade the performance of the unfolding solution while the NKD solution is robust to inter-layer coupling effects. Numerical experiments show efficacy of our methods.
CyFi-TTS: Cyclic Normalizing Flow with Fine-Grained Representation for End-to-End Text-to-Speech

Advanced end-to-end text-to-speech (TTS) systems directly generate high-quality speech. These systems demonstrate superior performance on the seen dataset from training. However, inferring speech using unseen transcripts is challenging. Usually, the generated speech tends to be mispronounced because the one-to-many problem creates an information gap between the text and speech. To address these problems, we propose a cyclic normalizing flow with fine-grained representation for end-to-end text-to-speech (CyFi-TTS), which generates natural-sounding speech by bridging the information gap. We leverage a temporal multi-resolution upsampler to progressively produce a fine-grained representation. Furthermore, we adopt a cyclic normalizing flow to produce an acoustic representation through cyclic representation learning. Experimental results reveal that CyFi-TTS directly generates speech with clear pronunciation compared to recent TTS systems. Furthermore, CyFi-TTS achieves a mean opinion score of 4.02 and a character error rate of 1.99%.
GANStrument: Adversarial Instrument Sound Synthesis with Pitch-Invariant Instance Conditioning

We propose GANStrument, a generative adversarial model for instrument sound synthesis. Given a one-shot sound as input, it is able to generate pitched instrument sounds that reflect the timbre of the input within an interactive time. By exploiting instance conditioning, GANStrument achieves better fidelity and diversity of synthesized sounds and generalization ability to various inputs. In addition, we introduce an adversarial training scheme for a pitch-invariant feature extractor that significantly improves the pitch accuracy and timbre consistency. Experimental results show that GANStrument outperforms strong baselines that do not use instance conditioning in terms of generation quality and input editability. Qualitative examples are available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
End-to-End Spoken Language Understanding Using Joint CTC Loss and Self-Supervised, Pretrained Acoustic Encoders

It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information. Popular end-to-end (E2E) SLU models utilize sequence-to-sequence automatic speech recognition (ASR) models to extract textual embeddings as input to infer semantics, which, however, require computationally expensive auto-regressive decoding. In this work, we leverage self-supervised acoustic encoders fine-tuned with Connectionist Temporal Classification (CTC) to extract textual embeddings and use joint CTC and SLU losses for utterance-level SLU tasks. Experiments show that our model achieves 4% absolute improvement over the the state-of-theart (SOTA) dialogue act classification model on the DSTC2 dataset and 1.3% absolute improvement over the SOTA SLU model on the SLURP dataset.
SVMV: Spatiotemporal Variance-Supervised Motion Volume for Video Frame Interpolation

High-performance video frame interpolation is challenging for complex scenes with diverse motion and occlusion characteristics. Existing methods, deploying off-the-shelf flow estimators to acquire initial characterizations refined by multiple subsequent models, often require heavy network architectures that are not practical for resource constrained systems. We investigate the unary potentials of the characterizations to improve efficiency. Specifically, we design a lightweight neural network to construct motion volumes via ensembles of offset approximations, and propose a spatiotemporal variance-aware loss to supervise the network learning. For network compactness, our spatiotemporal variance-supervised motion volume (SVMV) utilizes shared spatiotemporal representations via correlations among approximations, of which the diversifications are exploited to better leverage the network’s expressiveness through the spatiotemporal variances of motions and occlusions within the time interval to be interpolated. Experiments on publicly available datasets show that our method performs favorably against existing methods with a more compact network and less runtime.
Kapsül Ağları Kullanılarak Gerçek ve Sahte Yüz Verilerinin Sınıflandırılması

Recently, with the advancement of technology, artificial intelligence has begun to be used in many areas. It is used in many fields such as artificial intelligence, image processing, natural language processing, suggestion systems. The increase in the use of artificial intelligence has revealed the need for data. This situation has led to the production of new data from existing data. Today, generative adversarial networks (GAN) synthesize a wide variety of data inspired by existing data. These fake data produced from real image data can sometimes cause undesirable situations. It is an important issue to know whether the images, which are especially important for security, are fake or not. In this study, the classification of real human face data and fake face data generate from these data has been made. Fake face data were generated with the help of StyleGAN2-ADA from a small-sized dataset created by collecting facial data of a famous person. It is aimed to classify the generated fake face data and real face data with the capsule network model.
General Category Network: Handwritten Mathematical Expression Recognition with Coarse-Grained Recognition Task

Handwritten Mathematical Expression Recognition (HMER) is an important task in pattern recognition. It is a challenging task due to symbols resembling each other in appearance("z/2", "B/β") and the complex mathematical syntax. The encoder-decoder architecture has been widely used in recent HMER methods. Several works introduce HMER-related tasks that enhance the performance of HMER. We propose the General Category Recognition Task (GCRT) and design the General Category Network(GCN) to perform HMER and GCRT in parallel. GCRT alleviates the symbol confusion problem and also helps the model generate results conforming to mathematical syntax. Compared with SOTA methods, the experimental results show that Expression Recognition Rates(ExpRate) of our method are increased by 1.12%, 2.45% and 1.84% on CROHME 2014, 2016 and 2019 test set respectively. In addition, current works explore the effect of a single auxiliary task on HMER. We investigated the effect of multiple tasks on HMER. We performed many experiments and drew multiple valuable conclusions.We will publish all codes in the future.
Stabilising and Accelerating Light Gated Recurrent Units for Automatic Speech Recognition

The light gated recurrent units (Li-GRU) is well-known for achieving excellent results in automatic speech recognition (ASR) tasks while being lighter and faster to train than a standard gated recurrent units (GRU). However, the unbounded nature of its rectified linear unit on the candidate recurrent gate induces a gradient exploding phenomenon disrupting the training process and preventing it from being applied to medium to large ASR datasets. In this paper, we theoretically and empirically derive the necessary conditions for its stability as well as engineering mechanisms to speed up by a factor of five its training time, hence introducing a novel version of this architecture named SLi-GRU. Then, we evaluate its performance both on a toy task illustrating its newly acquired capabilities and a set of three different ASR datasets demonstrating lower word error rates compared to more complex recurrent neural networks.
Supervised Hierarchical Clustering Using Graph Neural Networks for Speaker Diarization

Conventional methods for speaker diarization involve windowing an audio file into short segments to extract speaker embeddings, followed by an unsupervised clustering of the embeddings. This multistep approach generates speaker assignments for each segment. In this paper, we propose a novel Supervised HierArchical gRaph Clustering algorithm (SHARC) for speaker diarization where we introduce a hierarchical structure using Graph Neural Network (GNN) to perform supervised clustering. The supervision allows the model to update the representations and directly improve the clustering performance, thus enabling a single-step approach for diarization. In the proposed work, the input segment embeddings are treated as nodes of a graph with the edge weights corresponding to the similarity scores between the nodes. We also propose an approach to jointly update the embedding extractor and the GNN model to perform end-to-end speaker diarization (E2E-SHARC). During inference, the hierarchical clustering is performed using node densities and edge existence probabilities to merge the segments until convergence. In the diarization experiments, we illustrate that the proposed E2E-SHARC approach achieves 53% and 44% relative improvements over the baseline systems on benchmark datasets like AMI and Voxconverse, respectively.
HYPERDOC’ING A LEGAL ENGLISH LESSON. MY FIRST EXPERIENCE WITH HYPERDOCS

This article provides an overview of a group of students’ first experience with HyperDocs as a strategy to foster learning English as a Foreign Language (EFL). It then presents students’ insights on their learning experience along with comments and reflections made by their teacher over the process of implementing HyperDocs in a Legal English course at a Colombian private university. This qualitative study involved a focus group, a students’ survey and the students’ artifacts. The findings display the relevance of assisting students in taking control of their own learning process, being critical net surfers and taking advantage of online learning resources.
PERANCANGAN SISTEM INFORMASI ABSENSI KARYAWAN BERBASIS WEB PADA PT. MANDIRI CIPTA SEJAHTERA

Internet technology is needed to get information faster. One of the information technology is used to carry out employee attendance &#x0D; activities. PT. Cipta Mandiri Sejahtera (Morena Rent), a&#x0D; the company engaged in car rental, carries out attendance activities for its employees. Morena Rent takes attendance using a fingerprint machine. However, employee fingerprints are sometimes not detected, the attendance data storage is full, and queues occur when employees gather to take attendance, therefore a computerized system is needed that can solve the problem. The&#x0D; method used for this research is to use the Waterfall model using the PHP and MYSQL programming languages. Web testing is carried out using a black box. The results of this research&#x0D; are to design and build a web-based information system program that is expected to reduce system absence problems. With this website, it is hoped that employees can input absences properly without any more errors, making it easy for the admin to process absence data and leave requests.
Improving Occluded Human Pose Estimation Via Linked Joints

Keypoint heatmaps, which produce peak values of Gaussian distributions for individual human joints, are crucial components in 2D human pose estimation. However, existing merits using keypoint heatmaps are usually defeated in body occlusion, resulting in inaccurate joint predictions. We consider that the failure is mainly due to keypoint heatmaps’ insufficiency for distinguishing the joints from two occluded bodies. Therefore, in this paper, we propose a method termed SkeletonMap (SMap), which introduces the prior knowledge of body structure to constrain relative connection of joints. As an extension of keypoint heatmaps, SMap can be efficiently plugged into existing 2D human pose estimation models with negligible increase in computational cost. Extensive experiments are conducted to show the effectiveness and generalization of SMap. Without bells and whistles, SMap brings a significant performance boost to the existing heatmap-based 2D human pose estimation models. On the MPII dataset, SMap improves SimpleBaseline (ResNet-152) from89.7 PCKh@0.5 to 90.4, and HRNet (W32) from 90.5 to 90.9. On the COCO dataset, SMap improves SimpleBaseline (ResNet-152) from 72.4 AP to 73.9. On the more challenging OCHuman dataset, SMap improves HRNet (W32) from 61.9 AP to 64.5, achieving 2.6 AP gains. We hope our simple and efficient approach will serve as a solid component for future research in 2D human pose estimation.
MSN-net: Multi-Scale Normality Network for Video Anomaly Detection

Existing unsupervised video anomaly detection methods often suffer from performance degradation due to the overgeneralization of deep models. In this paper, we propose a simple yet effective Multi-Scale Normality network (MSN-net) that uses hierarchical memories to learn multi-level prototypical spatial-temporal patterns of normal events. Specifically, the hierarchical memory module interacts with the encoder through the reading and writing operations during the training phase, preserving multi-scale normality in three separate memory pools. Then, the decoder decodes the features rewritten by the memorized normality to predict future frames so that its ability to predict anomalies is diminished. Experimental results show that MSN-net performs comparably to the state-of-the-art methods, and extension analysis demonstrates the effectiveness of multi-scale normality learning.
CSSE - An agnostic method of counterfactual, selected, and social explanations for classification models

In some contexts, achieving high predictive capability may be sufficient for a machine learning model. However, in many scenarios, it is necessary to understand the model’s decisions to increase confidence in the predictions and direct the actions to be taken based on them. Therefore, it is essential to provide interpretable models. However, some authors have pointed out the need to improve current interpretability methods to provide adequate explanations, especially for non-specialists in machine learning. The solution is to expand studies beyond computational issues to understand better how people receive explanations. Based on the literature, we identified three aspects to be considered in the explanations: contrastive, selected, and social. The counterfactual approach, contrastive in nature, inform the user of how the decision by the model can be altered through minimal changes to the input features. Given this, we introduce the Agnostic Method of Counterfactual, Selected, and Social Explanations (CSSE), capable of generating local explanations for classification models using a genetic algorithm. Thus, as contributions, we highlight that the CSSE offers counterfactual explanations from learning models, presents explanations with diversity, without prolixity, and allows the user to restrict the features that appear in the explanation (actionability), besides other parameterization options for the user to communicate their preferences. A particular novelty of our work is the possibility for the user to adjust the importance he will give to sparsity (minimum number of changes) or similarity (minimizing the distance). Furthermore, we indicate other possibilities for the actionability functionality, inherently used to lock immutable features, allowing users to block features according to their interests or expertise. These resources can help the user obtain explanations more targeted to their objective and advance further in interpretability, considering computational and social aspects in generating explanations. The experiments showed that CSSE presents relevant results compared to some existing approaches. The work also includes a case study of predicting the academic performance of children and adolescents with ADHD, in which we applied the CSSE. Thus, the proposed method advances interpretability by offering explanations aimed at the end user, which can generate greater acceptance, confidence, and understanding regarding the models’ decisions. The method implementation is available at https://codeocean.com/capsule/7060371/tree.
Mining periodic trends via closed high utility patterns

High utility pattern mining (HUPM) plays a significant role in data mining technologies. Traditional HUPM algorithms may produce a large number of high utility patterns (HUPs) when the database is dense or the data is massive. To address this issue, closed high utility pattern (CHUP) mining was proposed, providing a high-level overview of the HUPs and helpful information for decision-makers. However, CHUPs do not consider the factors of period and recency. Therefore, this paper is the first to introduce period and recency into closed high utility pattern mining and proposes the CPR-Miner algorithm to mine closed periodic recent high utility patterns. These patterns have more practical value since they are closed sets of HUPs. Due to the increasing number of factors to be considered, new upper bounds and pruning strategies are also proposed, significantly improving the algorithm's efficiency. To test the performance of our algorithm and our new pruning strategies, we improved the PHM algorithm to generate the PR-Miner algorithm. Experimental results show a significant efficiency of the new pruning strategies and demonstrate that CPR-Miner outperforms the PR-Miner algorithm in all aspects.
Boosting Prompt-Based Few-Shot Learners Through Out-of-Domain Knowledge Distillation

Prompt-based learning improves the performance of Pre-trained Language Models (PLMs) over few-shot learning and is suitable for low-resourced scenarios. However, it is challenging to deploy large PLMs online. Knowledge Distillation (KD) can compress large PLMs into small ones; yet, few-shot KD for prompt-tuned PLMs is challenging due to the lack of training data and the capacity gap between teacher and student models. We propose Boost-Distiller, the first few-shot KD algorithm for prompt-tuned PLMs with the help of the out-of-domain data. Apart from distilling the model logits, Boost-Distiller specifically considers heuristically-generated fake logits that improve the generalization abilities of student models. We further leverage the cross-domain model logits, weighted with domain expertise scores that measure the transferablity of out-of-domain instances. Experiments over various datasets show Boost-Distiller consistently outperforms baselines by a large margin.
Last: Scalable Lattice-Based Speech Modelling in Jax

We introduce LAST, a LAttice-based Speech Transducer library in JAX. With an emphasis on flexibility, ease-of-use, and scalability, LAST implements differentiable weighted finite state automaton (WFSA) algorithms needed for training & inference that scale to a large WFSA such as a recognition lattice over the entire utterance. Despite these WFSA algorithms being well-known in the literature, new challenges arise from performance characteristics of modern architectures, and from nuances in automatic differentiation. We describe a suite of generally applicable techniques employed in LAST to address these challenges, and demonstrate their effectiveness with benchmarks on TPUv3 and V100 GPU.