Reviewerly/exHarmony huggingface dataset:
SPL-MLL: Selecting Predictable Landmarks for Multi-label Learning

Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels. Furthermore, modeling the relationships between input and some (dull) classes further increases the difficulty of accurately predicting all possible labels. In this work, we propose to select a small subset of labels as landmarks which are easy to predict according to input (predictable) and can well recover the other possible labels (representative). Different from existing methods which separate the landmark selection and landmark prediction in the 2-step manner, the proposed algorithm, termed Selecting Predictable Landmarks for Multi-Label Learning (SPL-MLL), jointly conducts landmark selection, landmark prediction, and label recovery in a unified framework, to ensure both the representativeness and predictableness for selected landmarks. We employ the Alternating Direction Method (ADM) to solve our problem. Empirical studies on real-world datasets show that our method achieves superior classification performance over other state-of-the-art methods.
Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders

Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the chat summarization highly context dependent. In this work, we propose a novel unsupervised framework called RankAE to perform chat summarization without employing manually labeled data. RankAE consists of a topic-oriented ranking strategy that selects topic utterances according to centrality and diversity simultaneously, as well as a denoising auto-encoder that is carefully designed to generate succinct but context-informative summaries based on the selected utterances. To evaluate the proposed method, we collect a large-scale dataset of chat logs from a customer service environment and build an annotated set only for model evaluation. Experimental results show that RankAE significantly outperforms other unsupervised methods and is able to generate high-quality summaries in terms of relevance and topic coverage.
SmartWalk BAN: Using Body Area Networks to Encourage Older Adults to Perform Physical Activity

Due to the demographic ageing of the world’s population and the respective consequences, it is necessary to guarantee that older adults can be active and maintain their independence and autonomy for longer. The aim of the SmartWalk system is to promote walks in the city in order to stimulate physically active lifestyles. Body area networks are used to aggregate data collected by different type of sensors, which are transmitted to a server to support informed decisions of caregivers when planning physical activities for their care receivers. This article presents the SmartWalk system and reports an experimental setup that was developed to assess the performance of the current implementation and the respective critical components. According to the results, the SmartWalk system presents good performance in terms of battery usage, data upload, capacity to recover from connectivity failures and wireless coverage of its body area network.
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization

The early phase of training has been shown to be important in two ways for deep neural networks. First, the degree of regularization in this phase significantly impacts the final generalization. Second, it is accompanied by a rapid change in the local loss curvature influenced by regularization choices. Connecting these two findings, we show that stochastic gradient descent (SGD) implicitly penalizes the trace of the Fisher Information Matrix (FIM) from the beginning of training. We argue it is an implicit regularizer in SGD by showing that explicitly penalizing the trace of the FIM can significantly improve generalization. We further show that the early value of the trace of the FIM correlates strongly with the final generalization. We highlight that in the absence of implicit or explicit regularization, the trace of the FIM can increase to a large value early in training, to which we refer as catastrophic Fisher explosion. Finally, to gain insight into the regularization effect of penalizing the trace of the FIM, we show that it limits memorization by reducing the learning speed of examples with noisy labels more than that of the clean examples, and 2) trajectories with a low initial trace of the FIM end in flat minima, which are commonly associated with good generalization.
Video Captioning of Future Frames

Being able to anticipate and describe what may happen in the future is a fundamental ability for humans. Given a short clip of a scene about "a person is sitting behind a piano", humans can describe what will happen afterward, i.e. "the person is playing the piano". In this paper, we consider the task of captioning future events to assess the performance of intelligent models on anticipation and video description generation tasks simultaneously. More specifically, given only the frames relating to an occurring event (activity), the goal is to generate a sentence describing the most likely next event in the video. We tackle the problem by first predicting the next event in the semantic space of convolutional features, then fusing contextual information into those features, and feeding them to a captioning module. Departing from using recurrent units allows us to train the network in parallel. We compare the proposed method with a baseline and an oracle method on the ActivityNet-Captions dataset. Experimental results demonstrate that the proposed method outperforms the baseline and is comparable to the oracle method. We perform additional ablation study to further analyze our approach.
Characterization and mapping of hematite ore mineral classes using hyperspectral remote sensing technique: a case study from Bailadila iron ore mining region

Abstract The study demonstrates a methodology for mapping various hematite ore classes based on their reflectance and absorption spectra, using Hyperion satellite imagery. Substantial validation is carried out, using the spectral feature fitting technique, with the field spectra measured over the Bailadila hill range in Chhattisgarh State in India. The results of the study showed a good correlation between the concentration of iron oxide with the depth of the near-infrared absorption feature (R 2 = 0.843) and the width of the near-infrared absorption feature (R 2 = 0.812) through different empirical models, with a root-mean-square error (RMSE) between &lt; 0.317 and &lt; 0.409. The overall accuracy of the study is 88.2% with a Kappa coefficient value of 0.81. Geochemical analysis and X-ray fluorescence (XRF) of field ore samples are performed to ensure different classes of hematite ore minerals. Results showed a high content of Fe &gt; 60 wt% in most of the hematite ore samples, except banded hematite quartzite (BHQ) (&lt; 47 wt%).
Efficient Deep Image Denoising via Class Specific Convolution

Deep neural networks have been widely used in image denoising during the past few years. Even though they achieve great success on this problem, they are computationally inefficient which makes them inappropriate to be implemented in mobile devices. In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification. Despite using a computationally efficient network cannot effectively remove the noises from any content, it is still capable to denoise from a specific type of pattern or texture. The proposed method follows such a divide and conquer scheme. We first use an efficient U-net to pixel-wisely classify pixels in the noisy image based on the local gradient statistics.Then we replace part of the convolution layers in existing denoising networks by the proposed Class Specific Convolution layers (CSConv) which use different weights for different classes of pixels. Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method can reduce the computational costs without sacrificing the performance compared to state-of-the-art algorithms.
Multi-Scale Progressive Attention Network for Video Question Answering

Understanding the multi-scale visual information in a video is essential for Video Question Answering (VideoQA).Therefore, we propose a novel Multi-Scale Progressive Attention Network (MSPAN) to achieve relational reasoning between cross-scale video information.We construct clips of different lengths to represent different scales of the video.Then, the cliplevel features are aggregated into node features by using max-pool, and a graph is generated for each scale of clips.For cross-scale feature interaction, we design a message passing strategy between adjacent scale graphs, i.e., topdown scale interaction and bottom-up scale interaction.Under the question's guidance of progressive attention, we realize the fusion of all-scale video features.Experimental evaluations on three benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA show our method has achieved state-of-the-art performance.
An image denoising method based on BP neural network optimized by improved whale optimization algorithm

Abstract As an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.
Mind-the-Gap! Unsupervised Domain Adaptation for Text-Video Retrieval

When can we expect a text-video retrieval system to work effectively on datasets that differ from its training domain? In this work, we investigate this question through the lens of unsupervised domain adaptation in which the objective is to match natural language queries and video content in the presence of domain shift at query-time. Such systems have significant practical applications since they are capable generalising to new data sources without requiring corresponding text annotations. We make the following contributions: (1) We propose the UDAVR (Unsupervised Domain Adaptation for Video Retrieval) benchmark and employ it to study the performance of text-video retrieval in the presence of domain shift. (2) We propose Concept-Aware-Pseudo-Query (CAPQ), a method for learning discriminative and transferable features that bridge these cross-domain discrepancies to enable effective target domain retrieval using source domain supervision. (3) We show that CAPQ outperforms alternative domain adaptation strategies on UDAVR.
Virtual reality system for industrial training

The virtual training system is mainly aimed at practical problems such as highly training costs, much producing time, and repeated training for various dangerous production courses in the industrial manufacturing fields. This project builds a realistic virtual environment and provides a reliable visual operations for industrial production. The system includes technical support such as reproduction of real producing environment, multi- dimensional data input, demonstration of various processes, establishment of interactive feedback system, and error demonstration. The construction of this system provides a solution for the training of workers in industrial production enterprises and the improvement of industrial safety production efficiency and quality.
Distilling Knowledge by Mimicking Features

Knowledge distillation (KD) is a popular method to train efficient networks ("student") with the help of high-capacity networks ("teacher"). Traditional methods use the teacher's soft logits as extra supervision to train the student network. In this paper, we argue that it is more advantageous to make the student mimic the teacher's features in the penultimate layer. Not only the student can directly learn more effective information from the teacher feature, feature mimicking can also be applied for teachers trained without a softmax layer. Experiments show that it can achieve higher accuracy than traditional KD. To further facilitate feature mimicking, we decompose a feature vector into the magnitude and the direction. We argue that the teacher should give more freedom to the student feature's magnitude, and let the student pay more attention on mimicking the feature direction. To meet this requirement, we propose a loss term based on locality-sensitive hashing (LSH). With the help of this new loss, our method indeed mimics feature directions more accurately, relaxes constraints on feature magnitudes, and achieves state-of-the-art distillation accuracy. We provide theoretical analyses of how LSH facilitates feature direction mimicking, and further extend feature mimicking to multi-label recognition and object detection.
Remote Sensing Image Object Detection Based on Angle Classification

Arbitrarily-oriented object detection is a challenging task. Since the object orientation in remote sensing images is arbitrary, using horizontal bounding boxes will lead to low detection accuracy. Existing regression-based rotation detectors can lead to the problem of boundary discontinuity. In this paper, we propose a remote sensing image object detection method based on angle classification that uses rotation detection bounding boxes with angle information to detect objects. Specifically, we incorporate the neural architecture search framework with feature pyramid network (NAS-FPN) module in a dense detector (RetinaNet) and use a binary encoding method in angle classification. This method reduces the background influence, so that there is almost no overlap between detection boxes. Based on the angles of the detection boxes, we can infer the information of the motion direction of the target and further determine the motion trajectory of the target. We conducted ablation experiments on a large publicly available dataset for object detection in an aerial imagery (DOTA) dataset to verify the effectiveness of each module in the method and compared the method with several other detection methods. The experimental results demonstrate the effectiveness of our method.
SignGD with error feedback meets lazily aggregated technique: Communication-efficient algorithms for distributed learning

The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems. However, the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning systems. In this paper, we design two communication-efficient algorithms for distributed learning tasks. The first one is named EF-SIGNGD, in which we use the 1-bit (sign-based) gradient quantization method to save the communication bits. Moreover, the error feedback technique, i.e., incorporating the error made by the compression operator into the next step, is employed for the convergence guarantee. The second algorithm is called LE-SIGNGD, in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated information. LE-SIGNGD saves communication costs both in transmitted bits and communication rounds. Furthermore, we show that LE-SIGNGD is convergent under some mild assumptions. The effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.
Unsupervised Paraphrasing with Pretrained Language Models

Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures.However, previous explorations have largely focused on supervised methods, which require a large amount of labeled data that is costly to collect.To address this drawback, we adopt a transfer learning approach and propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting.Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking (DB).To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step.We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair (QQP) and the ParaNMT datasets and is robust to domain shift between the two datasets of distinct distributions.We also demonstrate that our model transfers to paraphrasing in other languages without any additional finetuning.
Single Image Haze Removal Based on a Simple Additive Model With Haze Smoothness Prior

Single image haze removal, which is to recover the clear version of a hazy image, is a challenging task in computer vision. In this paper, an additive haze model is proposed to approximate the hazy image formation process. In contrast with the traditional optical model, it regards the haze as an additive layer to a clean image. The model thus avoids estimating the medium transmission rate and the global atmospherical light. In addition, based on a critical observation that haze changes gradually and smoothly across the image, a haze smoothness prior is proposed to constrain this model. This prior assumes that the haze layer is much smoother than the clear image. Benefiting from this prior, we can directly separate the clean image from a single hazy image. Experimental results and comparisons with synthetic images and real-world images demonstrate that the proposed method outperforms state-of-the-art single image haze removal algorithms.
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving

Math word problem solving has attracted considerable research interest in recent years.Previous works have shown the effectiveness of utilizing graph neural networks to capture the relationships in the problem.However, these works did not carefully take the edge label information and the long-range word relationship across sentences into consideration.In addition, during generation, they focus on the most relevant areas of the currently generated word, while neglecting the rest of the problem.In this paper, we propose a novel Edge-Enhanced Hierarchical Graph-to-Tree model (EEH-G2T), in which the math word problems are represented as edge-labeled graphs.Specifically, an edge-enhanced hierarchical graph encoder is used to incorporate edge label information.This encoder updates the graph nodes hierarchically in two steps: sentence-level aggregation and problem-level aggregation.Furthermore, a tree-structured decoder with a split attention mechanism is applied to guide the model to pay attention to different parts of the input problem.Experimental results on the MAWPS and Math23K dataset showed that our EEH-G2T can effectively improve performance compared with state-of-the-art methods.
Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network

Clinically, red blood cell abnormalities are closely related to tumor diseases, red blood cell diseases, internal medicine, and other diseases. Red blood cell classification is the key to detecting red blood cell abnormalities. Traditional red blood cell classification is done manually by doctors, which requires a lot of manpower produces subjective results. This paper proposes an Attention-based Residual Feature Pyramid Network (ARFPN) to classify 14 types of red blood cells to assist the diagnosis of related diseases. The model performs classification directly on the entire red blood cell image. Meanwhile, a spatial attention mechanism and channel attention mechanism are combined with residual units to improve the expression of category-related features and achieve accurate extraction of features. Besides, the RoI align method is used to reduce the loss of spatial symmetry and improve classification accuracy. Five hundred and eighty eight red blood cell images are used to train and verify the effectiveness of the proposed method. The Channel Attention Residual Feature Pyramid Network (C-ARFPN) model achieves an mAP of 86%; the Channel and Spatial Attention Residual Feature Pyramid Network (CS-ARFPN) model achieves an mAP of 86.9%. The experimental results indicate that our method can classify more red blood cell types and better adapt to the needs of doctors, thus reducing the doctor's time and improving the diagnosis efficiency.
Neural echo state network using oscillations of gas bubbles in water

In the framework of physical reservoir computing (RC), machine learning algorithms designed for digital computers are executed using analog computerlike nonlinear physical systems that can provide energy-efficient computational power for predicting time-dependent quantities that can be found using nonlinear differential equations. We suggest a bubble-based RC (BRC) system that combines the nonlinearity of an acoustic response of a cluster of oscillating gas bubbles in water with a standard echo state network (ESN) algorithm that is well suited to forecast chaotic time series. We confirm the plausibility of the BRC system by numerically demonstrating its ability to forecast certain chaotic time series similarly to or even more accurately than ESN.
How to Improve the Digital Competence for E-Learning?

Digital competence for learning (skills, knowledge and attitudes needed for learning with digital devices) is a factor that affects the effectiveness of both the traditional and e-learning process. More specifically, technical competence is considered to be one of the four pillars of successful e-learning. Several studies show that in practice, digital technology has not always been used successfully, even in countries with very high digital readiness. Therefore, it is important to assess the different dimensions of digital competence for learning and analyse the interrelations between these dimensions in order to make suggestions for advancements. In our study, we applied a test with students from primary and lower secondary schools in Estonia to assess their digital competence for learning and used Structural Equation Modelling to understand how attitudes predict digital skills and knowledge that can be acquired in the individual and social settings. The findings confirm that only behavioural intention to use digital devices predicts the development of digital skills and knowledge. Moreover, some knowledge and skills acquired in the individual settings predict the development of knowledge and skills acquired in the social settings. The study provides researchers and practitioners with suggestions for improving the structure and quality of e-learning.