Reviewerly/exHarmony huggingface dataset:
Attention-Based Deep Autoencoder for Hyperspectral Image Denoising

Hyperspectral Image (HSI) denoising is a crucial pre-processing task due to its widespread applications in areas that include geology, medicine, agriculture, surveillance, and the food industry. Denoising HSI data improves high-level vision tasks like classification, object tracking, and video surveillance. In this work, we propose a novel attention-based deep auto-encoder for HSI denoising which learns a mapping from noisy observation to clean the data. To exploit the spatial-spectral information present in HSI data, 3D symmetric convolution and deconvolution are used. The 3D features extracted from 3D convolutional layers (CL) helps the restoration across the depth dimension. However, feature maps obtained from the convolutional layers are localized and are not able to capture long-range dependencies in the data. Furthermore, CL does not differentiate between low and high degradation levels; producing smoothed results in low-textured areas of the image and artifacts in the high-textured regions. To avoid these adverse effects, attention blocks are plugged into the network to focus on more relevant features during the restoration process. Features obtained from attention blocks are fed as symmetric skip connections into the corresponding deconvolution layers. The proposed model is fully convolutional; it can process 3D images of arbitrary dimensions during training and inference. The experimental results show that our proposed model: Attention-based Deep Auto-encoder (AbDAE) outperforms the state-of-the-art methods in terms of visual as well as quantitative results.
DenseASPP Enriched Residual Network Towards Visual Saliency Prediction

Predicting regions of interest, otherwise called salient regions tend to top out with the rise of deep learning techniques. Although convolutional neural networks have evaded the domain to let it reach newer heights, there still exists room for improvement on how to integrate the hierarchical features efficiently. In fact, the rich features at multiple spatial scales are found to be powerful towards accurate prediction. This paper proposes a novel end-to-end visual saliency prediction technique, based on DenseASPP (Dense Atrous Spatial Pyramid Pooling) and residual connections. It enriches the multi-scale contextual features via DenseASPP module, that gathers information via dense connections across multiple scales. Further, incorporation of residual connections between encoder and decoder blocks allow learning of more robust features that can result in better prediction. The model is trained on the largest dataset for saliency prediction, SALICON and experimental results on two public datasets, OSIE and PASCAL-S verify the effectiveness of the proposed framework compared with state-of-the-art results.
Video Enhancement with Single Frame

Hazy scenes captured by the camera in a hazy environment are the main cause of the deterioration of captured video quality. Hazy environmental conditions degrade the quality of captured videos which leads to poor visibility and color distortion in videos. In this paper, we propose a video enhancement technique with a single frame dehazing encoder-decoder network. The proposed method focuses to restore the haze-free video from the hazy video by developing an encoder-decoder-based dehazing network. The framework comprises of encoder block (EB) and decoder block (DB). Both EB and DB are used to extract the haze-relevant feature maps and to give more attention to haze-specific features by re-calibrating the weights of learned feature maps automatically and helps to recover the haze-free frame. The use of dilated residual module (DRM) helps to improve output results and gain more contextual information by widening the receptive field. The extensive analysis of the proposed method is carried out by considering benchmark synthetic hazy video databases and analyzed quantitative results. Experimental results show that the proposed method out performs the other state-of-the-art (SOTA) existing approaches for video frame dehazing.
Performance Evaluation of Deep Learning Models for Ship Detection

Although enormous success is achieved in the field of object detection, yet ship detection in high-resolution images is still an crucial task. Ship detection from optical remote sensing images plays a significant role in military and civil applications. For maritime surveillance, monitoring and traffic supervision ship detection deserves optimal solutions to identify objects accurately with faster speed. In this work, various object detection methods such as You Only Look Once (YOLO) v3, YOLO v4, RetinaNet152, EfficientDet-D2 and Faster-RCNN have been implemented to improve efficiency, speed and accuracy. Numerous experiments were conducted to evaluate the efficiency of object detection methods. The YOLO v4 with custom selection of anchor boxes using K-means++ clustering algorithm outperformed as compared to other detection methods in terms of accuracy, which is evaluated using COCO metrics, training and detection time. All the experiments are performed on the Airbus detection dataset from https://www.kaggle.com/c/airbus-ship-detection .
Fuzzy Entropy k-Plane Clustering Method and Its Application to Medical Image Segmentation

MRI images are complex, and the data distribution of tissues in MRI are non-spherical and overlapping in nature. Plane-based clustering methods are more efficient in comparison to centroid based clustering for non-spherical data, and soft clustering methods can efficiently handle the overlapping nature by representing clusters in terms of fuzzy sets. In this paper, we propose fuzzy entropy k-plane clustering (FEkPC), which incorporates the fuzzy partition entropy term with a fuzzy entropy parameter in the optimization problem of the conventional kPC method. The fuzzy entropy parameter controls the degree of fuzziness, the same as the fuzzifier parameter in the fuzzy clustering method. The fuzzy entropy terms try to minimize averaged non-membership degrees in the cluster. The performance of the proposed method has been evaluated over three publicly available MRI datasets: one simulated and two real human brain MRI datasets. The experimental results show that the proposed FEkPC method outperforms other state-of-the-art methods in terms of ASA and Dice Score.
Melanoma Cell Detection by Using K-means Clustering Segmentation and Abnormal Cell Detection Technique

Melanoma diagnosis in earlier stage is a challenging task, and it is ideally done by the pathologist to automate the detection of melanoma cells. Detection of melanocytes from histopathological image is difficult because melanocytes are very much similar to the keratinocytes. To detect melanoma cells at first, segmentation is required followed by calculation of perimeter of the cells. If it is greater than the threshold value, then it is identified as melanoma cells. K-means clustering unsupervised learning technique is used to segment the image followed by ellipse fitting algorithm to find out abnormally large cells. The centroid is then calculated and marked as nuclei. Experiment is performed on 30 images and is evaluated based on PPV, sensitivity, and NSR. The proposed work achieved 90% PPV, 85% sensitivity, and 13% SNR value.
EMD-Based Binary Classification of Mammograms

Mammography is an inexpensive and noninvasive imaging tool that is commonly used in detection of breast lesions. However, manual analysis of a mammogramic image can be both time intensive and prone to unwanted error. In recent times, there has been a lot of interest in using computer-aided techniques to classify medical images. The current study explores the efficacy of an Earth Mover's Distance (EMD)-based mammographic image classification technique to identify the benign and the malignant lumps in the images. We further present a novel leader recognition (LR) technique which aids in the classification process to identify the most benign and malignant images from their respective cohort in the training set. The effect of image diversity in training sets on classification efficacy is also studied by considering training sets of different sizes. The proposed classification technique is found to identify malignant images with up to $$80\%$$ sensitivity and also provides a maximum F1 score of $$72.73\%$$ .
Anti-Occlusion Target Tracking Based on Joint Confidence

Abstract Aiming to the challenge of occlusion during tracking, this paper proposes an anti-occlusion tracking based on joint confidence. Under the framework of the kernel correlation filter (KCF) tracking, the dimension of the feature is extended to construct a robust target appearance model, and the size of the target is estimated during the tracking process. We first judge whether occlusion occurs or not by the measurement by combining the maximum of the detection response map with the average peak correlation energy, then design the corresponding anti-interference tracking strategy. If the occlusion does not occur during the tracking process, the KCF tracking is performed, otherwise, re-detection is introduced to locate the target position, and the region corresponding to the re-detection is added to the regulation term of the KCF for context learning. The fusion of the filter template before occlusion and the context model learned during occlusion is used to locate the target and to update the model. Experimental evaluations on the datasets OTB2013, OTB100 and TC128 show that compared with the state-of-the-art algorithms such as KCF, Siamese and other algorithms, our proposed algorithm has stronger robustness and higher tracking accuracy when occlusion occurs.
Dns Tunneling Detection with New Patterns Emerging: A Forest-Based Classifier with an Unknown Option

Arbitrary data on a computer having external network connectivity can be sent to the attacker by making Domain Name System ( DNS ) queries. This technique is known as DNS tunneling. DNS tunneling detection can be seen as a classification task in the view of machine learning. However, the traditional classifiers cannot accurately predict the classes of queries whose patterns do not appear during training and these queries are inevitable in the live network. For convenience, we call them unknown queries. This paper first discussed the risks of DNS tunneling and the difficulties faced by DNS tunneling detection. Then we described DNS tunneling detection as a machine learning problem, i.e., Classification with New Patterns Emerging ( CNPE ), and presented its definition. Finally, we proposed a forest-based classifier to tackle this problem. Our model can identify unknown samples as a new class as well as accurately classify samples that are not unknown. The computational overhead of our model is near-linear with respect to the number of samples, and it is memory-friendly. Experiments are conducted on queries obtained from the live network and on public datasets . Experimental results show the effectiveness of the proposed algorithm.
Quantifying dynamical total coherence in a resource non-increasing framework

We quantify the dynamical quantum resource in the resource non-increasing (RNI) framework, namely the free dynamical resource is defined by the channels that cannot increase the static “resourcefulness” of any input state. We present two kinds of approaches to quantifying the dynamical resource, the distance measures and the maximal increasing static resource. As a demonstration, we quantify the dynamical total coherence with our presented measures. It is shown that the distance-based measures have good operational interpretation through quantum processing tasks and can be numerically calculated by semidefinite programming (SDP) and the measures based on the maximal increasing static resource could lead to the analytical solution. As an application, we consider the dynamical total coherence of the qubit amplitude damping channel. Both the analytical measure based on the static $$l_2$$ norm and the numerical illustrations based on the SDP are given.
ActiveMatch: End-To-End Semi-Supervised Active Representation Learning

Semi-supervised learning (SSL) is an efficient framework that can train models with both labeled and unlabeled data, but may generate ambiguous and non-distinguishable representations when lacking adequate labeled samples. With human-in-the-loop, active learning can iteratively select informative unlabeled samples for labeling and training to improve the performance in the SSL framework. However, most existing active learning approaches rely on pre-trained features, which is not suitable for end-to-end learning. To deal with the drawbacks of SSL, in this paper, we propose a novel end-to-end representation learning method, namely ActiveMatch, which combines SSL with contrastive learning and active learning to fully leverage the limited labels. Starting from a small amount of labeled data with unsupervised contrastive learning as a warm-up, ActiveMatch then combines SSL and supervised contrastive learning, and actively selects the most representative samples for labeling during the training, resulting in better representations towards the classification. Compared with MixMatch and FixMatch with the same amount of labeled data, we show that ActiveMatch achieves the state-of-the-art performance, with 89.24% accuracy on CIFAR-10 with 100 collected labels, and 92.20% accuracy with 200 collected labels.
Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the network weights. This minimization corresponds to the common approach of training a neural network with weight decay. We quantify the performance (mean-squared error) of these neural network estimators when the data-generating function belongs to the second-order Radon-domain bounded variation space. This space of functions was recently proposed as the natural function space associated with shallow ReLU neural networks. We derive a minimax lower bound for the estimation problem for this function space and show that the neural network estimators are minimax optimal up to logarithmic factors. This minimax rate is immune to the curse of dimensionality. We quantify an explicit gap between neural networks and linear methods (which include kernel methods) by deriving a linear minimax lower bound for the estimation problem, showing that linear methods necessarily suffer the curse of dimensionality in this function space. As a result, this paper sheds light on the phenomenon that neural networks seem to break the curse of dimensionality.
Controllable Dialogue Generation With Disentangled Multi-Grained Style Specification and Attribute Consistency Reward

Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs. In this paper, we propose a controllable dialogue generation model to steer response generation under multi-attribute constraints. Specifically, we define and categorize the commonly-used control attributes into global and local ones, which possess different granularities of effects on response generation. Then, we significantly extend the conventional seq2seq framework by introducing a novel two-stage decoder, which first uses a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-grained</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>style specification layer</b></i> to impose the stylistic constraints and determine word-level control states of responses based on the attributes, and then employs a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>response generation layer</i></b> to generate final responses maintaining both semantic relevancy to the contexts and fidelity to the attributes. Furthermore, we train our model with an attribute consistency reward to promote response control with explicit supervision signals. Extensive experiments and in-depth analyses on two datasets indicate that our model can significantly outperform competitive baselines in terms of response quality, content diversity and controllability.
Sensor-Based Hand Gesture Detection and Recognition by Key Intervals

This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is regarded as an object with a pair of key intervals. The detection and classification of each gesture are equivalent to the identification and matching of the corresponding key intervals. A simple automatic labelling is proposed for the identification of key intervals without manual inspection of sensory data. This could facilitate the collection and annotation of training data. To attain superior generalization and regularization, a multitask learning algorithm for the simultaneous training for gesture detection and classification is proposed. A prototype system based on smart phones for remote control of home appliances was implemented for the performance evaluation. Experimental results reveal that the proposed algorithm provides an effective alternative for applications where accurate detection and classification of hand gestures by simple networks are desired.
A Comprehensive Review on Temporal-Action Proposal Generation

Temporal-action proposal generation (TAPG) is a well-known pre-processing of temporal-action localization and mainly affects localization performance on untrimmed videos. In recent years, there has been growing interest in proposal generation. Researchers have recently focused on anchor- and boundary-based methods for generating action proposals. The main purpose of this paper is to provide a comprehensive review of temporal-action proposal generation with network architectures and empirical results. The pre-processing step for input data is also discussed for network construction. The content of this paper was obtained from the research literature related to temporal-action proposal generation from 2012 to 2022 for performance evaluation and comparison. From several well-known databases, we used specific keywords to select 71 related studies according to their contributions and evaluation criteria. The contributions and methodologies are summarized and analyzed in a tabular form for each category. The result from state-of-the-art research was further analyzed to show its limitations and challenges for action proposal generation. TAPG performance in average recall ranges from 60% up to 78% in two TAPG benchmarks. In addition, several future potential research directions in this field are suggested based on the current limitations of the related studies.
On the inductive biases of deep domain adaptation

Even if it is not always stated explicitly, the majority of recent approaches to domain adaptation are based on theoretically unjustified assumptions, on the one hand, and (often hidden) inductive biases on the other hand. This paper highlights that achieving feature alignment, which is commonly assumed to minimize theoretical upper bounds on risk in the target domain, does not guarantee low target risk. Furthermore, through a series of experiments, this paper reveals that deep domain adaptation methods heavily rely on hidden inductive biases present in common practices, including model pretraining and encoder architecture design. In a third step, the paper argues that using handcrafted priors might not be sufficient to bridge distant domains: powerful parametric priors can be instead learned from data, leading to a large improvement in target accuracy. This paper proposes a meta-learning strategy for discovering inductive biases that effectively solve specific domain transfers. It outperforms handcrafted priors on several image classification tasks.
Blockchain for requirements traceability: A qualitative approach

Abstract Blockchain technology has emerged as a “disruptive innovation” that has received significant attention in academic and organizational settings. However, most of the existing research is focused on technical issues of blockchain systems, overlooking the organizational perspective. This study adopted a grounded theory to unveil the blockchain implementation process in organizations from the lens of blockchain experts. The results revealed three main categories: key activities, success factors, and challenges related to blockchain implementation in organizations, the latter being identified as the core category, along with 17 other concepts. Findings suggested that the majority of blockchain projects stop at the pilot stage and outlined organizational resistance to change as the core challenge. According to the experts, the following factors contribute to the organizational resistance to change: innovation–production gap, conservative management, and centralized mentality. The study aims to contribute to the existing blockchain literature by providing a holistic and domain‐agnostic view of the blockchain implementation process in organizational settings. This can potentially encourage the development and implementation of blockchain solutions and guide practitioners who are interested in leveraging the inherent benefits of this technology. In addition, the results are used to improve a blockchain‐enabled requirements traceability framework proposed in our previous paper.
Under Pressure: Cognitive Effort During Website-Based Task Performance is Associated with Pupil Size, Visual Exploration, and Users’ Intention to Recommend

We aimed to validate objective usability methods to measure cognitive workload continuously that could complement traditional methods such as the NASA-TLX scale. To achieve this goal, we investigated advanced eye-tracking (ET) indices of cognitive effort and visual exploration (i.e., pupil size and exploration coefficient K) in a realistic website-based task. We experimentally varied time pressure to test the effect of momentary cognitive effort on these metrics. We found that our ET metrics covaried with NASA-TLX scores and they responded to our time pressure manipulation. Moreover, we showed that visual search became more exploratory under time pressure, which supports current theory in neuroscience that links behavioural exploration, task performance, and arousal. Our study thus applied fundamental research from cognitive neuroscience to the domain of usability testing and showed that advanced ET metrics can be useful tools for usability research.
An Entity-Matching System Based on Multimodal Data for Two Major E-Commerce Stores in Mexico

E-commerce has grown considerably in Latin America in recent years due to the COVID-19 pandemic. E-commerce users in English-speaking and Chinese-speaking countries have web-based tools to compare the prices of products offered by various retailers. The task of product comparison is known as entity matching in the data-science domain. This paper proposes the first entity-matching system for product comparison in Spanish-speaking e-commerce. Given the lack of uniformity of e-commerce sites in Mexico, we opted for a bimodal entity-matching system that uses the image and textual description of products from two of the largest e-commerce stores in Mexico. State-of-the-art techniques in natural language processing and machine learning were used to develop this research. The resulting system achieves F1 values of approximately 80%, representing a significant step towards consolidating a product-matching system in Spanish-speaking e-commerce.