Reviewerly/exHarmony huggingface dataset:
Multi-view Vision Transformers for Object Detection

Object detection has been thoroughly investigated during the last decade using deep neural networks. However, the inclusion of additional information given by multiple concurrent views of the same scene has not received much attention. In scenarios where objects may appear in obscure poses from certain view points, the use of differing simultaneous views can improve object detection. Therefore, we propose a multi-view fusion network to enrich the backbone features of standard object detection architectures across multiple source and target view points. Our method consists of a transformer decoder for the target view that combines the remaining source views feature maps. In this way, the feature representation of the target view can aggregate feature information from the source view through attention. Our architecture is detector-agnostic, meaning it can be applied across any existing detection backbone. We evaluate performance using YOLOX, Deformable DETR and Swin Transformer baseline detectors, comparing standard single view performance against the addition of our multi-view transformer architecture. Our method achieves a 3% increase of the COCO AP over a four view X-ray security dataset and a slight 0.7% increase on a seven view pedestrian dataset. We demonstrate that the integration of different views using attention-based networks improves the detection performance of multi-view datasets. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Modulation Recognition Method Based on Bispectrum and Ensemble Learning

Modulation recognition of emitters is of great significance for reconnaissance signal analysis. In this paper, a method based on bispectrum features and ensemble learning algorithm is proposed for modulation recognition. First, according to the application of signal modulation patterns in radar and communication fields, a set of signal samples including four modulation patterns is simulated. Then, statistical features such as variance, kurtosis and kurtosis are extracted from the bispectrum diagonal slices of signals to construct a bispectrum feature set, which used as the input feature of the recognition model. Finally, an identifier based on extreme gradient boosting (XGBoost) algorithm is constructed. Experimental results show that the bispectrum statistical features have satisfied feature expression and discrimination ability for signals with different modulation patterns, and the recognition model based on XGBoost algorithm has better recognition performance than the other seven recognition algorithms.
Dynamic Mask Curriculum Learning for Non-Autoregressive Neural Machine Translation

Non-autoregressive neural machine translation is gradually becoming a research hotspot due to its advantages of fast decoding. However, the increase of decoding speed is often accompanied by the loss of model performance. The main reason is that the target language information obtained at the decoder side is insufficient, and the mandatory parallel decoding leads to a large number of mistranslation and missing translation problems. In order to solve the problem of insufficient target language information, this paper proposes a dynamic mask curriculum learning approach to provide target side language information to the model. The target side self-attention layer is added in the pre-training phase to capture the target side information and adjust the amount of information input at any time by way of curriculum learning. The fine-tuning and inference phases disable the module in the same way as the normal NAT model. In this paper, we experiment on two translation datasets of WMT16, and the BLEU improvement reaches 4.4 without speed reduction.
Rapid Delivery of Software: The Effect of Alignment on Time to Market

In Scrum, teams working collaboratively on interdependent pieces of software face alignment issues as they need to coordinate their work. Organisations aim to minimise time to market of their products, which makes it relevant to identify how alignment issues affect time to market. Currently, empirical evidence of the effect of implementing alignment activities on delivering software is scarce. This research aims to identify those alignment activities that shorten the time to market of backlog items. First, examination of key concepts led to a grounded choice of alignment activities taken into account. Use of alignment activities in development of features was identified by sending feature owners a close-ended questionnaire on the alignment of their collaborating Scrum teams. The cycle times of backlog items were measured by using the application programmable interface of the agile tool used for tracking backlog items. Results show that when user stories were developed using a shared Definition of Ready, process and lead time decreased significantly. Process and lead time also differed between user stories implementing a different number of shared feedback sessions, where using two shared feedback sessions per sprint resulted in the lowest process and lead time.
Integrated Deconvolution Keypoint Detector and Descriptor Network

In this paper, we introduce a deconvolution-based approach to learn detector and descriptor for repeatable key-points under drastic environmental changes of weather and lighting conditions. As opposed to patch-based neural network, our integrated deconvolution keypoint detector and descriptor (IDKDD) network operates on full-sized images and jointly computes pixel-level keypoint locations and associated descriptors in one forward pass at both training and testing stages. We introduce the deconvolution-based detector and descriptor considering both low-level and high-level information of CNN network to deal with the challenging issue, temporal variations. We conduct comprehensive quantitative and qualitative experiments on standard dataset and demonstrate that our IDKDD network significantly outperforms the state-of-the-art methods on keypoint detection and description.
How Likely is a Random Network Graph Shift-Enabled?

In graph signal processing, the shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random network graph is shift-enabled. In particular, popular network graph models Erdős–Rényi (ER), Watts–Strogatz (WS), Barabási–Albert (BA) for both weighted and unweighted are considered. Moreover, both balanced and unbalanced signed graphs constructing using ER are considered. Our results show that the considered unweighted connected random network graphs are shift-enabled with high probability when the number of edges is moderately high. However, very dense graphs, as well as fully connected graphs, are not shift-enabled. Interestingly, this behaviour is not observed for weighted connected graphs, which are always shift-enabled unless the number of edges in the graph is very low. Finally, we evaluate the shift-enabled property of nine real-world graphs. The experimental results are consistent with our findings on randomly generated data. The results provide the basis for the filter design in a graph network.
Mining Twitter Multi-word Product Opinions with Most Frequent Sequences of Aspect Terms

Given a corpus of microblog texts from a social media platform such as Twitter (e.g., “the new iPhone battery life is good, but camera quality is bad”), mining multi-word aspects (e.g., battery life, camera quality) and opinions (e.g., good, bad) of these products is challenging due to the vast amount of data being generated. Aspect-Based Opinion Mining (ABOM) is thus a combination of automatic aspect extraction and opinion mining that allows an enterprise to analyze the data on relevant features of products in detail, saving time and money. Existing Twitter ABOM systems such as Hate Crime Twitter Sentiment (HCTS) and Microblog Aspect Miner (MAM) generally go through the four-step approach of obtaining microblog posts, identifying frequent nouns (candidate aspects), pruning the candidate aspects, and getting opinion polarity. However, they differ in how well they prune their candidate features. This paper proposes a system called Microblog Aspect Sequence Miner (MASM) as an extension of Microblog Aspect Miner (MAM) by replacing the Apriori algorithm with a modified frequent sequential pattern mining algorithm based on CM-SPAM to also enable mining multi-word aspects more efficiently. The proposed system is able to determine the summary of most common aspects (Aspect Category) and their sentiments for a product. Experimental results with evaluation metrics of execution time, precision, recall, and F1-measure indicate that our approach has higher recall and precision than these existing systems on Sanders Twitter corpus dataset.
Dental Image Segmentation Using Clustering Techniques and Level Set Methods

Segmentation is necessary for dental images for finding the parts of the teeth, surrounding tissues, and bones. The human identification system in dental methodology is a tedious and time-consuming process. The automatic identification system is the best solution for dental diagnosis and dental treatment systems. Choosing an appropriate region of interest with high accuracy and success rate is a challenging one. This can be attained with the help of proper segmentation methodologies. The segmentation techniques proposed for the root canal treatment are analyzed and compared. Clustering techniques and level set methods with different edge maps are implemented for the proper analysis of segmentation in dental images. Finally, the integration of coherence-enhanced diffusion filtering in basic level set segmentation methodology seems to be effective in improving the segmentation performance of dental images.
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks

Deep Neural Networks, particularly Convolutional Neural Networks (ConvNets), have achieved incredible success in many vision tasks, but they usually require millions of parameters for good accuracy performance. With increasing applications that use ConvNets, updating hundreds of networks for multiple tasks on an embedded device can be costly in terms of memory, bandwidth, and energy. Approaches to reduce this cost include model compression and parameter-efficient models that adapt a subset of network layers for each new task. This work proposes a novel parameter-efficient kernel modulation (KM) method that adapts all parameters of a base network instead of a subset of layers. KM uses lightweight task-specialized kernel modulators that require only an additional 1.4% of the base network parameters. With multiple tasks, only the task-specialized KM weights are communicated and stored on the end-user device. We applied this method in training ConvNets for Transfer Learning and Meta-Learning scenarios. Our results show that KM delivers up to 9% higher accuracy compared to other parameter-efficient methods on the Transfer Learning benchmark.
An Evolutionary Stream Clustering Technique Outlier Detection in Medical Data

Clustering data streams from data mining become evident as one of the most well-liked studies among the researchers correspond to their evolutionary field. Numerous threats addressed by data streams on clustering like limited time, memory and single scan data. In generic terms, data streams are defining an infinite sequence of the element data and evolve without prior knowledge number of the clusters. Many factors such as noise (outliers) have appeared periodically have a negative impact on data streams environment. The density-based technique has demonstrated to be an astonishing method in clustering data streams. It is computationally competent to generate arbitrary shape clusters and detect noise instantaneously. The number of the clusters are not required in advance to set as a parameter during the assessment begin. In contradictory, traditional density-based clustering is not relevant to conduct in data streams due to its own characteristics. Mostly all traditional density-based clustering can be extended to the updated version of algorithms to achieve the objective of data streams research. The idea is emphasizing on the density-based technique in the clustering process dominate the restrain from data streams nature. The objective of this paper intends a preliminary result on a density-based algorithm, named evoStream to explore outlier detection on medical data sets, heart failure clinical records and gene expression cancer RNA-seq. In due course, extensive evoStream later to develop to optimize the model to detect outlier in data streams.
Applications of Cancer Classification Using Elman Neural Network

This paper presents an Elman neural network (ENN) approach using biogeography-based optimization (BBO) algorithm augmented with classification rate for the breast cancer and lung cancer classification applications. In order to investigate the efficiencies of BBO method, real-time recurrent learning (RTRL), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm are also implemented for comparison. The mean square error (MSE), standard deviation (SD), and classification rate are conducted and compared to demonstrate the classification performances.
M²NAS: Joint Neural Architecture Optimization System With Network Transmission

Differentiable neural architecture search (NAS) methods have achieved comparable results for low search costs and high performance. Existing differentiable methods focus on searching microstructures in micro space, lacking in exploring macrostructures. However, different networks should have different macrostructures rather than all evenly distributed. This article proposes an M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NAS optimization system to optimize macrostructure and microstructure jointly. Specifically, we initialize a network with maximum complexity, where down-samplings occur at the beginning. Then, iteratively optimize the macrostructure and microstructure. For macro search, we establish a macro space and explore this space by generating candidates and initializing weights and architectural parameters for candidate networks, i.e., network transmission. For micro search, we propose a progressive pruning method to eliminate the vast quantization error caused by one-time pruning. With the number of parameters decreasing during the search, M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NAS obtains a series of networks with different complexity through network selection, forming a Pareto-optimal set. Results show that networks obtained by M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NAS have a better tradeoff between accuracy and complexity. A network searched on ImageNet achieves 77.4% and 76.3% top-1 accuracy, with and without data augment during retraining. When taking different pretrained models as backbones and combining them with Faster RCNN on COCO, our model can get 35.6% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(AP)$ </tex-math></inline-formula> and 55.7% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(AP_{50})$ </tex-math></inline-formula> , higher than typical NAS models, second only to ResNet-50 results.
Improving Events Classification with Latent Space Clustering-Based Similarities

The research on event intelligent analysis based on big data refers to the intelligent classification of monitoring events through the analysis of monitoring event alarm information in the operation and maintenance platform, to automatically recommend monitoring event processing solutions according to the event knowledge base. However, there are currently few methods to classify monitoring events. To solve this problem, our method relies on the BERT model and the Jieba word segmentation tool to perform keyword extraction, keyword word vector transformation and event representation vector generation for event information in training data. We then pre-classify the training data using the clustering algorithm and similarity to obtain information about each cluster. We establish the relationship between clusters and event classifications based on the pre-classification results and the classification labels of the training data. Finally, we process and analyze the new monitoring events that appear in the operation and maintenance platform, and effectively classify the new events according to the model training results. In addition, our method can periodically train the model to optimize the classification performance based on dynamically added data from the monitoring event database. We perform experiments on the real-life datasets and the results validate the effectiveness of our proposed method.
It’s all about the baseline - geochemical perturbations in hydrochronology

At surface recharge, atmospheric-equilibrated groundwater contains multiple geochemical components at trace levels, including short-lived isotopes (e.g. 3 H, 14 C, 81 Kr) produced via a combination of anthropogenic and natural processes.The initial concentrations of these 'tracers' are highly dependent on the environmental and geographic setting at the point of recharge.Once isolated from the surface, concentrations of these tracers progressively decrease over time through radioactive decay.Consequently, it is possible to constrain the last time surfacerecharged groundwater was in contact with the atmosphere through precise measuring of the absolute and relative abundances of these degradative tracers compared to initial concentrations and stable isotope signatures.However, subsurface processes such as mixing, dissolution, and precipitation, can produce groundwaters with multiple components of different provenance.This can result in apparent temporal discrepancies between tracers which need to be considered when applying this kind of hydrochronologic modelling approach.One complex set of processes less commonly considered arises due to in situ subsurface production through naturally occurring nucleogenic reactions involving U and Th decay.Specifically, neutron capture reactions are known to produce 3 H, 14 C, 36 Cl, 39 Ar, and 81 Kr, in the host matrix as well as in any associated fluids e.g.1-3 .Remarkably, to date, the dependence of geochemistry, host rocks and geologic settings and impact on such tracer production and alteration of "baseline" assumptions remains poorly constrained.This study investigates how the geochemistry of the host rock and fluids control in situ production of tracers via neutron capture of parent elements.The rates and production routes of 3 H, 14 C, 36 Cl 39 Ar, and 81 Kr are modelled for a variety of host rock lithologies and fluids to evaluate the impact baseline fluid production may have on apparent residence times.This approach demonstrates how the combination of high radioelement concentration in host rocks, coupled with multiple parent elements in the fluids, may result in non-negligible effects for 36 Cl, 39 Ar, (and potentially 14 C)-derived residence times, while 3 H and 81 Kr remain principally related to surficial recharge.
AMIF: A Hybrid Model for Improving Fact Checking in Product Question Answering

Fact checking in product-related community question answering is the task of verifying the truthfulness of an answer towards a given question, where the study has just begun. Most existing related work has focused on tailoring solutions to shallow feature fusion for the single-text claim involved with fact-checked evidence, limiting their success and generality in such answer truthfulness prediction task on E-commerce platforms. In this study, we propose an attention-based hybrid framework for multi-feature interaction fusion to determine the truthfulness of the answer towards a product-related question in E-commerce, which could not only support fine-grained semantic calibration between question-answer pairs for better understanding of the target answers, but also substantially cross-check all retrieved evidence to mine coherent opinions towards the pair. In addition, our framework further integrates non-textual features from metadata for improving performance. Extensive experiments conducted on real-world representative benchmark data show that our proposed model achieves superior performance on the task of answer veracity prediction.
Find Your ASMR: A Perceptual Retrieval Interface for Autonomous Sensory Meridian Response Videos

Autonomous sensory meridian response (ASMR) is a type of video contents designed to help people relax and feel comfortable. Users usually retrieve ASMR contents from various video websites using only keywords. However, it is challenging to examine satisfactory contents to reflect users’ needs for ASMR videos using keywords or content-based retrieval. To solve this issue, we propose a perceptual video retrieval system for ASMR videos and provide a novel retrieval user interface that allows users to retrieve content according to watching purpose and anticipated expectations, such as excitement, calmness, stress and sadness. An ASMR video perception dataset is constructed with annotations on affective responses after watching the videos. To verify the proposed video retrieval system, a user study is conducted showing that users can retrieve satisfactory ASMR contents easily and efficiently compared to conventional keywords-based retrieval systems.
Blended QR Code for Digital Advertising

Quick Response (QR) code has been widely used in everyone's daily lives for advertising purpose. This is a new norm lifestyle for worldwide customers during the pandemic and post-pandemic. Due to the dull appearance of traditional QR code, blended QR code is created by overlaying an advertisement with a QR code to strengthen the advertising effectiveness. Creating pleasant visibility of blended QR codes can catch the attention of customers and thus able to further engage with them. However, the direct embedding of a traditional QR code with its black and white modules will negatively affect the advertising impact when the advertisement is distorted. Hence, these data modules are the major reason that affects the appearance and yet they are the most critical aspect of decoding capability. As a result, it is a challenge to identify the tradeoff between decoding capability and visual appearance. Therefore, this paper proposes an algorithm that manages the number of data modules and the size of each data module to increase the advertising impact. The proposed algorithm provides more weightage to the pixels that are closer to the center region of each module, which maintains the data on hold inside the modules. The performance comparison between advertisement visibility and decoding capability is presented to verify the robustness of the proposed algorithm.