Reviewerly/exHarmony huggingface dataset:
Progressive Erasing Network with consistency loss for fine-grained visual classification

Fine-grained Visual Categorization (FGVC) in computer vision aims to recognize images belonging to multiple subordinate categories of a super-category. The difficulty of FGVC lies in the close resemblance among inter-classes and large variations among intra-classes. Most existing networks only focus on a few discriminative regions, while ignoring many subtle complementary features. So we propose a Progressive Erasing Network (PEN). In PEN, a Multi-Grid Erasure mechanism augments data samples and assists in capturing the local discriminative features, where the overall structure of the image is destroyed indirectly through pixel-wise erasure. Cross-layer feature aggregation by extracting salient class features is of great significance in FGVC. However, the capability of cross-layer feature representation based on a simple aggregation strategy is still inefficient. To this end, the proposed Consistency loss explores the cross-layer semantic affinity, which guides the Cross-Layer Incentive (CLI) block to mine more efficient feature representations of different granularity. We also integrate Cross Entropy and Complementary Entropy to take the distribution of negative classes into account for better classification performance. Our method uses end-to-end training with only classification labels. Experimental results show that our model outperforms the state-of-the-art on three fine-grained benchmarks.
Real-time power optimization for application server clusters based on Mixed-Integer Programming

In the environment of peer competition and energy conservation, optimizing the deployment of application server clusters in real time according to actual workload conditions to reduce operating costs and energy consumption is an important issue that must be urgently addressed. In this paper, we propose a real-time power optimization strategy for application server clusters, and the optimization measures include CPU dynamic voltage/frequency scaling and server dynamic switching. First, the feasibility of defining variables for server types is proved, and appropriate variables are defined to describe the cluster power optimization as a mixed-integer programming (MIP) problem. Then, two solution methods are proposed: the exact method based on the Gurobi optimizer and the approximate method based on primary–secondary optimization and differential evolution with two mutations (PSODE). The former turns the MIP problem into a standard mixed-integer quadratic programming form by introducing intermediate variables and solves it using the Gurobi optimizer. The latter rewrites the MIP problem as a primary–secondary optimization problem and proposes a differential evolutionary-based solution algorithm for the primary optimization problem. The evolutionary process consists of two mutation operations, inter- and intraindividual mutations, which both use a heuristic policy to accelerate the evolution convergence. The test results reveal that the Gurobi-based method can quickly determine the global optimal deployment when the cluster size is small. The PSODE-based method can quickly determine the global optimal deployment or high-quality suboptimal deployment when applied to large-scale clusters.
An Integration of TextGCN and Autoencoder into Aspect-Based Sentiment Analysis

Due to the rapid increase in User-Generated Content (UGC) data, opinion mining, also called sentiment analysis, has attracted much attention in both academia and industry. Aspect-Based Sentiment Analysis (ABSA), a subfield of sentiment analysis, aims to extract the aspect and the corresponding sentiment simultaneously. Previous works in ABSA may generate undesired aspects, require a large amount of training data, or produce unsatisfactory results. This paper proposes a Graph Neural Network based method to automatically generate aspect-specific sentiment words using a small number of aspect seed words and general sentiment words. It subsequently leverages the aspect-specific sentiment words to improve the Joint Aspect-Sentiment Autoencoder (JASA) model. We conduct experiments on two datasets to verify the proposed model. It shows that our approach has better performance in the ABSA task when compared with previous works.
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning

Abstract This paper focuses on inverse reinforcement learning for autonomous navigation using distance and semantic category observations. The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert’s observations and state-control trajectory. We develop a map encoder, that infers semantic category probabilities from the observation sequence, and a cost encoder, defined as a deep neural network over the semantic features. Since the expert cost is not directly observable, the model parameters can only be optimized by differentiating the error between demonstrated controls and a control policy computed from the cost estimate. We propose a new model of expert behavior that enables error minimization using a closed-form subgradient computed only over a subset of promising states via a motion planning algorithm. Our approach allows generalizing the learned behavior to new environments with new spatial configurations of the semantic categories. We analyze the different components of our model in a minigrid environment. We also demonstrate that our approach learns to follow traffic rules in the autonomous driving CARLA simulator by relying on semantic observations of buildings, sidewalks, and road lanes.
Meta Flowers

“Meta Flowers” is a multiparticipant installation artwork with Cross Reality (XR) . Wearing a glove-type tactile device consisting of linear resonant actuators (LRA) and a microcontroller with Wi-Fi unit, and HoloLens 2, participants can experience XR through the act of arranging virtual flowers (VFs) which have ‘shadow,’ ‘rigid-body,’ and ‘sound (see Figure 1 (a)).’ The VF blooms at the location of the VIVE Tracker, which is installed at the tip of a metal rod inserted into a vase on the table (see Figure 1 (b) (c)). Real shadows of the vases, sticks, and artificial flowers, as well as the virtual shadows of the VFs, are projected onto the table by the light and images projected from the projector installed on the celling, creating a fusion that is not unnatural. Participants can move the VFs and arrange them in vases on the table. When participants touch the VF, petals of the VF fall and participants get tactile sensations through the haptics glove (see Figure 1 (d)). VFs play sounds while they are in bloom, and their pitch change depending on the position of them. In the other words, the sound and appearance of VFs will change depending on the relationship among participants and VFs. In addition, when real water is poured by the sensor-equipped jug into a vase in which a metal rod without petals is put, the VF blooms again (see Figure 1 (e) (f)).
Nonlinear materials for preparation of entangled states of light

In this paper we present a brief analysis of existing materials and structures with quadratic-nonlinear optical properties that can be used to generate a difference frequency in both THz and sub-THz frequency ranges. This task is actual for future hybrid radio-over-fiber systems exploiting ultra high frequencies (6G etc.). It is shown that such a device may be of interest regarding the problems of generating (preparation) quantum-entangled states of photons, which can find application in high-performance computing and information systems. It is shown that the proposed concept of nonlinear optical-to-radio converter can be applied for entangled photons preparation as well.
Comparing of linear and conical interferograms for wavefront aberrations analysis based on neural networks

Recently, data mining and neural networks are increasingly used for wavefront recognition from interferograms. In this case, there is considerable freedom in choosing the structure of the reference beam. In this work, a comparative study of the effectiveness of using neural networks for solving the problem of recognizing wavefront aberrations based on linear (flat reference beam) and conical (conical reference wavefront) interferograms is carried out. The effectiveness of recognition of types and levels of aberrations by conical interferograms based on the use of neural networks is shown: the average absolute error is reduced by 3 times, compared with linear interferograms. This effect is related to the rotational invariance of the introduced aberrations.
Toward understanding deep learning classification of anatomic sites: lessons from the development of a CBCT projection classifier

Purpose: Deep learning (DL) applications strongly depend on the training dataset and convolutional neural network architecture; however, it is unclear how to objectively select such parameters. We investigate the classification performance of different DL models and training schemes for the anatomic classification of cone-beam computed tomography (CBCT) projections. Approach: CBCT scans from 1055 patients were collected and manually classified into five anatomic classes and used to develop DL models to predict the anatomic class from single x-ray projections. VGG-16, Xception, and Inception v3 architectures were trained with 75% of the data, and the remaining 25% was used for testing and evaluation. To study the dependence of the classification performance on dataset size, training data was downsampled to various dataset sizes. Gradient-weighted class activation maps (grad-CAM) were generated using the model with highest classification performance, to identify regions with strong influence on CNN decisions. Results: The highest precision and recall values were achieved with VGG-16. One of the best performing combinations was the VGG-16 trained with 90 deg projections (mean class precision = 0.87). The training dataset size could be reduced to ∼50% of its initial size, without compromising the classification performance. For correctly classified cases, Grad-CAM were more heavily weighted for anatomically relevant regions. Conclusions: It was possible to determine those dependencies with a higher influence on the classification performance of DL models for the studied task. Grad-CAM enabled the identification of possible sources of class confusion.
Constructing the deep-spreading pattern of tectono-geochemical anomalies and its implications on the Huangshaping W–Sn–Pb–Zn polymetallic deposit in southern Hunan, China

Huangshaping large W–Mo–Pb–Zn polymetallic deposit is a typical representative of the Cu–Sn metallogenic system in Nanling Range, South China. This deposit located in the superimposed tectonic setting of the Qinzhou–Hangzhou and Nanling metallogenic belts. Skarn-type W–Sn orebodies and hydrothermal vein-type Pb–Zn–Cu orebodies located in the faults and contact zone between late Jurassic porphyry and late Paleozoic limestone. Many studies have been conducted on the geological and geochemical characteristics of the deposit, but the comprehensive understanding of the deep-spreading pattern of the tectono-geochemical anomalies remains unclear. The geochemical data of tectonites in deep level adits (−136 m, −176 m, −256 m and −296 m) were studied the distribution characteristics of ore-forming trace elements and the depth index of tectonic geochemical anomalies. Finally, four element combination anomalies were extrated from the cluster analysis and factor analysis. They are generally consistent with the known Cu–Pb–Zn and W–Sn–Mo–Bi metallogenic systems. In addition, from altered porphyry to limestone of the Shidengzi Formation, the plane and profile anomaly contour maps delineated by different factors have regular variation characteristics. High anomalous values usually occur in the part where ore-forming porphyry changes from convex to concave. Comprehensive study trace element combination anomalies shows that the connection line of high value anomalous zones corresponds to the swelling and shrinking characteristics of porphyry, showing periodic wave-like changes. Thus, the deep index and deep-spreading pattern of ore-controlling structure were constructed. The length, width and height of the deep-seated orebodies indicated by the main anomalies are evaluated. The anomalous vertical variation indicates the migration of ore-forming fluid from SE to NW to the shallow. The strike of orebodiesy are consistent with the ore-controlling faults and dip to SE. Overall, the tectonic geochemical anomalies are consistent with the locations where the orebodies were identified, indicating that the method is feasible and effective. This study has an important significance for the layout of deep-prospecting and exploration projects in the Huangshaping deposit and even in southern Hunan.
Classifying Anti-Mask Tweets into Misclassification vs. Rejection: A Year-Long Study

The debate over masks has played out vigorously over social media platforms such as Twitter over the course of the Covid-19 pandemic. Anti-maskers oppose the use of face masks on two philosophical grounds. First, they question their effectiveness and second, they reject them as an infringement of their personal liberties and freedoms. Both these narratives can be damaging in their own respective ways; misinformation can mislead people to abandon this simple public health measure, and rejection can incite unrest, disobedience and violence. Different policies, ranging from completely removing the tweet to simply placing a warning label, may be applied to these two types of anti-mask tweets to mitigate their damage. To facilitate these differentiated policy decisions, driven by the state of the pandemic and the surrounding social and political circumstances, this paper proposes a machine learning approach to separate anti-mask tweets into misinformation and rejection. Linguistic, social, auxiliary, and sentiment features are extracted from this corpus of tweets collected over the first year. A combination of these features is used to train ensemble and neural network classifiers. The results show that our machine learning framework can separate between misinformation and rejection tweets with a F1-score of around 0.90. These results are noteworthy because the framework can classify between two groups of tweets that share a common overall theme of anti-masking yet have only subtle differences. Moreover, the data collected over a period of one year implies that this separation is achieved even when the anti-masking rhetoric is embedded in widely varying social and political contexts.
Sentimental Analysis on Amazon Reviews Using Machine Learning

Because of the rapid advancement of web technology, Internet users now have access to a significant amount of data on the web. This information is primarily derived from social media platforms such as Facebook and Twitter, where millions of people express their views in their everyday interactions. Many online buying platforms, such as Amazon, Flipkart, and Ajio, contain a wealth of information in the form of reviews and ratings. Amazon is one of the many e-commerce perks that people use every day to purchase online, since it allows you to browse thousands of other consumer evaluations about the things you are interested in. These evaluations offer useful information about the product, such as its qualities, quality, and suggestions. The goal of this research is to do Sentimental Analysis on product-based evaluations. Product-based reviews from online buying sites such as Amazon.com might be classified as positive, negative, or neutral. Random Forest and Logistics Regression, a machine learning technique, is used to examine the suggested task and has obtained an overall accuracy of 96%.
Mobile news applications quality analysis using web quality and importance performance analysis

The growing necessity for high news information encourages news portal providers to improve quality. This study aims to analyze the level of service quality on mobile news applications. Measurement of application quality is conducted by employing the Web Quality (WebQual) and Importance Performance Analysis (IPA) methods. The case study was conducted on mobile news applications. The respondents of this study were 120 people with 23 attributes from 4 WebQual dimensions. The analysis results of the quality level of the application features indicate that the attributes of the Usability, Information Quality, and Service Interaction dimensions require corrective action, gathering the seven attributes required to be prioritized for improvement.
Real Time Video Image Edge Detection System

In Surveillance and Medical imaging applications edge detection acts as a vital character in finding the edges. Problems such as missing edges due to noise may arise during edge localization and any edge detection technique takes high computational time. Therefore, edge detection with high accuracy is a critical issue to handle in these applications. One of the solutions to overcome this problem is to use canny edge detection which is one of the superior edge extraction schemes. It reduces the number of false edges and consequently make a superior starting point for other additional processes. This project deals with real time video image edge detection using MyRIO (Reconfigurable Input Output) hardware. A camera which is supported by the device is used to capture the images. Canny edge operator is utilized to identify the edges present in the image. The edge can be handled best by adjusting the extra to an assortment of settings. Depending on the threshold values the edges will be detected. Edge detection plays an essential role in many of the applications like Finger print recognition, This work developed a portable and manageable device that detects the edges in real time environment which can be utilised in any of the applications.
A Comparative Review on Image Analysis with Machine Learning for Extended Reality (XR) Applications

Progressions in Medical, Industrial 4.0, and training require more client communication with the information in reality. Extended reality (XR) development could be conceptualized as a wise advancement and a capable data variety device fitting for far off trial and error in image processing. This innovation includes utilizing the Head Mounted Devices (HMD) built-in with a functionalities like data collection, portability and reproducibility. This article will help to understand the different methodologies that are used for 3Dimensional (3D) mode of interaction with the particular data set in industries to refine the system and uncovers the bugs in the machineries. To identify the critical medical issues, the future technology can give a comfort for the medic to diagnose it quickly. Educators currently utilizing video animation are an up-rising pattern. Important methods used for various applications like, Improved Scale Invariant Feature Transform (SIFT), Block Orthogonal Matching Pursuit (BOMP), Oriented fast and Rotated Brief (ORB) feature descriptor and Kanade-Lucas-Tomasi (KLT), Semi-Global Block Matching (SGBM) algorithm etc., In high-speed real time camera, the position recognition accuracy of an object is less than 65.2% as an average in motion due to more noise interferences in depth consistency. So, more optimization is needed in the algorithm of depth estimation. Processing time of a target tracking system is high (10.670 s), that must be reduced to provide increased performance in motion tracking of an object in real-time. XR is a key innovation that is going to work with a change in perspective in the manner in which clients collaborate with information and has just barely as of late been perceived as a feasible answer for tackling numerous basic requirements.
Enhanced Mask-RCNN for Ship Detection and Segmentation

Ship detection and segmentation using satellite remote sensing imagery have become a hot issue in the scientific community. This sector helps control maritime violence, illegal fishing, and cargo transportation. Most available methods used for detecting ships perform object detection but don't perform semantic segmentation. Besides, previous papers had various flaws, such as the inability to detect small ships, greater false positives, and severe noise in the given Synthetic Aperture Radar (SAR) images, which affects low-level feature learning in shallow layers and makes object detection more difficult. The intricacies of SAR images also significantly reduce the benefits of Convolutional Neural Networks (CNNs). Then there are a few models that can't tell the difference between things that appear to be ships. Furthermore, some existing models performed somewhat worse than other state-of-the-art given and successful frameworks. The cost of computing resources is higher for some models. This research provides a ship detection approach based on an improved Mask Region-Based Convolutional Neural Networks (Mask RCNN). At the pixel level, the proposed approach can detect and segment ships. For object detection and segmentation, Mask RCNN comprises two parts. In the segmentation part, more Convolutional Layers are added, and hyperparameters are changed to improve the overall output. Because of these changes, the proposed model can work more accurately than existing models. Using this mentioned method on the Airbus Ship Detection dataset on Kaggle, we achieved an accuracy of 82.9% on the proposed model.
Multi-Label Contrastive Learning for Abstract Visual Reasoning

For a long time the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence. Recent advances in application of deep learning (DL) methods led, as in many other domains, to surpassing human abstract reasoning performance, specifically in the most popular type of such problems - the Raven's Progressive Matrices (RPMs). While the efficacy of DL systems is indeed impressive, the way they approach the RPMs is very different from that of humans. State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on identification of the rules / concepts underlying the RPM (or generally a visual reasoning task) to be solved. Motivated by this cognitive difference, this work aims at combining DL with human way of solving RPMs and getting the best of both worlds. Specifically, we cast the problem of solving RPMs into multi-label classification framework where each RPM is viewed as a multi-label data point, with labels determined by the set of abstract rules underlying the RPM. For efficient training of the system we introduce a generalisation of the Noise Contrastive Estimation algorithm to the case of multi-label samples. Furthermore, we propose a new sparse rule encoding scheme for RPMs which, besides the new training algorithm, is the key factor contributing to the state-of-the-art performance. The proposed approach is evaluated on two most popular benchmark datasets (Balanced-RAVEN and PGM) and on both of them demonstrates an advantage over the current state-of-the-art results. Contrary to applications of contrastive learning methods reported in other domains, the state-of-the-art performance reported in the paper is achieved with no need for large batch sizes or strong data augmentation.