Reviewerly/exHarmony huggingface dataset:
Adaptive data augmentation network for human pose estimation

With the rapid development of convolutional neural networks (CNNs), the performance of human pose estimation has been significantly improved. However, state-of-the-art methods still face specific challenges, such as occluded keypoints and nearby persons. Unlike the classical network improvement strategy, our approach obtains adaptive data to obtain more accurate keypoints through data augmentation. In this paper, we propose Adaptive Data Augmentation Network (ADA-Net), which brings more adaptive training data by adding occlusion and interleaving information to the original image. First, we introduce Active Transmission Network (ATNet), which actively learns the transformation matrix from the original image and uses the matrix to synthesize new images for training. Second, we adopt an adversarial training strategy combined with the ATNet that allows us to capture more challenging cases. Extensive experiments show that our approach achieves comparable or even better results than most state-of-the-art methods. In particular, our ADA-Net outperforms High-Resolution Network (HRNet) by 1.1 and 0.7 points on the COCO test-dev set and MPII validation set, respectively. • The diversity of training samples will enhance the performance of human pose estimation. • The appropriate number of pasted samples on the original image can affect network performance. • Pasting the complete person is beneficial in enhancing the original image information. • The generative adversarial network brings the synthetic image closer to real challenging cases.
Non-Contrastive Self-Supervised Learning for Utterance-Level Information Extraction From Speech

In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of utterance-level speech representation can be used in speech applications that require discriminative representation of consistent attributes within an utterance: speaker, language, emotion, and age. Existing frame-level self-supervised speech representation, e.g., wav2vec, can be used as utterance-level representation with pooling, but the models are usually large. There are also SSL techniques to learn utterance-level representation. One of the most successful is a contrastive method, which requires negative sampling: selecting alternative samples to contrast with the current sample (anchor). However, this does not ensure that all the negative samples belong to classes different from the anchor class without labels. This paper applies a non-contrastive self-supervised method to learn utterance-level embeddings. We adapted DIstillation with NO labels (DINO) from computer vision to speech. Unlike contrastive methods, DINO does not require negative sampling. We compared DINO to x-vector trained in a supervised manner. When transferred to down-stream tasks (speaker verification, speech emotion recognition (SER), and Alzheimer's disease detection), DINO outperformed x-vector. We studied the influence of several aspects during transfer learning such as dividing the fine-tuning process into steps, chunk lengths, or augmentation. During fine-tuning, tuning the last affine layers first and then the whole network surpassed fine-tuning all at once. Using shorter chunk lengths, although they generate more diverse inputs, did not necessarily improve performance, implying speech segments at least with a specific length are required for better performance per application. Augmentation was helpful in SER.
Prior Guided Transformer for Accurate Radiology Reports Generation

In this paper, we propose a prior guided transformer for accurate radiology reports generation. In the encoder part, a radiograph is firstly represented by a set of patch features, which is obtained through a convolutional neural network and a traditional transformer encoder. Then an Additive Gaussian model is applied to represent the prior knowledge based on unsupervised clustering and sparse attention. In the decoder part, prior embeddings are acquired by probabilistically sampling from the radiograph prior. Then the visual features, language embeddings, and prior embeddings are fused by our proposed Prior Guided Attention to generate accurate radiology reports. Experiment results show that our method achieves better performance than state-of-the-art methods on two public radiology datasets, which proves the effectiveness of our prior guided transformer.
A Comparative Study of Data Protection Laws: Current Global Trends, Challenges and Need of Reforms in India

“The best way to get a bad law repealed is to enforce it strictly.” Abraham Lincoln’s legacy is still relevant in this decade. The 21st century is sometimes referred to as “the information age” because of the tremendous growth in the variety of ways we use information. India is rapidly transforming into digital economy. India has also been affected by the digital revolution. Recognizing its importance and the potential for significant disruption in practically every area of society, Indian government devised and implemented the “Digital India” plan. India is on track to develop into a digital economy with a substantial market for foreign businesses. In January 2022, there were 658.0 million internet users in India. Between 2021 and 2022, there were 47 million more internet users in India (+8.2 percent). Internet penetration in India stood at 47.0% in January 2022.&#x0D; Data security and protection are crucial given the world’s increasing digitization. Processing of personal data is increasingly widespread, despite the fact that the shift to a digital economy is still in progress. Today’s digital environment makes it so that almost every action a person takes involves some sort of data transaction. New markets have emerged as a result of the Internet, particularly those whose business plans directly or indirectly involve the collection, organisation, and processing of personal data.&#x0D; It is pertinent and strange to mention the fact here that, “the world’s largest taxi company UBER owns no vehicles; Facebook, the world’s most popular social media platform, creates no content; Alibaba, the most valuable retailer, has no inventory; similarly, Airbnb, the world’s largest accommodation provider, owns no real estate”. All of these are the “Data Driven” and not the “information driven” companies. It indicates that the company collects, analyses, and applies data to make important decisions. It is a serious matter of concern around the world when it comes with right to privacy of an individual. This study deals with current data protection laws, challenges and reforms in India.
Breast cancer patient characterisation and visualisation using deep learning and fisher information networks

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.
Cooperative Particle Swarm Optimization With a Bilevel Resource Allocation Mechanism for Large-Scale Dynamic Optimization

Although cooperative coevolutionary algorithms are developed for large-scale dynamic optimization via subspace decomposition, they still face difficulties in reacting to environmental changes, in the presence of multiple peaks in the fitness functions and unevenness of subproblems. The resource allocation mechanisms among subproblems in the existing algorithms rely mainly on the fitness improvements already made but not potential ones. On the one hand, there is a lack of sufficient computing resources to achieve potential fitness improvements for some hard subproblems. On the other hand, the existing algorithms waste computing resources aiming to find most of the local optima of problems. In this article, we propose a cooperative particle swarm optimization algorithm to address these issues by introducing a bilevel balanceable resource allocation mechanism. A search strategy in the lower level is introduced to select some promising solutions from an archive based on solution diversity and quality to identify new peaks in every subproblem. A resource allocation strategy in the upper level is introduced to balance the coevolution of multiple subproblems by referring to their historical improvements and more computing resources are allocated for solving the subproblems that perform poorly but are expected to make great fitness improvements. Experimental results demonstrate that the proposed algorithm is competitive with the state-of-the-art algorithms in terms of objective function values and response efficiency with respect to environmental changes.
Multi-Scale Deep Subspace Clustering With Discriminative Learning

Deep subspace clustering methods have achieved impressive clustering performance compared with other clustering algorithms.However, most existing methods suffer from the following problems: 1) they only consider the global features but neglect the local features in subspace self-expressiveness learning; 2) they neglect the discriminative information of each self-expressiveness coefficient matrix; 3) they ignore the useful long-range dependencies and positional information in feature representation learning.To solve these problems, in this paper, we propose a novel multi-scale deep subspace clustering with discriminative learning (MDSCDL) to obtain a high-quality self-expressiveness coefficient matrix.Specifically, MDSCDL bridges multiple fully-connection layers between encoder and decoder to learn multi-scale self-expressiveness coefficient matrices from global and local features, representing the more comprehensive relationship among data.By modeling the interdependencies of the multi-scale selfexpressiveness coefficient matrices, MDSCDL adaptively assigns discriminative weights for each matrix and fuses them with convolution operation.Moreover, to increase representation power, MDSCDL introduces the coordinate attention mechanism to extract the long-range dependencies and positional features for subspace self-expressiveness learning.Extensive experiments on the face and object datasets have shown the superiority of MDSCDL compared with several state-of-the-art methods.
Multimodal Sentiment Analysis Based on Cross-Modal Attention and Gated Cyclic Hierarchical Fusion Networks

Multimodal sentiment analysis has been an active subfield in natural language processing. This makes multimodal sentiment tasks challenging due to the use of different sources for predicting a speaker’s sentiment. Previous research has focused on extracting single contextual information within a modality and trying different modality fusion stages to improve prediction accuracy. However, a factor that may lead to poor model performance is that this does not consider the variability between modalities. Furthermore, existing fusion methods tend to extract the representational information of individual modalities before fusion. This ignores the critical role of intermodal interaction information for model prediction. This paper proposes a multimodal sentiment analysis method based on cross-modal attention and gated cyclic hierarchical fusion network MGHF. MGHF is based on the idea of distribution matching, which enables modalities to obtain representational information with a synergistic effect on the overall sentiment orientation in the temporal interaction phase. After that, we designed a gated cyclic hierarchical fusion network that takes text-based acoustic representation, text-based visual representation, and text representation as inputs and eliminates redundant information through a gating mechanism to achieve effective multimodal representation interaction fusion. Our extensive experiments on two publicly available and popular multimodal datasets show that MGHF has significant advantages over previous complex and robust baselines.
Students’ Daily English Situational Teaching Based on Virtual Reality Technology

Since China is not a native English-speaking country, Chinese English learners generally have poor English application ability. Therefore, this paper aims to study the combination of virtual reality and English learning to create an immersive English learning system, which provides new solutions for students’ daily English to relieve from foreign language anxiety and improve their English application ability. This paper proposes that starting from the research needs, this research has developed a VR English learning system for scene-based teaching. This system can bring visual, auditory, and tactile multisensing experiences to daily English users’ learning. The user is fully immersed in the virtual scene, and the feasibility of the system is increased by data demonstration through actual teaching experiments. The experimental results of this paper show that 90% of the learners are satisfied with the learning effect in the virtual simulation learning situation, and more than 80% of the learners believe that using the avatar learning method to learn English in the virtual simulation learning situation improves the ability of all aspects and the relationship between teachers and students. In addition, 85% of the learners believe that they can improve their interest in daily English learning in the virtual simulation learning situation. Therefore, the feasibility of the system is high.
Overview of the ImageCLEF 2022: Multimedia Retrieval in Medical, Social Media and Nature Applications

This paper presents an overview of the ImageCLEF 2022 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2022. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2022, the 20th edition of ImageCLEF runs four main tasks: (i) a medical task that groups two previous tasks, i.e., caption analysis and tuberculosis prediction, (ii) a social media aware task on estimating potential real-life effects of online image sharing, (iii) a nature coral task about segmenting and labeling collections of coral reef images, and (iv) a new fusion task addressing the design of late fusion schemes for boosting the performance, with two real-world applications: image search diversification (retrieval) and prediction of visual interestingness (regression). The benchmark campaign received the participation of over 25 groups submitting more than 258 runs.
Compiling regional structures in geological databases: The giant quartz veins of the Pyrenees as a case study

Gathering and arranging specific data related to different types of geological structures in thematic and interactive databases has become central for the geosciences. However, a well-defined workflow to achieve this goal is currently lacking. We present a Geographical Information System-based method for the systematic compilation, evaluation, and interpretation of regional geological structures. The method is demonstrated by indexing 741 giant quartz veins cropping out along the Pyrenees (SW Europe) in a database called GIVEPY (GIant quartz VEins of the PYrenees). The GIVEPY database incorporates information of the geometry, distribution, and statistics of the indexed veins, as well as of their host rocks, and can serve as a starting point for boosting the understanding of giant quartz veins in the Pyrenees and other areas. The first-order structural controls on vein emplacement are hypothesized on the basis of vein distribution and linkage with other brittle and ductile structures, such as faults, folds, shear zones, and regional foliations. Furthermore, we discuss the strengths and limitations of the workflow and its potential application to other geological structures. The GIVEPY database is dynamic and freely available at https://givepy.info/.
Selecting Surface Inclination for Maximum Solar Power

Maximum efficiency of surfaces that exploit solar energy, including Photovoltaic Panels and Thermal collectors, is achieved by installing them in a certain inclination (tilt). Most common approach is to select an inclination angle equal to the location’s latitude. This is based on the astronomical calculations of the sun’s position throughout the year but ignores meteorological factors. Cloud coverage and aerosols tend to change the direct irradiance but also the radiance sky distribution, thus horizontal surfaces receive larger amounts than tilted ones in specific atmospheric conditions (e.g., cases of cloud presence). In the present study we used 15 years of data, from 25 cities in Europe and North Africa in order to estimate the optimal tilt angle and the related energy benefits based in real atmospheric conditions. Data were retrieved from Copernicus Atmospheric Monitoring Service (CAMS). Four diffuse irradiance, various models are compared, and their differences are evaluated. Equations, extracted from solar irradiance and cloud properties regressions, are suggested to estimate the optimal tilt angle in regions, where no climatological data are available. In addition, the impact of cloud coverage is parameterized using the Cloud Modification Factor (CMF) and an equation is proposed to estimate the optimal tilt angle. A realistic representation of the photovoltaic energy production and a subsequent financial analysis were additionally performed. The results are able to support the prognosis of energy outcome and should be part of energy planning and the decision making for optimum solar power exploitation into the international clean energy transitions. Finally, results are compared to a global study and differences on the optimal tilt angle at cities of Northern Europe is presented.
Research on Teaching Quality Evaluation Model of Higher Education Teachers Based on BP Neural Network and Random Matrix

Improving teaching quality is the first task of higher education, and evaluating teaching quality is an effective measure to improve teaching quality. Combining the advantages of BP neural network and random matrix algorithm, the teaching quality evaluation model of higher education teachers is established. In this paper, the improved BP neural network and the random matrix structure are used to normalize the indicators, evaluate the teaching indicators, and build the teacher teaching quality evaluation system model. Through experimental design, the training data set is input into the model for training. In the training process, the increase and decrease ratio of learning rate, momentum term, and other parameters are adjusted to improve the prediction accuracy and convergence speed of the model. Iteration times, training time, MSE, and prediction accuracy were taken as performance comparison indexes of the model. Experiments show that the model solves the shortcomings of the existing teaching quality evaluation methods and models to a certain extent, and improves the accuracy of evaluation prediction. When the number of iterations is 133, the prediction accuracy is as high as 94.9%, which verifies the effectiveness of the model in the evaluation of teaching quality in colleges and universities. Finally, the evaluation index system of teacher teaching quality is comprehensively analyzed, and the results prove that the evaluation model of teacher teaching quality of A university is suitable for the situation of the school, can highlight the guidance, and is scientific and measurable of evaluation.
The Hidden Parallelepiped Is Back Again: Power Analysis Attacks on Falcon

FALCON is a very efficient and compact lattice-based signature finalist of the NIST’s Post-Quantum standardization campaign. This work assesses Falcon’s sidechannel resistance by analyzing two vulnerabilities, namely the pre-image computation and the trapdoor sampling. The first attack is an improvement of Karabulut and Aysu (DAC 2021). It overcomes several difficulties inherent to the structure of the stored key like the Fourier representation and directly recovers the key with a limited number of traces and a reduced complexity. The main part of this paper is dedicated to our second attack: we show that a simple power analysis during the signature execution could provide the exact value of the output of a subroutine called the base sampler. This intermediate value does not directly lead to the secret and we had toadapt the so-called hidden parallelepiped attack initially introduced by Nguyen and Regev in Eurocrypt 2006 and reused by Ducas and Nguyen in Asiacrypt 2012. We extensively quantify the resources for our attacks and experimentally demonstrate them with FALCON’s reference implementation on the ELMO simulator (McCann, Oswald and Whitnall USENIX 2017) and on a ChipWhisperer Lite with STM32F3 target (ARM Cortex M4).These new attacks highlight the need for side-channel protection for one of the three finalists of NIST’s standardization campaign by pointing out the vulnerable parts and quantifying the resources of the attacks.