Reviewerly/exHarmony huggingface dataset:
Dynamic Face Recognition System Using Histogram of Oriented Gradients and Deep Neural Network

Face recognition is a rapidly growing field. Face recognition is the ability to recognize and detect a person based on their visual features. Many methods are already proposed in which the face recognition system can recognize only a fixed set of people. This paper provides a revolutionary face recognition system implementation process that can recognize a large number of persons whose facial features may be derived from an input image. Face detection and recognition are the two phases of the human face recognition technique. In this model, face recognition is achieved by using histogram of oriented gradients and deep neural network. Histogram of oriented gradients (HOG) extracts all the important features from an image. HOG coupled with support vector machine (SVM) classifier detects the face from an input image. The localized face image is passed to the regression tree for facial landmark detection to obtain a centered face image which is fed as input to the deep neural network for face recognition. The proposed model gives an accuracy of 94.44% and is also implemented on video. This model can be used for biometric attendance systems.
Non-Global Privacy Protection Facing Sensitive Areas in Face Images

Face images, as an information carrier, are naturally weak in privacy. If they are collected and analyzed by malicious third parties, personal privacy will leak, and many other unmeasurable losses will occur. Differential privacy protection of face images is mainly being studied under non-interactive frameworks. However, the ε-effect impacts the entire image under these frameworks. Besides, the noise influence is uniform across the protected image, during the realization of the Laplace mechanism. The differential privacy of face images under interactive mechanisms can protect the privacy of different areas to different degrees, but the total error is still constrained by the image size. To solve the problem, this paper proposes a non-global privacy protection method for sensitive areas in face images, known as differential privacy of landmark positioning (DPLP). The proposed algorithm is realized as follows: Firstly, the active shape model (ASM) algorithm was adopted to position the area of each face landmark. If the landmark overlaps a subgraph of the original image, then the subgraph would be taken as a sensitive area. Then, the sensitive area was treated as the seed for regional growth, following the fusion similarity measurement mechanism (FSMM). In our method, the privacy budget is only allocated to the seed; whether any other insensitive area would be protected depends on whether the area exists in a growing region. In addition, when a subgraph meets the criterion for merging with multiple seeds, the most reasonable seed to be merged would be selected by the exponential mechanism. Experimental results show that the DPLP algorithm satisfies ε-differential privacy, its total error does not change with image size, and the noisy image remains highly available.
Unsupervised Multimodal MR Images Synthesizer Using Knowledge From Higher Dimension

Magnetic Resonance Images (MRIs) of different modalities have different reference values for pathological diagnosis. But it is difficult to obtain multimodality MRIs. So, medical image synthesis has been proposed as an effective solution, with which any missing modalities are synthesized from the existing ones. To train a multimodal MRI synthesizer with limited number of unpaired MRIs, in this paper, we have proposed a novel High-dimensional Knowledge Guided Generative Adversarial Network (HKG-GAN). In our HKG-GAN, a cross-dimensional knowledge transfer network is utilized to extract features from 2D images (slices of MRIs) to measure the perceptual similarity of images of source and synthesized modalities, whose knowledge is transferred from a pre-trained 3D network without accessing its private training dataset. Nevertheless, based on code-splitting and cross-decoding, HKG-GAN is a one-for-all network that encodes MRIs into content codes and style codes, and then cross-decodes the encoding of a random image of different modality to convert MRI to target modality. The effectiveness has been proofed through comparative experiments.
Enhancing Medical Data Transparency and Integrity with Blockchain Based Implementation

Electronic Health Record (EHR) is one of the most common ways to represent patients' medical information digitally. It contains the patients' medical data such as medical history, diagnoses, medication, treatment plans, allergies, and much more medical-related information. However, medical data stored through this method is often centralized and is vulnerable to attacks. Hence its integrity and various aspects such as lack of transparency are constantly questioned and criticized. The implementation of blockchain, a decentralized technology that is immutable and transparent by nature, is proposed to investigate an alternative solution to these problems. Furthermore, blockchain's inherent implementation of cryptography and its consensus mechanisms allow data to be stored in the blockchain network immutable and transparent. By utilizing such characteristics, medical data can be stored and accessed by the stakeholders without worrying about data tampering. At the same time, the mandatory encryption ensures access is only available to those who are authorized. This paper discusses how medical data can be stored inside the blockchain network to retain the transparency and integrity of medical data.
Measuring Internet Slang Style in the Marketing Context: Scale Development and Validation

As an emerging language variant, practitioners have extensively used Internet slang in advertising and other communication activities. However, its unique characteristics that differ from standard language have yet to be explored. Drawing upon interdisciplinary theories on schema and communication styles, this research makes the first attempt to conceptualize and measure these characteristics by introducing a new multi-dimensional construct, "Internet slang style," in the marketing context. It develops and validates a new scale to measure Internet slang style along the dimensions of amiability, overtness, candor, and harshness through a series of in-depth interviews, two surveys, and one experiment with consumers. In addition, this research investigates the impact of Internet slang styles on brand personality and brand attitude. The results indicate that different Internet slang style dimensions positively correspond to different brand personality dimensions but exert no influence on brand attitude. Practically, the scale provides an easy-to-use instrument to evaluate Internet slang styles from a consumer perspective to help companies appropriately employ Internet slang in marketing communication activities.
BiCSNet: A Bidirectional Cross-Scale Backbone for Recognition and Localization

Recognition and localization models can be generally decomposed into three components: encoder, decoder, and task head. In this paper, we rethink the necessity of decoder, as we observe that it brings additional computational and parametric burden. We thus propose to remove the decoder and present a bidirectional cross-scale architecture that is able to obtain rich semantic information and precise localization in a unified backbone. Extensive experiments demonstrate that, different from common encoder-decoder models and other down-sampling and up-sampling backbones, the proposed BiCSNet achieves improved performances compared to existing architectures for pixel-level tasks. In object detection, our BiCSNet brings significant performance improvement by ~ 3% AP at various scales with 13% – 23% fewer FLOPS, compared with ResNet-FPN models on COCO dataset. In Instance segmentation, the AP can be improved by 1% over SpineNet. BiCSNet is also promising for semantic segmentation tasks, as the proposed BiCSNet pre-trained on ImageNet alone significantly outperforms DeepLabv3 pre-trained on both ImageNet and COCO dataset by 1.3% in mIOU with 89% fewer FLOPs on PASCAL VOC 2012.
Evolution of neural networks

tutorial Public AccessEvolution of neural networks Author: Risto Miikkulainen The University of Texas at Austin and Cognizant Technology Solutions The University of Texas at Austin and Cognizant Technology SolutionsView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 426–442https://doi.org/10.1145/3449726.3461432Published:08 July 2021Publication History 1citation180DownloadsMetricsTotal Citations1Total Downloads180Last 12 Months94Last 6 weeks21 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Deep Structured Clustering of Short Text

Short text clustering is beneficial in many applications such as articles recommendations, user clustering and event exploration. Recent works of short text clustering boost the clustering results by improving the representation of short text with deep neural networks, such as CNN and autoencoder. However, existing short text deep clustering methods ignore the structure information of short texts. In this paper, we present a GCN-based clustering method for short text clustering, named as Deep Structured Clustering (DSC) method, to explore the relationships among short texts for representation learning. We first construct a $${\boldsymbol{k}}$$ -nn graph to capture the relationships among the short texts, and then jointly learn the short text representations and perform clustering with a dual self-supervised learning module. The experimental results demonstrate the superiority of our proposed method, and the ablation experimental results verify the effectiveness of the modules in our proposed method.
A Just-In-Time Compilation Approach for Neural Dynamics Simulation

As the bridge between brain science and brain-inspired computation, computational neuroscience has been attracting more and more attention from researchers in different disciplines. However, the current neural simulators based on low-level language programming or pseudo-programming using high-level descriptive language can not full fill users' basic requirements, including easy-to-learn-and-use, high flexibility, good transparency, and high-speed performance. Here, we introduce a Just-In-Time (JIT) compilation approach for neural dynamics simulation. The core idea behind the JIT approach is that any dynamical model coded with a high-level language can be just-in-time compiled into efficient machine codes running on a device of CPU or GPU. Based on the JIT approach, we develop a neural dynamics simulator in the Python framework called BrainPy, which is available publicly at https://github.com/PKU-NIP-Lab/BrainPy. BrainPy provides a friendly and highly flexible interface for users to define an arbitrary dynamical system, and the JIT compilation enables the defined model to run efficiently. We hope that BrainPy can serve as a general software for both research and education in computational neuroscience.
Studi On Big Data Analytics Framework in Smart City Context

The issue of global urbanization, which is a separate problem faced by the government, is the very rapid growth of population density in cities. To face this challenge, the government launched a smart city project by targeting sustainable economic growth and improving the quality of life. Information and Communication Technology governance is the key to realizing a smart city. However, each of these I.C.T. tools produce large amounts of data known as Big Data. Data processing with the Big Data approach is becoming a trend in information systems to provide better public services and provide references in the policy-making process. However, to obtain important information in the scope of big data, a Big Data Analytics process is needed, also known as Big Data Value Chain. Extracting knowledge from the related literature can identify the characteristics of the big data analytic framework for smart cities. This paper reviews several big data analytic frameworks applied to smart cities. This paper is to find the advantages and disadvantages of each framework so that it can be a direction for future research
Perceiving, Navigating and Inhabiting: Performance design through sonic strategies

In the intersecting fields of performance, technologically mediated live arts and artistic research, the significant potential of deploying sonic strategies is by now well documented. Sound-driven performance design is becoming widespread due to its efficacy in raising awareness of, critically engaging with and traversing and intervening in the complexities of the everyday. Site-specific embodied approaches in particular, such as binaural field-recording and audiowalk composition, have notably underlined the relational, exchange-oriented constellation of interactions between individual and collective bodies, and their diverse surroundings.The audiowalk format – defined as an immersive soundscape composition, which is anchored in an exploratory embodied perspective and based on listening while interacting with a specific environment – particularly stands out in enabling innovative ways of perceiving, navigating and inhabiting places and communities in contemporary societies. It is implicitly performative, in the sense that the listener’s immediate bodily agency is at stake when a specific choreographic engagement is suggested (following a path and finding your bearings; walking, running, crouching, paying attention to the near and to the far; listening to the layering of composed sounds, local noises leaking in, and “added” sounds brought forth from induced aural imagination). Through a combination of headphone-dedicated aural scenography, choreography, and sound design, the audiowalk activates modes of being embedded in site and situation. This article focuses on presenting and discussing artistic methods and performative strategies developed to counter the aural passivity of everyday listening. It borrows from selected case-studies of artistic production as well as from my own experience as a sound artist creating, researching, and teaching process-oriented sonic strategies for performance design.
MeTGAN: Memory Efficient Tabular GAN for High Cardinality Categorical Datasets

Generative Adversarial Networks (GANs) have seen their use for generating synthetic data expand, from unstructured data like images to structured tabular data. One of the recently proposed models in the field of tabular data generation, CTGAN, demonstrated state-of-the-art performance on this task even in the presence of a high class imbalance in categorical columns or multiple modes in continuous columns. Many of the recently proposed methods have also derived ideas from CTGAN. However, training CTGAN requires a high memory footprint while dealing with high cardinality categorical columns in the dataset. In this paper, we propose MeTGAN, a memory-efficient version of CTGAN, which reduces memory usage by roughly 80%, with a minimal effect on performance. MeTGAN uses sparse linear layers to overcome the memory bottlenecks of CTGAN. We compare the performance of MeTGAN with the other models on publicly available datasets. Quality of data generation, memory requirements, and the privacy guarantees of the models are the metrics considered in this study. The goal of this paper is also to draw the attention of the research community on the issue of the computational footprint of tabular data generation methods to enable them on larger datasets especially ones with high cardinality categorical variables.
Direct Dense Pose Estimation

Dense human pose estimation is the problem of learning dense correspondences between RGB images and the surfaces of human bodies, which finds various applications, such as human body reconstruction, human pose transfer, and human action recognition. Prior dense pose estimation methods are all based on Mask R-CNN framework and operate in a top-down manner of first attempting to identify a bounding box for each person and matching dense correspondences in each bounding box. Consequently, these methods lack robustness due to their critical dependence on the Mask R-CNN detection, and the runtime increases drastically as the number of persons in the image increases. We therefore propose a novel alternative method for solving the dense pose estimation problem, called Direct Dense Pose (DDP). DDP first predicts the instance mask and global IUV representation separately and then combines them together. We also propose a simple yet effective 2D temporal-smoothing scheme to alleviate the temporal jitters when dealing with video data. Experiments demonstrate that DDP overcomes the limitations of previous top-down baseline methods and achieves competitive accuracy. In addition, DDP is computationally more efficient than previous dense pose estimation methods, and it reduces jitters when applied to a video sequence, which is a problem plaguing the previous methods.
A Blended Teaching Mode of University Mathematics Based on “Micro Class” and “Rain Class” under the Internet plus Background

In the context of the Internet, the popularity of smart phones has brought an impact on the traditional mathematics teaching mode.This paper studies the blended teaching mode of "micro class" and "rain class" based on the Internet, which makes up for the shortcomings of the traditional teaching mode and improves the learning effect.
Visual Object Detection and Tracking System Design based on MobileNet-SSD

In order to realize real-time object detection and collaborative control of multi pan-tilts for unmanned platform, a scheme of visual object detection and tracking system based on MobileNet-SSD algorithm is proposed. This scheme can realize real-time visual object detection of people and vehicles in mobile platform without GPUs. First, objects are detected based on MobileNet-SSD algorithm. Second, the result of object detection is used for calculating attitude variation matrix for controlling multi pan-tilts. Finally, the multi pan-tilts can equip with different equipment, namely, three two-axis pan-tilts tracking objects to complete a various of tasks together. The experimental results show that the real-time performance of object detection is feasible, and the two-axis pan-tilts can track objects effectively.
Extractive Text Summarization Using BERT

Automatic Text Summarization is one of the most challenging and exciting problems in Natural Language Processing (NLP). With the data explosion, summarizing textual content effectively without losing the original meaning, but reducing the text size and reading time for a user is the need of the hour. Recently, with the use of deep learning models like recurrent neural networks, long short-term memory networks for text summarization have led to high performance. However, the breakthrough has been with transformer-based Bidirectional Encoder Representation from Transformers (BERT) which is non-sequential. This paper presents extractive text summarization using BERT to obtain high accuracy of average Rogue1—41.47, compression ratio of 60%, and reduction in user reading time by 66% on CNN Daily News dataset. Therefore, BERT based extractive text summarization is highly effective.
An Abused Webpage Detection Method Based on Screenshots Text Recognition

With the rapid development of the Internet, webpages containing abused information such as pornography and gambling have emerged in an endless stream. These webpages are using various methods to evade traditional detection methods and which seriously make the Internet environment worse. Thus, how to accurately identify these webpages are becoming more and more significant. In response to this problem, by combining text recognition and text classification, this paper proposes an abused webpage detection method based on screenshots, which can efficiently detect and classify webpages by acquiring the user's real visible webpage information. Also, this paper uses the traditional web crawler method to conduct a comparative experiment, and the accuracy and the advantage of the method have been verified. This work will provide technical support for fighting against illegal activities and purifying the Internet environment.
College English Teaching between Chinese and English Idioms

An idiom is a phrase which is an established and fixed part of a particular language.Idioms are the essence of language and they bear the cultural characteristics and cultural information of a nation.The culture differences reflected in idioms are mainly shown in four aspects.They are differences in context, custom, religion and history.Idioms' characteristics have a close relationship with source.The two languages between Chinese and English have some similar cultural characteristics.These differences and similarities make translation of idioms quite difficult.Therefore, cultural translation methods are very important.In order to have a better transmit in idioms culture, we should avoid making mistakes in idiom translation.English-Chinese translation is one of the important means of learning English, especially the translation of English idioms.Translators often need careful scrutiny, should not take the words literally.Making the translation of idioms is in line with the idioms using habits, but also with their implied meaning.This paper tries to analyze some idioms from the background knowledge.Therefore, discussion on correct understanding idioms is an important prerequisite for translation.
Virtual Reality in Healthcare

Over the past few years, the rapid development of virtual reality has led to the technology finding its way into the professional sector in addition to the gaming market. It plays a particularly important role in medical applications by providing a virtual environment to enable therapy, rehabilitation, and serving as an educational platform. The chapter provides an overview of the applications of virtual reality in medicine about some of the most important areas. Both scenario development and application validation methods are presented, as well as their impact on the end user. Finally, the technological potential and future development of VR applications used for improving medical service delivery are summarized and briefly discussed.
Adversarial Social Recommendations With Capturing Multi-Modal Views Of Social Friends

Social recommendation aims to collect social relationship information to promote user preference learning, which is an important branch of the recommendation system. Although the existing works using graph neural network have been fruitful, they still face the following three challenges: (1) insufficient capture of user's fine-grained preferences at aspect level; (2) unable to capture the unique multi-modal distribution of user and friend aspect-level preferences; (3) lack the ability to weigh the importance of aspect information adaptively. To fill this gap, we propose a social recommendation model called AMMSR based on adversarial multi-modal viewpoint capturing. In order to achieve fine-grained modeling, AMMSR firstly projects candidate item into multiple latent aspect spaces to realize one-to-one query of user preferences at aspect level. Secondly, the adversarial variational bayes technique is introduced to capture the multi-modal preferences of users and their friends in latent aspects. Finally, two self-attention modules are set up on the user end and the commodity end to achieve the purpose of personalized information fusion. Extensive experiments on three public datasets show that AMMSR is generally superior to the existing methods, and several visual case studies verify that our model can effectively capture multi-modal viewpoints.