Reviewerly/exHarmony huggingface dataset:
VBNet: A VLC Enabled Hybrid Data Center Network

Visible Light Communication (VLC) has the potential to provide dense and fast connectivity at low cost. In this paper, we propose a novel VLC-enabled hybrid data center network VBNet. It extends the design of wireless data center networks further into three aspects: (1) full wireless: server level is completely wireless; (2) easy to deploy: no need to change the existing infrastructure in the data center; (3) plug-and-play: no additional centralized control operations. VLC link reduces the path length of communication between servers and improves network performance. The unicast and multicast routing of VBNet are also given. Using VLC links can avoid hierarchical switches and cables and reduce hardware investment and maintenance costs.
An improved Faster RCNN for Pedestrian Detection

Pedestrian detection plays a pivotal role in applications such as robotics, automated driving, assistive living, and surveillance. The problem of pedestrian detection, although approached by many computer vision researchers is far from solved. The scale, pose, occlusion, illumination, and many such factors affect the performance of the methods. In this work, a modification of the most commonly used deep convolutional neural network model ResNet18 is proposed. The modified CNN structure forms the base of the Faster RCNN model utilized to predict the locations of pedestrians in the image. The proposed method has been improved in terms of the feature map extraction of the image. To evaluate the proposed method, two benchmark datasets INRIA Pedestrian and PASCAL VOC 2012 are considered. The performance metrics used for evaluation are Detection Error Trade-off and Precision-Recall Curve. A statistical analysis is also conducted. The proposed method is compared against state-of-the-art detection methods.
ILMICA - Interactive Learning Model of Image Collage Assessment: A Transfer Learning Approach for Aesthetic Principles

The beauty of moments can be expressed in many ways. One of them is the image collage which captures events and expresses emotions. Nowadays there is a large number of digital images. Aesthetic analyses of image collages are rarely performed due to their complexity and time-consuming nature. For this reason, this is an important issue that has to be addressed. In this paper, we propose an interactive learning model for image collage assessment. It consists of two components: A pre-trained convolutional neural network with built-in knowledge about aesthetics obtained from single image analysis, and an “Interactive Transfer Learning” component specialized in collage aesthetics which is adaptable via Active Learning. We present a mixed method study in which rules for software-based collage generation are identified and a dataset of automatically generated collages representative of the rules is created. ILMICA’s performance is analyzed by a user survey. It is found that the knowledge transfer from single image assessment to collage assessment works: ILMICA can assess collage aesthetics based on predefined rules, thereby demonstrating the system’s ability to learn. Thus, this process can alleviate the end user and simplify aesthetic collage evaluations.
Script Identification of Camera Based Bilingual Document Images Using SFTA Features

In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.
Identification of Speech Signal Using Mfcc Imfcc Features Through Gaussian Mixture Matching Model

Speaker identification is one of the foreground methods for establishing which speaker produces a better voicing statement among the different speakers. This process needs speaker recognition technique to determine which speaker can be recognized depending on the data in speech signal and for the speaker identification is use as a subordinate technique. The two phases of speaker identification include enrolment phase and testing phase. In first phase, the number of speakers were identified and recognition is based on their speech signal. For all the speakers a construction model is developed. In the second phase, a reference speech signal is made to compare with the recognized speech signal from the speakers. This is mostly used in checking and authenticating their integrity, status and control to services like voice based attendance and many other applications which involves speech identification and recognition. There are three main tasks in speaker identification. They are preprocessing, feature extraction and feature matching. The preprocessing of the signal involves pre-emphasis, framing, windowing and spectrum conversion. In this paper feature extraction involves the extraction of MFCC and IMFCC features. Feature matching is done with the help of Bayesian Gaussian Mixture Model to compare the training and test set features to identify the speaker. In this paper speaker identification accuracy and error rate are found.
[Invited Paper] HDR Tone mapping: System Implementations and Benchmarking

Ever-increasing demand for high quality display has ensued two decades of research in high dynamic range (HDR) imaging, as it has the potential to replace the standard dynamic range imaging. The most significant feature of HDR is the accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Additionally, display of HDR video content on a standard display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping (TM) operators, have been studied and proposed in these two decades. In this paper, we will discuss those TM algorithms which have been implemented on graphics processing unit (GPU)1-12), field-programmable gate array (FPGA)13-47), and application specific integrated circuit (ASIC)48-60). Like any other image enhancement algorithms tone mapping is resource intensive; accordingly, they have to be accelerated using hardware for real-time performance. This adaptation effort leads to various design challenges that are encountered during the hardware (HW) development. Any real-time application poses strict timing constraints which requires time exact processing of the algorithm. Also, most of the embedded systems would have limited system resources in terms of battery, computational power and memory resources. These design challenges require novel solutions, and in this paper we focus on these HW issues. Recently machine-learning (ML) algorithms have been providing different approaches to solve various image processing problems including tone mapping. Besides HW-TMOs we briefly review learning-based TM algorithms, and discuss important architectures & datasets. Additionally, we present HDR-Net like neural networks as ideal candidate baselines for realizing TMOs with deep neural network (DNN) on hardware/embedded systems.
Emoji Use in Research about Tourism and Recreational Activities Preferences for Children with Disabilities

Tourism for people with disabilities offers entertainment, improves both mental and physical health, as well as socialization and contributes significantly to improving their quality of life. The purpose of this study was to reveal, with the use of emoji, the preferences of children with disabilities regarding their participation in recreational activities during the holidays. For this purpose, an electronic questionnaire was developed, which included questions with emoji. The research sample consisted of 72 children with motor and intellectual disabilities and sensory disorders. The results of the research showed that the majority stated that they like to socialize with other children and that they would like to participate in various games and activities during their vacation. Finding new friends and avoiding the feeling of loneliness was the most important reason for their participation in recreational activities during their holidays. Their participation contributed significantly to the cultivation and maintenance of their autonomy, the prevention of depression and the development of sociability. The results of the present research can significantly contribute to the design of services provided in the field of tourism.
The development of cognitive information technology in physics education

The work is devoted to the development of informational techniques to improve the cognitive motive of students’ learning. The purpose is the creation of IT content reflecting the most striking achievements of science in the last seven years in the form of a video system of electronic documents. The system includes the achievements of physics in 2012-2019 that made it possible to open global synergetic processes. The ability to use color illustrations provides involving a visual channel of information perception with the ability of the adjusting the list of studied topics and their content. The work reveals that such stimulation of a cognitive motive leads to a cognitive effect, which allows increasing the professional motive of student learning and achieving the maximal effect of learning.
Computer Assisted Design of Motor and Transformer

Transformer is a very important electrical equipment, carrying a variety of functions such as voltage conversion, current conversion and voltage regulation. This design is mainly to design a transformer circuit, complete 220V ac into 5V direct current function. The designed circuit was tested based on Mulitism simulation software.
Obfuscation-Based Mechanisms in Location-Based Privacy Protection

Protection of personal data is vital to individuals. With the prevalence of cyber-crime, it is important to leverage the power of artificial intelligence (AI) to protect the user. The more someone else knows about a person and their whereabouts, the more power they can have over that person. This power takes the form of influence over their decisions, perhaps reveals to others their behaviours, where they live, and subsequently can affect their reputation or even safety. This review will provide an exploration into three methods of obscuring a person’s geographical location when using location-based services. It will explain to the reader how artificial intelligence can be used in their favour. Unfortunately, there is no perfect protection of privacy because location-based services (LBS) rely on the device’s accurate location to provide services, such as restaurant recommendations or games like Pokemon. But we should at least aim for a favourable balance between total privacy and the exposure of information on an individual. Three obfuscation-based mechanisms were compared on their data privacy efficacy, data utility, and ease of implementation. These mechanisms are: location generalisation (cloaking), location perturbation (differential privacy), and location spoofing (using dummy locations). Gowalla is a location-based social networking website where users share their locations by checking-in. A subset of the Gowalla dataset was used to evaluate these three mechanisms. In this work, three obfuscation methodologies for location-based services to protect the user’s location and privacy were evaluated. For the Gowalla dataset subset in London, the differential privacy using logistic regression with an epsilon of 0.8 seemed to have the best performance in terms of privacy, utility and overhead. k-Anonymity came second in its reasonable performance across all three metrics. t-Closeness obfuscation performed better in privacy, but worse than l-diversity in utility. Finally, these were followed by the application of dummy based locations. This method was the simplest but was the least refined in its ability to hide user datapoints and the most vulnerable to malicious attacks. The differential privacy using k-means was considered separately, since it is an unsupervised artificial intelligence learning algorithm as opposed to the others. Differential privacy clustering showed the ease of being able to create varying clusters each time a query is run, which could be highly effective in hiding user device locations.
Obstacle Avoidance for AUV Based on Null Space Modified Guidance Vector Field and Fuzzy Coefficient Regulator

An efficient method called null space modified guidance vector field (NSMGVF) is proposed to solve the obstacle avoidance problem for autonomous underwater vehicle (AUV) in complex three-dimensional environment. The modified guidance vector field is firstly constructed to guide AUV to avoid static obstacles when moving towards destination. The null space strategy is introduced to integrate different modulation velocities generated by obstacles, and then AUV can avoid multiple dynamic obstacles at the same time. In addition, the fuzzy coefficient regulator (FCR) is introduced to adjust the coefficients of each obstacle online, and a better path is obtained in this way. Finally, the simulation results show that this method applies to the obstacle avoidance mission for AUV in complex marine environment and has a better real-time performance.
The Development of Computer Multimedia Technology in English Entrepreneurship Training under the Background of School-Enterprise Cooperation

The penetration of big data technology in English education in my country is an important prerequisite for facilitating a comprehensive reform of education and information technology. In this article, I will analyze the current state of English education in my country, fully clarify research theory, and properly measure research and analytical innovation. This article analyzes two identities of a user. The BD era helps teachers make decisions, change the way people think, and explore the impact of English college education in the BD era. Finally, we will analyze the research on mixed methods of English education in the BD era in a graph and contribute to improving the quality of English education. This article combines the English teaching methods commonly used by students, analyzes the benefits of teaching English, and proposes effective measures to establish new ways of teaching English to teachers. Not only do they play a role in student autonomy, they also enable teachers to use online teaching methods in and out of the classroom to further reform new models of teaching English. The results of experimental studies show that in a BD environment, English teachers change traditional teaching methods, recognize the benefits of current BD technology, introduce it into education, and adapt teaching methods according to the actual needs of today. Indicates that it needs to be changed. Here are some suggestions on how to view and book English education at the university.
Research on Outsourced PSI Protocols for Privacy Preserving Data Sharing

Private set intersection (PSI), a basic privacy preserving technology for data sharing, is widely used in various practical applications such as educational system, anti-epidemic system and credit system. In traditional PSI solutions, two participants collaboratively calculate the intersection based on the plaintext sets they hold. In cloud computing scenario, cloud users would like to upload the encrypted data to the cloud rather than storing their own datasets locally, which needs to delegate cloud servers to execute PSI operations for data uses. Therefore, how to implement outsourced PSI computation is a hot topic of current research. In this paper, we mainly analyze the current researches focusing on the security and scalability of various existing out-sourced PSI protocols, including verifiable PSI, multi-party PSI, PSI-CA, quantum-based PSI, post-quantum-based PSI, fog-based PSI, blockchain-based PSI, attribute-based PSI, etc. Through the comparative analysis and summary of the existing schemes, we further discussed the future research direction.
DeepMnist: A method of white box testing based on hierarchy

Deep learning systems are increasingly used in safety-critical areas such as self-driving cars, and systematic testing of their behavior has become a key issue. Research in recent years has mainly proposed new coverage metrics and new neuron selection strategies but did not provide a suitable neuron selection strategy for the proposed coverage metrics. Our purpose is to experiment with different coverage metrics to explore further the influence of different coverage metrics on neuron guidance. DeepMnist selected Top-k Neuron Coverage(TKNC) for experiments and proposed a TKNC-based neuron selection strategy to test the MNIST dataset. Compared with Deepxplore and DLFuzz, the coverage rate is greatly improved, which is more suitable for the coverage measurement of TKNC.
DRSGN: Dual Revised Semantic Graph Structured Network for Image-Text Matching

Image-Text matching has made significant contributions to bridge the gap in multi-modal retrieval for advanced pattern recognition systems. The key point of this task is mapping images and text semantics into a common space, where the correlations of similar pairs and dissimilar ones are distinguishable. To concentrate on obtaining semantics, most of existing methods utilize an external object detector to extract compatible region-level visual representations to match the word-level textual clues in captions. In addition to perform the local region matching, several recent studies design a classification task using global information in the final matching layer, but such global supervision signal is difficult to transmitted to the shallow feature descriptor learning layer. To address this issue, in this paper, we propose a novel Dual Revised Semantic Graph Structured Network (DRSGN) to supplement global supervision to the regional semantics adaptively in the shallow layers. In principle, DRSGN integrates regional and global descriptors to formulate an attention supervising mechanism, aiming to simultaneously highlight the regional instances and global scene to obtain complementary visual clues. A dual semantic supervising module is then established to interact between two modalities to extract the genuine matching pairs. Finally, a semantic graph consisting of the obtained multi-modal clues is designed to perform similarity reasoning between the positional relation embedded textual nodes and semantic related visual nodes. The dedicated global signals provide complementary supervision against local regions to support improved matching capacity. The experimental results on Flikr30K and MSCOCO demonstrate the effectiveness of the proposed DRSGN, with improved matching performance against the local region-based approaches
Hierarchical Structure-Feature Aware Graph Neural Network for Node Classification

In recent years, graph neural network is used to process graph data and has been successfully applied to graph node classification task.Due to the complexity of graph structure and the difficulty of obtaining node labels, node classification in datasets with fewer labels becomes a challenge.Existing node classification problems with few-label nodes datasets usually use neighbor aggregation schemes.However, these methods lack the ''graph pooling mechanism,'' which makes these models impossible to make full use of the global graph information.To solve the problem, we propose a Hierarchical Structure-Feature aware Graph Neural Network (HSFGNN) model.The model learns node features by a hierarchical mechanism that repeatedly utilizes coarsening and refining methods at different levels.Firstly, we use topological information, node characteristics and connectivity of the graph to construct an intuitive and effective coarsening part based on multivariable analysis technology.Secondly, we aggregate the unselected nodes' features into the top-k nodes, so that the coarser nodes contain more abundant valid graph information.Finally, we propose an additional attention mechanism to obtain more accurate final representations of nodes.Experimental results show that the HSFGNN model achieves outstanding classification accuracy on the dataset with less labeled data.
LFM-D2GAIN: An Improved Missing Data Imputation Method Based on Generative Adversarial Imputation Nets

For improving the accuracy of missing data imputation in data completion task and making the imputed data further obey the real distribution law, dual discriminator generative adversarial imputation nets pre-trained by latent factor model has been proposed. LFM makes each training step transition coherently and reduces the reconstruction error of missing data. Combined with the design idea of D2GAN, another discriminator has been added into the standard GAIN. The loss function in the network has been designed for the data completion task, so that multi-modes of real data could be captured in training process to avoid the mode collapse of data after completion. By comparing multiple baseline on multiple open data sets, the results obtained by LFM-D2GAIN show that RMSE decreased 32&#x0025; and global KL divergence decreased 16&#x0025; on average. It shows that the proposed method has excellent performance and can provide a positive impact on subsequent data mining tasks.
A generalized precision matrix for t-Student distributions in portfolio optimization

The Markowitz model is still the cornerstone of modern portfolio theory. In particular, when focusing on the minimum-variance portfolio, the covariance matrix or better its inverse, the so-called precision matrix, is the only input required. So far, most scholars worked on improving the estimation of the input, however little attention has been given to the limitations of the inverse covariance matrix when capturing the dependence structure in a non-Gaussian setting. While the precision matrix allows to correctly understand the conditional dependence structure of random vectors in a Gaussian setting, the inverse of the covariance matrix might not necessarily result in a reliable source of information when Gaussianity fails. In this paper, exploiting the local dependence function, different definitions of the generalized precision matrix (GPM), which holds for a general class of distributions, are provided. In particular, we focus on the multivariate t-Student distribution and point out that the interaction in random vectors does not depend only on the inverse of the covariance matrix, but also on additional elements. We test the performance of the proposed GPM using a minimum-variance portfolio set-up by considering S\&P 100 and Fama and French industry data. We show that portfolios relying on the GPM often generate statistically significant lower out-of-sample variances than state-of-art methods.