Reviewerly/exHarmony huggingface dataset:
Traffic Sign Recognition with Binarized Multi-Scale Neural Networks

Computer vision has grown tremendously thanks to advances in convolutional neural networks (CNNs), the field of traffic sign recognition and intelligent transportation performs better because of that. However, as traffic sign recognition systems are often deployed in computing and storage constrained environments, it is critical for the network design to be accurate and real-time in these instances. Whether it is training or running an ordinary neural networks framework, it requires one or many high-power Graphic Processing Units (GPUs) and considerable memory resources. To solve this problem, this project creatively propose a 2-stage binarized multi-scale neural network framework, called B-MNN, extends Pierre Sermanet and Yann LeCun's Convolutional Networks. When training a B-MNN, we intentionally restrict the weights and activations of the model to +1 or -1. The mathematical calculation (addition, subtraction, multiplication and division) of the binarization network can be implemented using bit-wise bit operations, which is convenient for hardware to implement the algorithm, faster and more energy efficient. B-MNN approaches state-of-the-art results on the GTSRB traffic sign dataset implemented with the Keras artificial neural network library. We achieved 91.34% accuracy. Although it is slightly less accurate than models based on CNNs, the same ordinary neural network structure is an order of magnitude larger than ours. It is an important direction to realize the neural network into the realistic scene in the future.
MPMSVC: Multiple Parametric-Margin Support Vector Clustering

In this work, we propose an unsupervised multiple parametric-margin support vector clustering (MPMSVC) for noisy clustering tasks. The main idea of MPMSVC is to find a parametric-margin center hyperplane for each cluster in a manner that gathers the within-cluster instances around the corresponding center hyperplane, and keeps the between-cluster instances far away. Specifically, our MPMSVC owns the following attractive merits: i) The primal of MPMSVC is enhanced in the least squares sense, which enjoys an effective learning procedure. ii) The utilization of the linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm loss makes MPMSVC be more robust to noisy clustering tasks. iii) An efficient iterative algorithm is presented to optimize the non-smooth problem in MPMSVC, which only involves a set of linear equations. Also, the convergence of the proposed algorithm is guaranteed theoretically. iv) The nonlinear extension is further derived via kernel technique to deal with more complex clustering tasks. Finally, the feasibility and effectiveness of MPMSVC is validated by extensive experiments on both synthetic and real-world datasets.
Automatically Identifying Parameter Constraints in Complex Web APIs: A Case Study at Adyen

Web APIs may have constraints on parameters, such that not all parameters are either always required or always optional.Moreover, the presence or value of one parameter could cause another parameter to be required, or parameters could have restrictions on what kinds of values are valid.Having a clear overview of the constraints helps API consumers to integrate without the need for additional support and with fewer integration faults.We made use of existing documentation and code analysis approaches for identifying parameter constraints in complex web APIs.In this paper, we report our case study of several APIs at Adyen, a large-scale payment company that offers complex Web APIs to its customers.Our results show that the documentationand code-based approach can identify 23% and 53% of the constraints respectively and, when combined, 68% of them.We also reflect on the current challenges that these approaches face.In particular, the absence of information that explicitly describes the constraints in the documentation (in the documentation analysis), and the engineering of a sound static code analyser that is sensitive to data-flow, maintains longer parameter references throughout the API's code, and that is able to symbolically execute the several libraries and frameworks used by the API (in the static analysis).
Single frame phase estimation based on Hilbert transform and Lissajous Ellipse Fitting method in fringe projection technology

Fringe projection three-dimensional (3D) measurement technique is an effective technique to measure 3D shape of objects, in which phase retrieval is an important procedure. To overcome the need of pre-filtering process in the existing single-frame methods, this paper proposes a novel method to extract phase distribution solely from one-frame fringe pattern without pre-filtering, which reduces its complexity and retains more phase details. The proposed method consists of two stages. Firstly, the Hilbert transform is applied to one-frame fringe pattern to generate another frame of fringe pattern. Secondly, the phase distribution is calculated based on Lissajous Ellipse Fitting (LEF) technique by using original fringe pattern and the Hilbert transformed fringe pattern. The results provides a practically efficient methodology for single frame phase reconstruction, which shows potential useful means for reconstructing object in rapid measurement.
Automated Segmentation of Overlapping Cells in Cervical Cytology Images Using Deep Learning

In this paper, we deal with segmentation of overlapping cells in cervix cytological images. One of the major challenges in analysing cervical cytology images is the segmentation of overlapping cells. The complexity is mainly due to low contrast, wide range of overlapping ratios and other debris. We propose to address these issues using deep learning. Our method is a three-stage process and uses the convolutional network U-Net in two of these stages. We showcase the process with the Overlapping Cervical Cytology Image Segmentation Challenge dataset. Innovations by using available information provide results that are more promising than existing methods. These include the use of overlap percentage, and ensuring that the inputs to the second U-Net are the original image, the cell mass, and the masks of the individual nucleus.
StARboard &amp; TrACTOr: Actuated Tangibles in an Educational TAR Application

We explore the potential of direct haptic interaction in a novel approach to Tangible Augmented Reality in an educational context. Employing our prototyping platform ACTO, we developed a tabletop Augmented Reality application StARboard for sailing students. In this personal viewpoint environment virtual objects, e.g., sailing ships, are physically represented by actuated micro robots. These align with virtual objects, allowing direct physical interaction with the scene. When a user tries to pick up a virtual ship, its physical robot counterpart is grabbed instead. We also developed a tracking solution TrACTOr, employing a depth sensor to allow tracking independent of the table surface. In this paper we present concept and development of StARboard and TrACTOr. We report results of our user study with 18 participants using our prototype. They show that direct haptic interaction in tabletop AR scores en-par with traditional mouse interaction on a desktop setup in usability (mean SUS = 86.7 vs. 82.9) and performance (mean RTLX = 15.0 vs. 14.8), while outperforming the mouse in factors related to learning like presence (mean 6.0 vs 3.1) and absorption (mean 5.4 vs. 4.2). It was also rated the most fun (13× vs. 0×) and most suitable for learning (9× vs. 4×).
Mini neural network based on knowledge distillation for dynamic gesture recognition in real scenes

At present, gesture interaction has become one of the important ways of human-computer interaction, and the human hand, as the most flexible organ in the body, will make non-contact dynamic gesture interaction more convenient and more versatile. However, because of the diversity and uncertainty of gestures in time and space, and the complex deformable body of the human hands, it finally leads to the low recognition rate of dynamic gesture recognition in real scenes. Aiming at the difficulty of hand feature recognition and positioning, and the interference problem of unconstrained environment, this paper proposes a lightweight neural network based on distillation collaborative training for dynamic gesture recognition. This method uses knowledge distillation to train the deep network and the lightweight network together to improve the recognition performance of the lightweight neural network. At the same time, LSTM is used to improve the network's recognition rate of dynamic gestures of different durations in real scenes. In this paper, the lightweight dynamic gesture recognition network was tested on 20BN-Something-Something V2, and the accuracy rate was as high as 64.3%.
ROLAP DW transformation proposal for OLAP architecture in <i>NoSQL</i> database

This paper aims to present a case study related to the migration of a ROLAP architecture Data Warehouse of the University of Brasilia to an OLAP architecture in the NoSQL DB family of columns. This migration starts from the need to study new paradigms of architectures for decision support systems due to the new reality of the problems generated by the use of Big Data in the present moment at the university. We made two approaches: the first one by migrating to the Cassandra DBMS and the second one by migrating to Apache Hive. State of the art was made from Web of Science and we used the TEMAC methodology. We made a transformation of integrating all dimensions and table of facts into a single table for both Cassandra and Hive and we made a comparison by running two queries from these tables. Besides that, we also made a qualitative analysis, addressing the advantages and disadvantages of each approach. In the end, we concluded that Apache Hive should be the best choice for the University of Brasilia.
A convolutional neural network-based reviews classification method for explainable recommendations

Recent advances in information filtering have resulted in effective recommender systems that are able to provide online personalized recommendations to millions of users from all over the world. However, most of these systems ignore the explanation purpose while producing recommendations with high-quality results. Moreover, the classification of reviews given to users as explanations is not fully exploited in previous studies. In this paper, we develop a convolutional neural network-based reviews classification method for explainable recommendation systems. The convolutional neural network is used to extract the reviews features for predicting whether the reviews provided as explanations are positive or negative. Based on such additional information, users can understand not only why certain items are recommended for them but also get support to know the nature of such explanations. We conduct experiments on a dataset from Amazon. The experimental results show that our method outperforms state-of-the-art methods.
Robust and Imperceptible Digital Image Watermarking Based on DWT-DCT-Schur

The advent of internet and hence multimedia communication has been very useful to quickly transmit data, in all fields, including the medical field. In this scenario, security breach becomes a major concern. Watermarking is one of the schemes used for authentication purposes, copyright protection etc. Watermarking involves two images, one watermark and one cover-image. The watermark is embedded into the cover-image imperceptibly and has to be extracted later for it to be authenticated. Both the cover image and the watermark undergo various transforms. A non-blind, robust, imperceptible watermarking, in the frequency domain is presented here. Two different frequency domain transforms have been applied on the cover-image successively followed by a mathematical decomposition to improve the robustness, they are, namely, DWT (Discrete Wavelet Transform), DCT (Discrete Cosine Transform) and Schur decomposition. A two level DWT is applied to the encrypted watermark before embedding it to the cover image. This method is highly effective on X-ray images and color images alike. This can be used on medical images for authentication purposes. Robustness and imperceptibility of the image under various attacks have been simulated on Matlab and examined.
Sentiment Analysis for Review Rating Prediction in a Travel Journal

This paper presents sentiment analysis to predict numerical rating of text reviews in a web-based travel journal application. The application allows users to record and provide text reviews on tourist spots visited. Text reviews undergo parts-of-speech (POS) tagging, rule-based phrase chunking and dependency parsing to extract opinion phrases in noun-adjective and noun-verb pairs from the original text. Each pair is further classified to one of the four categories: accommodation, food, entertainment and tourist attraction using the noun against a curated bag-of-words (BOW) to ensure that only relevant statements are included in the scoring. Word Sense Disambiguation is performed to correctly identify the word sense that matches the meaning of the sentence using WordNet. SentiWordNet, a lexical resource for sentiment analysis, was used to determine polarity score representing the emotional intensity of the review. The system predicted star rating was compared with the actual author rating in Google Maps and with human annotator ratings who are asked to label the text reviews. The predicted rating scored low mean absolute error (MAE) between the system and human rating which means that the rating predicted is closer to human interpretation of the text reviews. Overall rating prediction accuracy is 82%.
Identity Based Security Framework For Smart Cities

A Smart City is a next generation city where it uses information and communication technologies(ICT) to provide numerous services and amenities with improved efficiency and reliability in a hierarchical manner. Despite the benefits, securing smart cities is of paramount importance for safeguarding communications from malicious entities and preserving user privacy. Thus a need for developing a security framework for smart cities becomes necessary for integrating smart city services and platforms in a hierarchical manner. In this paper, we develop an identity based security framework for smart cities. The proposed framework leverages the hierarchical nature of smart cities and builds a pseudonym-based key management scheme that uses identities as public keys. Our approach replaces the traditional Public Key Infrastructure in smart cities thus providing improved benefits in terms of security, reliability and efficiency. Evaluation of the proposed scheme shows that our approach is lightweight compared to traditional public key infrastructure and that it eliminates the problem of single point of failure in Private Key Generators (PKG).
Gladius: LWR Based Efficient Hybrid Public Key Encryption with Distributed Decryption

Standard hybrid encryption schemes based on the KEM-DEM framework are hard to implement efficiently in a distributed manner whilst maintaining the CCA security property of the scheme. This is because the DEM needs to be decrypted under the key encapsulated by the KEM, before the whole ciphertext is declared valid. In this paper we present a new variant of the KEM-DEM framework, closely related to Tag-KEMs, which sidesteps this issue. We then present a post-quantum KEM for this framework based on Learning-with-Rounding, which is designed specifically to have fast distributed decryption. Our combined construction of a hybrid encryption scheme with Learning-with-Rounding based KEM, called Gladius, is closely related to the NIST Round 3 candidate called Saber. Finally, we give a prototype distributed implementation that achieves a decapsulation time of 4.99 s for three parties.
Privacy-Aware Face Recognition with Lensless Multi-pinhole Camera

Face recognition and privacy protection are closely related. A high-quality facial image is required to achieve a high accuracy in face recognition; however, this undermines the privacy of the person being photographed. From the perspective of confidentiality, storing facial images as raw data is a problem. If a low-quality facial image is used, to protect user privacy, the accuracy of recognition decreases. In this paper, we propose a method for face recognition that solves these problems. We train a neural network with an unblurred image at first, and then train the neural network with a blurred image, using the features of the neural network trained with the unblurred image, as an initial value. This makes it possible to train features that are similar to the features trained with the neural network using a high-quality image. This enables us to perform face recognition without compromising user privacy. Our method consists of a neural network for face feature extraction, which extracts suitable features for face recognition from a blurred facial image, and a face recognition neural network. After pretraining both networks, we fine-tune them in an end-to-end manner. In experiments, the proposed method achieved accuracy comparable to that of conventional face recognition methods, which take as input unblurred face images from simulations and from images captured by our camera system.
Meta-Path Hierarchical Heterogeneous Graph Convolution Network for High Potential Scholar Recognition

Recognizing high potential scholars has become an important problem in recent years.However, conventional scholar evaluating methods based on hand-crafted metrics can not profile the scholars in a dynamic and comprehensive way.With the development of online academic databases, large-scale academic activity data become available, which implies detailed information on the scholars' achievement and academic activities.Inspired by the recent success of deep graph neural networks (GNNs), we propose a novel solution to recognize high potential scholars on the dynamic heterogeneous academic network.Specifically, we propose a novel Mate-path Hierarchical Heterogeneous Graph Convolution Network (MHHGCN) to effectively model the heterogeneous graph information.MHHGCN hierarchically aggregates entity and relational information on a set of metapaths, and can alleviate the information loss problem in the previous heterogenous GNN models.Then to capture the dynamic scholar feature, we combine MHHGCN with Long Short Term Memory (LSTM) network with attention mechanism to model the temporal information and predict the potential scholar.Extensive experimental results on real-world high potential scholar data demonstrate the effectiveness of our approach.Moreover, the model shows high interpretability by visualization of the attention layers.
Automated system of remote holding competitive and assessment procedures

Abstract At present, organizational and reporting activities during the admission of exams, the defense of students’ FQPs, dissertations using on-line services and videoconferencing are carried out manually and require laborious routine work of their organizers. The purpose of the work is to develop a web application that provides automation of events during the assessment and competitive procedures in on-line and off-line modes. The design of a formal educational or scientific attestation event is carried out according to the model of expert competitive assessment of objects: collection of evaluated objects-selection of evaluation criteria-expert evaluation of objects. The creation of a web application is realized using the latest technologies for developing single-page web applications (SPA). The use of the developed web application allows organizing the procedure for expert assessment of objects/subjects in real time in conjunction with video communication systems. The organizer can be any user who takes on the role of a moderator of the event. The attracted experts can, in parallel with the video presentation of the participants, familiarize themselves with their materials, give marks in real time according to the criteria set by the organizer. Experimental testing of the developed application showed the possibility of its application in carrying out various competitive and assessment procedures in education. The resulting software product can be widely used in the educational process of any educational institution and its development is not laborious.
Android Application Food Delivery Services

The following topics are dealt with: mobile computing; computer aided instruction; smart phones; data mining; Internet; pattern classification; learning (artificial intelligence); educational institutions; educational administrative data processing; and social networking (online).
Super-resolution Reconstruction of Rock Thin-Section Image based on SinGAN

The rock thin section images are of great significance to the study of petroleum geological characteristics and oil and gas exploration. Due to the limitations of various factors, the obtained images of rock thin-sections often have low resolution, which limits the researchers' grasp of their detailed information to some extent. The traditional super-resolution algorithm of neural network will need a large amount of data as a training set, in order to improve rock thin-section image super-resolution reconstruction algorithm texture detail information reduction ability, the paper using single image generative adversarial network for rock thin-section image super-resolution reconstruction, does not need to input a large number of data sets, of single image super-resolution reconstruction image. Rock cast thin section images from an oil field area in Ordos basin were used for training, and peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) evaluation indexes were used for model evaluation. The experimental results show that the super-resolution image processing based on this method has good visual effects and evaluation indexes.
Fast Distance Metrics in Low-Dimensional Space for Neighbor Search Problems

We consider popular dimension reduction techniques that project data on a low dimensional subspace. They include Principal Component Analysis, Column Subset Selection, and Johnson-Lindenstrauss projections. These techniques have been classically used to efficiently compute various approximations. We propose the following three-step procedure for enhancing the accuracy of such approximations: 1. Unknown quantities in the approximation are replaced with random variables. 2. The Maximum Entropy method is applied to infer the most likely probability distribution. 3. Expected values of the random variables are used to compute the enhanced estimates. Our use of the Maximum Entropy method requires knowledge of vector norms that can be easily computed during the dimension reduction. We demonstrate significant enhancements in average accuracy for Euclidean distance and Mahalanobis distance, and improvements in evaluating k-nearest neighbors and k-furthest neighbors by using the enhanced Euclidean distance formula.