Reviewerly/exHarmony huggingface dataset:
Recognition of Handwritten Digits Based on Images Spectrum Decomposition

Recognition of handwritten digits by convolutional neural network (CNN) using Fourier transforms of images as a preprocessing is considered. An algorithm of image preprocessing for effective CNN training and handwritten digits recognition is proposed. A discrete two-dimensional Fourier transform is applied to the original images. The real and imaginary parts are separated from the obtained complex values, as well as the amplitude and phase are calculated. Convolutional neural network is trained on the resulting characteristics obtained after Fourier transform. The proposed approach is tested on the MNIST database. The effects of image preprocessing using spectral decomposition and application of obtained different essential characteristics on the errors of handwritten digits recognition are estimated.
Entity and Entity Type Enhanced Capsule Network for Distant Supervision Relation Extraction

In the task of distant supervision relation extraction, attention mechanism is introduced to distinguish correct instances from noise. However, the existing attention is often single-headed, and unable to recognize fine-grained word-level features from multiple subspaces. In addition, most current models do not fully utilize the external information of entities, and lack effective use of potential relation between the pre-trained entities. They only encode entities and other components of sentence into the vector, which is mixed with noise also weakens the potential relation. Therefore, we propose a dynamic double multi-head attention to filter out noise, and an entity supervisor to enhance the potential relation, and use capsule network to solve multi-label classification problem. We also propose auxiliary BGRU (Bidirectional Gated Recurrent Unit) to improve feature extraction performance. Experimental results show that our model achieves state-of-the-art performance on the precision-recall curve; Performance in processing of long-tail relations is also quite outstanding.
Background Object Removal and Image Inpainting to Fill Irregular Holes

In the current scenario, nearly all deep learning methods to perform image inpainting use a standard convolution network in order to regenerate the holes created by removing the object. However, the result of this procedure is not up to the mark as the resultant image often appears to be distorted and blurry. Post-processing techniques do address this issue but prove to be computationally expensive. We aim to remove the background object from the image while ensuring that the regenerated image has minimal discrepancies. In our approach, we make use of partial convolution in which at every stage of the convolution network, we replace the convolution block with a partial convolution block. We have also focused on producing a high-quality mask dataset which ensures that our model works well on real life images where aberrantly shaped and sized objects are needed to be removed. Qualitative comparisons help analyze the predictions of our model in relation to those made by the standard image inpainting model of OpenCV.
Energy Loan Program with Blockchain Safe Credit under Scenario of Industrial Internet of Things

In order to solve the problems of high carbon footprint and energy consumption cost in the Internet of vehicles (IoV), point-to-point energy transactions are widely conducted in wireless vehicular ad hoc networks (VANET). However, due to the impact of untrustworthy and non- transparent energy market, there are many common security and privacy protection issues in the energy transaction process. To deal with these problems, this paper takes advantages of the distributed management structure and blockchain technology to study a new safe energy trading system based on the consortium blockchains. Meanwhile, this paper proposes a lightweight intelligent contract to evaluate and update the reputation value of each vehicle participant, in order to provide a credible computing environment and avoid the massive resource consumption caused by the "mining" process.
Reconnaissance and Experiment on 5G-SA Communication Terminal Capability and Identity Information

With the rapid development of mobile communication technology, the reconnaissance on terminal capability and identity information is not only an important guarantee to maintain the normal order of mobile communication, but also an essential means to ensure the electromagnetic space security. According to the characteristics of 5G mobile communication terminal’s transporting capability and identity information, the smart jamming is first used to make the target terminal away from the 5G network, and then the jamming is turned off at once. Next the terminal will return to the 5G network. Through the time-frequency matching detection method, interactive signals of random access process and network registration between the terminal and the base station are quickly captured in this process, and the scheduling information in Physical Downlink Control Channel (PDCCH) and the capability and identity information in Physical Uplink Shared Channel (PUSCH) are demodulated and decoded under non-cooperative conditions. Finally, the experiment is carried out on the actual 5G communication terminal of China Telecom. The capability and identity information of this terminal are extracted successfully in the Stand Alone (SA) mode, which verifies the effectiveness and correctness of the method. This is a significant technical foundation for the subsequent development on the 5G terminal control equipment.
Performance of the quantum MaxEnt estimation in the presence of physical symmetries

When an informationally complete measurement is not available, the reconstruction of the density operator that describes the state of a quantum system can be obtained, in a reliable way, by adopting the maximum entropy principle (MaxEnt principle), as an additional criterion, to achieve the least biased estimation. In this paper, we study the performance of the MaxEnt method for quantum state estimation when there is prior information about symmetries of the unknown state. We explicitly describe how to work with this method in the most general case, and we present an algorithm that allows to improve the estimation of quantum states with arbitrary symmetries. Furthermore, we implement this algorithm to carry out numerical simulations estimating the density matrix of several three-qubit states of particular interest for quantum information tasks. We observed that, for most states, our approach allows to considerably reduce the number of independent measurements needed to obtain a sufficiently high fidelity in the reconstruction of the density matrix. Moreover, we analyze the performance of the method in realistic scenarios, showing that it is robust even when the effects of finite statistics and experimental noise are considered.
Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing

Feature selection for deep learning architectures is one of the important and challenging steps in developing an efficient image parsing application. In this paper, a novel image parsing architecture which makes use of unique feature selection is proposed. It introduces the idea of weighted relationship awareness to reduce the redundancy of features and optimally select an efficient subset of feature representations. The proposed architecture is evaluated on Cam Vid benchmark dataset. A comparison with state-of-the-art methods was conducted which showed significant improvements in terms of segmentation and classification accuracy.
Face Detection with Viola Jones Algorithm

This article aims to present a face detection system for one or more people moving through a video. To achieve this, we use Viola Jones algorithm and a diagram of a face detection algorithm using Boosting algorithm was prepared. Learning the Boosting algorithm is necessary to build a strong classifier from a set of weak classifiers. Subsequently, the Boosting algorithm was combined with a tracking filter and the particulate filter, in order to track each face from its appearance until its disappearance. The particulate filter is one of the best solutions for plotting trajectories and predicting the position of faces in a scene. The algorithm proposed for this task includes four components: learning, detection, tracking and prediction.
Automated Attribute Weighting Fuzzy k-Centers Algorithm for Categorical Data Clustering

Cluster analysis plays an important role in exploring the correlations in data by dividing datasets into separate clusters so that similar objects are located in the same cluster. Moreover, fuzzy cluster analysis can reveal the mixtures of clusters in datasets containing multiple distributions. Certainly, the outcome of clustering methods is approximately determined by the similarity definition. Thus, the similarity measurement is exceedingly important to the formation of fuzzy clusters. In fact, the similarity between two objects is mostly calculated by the mean of differences across multiple dimensions. However, the dissimilarity in some dimensions has little or no effect on the fuzzy clustering outcome. In this study, we explore such impacts for fuzzy clustering of data with categorical attributes. Accordingly, the impact of each attribute on each fuzzy cluster is calculated using an optimizer, and the overlapping dissimilar values are then adjusted by the corresponding weights. We propose to apply this approach to the Fk-centers clustering algorithm, and the experimental results show that our proposed method can achieve higher fuzzy silhouette scores than other related works. These results demonstrate the applicability of deploying of the proposed method in real-world application.
A Fully-Integrated CMOS Hyperchaotic Map for Obfuscated IoT Communications

This paper describes the analysis and practical integrated circuit (IC) implementation of synchronized hyperchaotic maps for energy-efficient private data communication between (or within) IoT nodes. Either analog or digital data can be obfuscated in the physical layer using chaotic masking and recovered by a synchronized hyperchaotic map at the receiver, resulting in compact and low-power private links that operate independently of the digital processor and do not require additional analog-to-digital conversions. The proposed map was implemented in 180 nm CMOS using fully-differential switched-capacitor (SC) circuits, and a serial peripheral interface (SPI) control interface was added for changing the map parameters (thus allowing post-fabrication trimming and changing of obfuscation keys). The complete system (active die area = 2 mm×1 mm) consumes ~4.5 mW from a 2 V power supply at a clock frequency of 2 MHz. The chip can operate at frequencies up to 6.25 MHz, which results in a link energy efficiency of 4.3 nJ/bit for transmitting analog data with 6-bit precision at a data rate of 3.75 Mbps.
Gaze-Based Interaction for Interactive Storytelling in VR

Interactive stories in Virtual Reality need a way of making decisions that influence the further progress of the story. The decision making should be easy and should not disturb the feeling of presence in the virtual world. As many virtual reality glasses come with an integrated eye tracker, it suggests itself to use gaze for making the decisions. We created an interactive story and implemented three different gaze interaction methods, which we evaluated in a user study with 24 participants. The three interaction methods used a dwell-time mechanism, one with a gaze button, one with a texture change of the object looked at, and one method without any feedback. The texture change was the favorite of the users, however, the choice of the interaction method may also depend on the intended dramaturgy and aesthetic aspects of the story.
Correlation Tracking via Mask and Multi-peaks Re-prediction

At present, most of the classic Discriminative Correlation Filter (DCF) use rectangle to track targets. Although this method of selecting targets by rectangle is convenient, the shortcomings are also particularly obvious. For example, DCF may mistake some background information as target. Moreover, the DCF simply uses the linear method to update the target template, which will introduce interference into the model. In order to solve the above problems, we propose a mask and multi-peaks re-prediction correlation filter algorithm. First, the segmentation algorithm is used to extract the edge information in the target region, and the target mask is made according to the edge information. In this way more accurate target information is obtained. Secondly, when the target is interfered, the filtered response graph will fluctuate drastically. We compare the similarity between the target of the first frame and the alternative region that made through multiple peaks to select the most accurate and effective target, and use this target to update the model. We evaluated the proposed algorithm on the OTB-2013 dataset and UAV123 dataset, experiments show that our algorithm can better deal with background interference and has better performance than other advanced algorithms.
Plankton Classification on Imbalanced Dataset via Hybrid Resample Method with LightBGM

Plankton monitoring plays an essential role in marine ecological environment protection, effective identification of its species and quantity can assess the health of the marine ecosystem. Thus, it is valuable to build an automatic classification system for plankton. However, the data of plankton naturally exhibit an imbalance in their class distribution. As a result, we need to take the class-imbalance problem into account for plankton classification. In this paper, we propose a classification model based on a hybrid resample method with LightBGM classifier. Our hybrid resample method combines borderline-SMOTE oversampling and Fuzzy C-means cluster-based undersampling (BSFCM), which is available for handling both within-class and between-class imbalance. In addition, to eliminate the irrelevant factors, dataset preprocessing and feature dimension reduction are employed for the in situ plankton images. The F1-measure and G-mean are used as the evaluation criterion to assess the classification performance. The experimental results show that our BSFCM method using LightBGM classifier is superior to the compared benchmark methods, and achieves good performance on the imbalanced plankton dataset.
E-Business Startup: "Universia" As University Collaborator System

According to Covid 19 pandemic every process has to shifting to collaborate with digital technology. One of the field that pushes to shift is Education field. Undergraduate education level, is one of many strategies to improve people competency. In Indonesia, the number of people has steadily increased. However, they have to improve their level of competency to compete with their competitor. According to this condition, many prospective student difficult to find information that their need to decide a place to study. There are many consideration factors that will involved in this decision. Therefore, in this research will build university collaborator system that can support analysis from prospective student perspective to choose education institution. To build this business design this study involve some analysis such as industrial competition will be using Porter Five Force’s and supported by Business Model Canvas to define Universia’s business model, as well as financial projections for the next seven years. According to the Porter Five Forces model, the information services market around universities still has a lot of room for new entrants. The results of the business model canvas analysis show that, despite being a newcomer to the sector, Universia has a number of value propositions that its competitors do not. Universia’s product will be generated in the web based application. The application will be designed using Object Oriented Analysis and Design (OOAD) in conjunction with the Extreme Programming approach, which includes steps such as planning, designing, coding, and testing. The result of this study is a system collaborator that can be bridge between prospective student and university, so the prospective student can easily find the information that their need to choose university.
Human-machine collaboration on data annotation of images by semi-automatic labeling

Deployment of deep neural network architectures in computer vision applications requires labeled images which human workers create in a manual, cumbersome process of drawing bounding boxes and segmentation masks. In this work, we propose an image labeling companion that supports human workers to label images faster and more efficiently. Our data-pipeline utilizes One-Shot, Few-Shot and pre-trained object detection models to provide bounding box suggestions, thereby reducing the required user interactions during labeling to corrective adjustments. The resulting labels are then used to continuously update the underlying suggestion models. Optionally, we apply a refinement step, where an available bounding box is converted into a finer segmentation mask. We evaluate our approach with a group of participants who label images using our tool - both manually and with the system. In all our experiments, the achieved quality is consistently comparable with manually created labels at factor 2 to 6 faster execution times.
Discovering Proper Neighbors to Improve Session-Based Recommendation

Session-based recommendation shows increasing importance in E-commerce, news and multimedia applications. Its main challenge is to predict next item just using a short anonymous behavior sequence. Some works introduce other close similar sessions as complementary to help recommendation. But users' online behaviors are diverse and very similar sessions are always rare, so the information provided by such similar sessions is limited. In fact, if we observe the data at the high level of coarse granularity, we will find that they may present certain regularity of content and patterns. The selection of close neighborhood sessions at tag level can solve the problem of data sparsity and improve the quality of recommendation. Therefore, we propose a novel model CoKnow that is a collaborative knowledge-aware session-based recommendation model. In this model, we establish a tag-based neighbor selection mechanism. Specifically, CoKnow contains two modules: Current session modeling with item tag(Cu-tag) and Neighbor session modeling with item tag (Ne-tag). In Cu-tag, we construct an item graph and a tag graph based on current session, and use graph neural networks to learn the representations of items and tags. In Ne-tag, a memory matrix is used to store the representations of neighborhood sessions with tag information, and then we integrate these representations according to their similarity with current session to get the output. Finally, the outputs of these two modules are combined to obtain the final representation of session for recommendation. Extensive experiments on real-world datasets show that our proposed model outperforms other state-of-the-art methods consistently.
Image-Based Facial Emotion Recognition Using Convolutional Neural Networks and Transfer Learning

Emotions play a key role in communication between humans. However, it is a characteristic missing in standard human-machine interaction. Therefore, the development of automatic systems for recognizing human emotions has a high value nowadays. In this article is studied the impact of Transfer Learning (TL) methodology over a Deep Learning (DL) system created for Facial Emotion Recognition (FER). A Convolutional Neural Network with a VGG-type architecture is developed with the use of a large image-based FER data set (known as FER2013). TL methodology using different approaches is performed in order of reusing the knowledge assimilated over FER2013 data set, and it is evaluated over another FER data set (known as CK+). We show how a TL procedure can increase the accuracy and generalization ability of a DL model for recognizing basic emotions.
Matrix Shuffle- Exchange Networks for Hard 2D Tasks

Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that prevents its applications to more complex 2D tasks. We propose a new neural model, called Matrix Shuffle-Exchange network, that can efficiently exploit long-range dependencies in 2D data and has comparable speed to a convolutional neural network. It is derived from Neural Shuffle-Exchange network and has O(log N) layers and O(N ^ 2 log N) total time and O(N^2) space complexity for processing a NxN data matrix. We show that the Matrix Shuffle-Exchange network is well-suited for algorithmic and logical reasoning tasks on matrices and dense graphs, exceeding convolutional and graph neural network baselines. Its distinct advantage is the capability of retaining full long-range dependency modelling when generalizing to larger instances - much larger than could be processed with models equipped with a dense attention mechanism.
Resume Screening Using Machine Learning

The recruitment of new employees and allocation of suitable work has always been a random approach for many multinational companies [MNC]. With the use of the data obtained from the submitted resumes, this research work aims to separate the resume levels by analyzing the expertise data from the resumes. To accomplish this, the proposed research work has used machine learning and Naïve-Bayes methodology and it also attempts to find which process provides best possible result for the segregation using one vs. rest classifier. Furthermore, this research work attempts to find the accuracy and performance of the proposed methodology and incorporate it in the IT firms and other regulations for the prevention of manual screening and establish a safe allocation of resources for the companies.
Design of Self-Balancing Virtual Reality Robot Using PID Control Method and Complementary Filter

In this paper, a self-balancing virtual reality robot was designed. It is a type of robot that can maintain a upright position and is equipped with a camera for survey and exploration purposes. The self-balancing robot control system uses the principle of an inverted pendulum with two wheels. The system was connected to Android application as a means of streaming data from the camera module and as a guide for servo movement. This system was created using the CODESYS application installed on the Raspberry Pi 3B+. The main objective of this research is to implement a virtual reality self-balancing robot design with wheel movement using the PID control method and a complementary filter. The MPU6050 sensor and Complementary filter are used as feedback controls to estimate the robot relative upright position which is then calculated using PID to control the motor so that the speed and acceleration of the motor can be varied to keep the robot's balanced in its upright position.