Reviewerly/exHarmony huggingface dataset:
Learning Semantic Representations via Joint 3D Face Reconstruction and Facial Attribute Estimation

We propose a novel joint framework for 3D face reconstruction (3DFR) that integrates facial attribute estimation (FAE) as an auxiliary task. One of the essential problems of 3DFR is to extract semantic facial features (e.g., Big Nose, High Cheekbones, and Asian) from in-the-wild 2D images, which is inherently involved with FAE. These two tasks, though heterogeneous, are highly relevant to each other. To achieve this, we leverage a Convolutional Neural Network to extract shared facial representations for both shape decoder and attribute classifier. We further develop an in-batch hybrid-task training scheme that enables our model to learn from heterogeneous facial datasets jointly within a mini-batch. Thanks to the joint loss that provides supervision from both 3DFR and FAE domains, our model learns the correlations between 3D shapes and facial attributes, which benefit both feature extraction and shape inference. Quantitative evaluation and qualitative visualization results confirm the effectiveness and robustness of our joint framework.
Improving the Cultivation Ability of University International Students in China based on the Citespace

The impact of information technology on the education industry is revolutionary. Universities should improve the level of information technology, update concepts, and further improve the training capabilities of foreign students. Taking Dalian University of Technology as an example, this paper uses citespace software to visually analyze the training of Chinese students abroad, and analyzes the problems existing in the training of Chinese students abroad, and puts forward suggestions to further improve their training ability.
Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and Defence Methods

With more research published on adversarial examples, we face a growing need for strong and insightful methods for evaluating the robustness of machine learning solutions against their adversarial threats. Previous work contains problematic and overly simplified evaluation methods, where different methods for generating adversarial examples are compared, even though they produce adversarial examples of differing perturbation magnitudes. This creates a biased evaluation environment, as higher perturbations yield naturally stronger adversarial examples. We propose a novel “accuracy-perturbation curve” that visualizes a classifiers classification accuracy response to adversarial examples of different perturbations. To demonstrate the utility of the curve we perform evaluation of responses of different image classifier architectures to four popular adversarial example methods. We also show how adversarial training improves the robustness of a classifier using the “accuracy-perturbation curve”.
Survey of Lattice to Design Post Quantum Cryptographic Algorithm Using Lattice

Objective: Quantum algorithm are stronger and more secure than the classical computers not because they run on faster harder computers but also, they require fewer steps. With Quantum computers the attackers have high computing power and with quantum algorithm can easily break the cryptographic system. Lattice is regularly spaced grid of points stretching to infinity. Quantum safe security algorithms are resistant to both attacks caused by classical computers and the attack caused by quantum computers. Lattice based cryptography are the post quantum cryptographic standards which are resistant to the attacks from quantum computers hence has advantage of strong security and high efficiency. The main objective of the paper is to study lattice, lattice properties, lattice – based cryptographic algorithm to design new lattice based cryptographic algorithm which are quantum resistant in future.Methods: In this paper the lattice-based cryptography is discussed right from its seminal work to its efficient cryptographic schemes. Paper discusses what is lattice, lattice properties, lattice problem and algorithmic solution to lattice problem and lattice-based cryptography.Findings: After studying post quantum cryptographic algorithms using lattice it was found that lattice-based post quantum cryptographic algorithms are resistant to quantum computer attacks.Novelty: Paper discusses lattice, properties of lattice in a simple way. Widely used cryptographic algorithms like RSA, Diffie-Hellman Key exchange, Elliptic Curve Cryptography are not resistant to quantum computers attacks. Paper discusses the importance of post quantum algorithm using lattice that are resistant to quantum computer attacks.
ACCLVOS: Atrous Convolution with Spatial-Temporal ConvLSTM for Video Object Segmentation

Semi-supervised video object segmentation aims at segmenting the target of interest throughout a video sequence when only the annotated mask of the first frame is given. A feasible method for segmentation is to capture the spatial-temporal coherence between frames. However, it may suffer from mask drift when the spatial-temporal coherence is unreliable. To relieve this problem, we propose an encoder-decoder-recurrent model for semi-supervised video object segmentation. The model adopts a U-shape architecture that combines atrous convolution and ConvLSTM to establish the coherence in both the spatial and temporal domains. Furthermore, the weight ratio for each block is also reconstructed to make the model more suitable for the VOS task. We evaluate our method on two benchmarks, DAVIS-2017 and Youtube-VOS, where state-of-the-art segmentation accuracy with a real-time inference speed of 21.3 frames per second on a Tesla P100 is obtained.
Research on the reform of foreign training teaching under the background of informatization

Foreign Training Teaching is an important part of teaching and training in our academy. In recent years, our college has adopted the strength of the whole academy, integrated various resources, and focused on improving the quality of teaching, and better met the needs of foreign training students. However, due to the particularity of foreign training teaching, our academy has always adopted the traditional teaching mode, which has been greatly challenged in the wave of educational informatization. We should take the initiative to meet the challenge and establish a student-centered and learning-oriented classroom teaching mode to realize educational informatization. Based on the practice and experience of foreign training, this paper introduces some effective teaching methods and teaching reform ideas, starting, from the characteristics and rules of foreign training and aiming at improving teaching quality.
Research on feature extraction of mechanical operators with abnormal behavior

High frequency of safety accidents in the process of mechanical production has brought great pressure and loss to the social economy. Relevant statistics show that the proportion of illegal operation of operators is the main factor. In order to reduce the occurrence of safety accidents, the computer vision research technology based on monitoring video is applied to the direction of mechanical safety behavior recognition. The content of abnormal behavior of mechanical operators is relatively broad. A foundation for the subsequent simulation analysis can be formed through extracting the characteristics of the actor, calculating the characteristic operator of human behavior, and debugging and modifying the threshold value of the characteristic operator for judging the abnormal behavior.
TIDY: A PBE-based framework supporting smart transformations for entity consistency in PowerPoint

Programming by Example (PBE) is increasingly assisting human users by recognizing and executing repetitive tasks, such as text editing and spreadsheet manipulation. Yet, existing work falls short on dealing with rich-formatted documents like PowerPoint (PPT) files, when examples are few and collecting them is intrusive. This article presents TIDY, a PBE-based framework, to assist automated entity transformations for their layout and style consistency in rich-formatted documents like PowerPoint, in a way adaptive to entity contexts and flexible with user selections. TIDY achieves this by examining entities’ operation histories, and proposes a two-stage framework to first identify user intentions behind histories and then make wise next-operation recommendations for users, in order to maintain the entity consistency for rich-formatted documents. We implemented TIDY as a prototype tool and integrated it into PowerPoint as a plug-in module. We experimentally evaluated TIDY with real-world user operation data. The evaluation reports that TIDY achieved promising effectiveness with a hit rate of 77.3% on average, which was stably holding for a variety of editing tasks. Besides, TIDY took only marginal time overhead, costing several to several tens of milliseconds, to complete each recommendation. TIDY assists users to complete repetitive tasks in rich-formatted documents by non-intrusive user intention recognition and smart next-operation recommendations, which is effective and practically useful.
Q-Learning using Retrospective Kalman Filters

Reinforcement Learning allows us to acquire knowledge without any training data. However, for learning it takes time. We discuss a case in which an agent receives a large negative reward. We assume that the reverse action allows us to improve the current situation. In this work, we propose a method to perform Reverse action by using Retrospective Kalman Filter that estimates the state one step before. We show an experience by a Hunter Prey problem. And discuss the usefulness of our proposed method.
Governance and the Capital Flight Trap in Africa

The study examines the use of governance tools to fight capital flight by reducing the capital flight trap. Two overarching policy syndromes are addressed in the study. It first assesses whether governance is an effective deterrent to the capital flight trap in Africa, before examining what thresholds of government quality are required to fight the capital flight trap in the continent. The following findings are established. Evidence of a capital flight trap is apparent because past values of capital flight have a positive effect on future values of capital flight. The net effects from interactions of the capital flight trap with political stability, regulation quality, economic governance and corruption-control on capital flight are positive. The critical masses at which “voice & accountability” and regulation quality can complement the capital flight trap to reduce capital flight are respectively, 0.120 and 0.680, which correspond to the best performing countries. Policy implications are discussed.
Makar: A Framework for Multi-source Studies based on Unstructured Data

To perform various development and maintenance tasks, developers frequently seek information on various sources such as mailing lists, Stack Overflow (SO), and Quora. Researchers analyze these sources to understand developer information needs in these tasks. However, extracting and preprocessing unstructured data from various sources, building and maintaining a reusable dataset is often a time-consuming and iterative process. Additionally, the lack of tools for automating this data analysis process complicates the task to reproduce previous results or datasets.To address these concerns we propose Makar, which provides various data extraction and preprocessing methods to support researchers in conducting reproducible multi-source studies. To evaluate Makar, we conduct a case study that analyzes code comment related discussions from SO, Quora, and mailing lists. Our results show that Makar is helpful for preparing reproducible datasets from multiple sources with little effort, and for identifying the relevant data to answer specific research questions in a shorter time compared to state-of-the-art tools, which is of critical importance for studies based on unstructured data. Tool webpage: https://github.com/maethub/makar.
Tracking and application of IMM algorithm in mid-course of Booster-glide vehicle

The booster-glide vehicle is one of the most important research targets in recent years that can change orbit by maneuvering and glide by jumping during flight. This paper focuses on the research and analysis of tracking the midcourse booster-glide vehicle's trajectory. The interactive multi-model algorithm established by establishing the CS-CKF model and the free segment precise dynamics model is compared with the single CS-CKF model algorithm for analysis. The simulation results show that the interactive multi-model (IMM) algorithm based on CS-CKF and precision dynamics model has better performance in the midcourse tracking of the booster-glide vehicle.
Affordance Judgments in Mobile Augmented Reality with Cues

We investigated two judgments of action capabilities with virtual objects presented through smartphones: passing through an aperture and stepping over a gap. The results showed that users were conservative in their affordance judgments for the two actions, but that judgments became more accurate with training by AR cues. In the post-cue trials, passing through judgments improved; in contrast, stepping over judgments became more precise when the cue was present, but did not display the same generalization in the post-cue block of improved estimates.
Research on Satisfaction of High School Mathematics Teaching Efficiency Based on OBE -Take Gansu and Qinghai Tibetan Ethnic Middle Schools as examples

Taking the three Tibetan middle schools in Gansu and Qinghai-Tibet areas as examples, there are many reasons for the poor efficiency of high school math classroom teaching. We are based on OBE teaching philosophy, the investigation and analysis were carried out from three dimensions: interest and feelings of mathematics learning, motivation of mathematics learning, and attitude of math teaching, and use the fuzzy comprehensive analysis method to conclude that the satisfaction of teaching efficiency is general. Based on the OBE teaching philosophy, student-centered and reverse-designed teaching plans should start from the following aspects: Stimulate interest and set reasonable goals; student-centered, positioning the driving force; with a smile and friendly get along.
Research on Comprehensive Evaluation of Military Aircrafts’ Overhaul Performance Based on Entropy Weight and Cloud Model

Due to the complex evaluation of the current military aircrafts’ overhaul performance with many influencing factors, an evaluation index system of military aircrafts’ overhaul performance was constructed. It was proposed that the entropy weight method (EWM) was used to calculate the performance results, and then the performance of the reliability and validity of the results was verified based on the cloud model. By collecting the performance index data of a certain type of military aircraft overhaul from 2015 to 2018, the value of the overhaul performance of this type of military aircraft was calculated, and the feasibility and effectiveness of the method were verified.
Anomaly Vehicle Detection Using Deep Neural Network

This project is focusing more on educational purposes. The number of vehicles keeps increasing on the road since almost all people have a personal vehicle. However, tally with the increment of the vehicles on the road, the case of accidents on the road also increases and this makes the road not safer for being used even with extra care. Accidents are caused by people that are careless and do not follow the traffic law on the road. This project is proposed to detect and classify the anomaly vehicle since the anomaly vehicle leads to vehicle accidents on the road. The primary objective is to develop a video processing algorithm to detect and classify vehicles. The objective also extends to construct an anomaly vehicle detection algorithm and propose an alert system for anomaly vehicle detection using a deep neural network. The vehicles are detected using algorithms developed using the python programming language, along with few open-source libraries, namely, OpenCV and the Visual Studio code. A convolutional neural network is used for image processing to reframe and resize the image thus train the image for detection. The bounding box was drawn by using the YOLO object detection method. The data is visualized using the Orange3 software found in the Anaconda python distribution. The Orange3 software is also used for machine learning and data analytics which is used to identify the anomaly vehicle.
Design and Analysis of a Gesture Remote-Controlled Pick-Up Car Based on ARM

Gesture remote control pick-up car is divided into remote control part and car part. The remote-control part can set the control type, capture the hand posture, display data and transmission data; the car part captures the manipulator's posture and display data, and calculates and controls by receiving the hand data. Make the manipulator and man's hand posture consistent, and also can control the car's driving by the man's hand posture. The manipulator is made with a 3D printer and uses a steering gear as a power device with three degrees of freedom. Use the STM32 series microprocessor as the control chip, read the data from the attitude sensor, perform calculation and filtering, solve the attitude, and achieve remote object retrieval by controlling peripheral devices.
Universitas Nasional Lecturer Attendance System Design Using Web-Based Low Fidelity Prototype Method

The Universitas Nasional  Lecturer Attendance Website is a web-based application that functions for lecturers to do attendance while the lecturer or admin secretariat can validate the attendance of the lecturer if the lecturer is not present because he is unable to input attendance. On the website, there are several features that can be used by users, including student features, lecturer features, and admin features. students can update their profile and can ask questions to academics through the contact us feature. Likewise, lecturers and admins will validate the absences of lecturers in the admin feature. This can make it easier for the admin to go through the database. In addition, this website can make it easier for admins in terms of archiving / recapping data because the system is connected directly through the database.
Semantic Discovery of Composite GIS Services

With an increasing number of Geographical Information System (GIS) services publicly available on the Web, the discovery of composite GIS services is promising when novel requirements are to be satisfied. GIS services in the repository like ArcGIS software are organized in a tree hierarchy, where a parent node represents a categorial GIS service with a coarser-granularity than its child GIS services, while leaf nodes correspond to atomic and exercisable GIS services. In this setting, discovering appropriate atomic GIS services is challenging. To remedy this issue, this paper proposes a composite GIS service discovery mechanism. Specifically, for the given requirement, select the parent nodes that take the given input parameters as input and remove their inactivated children. Use remaining children to build the network and repeat the previous operation until finding the services that contain the required output. Then record the semantic similarity degree, calculated by services functional description, in this network. By using the simulated annealing algorithm, a composite GIS services solution will be recommended from this semantic network. Evaluation results demonstrate that our approach could give more significant solution compared with the state-of-the-art techniques.
Research on Real-time Monitoring System of Motion Carrier Based on Satellite Positioning

Abstract The system uses CC2541 as the main control chip and mounts PCB antenna to realize Bluetooth wireless communication. GPRS wireless communication adopts SIM900A chip, which provides wireless link for users to upload data to the cloud. The satellite positioning module adopts ZOE-M8Q chip, supporting up to 12 satellite positioning; The cloud SERVER uses Windows Server 2012RC operating system to realize data monitoring and collection. Its based on satellite positioning data can be in the service of Android, IOS, and remote server monitoring system, and serving for the latitude and longitude, speed, time, mileage and other status information recording, storage of the carrier (unmanned vehicle, unmanned aerial vehicles (uavs), etc.). At the same time it can realize the solar energy conservation and emissions reduction, the cloud server storage and data processing through technologies such as GPRS and Bluetooth.
Zero Hunger: Smart Food Donation System using IoT

In the Coronavirus circumstance hungry individuals confronting food issues and loads of individuals kicked the bucket in the Coronavirus lockdown by considering this continuous issue we are attempting to give a constant answer for that. We are attempting to construct the system 'Zero Hunger' utilizing ML and IoT innovation with the mission to end food wastage and craving is a food wastage decrease based innovation in which gets surplus food from public capacity café, birthday celebrations show corridors home, and so on and convey to those who are directing NGOs.
Symbiotic Trackers’ Ensemble with Trackers’ Re-initialization for Face Tracking

Visual object tracking aims to deliver accurate estimates about the state of the target in a sequence of images or video frames. Nevertheless, tracking algorithms are sensitive to different kinds of image perturbations that frequently cause tracking failures. Indeed, tracking failures result from the insertion of imprecise target-related data into the trackers’ appearance models, which leads the trackers to lose the target or drift away from it. Here, we propose a tracking fusion approach, which incorporates feedback and re-initialization mechanisms to improve overall tracking performance. Our fusion technique, called SymTE-TR, enhances trackers’ overall performance by updating their appearances models with reliable information of the target’s states, while resets the imprecise trackers. We evaluated our approach on a facial video dataset, which characterizes a particular challenging tracking application under different imaging conditions. The experimental results indicate that our approach contributes to enhancing individual tracker performances by providing stable results across the video sequences and, consequently, contributes to stable overall tracking fusion performances.
Binary Image Geometric Calculation Algorithm Based on SNAM Representation

Aiming at problems such as difficulties in determining sub-mode position relations and complexity in image calculations caused by asymmetric segmentation in SNAM image representation, the present study first recovers submodule spatial position relations of SNAM representation with grid arrays and improves the grid arrays satisfying geometric calculation of images. Then, it proposes a geometric calculation algorithm based on SNAM. According to experimental results compared to geometric calculations represented by linear quad-tree and arrays or the like, the proposed algorithm has obvious higher efficiency. It is an eight-neighborhood image geometric calculation algorithm with excellent performance and adaptability to SNAM representation.
Linear Fuzzy Clustering of Distributed Databases Considering Privacy Preservation

Collaborative data clustering is a promising approach for extracting intrinsic cluster structures from distributed databases keeping personal privacy, where the goal of collaborative analysis is to find richer information rather than independent analysis of each database. In this research, a novel privacy preserving linear fuzzy clustering model is proposed by enhancing the scheme of k-Means-type model into Fuzzy c-Lines (FCL) in conjunction with cryptographic calculation. The element-wise clustering criterion enables to derive local principal component vectors in each data sources without handling fuzzy scatter matrices. The characteristics of the proposed method are demonstrated in an experiment with an artificial data set followed by an application to analysis of human behavior from sensor data.