Reviewerly/exHarmony huggingface dataset:
Branch Interaction Network for Person Re-identification

Most existing Person Re-identification (Re-ID) models aim to learn global and multi-granularity local features by designing a multi-branch structure and performing a uniform partition with the various number of divisions in different branches. However, the uniform partition is likely to separate meaningful regions in a single branch, and interaction between various branches disappeared after the split. In this paper, we propose the Branch Interaction Network (BIN), a multi-branch network architecture with three branches for learning coarse-to-fine features. Instead of traditional uniform partition, a horizontal overlapped division is employed to make sure essential local areas between adjacent parts are covered. Additionally, a novel attention module called Inter-Branch Attention Module (IBAM) is introduced to model contextual dependencies in the spatial domain across branches and learn better shared and specific representations for each branch. Extensive experiments are conducted on three mainstream datasets, i.e., DukeMTMC-reID, Market-1501 and CUHK03, showing the effectiveness of our approach, which outperforms the state-of-the-art methods. For instance, we achieve a top result of 90.50% mAP and 92.06% rank-1 accuracy on DukeMTMC-reID with re-ranking.
A system of digital simulation models for distance learning of computer subsystems in IT courses

Abstract Modern education actively uses new forms, such as distance learning, information technology, including the use of digital models. In the article, a system of digital software models to simulate the basic components of modern computers is considered. The described system is focused on the study of the composition and processes in the computer in IT courses for university students. The structure of system simulation models are designed for the study of the groups of the main computer subsystems: input/output subsystem, cache and multiprocessor memory, pipeline, superscalar, microarchitecture EPIC, the topology of the main configurations of local computer networks. A distinctive feature of the proposed system of models is the advanced animation of processes in the studied computer subsystems. An example of a superscalar processor digital model is considered, a model screen form and list of monitored parameters are presented. A data envelopment analysis technique for assessing the effectiveness of training is proposed. A linear programming problem is formulated based on the Banker-Charnes-Cooper model. The graphs for evaluating the results obtained on the test group of students are given.
A Quantum Computer Architecture Based on Silicon Donor Qubits Coupled by Photons

We describe an architecture for fault-tolerant universal quantum computation suited for donor qubits in silicon, coupled by photonic interconnects. The required quantum primitives are local measurements/unitaries, and a non-local Pauli measurement.
Social Study Based Review on Image Aesthetic Assessment and Editing

Previous research and review work on image aesthetic assessment mainly focused on technical developments and accomplishments, but none of them discussed the social influence. This paper not only reviews recent researches done in the field of image aesthetic assessment and editing, introduces typical image datasets, but also discusses the latent and potential moral and cultural dilemma that may be caused by these new techniques. It is believed that social study should be more emphasized in the future as the relevant techniques become more developed and mature.
SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds

Detection and classification of road-objects like cars, pedestrians, and cyclists is the first step in autonomous driving. In particular, point-wise object segmentation for 3D point clouds is essential to estimate the precise appearances of the road-objects. In this paper, we propose SPriorSeg, a fast and accurate point-level object segmentation for point clouds by integrating the strengths of deep convolutional auto-encoder and region growing algorithm. Semantic segmentation using the light-weighted convolutional auto-encoder generates semantic prior by labeling a spherical projection image of point clouds pixel-by-pixel with classes of road-objects. The region growing algorithm achieves pixel-wise instance segmentation by taking into account semantic prior and geometric features between neighboring pixels. We build a well-balanced, pixel-level labeled dataset for all classes using 3D bounding boxes and point clouds from the KITTI object dataset. The dataset is employed to train our light-weighted neural network for semantic segmentation and demonstrate the performance of both semantic and instance segmentation of SPriorSeg.
Mobile Application to Improve Reading Habits Using Virtual Reality

Education is changing rapidly, so emerging technologies are being used to improve this process. One of these technologies is the Virtual Reality (VR), whose field of action is increasingly broad, so it has been incorporating new methods in teaching having a great positive impact in recent years, but the applicability in the area of communication is minimal. Seeing the challenges faced by the Ministry of Education in the development of reading skills and strengthening the capabilities of students in our country. In the this research a new alternative is proposed, to improve the beginnings of the habit of reading in students of second grade of Elementary School having to use of a mobile application with VR named Diverticuentos that generates scenes of the readings in 360° besides being connected to a databases like firebase that allows us to see the progress of each student verifying that it is possible to integrate this new technology in the sessions and to generate a beginning of habit of reading of the students.
The specific of the manifestations of the motivational sphere of higher school students of different areas of professional training

The paper presents the results of a study devoted to identifying the features of the motivational sphere of higher school students in different areas of professional training. The empirical research was conducted on the basis of the Far Eastern State Transport University (Khabarovsk). The empirical study involved students of 1-3 years of study in the humanities and technical areas of training. Sixty students participated in the study (30 students studying in the humanities, 30 students studying in technical areas of training). In the conducted empirical research, it was found that the motivational sphere of students of different training areas has specificity. To maintain high motivation, it is important for students of the humanities to be satisfied with their professional decisions, to be satisfied with their activities, to be useful to society with their work, to strive for diversity, to develop mentally, morally, ethical, creatively and physically. In order to maintain a high level of motivation, students of technical areas of professional training need to be aware of the world of the profession, be able to plan their professional life, and be able to make professional decisions independently, they have almost no fear of making mistakes and receiving criticism and punishment for mistakes.
An Automatic Scheme for Optimizing the Size of Deep Networks

Large-scale datasets and complex architectures promote the development of CNN models. Although with stronger representation power, larger CNNs are more resource-hungry, which makes it difficult to deploy on resource-constrained Internet of Things (IoT) devices. Another serious challenge occurring with larger CNNs is their susceptibility to overfit with a small training dataset. In this paper, we ask the question: can we find an optimized compact model for a particular data set? We propose a novel scheme to optimize the model size so as to obtain a compact model instead of a larger model. The optimized model achieves higher accuracy than the widely used deeper model. In addition, it decreases the run-time memory and reduces the number of computing operations. This is achieved by applying Minimum Description Length (MDL) to find the optimal size of the model for a particular data set mathematically. MDL--the information-theoretic model selection principle assumes that the simplest, most compact representation model is the best model and most probable explanation of the data. We call our approach OptSize, model size is automatically identified, yielding compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet on various image classification datasets. The result shows that compact nets obtained by our proposed method perform better to complex, well-engineered, deeper convolutional architectures.
Have We Met the Vision of Engineering 2020 and What is Our Vision for 2040?

In this special session, we will reflect back on the early years of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, when much had been written about the future of engineering and engineering education. There had been global calls on changing the culture of engineering in the new century. In 2004, the National Academy of Engineering published the consensus report titled, The Engineer of 2020: Visions of Engineering in the New Century. The accompanying report came out the following year, with the title, Educating the Engineer of 2020: Adapting Engineering Education to the New Century. The first one envisioned the future, and the second one explained how we prepare for that future. As we entered the year 2020, it is now time to look back, review, and reflect: Have we met the vision of engineer 2020? This session will promote thought-provoking, reflective thinking about the past and visionary thinking about the future of engineering education.
Anatomization of the systems of dimension relaxation for facial recognition

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.
Implementasi Metode Background Subtraction dan Morfologi untuk Mendeteksi Objek Bergerak Pada Video

Monitoring systems are a rapidly increasing need in almost all lines of public, such as roads, parks, buildings, terminals and many places that have a monitoring system. Detection adapts learning of moving objects in video observation to obtain results such as the human sense of sight. By detecting objects in the monitoring system, we can determine the frame that shows the position of the moving object. T he methods that can be used to de-tect objects are background subtraction and morphology, these methods are considered appro-priate to be implemented in the designed program. The research method used is divided into two stages, namely data collection to collect CCTV video samples and search for related research references. Then the stages that the program goes through include frame extraction, implemen-tation of background subtraction, conversion of grayscale images and converting them into bi-nary form, performing opening morphological operations, making masking and implementing them into video. From the test results the program has a success rate of 93.3% of the test with bright lighting and 83.3% of the test with dim lighting.
RETRACTED: Research and development of fitness equipment embedded system based on distributed virtual environment

Embedded system based virtualization, which appreciates massive prominence in the venture and individualized computing spaces, is as of late increasing critical interest in the inserted area—beginning from a correlation of crucial qualities of big business fitness equipment and fitness action monitoring. So in the proposed method difficult that current wellness gear has a solitary capacity, the absence of diversion and intuitive highlights, the smart wellness hardware installed virtually with wellness, and double amusement capacity is explored and created by appropriated virtual innovation. The design, equipment, and programming structure for the wellness hardware and distinction in inspiration for the utilization of embedded system machines and the subsequent contrasts in the innovation's necessities. The differences are very considerable, and that virtualization can't meet the unique prerequisites of implanted frameworks. All things being equal, broader working virtual innovations are required, which uphold virtualization as an extraordinary case.
Application of the maintenance text data of transformers based on SimHash and Hamming distance algorithm

Power companies have accumulated a large amount of maintenance data of power equipment in text format. To extract valuable information, text mining is expected. Text similarity is an important method for text mining; however, the feature dimensions increase for long texts. In this paper, the SimHash algorithm is used to map the original text into a 64-bit binary fingerprint, and the similarity between texts is then determined with Hamming distance. With this method, the recommendation model of maintenance measures is established. The verification results show that the recommendation model has good predictions based on the SimHash and Hamming distance algorithm, and is potential to apply in the practical field.
Accelerating the Training of an LP-SVR Over Large Datasets

This paper presents a learning speedup method based on the relationship between the support vectors and the within-class Mahalanobis distances among the training set. We explain how statistical properties of the data can be used to pre-rank the training set. Then we explain the relationship among the pre-ranked training set indices, convex hull indices, and the support vector indices. We also explain how this method has better efficiency than those approaches based on the convex hull, especially at large-scale problems. At the end of the paper we conclude by explaining the findings of the experimental results over the speedup alternative.
An Improved Leaky-ESN for Electricity Load Forecasting

In the recent decade, recurrent neural networks become a hot research field, with the powerful capability to capture temporal information from time series. To avoid the vanishing/exploding gradient problem caused by the gradient descent algorithm involved in most recurrent networks, echo state network (ESN) was proposed to contain a large but sparse reservoir instead of traditional hidden layers. However, the performance of ESN is very sensitive to the parameters of the reservoir. In this paper, we focus on the improvements of ESN in the background of electricity load forecasting. With the goal of effectively and efficiently computation in the context of pervasive and cloud computing, two versions of adaptive echo state network (AESN) are designed to adopt a modular control strategy to automatically adjust some parameters of the reservoir. Applying AESN on two synthetic datasets and two real world electricity datasets, experimental results demonstrate that AESN is viable.
An Unprecedented PSO-PID Optimized Glucose Homeostasis

Particle Swarm Optimization) is a population based stochastic enhancement of improvements. This paper presents an unprecedented (UPSO-PID)and does the hypothesis examination of the PSO with multi-target improvement in PSO and its designing applications.
Implementation of KOHA Integrated Library Management System in Wollega University Main Library, Nekemte, Ethiopia: A Study

KOHA is widely used library automation software in the world with provisions for all library operations. This paper examines the implementation of Koha in Wollega University Library the challenges faced in the implementation of Koha and provides solutions to some of the challenges faced. This paper aims to elaborate the method of installation of KohaKoha, steps for data migration from Excel to KOHA and upgrading process of KOHA version from 17.05 to 19.05.05. This is a case study conducted at Wollega University, Nekemte. The paper identifies several issues concerning data migration within a local scenario and elaborates how Koha software installed and customised for university needs. The lesson learnt and the experience gained would help in similar exercises elsewhere.
The Research on the Application of Design Pattern of Object-Oriented Software Development Technology in Aerospace Embedded Software Development

In this paper, the application of several “design pattern” of object-oriented software development technology in aerospace embedded software development was mainly discussed. aerospace embedded software is the core of supporting spacecraft in orbit, Its quality directly determines the performance and the saftey of the spacecraft. In order to adapt to the improvement and development of spacecraft in recent years, the software becomes more and more complex. “Design pattern” is necessarily adopted to help designing software in order to develop high-quality software with high reusability and scalability. This article focuses on three applicable design patterns. They mainly included the following patterns, including “intermediary pattern” and “strategy pattern” among the “Behavioral pattern” and “bridge pattern” among the “Structural pattern”. They were analyzed aiming at the characteristics of aerospace embedded software and in detail for specific application scenarios. The main purpose of applying these “design patterns” is to solve the problems such as software body architecture design and complex algorithm design, etc.
An Edge Colouring-based Collaborative Routing Protocol for Blockchain Offline Channels

A Path-based Fund Transfer (PBT) in blockchain offline channel networks or in credit networks, uses a path among the offline channels to transfer funds among the peers who do not have mutual channels. A routing algorithm for PBT finds a suitable path for PBT execution. The problems with the landmark-based routing algorithms for PBT executions are as follows: (1) PBTs through hubs may cause privacy problems as a few landmarks can collude to find the sender and the receiver of a PBT, (2) Landmarks can be targeted with DoS or Eclipse attack. Unavailability of landmarks will lead to a high failure rate of PBT and (3) the unavailability of nodes for PBT execution creates cuts in the trees maintained by landmark-based routing protocols, which will lead to failure of PBT execution. In this paper, we mitigate the above problems with routing algorithms for PBT execution with a graph edge colouring-based routing protocol. In this routing protocol, every peer maintains a set of small subgraphs of the channel network with a particular topology. The peers exchange such subgraph information to find an appropriate path for PBT execution. Our contributions are as follows: (1) We develop a distributed algorithm to find subgraphs maintained by a peer. We prove that despite sharing subgraph information, our protocol preserves the privacy of the sender and the receiver of a PBT. (2) We prove that the proposed protocol is secure against adversarial peers initially agrees to participate in PBT execution and included in the trees or subgraphs computed by landmark-based or our edge colour-based routing algorithm, but later they do not participate in PBT execution. (3) We show that trees built by landmark-based algorithms require more frequent rebuilding as values in individual channels are changed over time compared with subgraphs to be maintained by the peers. (4) We show that the success rate and time to execute PBT for the proposed edge colouring-based routing algorithm is competitive against the landmark-based routing algorithm. (5) We show that DoS attacks resulting unavailability of peers have less impact on the proposed routing algorithm compared with landmark-based routing algorithms.
Pixel Level Alignment Person Re-Identification based on Multi-Branch Part Reconstructing

This paper proposes a person re-identification method to solve the misalignment problem caused by pose/viewpoint variations, occlusion, etc. By applying the densely aligned part key points, we design a multi-branch part reconstructing network for pixel-level alignment to map the pixels on the pedestrian image to a unified densely aligned space. Concretely, we utilize the scheme of multi-task learning. In our reconstructing subnet, we assign the task of reconstructing 24-part maps, while we conduct the feature representation learning task in the backbone network to learn the features with densely aligned information. Our method achieves high performance, outperforming most state-of-the-art methods.
Development of a Mobile Application for English Language Learning Through Corrective Feedback

Learning English as a foreign language is committed to using learning models that are linked to error correction by analyzing comments provided through corrective feedback to correct the error or to confirm the correct response. Having the chance to receive immediate corrective feedback allows students to be in contact directly with their learning process. This article implements an augmented reality application for learning English as a foreign language using corrective feedback and thus creating an accessible teaching tool that allows students to have additional teaching material to interact with the real world with virtual objects and animations. The application was designed by using Vuforia, Unity, Visual and Android Studio software. The objective of this article is to teach the student in an interactive way and consequently create a user-friendly link. The results show a great application in functionality using augmented reality for English language learning.
Creating E-Courses for Learning the CAD Basics

Article is devoted to the peculiarities of creating an electronic course on learning the basics of three-dimensional design of wind power plant elements using modern software systems. The iSpring Suite program was chosen as a course development tool. It has support for the SCORM 2004 format and extensive integration with office suites. In order to minimize problems with working in various distance learning systems, the course was divided into nine modules. The first module is dedicated to the basics of wind energy. The second module provides an overview of existing CAD programs. The third and fourth modules are dedicated to 2dimensional design. The fifth and sixth modules introduce commands for creating 3-dimensional parts from both other parts and 2-dimensional sketches. Module 7 is devoted to algorithms for creating assemblies from existing 3-dimensional parts. Module 8 and 9 represent the implementation of the multiplier/reducer Assembly algorithm from ready-made 3dimensional parts that are present in the course resources.