Reviewerly/exHarmony huggingface dataset:
Measurement and comparison study of deformation using extensometer and 2D DIC technology

Abstract The article deals with a short study of deformation measurement two different approaches. First is measurement by extensometer which is the contact technology and second is 2D DIC, which is the optical and contactless method of measurement. The paper starts with introduction to the 2D DIC measure method and continue with results of measurements of both methods. In the conclusion there is comparing results of two presented experimental methods.
Reply to Nock and Nielsen: On the work of Nock and Nielsen and its relationship to the additive tree

The observation that decision trees are boosting algorithms, as cited in our work (1) and acknowledged by Nock and Nielsen (2), was first established by refs. 3 and 4. This was later used by refs. 5 and 6 to develop, to the best of our knowledge, the first decision tree algorithms based purely on boosting. This work, cited in our article, precedes refs. 7 and 8 cited by Nock and Nielsen (2). The original and important contributions of refs. 7 and 8 as they pertain to this discussion was to theoretically prove convergence rates for decision tree algorithms built with boosting, along with … [↵][1]1To whom correspondence may be addressed. Email: gilmer.valdes{at}ucsf.edu. [1]: #xref-corresp-1-1
An Improved Squirrel Search Algorithm with Reproduction and Competition Mechanisms

The performance of the recently-proposed Squirrel Search Algorithm (SSA) is improved in this paper. SSA is a swarm intelligence algorithm that simulates the dynamic foraging behavior of squirrels. The traditional SSA is prone to premature convergence when solving optimization problems. This work proposed a propagation and diffusion search mechanism to alleviate these drawbacks by expand the search space using the Invasive Weed Algorithm (IWO). The proposed algorithm, which called SSIWO, has high ability to improve the exploration and local optimal avoidance of SSA. In order to investigate the performance proposed SSIWO algorithm, several experiments are conducted on eight benchmark functions and using three algorithms. The experimental results show the superior performance of the proposed SSIWO algorithm to determine the optimal solutions of the benchmark function problems.
Analyzing Architectures of Reservoir Computing Models Using Particle Swarm Intelligence

The Reservoir Computing (RC) area has gain relevance during the last years. An RC model is a neural network with a recurrent hidden subnetwork (named reservoir) composed by randomly initialized hidden-hidden weights over the edges, and the rest of the model parameters are trained using linear regression. In this paper, we study the effect of training a selected subset of hidden-hidden weights and the linear regression parameters. For the training, we use a popular evolutionary technique named Particle Swarm Intelligence. The proposed method is inspired in Extreme Learning machines, where only a good selection of adjustable parameters is trained. Empirical analysis was made over well-known simulated time-series benchmarks and a real-world problems.
An Accurate Weight Binarization Method for a CNN Object Detector Using Double Scaling Factors

In recent years, object detectors such as you-only-look-once (YOLO) have been intensively studied owing to applications in robotics, autonomous driving, and drones. However, memory and computation complexity is widely known as the bottlenecks in implementing YOLOv2 in hardware. A common approach is to apply weight binarization. However, the existing methods suffer from a substantial degradation in detection performance. This study proposes an accurate weight binarization method with two scaling factors. Experimental results show that the proposed method reduces the performance degradation by 32.18% while maintaining the similar memory and computation requirements as the state-of-art methods.
A Signal Sorting Algorithm Based on LOF De-Noised Clustering

In this paper, an algorithm for removing outliers is proposed for low SNR signals. Firstly, the coarse separation of signals is performed by using the isolated point removal algorithm based on Euclidean distance, and then the coarsely separated data is finely separated by the LOF algorithm based on density detection. The remaining signal data after fine separation is clustered. Through simulation analysis, the algorithm can remove all isolated points at the cost of useful signal loss at low SNR, and the residual signal clustering effect is better.
A Novel Gradient L0-Norm Regularization Image Restoration Method Based on Non-local Total Variation

This paper proposes a novel image restoration method based on non-local total variation (TV). Firstly, the image is divided into two types of regions by the gradient L0 norm. The one regularized by the local TV term contains edges and flat regions, the other regularized by the non-local TV term contains rich image details. Then, in order to simplify complex numerical algorithms, we adopt the alternating direction method of multipliers (ADMM) algorithm to optimize the object function. Finally, we carry out comparative experiments with several recent state-of-the-art methods to verify the performance of the proposed method. Experimental results show that the proposed method has better performance in the efficiency and get a good balance between balance between easing staircase effects and retaining image details.
Research on cost control of real estate project based on contract planning and information system

Abstract China’s real estate project cost control has entered the stage of refinement and modernization. Under the environment of promoting information construction industry in the whole country, it is urgent to build a real estate enterprise information system through effective combination of Internet + project cost control. This paper studies the project cost control system based on contract planning and information system, through the construction of the system and analysis of its application to further study the mechanism of contract planning and information system.
Normalized Metric Learning Based on Multi-feature Fusion for Person Re-identification

Person re-identification remains a critical conundrum of intelligent video surveillance system, which intends to query pedestrians with the same identity. It is of great significance to extract features with sharp discrimination and robustness, especially for images captured in complex scenes, such as illumination variance, different pose and viewpoints. In this paper, we propose a normalized distance aggregation strategy, which integrates three discriminative pedestrian feature extraction models, i.e., low-level local maximal occurrence (LOMO), mid-level salient color names based color descriptor (SCNCD), and high-level feature fusion net (FFN). We utilize each feature descriptor to train two distance metric learning models, including large-scale similarity learning (LSSL) and cross-view quadratic discriminant analysis (XQDA), respectively. Accordingly, the ultimate distance is calculated as the sum of six optimized distance metrics by the min-max normalization with respective weights. In experiments, comprehensive evaluation results indicate that our framework gains superior performance with robustness to illumination, viewpoint as well as pose changes, and exceeds the existing methods on the public dataset VIPeR.
Using Artificial Intelligence to Improve Camera’s Recognition Function on Mobile Phone

Abstract There is a rising trend in the numbers of Artificial Intelligence (AI) applications in different fields include interaction design and mobile phone. In addition, mobile devices have been tools that people interact with every day. Thus, applying the combination of AI and interaction design to camera’s recognition of mobile devices is the central concern of this research. This report includes the discussion around two aspects towards mobile phones which are holography and augmented reality (AR). Within these two aspects, machine-learning-based technologies such as image recognition and face recognition have been applied to improve the user experience and interactions. In order to build a new bridge for the interactions between people and mobile phones, the proposal of this research is to design an AR-based 3D hologram system on mobile phones. This proposal will also include technologies of computer vision and eye tracking. Importantly, this combination will allow users to create their own new ways to connect virtual objects in the real world through mobile phones. To design this product, some methods such as concept mapping, case studies and prototyping will be used in the process. Moreover, this product may change the way between human and machine and take the interaction to a new degree.
Research on Control Flow Integrity Scheme for Android Platform

Although there are protection mechanisms like ASLR, DEP, SELinux in the Android system, they are not very effective. Although the control flow integrity scheme already has multiple schemes, some of them are protected at the source code level, some are protected by binary files, and the protection of binary files requires a large scope to rewrite binary files. This paper breaks through the difficulty of binary file rewriting, and innovatively proposes a control flow integrity protection scheme for binary files already existed in the Android system, which can effectively protect heap overflow in the Android system with a small amount of modification to binary. Attacks such as memory uninitialization, re-use after release, double release, and other memory-destroyed vulnerabilities.
Calibration methodology of program package for optic measuring of strain and crack resistance determination of compact tension specimens

Abstract In the work, the problem of eccentric tension of a compact specimen with a crack was solved by numerical methods. It was proposed to use the results of numerical modeling for setting up and testing the original software package for measuring the deformation of materials by optical methods. The values of the fracture parameters obtained by software coincide with the experimental and numerical results. The proposed testing methodology made it possible to avoid problems with determining the error in the quality of calculation of the optical flow during verification. The dependences of the change of the values of stress intensity factor and J-integral on the applied tensile loading are determined for compact tension specimen with a crack from aluminum alloy D16.
Conversation Extraction over Short Text Stream with Hierarchical LSTM Neural Networks

Conversation extraction associate topics of utterances in short text stream conversation. The problem of associating topic labels to utterances can be treated as a sequence labeling task. In this work, we build a hierarchical recurrent neural network, using LSTM as the base unit to classify each utterance into its corresponding topic. We apply the results of the DA recognition to the topic classification and define interdependencies in short text stream conversations to improve classification accuracy. Our model achieves state-of-the-art results on two different short text stream conversation datasets.
Building an Inclusive Distributed Ledger System

In 2008, bitcoin disrupted the transactional ecosystem with its value propositions. However, sustaining autonomous, deregulated systems in markets filled with laws and regulations has had its challenges. There are also open questions of scalability, resilience, availability, speed and finality of transactions. Sound technical components have emerged but we need to take this one step further and integrate the units to provide an accepted packaged solution. Using a game application as an entry point, we propose a simple, inclusive asset transfer system which will stimulate adoption and create traction in the distributed ledger universe.
A Survey of Pedestrian Detection Based on Deep Learning

The purpose of pedestrian detection is to accurately locate each pedestrian belonging to the detection range from a specific scene. When combined with pedestrian recognition and pedestrian tracking technology, it has important applications in areas such as autonomous driving, human-computer interaction, intelligent video surveillance, and character object behavior analysis. The research progress of deep learning technology in the field of pedestrian detection is studied. The main problems and challenges of pedestrian detection are analyzed. The paper also summarizes the data sets and evaluation criteria of pedestrian detection. Provide reference and basis for comprehensive research in the field.
Improving students’ science learning outcomes through joyful-inquiry interactive demonstration assisted by game android

This study aimed to analyze the improvement of students’ cognitive learning outcomes after the application of joyful-inquiry interactive demonstration (J-IID) learning assisted by android games on the material of human respiratory system. This research used mixed methods research with embedded experimental. The design of this study used Pretest-Posttest Control Design. The study was conducted at SMPN 5 Malang class VIII B in the academic year of 2018/2019. The sample selection technique used was purposive sampling. The quantitative data were in the form of pretest and posttest scores. The qualitative data were interview results at the end of the learning process and students’ responses to the test. The quantitative data were analyzed with Kolmogorov Smirnov, paired t-test, N-Gain, and d-effect size. The qualitative data was analyzed with data reduction and scatter plot. The improved learning outcomes were revealed in an increase in the value of pretest to posttest, from 68.74 to 81.42. The t-test results showed that there was a difference between the pretest and posttest scores. The N-Gain test results, namely 0.40, showed an increase in students’ learning outcomes in the medium category. The d-effect size test results indicated the effect of J-IID learning has a high impact on improving students’ learning outcomes with score of 1.03. The data analysis showed that the learning used the joyful-inquiry interactive demonstration model assisted by Android games can improve students’ cognitive learning outcomes on the material of human respiratory system.
Network Security Monitoring (NSM): Can it be Effective in a World with Encrypted Traffic?

HTTPS is gaining widespread popularity for secure transactions. Most popular sites have made default choice as HTTPS. This development of encrypted traffic has brought in new challenges in the areas of network security monitoring and analysis. This paper makes a survey through various study done in the area on novel approaches for identification and investigating HTTPS traffic and its effect on network security monitoring. This work makes a complete analysis and evaluation of HTTPS protocol -is it ensuring security or are we entering in a vicious cycle of finding weaknesses and trying to fill the gaps in Network security Monitoring. There are couple of vacuums that exist along with encrypted data, namely firewalls, IDS becoming blind to data being exchanged, enhancing vulnerabilities by making it tough to implement security policy and probability of malicious activities hiding in the cipher ed traffic. Most of the current techniques namely DPI to port based to IP address to DNS to SNI filtering is prone to be ineffective in front of HTTPS traffic. The emphasis is upon the new ways to explore the expanding HTTPS volume with security breaches to cover new challenges related to Network Security Monitoring. Data collected from couple of up to date research and their conclusion has been discussed to provide a brief overview so as to provide the reader with an in-depth understanding of the research progress in this area.
FACT-from actual to conceptual tie-ins

Knowledge Graphs have increasingly become the preferred approach to complex problems that involve low-level business data. It is well known that Google, LinkedIn, Facebook, Twitter - all have a knowledge graph at their core.
Cross-Domain Secure Sharing of Video Based on White-Box Encryption

In the vertical multi-level public network, the ciphertext video needs to support the distribution of key in the whole network when it is shared across different security domains horizontally. However, the traditional key management mode faces great pressure on the security classification and encryption efficiency, and especially, it cannot fully ensure the security of content and key when sharing across-domains. Based on the above analysis, this paper proposes a cross domain video security sharing solution based on white box encryption theory to improve the security of video data sharing. In this solution, the white box encryption technology is adopted to establish the data sharing background trust mechanism based on the key management center, and we study the white box key protection technology of the video terminal to support the mass level key distribution in the network and the online security generation and replacement of the white box password module, so that the safe and fast cross domain exchange and sharing of video data are realized.
Roof Edge Detection for Solar Panel Installation

Edge detection is a crucial factor in extracting important information from an image. Edges are boundaries where the image brightness tends to discontinue. There is a sudden increase in solar panel installation on roofs for absorbing energy from sunlight and generating electricity through it. This not only helps in reducing our dependency on utility sources but also is environmentally friendly and cost efficient. Satellite view of roof images provides us a view of the roof along with trees and other obstructions. In this paper, we developed a methodology that will remove the obstruction that is hiding the edges of the roof, restore the image, and then detect the roof edges. This methodology will help in installation planning of solar panels on roofs.
EGAN: An Effective Code Readability Classification using Ensemble Generative Adversarial Networks

Classification of code script readability (which corresponds to categorizing a source code as either readable or unreadable) has prompted severe concern in academia and industry. To construct accurate classification models, previous studies depended mainly upon handcrafted features. However, the manual feature engineering process is usually labor-intensive and can capture only partial information about the source code, which is likely to limit the model performance. To improve this, we propose the use of GAN's (Generative Adversarial Networks). First, we present a representation approach (with multiple granularities) to encode source codes as the input to GAN's into integer matrices. Then we propose an EGAN based on GAN's for the code readability classification. EGAN consists of three separate GANs with identical architectures that are trained on data preprocessed in different ways. We evaluate our approach against five state-of-the-art code readability models. The findings of the tests demonstrate that RGA can surpass previous approaches. The quality increase varies from 2% to 15%. EGAN offers reasonably improved performance by removing the need for manual interface design, demonstrating the usefulness of GAN's approaches in code readability classification tasks.
LibGuides: Information Technology Help: Google

Resources to help Central Penn College students navigate the technology required in their courses.
Second Workshop on Natural Language Generation for Human-Robot Interaction

This workshop is the second in a series bringing together the Natural Language Generation and Human-Robot Interaction communities to discuss topics of mutual interest with the goal of developing an HRI-inspired NLG shared task. The workshop website is urlhttps://purl.org/nlg-hri-workshop/2020.
Real-Time Monitoring System for Passenger Flow Information of Metro Stations Based on Intelligent Video Surveillance

The vigorous development of urban rail transit provides an important guarantee for people’s travel, and it also brings potential safety hazards to people. Metro operation often faces peak passenger flow pressure. Although the application of video surveillance can reduce the occurrence of such events, there are still some shortcomings in the existing video surveillance system. This paper first analyzes the shortcomings of the video surveillance system of the existing metro station and then introduces the function and advantages of the real-time monitoring system of the passenger flow information of the metro station based on intelligent video surveillance. The system can realize the real-time detection of passenger flow basic information and the statistical analysis of passenger flow basic information in each monitoring area of the subway station, fully consider the monitoring of key areas of metro stations, effectively reducing or avoiding the occurrence of emergencies and ensuring the safety of subway operations.
New generalized secret-sharing schemes with points on a hyperplane using a Wronskian matrix

A new secret-sharing scheme is constructed using elementary tools from different fields of mathematics. A method is introduced which uses the assignment of points on a hyperplane, serving as terminal points of vectors which meet an outlined criterion for linear independence. Submatrices of a Wronskian matrix are used in the assignment of these points. This method is also generalized to include a weighted scheme and a multilevel hierarchical model.
Research on the Evaluation of Information Awareness of Teachers in Contemporary Colleges and Universities Based on Grey Relation Degree Analysis

Abstract The information awareness of college teachers directly affects their speed and ability to obtain, judge and use information, and then affects the effectiveness of information teaching. In view of this, on the basis of constructing a scientific and systematic Information awareness evaluation index system for university teachers in the information age, eight experts were invited to empower the first-level indicators of the constructed indicator system; The evaluation model of information consciousness of college teachers in the information age was constructed by using grey correlation degree analysis method. Ten teachers of university in Changsha were selected to make an empirical evaluation of their information awareness, and the evaluation results were analyzed briefly.