Reviewerly/exHarmony huggingface dataset:
Identity Recognition Using Same Face in Different Context

Human face is a God gift to a human body. It really makes sense use to make expressions. Human face properties adhere to the parents so it is used to recognize the person. There are many researches going on face property. Today face is a very important human body part that is using for Machine Learning. The main objective of the research is to present the current scenario of the system to recognize humans by his face and matching it with their photo on ID card. There are many cases where human is failing to recognize with his Photo ID card. Some situations where a person is genuine but a photo on ID card is not clear, due to this problem many security leaks. This research is highly focused on these problems and the solutions finding. Some literature were reviewed by foreign authors on the same problem; they also clarify the identical problem. To clearly express the problem in terms of Image Processing, we have used MatLab and taken sample photos. These images were deeply analyzed and Region of Interest obtained. The very simple conversion of ROI images into Black and White images gives the real difference between images of the same face. Some more techniques can be applied to get different additional outputs and impacts. The result of the paper is to present a real example of face image and its processing in MatLab. Overall to improve this identification system one can update his/her face photo in ID card but sometimes it is feasible.
Current Frame Priors Assisted Neural Network for Intra Prediction

Intra prediction is the key technology to reduce spatial redundancy in the modern video coding standard. Recently, deep learning based methods that directly generate the intra prediction by neural network achieve superior performance than traditional directional based intra prediction. However, these methods lack the ability to handle complex blocks which contain mixed directional textures or recurrent patterns since they only use the neighboring reference samples of the current one. The other intermediate information denoted as reference priors in this paper generated during the coding process is not exploited. In this paper, a Current Frame Priors assisted Neural Network (CFPNN) is presented to improve the intra prediction efficiency. Specifically, we utilize the local contextual information provided by the neighboring multiple references as the primary inference source. In addition to the neighboring references, we additionally use the other two reference priors within the current frame - the predictor searched by intra block copy (IntraBC) and the corresponding residual component. The IntraBC predictor provides useful nonlocal information to help generate more accurate prediction for complex blocks together with neighboring local information. While the residual component contains unique information that reflects the characteristics of the block to some extent is utilized to reduce the noise contained in the reconstructed reference samples. Moreover, we investigate the best way to integrate the proposed method into the codec. Experimental results demonstrate that compared to HEVC, our proposed CFPNN achieves an average of 4.1% BD-rate reduction for the luma component under the All Intra configuration.
Student Educational Learning Experience Through Cooperative Research

Two state universities are working together on a two-part research project. The first part revolves around creating a learning experience for these universities’ students where they will develop key cybersecurity skills. They are tasked with creating and assessing fictional businesses to produce mock data that will be vital to the second part of this project. This will require them to develop a secure network architecture for their business along with security policies that consider the physical security of the organization not just the cybersecurity. The second part will be to design and develop a tool that will assist businesses in risk management. The created mock data will be used to test the tool during its development. We have hired students from both universities to work together to create several fictional businesses. These fictional businesses, once created, will be assessed using the Department of Homeland Security’s Cyber Security Evaluation Tool (CSET). This tool produces a report from a business security assessment that will be used in the second part of this project. The student workers are split into groups, each of which have at least one student from each university with different levels of expertise to ensure that students have the chance for peer-to-peer learning and to network with students outside of their university. The goal for the first part of this project is to help students develop cybersecurity skills through cooperative research.
CIPP Design Reconciliation: How It Works and Why It’s Important

The cured in place pipe (CIPP) rehabilitation process involves the installation and curing of a resin impregnated felt tube in an existing sewer resulting in a close fit, structural liner. CIPP liners can be manufactured and designed to support internal pressure, internal vacuum, and externally applied loads resulting in a rehabilitated pipe structure with a full design life. However, unlike manufactured pipe products, moving the manufacturing process from a factory environment to the field increases variability in the final end product. To ensure the installed product meets the long-term design objectives for the project, quality assurance testing is of the upmost importance to confirm the in-place liner thickness and material properties. When the in-place liner thickness and/or material properties are lower than those identified in the original design, a design reconciliation must be undertaken to determine if the installed liner has sufficient structural resistance to meet the desired design objectives. It is imperative that the designer, contract administrator, and contractor work together during the design reconciliation process to assess the suitability of the installed liner. This is especially critical on larger, more complex installations, where it may not be feasible or may require significant efforts to be expended by the contractor and other parties to rectify thickness or material properties issues. This paper addresses the quality assurance and design reconciliation process for CIPP liners, with a focus on a holistic approach which begins in the design phase and ends with final acceptance of the end product. To illustrate the importance of a holistic approach, the paper includes lessons learned from managing both large and small rehabilitation programs.
The Technical Buzzword: Artificial Intelligence

AI is the incredibly exciting technique to the world. According to John McCarthy it is “The science and engineering of making intelligent machine, especially intelligent computers”. AI is the way of creating extraordinary powerful machine which is similar as human being. The AI is being accomplished by studying how human brain think, how they learn, decide, work, solving the real world problem and after that verify the outcomes and studying it. Primarily you can learn here what AI is and how it works, its types, its history, its agents, its applications, its advantages and disadvantages.
Towards Version Controlling in RefactorErl

Static source code analyser tools are operating on an intermediate representation of the source code that is usually a tree or a graph. Those representations need to be updated according to the different versions of the source code. However, the developers might be interested in the changes or might need information about previous versions, therefore, keeping different versions of the source code analysed by the tools are required. RefactorErl is an open-source static analysis and transformation tool for Erlang that uses a graph representation to store and manipulate the source code. The aim of our research was to create an extension of the Semantic Program Graph of RefactorErl that is able to store different versions of the source code in a single graph. The new method resulted in 30% memory footprint decrease compared to the available workaround solutions.
Inside the Black Box of Bitcoin Mining: A Theoretical Framework and Simulation Evidence

The incentive compatibility of Bitcoin mining is vital for maintaining the network trustworthy and immutable - its empirical composition, however, remains widely opaque. This paper proposes a conceptual framework that leverages innovation-driven convergence to reconstruct market conditions and to study miners' behavior. The theoretical implications are corroborated with empirical evidence using numerical simulation and a set of 10,000 random samples. The results quantify large variation in miners' profits and costs but demonstrate that the observed behavior is consistent with the proposed theory. Lower bound estimates indicate that the fixed costs required for a majority of computing power and thus the ability to successfully attack the network can be astoundingly small (e.g., $2.55 million in May 2020) when adverse events coincide. This pronounces the relevance of cyclical patterns when assessing the stability of proof-of-work consensus.
Digital Watermarking Technique in Transform Domain Using Meta-Heuristic Algorithm

The digital data transmission or sharing through the inter-networked systems has been increased heavily in recent decades as Internet access is reachable. Due to that, illegal digital data distribution becomes the main concern as the copyrights of the owner can be breached through the tools available on the Internet. Digital multimedia (in the form of audio, text, image, and video) can be easily modified and shared for profit. To address this issue, digital watermarking (WMG) has been introduced as one of the solutions. In this article, an optimized blind dual WMG technique for the digital images in the transform domain using the Slantlet transform and discrete cosine transform (DCT) is proposed. The consideration factors of WMG such as robustness and imperceptibility are both contradicting. So, to provide a trade-off between these factors, a meta-heuristic algorithm is used to balance these factors. The visual quality of the watermarked image (WMI) is verified with the reference image quality (RIQ) measures and no-reference image quality (NRIQ) measures and shown that the proposed method produces high-quality WMIs.
Profile-guided Frequency Scaling for Latency-Critical Search Workloads

Dynamic frequency scaling is a technique to reduce power consumption in computer systems. However, this technique poses challenges when adopted in latency-critical applications. Prior work on dynamic frequency scaling is application agnostic and coarse-granulated in the sense that it considers the entire application process utilization for decision making, without the distinction between individual threads or functions.This work proposes a finer-grained dynamic frequency scaling approach for multi-core processors that leverages information about the computational intensity of certain functions in a latency-critical web search application. First, our approach profiles the running application to identify hot functions for typical workloads. Next, a run-time scheme is devised to adapt the individual core frequency whenever a compute-intensive thread enters or exits a hot function. We implemented and evaluated our proposal in a real multi-core system. We observed energy consumption savings up to 28% when compared to the recent Linux's Ondemand frequency scaling governor, while attaining acceptable levels of tail latency constraints.
vlda: An R package for statistical visualization of multidimensional longitudinal data

The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.
Analysis of Knowledge Map of Online Educational Resources in the 21st Century

With the development of the times, the level of science and technology is changing with each passing day. Online educational resources have become one of the research directions in the field of educational technology, which is an indispensable part of the sustainable development of individuals in the life-long learning society. Analyzing of the relevant literature in CNKI, the knowledge map of online educational resources in China from 2000 to 2020 is generated based on the visualization analysis tool CiteSpace, and the online educational resources in China in the 21st century are displayed and analyzed in the way of knowledge map, so as to explore the change and evolution of the research of online educational resources in China. Based on this, the conclusions and suggestions are drawn to provide the reference for the theoretical and practical construction of online educational resources.
Lightweight Detection Network of Trunk Contents

During the annual vehicle review, real-time identification of oil-to-gas vehicles is one of the most important contents. Aiming at the characteristics of objects in the trunk of such vehicles, a Trunk-YOLO lightweight real-time detection network is proposed. Firstly, by streamlining the number and structure of CSP modules, and cutting the convolutional layer to perform feature extraction, the cost of feature extraction will be reduced while modifying the activation function. Secondly, a faster K-means algorithm is proposed to perform target candidate frames for the features of the trunk object cluster analysis of numbers. Finally, the YOLO-Head module performs classification and regression, and outputs the prediction results. The method proposed in this paper is tested on the self-made dataset TRUNK-Dataset and compared with the classic lightweight target detection network. The test results show that the network proposed in this paper has achieved good results in detection speed and accuracy.
Open learning for museum educators professional development: an online experience in time of a pandemic

During the COVID-19 pandemic, many open and distance learning experiences were carried out by different museum and cultural institutions in order to promote the development of digital skills in museum professionals. This contribution aims to illustrate the results of an ERE (Emergency Remote Education) experience carried out in 2020 by the Center for Museum Studies (CDM) of Roma TRE University and aimed at soliciting digital, professional and transverse skills, such as critical thinking, in museum operators and educators attending the post-graduate courses in "Museum Education. Theoretical aspects" and "Advanced Studies in Museum Education". The results show a good level of self-assessment of the skills solicited in the participants and provide useful directions for the realization of online and open learning experiences for museum professionals.
Realization and optimization of Chinese syntactic analyzer based on PCFG

Abstract As a basic research work, syntactic analysis is also a key technology in the field of natural language processing, and is the basis for deep understanding of language. The syntactic analyzer is an important tool for syntactic analysis. The Probabilistic Context Free Grammar (PCFG) model is the basic model of most parsers with excellent performance. Good effect. This article integrates named entity clues into PCFG-based Chinese syntax analysis models, completes joint learning and decoding of named entity recognition, syntactic analysis, and implements a fast and reliable decoding parser.
Algorithms for Mastering Board Game Nanahoshi Considering Deep Neural Networks

With the recent rapid progress of AI (Artificial Intelligence) technology, the performance of AI in board games such as Chess, Shogi, and Go has become very high. The combination of advanced search-tree methods and deep learning has made it possible for AI to outperform professionals. “Nanahoshi” is a board game similar to Chinese Anqi. Unlike Go and Shogi, Nanahoshi is not a perfect information game, because all pieces are faced down and hidden in the initial state. The uncertainty largely increases the difficulty of searching for game AI. In this paper, we design some algorithms that masters Nanahoshi. First, we design an algorithm using the Monte Carlo tree search. Next, we design an algorithm using deep neural networks. Furthermore, we evaluate the performance of these algorithms based on winning rate by playing games between these algorithms.
The Effect of Character-Based Teaching Flipbook Media on The Results of Calculus Learning

Research on the Effect of Using Character-Based Flipbook Teaching Media on Calculus Learning Outcomes. This research is based on problems in the world of education, especially the use of multimedia learning with the calculus of integral material in rotating objects. Research conducted at FKIP UHAMKA with the research method used is experimental design posttest only form and the research sample was 36 students divided into 2 class groups, the experimental class and the control class. Based on the results of the analysis calculation on the analysis prerequisite test that is using the post test sample the learning outcomes of calculus are normally distributed, namely 5.172 <7.815. Likewise for the calculated X2 value (control class) <of X2 table that is 2,183 <7,815. So for the testing phase you can use the t-test. with a significant level α = 0.05 For the hypothesis test using the t-test the results obtained were 8.621 ≥ 2.014, which means it was rejected and Ha was accepted, while the characters with an average post-test score of 76.67 with the development of the character of the questionnaire was 83, 1 Based on the calculation of hypothesis testing, and character questionnaires it can be concluded that there is an influence of the use of character - based flipbook teaching media on calculus learning outcomes.
Ant Hole: Data Poisoning Attack Breaking out the Boundary of Face Cluster

With the continuous improvement of the open ability of machine learning, more and more users are benefited. Therefore, the impact on its security also expands. However, most of the research on the security of machine learning focuses on supervised learning, while the security of unsupervised learning has not been paid enough attention. In this paper, we propose a data poisoning attack method aimed at the face clustering open source project. We innovatively propose a fusion iterative method. It can smoothly generate a series of fusion face images which will fool the clustering algorithm.
A Chinese automatic question answering technique based on semantic similarity

In this paper, for the Chinese automatic question answering technology in open domain, in addition to considering the traditional association between questions and questions, the correlation between questions and answers is added. The cosine similarity between questions and answers is used as the semantic similarity between them. A bi-directional long short-term memory network (BiLSTM) is added between the question and question, answer and the answer to seek the association between the contexts. and an attention mechanism is added to make question and answer related. Finally, the experimental verification shows that the accuracy of automatic question answering by the proposed method reaches 70%.
Collaborative Filtering Auto-Encoders for Technical Patent Recommending

To find the exact items from the massive patent resources for users is a matter of great urgency. Although the recommender systems have shot this problem to a certain extent, there are still some challenging problems, such as tracking user interests and improving the recommendation quality when the rating matrix is extremely sparse. In this paper, we propose a novel method called Collaborative Filtering Auto-Encoder for the top-N recommendation. This method employs Auto-Encoders to extract the item's features, converts a high-dimensional sparse vector into a low-dimensional dense vector, and then uses the dense vector for similarity calculation. At the same time, to make the recommendation list closer to the user's recent interests, we divide the recommendation weight into time-based and recent similarity-based weights. In fact, the proposed method is an improved, item-based collaborative filtering model with more flexible components. Experimental results show that the method consistently outperforms state-of-the-art top-N recommendation methods by a significant margin on standard evaluation metrics.
Development of application tools for determining cropping patterns based on water availability

Abstract Innovative irrigation is a vital factor in increasing water efficiency, gaining benefits, and reducing environmental burdens. Fulfilling irrigation water demand appropriately from time, space, quantity, and quality is the main factor in the irrigation water distribution pattern. A simple information system process, transparent and real-time reporting can reduce water sharing conflicts and improve irrigation operator performance. This research aims to determine the planting area refers to water availability through an automatic calculation application, where the water distribution is carried out based on the condition of the plant’s water demand at that time and several days after. The process of collecting the latest data by installing sensors or recording devices related to the analysis of water demand and availability parameters, such as climatological conditions, plants, and water in the rice fields and their sources, was needed as an input process the application. When the automation of operations operates, sensors on the rice fields plots send information directly to data companies that perform information analysis and send agronomic instructions to machines that regulate the release of the amount of water to the motion weir (smart agriculture management system). The way for determining the planting area and planting pattern calculated using the calculation application of the irrigation water demand, which developed with potential evapotranspiration values, reliable rain (R 80 ), crop coefficient (Kc), and dependable discharge data (Q 80 ) as the input data. This system’s output data consist of a table of irrigation water demand, a graph of the relationship between irrigation water demand and discharge availability for a year.