Reviewerly/exHarmony huggingface dataset:
Patent Recommendation Engine Using Graph Database

Accurate analysis of patents is an essential tool for modern companies. The idea behind the patent recommendation engine is to build solutions that enhance the quality as well the quantity of extractable data from a patent and discover meaningful relations, helping companies by spending fewer resources in terms of time and manpower. The recommendation engine is a concept of having the patent data transferred into a graph database and executing queries to answer questions specific to certain business use cases such that the task is significantly easier, less resource-intensive, and less complex when compared to the same being performed by a conventional relational database. The engine concept accepts a single input and forming clusters from the single starting point based on chain queries. It can then run the required algorithms on the clusters formed to select the best-fit data. The recommendation engine uses Neo4j as the database. Neo4j is a NoSQL graph database that focuses more on the relationship between various data rather than the data itself. We extract the data from our existing databases and then ingest them into Neo4j. Cypher queries power the engine that answers very specific questions within very little time.
Referring Expression Comprehension by Composing Semantic-based Visual Attention

In recent years, multimodal tasks are receiving more and more attentions. Referring expression comprehension aims to find the object by a natural language sentence. The challenge of this task is that the system should be able to recognize images and understand the text to determine the corresponding target. In this paper, an interpretable method is proposed to perform referring expression comprehension by decomposing the input sentences into semantic units. The proposed method learns the joint text and visual features and generates the attention map of the target object. Visualization of comprehension progress can be obtained in the end to further analyze how deep model interpret the query object.
CNN-based Human Recognition and Extended Kalman Filter-based Position Tracking Using 360o LiDAR

The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.
Evaluation of deep learning models for sentiment analysis

Sentiment analysis is a task that belongs to natural language processing and it is highly used in texts extracted from social networks. This task consists of assigning the labels or classes: positive, negative or neutral to the text. However, analyzing a piece of text extracted from social networks to determine if it represents a positive or negative sentiment is a difficult task, because social media texts contain slangs, typographical errors and cultural context. The shortcomings of traditional frequency based feature extraction models such as bag of words or TF-IDF affect the accuracy of sentiment classification. To improve the precision in the sentiment classification task, it is possible to use natural language modelling methods that are able to learn contextual information from words. In this work, word embedding such as Word2Vec, GloVe and Doc2VecC with different dimensions are used. The resulting word vectors will be used to train recurring neural networks such as LSTM, BiLSTM, GRU and BiGRU, to improve sentiment classification.
Neural Architecture Search Using Harmony Search Applied to Malaria Detection

Over 200 million malaria cases worldwide lead to half a million deaths annually. Although significant progress has been made, eradication remains elusive. One of the main challenges to overcome is diagnosis. Currently, there are many techniques available, among them light microscopy being the golden standard. However, this method is slow and expensive, since it requires a professional microscopist, manually counting red blood cells (RBCs). Some automation attempts have been made, but thus far, no commercial solution has been developed, and there is no consensus on the right approach to the matter. One remarkable prospect is the use convolutional neural networks (CNNs) to classify and count RBCs. This method has proven to be highly accurate, but computationally intensive. This work seeks to find more cost-effective topologies, while still maintaining reasonable accuracy. To do so, it will use Harmony Search, a music-inspired metaheuristic. The main contributions of this work is the search of more efficient topologies and the suggestion of a new metric for kind of topology implementation, one that utilizes both accuracy and computation cost indicators.
Deep Learning AI Modeling

This chapter deals time series data modeling using neural networks, especially multilayer perceptron deep learning types. The first half of the chapter provides mathematical bases of neural networks, and the latter half shows practical application examples of a step response model and sudden event prediction model.
Formation of a Social and Humanities Educational Strategy in Educational Institutions of the System of the Ministry of Internal Affairs of Russia: Philosophical and Anthropological Foundations

The aim of the study is to substantiate the social and humanities methodology for the educational strategy implementation in higher educational institution of the system of the Ministry of Internal Affairs of Russia through the prism of the fundamental foundations of Russian philosophy and philosophy of education, highlighting the cultural and historical vectors of national identity in the context of the “new historical choice”. The article reflects on the methodological foundations for improving the educational strategy in educational institutions of the system of the Ministry of Internal Affairs of Russia in the direction of its “human dimension”. The scientific novelty of the study lies in the theoretical reconstruction of the content of the commonality conceptual sphere as a category and socio-political value of Russian philosophy and culture in relation to the content and structure planning of education, in particular the departmental “educational paradigm”. As a result of the study, based on the connection between fundamental philosophical foundations and the methodology for implementing the educational strategy, the anthropological content of education, in particular departmental education, its potential for the formation of an identity trajectory necessary to preserve statehood in the face of modern challenges and threats is revealed. The methodological and instrumental conditions for modelling the social and humanities educational strategy of a higher educational institution of the system of the Ministry of Internal Affairs of Russia are also determined.
Performance Analysis of Federated Learning Aggregation Algorithms for Secure and Efficient Data Handling

Traditional machine learning projects have revolved around training the model with the help of previously observed data to be able to predict output for future unknown data. In the current scenario, when the data generated are huge, centralized training of the model becomes inefficient. Hence, distributed approach with client server model has to be used for training the models. This introduces data handling and critical data privacy issues. This paper concentrates on Federated learning (FL) which builds a model for the server by aggregating the parameters obtained from the local models of the client devices. The research work focuses on design and evaluation of three new FL algorithms against the average of the performances of the local models. The evolved approach considering weights of the local models proportional to accuracy of the local model is found to be the most accurate and better than the centralized approach. The evaluation is done using three different algorithms belonging to regression and classification on multiple datasets. It is observed that there is only one round of communication between the clients and server required in the federated learning setup to achieve the benchmarked accuracy set by the centralized setup. This is a considerable development and state-of-the-art approach to reduce communication and computation costs.
3D terrain estimation from a single landscape image

Abstract This article presents the first technique to estimate a 3D terrain model from a single landscape image. Although monocular depth estimation also offers single‐image 3D reconstruction, it assigns depth only to pixels visible in the input image, resulting in an incomplete 3D terrain output. Our method generates a complete 3D terrain model as a textured height map via a three‐stage framework using deep neural networks. First, to exploit the performance of pixel‐aligned estimation, we estimate terrain's per‐pixel depth and color free from shadows or lights in the perspective view. Second, we triangulate the RGB‐D data generated in the first stage and rasterize the triangular mesh from the top view to obtain an incomplete textured height map. Finally, we inpaint the depth and color in the missing regions. Because there are many possible ways to complete the missing regions, we synthesize diverse shapes and textures during inpainting using a variational autoencoder. Qualitative and quantitative experiments reveal that our method outperforms existing methods applying a direct perspective‐to‐top view transform as image‐to‐image translation.
Design of Fruit-V2 and Fruit-80 Stream Ciphers Using Basic Version

Nowadays, technology has improved a lot. The use of computing power for reducing human power and time has increased. The use of low-resources devices like WSN, RFID, and IIOT is also increased. Xilinx FPGA solutions provide real-time processing and flexibility. For all these, the main challenging problem is the confidentially with limited resources. eSTREAM portfolio was started to discover the efficient lightweight stream ciphers. Many lightweight stream ciphers came into existence like Sprout. Sprout was introduced to achieve all the goals but researchers have proven that Sprout was insecure, i.e., it cannot withstand the attacks. Fruit-v2 is introduced with improved features. It has proven that is very secure and ultra-lightweight. This paper discusses the design of the basic version of Fruit-v2 and Fruit-80. The main objective is to obtain a high frequency so that the speed is achieved.
Software Implementation of Plantlet Stream Cipher Using Verilog Hardware Description Language

Nowadays, use of resources like area, energy, or power is restricted. The fewer resources should be used as much as possible. Thus, there is a lot of scope for lightweight stream ciphers. In this paper, software implementation of plantlet stream cipher has been performed using Verilog hardware description language on the Xilinx ISE design suite. One approach to obtain a lightweight stream cipher is to store the cipher key in non-volatile memory. This cipher key is used not only for the initialization process but it is also used during the encryption/decryption process. Plantlet stream cipher was proposed to improve the design of sprout that has minimum area compared to all other stream ciphers. In this paper, plantlet stream cipher is compared with two other ciphers. It is also a secured stream cipher to overcome the time-memory-data attacks. The main application of plantlet stream cipher is for security and lightweight constrained devices. In the plantlet, a double-layer LFSR is used such that the area is minimized and an all-zero state is avoided.
Perancangan Aplikasi Sistem Penggajian Karyawan Berbasis Web Terintegrasi Absensi Fingerprint

Salary is the payment in the form of money received by an employee, the results of his contributions as an employee and his ideas to achieve the company's goals. At PT. Taishan Alkes Indonesia Management in the payroll sector is still implementing a manual / conventional way payroll system. This can lead to errors at the time of salary calculation. As a new company that is growing, PT. Taishan Alkes Indonesia needs a data processing application that can help in processing employee payroll data quickly, precisely, and accurately. The method used in this payroll system is the waterfall method, which is a structured framework (framework) that is arranged in a series in an effort to develop information systems (software). The creation of this application uses the CodeIgniter framework and also web programming using PHP.
A Case Study of Distance Learning Using Native Speakers in Chinese Education

In this study, a blended class was designed and operated in the form of learning through lectures by instructors and learning through exchanges with native Chinese speakers in major classes of Chinese related departments. This study attempted to describe the overall effect of this program by analyzing the cases of classes using native speakers in overseas regions, and surveying native speakers, teachers, and Korean learners who participated in the program. As a result of the operation, it was found that the class model in which native speakers participate can be used in all major subjects of Chinese-related majors, and most learners show high satisfaction. The class model could consider different countermeasures in language courses and theory courses. In the case of language courses, the teacher actively intervenes to identify the students, and it is necessary to try individual guidance or conduct interviews. It was found that educational understanding is necessary, and in the case of theoretical lectures, it is generally applicable to upper grade subjects. analyzed as possible.
Fast-tracking Healthcare Services for Students through the Design of a Hospital Information System

Students with good health status tend to attain better educational performance with low risk of academic failure unlike students with health issues.The discrepancies in the loss of instructional hours due to unplanned and delayed medical care widens the gap in academic performance between healthy and unhealthy students.Systems with the potential of providing excellent information access to relevant health personnel and eliminating absenteeism and lateness to duty which are paramount to timely healthcare delivery have now become a necessity.The urgent need for such a system, flexibility in the development process and the potential for adding or modifying later requirement specifications influenced the selection of the Rapid Application Development (RAD) Software Development Model for this study.This project designed and implemented a system that offers prompt healthcare delivery to narrow the gap between healthy and unhealthy students.
Current Perspective of Education in the Face of Psychological Challenges and Addictions Caused by the Pandemic

When talking about education, many meanings and references come to mind, since each person interprets this concept based on their ideals and knowledge, and therefore, it is necessary to know all the actions that are carried out for their benefit, from the level basic to superior, because in the end it seeks to implement a quality education that allows increasing the knowledge of students through the teaching-learning process. In 2021, the global pandemic caused by COVID 19 caused a new vision, perception, reality, challenges and empathy with everyone in the world. In this sense, the central issue of highest priority is education, since the pandemic teaches us as a society to understand and reflect on all the cases and actions in our day to day. This pressure goes beyond news regarding the health contingency, emphasis should be placed on 3 specific issues that are Education, Psychology and Addiction. These terms involve a deep analysis about what happened on the educational side,
Development of a Domain Specific Modeling Language for Educational Data Mining

In data mining solutions, the data selection phase plays an essential role in the success of decision-making.The tools that operate at this phase need to cater to each domain's technical and management challenges.Using a Domain-Specific Modeling Language (DSML), we found an alternative to abstract data and simplify the selection for Educational Data Mining (EDM) process.This work presents a graphic DSML to represent the problem.We used a case study methodology and implemented a CASE tool for the language evaluation.We acquired evidence that the proposed language simplifies the data selection phase for EDM because it solves the technical and management challenges addressed to this domain.
Taint Trace Analysis For Java Web Applications

Taint analysis is concerned about whether a value in a program can be influenced, or tainted, by user input.Existing works on taint analysis focus on tracking the propagation of taint flows between variables in a program, and a security risk is reported whenever a taint source (user input) flows to a taint sink (resource that requires protection).However, a reported bug may have its taint source and taint sink located in different software components, which complicates the bug tracking and bug confirmation for developers.In this paper, we propose Taint Trace Analysis (TTA), which extends P/Taint, a context-sensitive Java taint analysis project, by making the taint information flow explicit.Thanks to the underlying Datalog semantics, we describe a way to extract traces of taint flows across program contexts and field accesses in the Doop framework.Different from existing works that produce only source-sink pairs, the output of TTA can be visualized as a set of traces which illustrate the inter-procedural taint propagation from taint sources to their corresponding sinks.As a consequence, TTA provides more useful information for developers and users after a vulnerability is reported.Our implementation is also efficient, and as shown in our experiment, it adds only a small run-time overhead on top of P/Taint for a range of analyses with different types of context-sensitivities applied.
An Exploratory Study of Bug Prioritization and Severity Prediction based on Source Code Features

Software systems generate a large number of bugs during their lifecycles.Managing and assigning these bug reports is a challenging task.Building prediction models for the priority or severity levels of bugs through bug reports can help developers prioritize highly urgent bugs.Traditional prediction models are based on the textual description information in bug reports.However, most of the description is little or no.According to the bug report, developers need to fix the corresponding source code files.If the corresponding source code file is a core module in a software system, the report is likely to have high-level assignment rights.Therefore, in this paper, we investigate the effect of using the source code file feature sets on classification performance.In addition, we evaluate the effect of different sampling methods on the data, namely SMOTE, RUS, SMOTEEN, Adaboost, and GAN.Extensive experiments were conducted on five open-source projects.The experimental results show that the source code file feature sets do not perform as well as the textual description features in bug reports.Besides, over-sampling methods do not alleviate the data imbalance problem in the case of insufficient data, while GAN performs best in the case of sufficient data.
Agile and Lean Software Engineering and the SWEBOK - Position Paper for Panel: Educational and Professional Implications of SWEBOK

This position paper addresses the usefulness (or otherwise) of the Software Engineering Body of knowledge (SWEBOK) version 3 for software practitioners in industry, and the consequent need for the SWEBOK to evolve to better address current industry practice.The position taken in this paper is that agile and lean methods are now the predominant approach to software engineering, and that the limited and anachronistic coverage of agile methods in the SWEBOK, coupled with the absence of any acknowledgement of lean approaches, is undermining software engineering education, the career prospects of graduates, and the software industry as a whole.It is therefore proposed that the agile methods section of the SWEBOK is revised and expanded such that it provides a valid body of knowledge for contemporary software engineering
Scanning Angle Control of MEMS Micro-mirror Used in LIDAR

In this paper, a robust-control scheme of scanning angle of MEMS micro-mirror-based LIDAR is proposed. The structure of LIDAR using an electromagnetically driven scanning micro-mirror for laser beam scanning is presented. Then, based on the analysis of the characteristic of the micromirror, a nonlinear hysteresis compensation scheme is used for compensating the effect of hysteresis, then a PID strategy based on robust design is proposed for controling the angular movement. The corresponding experimental results are illustrated.
Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Image enhancement is an important method in the process of image processing.This paper proposes an image enhancement method base on an improved fuzzy c-means clustering.The method consists of the following steps: firstly, proposed a fuzzy c-means clustering with a cooperation center(FCM-co).Secondly, using the FCM-co, divide the image pixels into different clusters and marked membership values to those clusters.Thirdly, modify the membership values.Finally, calculate the new pixel gray levels.This enhancement method can overcome the disadvantage of overexposure and better retain image details.Through the experiment, the test results show that the proposed enhancement method could achieve better performance.
Reference and research on the analysis method of comprehensive implementation of car touring carnival activities

China's auto market has finally reached an inflection point, and the carnival event has become a milestone car show built at the inflection point.Through the implementation effect of the past automobile industry touring activities, this paper analyzes the implementation significance of automobile touring carnival activities, analyzes the automobile market, briefly describes automobile activities' planning principles, lists the activities' planning, implements protective measures, and introduces successful activities.Cases, learn implementation methods from successful cases and raise issues that need attention in implementing such activities.This article has made a critical discussion on the safety issues related to automobiles.It is hoped that while promoting the development of China's automobile industry, carnival carnivals can pay attention to automobile-related traffic safety issues and transmit the positive energy of China's automobile safety.