Reviewerly/exHarmony huggingface dataset:
The Digital Perception of Architectural Space

The introduction of digital technologies in the professional field of architecture is questioning the traditional way of learning some topics such as the perception of architectural space. The purpose of this research is to compare the perception of architectural space using three digital visualization media: conventional video, 360° video and 3D digital model in 53 students of the first year of studies of the professional career of Architecture in Peru. For the experimentation, the chapel in Ronchamp designed by Le Corbusier was used and five components of spatial perception were determined: measurements and proportions, spatial relationships and orientation, forms and materials, lighting and shadows and experienced sensations. The results were obtained based on an ad-hoc questionnaire and based on them it is concluded that the 360° video is the most highly rated visualization medium in all components and in the overall perception of space; however, the other two media have advantages in specific aspects, which allows us to recommend their complementary use to achieve a perception closer to the real one.
Data Incremental Learning for Fault Detection and Classification in Photovoltaic Arrays

In this paper, a data incremental learning algorithm known as Learn++ is embedded in a very simple Multi-Layer Perceptron for fault detection and classification in Photovoltaic arrays. Usually in PV arrays, classifiers are limited to classical batch setting where data are provided before training, and thus optimization of meta-parameters and the selection of model become dataset dependent. Moreover, during training it is assumed that the dataset and its underlying structure are static and since in PV systems constantly new batch of data arrives over time, the classifiers are likely to misclassify the previously unseen data. In an attempt to learn new information, completely stable classifiers lose previously acquired knowledge which is very undesirable. Moreover, completely plastic classifiers learn new information but cannot retain their previous knowledge. Learn++ algorithm is employed in this paper to address the stability-plasticity dilemma of feedforward MLP as base classifier to learn incrementally without undergoing catastrophic forgetting. Using MATLAB simulations, the incremental learning of the MLP based on Learn++ is demonstrated by classifying line-ground and line-line faults in two PV arrays system with 97% classification performance on a test set, TEST , containing 300 instances.
Gradient boosting regression for faster Partitioned Iterated Function Systems‐based head pose estimation

Head pose estimation (HPE) notoriously represents a crucial task for many computer vision applications in robotics, biometry and video surveillance. While, in general, HPE can be performed on both still images and frames extracted from live video or captured footage, its functional approach and the related processing pipeline may have a significant impact on suitability to different application contexts. This implies that, for any real-time application in which HPE is required, this information, namely the angular value of yaw, pitch and roll axes, should be provided in real-time as well. Since, so far, the primary aim in HPE research has been on improving estimation accuracy, there are only a few works reporting the computing time of the proposed HPE method and even less explicitly addressing it. The present work stems from a previous Partitioned Iterated Function Systems-based approach providing state-of-the-art accuracy with high computing cost, and improve it by means of two regression models, namely Gradient Boosting Regressor and Extreme Gradient Boosting Regressor, achieving much faster response and an even lower mean absolute error on the yaw and roll axis, as shown by experiments conducted on the BIWI and AFLW2000 datasets.
Optimally Efficient Multi-party Fair Exchange and Fair Secure Multi-party Computation

Multi-party fair exchange (MFE) and fair secure multi-party computation (fair SMPC) are under-studied fields of research, with practical importance. In particular, we consider MFE scenarios where at the end of the protocol, either every participant receives every other participant’s item, or no participant receives anything. We analyze the case where a trusted third party (TTP) is optimistically available, although we emphasize that the trust put on the TTP is only regarding the fairness , and our protocols preserve the privacy of the exchanged items against the TTP. In the fair SMPC case, we prove that a malicious TTP can only harm fairness, but not security . We construct an asymptotically optimal multi-party fair exchange protocol that requires a constant number of rounds (in comparison to linear) and O(n 2 ) messages (in comparison to cubic), where n is the number of participating parties. In our protocol, we enable the parties to efficiently exchange any item that can be efficiently put into a verifiable encryption (e.g., signatures on a contract). We show how to apply this protocol on top of any SMPC protocol to achieve fairness with very little overhead (independent of the circuit size). We then generalize our protocol to efficiently handle any exchange topology (participants exchange items with arbitrary other participants). Our protocol guarantees fairness in its strongest sense: even if all n-1 other participants are malicious and colluding with each other, the fairness is still guaranteed.
An Improved Arithmetic Optimization Algorithm with a Strategy Balancing Exploration and Exploitation

With the increasing complexity and difficulty of practical problems, higher requirements are put forward to optimization techniques, especially the improvement on reliability and performance of meta-heuristic algorithm. In this paper, an improved arithmetic optimization algorithm (IAOA) is proposed, and it is compared with two algorithms—particle swarm optimization (PSO) and arithmetic optimization algorithm (AOA) on 13 benchmark functions. Experimental results show that the proposed algorithm performed better than the compared algorithms in solving particle problems in most cases.
Indonesia China Trade Relations, Social Media and Sentiment Analysis: Insight from Text Mining Technique

Sentiment Analysis (SA) employed for detecting, extracting, and classifying people opinions about an issue. Social media is a channel to show people opinions and thoughts. This study aimed to detect and classify Indonesia public opinion from Twitter written in Indonesia language for trade relations between Indonesia and China topic with text mining techniques. The result was the model that detected and classified sentiment of public opinion into negative, neutral or positive sentiment. The sentiment detected by lexicon-based and rule-based sentiment analysis. VADER was chosen as a tool for sentiment analysis lexicon-based. The text classification process was a training stage for the model. The experiment revealed SVM classifier performed higher accuracy value than Naïve Bayes, 67.28% and 64.68% respectively.
Achieving Standard Software Security in Agile Developments

The introduction of agile software development methods has brought about diverse benefits to the industry. These range from rapid deployment of software products to satisfying the ever changing customer requirements among others. These have in turn fueled the rapid adoption of these methodologies within the industry. On the down side, agile approach has been proved to be weak in handling security and therefore not appropriate for developing secure systems. The framework we present in this paper addresses this by ensuring that security is in the development process from the beginning of the project to the end. It is modeled after the SSE-CMM as a standard for security engineering. Considering its process areas, it aims to achieve the intended security goals within the agile process to appropriate levels by adopting agility in the implementation of these activities. The criteria of implementation are agility plus the SSE-CMM process areas goals. The framework is tested through a case study to check the capability levels achieved and the agility of the resultant process.
A Fast Image Encryption Based on Improved Logistic Map

Chaotic map has the properties of pseudorandomness, sensitivity to parameters and initial values, which is beneficial to enhance the performance of encryption algorithm. In the era of 5G business and the Internet of Everything, high reliability and low delay of information on high-speed channels provide good basis for the application of fast image encryption algorithm. In this paper, a fast image encryption algorithm based on the improved chaotic model was proposed by using the multiplicative large integer modulo method to homogenize the Logistic map. In the encryption algorithm, the scrambling and diffusion are processed simultaneously, which makes this scheme own high speed. Simulation experiments and performance analysis show that the scheme is safe and reliable, and can effectively meet the needs of safe image transmission in the network.
Cross-Platform Immersive Visualization and Navigation with Augmented Reality

Navigation and situation awareness are amongst the most important aspects of collaborative analysis in 3D environments. This paper investigates the latest technology of mixed reality to improve the team navigation through effective real-time communication. We develop a cross-platform collaboration system, supporting different types of devices and operating systems, including Microsoft HoloLens and iOS devices. Our system provides essential coordination and information sharing functions by leveraging on device sensors and Vuforia API of image marker to localize users inside a building. We provide a set of essential building navigation, visualization, and interaction methods to support joint tasks in the physical building environments among participants with mobile devices in real-time. We have performed a user study to evaluate different devices used in coordination tasks. Our results demonstrate the effects of immersive visualization for improving 3D navigation and coordination.
Research on the Application of Information-based Financial Sharing Center in Enterprise Financial Management

With the development of economic globalization, China's socialist market economy continues to prosper. Nowadays, the quality of life of the people has been greatly improved. At present, the financial management level of enterprises is difficult to meet the needs of the rapid development of enterprises and needs to be improved urgently. The advantage of financial management is the main factor reflecting the economic advantage and competitive advantage of enterprises. In recent years, with the continuous improvement of information level in various fields of society, computers have been gradually applied to financial management in various fields. Today, with the continuous development of enterprise management informationization, the advantages of financial sharing center are constantly being tapped, and this new management mode is being adopted by more and more enterprises, which brings many benefits to the financial management of enterprises. Based on the information background, this paper analyzes the role of financial sharing center in enterprise financial management, so as to give full play to the advantages of financial sharing center and provide a solid guarantee for improving the level of enterprise financial management.
Analysis of the Influence of Various Factors on the Ultraviolet Discharge Detection of 110kV Contaminated Insulators

In order to improve the accuracy of the discharge detection of the contaminated insulator UV imager and collect the detection data under different humidity and contamination levels, this article used the 110kV voltage class contaminated insulator string as the research object, using the UV imaging produced by Beijing Ruiying Zhituo Technology Development Co., Ltd. This article studied the relationship between the number of detected photons and environmental humidity and pollution degree during insulator pollution discharge.The test results show that: the photon number and humidity approximately satisfy the power function variation characteristics; the photon number and the pollution degree approximately satisfy the power function variation characteristics. The research results lay the foundation for improving the accuracy of UV discharge detection of polluted insulators at 110kV voltage level.
Unmanned Aerial Vehicle with Hybrid Lift and Propulsion

Diminishing energy consumption with the greatest possible autonomy on flight time and distance is one of the main goals of the development of unmanned aerial vehicles (UAV). Vertical takeoff and landing is also another feature that makes it easier to handle such platforms. Considering these requirements, we introduce the architectural design of a UAV that is conceptualized and built as a flying wing with a pusher configuration with combustion engine propeller and a multirotor composed of 3 propeller sets with electric motors. We performed several tests, including takeoff, horizontal flight, and transition tests successfully, and also tests on autonomy. Results show the versatility of the final-built hybrid prototype for operations in the field. The hybrid system has proven to be functional and effective, such that the architectural sketch has been submitted as a patent. So the main contribution here is the architectural design and construction details of the hybrid UAV system composed of a multirotor with 3 electric powertrain sets and a flying wing with a combustion powertrain set.
Design and analysis of Gaussian sum high-order CKF for nonlinear/non-Gaussian dynamic state estimation

In order to solve the problem of state estimation for nonlinear, non-Gaussian system, a novel Gaussian sum high order cubature Kalman filter (GS-HCKF) is proposed. In the GS-HCKF algorithm, a new version of high-order cubature Kalman filter (HCKF) is combined with Gaussian sum filter (GSF). The key idea of the proposed algorithm is that the posterior probability density of state can be approximated by a set of Gaussian distribution and each Gaussian distribution is estimated by high-order cubature Kalman filter. Numerical simulation results show that the proposed GS-HCKF is compatible with the advantages of traditional GSF and Gaussian sum unscented Kalman filter (GS-UKF), that is, the proposed GS-HCKF has low computational complexity and high estimation accuracy.
Research on posts analysis based on data process automation

The structural contradiction between talent supply and demand is the key problem to be solved in higher vocational colleges. Recruitment website provides massive recruitment data. The analysis of recruitment data has important practical significance for promoting the reform and innovation of talent training mode. Based on big data technology, the distributed real-time incremental collection of posts information is realized by Redis and Scrapy technology. The crawled posts information is stored in HBase database. The posts data is analyzed by spark platform, and the analysis result is stored in MySQL database. The charts are displayed by Flask framework and Echarts library. The system is closely linked to the pain spot of the current higher vocational talent training, and it is closely combined the skills required by the post with the courses offered by the school. It is helpful to improve the quality of talent training and cultivate more high-quality skilled talents.
Psychological test of patients with mental disorders based on eye movement data fusion algorithm

Eye movement (EM), as a mature observation technology, has been widely used in the research of psychology, and it is also one of the important methods of multi-quality psychological testing technology. However, there are relatively few researches on psychological testing based on EM technology at present. By introducing convolution neural network (CNN) network into deep long short memory network (DLSTM), this paper develops a new network structure, designs a fusion strategy, and proposes an EM tracking data fusion algorithm based on deep learning (EYE-CNN-DLSTM). By comparing the fusion effect and index values of EYE-CNN-DLSTM algorithm with two deep learning algorithms MLP and DLSTM on 10 sets of real EM data sets and 10 sets of real tracking data sets, the experimental results show that EYE-CNN-DLSTM algorithm performs well in fusion quality. It provides a theoretical basis for the new objective evaluation index of patients with mental disorders.
Spliced Image Forgery Detection Based on the Combination of Image Pre-processing and Inception V3

The paper proposes a method to detect the splicing in images which the spliced regions are created from other images. Our model is built to classify images with fake or not in a set of images. The image classification aims to save time when collecting and removing the fake images in a huge dataset and skip the step of searching the forged areas if there is no forgery in images. Previous studies mainly focused on the finding forged areas on the interfered image. The image preprocessing step and Inception V3 are combined to improve the detection. The objective of preprocessing step is to highlight the cropped area of the spliced images. The proposed model is experimented on Columbia Uncompressed Image Splicing Detection Evaluation Dataset and our built-in dataset with the average accuracy of spliced image detection up to 93.7%. The high accuracy demonstrates the effectiveness of the proposed method and is also a new contribution to the field of image forensics.
Facial Expression Recognition Using Convolutional Adaptive Neuro-Fuzzy Inference System (CANFIS)

Facial Expression gives significant information about the emotion of a person. In the research area of computer vision, automated facial expression recognition is an important area because of its significance in Human Computer Interaction (HCI). To improve the level of interaction in man machine communication systems, extraction and validation of emotional information by facial expression analysis plays a major role. The proposed method in facial expression recognition is based on extracted features like mean, mode, minimum intensity, maximum intenstiy, Perimeter, Standard Deviation (S.D), orientation (about X and Y axis) and centroid (about X and Y axis). A random mask is generated to find the convolution of the feature set from the pooling layer to the fully connected layers of the Feed Forward (FF) Artificial Neural Network (ANN). A neuro-fuzzy based system called Adaptive Neuro Fuzzy Inference System (ANFIS) is used for classification by tuning the weight vectors in the fully connected layers of ANN which produces a robust algorithm called the Convolutional Adaptive Neuro-Fuzzy (CANFIS). The performance of this method is evaluated using Recognition Efficiency (RE) which yields an effective recognition system in comparison with the conventional ANFIS structure.
Credit Card Fraud Detection Using Soft Computing

In the money-related sector, fraud is becoming exceptionally quick. Data mining can identify extortion without any problem. Visa misrepresentation location is likewise an information mining issue and it is trying for certain reasons, for example, the conduct of the cheats isn’t steady and the dataset of credit card frauds is exceptionally imbalanced. This paper explores the presentation of some machine learning and soft computing procedures on profoundly imbalanced Visa misrepresentation exchange information set. We have downloaded one dataset from Kaggle (creditcard.csv) which contains 284,807 exchanges. We actualize this project in Python which is a famous object-oriented programming language, especially used for data science and data analysis, and machine learning projects. We have that the presentation of the methods assessed is dependent on exactness, affectability, explicitness, accuracy, Matthews relationship coefficient, and adjusted arrangement rate. The results show the optimal accuracy for Naïve Bayes, k-nearest neighbor means Neural network and logistic regression classifiers. The comparative results show that k-means clustering performs better than other techniques. It performs better than other techniques and gives more accurate results.
Research on the Advantages of Digital Sensor Equipment in Language Audio-Visual and Oral Teaching

Digital sensors use biotechnology and information processing technology to strengthen the processing of relevant visual and auditory information, which is helpful to ensure that the receiver can obtain more accurate information, so as to improve the learning effect and reduce the impact on the environment. This paper designs an experiment to explore the role of digital sensors in language audio-visual teaching, which provides a reference for the application of digital sensors in the future. The impulse response function in sensor technology is introduced. The speech time domain envelope and time-varying mouth area of the sensor device are calculated. The auditory attention transfer detection based on line of sight rotation estimation is carried out through the auditory attention decoding fusion technology and the sensor auditory attention conversion detection method. At the same time, the characteristic of sensor heog signal is analyzed. The results show that the algorithm proposed in this paper has good results.
Strengthening University Computing Programs to Meet National Security Needs

In the wake of a pandemic, it has been essential that programs move online quickly while remaining the same academic rigor as being on the ground. Therefore the cybersecurity coursework and labs were taught entirely online while representing real-world scenarios. The College of Computing at Illinois Institute of Technology has recently been actively creating new partnerships, strengthening their cybersecurity, Information Technology, and other computing programs to meet national security needs. These activities have allowed partnerships with the United States Armed Services, Department of Defense, and numerous industry partners. The newly found focus on national security has provided a different perspective on cybersecurity-related to global events and how it can shape those currently, such as political events.
Research on student achievement evaluation and management mechanism based on support vector machine

Student achievement evaluation is of great significance for supervision and guidance of teaching management. However, the traditional method of evaluation and analysis based on absolute score has some defects, which can not fully reflect the information contained in student achievement, so that the impact of student achievement evaluation on teaching management is limited. To solve this problem, this paper uses support vector machine (SVM) to study students' performance, takes students' usual performance as the input of the model, and the performance label as the output of the model. And through empirical analysis, it verifies the importance of the intrinsic information hidden in the achievement data to evaluate students' achievements, thus providing a favorable basis for teaching management. Using scientific methods to predict students' grades can not only guide students' targeted tutoring, but also provide scientific decision-making basis for teaching administrators.