Reviewerly/exHarmony huggingface dataset:
NeLF: Practical Novel View Synthesis with Neural Light Field.

In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color when reaching the image plane. For efficient novel view rendering, we adopt a 4D parameterization of the light field, where each ray is characterized by a 4D parameter. We then formulate the light field as a 4D function that maps 4D coordinates to corresponding color values. We train a deep fully connected network to optimize this implicit function and memorize the 3D scene. Then, the scene-specific model is used to synthesize novel views. Different from previous light field approaches which require dense view sampling to reliably render novel views, our method can render novel views by sampling rays and querying the color for each ray from the network directly, thus enabling high-quality light field rendering with a sparser set of training images. Our method achieves state-of-the-art novel view synthesis results while maintaining an interactive frame rate.
Tennis Assistant Referee System based on Intelligent Sensor Network and Data Understanding

Tennis assistant referee system based on intelligent sensor network and data understanding is designed in this paper. These integrated sensors perceive, collect and also then monitor the information of the target object through mutual cooperation. It is essential while dealing with the data complex issues. In order to improve the tracking accuracy of the model, the method of multi-granularity feature is used to judge the tracking result, and with the tracking model, the assisted model is considered. The experiment compared with state-of-the-art ones have proven the efficiency and the QoS performance.
Hardware Passwords Manager Based on Biometric Authentication

This paper presents a portable passwords manager which has a two-stage biometric-based access procedure. Data security using biometric methods was chosen as a variant of reduced complexity but was very effective in preventing cyber theft. The implementation of biometrics for the purpose of identification in high-security systems has become essential with the evolution of technology and the spike in identity theft. Unlike passwords or IDs, a biometric feature is an identifier that cannot be lost, stolen, or replicated, which provides biometric authentication systems with an increased level of security. During the first accessing step, the 3DPassManager portable device measures the heartbeat and uses fingerprint and iris features to realize a unique biometric-based authentication. While the specific characteristics of fingerprint and iris features are integrated to ensure that the person using the device is the rightful owner, the pulse is utilized to verify if previously acquired static images are not used. During the second accessing step, a password is generated based on fingerprint details, valid only for a small-time interval. The fingerprint is stored in a secret key with a 1024-bit length. Once access is allowed, the passwords are made available through an extension installed on the web browser. The device is the size of a cigarette pack and communicates with the PC by scanning a QR code. It is safe and was previously tested for dictionary and brute force attacks.
Fast and Accurate Light Field View Synthesis by Optimizing Input View Selection

There is a trade-off between spatial resolution and angular resolution limits in light field applications; various targeted algorithms have been proposed to enhance angular resolution while ensuring high spatial resolution simultaneously, which is also called view synthesis. Among them, depth estimation-based methods can use only four corner views to reconstruct a novel view at an arbitrary location. However, depth estimation is a time-consuming process, and the quality of the reconstructed novel view is not only related to the number of the input views, but also the location of the input views. In this paper, we explore the relationship between different input view selections with the angular super-resolution reconstruction results. Different numbers and positions of input views are selected to compare the speed of super-resolution reconstruction and the quality of novel views. Experimental results show that the speed of the algorithm decreases with the increase of the input views for each novel view, and the quality of the novel view decreases with the increase of the distance from the input views. After comparison using two input views in the same line to reconstruct the novel views between them, fast and accurate light field view synthesis is achieved.
Clustering of Remote Sensing Data Based on Spherical Evolution Algorithm

Many meta-heuristic algorithms have been used in many areas, e.g., pattern recognition, machine learning, information retrieval, data mining, and image analysis. These algorithms have become powerful and popular in image clustering. In this study, a Spherical Evolution Algorithm (SEA) is applied during image clustering. The SE adopts a novel spherical search mechanism instead of the conventional hypercube search mechanism. First, a synthetic image is classified using the spherical evolution algorithm by varying the Scale Factor (SF) and the number of the function evaluations (FES) parameters. The best values of these parameters obtained by the first classification will be used for the classification of the real satellite image (Landsat TM imagery). Secondly, SEA and other meta-heuristic algorithms as the Genetic Algorithm (GA), the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are implemented and applied on real image. The comparison results demonstrate that SEA approach outperforms other methods in terms of execution times and global minimum values.
Rapid Completion Design Changes with Fiber Optics

Abstract One of the latest developments in permanent fiber optics is ability to install and complete a well in rapidly based on the needs of the program. This paper will present the drivers leading into the operation, the data collected and the completion advances resulting from a permanent fiber conceived and executed in under four weeks. Completion changes were conceived following direct observations of distribution of flow rate, defined in a Uniformity Index. The resulting changes were cost neutral to the overall program but showed improved completion results and well performance.
SWARM OPTIMIZATION ALGORITHM BASED ON THE ANT COLONY LIFE CYCLE

Optimization is very important to the success of any business. One technique for solving optimization is swarm intelligence; it has been successfully applied to solve a wide range of optimization problems. We devised a new swarm intelligence optimization algorithm based on the cooperative behavior of three different kinds of ants in a colony. Our algorithm consists of both exploration and exploitation processes to achieve better search performance. A new local search, inspired by the foraging of desert ants, was introduced to help the search move away from the local optima. Performance was evaluated on 23 standard benchmark functions of varying complexity. Our algorithm was able to find the global optima in more than 80 percent of the test functions, whereas the second-place algorithm only found around 10 percent of the functions tested.
Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks

This paper introduces our methodology to estimate sidewalk accessibilities from wheelchair behavior via a triaxial accelerometer in a smartphone installed under a wheelchair seat. Our method recognizes sidewalk accessibilities from environmental factors, e.g. gradient, curbs, and gaps, which influence wheelchair bodies and become a burden for people with mobility difficulties. This paper developed and evaluated a prototype system that visualizes sidewalk accessibility information by extracting knowledge from wheelchair acceleration using deep neural networks. Firstly, we created a supervised convolutional neural network model to classify road surface conditions using wheelchair acceleration data. Secondly, we applied a weakly supervised method to extract representations of road surface conditions without manual annotations. Finally, we developed a self-supervised variational autoencoder to assess sidewalk barriers for wheelchair users. The results show that the proposed method estimates sidewalk accessibilities from wheelchair accelerations and extracts knowledge of accessibilities by weakly supervised and self-supervised approaches.
Intellectual Property Assurance Method for Digital University Ecosystem based on Blockchain Technology

In the university digital ecosystem it is very important to have a proper process of collecting, processing and storing the data about students educational results which could confirm the level of the achieved outcomes within their study.In the paper authors provide an analysis of the data which should be stored and existing system which are used for diploma verification.According to the authors' research, the strategy for the digital transformation of the high educational institutes education system should include the modernization of the management of the intellectual property and learning outcomes (competencies and skills).To provide an intellectual property assurance for the students' documents there were chosen blockchain technology.Intellectual property assurance method for digital university ecosystem based on blockchain technology was proposed by the authors.
Survey of Smartphone Contact-Tracing Apps Architectures

The outbreak of COVID-19 has caused a cataclysmic impact on the world. The highly infectious virus is known to spread rapidly where the person exposed does not exhibit the symptoms initially. The respondent governments are using contact tracing apps to help in the automation of the process to trace the recent contacts of the infected person. This is a survey to review and examine various smartphone contact tracing App architectures and Technologies used to connect the smartphones. So as to compare them to find the best solution to the problem. We have analyzed a few research papers and their results are compared. We have seen that many studies found that contact tracing could stop an outbreak by reducing the reproduction rate and thus reducing the total number of infections.
A Multinomial Processing Tree Model of RC Attachment

In the field of sentence processing, speakers' preferred interpretation of ambiguous sentences is often determined using a variant of a discrete choice task, in which participants are asked to indicate their preferred meaning of an ambiguous sentence.We discuss participants' degree of attentiveness as a potential source of bias and variability in such tasks.We show that it may distort the estimates of the preference of a particular interpretation obtained in such experiments and may thus complicate the interpretation of the results as well as the comparison of the results of several experiments.We propose an analysis method based on multinomial processing tree models (Batchelder and Riefer, 1999) which can correct for this bias and allows for a separation of parameters of theoretical importance from nuisance parameters.We test two variants of the MPT-based model on experimental data from English and Turkish and demonstrate that our method can provide deeper insight into the processes underlying participants' answering behavior and their interpretation preferences than an analysis based on raw percentages.
Dynamically Controlled Kernel Estimation

We introduce a data driven and model free approach for computing conditional expectations. The new method is based on classical techniques combined with machine learning methods. In particular, we consider kernel density estimation based on simulated risk factors combined with a control variate. This is used in a Gaussian process regression for finally approximating the conditional expectation. In this way we increase not only the stability of the estimator but we also need a significantly lower amount of simulations due to the variance reduction. Since we apply Gaussian process regression, we do not only get a point estimate, but also the full distribution. It turns out that the optimal coefficient for the control variate is the minimal variance delta. Thus, in this way we obtain model free and purely data driven hedges. Finally, we apply our method to several examples from option pricing including exotic option payoffs and payoffs with multiple underlyings for different models including the rough Bergomi model. A discussion on the challenges to extend the method to a large dimensional settings is provided and is partially solved by using Quasi random number sequences.
STUDENTS’ ANXIETY IN SPEAKING ENGLISH DURING DISTANCE LEARNING

The ability to speak a foreign language is prominently influenced by one of the obstacles, namely speaking anxiety. The students tend to be anxious in speaking English in class during their presentation even though it is conducted through distance learning because of the Covid-19 pandemic. Referring to this background, this study aims to examine what kinds of the students’ anxiety in speaking English are during the distance learning and how they cope with the anxiety during the distance learning. A case study was employed in this research as one of descriptive designs in qualitative research. Moreover, the data was collected through analysing students’ self-reflection documents, a close-ended questionnaire, and an interview as the research instruments. Additionally, the participants of this study were 36 Science students at grade X of SMA Kristen Petra 1 Surabaya. After conducting the research, the researcher discovered that communication apprehension, fear of negative evaluation, and test anxiety often made the students anxious in speaking English during distance learning due to Covid-19 pandemic. Hence, it is significant for teachers as the facilitator can play their roles to reduce students’ speaking anxiety in a foreign language classroom.
A Novel and Secure Image Encryption Scheme Based on Chaotic Logistic Map and Random Scrambling Using a Special Parameter of Gray Code Transformation

Background: Image security is a major concern in the digital communication media. So there are so many algorithms symmetric and asymmetric are available for securely transmit the image. In symmetric key algorithm many algorithms are available based on bit plane decomposition and random scrambling using chaotic maps. Objective: A secure image encryption algorithm is proposed based on the combination of (n, k, p) gray code and chaotic logistic map to improve the performance of decomposition encryption methods. Methods: We suggest a new algorithm that improves the security of image encryption based on (n, k, p) gray code and bit plane decomposition method. This new encryption technique uses the bit plane decomposition of (n, k, p) gray code then random scrambling of each bit plane and chaotic logistic by map using pixel substitution method. Results: The performance and security of proposed algorithm have been analyzed on different evaluation metrics like MSE, PSNR, UAIC and NPCR and Correlation Coefficient etc. The experimental results show that the proposed algorithm is secure and provides better results from the XOR based substitution method. Conclusion: This new method can be utilized in imaging systems like tomography images of medical field, tele-medicine and digital video, video conferencing in communication system. Further, we will study to enhance and examine the execution of (n, k, p) gray code for data hiding and picture de-noising.
Designing Bkd Website Based In Industrial Engineering Department Of Universitas Pembangunan Nasional “Veteran” Jawa Timur

Lecturer Workload (BKD) is a portrait of the lecturer workload in the implementation of University’s Tri Dharma in the next semester. In college academic activities, the reporting of Lecturer Workload (BKD) is important because it is concerned with the productivity of lecturer’s performance. The lecturer workload is commensurate with a minimum of 12 credits and a maximum of 16 credits in each semester in accordance with academic qualifications. The Department of Industrial Engineering of UPN “Veteran” Jawa Timur has implemented a manual lecturer workload reporting system and indirectly the high potential obstacles to the evaluation process. So that in its application Lecturer Workload reporting (BKD) must be able to facilitate between lecturers, assessors and head of department in coordination. This research aims to produce Lecturer Workload Information System (BKD) effectively and efficiently. The design of this system uses the System Development Life Cycle (SDLC) method. From the results obtained, the output of this system is that lecturers report their performance online, assessors can verify files easily, and study programs can monitor lecturers' performance. It is hoped that the Lecturer Workload Information System (BKD) can help the Industrial Engineering Study Program of the East Java National Veterans Development University in conducting the lecturer workload reporting process (BKD)
Search-Space Reduction for S-Boxes Resilient to Power Attacks

The search of bijective n×n S-boxes resilient to power attacks in the space of dimension (2n)! is a controversial topic in the cryptology community nowadays. This paper proposes partitioning the space of (2n)! S-boxes into equivalence classes using the hypothetical power leakage according to the Hamming weights model, which ensures a homogeneous theoretical resistance within the class against power attacks. We developed a fast algorithm to generate these S-boxes by class. It was mathematically demonstrated that the theoretical metric confusion coefficient variance takes constant values within each class. A new search strategy—jumping over the class space—is justified to find S-boxes with high confusion coefficient variance in the space partitioned by Hamming weight classes. In addition, a decision criterion is proposed to move quickly between or within classes. The number of classes and the number of S-boxes within each class are calculated, showing that, as n increases, the class space dimension is an ever-smaller fraction of the space of S-boxes, which significantly reduces the space of search of S-boxes resilient to power attacks, when the search is performed from class to class.
Evaluating the Distance Learning Experience During the Corona Pandemic Using the Multiple Decision Model (CIPP) - Field Study on Students of Saudi Universities-: تقويم تجربة التعليم عن بعد في ضوء جائحة كورونا باستخدام نموذج القرارات المتعددة (CIPP) – دراسة ميدانية على طلبة الجامعات السعودية-

The study aimed to evaluate the distance learning during the Corona pandemic using the Multiple Decision Model (CIPP), through evaluating the context, inputs, processes and outputs of the experience from the viewpoint of male and female students at the undergraduate level in Saudi universities. To achieve the goals of the study, the descriptive survey approach was used, and the questionnaire as a study tool that consisted of (22) phrases divided into four axes (context, inputs, processes, outputs). It was applied to a simple random sample consisting of (502) male and female students in the study population which included all male and female students of the public universities in the Riyadh region, who numbered (184, 035). The study found that the input axis was the highest achieved with a high degree (3.52), followed by the outputs with a medium degree (3.39), operations with a medium degree (3.26) and assessing the context with an average degree (3.23). A number of recommendations were given, the most important of which was developing the infrastructure and academic accreditation for distance education programs, and providing support students in financial need.
GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST

Clothing also means clothing is needed by humans. Besides the need for clothing in terms of function, clothing sales or business is also very potent. About 75 million people worldwide are directly involved in textiles, clothing, and footwear. In this case, a common problem in this industry is that the actual productivity of apparel employees sometimes fails to reach the productivity targets set by the authorities to meet production targets on time, resulting in huge losses. Experiments were conducted using the random forest model, linear regression, and neural network by looking for the values ​​of the correlation coefficient, MAE, and RMSE. This aims to predict the productivity of garment employees with data mining techniques that apply machine learning and look for the minimum MAE value. The results of testing the proposed algorithm on the garment worker productivity dataset obtained the smallest MAE, namely the random forest algorithm, namely 0.0787, linear regression 0.1081, and 0.1218 neural networks
Using Open Street Map for Content Creation in Location-Based Games

Location-based games have been around since 2000. In these games, players are required to move and interact with objects in the physical world. Players need to reach a set of targets by moving outdoors. The targets are real objects (amenities) like statues, restaurants, bridges and contain the name, location, and a representative image. The main challenge is finding content and creating games where there is playing interest. In this paper, we study the use of OpenStreetMap (OSM) for content creation. We study the availability of amenities in different regions and whether they contain sufficient metadata for generating targets. We found that even if the data within OSM itself lacks images; many amenities refer to external links like Wikipedia pages and individual websites, from which we can extract the desired content about 21% of the time. This approach outperforms previously studied methods including Web crawling, geotagged photo datasets, and photo-sharing services such as Flickr. However, OSM data is greatly underrepresented in Asian countries and good results are limited to urban and downtown areas.
Students’ Digital Literacy and Collaboration Abilities: An Analysis in Senior High School Students

This study aims to describe and analyze the level of digital literacy and collaboration skills of students. The research method used a quantitative descriptive survey with a research instrument in the form of a questionnaire. The study involved 10 physics teachers and 105 high school students in Lampung Province. The results showed that the digital literacy of students was in the sufficient category with the achievement percentage of 54.68%, while the collaborative abilities of students were in the poor category with the achievement percentage of 39.17%. Digital literacy has not been maximally achieved and the ability to collaborate in students is influenced by the lack of efforts of students to use the internet as a learning resource and the lack of student-centered learning.
A Decade of Evidence of Trend Following Investing in Cryptocurrencies

Cryptocurrency markets have many of the characteristics of 20th century commodities markets, making them an attractive candidate for trend following strategies. We present a decade of evidence from the infancy of bitcoin, showcasing the potential investor returns in cryptocurrency trend following, 255% walk-forward annualised returns. We find that cryptocurrencies offer similar returns characteristics to commodities with similar risk-adjusted returns, and strong bear market diversification against traditional equities. Code available at https://github.com/Globe-Research/bittrends.