Reviewerly/exHarmony huggingface dataset:
Novel Hash-Based Radix Sorting Algorithm

Sorting remains a quintessential problem in computer science; henceforth, considerable research has focused on optimizing runtime efficiency when sorting a collection of elements. Most algorithms for sorting objects are comparison-based. Bucket Sort and Radix Sort are non-comparison based sorting algorithms that can sort objects in linear time. In both cases, the corresponding array indices represent a hash for the object. Nevertheless, Radix Sort still requires an auxiliary array. In this paper, replacing Radix Sort's auxiliary array with a hash table is proposed. Use of a hash table in Radix Sort should avoid the calculations for the array and be better suited for handling objects. As with an array-based Radix sort, this hash-based approach should maintain linearity, thereby sorting objects more efficiently. Following successfully programming this novel sorting algorithm, as the number of elements increases, the runtime progresses linearly, not exponentially. Moreover, as the number of digits increases, the sorting runtime still lengthens linearly. Thus, a hash-based Radix sort is feasible and overcomes many issues associated with current sorting algorithms.
Receding Path Planning for UAV Swarms Using Priority-based Artificial Potential Field

This paper presents a receding path planning method using priority-based artificial potential field (PAPF) for UAV swarms considering moving obstacle and inter-UAV collision avoidance constraint. To enhance computational efficiency, PAPF is proposed to decouple the UAV swarm cooperative path planning problem into a series of single-UAV path planning problems. The receding planning mechanism is used to reduce the horizon of the path planning to further reduce the computational burden. In each planning horizon, when sequentially generating the paths of multiple UAVs, lower priority UAVs treat the path points of higher priority ones as obstacles for collision avoidance. Simulation studies show that PAPF can generally generate swarm paths in around Is on the scenario involving 16 UAVs, which verifies the efficiency merit of PAPF in solving dynamic path planning problems for UAV swarms.
Robust H<sub>∞</sub> Fuzzy Logic Control of Intelligent Electrically Powered Wheelchair

A robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> Fuzzy Logic Control structure for a two-wheeled intelligent electrically powered wheelchair is proposed in this paper, which can compensate the effects caused by unmodeled dynamics, disturbances, approximate errors and varying external load acting on the drive system. The control structure employs a fuzzy logic controller with a simple structure incorporating an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller based on an iterative linear matrix inequality algorithm via an acceleration feedback signal. The proposed scheme is easy to be carried on practical wheelchairs and robust to variance of the motor dynamics, sensor noise, and unmodeled vehicle dynamics. Finally, the simulation experiments show the effectiveness of the proposed scheme.
The Efficient Data Classification using SVMcW for IoT Data Monitoring and Sensing

The developing of the wireless network supports the high transmission data rate and low latency for UEs. Hence, extensive data and application are the main reason for enhancing the usage of IoT. With the IoT device increasing; it means that more data need to be processed and analyzed. In order to reduce the process time and power consumption, we proposed the method of the smart monitor to the director the IoT data, and the Support Vector Machine (SVM) to classify data. By this way, the data process can achieve real-time processing. Besides, to solve the disadvantage of SVM and reduce the training time, we involved the concept of weight in the feature of SVM. Finally, the simulation results show that our method can reduce the training time and guarantee the classifier accuracy.
Searching Behavior Analysis of Online Shopping Based on Information Content of Query Words

With the spread of the internet as social infrastructure, more and more people are shopping online. Online sites that formerly dealt with such specific products as books and clothing have also expanded to mall-type shopping sites by incorporating various kinds of stores. As a result, searching for products has become more complicated and prolonged. In this paper, we propose a method that models product-searching behavior based on the transition of the search words input by users. Since a query is generally composed of one or more search words, their information content is calculated in advance from query logs. Thus, varying the information content of the user's query sequences can be classified as a model of user searching behaviors. From analysis results using actual data, we confirmed that our proposed method effectively models product-searching behavior.
Understanding Confounding Factors in Face Detection and Recognition

Currently, face recognition systems perform at or above human-levels on media captured under controlled conditions. However, confounding factors such as pose, illumination, and expression (PIE), as well as facial hair, gender, skin tone, age, and resolution, can degrade performance, especially when large variations are present. We utilize the IJB-C dataset to investigate the impact of confounding factors on both face detection accuracy and face verification genuine matcher scores. Since IJB-C was collected without the use of a face detector, it can be used to evaluate face detection performance, and it contains large variations in pose, illumination, expression, and other factors. We also use a linear regression model analysis to identify which confounding factors are most influential for face verification performance.
Visualizing Modular Forms

We examine several currently used techniques for visualizing complex-valued functions applied to modular forms. We plot several examples and study the benefits and limitations of each technique. We then introduce a method of visualization that can take advantage of colormaps in Python’s matplotlib library, describe an implementation, and give more examples. Much of this discussion applies to general visualizations of complex-valued functions in the plane.
Survey on Recent trends in Image Morphing Techniques

To accomplish this, Image Morphing has attracted a lot of attention from multimedia users and entertainment seekers in order to obtain fancier transitions and animations. The current trends in Morphing are Cross Dissolving, Mesh Warping, Field Morphing and Transition Control. These trends work on the important facial features by extracting the Landmarks (control points). These landmarks highlight the important facial attributes. The landmarks can be extracted either manually or by using a pre-trained Dlib model for landmarks. In this paper, various trends that are recently adapted for Image Morphing are reviewed.
Automated Detection of White Blood Cells Cancer Disease

Leukemia is a blood disease . The pace of cure be dependent upon the kind of Leukemia just as the period of unfortunate casualties. Intense lymphocytic Leukemia, Acute myeloid Leukemia and typical instances of Microscopic pictures of blood marrow spreads at first separated the core by evacuating foundation utilizing division. At that point the impacted cores' shading, GLCM and geometric highlights are separated lastly these cells are named carcinogenic or nonmalignant cell and its subtypes utilizing bolster vector machine (SVM) and KNN classifier. The precision of the classifier assessed up to 94.3%. The trial results demonstrates that proposed calculation could achieve a sufficient exhibition for the analysis of AML, ALL and their sub-types.
Are Humans Bayesian in the Optimization of Black-Box Functions?

Many real-world problems have complicated objective functions whose optimization requires sophisticated sequential decision-making strategies. Modelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. The Gaussian Process based Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper we focus on Bayesian Optimization and analyse experimentally how it compares to humans while searching for the maximum of an unknown 2D function. A set of controlled experiments with 53 subjects confirm that Gaussian Processes provide a general model to explain different patterns of learning enabled search and optimization in humans.
Tokens and Tokenization

The authors have carried out an examination of the status of tokens and tokenization in financial markets with regulatory problems, which lack proposals for solutions based on a generalized consensus. Overall, it seems to authors that cutting the Gordian knot of tokenization and tokens is the essential need to achieve a consensual and efficient protocol of unequivocal attribution of legal responsibility to obtain satisfactory levels of transparency and reliability in all transactions. In particular, tokens that claim to be money appear to have a more complicated potential development than the rest, since there is a controversy between the pressures of the sector agents and the specific restrictions indicated by Knapp and Ingham and maintained by the states and regulators with respect to its exclusive regulatory capacity over money as a means of payment of a wide and reliable acceptance.
Learning Aerial Image Similarity Using Triplet Networks

Unmanned aerial vehicles (UAV) faces localization challenges in satellite navigation systems denied environments. Images taken from on-board cameras can be used to compare against orthophotographical map to support visual localization algorithms. Image similarity estimation can be achieved calculating various similarity metrics. Pearson correlation was found to be the best choice for evaluating areal images similarity in our experiments. Still is not robust against image displacement caused by aircraft frame movement. We propose a new architecture of triplet neural network to learn image similarity measure. The proposed architecture incorporates VGG16 network base layers. Top layer structure, loss function and performance metrics being suggested by authors. Images were matched to the maps from satellite photo. The matching results from proposed neural network architecture were compared and evaluated against Pearson correlation.
Approximating Learning Curves for Imbalanced Big Data with Limited Labels

Labeling data for supervised learning can be an expensive task, especially when large amounts of data are required to build an adequate classifier. For most problems, there exists a point of diminishing returns on a learning curve where adding more data only marginally increases model performance. It would be beneficial to approximate this point for scenarios where there is a large amount of data available but only a small amount of labeled data. Then, time and resources can be spent wisely to label the sample that is required for acceptable model performance. In this study, we explore learning curve approximation methods on a big imbalanced dataset from the bioinformatics domain. We evaluate a curve fitting method developed on small data using an inverse power law model, and propose a new semi-supervised method to take advantage of the large amount of unlabeled data. We find that the traditional curve fitting method is not effective for large sample sizes, while the semi-supervised method more accurately identifies the point of diminishing returns.
A Formal Proof in Coq of Cantor-Bernstein-Schroeder’s Theorem without axiom of choice

This paper describes a formal proof of Cantor-Bernstein-Schroeder’s Theorem based on Morse-Kelley axiomatic set theory, which use inductive without axiom of choice in the proof assistant Coq. Firstly, we formalize a few definitions, axioms and theorems from the axiomatic set theory, just needed for formal proof. Furthermore, some new definition were added to make the system self-closed. Then, some reusable lemmas and Integrated strategy are given to make the code simpler and more automated. At last, we give a formal proof of the theorem in detail. All the proofs are formally checked by Coq proof assistant. The formalizations embody that mechanical proving of mathematical theorem based on Coq has the characteristics of readability and interactivity. Every step proves to normalized, rigorous and credible. The Cantor-Bernstein-Schroeder’s Theorem is the key result in the set theory that allows comparison of infinite sets, and its formalization lay a foundation for formal proof of lots of important theorems and puzzles in set theory.
A function level Java code clone detection method

In this article, we mainly propose a method of function level code clone detection for Java language. The method includes a source code preprocessing strategy which unifies some Java grammatical features such as lambda expressions, anonymous classes and generics through code preprocessing. After the source code is preprocessed, the function extraction is performed to get all functions from source code, and then a filtering mechanism is carried out to filter out the useless comparing pairs with large difference in sequence length. Finally, we combine the longest common subsequence algorithm and the suffix array algorithm to perform the similarity calculation according to the size of the function. The experimental results in twenty open source software show that our method improves both accuracy and efficiency.
Attribute value generation using association rule mining

This paper presents an approach to generate attribute values of a record in a table using association rule mining. The proposed method generates a new record based on an existing record in the table. The generation of new values is subject to the constraints specified in the form of association rules. This approach can be used in automatic composition of music out of a `raaga' and in generation of dance steps in choreography. The proposed approach has been experimented to generation a new composition on Indian classical music raaga Bhupali on Sitar.
Research On Underwater Multi-clutter Data Association Based On Fuzzy Clustering

In this paper, a multi-target tracking association algorithm based on fuzzy clustering is proposed to cope with the multi-clutter phenomenon and large measurement error in underwater environment. The algorithm obtains the membership degree of each measurement to the clustering center point by using the Fuzzy Clustering (FCM) algorithm, and then weights the associated measurement values as a weight coefficient. Finally, the Kalman filter algorithm is used to obtain the estimated value of the state, so that multiple targets can be tracked. Simulation results show that the traditional algorithm is easy to generate false alarms or even filter divergence in the multi-clutter environment. However, the algorithm proposed in this paper takes all the measured values falling within the tracking gate into account, which greatly improves the tracking accuracy and stability. So that our method can be applied to actual engineering projects.
Syllabus Mining for Faculty Development in Science and Engineering Courses

Creating an informative syllabus that can be searched and fully utilized by students is essential for effective education at universities. In this study, we empirically investigate the searchability of a collection of syllabi. In our study, 6,493 online syllabi of a national university in Japan are examined. First, we compare a baseline method and our ameliorated method of query expansion using the book information in syllabi. The results of our experiment demonstrated that book information in syllabi is effective in improving the searchability. Next, we compare methods for word suggestions using deep learning approaches and large text corpora. In the experiment, we used a bibliographic database of university libraries in Japan, which contains 3,990,646 bibliographic entries, and a version of Japanese Wikipedia, which contains 2,351,545 articles. The results indicate that a wide range of vocabulary is advantageous for improving the searchability of syllabi. Finally, we propose some guiding principles for writing a better syllabus based on our findings.
Sampling error correlated among observations: origin, impacts, and solutions

Geoscientific datasets can contain individual data for more than 50 different chemical elements. The association between these variables is as important as their individual values. However, it is commonly overlooked that the observed covariance may be overestimated due to correlated errors. Dependent errors arise from many sources, such as the segregation process of minerals associated with these variables during delimitation, extraction, and preparation steps. This study extends a classical model composed of grade-independent (additive) and grade-proportional (multiplicative) errors to a generalised multivariate model that can estimate the real variance, covariance, and correlation from observations affected by shared errors. The use of estimates of the real covariance is recommended when the study objective is to evaluate or estimate the association between processes instead of the association between observations. A numerical example illustrates the bias in statistics and discusses the relevance of considering shared errors in linear regression and kriging.
Applying the Teach-Back Method and Mobile Technology to Support Elementary Students’ Mathematics Problem-Solving Strategies

A great amount of elementary students encounter failure to solve mathematical problems because they cannot understand the meanings of the problems. As indicated, problem-solving is the core of the mathematics curriculum. Thus, students need to develop reading comprehension ability to solve mathematics problems. The teach-back is a constructive method that can help students present the understanding of a certain topic through dialogues. This study investigated the effects of the teach-back approach, exploring elementary students’ mathematics learning efficacy of problem-solving and attitudes towards an interactive learning platform, HiTeach. The participants in this study were six-graders from a public school in Taiwan. There were twenty-four participants in the experimental group and twenty-four participants in the control group. The results indicated that students in the teach-back approach group performed better than those in the traditional collaborative group. Moreover, the teach-back approach helped students in comprehending the meaning of questions in problem-solving.
Goal-Oriented Conversational System Using Transfer Learning and Attention Mechanism

Goal-Oriented (GO) conversational system or GO chatbot is making a significant difference in information availability and customer satisfaction in different business areas. This article describes a GO chatbot model, where transfer learning and attention mechanism are used. Usage of these deep learning techniques widens the application areas of the GO chatbot. Application of transfer learning allows the GO chatbot to transfer the common knowledge of one domain to another domain, which solves the problem of inadequate data for a particular domain. On the other hand, the attention mechanism helps the model to perform domain-specific chatting. The proposed model produces better results on previous research work datasets and also for a newly introduced organ transplant information dataset. Two main contributions of this research are, using transfer learning and attention mechanism for GO chatbot and introducing a new dataset for organ transplant information.
Development of Accreditation Information System (AIS) for vocational higher education with NAAHE standard

Abstract Higher Education Accreditation is an activity that must be implemented by every college for every 5 years where this activity becomes a benchmark for college sustainability. This accreditation must be in line with the National Accreditation Agency for Higher Education (NAAHE) standards. Astra Manufacturing Polytechnic (Polman Astra) as one of the vocational higher education also conducts the accreditation. In compiling the accreditation instrument Polman Astra has a problem such as the inaccuracy of accreditation data between one department and another and the difficulty of coordinating among departments in providing data accreditation compilation. This problem may cause the late of accreditation according to a predetermined time. Due to this problem, an information system was built, namely Accreditation Information System (AIS). It is a web-based information system and designed according to NAAHE standard. AIS is built using the .NET framework and SQL Server DBMS with prototyping methodology. After implementation, AIS was able to integrate the needs of data accreditation by providing data requirements and eliminates delaying data preparation by the related department since it was directly generated from the Polman Astra database. AIS is also able to manage the accreditation data table dynamically and transparently, eliminates paper printing costs into zero and prevent delays in the preparation of accreditation instruments so that the accreditation can be completed according to a predetermined time
Reactive Obstacle Avoidance Strategy Based on Fuzzy Neural Network and Arc Trajectory

To solve the obstacle avoidance of a wheeled mobile robot in the target navigation process, a method based on arc trajectory and fuzzy neural network is proposed. Although some related works consider linear and angular velocities as control inputs, the sample inputs of the training neural network are too subjective and not very easy to select. In this paper, a very clear sample set can be established by CAD software. Then through network training and kinematics model transformation, the linear velocity of two wheels is obtained as the control input. Finally, the advantages of this method are demonstrated by STDR and ROS simulation.
Deep belief network based NOx emissions prediction of coal-fired boiler

The emissions of nitrogen oxides (NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> ) from boilers is related to various production parameters and has strong nonlinear characteristics, which is difficult to accurately predict. To address this problem, a comprehensive NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> emissions modeling method based on deep belief network (DBN) is proposed. In the algorithm, classification regression tree (CART) is used to select input variables by sorting the importance of input variables. In addition, a DBN is employed to establishe the NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> emissions prediction model. In this paper, the experimental data is obtained from the historical data of coal-fired power plants. The experimental results show that the average relative error of DBN prediction results is lower than extreme learning machine (ELM) and least squares support vector machine (LSSVM).