Reviewerly/exHarmony huggingface dataset:
An Empirical Study on Code Smells Co-occurrences in Android Applications

Android applications (apps) evolve quickly to meet users requirements, fix bugs or adapt to technological changes. Such changes can lead to the presence of code smells - symptoms of poor design and/or implementation choices that may hinder the project maintenance and evolution. Previous research studied the characteristics of traditional object-oriented (OO) code smells affecting source code files in desktop software systems, and advocated that the interaction and co-presence of code smells reduce the ability of developers to understand and maintain source code. However, little knowledge is available on emerging categories of Android-specific code smells and their interactions, i.e., co-occurences, with traditional OO smells, in the context of Android apps. To provide a broader understanding of this phenomenon, we conduct an empirical study on 1,923 open source Android apps taking into account 15 types of Android-specific and 10 types of traditional OO code smells. Our results show that the co-occurrence phenomenon is indeed prevalent in Android apps and several smell types have strong associations. Developers need to be aware of this phenomenon and consider detecting and eliminating both traditional and Android smells separately, using dedicated tools.
Realization of an Adaptive Test Paper Generation Function based on DPC Algorithm

Abstract Before COVID-19, although the online assessment platform has developed, it is relatively slow, and people prefer to organize on-site examinations. After the outbreak of the epidemic, people realize the urgency and necessity of information construction in all walks of life. More and more researchers begin to pay attention to and explore the use of advanced machine learning methods to improve the practicability of online examinations platform. The test paper generation function is the core link, and a good test paper generation function can ensure the quality of a test paper. In this paper, an advanced unsupervised algorithm DPC(Clustering by Fast Search and Find of Density Peaks) is used to conduct deep mining and test paper generation adaptive based on the question bank and historical assessment such as assessment frequency, accuracy or score rate, and a more reasonable test paper generation function is realized. By comparing and testing the experimental results, it can be proved that the idea is correct and feasible.
Design and Optimization of an Intelligent Fuzzy Logic Controller for a Nonlinear Dynamic System

Owing to storage, mixing and blending tasks carried out in process industries with spherical tank systems (STIS), the need arises for liquid level control to ensure quality of production, less reaction losses and safety. For this reason, this paper proposes the design of intelligent fuzzy logic controllers to solve the liquid level control problem. Using MATLAB Fuzzy Inference System and Simulink, four fuzzy logic control (FLC) design schemes have been developed with liquid level, error, liquid level change rate and error change rate as inputs. The optimized controller proposed is a hybridization of FLC and Proportional-Integral (PI) control abbreviated (PI-Like FLC). The results showed the PI-Like FLC had superior performance with respect to settling time and steady state error compared to other schemes. The steady state error was found to be 0.0082 and further optimization resulted in an error drop of 95 percent with disturbance rejection of 3000s at inflow range of 61 to 90 cube meters per second.
A Case of Teaching and Assessing an Introduction to Information Technology Course

Society witnesses that technology, especially information technology, plays a more and more important role in business and other areas. Not surprisingly, the new 2020 business accreditation standards by the Association to Advance Collegiate Schools of Business International (AACSB) have added technological competency and agility to reflect this trend. While most students know that information technology is important, teaching information technology in a business course of a business program has always been a challenge. Due to the AACSB requirement, technological competency may become one of the learning goals of business programs, and how to assess it becomes a new task to most business programs. This paper presents a new approach to teaching information technology courses and an innovative technological competency assessment instrument and effectiveness at an AACSB accredited school of business.
Keris Geometry Accuracy Using 3D Scanning Technique

Keris or kris is a traditional Indonesian stabbing weapon with distinctive and unique geometries which differentiate it from other weapons. Due to the different varieties of keris manufactured in various regions, it is necessary to develop a method of measuring the geometry of the keris, to assist in its identification process as well as to preserve its original shape. This research presents a method of creating a model representative of the keris using 3D scanning technique. The scanning method was illustrated for the measurement of three geometrically simple to complex keris, namely Lurus luk 1, Naga Kikik luk 3, and Sugguh Tamu luk 7. Consequently, geometric accuracy was measured by comparing the results of manual measurements to the coordinates of the 3D model, and results have shown a relatively small error in the range of 0.47% to 3.92 %. The results are significant as it shows that the 3D scanning method may be used to assist in the identification of the different varieties of keris.
Denoising biomedical signals via adaptive low-rank matrix representation by singular value decomposition using wavelets

Noise reduction of considerable recorded data, e.g., EEG, PPG signals, is significantly important in biomedical signal processing. Singular value decomposition (SVD) method has shown optimistic results in denoising biomedical dataset of images and signals via dimension reduction. However, a still challenge in SVD approach is to find the low-rank representation of the matrix obtained by matricification of the signal of interest adaptively which retrain the energy in signal subspace and neglect the energy in noise subspace. Here, we develop an adaptive rank estimation by the SVD for denoising purpose based on estimating the noise level σ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">est</inf> using the first level detail symmlet-wavelet's coefficients d1. The optimal rank is obtained at the point where the difference between the noisy and the reduced rank dataset is approximately below the estimated noise level. The proposed method has successfully estimated the optimal rank which gives the best denoising performance.
Multiclass Model for Quality of Service Using Machine Learning and Cloud Computing

Many web services on the internet have sprung up as a result of the rapid advancement of technology and the deployment of computing. Since users need many web services to achieve their requests, there are many web services that share the same functionality at different qualities. This requires the identification of the quality of web services. Using cloud computing, this paper proposes a model of multi-classification to predict the quality of web services by using machine learning techniques. There are four algorithms of machine learning applied in this work: Multiclass Logistic Regression, Multiclass Decision Forest (DF), Multiclass Decision Jungle (DJ), and Multiclass Neural Network (NN), After comparing the results, it has been found that the Multiclass Neural Network obtained the highest overall accuracy and average accuracy. By using features selection and normalization in this work and compare the algorithms. The selection of the best model is followed by the creation of a web service for the prediction of quality using Azure ML studio.
Convergence Analysis of Gravity-Based Clustering (GBC) Algorithm

Clustering algorithms have been adopted in a wide range of applications in big data and pattern recognition. Optimized clustering is generally known as an NP-hard problem, and the convergence time is hard to determine, which results in challenging issues in deploying clustering methods in resource-constrained systems. In this paper, we present a mathematical framework to prove the convergence of the Gravity Based Clustering (GBC) algorithm, which is a recently introduced centroid-based clustering method. First, we provide an analysis of the convergence of cluster centers generated by GBC in the Euclidean norm. Following this convergence analysis, we introduce cluster sets as random variables and verify the convergence of cluster sets in probability. Then, in addition to GBC, we show that this framework can also be applied to model the convergence of the cluster sets generated by any centroid-based clustering algorithm with convergent cluster centers. To demonstrate the accuracy of our framework, we carry out extensive experiments with several benchmarking datasets. Moreover, the values of parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> and ∊, which play the key role in the convergence rate of GBC, are determined. We compare the convergence rate of the K-means and the C-means methods with GBC, where we use the best values of the parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> and ∊ to implement GBC. Our results point out that GBC is faster than the baseline methods in several datasets.
Nonlinear Control of Hexarotor System Using Proportional Derivative Sliding Mode Controller (PD-SMC)

An Unmanned Aerial Vehicle (UAV) or Uncrewed Aerial Vehicle is a multirotor type of vehicle and is commonly known as a drone. Hexarotor type of UAV has six rotors and has several characteristics that give more operational advantages over lower rotors of UAV. This paper presents the mathematical modeling of the hexarotor system with the Proportional Derivative Sliding Mode Controller (PD-SMC) approach as the nonlinear controller. The mathematical model of the UAV’s body dynamics was modeled using the Newtonian method. This research implemented the SMC controller to the hexarotor system and coupled it with PD as the sliding surface for the attitudes controller. For comparison, Proportional Integral Derivative (PID), PD, and Linear Quadratic Regulator (LQR) controllers were also applied to the hexarotor system. Hence, better attitudes controller performances were achieved using the coupled controller, which is the PD-SMC controller. The performances were analyzed in percentage overshoot, settling time, rise time, and steady-state error. Matlab Simulink simulation was used throughout the research to measure the performances of hexarotor. As a result, for roll angle, rise time was 0.06 s, settling time was 0.50 s, percentage of overshoot was 0.0002%, and the steady-state error was 0.0001. In conclusion, PD-SMC shows the best stabilization controller for the hexarotor system with almost zero overshoot, zero steady-state errors, and faster settling times, and faster rising time.
Investigating Genetic Network Programming for Multiple Nest Foraging

Genetic Network Programming is a relatively unexplored evolutionary algorithm, particularly for more advanced tasks. Foraging is a challenging domain within swarm robotics, since it requires an aptitude for multiple rudimentary behaviours. The work herein thus investigates the application of Genetic Network Programming for multiple nest foraging. Further, a variant of Genetic Network Programming, which incorporates neural network benefits is proposed and evaluated. The results are compared to state-of-the-art foraging algorithms including the generic Neuro-evolution of Augmented Technologies and Novelty Search algorithms and the more application specific Multiple-Place Foraging Algorithm. Results indicate that Genetic Network Programming shows promise.
On the Innocence and Determinacy of Plural Quantification

Abstract Plural logic is widely assumed to have two important virtues: ontological innocence and determinacy. Both assumptions are problematic, as is shown by providing a Henkin-style semantics for plural logic that does not resort to sets but takes a plural term to have plural reference. This semantics gives rise to a generalized notion of ontological commitment, which is used to develop some ideas of earlier critics of the alleged ontological innocence of plural logic.
Educational Aids for Teaching Hacking Principles to Strengthen Computational Thinking Skills to Elementary

Modern society is changing into an era of digital transformation with the development of advanced information and communication technology (ICT) and information systems. Various information is digitized and stored, and the value of information is increasing. As the value of information increases, hackers' techniques to take advantage of unfair profits using them are subdivided and advanced, causing unexpected big problems in society. In particular, it was noted that cases in which elementary school students, a digital native generation, are often reported to be involved in crimes. Therefore, based on the basic concepts and principles of computing for elementary school students, we developed a learning teaching tool that can provide hacking-related education in an interesting way using gamification based on computing thinking ability that can efficiently solve unexpected problems. The learning goal was set for the development of educational aids by analyzing the cyber-attack process, and the game material was selected by applying the information security hacking principle learning to Samuel Livingstone's simulation game model. Then, we set game figures, roles, resources, external factors, and goals of the educational game and designed game boards, mission papers, and worksheets. We also established the game rules using the game data designed. Lastly, we found complementary points with security experts and education experts to supplement the learning aid while running the game. This paper helped learners to understand the security equipment applied in the network environment. In the end, we developed a teaching aid that can strengthen learners' computational thinking and understand hacking principles. This game ensures infinite scalability of the challenge by having students perform three missions per challenge. The educational aids proposed in this paper are limited at the primary level that elementary school students can understand. However, it can be designed and extended to infer interleaving by combining transaction and lock functions.
Simultaneous Causal Noise Removal for Causal Rule Discovery and Learning

Identifying cause and effect, or for that matter correlations, between events is critical for the operations of general AI or robotic systems. Being able to learn correlations or causalities enables the system to predict what future events may occur based on the currently observed events, or to know what actions can be taken to effect future consequences. This is useful for prediction and problem solving processes that are very important aspects of intelligent capabilities. However, the purported correlations or causalities are often accompanied by unrelated noisy events. The noise can intervene between the relevant events, and are termed diachronic causal or correlational noise. The noise can also occur concomitantly with the events of interest, and are termed simultaneous causal or correlational noise. While there has been previous work that addresses the issue of diachronic causal or correlational noise, simultaneous causal or correlational noise, especially in a symbolic rule learning process, has not been adequately addressed. This paper presents an algorithm to remove simultaneous causal noise in order to recover the correct causal rules involved. The same method can also be applied to a correlational situation.
Attacking the Knapsack Public-key Cryptosystem

Merkle–Hellman knapsack cryptosystem is a public-key cryptosystem, which means, two keys are used, a public key for the encryption and a private key for the decryption. This cryptosystem is not secure against the cryptosystem attacks, such as LLL Algorithm. In this paper, we introduce a new study to attack the knapsack problem. This study is based on discussing certain facts regarding the public key to extract the multiplier. Also, based on some facts we can find the modulus that is used in the cryptosystem. Therefore, the experimental outcome shows that this method is easier and faster to attack the knapsack cryptosystem.
Reinforcement Learning-Based Differential Evolution for Global Optimization

Reinforcement learning is a computational approach that mimics learning from interaction and supplements the existing supervised and unsupervised learning methods within the machine learning field. It bases on the mapping of a given situation to the action, and each action is evaluated by a reward. Of crucial concern, here is that the mapping is performed using suitable policies that correspond to a set of the so-called psychological stimulus-response rules (associations). However, in reinforcement learning, we are not interested in immediate rewards, but in a value function that specifies how good the rewards were in the long run. Reinforcement learning differential evolution is proposed in this study. On the one hand, a Q-learning algorithm capable of ensuring the good behavior of the evolutionary search process by explicit strategy exploration is engaged to collect the more prominent mutation strategies within an ensemble of strategies. On the other, the reinforcement learning mechanism selects among the strategies incorporated from the original L-SHADE algorithm using the ‘DE/current-to-pbest/1/bin’ mutation strategy toward the iL-SHADE to jSO using the ‘DE/current-to-pbest-w/1/bin’ mutation strategies. Testing the proposed RL-SHADE algorithm was conducted on the well-established function benchmark suites from the popular CEC special session/competition on real-parameter single-objective optimization during the last decade, where three different benchmark suites were issued. We expected that the results of the proposed RL-SHADE algorithm would outperform the results of the three original algorithms in solving all the observed benchmarks.
Mimicking the Human Approach in the Game of Hive

While Deep Blue and AlphaGo make it seem like board games have been solved, there are still plenty of games for which no good game playing program exists. Hive is such a game. It is, combinatorically, of similar complexity as chess or Go, yet the rules of the game are such that current methods can barely beat a randomly playing agent. A major bottleneck for progress is the high branching factor of the game. We apply state of the art methods for which we develop new heuristics that are based on human domain-knowledge, attempting to improve upon the current dire state of Hive agents. Our methods have improved playing strength compared to the state of the art, although our AI still fails against actual Humans. We also find that, while in most board games, brute force or deep learning approaches work best, in Hive, an approach based on mimicking human knowledge outperforms these other approaches, including Monte Carlo Tree Search or Deep Reinforcement Learning. Future work will show if this anomalous situation is inherent to the game.
Image Processing for Improvement of Facial Keypoints Detector

This paper discusses the improvement of facial detection algorithms using the DCT algorithm and image processing. Face key point is very needed in the face recognition system. Some important factors that have effects to detect its result are noise and illuminations. These two factors can be overcome by eliminating some DCT coefficients, both high and low. However, after handling the problem, most likely the image quality will become decrease, which will adversely influence the performance of the feature detector algorithm. Therefore, it is very important to test the performance of the feature detector algorithm on images that are implemented noise and illumination handling and how to improve the quality again. This research implemented Discrete Cosine Transform (DCT), by eliminating the high and low coefficient because there is noise and illumination. However, it is not known at what coefficient level is the most effective, so testing in this study was carried out. Four deblurring algorithms are tested in this research, Blind Deconvolution, Wiener Filter Deconvolution, Lucy-Richardson Deconvolution, and Regularization Deconvolution. And tested the CLAHE algorithm to overcome the effect of removing low coefficient DCT. The best coefficient value to be removed at the DCT frequency is 0.75 with the best SURF algorithm, without the use of other algorithms. Also, the highest F-score is produced by the SURF detector at removing DCT low frequency in combination with the CLAHE algorithm. With the most ideal coefficient of 0.25.
Rotation Invariance for Offline Handwritten Chemical Organic Ring Structure Symbols Recognition

Auto grading is an instruction tool which could reduce teachers' burden, provide students with instant feedback and support highly personalized learning. One key technique is to recognize students' handwritten assignments. This work will be focused on the task of recognizing handwritten chemical organic ring structure symbols which exhibit the characteristic of rotational symmetry. Due to the global parameter sharing mechanism and pooling operation, convolutional neural networks (CNNs) have the power to learn translation-invariance features. However, the design of the standard CNN itself does not specifically consider the rotation invariance. In this paper, we explore different methods to improve the property of rotation invariance of CNN and apply them for semantic recognition of handwritten chemical organic ring structure symbols. The first one is a test-time augmentation method with voting strategy without modifications to the network architecture. For the second method, we add 2 new layers into the model to endow it with the property of rotation invariance. These 2 proposed methods are evaluated on a self-collected data set and achieve the recognition aecuracy of 98.75% and 93.125% respectively.
Presenting a Protocol to Increase IOT-Based Security

The Internet of Things (IoT) has expanded access to information technology by combining both digital and physical fields as an emerging technology. IoT will increasingly overshadow human life as it becomes more pervasive. IoT will be applied to important areas of the national economy, such as health care and medical care, and smart transportation. Hence, data security in IoT must be met with highlights such as distinguishing proof, unwavering quality, integration, and verifiable, and so on. Security within the field of IoT is more vital in terms of openness and reliance. Security is an critical issue for IoT applications and proceeds to confront major challenges. This think about pointed to supply a convention to extend IoT-based security. The method proposed in this paper is to show modern conveyance engineering to extend IoT security. This unused design is based on a blockchain and can be actualized as a dispersed design in all layers of IoT. The findings of recreations and usage in genuine IoT scenarios appear that the execution of the proposed security conventions can be proficient and viable within the productivity and adequacy of distinctive levels of security and IoT accessibility.