Reviewerly/exHarmony huggingface dataset:
1-Norm twin random vector functional link networks based on Universum data for leaf disease detection

Due to rapid climate change and man-made activities, the types of leaf diseases are gradually increasing. As a result, taking the essential measures to recognize and diagnose the problem becomes a top priority. Though manual techniques for identifying leaf disease have been used in the past, research-based studies may produce a better result and overcome the disadvantages of the manual process. Nowadays, the random vector functional link (RVFL) network gaining popularity day by day due to the better generalization ability for various applications. In the spirit of the popular twin support vector machine (TSVM) and the twin variant of the random vector functional link (TRVFL) network, a novel twin RVFL (TRVFL1norm) is suggested in this work. TRVFL1norm overcomes the inherent limitation of TRVFL, which loses the sparsity due to the use of 2 norm and also gives a high penalty to outlier points which leads to degrade the generalization performance. Moreover, it is well known that Universum data can be used as prior knowledge in the training model. Therefore, to make the model more robust and generalized, we propose another model, TRVFL1norm based on the Universum data (UTRVFL1norm). UTRVFL1norm possesses the advantages of both 1-norm as well as Universum data. For both TRVFL1norm and UTRVFL1norm, few of the components in the output matrix will be zero due to sparsity hence, the resultant classifier must depend on the lesser output nodes compared to TRVFL. To verify model’s performance, we have compared the classification performance of proposed TRVFL1norm and UTRVFL1norm models with support vector machine (SVM), Universum SVM (USVM), twin support vector machine (TSVM), Universum TSVM (UTSVM), random vector functional link (RVFL), multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN) on few interesting benchmark datasets. Further, we have also checked the performance of TRVFL1norm and UTRVFL1norm for leaf disease detection as one of the possible applications. The experimental results indicate the efficiency and applicability of the proposed models.
SAR ship detection based on salience region extraction and multi-branch attention

Ship detection of synthetic aperture radar (SAR) images has received much attention in the field of military and people's livelihood. The radar pulse signals reflected by buildings and sea clutter would reduce the salience of ships in images, making ship features blurrier. This leads to interference and erroneous judgments in SAR ship detection. To solve this problem, a novel SAR ship detection method based on salience region extraction (SRE) and multi-branch attention (MBA) is proposed in this paper. The designed SRE module extracts all regions where ships may exist according to the maximum inter-class variance, and filters out irrelevant background information. Then, the proposed MBA module is used to enhance the expressive ability of ship features, so as to improve the salience of the ship features. Extensive comparison experiments have been conducted to prove the effectiveness of SRE and MBA modules. The average precision (AP0.5) is increased by 3.20% and 2.13% through SRE module and MBA module, respectively. The proposed method could achieve 0.8966 and 0.9697 in AP0.5for inshore and offshore scenes, which gives the best results.
Moderation, Networks, and Anti-Social Behavior Online

Major open platforms, such as Facebook, Twitter, Instagram, and Tik Tok, are bombarded with postings that violate platform community standards, offend societal norms, and cause harm to individuals and groups. To manage such sites requires identification of content and behavior that is anti-social and action to remove content and sanction posters. This process is not as straightforward as it seems: what is offensive and to whom varies by individual, group, and community; what action to take depends on stated standards, community expectations, and the extent of the offense; conversations can create and sustain anti-social behavior (ASB); networks of individuals can launch coordinated attacks; and fake accounts can side-step sanctioning behavior. In meeting the challenges of moderating extreme content, two guiding questions stand out: how do we define and identify ASB online? And, given the quantity and nuances of offensive content: how do we make the best use of automation and humans in the management of offending content and ASB? To address these questions, existing studies on ASB online were reviewed, and a detailed examination was made of social media moderation practices on major media. Pros and cons of automated and human review are discussed in a framework of three layers: environment, community, and crowd. Throughout, the article adds attention to the network impact of ASB, emphasizing the way ASB builds a relation between perpetrator(s) and victim(s), and can make ASB more or less offensive.
ON THE <i>N</i>-POINT CORRELATION OF VAN DER CORPUT SEQUENCES

Abstract We derive an explicit formula for the N -point correlation $F_N(s)$ of the van der Corput sequence in base $2$ for all $N \in \mathbb {N}$ and $s \geq 0$ . The formula can be evaluated without explicit knowledge about the elements of the van der Corput sequence. This constitutes the first example of an exact closed-form expression of $F_N(s)$ for all $N \in \mathbb {N}$ and all $s \geq 0$ which does not require explicit knowledge about the involved sequence. Moreover, it can be immediately read off that $\lim _{N \to \infty } F_N(s)$ exists only for $0 \leq s \leq 1/2$ .
Pedestrian Detection Based on Infrared Imaging Through Gray Transformation and Deep Learning

Infrared images are widely used in night security. Compared with visible images, infrared images have lower pixels and poor imaging quality. The accuracy of pedestrian detection will be extremely poor. Considering these complex situations when the ambient temperature is high or complex, we combine the object detection with the unique temperature information of infrared image and propose a better method that can significantly improve the detection accuracy of infrared image in above situations. We carry out a variety of different preprocessing of the image and then carry out pedestrian detection of the processed image. This method has achieved a higher recognition accuracy in our own infrared image dataset. This will be applied in night security automation.
Evaluation of Intrinsic Explainable Reinforcement Learning in Remote Electrical Tilt Optimization

This paper empirically evaluates two intrinsic Explainable Reinforcement Learning (XRL) algorithms on the Remote Electrical Tilt (RET) optimization problem. In RET optimization, where the electrical downtilt of the antennas in a cellular network is controlled to optimize coverage and capacity, explanations are necessary to understand the reasons behind a specific adjustment. First, we formulate the RET problem in the reinforcement learning (RL) framework and describe how we apply Decomposed Reward Deep Q Network (drDQN) and Linear ModelU-Tree (LMUT), which are two state-of-the-art XRL algorithms. Then, we train and test such agents in a realistic simulated network. Our results highlight both advantages and disadvantages of the algorithms. DrDQN provides intuitive contrastive local explanations for the agent’s decisions to adjust the downtilt of an antenna, while achieving the same performance as the original DQN algorithm. LMUT reaches high performance while employing a fully transparent linear model capable of generating both local and global explanations. On the other hand, drDQN adds a constraint on the reward design that might be problematic for the specification of the objective, whereas LMUT could generate misleading global feature importance and needs additional developments to provide more user-interpretable local explanations.
An FCM-Based Image De-Noising with Spatial Statistics Pilot Study

Image de-noising is an important scheme that makes an image visually prominent and obtains enough useful information to produce a clear image. Many applications have been developed for effective noise suppression that produce good image quality. This study assumed that a residual image consisted of noise with edges produced by subtracting the original image with a low-pass-filter-smoothed image. The Moran statistics were then used to measure the variation in spatial information in residual images and we then used this information as feature data input into the Fuzzy C-means (FCM) algorithm. Three clusters were pre-assumed for FCM in this work: they were heavy, medium, and less noisy areas. The rates for each position partially belonged to each cluster determined using an FCM membership function. Each pixel in a noisy image was assumed in de-noising processing as a linear combination of the product of three de-noised images with membership functions in the same position. Average filters with different windows and a Gaussian filter were a priori applied to this noisy image to create three de-noised versions. The results showed that this scheme worked better than the non-adaptive smoothing. This scheme‘s performance was evaluated and compared to the bilateral filter and non-local means (NLM) using the peak signal to noise ratio (PSNR) and structure similarity index measure (SSIM). The developed scheme is a pilot study. Further future studies are needed on the optimized number of clusters and smoother versions used in linear combination.
Sorting of Similar Shaped Objects Using Object Detection Method

To detect only specific similar shaped objects with YOLO, the paper proposes a series of procedures as an automatic detection method. The method is applied to quality control of precious paint products. Paints are produced through the milling process of powders. Tiny powders randomly attach to larger powders, called asteroid powders. These are degrading the quality of the product. The asteroid powders should be detected automatically by other powders similar in shape to them. In the paper, various YOLO models which differ in size are constructed by expanding the number of evaluators and data. YOLO models output class names and coordinate information. It needs to be optimized to evaluate performance. The method optimized some information and evaluated the performance of the models through validation. The paper discusses the evaluation characteristics of the evaluator and the model. In addition, it provides feedback to the evaluator, making the prediction results visible and understandable to the evaluator.
FPGA Embedded Signal Conditioning System Based on Fuzzy Logic for Temperature Measurement

Temperature is an important physical variable that is measured and controlled in many manufacturing stages in industrial processes. Commonly, a temperature measuring digital system requires additional signal conditioning circuitry, which represents a restriction for small area, weight, and power consumption applications. An FPGA embedded signal conditioning system based on fuzzy logic for temperature measurement is proposed in this paper. In this way external signal conditioning hardware is not needed for the temperature measurement. Particularly, a Takagi–Sugeno-Kang fuzzy inference system was employed. The temperature measurement system was implemented on a Spartan 6 FPGA using an LM35 temperature sensor. The sampled temperature value is real time monitored and displayed on a PC screen through RS-232 serial communication protocol. The measurement system using the proposed fuzzy logic signal conditioning was experimentally tested within a temperature values range, which was compared with the obtained values when it was not used. According to the statistical analysis of the obtained results, an average error reduction from 305.15% to 2% and a variance error reduction from 1851.3% up to 2.64% were achieved. The described fuzzy logic system can be extended to conditioning other analog signals by just adjusting the membership functions coefficients, which is left as future work by the authors.
An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.
Alignment of Learning Outcomes and Assessment

Too often there can be data for inputs and outputs, but there is often missing information for intermediate phases. Here then we show the development of intermediate parts of the design of an assessment process which aligns with the learning outcomes of a unit or subject within a course. This is prior to the design of any soft computing for implementing and automating the process. These unit learning outcomes are subsets of the learning outcomes of the course of which the unit forms a part, and these course learning outcomes reflect the graduate attribute of the institution: that is, those specific personal and professional qualities which a graduate of that institution will exhibit.
A Study on KOSPI Return Volatility Estimation Using Heterscedastic Time Series Model

In this study, return volatility prediction model was estimated by converting composite stock index into return variables. As for the heteroscedasticity test for the return, the Q-test and the LM-test showed that heteroscedasticity existed at the 2nd and 7th lags. Therefore, the order was determined using the SBC statistic, the parameters were estimated using the ARCH (2) model, and the model fit test was conducted. The parameter estimates of the ARCH(2) model were statistically significant, but the residual analysis showed that autocorrelation existed and did not satisfy the normality test. In the results of applying the GARCH (1,1) model, the parameter estimates, residual analysis, and normality were found to be satisfactory. Therefore, the GARCH (1,1) model was determined as the KOSPI return volatility estimation model and volatility was predicted. Volatility was predicted to show high volatility in the period of January and February 2023, and to stay calm after March for a long-term. Continuing a calm state means that the probability of large volatility is high. Therefore, those in charge of government agencies need to check system improvement or policy establishment and make efforts to identify trends in volatility by market.
Measuring the Sensitivity of Image Captioning Metrics to Caption Perturbations

Image captioning systems based on deep neural networks have made significant progress in generating image descriptions in fluent language. Despite the achieved success, one of the main challenges remains automatic evaluation of the results. Recently, research efforts focused on the development of increasingly sophisticated automatic metrics have intensified significantly, so finding an appropriate way to compare metrics has also gained importance. A good automatic metric should match the ratings human evaluators would assign to captions, so a common way to evaluate them is to compute the correlation with human judgments. In this paper, we chose a different approach that could complement the results of correlation analysis in order to get a more complete insight into a particular metric. In this regard, we analyzed how various caption perturbations affect the scores of selected image captioning metrics. We constructed a dataset of various caption modifications and analyzed the changes in scores resulting from such perturbations.
Customer Review and Customer Relationship Management Analysis of B2B Product Purchase Decisions

Customer reviews, which are often called testimonials, and relationship management are two things that are believed to be important factors in purchasing decisions in the digital marketing era. However, existing research is still dominated by end-user objects or individual consumers. This study aims to determine the effect of customer reviews (Testimonies) and customer relationship management on purchasing decisions made by organizations or companies. Using quantitative research methods. with 60 respondents who are customer companies from Brightindo (suppliers of cleaning chemicals). Data was collected through questionnaires in google forms which were sent to respondents and processed using SPSS software. The results of data processing show that customer review has an effect and is significant on purchasing decisions, for customer relationships it has no effect and is not significant on purchasing decisions, for customer reviews and customer relationships it simultaneously affects purchasing decisions.
Adaptation and Assimilation Measurement in Human-Computer Interaction: Implementation of Sustainable Society

This article has been retracted upon request of the author. Data has been used without permission from the author’s research team. This compromises the integrity of the research. The proceedings Editors and the Publisher are not responsible for the mistake. The Publisher approved the retraction.
Reward Function and Configuration Parameters in Machine Learning of a Four-Legged Walking Robot

In contemporary times, the use of walking robots is gaining increasing popularity and is prevalent in various industries. The ability to navigate challenging terrains is one of the advantages that they have over other types of robots, but they also require more intricate control mechanisms. One way to simplify this issue is to take advantage of artificial intelligence through reinforcement learning. The reward function is one of the conditions that governs how learning takes place, determining what actions the agent is willing to take based on the collected data. Another aspect to consider is the predetermined values contained in the configuration file, which describe the course of the training. The correct tuning of them is crucial for achieving satisfactory results in the teaching process. The initial phase of the investigation involved assessing the currently prevalent forms of kinematics for walking robots. Based on this evaluation, the most suitable design was selected. Subsequently, the Unity3D development environment was configured using an ML-Agents toolkit, which supports machine learning. During the experiment, the impacts of the values defined in the configuration file and the form of the reward function on the course of training were examined. Movement algorithms were developed for various modifications for learning to use artificial neural networks.
The Powerful Effect of Social Media and The Mental toughness on Achievement Motivation of the Students by Blended Learning in the Digital Era

The background of this research is the problem that in the digital era students follow the learning process with a blended learning system. In the blended learning, there are times when students and lecturer meet face to face, but there are times that the students study independently through forum discussions and assignments, both personal and group in online learning system. In the blended learning system, the achievement motivation of the students is very important. Both face-to-face and online learning in independent learning are influenced by social media and the mental toughness of the students. Therefore, the aim of this research is to show how powerful the influence of social media and mental toughness of the students on the achievement motivation of the students in blended learning system in the digital era. By using quantitative methods and literature review, this study concluded that Social Media and Mental toughness of the Students have a significant influence on the achievement motivation of the students. The implication is that the students must be encouraged to be able to use Social Media as well as possible to support their learning processes and outcomes and the mental toughness of the students must be increased, so that the mental toughness of the students can increase the achievement motivation of the students in the learning process using with the blended learning system in the digital era.
Garudeya Illustrated Digital Book as a Supporting Media to Children Learning

The lack of children’s reading interest in historical heritage books is because the visualization of books provided by the school library is less attractive. This study aimed to design a digital illustrated Garudeya book as a learning support medium for elementary school students in Malang City. This study uses the design thinking method by involving the reader in the empathize, define, ideate, prototype, and test stages. The results of data analysis obtained the word playful as a design concept originating from the target audience of children. This concept is used in most digital book designs illustrated by The Story of Garudeya. The Garudeya Storybook tells the story of Garudeya, who saves his mother, Sang Winata, from being enslaved by his younger brother Sang Kadru because he lost a bet when guessing the color of a horse’s tail. The Kadru deliberately cheated by painting the color of the ponytail black to win the bet. This illustrated digital book is essential to use as a learning support medium so students can easily understand Indonesian heritage. In addition, it can also be used as a digital reference to introduce Indonesian cultural heritage globally.
Investigating Imperfect Cloning for Extending Quantum Communication Capabilities

Quantum computing allows the implementation of powerful algorithms with enormous computing capabilities and promises a secure quantum Internet. Despite the advantages brought by quantum communication, certain communication paradigms are impossible or cannot be completely implemented due to the no-cloning theorem. Qubit retransmission for reliable communications and point-to-multipoint quantum communication (QP2MP) are among them. In this paper, we investigate whether a Universal Quantum Copying Machine (UQCM) generating imperfect copies of qubits can help. Specifically, we propose the Quantum Automatic Repeat Request (QARQ) protocol, which is based on its classical variant, as well as to perform QP2MP communication using imperfect clones. Note that the availability of these protocols might foster the development of new distributed quantum computing applications. As current quantum devices are noisy and they decohere qubits, we analyze these two protocols under the presence of various sources of noise. Three major quantum technologies are studied for these protocols: direct transmission (DT), teleportation (TP), and telecloning (TC). The Nitrogen-Vacancy (NV) center platform is used to create simulation models. Results show that TC outperforms TP and DT in terms of fidelity in both QARQ and QP2MP, although it is the most complex one in terms of quantum cost. A numerical study shows that the QARQ protocol significantly improves qubit recovery and that creating more clones does not always improve qubit recovery.