Reviewerly/exHarmony huggingface dataset:
Impact of Chemical Properties Crumb Rubber with Different Waste Tire in Hot Mix Asphalt Design

The purpose of this research is to assess the impact of chemical properties of rubber crumbs for various types of waste tires mixed in design asphalt. This paper gives a scientific contribution of the chemical properties in relationship with the mix design asphalt. Many tires are replaced and dumped as waste in many parts of the world every year. This has created severe environmental problems such as pollution which governments of many nations spend a lot of budgets to manage every year. However, incorporating rubber crumbs as part of asphalt construction would help solve the environmental pollution problem in the world. The analysis is important because chemical properties such as Zn, Carbon, and Mg, have different characteristics that must be considered in making the best asphalt mix design. This would help to achieve alternate solutions. Furthermore, Utilizing XRD, FTIR, and TGA, the granules of several kinds of waste tire were examined. To achieve the best mix design for the asphalt, the results had many viewpoints. The TGA result demonstrated that all waste tires decomposed at the temperature of 500°C. However, the truck tire waste had the highest maximum thermal decomposition temperature of 587°C. The FTIR result showed that all the tire waste has similar chemical functional groups of polystyrene. The result of XRF showed that truck tire waste possesses the highest Zn content which is considered as dangerous element to the environment.
​​TECHNICAL VIEW OF EDUCATIONAL PHILOSOPHY IN THE TECHNICAL AGE​

In education philosophy point of view, technology not only refers to the meaning of various education skills, skills, methods and means and all kinds of materialized tools, equipment and information media system, and means the verb in the meaning of technology process, is noun technology of education, bright and structure, is "education technology", for the educators to use knowledge, skills with various tools, media technology system knowledge, cultivating students emotion, hone students will and fitness process of student body, it has the educational, emotional and artistic characteristics. According to different standards, educational technology can be at least divided into original technology and interventional technology, "hard" technology and "soft" technology, general technology and special technology. The practice of educational philosophy and technology should uphold the original intention of education, "improve technical quality" for technology, "soften" hard technology; achieve man-machine collaboration, and make technology serve the overall development of people.
Unveiling the Spectrum of Hybrid Work in Software Engineering: Research Directions

Abstract Despite the heated debate on whether hybrid work would be the new normal in the post-pandemic world, it is an exciting, if not new, research phenomenon for Software Engineering (SE) researchers. Hybrid work has a wide range of dimensions and aspects that need exploration and understanding for modern software companies to truly benefit from it. In this paper, we propose a framework that incorporates multiple perspectives on hybrid work in software engineering. We applied the framework to group a set of research topics collected at the GoHyb (Global and Hybrid Work in Software Engineering) workshop collocated with the XP2023 conference, and extrapolated some new topics based on the framework, to demonstrate various research questions that can be asked on hybrid work in software engineering. We conclude the paper with a remark on the need of more contextual and nuanced understanding of hybrid work in software engineering.
FAFVTC: A Real-Time Network for Vehicle Tracking and Counting

In a complex traffic environment, the detection and association of moving objects can easily lead to tracking errors. This work proposes a novel attention mechanism called MCSA, which integrates multi-spectral attention and spatial attention. Additionally, a fast and anchor-free real-time vehicle tracking and counting model named FAFVTC is constructed. MCSA is used for extracting the features of moving objects, while FAFVTC is able to better detect and associate these objects. The effectiveness of the FAFVTC method is verified on the UA-DETRAC dataset. FAFVTC outperforms existing techniques with a 1.3 improvement in the PR-MOTA metric and a 2.16 improvement in the MOTA metric. The average tracking speed achieved is 27.9 FPS. The experimental results demonstrate that the proposed approach enables fast and accurate vehicle tracking and counting.
Approaching Explainable Artificial Intelligence Methods in the Diagnosis of Iron Deficiency Anemia Using Blood Parameters

Anemia is a global health disorder diagnosed by observing blood parameters. It is a tedious and time-consuming method for healthcare workers to analyze the data manually and may also lead to mistakes. This paper proposes a novel method to understand the impact of blood parameters in diagnosing anemia. Machine learning methods have been used to classify the data, and the impact of the attributes was explained using explainable AI tools to bring transparency and trust to the architectures. XAI helps in ensuring fairness, accountability, and transparency. The models show a high accuracy of 80-100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> • The beeswarm plot explained the impact of the various attributes present in a complete blood count in the diagnosis of iron deficiency anemia. The methods introduced help in the quick diagnosis of anemia and save time for healthcare professionals. Improvement in the current technology in collaboration with healthcare workers will lead the medical domain to new heights.
A NON-PARAMETRIC APPROACH TO EXPLAINABLE ARTIFICIAL INTELLIGENCE AND ITS APPLICATION IN MEDICINE

The paper proposes a non-parametrical approach to explainable artificial intelligence based on the compactness postulate, which states that objects of one class in the feature space are, as a rule, located closer to each other than to objects of other classes. Objects are considered similar if they are located close to each other in the feature space. Meanwhile, the properties of objects in real life are often random values. Such objects are not described by a vector of features, but by a random sample or several samples of features, and the postulate of compactness should be replaced by the postulate of statistical homogeneity. Objects are considered statistically homogeneous if their features obey the same distributions. The paper describes a non-parametric measure of homogeneity and an illustration of its use in medical applications, in particular for the diagnosis of breast cancer within the framework of similarity-based explainable artificial intelligence.For comparison, the results of diagnostics of the same data set using deep learning of an artificial neural network are given. We formulate new statistical postulates of machine learning and propose to consider a machine learning algorithm as explanatory and interpretable if it satisfies these postulates.
Intelligent Sharding Decision for Blockchain-Enabled Industrial IoT Based on A3C Approach

As an essential concept and technology, the Industrial Internet of Things (IIoT) is a feasible way for the advancement of industrial digitization, networking and intelligence. However, it brings serious security and privacy risks to industrial data. Blockchain technology can address security and privacy risks into the IIoT effectively, but there are a few nonnegligible questions in the current researches: 1) The scalability of the conventional blockchain system is extremely limited, making it unable to handle the rapid uploading of massive data in the IIoT. 2) The large-scale nature of IIoT and complex consensus procedure of blockchain result in excessive computational overhead. 3) The balance between security and computational efficiency is often ignored. To address these problems, we propose the concept of sharding decision blockchain innovatively and formulate the optimization problem as a Markov decision process (MDP). Besides, the combining of mobile edge computing (MEC) and cloud computing is empowered the computational efficiency in the proposed scheme. An asynchronous advantage actor-critic (A3C) approach is taken into account and used to solve the optimization problem. Experimental results indicate that our proposed scheme provides a significant improvement on the IIoT versus other solutions.
Dynamics of Digitalisation and its Implications on the Nigerian Financial System Performance

This paper examines the impact of digitalisation on the performance of Deposit Money Banks (DMBs) in Nigeria. Using the Dynamic Ordinary Least Squares (DOLS) method, the results revealed that Web pay, and the Nigeria Inter-Bank Settlement System (NIBSS), Instant Payment (NIP) transactions, have a significant and positive impact on the Capital Adequacy Ratio expressed as a percentage of risk-weighted credit of banks’ exposure. However, Point of Sale (PoS) and Web payments (Web pay) transactions showed a positive, but insignificant impact on the Liquidity Ratio of banks for the period, indicating that both are sources of liquidity in the financial system, but not significant. Also, NIP showed a positive, but not significant impact on the Asset Quality of banks. Further, an insignificant negative effect of PoS transactions and NIP on the profitability of banks in Nigeria was also established. Importantly, mobile payment transactions exerted a positive, significant impact on banks’ profitability during the study period. These findings presuppose those technological innovations in the banking sector of Nigeria’s economy are key to the profitability of banks. Therefore, DMBs in Nigeria should continue to leverage technological innovations, using the various digital banking channels, especially, Web pay, NIP, and mobile payments to boost their performance.
Adaptive milliseconds tracking and zooming optics based on a high-speed gaze controller and liquid lenses

The high-speed gaze and high resolution are critical factors for actual monitoring systems. However, the conventional method cannot track and zoom as fast as expected due to the larger inertia and it results in a low resolution due to the digital zoom. In this paper, we proposed a high-speed tracking and zooming optics that is coaxial designed and with an active tracking unit and an optical zooming unit to overcome the above issues. The tracking unit always tracks the object in the center of view by a pan-tilt mirror controller and a visual feedback tracking algorithm within 4 milliseconds response order. The zooming unit can continuously change the magnification from 1X to 2X by three liquid lenses within milliseconds. Besides, the zooming unit provides a compensation algorithm to achieve accurate zoom and focus.
The Chemical Engine Algorithm and Realization Based on Unreal Engine-4

The Chemical Engine is a new concept introduced by Nintendo company as a counterpart to the traditional physics engine in game development. However, Nintendo has not released any details of the Chemical Engine, also Nintendo blurred the definition between “chemical” and “physical”. Therefore, this paper clarifies the concept of physical engine and chemical engine in game development, then based on the definition, two chemical engine algorithms are proposed. One is called the “elemental energy” algorithm, which is based on Nintendo’s philosophy and optimized for future scalability, “elemental energy” can be widely used in general game scenarios. The second one is called the “factorization and properties” algorithm, which is more in line with the definition of chemistry in academics, this method can realistically render chemical reactions, but the realization is more difficult and too costly to use in game development. Therefore, this paper provides a specific means of implementation in the Unreal Engine 4 engine based on the elemental energy algorithm Through the analysis of the achievement and experiment, the cost and method of the “elemental energy” algorithm are reasonable. Therefore, the scheme is more practical in this scenario, and it could be widely used in commercial game development.
Children's Language According to Phonological and Morphological Aspects in Vocal Learning at PAUD Al-Karimah

This research is important to determine children's language acquisition and development according to studies of phonology and morphology in Indonesian language learning. The method used in this research is qualitative method with a case study approach. Through this qualitative method, the phonetic system of Edwin Omar Malik Erdogan (2.6 years) and Edward Nazhan Naik Erdogan (4 years), who are students at PAUD Al-Karimah, will be described. These two subjects were used as research samples from ten students to make the findings more accurate regarding learning outcomes and descriptive evidence of early childhood language acquisition at PAUD Al-Karimah. The result of this study was the Acquisition of phonology in their daily lives. Edwin and Edward obtained five vowel sounds Indonesians produced. The vowel sound consists of [a], [i], [u], [e], [o]. So it can be concluded that Edwin and Edward have mastered all vowel sounds, and there are consonant sounds, as many as 19 consonants Indonesian produced by Edwin and Edward. The consonant sounds consist of [b], [c], [d], [g], [h], [j], [l], [m], [n], [p], [s], [t], [w], [y], [G], [S], [ṭ], [?]. The consonant sounds that are not pronounced are [k], [f], [q], [r], [v], [x], [z].
IMPLEMENTATION OF C4.5 ALGORITHM FOR ASSESMENT OF COMMUNITY SATISFACTION

This Research applies the C4.5 Algorithm in deciding community satisfaction of sommunity services. Algorithm C4.5 is a classification algorithm with a decision tree technique that is well known and preferred because it has many advantages. These advantages, for example, can process numeric and discrete data, can handle missing attribute values, produce rules that are easy to interpret and the fastest. This research is a classification with the concept of data mining involving 20 community questionnaire data which are categorized as: cheap, expensive, appropriate, inappropriate, fast, long, comfortable, uncomfortable. There are four attributes that affect community satisfaction including: handling costs, service procedures, handling time and convenience. From the results of this research that has been conducted by researchers, it can be concluded. The customer satisfaction of the community services can be predicted and evaluated by utilizing data mining techniques using the C4.5 Algorithm to predict community satisfaction. This research also provided that C4.5 Algorihtm can be used to get high result of accuracy
Can personalized Virtual Reality be better than Guided Imagery in enhancing the impact of Progressive Muscle Relaxation Training?: A Pilot Randomized Controlled Study on an Italian university sample (Preprint)

Background: Empirical evidence has shown that virtual reality (VR) scenarios can increase the effects of relaxation techniques, reducing anxiety by enabling people to experience emotional conditions in more vivid settings. Objective: This pilot randomized controlled study aims to investigate whether the progressive muscle relaxation technique (PMRT) associated with a personalized scenario in VR promotes psychological well-being and facilitates the recall of relaxing images more than the standard complementary intervention that involves the integration of PMRT and guided imagery (GI). Methods: On the basis of a longitudinal, between-subject design, 72 university students were randomly exposed to one of two experimental conditions: (1) standard complementary procedure (PMRT and GI exposure) and (2) experimental procedure (PMRT and personalized VR exposure). Individuals were assessed by a therapist before and after 7 training sessions based on measures investigating anxiety, depression, quality of life, coping strategies, sense of presence, engagement, and side effects related to VR exposure. Heart rate data were also collected. Results: Differences in changes between the 2 groups after the in vivo PMRT session conducted by the psychotherapist (T1) were statistically significant for state anxiety (F1,67=30.56; P&lt;.001) and heart rate (F1,67=4.87; P=.01). Individuals in the VR group obtained lower scores both before (t67=−2.63; P=.01; Cohen d=0.91) and after (t67=−7.23; P&lt;.001; Cohen d=2.45) the relaxation session when it was self-administered by participants (T2). A significant reduction in perceived state anxiety at T1 and T2 was observed for both groups (P&lt;.001). After the VR experience, individuals reported feeling higher engagement in the experience than what was mentioned by participants in the GI group (F1,67=2.85; P=.03; ηp2=0.15), and they experienced the environment as more realistic (F1,67=4.38; P=.003; ηp2=0.21). No differences between groups regarding sense of presence were found (F1,67=1.99; P=.11; ηp2=0.11). Individuals exposed before to the VR scenario (T1) referred to perceiving the scenario recalled in-imagination at T2 as more realistic than what those in the GI group experienced (F1,67=3.21; P=.02; ηp2=0.12). The VR group had lower trait anxiety levels than the GI group after the relaxation session during session 7 (T2; t67=−2.43; P=.02). Conclusions: Personalized relaxing VR scenarios can contribute to improving relaxation and decreasing anxiety when integrated with PMRT as a complementary relaxation method. Trial Registration: ClinicalTrials.gov NCT05478941; https://classic.clinicaltrials.gov/ct2/show/NCT05478941
Understanding Metaverse Adoption Strategy from Perspective of Social Presence and Support Theories: The Moderating Role of Privacy Risks

From time-to-time technological development offers technologies that respond to environmental requirements but are more complex. Metaverse is one of the most modern technologies that helps the individual to enter into a realistic, tangible experience, which is considered a revolution in the digital field. Entering the metaverse environment comes with concern about one’s privacy. Such as the loss of personal data and the possibility of its transmission and use by others. In light of these obstacles, is it possible to utilize metaverse as an adopted strategy? This study aimed to test the possibility of adopting the metaverse strategy, by two main elements, which are social presence and social support. Taking into account privacy risks as an interactive variable. The study data were collected from (210) users and tested statistically by PLS-SEM approach to address the hypotheses of the study. A set of results was reached that showed the impact of social presence and social support on the adoption of metaverse. With the emergence of the impact of privacy risks in that relationship. The study concluded that adopting metaverse should take into account the privacy risks of users in dealing with digital technologies.
New research for detecting complex associations between variables with randomness

Many correlation analysis methods can capture a wide range of functional types of variables. However, the influence of uncertainty and distribution status in data is not considered, which leads to the neglect of the regularity information between variables, so that the correlation of variables that contain functional relationship but subject to specific distributions cannot be well identified. Therefore, a novel correlation analysis framework for detecting associations between variables with randomness (RVCR-CA) is proposed. The new method calculates the normalized RMSE to evaluate the degree of functional relationship between variables, calculates entropy difference to measure the degree of uncertainty in variables and constructs the copula function to evaluate the degree of dependence on random variables with distributions. Then, the weighted sum method is performed to the above three indicators to obtain the final correlation coefficient R. In the study, which considers the degree of functional relationship between variables, the uncertainty in variables and the degree of dependence on the variables containing distributions, cannot only measure the correlation of functional relationship variables with specific distributions, but also can better evaluate the correlation of variables without clear functional relationships. In experiments on the data with functional relationship between variables that contain specific distributions, UCI data and synthetic data, the results show that the proposed method has more comprehensive evaluation ability and better evaluation effect than the traditional method of correlation analysis.
Optimized Text Summarization Using Abstraction and Extraction

In today’s world, huge amount of digital data is generated each and every day. Text data is one of the most primary and important digital data that has seen a lot of evolution in the last decade. It contains a lot of important and sensitive information that needs to be efficiently summarized as to extract meaningful information from it. Text summarization refers to the method of developing a concise version of a text document that preserves the important information and the overall meaning of the source data. Automated summarization of text has emerged as a crucial method for efficiently identifying pertinent information from lengthy texts within a brief timeframe and with minimal exertion. There are, essentially, two kinds of procedures for text summarization, namely extractive and abstractive. The objective of this paper is to develop a method that integrates these two approaches in order to assess the effectiveness of the resulting model. Additionally, the aim is to provide a comprehensive and integrated review of both extractive and abstractive approaches to text summarization by means of a comparative empirical analysis.
A low-computational cost face recognition system for high accuracy privileged access management to support the development of smart laboratories

Nowadays, privileged access management (PAM) is important for the development of smart laboratories, especially in modern universities and research centers. A biometrics-based PAM such as a face identification system is needed to safeguard identities to define specialized capabilities beyond regular users while accessing the labs or the equipment. However, real-world PAM implementation faces high computational costs and a rigid centralized system, making its scalability and the depth of implementation limited. This study proposes a design of a PAM system using face identification technology and low-powered embedded systems. A prototype is made using Raspberry Pi 4 as a computing module and a webcam as a visual sensor. The design considers scalability and performance through an edge-computing scheme and a low-complexity algorithm. The design is developed through two-stage architecture i.e., face detection and face recognition. The face detection algorithm is designed through Haar cascade classifier (HCC). In addition, the face recognition algorithm is performed using local binary pattern histogram (LBPH). An analysis is provided by comparing the methodology with deep learning (DL)-based algorithm. The average accuracy using 5-fold cross-validation using LBPH is 3 – 10 % lower than those using the DL model. Although the accuracy using LBPH is slightly lower, in real-time applications, the prototype can recognize lab member faces at a maximum distance of one meter with a frame rate reaches 10 FPS i.e., higher than those achieved by the DL model. Furthermore, the system can be enhanced with a centralized supervisory system that connects to the edge computing node making its scalability more manageable.