Reviewerly/exHarmony huggingface dataset:
Interpretability is not Explainability: New Quantitative XAI Approach with a focus on Recommender Systems in Education

The field of eXplainable Artificial Intelligence faces challenges due to the absence of a widely accepted taxonomy that facilitates the quantitative evaluation of explainability in Machine Learning algorithms. In this paper, we propose a novel taxonomy that addresses the current gap in the literature by providing a clear and unambiguous understanding of the key concepts and relationships in XAI. Our approach is rooted in a systematic analysis of existing definitions and frameworks, with a focus on transparency, interpretability, completeness, complexity and understandability as essential dimensions of explainability. This comprehensive taxonomy aims to establish a shared vocabulary for future research. To demonstrate the utility of our proposed taxonomy, we examine a case study of a Recommender System designed to curate and recommend the most suitable online resources from MERLOT. By employing the SHAP package, we quantify and enhance the explainability of the RS within the context of our newly developed taxonomy.
Pemanfaatan Limbah Plastik Polyethylene Terephthalate (PET) dan Batok Kelapa Sebagai Bahan Baku Paving Block

In 2021 the total amount of waste from various types of waste in Indonesia alone will reach 68,5 milion tons. Based on these data, as much as 17 percent, or around 11,6 milion tons, was contributed by jenna R.Jambeck from the University of Geogia in 2010, there were 275 tons of plastic waste produced worlwide. Around 4,8-12,7 milion tons of them are waste and pollute the sea. Indonesia itself has a coastal population of 187,2 ,milion which annually produces plastic waste. Around 0,48-1,29 milion tonnes of plastic waste is thought to have polluted the oceans. To overcome this, proper management of plastic waste is needed, one of which is by making paving blocks made from plastic waste. The study aims to determine the characteristics of thr various composition paving blocks according to the Dquality standard, which is used for garden pavements and other uses. In this study, aratio of 1:1:1 was used for plastic, sand, and oil. Then coconut shells were added to the treatment P1 as much as 3%, P2 as much as 6%, P3 as much as 9%, and P4 as much as 12% of the paving block volume paving block quality standardD has requirements, namely the maximum value obtained from the results of the compressive strength test is 10 Mpa and water absorption is 10%. From the result of the research that i did, it was found that the variations in the P treatment were closestto the quality standard D, with a compressive strength test value of 5,84 Mpa. Keywords : Coconut Shells, Paving Blocks, PET, Plastic, Waste.
An enhanced fletcher-reeves-like conjugate gradient methods for image restoration

Noise is an unavoidable aspect of modern camera technology, causing a decline in the overall visual quality of the images. Efforts are underway to diminish noise without compromising essential image features like edges, corners, and other intricate structures. Numerous techniques have already been suggested by many researchers for noise reduction, each with its unique set of benefits and drawbacks. Denoising images is a basic challenge in image processing. We describe a two-phase approach for removing impulse noise in this study. The adaptive median filter (AMF) for salt-and-pepper noise identifies noise candidates in the first phase. The second step minimizes an edge-preserving regularization function using a novel hybrid conjugate gradient approach. To generate the new improved search direction, the new algorithm takes advantage of two well-known successful conjugate gradient techniques. The descent property and global convergence are proven for the new methods. The obtained numerical results reveal that, when applied to image restoration, the new algorithms are superior to the classical fletcher reeves (FR) method in the same domain in terms of maintaining image quality and efficiency.
Efficiency of recurrent neural networks for seasonal trended time series modelling

Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for modelling seasonal time series with trend. For this purpose, we are interested in the learning stability of the established systems using the mean average percentage error (MAPE) as a measure. Both simulated and real data were examined, and we have found a positive correlation between the signal period and the system input vector length for stable and relatively efficient learning. We also examined the white noise impact on the learning performance.
Development of Convergence Education Materials Using GeoGebra of Forming Infinite Multiple Image: focusing on the students at S gifted school

Objectives In this study, it was analyzed the understanding of the principle of formation of infinite multiple image of first-year students in a gifted school, and developed convergence education materials for the students' level of understanding.&#x0D; Methods A questionnaire was developed in consideration of the formation of infinite multiple images, the way two mirror images appear in infinite multiple images, and the recognition of the formation of infinite multiple images according to situational changes. Afterwards, 16 first-year students in S gifted school in Korea were asked to respond to the questionnaire, and the results were analyzed.&#x0D; Results Analyzing the students' understanding of infinite multiple image formation, most of the students were aware of infinite multiple image, but they did not know exactly how the infinite multiple image was formed. Based on the results, convergence educational materials based on ray diagram for the infinite multiple image formation were developed using GeoGebra math software.&#x0D; Conclusions The convergence education materials developed in this study can be used for gifted school students' understanding of infinite multiple images, and can be used later for convergence classes for general school students.
Application Security Testing to Support Digital-Based Cultural Ecosystem in Jogja Smart Province

Jogja Smart Province (JSP) is a framework for utilizing the information technology to seek a regional collaboration to help resolving some strategic issues or problems. In addition, JSP also encourages the development of potentials in the Special Region of Yogyakarta. Several digital applications have been developed to support JSP including mobile and web-based applications. These applications are very important to realize the five dimensions of JSP, namely smart living, smart culture, smart society, smart environment, and smart governance. However, there are some serious problems that threaten the sustainability of JSP, including cyber-attacks on JSP applications. Therefore, this community engagement activity carried out by the Department of Electrical Engineering and Informatics, Vocational College of Universitas Gadjah Mada aims to perform penetration testing of applications owned by JSP. This community engagement activity is in partnership with the Communication and Informatics Office of Special Region of Yogyakarta, which is authorized to manage JSP applications. Some applications targeted for penetration testing include Jogja Istimewa, Visiting Jogja, e-Prima, BiroHukum, Paperless, Jogjaplan, LPSE, Peladen, Sadewa, Jogjaprov, and Simpeg2. By doing penetration testing on these applications, the potential vulnerabilities found in JSP applications can be identified. Furthermore, several suggestions or recommendations are proposed to improve the resilience of JSP applications from potential cyber-attacks.
Development and application of teaching and learning materials for middle school quadratic functions based on smartphone activity

Objectives In this study, teaching and learning materials were developed for middle school students to explore&#x0D; quadratic functions through smartphone-based activities. After the middle school students performed the activities,&#x0D; the results of the activities were analyzed to find out whether they were motivated to learn mathematics and&#x0D; what mathematical facts they found.&#x0D; Methods Teaching and learning materials for quadratic function for exploration activities were developed using&#x0D; GeoGebra for 5 2nd grade students at “A” middle school located in a small city. Afterwards, the developed teaching&#x0D; and learning materials were applied to middle school students, and the students' results were analyzed in&#x0D; terms of motivation, mathematical discovery, and content understanding.&#x0D; Results After analyzing the middle school quadratic function curriculum and various textbooks, smartphone activity-&#x0D; based quadratic function teaching and learning materials were developed. In addition, as a result of analyzing&#x0D; the results of applying the developed teaching and learning materials to students, students were motivated to explore&#x0D; quadratic functions using GeoGebra on their smartphones. In addition, while moving graphs of quadratic&#x0D; function and observing the changes in function formula, the experience of reasoning about the translation of the&#x0D; function was experienced. In addition, students gained more understanding and reasoning experience through&#x0D; geometry-centered teaching and learning activities such as drawing points, drawing graphs, and moving them in&#x0D; GeoGebra.&#x0D; Conclusions Using the teaching-learning materials developed in this study, middle school students were able to&#x0D; gain motivation for learning and infer and explore the contents of quadratic functions.
EVOAL: A Domain-Specific Language-Based Approach to Optimisation

Adapting optimisation algorithms, such as evolutionary algorithms, to a problem is a necessity. The required collection and exchange of domain information is an important but tedious task in real-world projects involving several experts from different areas of expertise (e.g. the domain and the optimisation area). This paper presents a structured approach that allows the experts to systematically provide their knowledge using domain-specific languages. The presented approach defines a different language for the involved experts that enables them to add their knowledge and use the information provided by other experts. The languages are extensible, allowing the addition of new optimisation aspects without changing the actual language. These languages are the front-end to a versatile open-source optimisation tool, that we built, enabling the actual execution of the optimisation. It additionally provides features for surrogate models as well as data generation and different benchmarks for evaluation. Conducting a user study, we show that the language is suitable to express the domain knowledge and domain experts can use the language to describe their domain knowledge after a short introduction. This way, the approach reduces the effort for domain experts in providing their information. As a side effect, the complete configuration of the optimisation execution through these languages allows an easy and reliable reproduction.
Distributed Energy Resource Management Systems: Preserving Customer Privacy through K-Anonymity

The smart grid represents the next generation of electricity distribution systems that utilizes recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. One of the many concerns in smart grid digital communication discussions is the possibility of violating customers’ privacy. Violating customers’ privacy imposes a significant barrier as smart grid desirable attributes are tightly tied to customers’ participation. Employing privacy models can address concerns regarding information privacy in smart grid digital communication. In this work, we provide an approach to utilizing K-anonymity to ensure data within the system excludes Personally Identifiable Information. Results suggest that a dynamically generated generalization hierarchy minimizes information loss incurred by the anonymization process.
Construction of Aviation Intelligent Computer Aided Training System Based on Swarm Intelligence Optimization Algorithm

In order to realize the digitization and intelligence of aircraft maintenance training, an aviation intelligent computer-aided training system based on swarm intelligence optimization algorithm was constructed. The system comprehensively considers the user-centered design of human-computer interaction strategies and cognitive psychological factors, and establishes an intelligent decision-making model based on information entropy theory. The model takes the training results of the trainees as the basis for intelligent decision-making. By extracting the knowledge element information that affects the training failure, it searches for the relevant knowledge chain, and then calculates the entropy value to queue the knowledge elements that cause the training failure. Cognitive theory conducts secondary organization of relevant knowledge elements to form a new targeted training library. Select swarm intelligence optimization algorithms with different characteristics for theoretical and applied research. In the research of optimization algorithm, the operation mechanism of swarm intelligence optimization algorithm and the construction of fitness function are improved respectively, trying to improve the convergence and convergence accuracy in the analysis of typical optimization problems in the aviation industry; Based on this theory, an aviation oriented aircraft maintenance training prototype system is established, which verifies the feasibility of the intelligent computer-aided training system based on information entropy.
Help! I need somebody. A Mapping Study about Expert Identification in Software Development

Context: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. Objective: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. Method: We carried out a systematic literature mapping and analyzed 17 publications. Results: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. Conclusion: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification.
Incremental Object Detection with Image-level Labels

Incremental object detection (IOD) aims to achieve simultaneous prediction of old and new samples on localization and classification when new concepts are provided. It is a challenging task due to the need for a joint interpretation of semantic and spatial information. While existing work accomplishes the detector generalization with the help of annotated sample classes and bounding boxes, this paper presents one astonishing finding on the unnecessity of new bounding boxes, which will significantly reduce the annotation cost and condition constraints in IOD. To enable the incremental detection process with image-level labels, we propose a multi-branch decoupling scheme in which the representation, classification, and regression branches are customized to accommodate new concepts with class labels with different semantic levels. The regression branch is firstly frozen to ensure generalizable localization while maintaining the stability of representation optimization. Then the representation branch is gradient-inverted to estimate the distribution drift from the old object-level set to the new image-level one, improving the effectiveness of feature updates in the absence of homogeneous semantic and spatial supervision. Finally, the classification branch is calibrated to balance the contribution of different images to the gradient, in which the frequency ratios of old instances and new images are calculated as a post-processing factor for the logits margin. Extensive experiments on two standard incremental detection benchmarks demonstrate the above-par recognition and localization performance while reducing the burden on new class annotation, outperforming state-of-the-art methods by 2% and 3%, respectively.
Artistic Line Drawing Rendering With Priors of Depth and Edge Density

Line drawing is a form of painting that uses lines as expressive elements and often employs the Combination of Abstraction and Figuration (CAF) to enhance artistic expression. However, existing methods tend to focus on generating semantically accurate line drawings, resulting in lackluster images. We observe that, by varying the details of lines in the drawing, it is possible to represent CAF and enhance the artistic effect of the line drawing more effectively. Therefore, we propose an optimization method for line drawing rendering based on the prior knowledge of depth and edge density, which adaptively adjusts the line details of input images using the double estimation. Our method allows generated line drawings to maintain correct semantic information while keeping consistent background structure contours and rich foreground details. Experiments show that, compared to existing line drawing generation methods, our algorithm can generate line drawings that are more aesthetically pleasing to human perception.
C2BNet: A Deep Learning Architecture with Coupled Composite Backbone for Parasitic EGG Detection in Microscopic Images

Internet of Medical Things (IoMT) enabled by artificial intelligence (AI) technologies can facilitate automatic diagnosis and management of chronic diseases (e.g., intestinal parasitic infection) based on two-dimensional (2D) microscopic images. To improve the model performance of object detection challenged by microscopic image characteristics (e.g., focus failure, motion blur, and whether zoomed or not), we propose Coupled Composite Backbone Network (C2BNet) to execute the parasitic egg detection task using 2D microscopic images. In particular, the C2BNet backbone adopts a two-path structure-based backbone and leverages model heterogeneity to learn object features from different perspectives. A novel feature composition style is proposed to flow the feature within the coupled composite backbone, and ensure mutual enhancement of feature representation ability among the different paths of the backbone. To further improve the accuracy of the detection results, we propose Multiscale Weighted Box Fusion (WBF) to fuse the location and confidence scores of all bounding boxes predicted from the multiscale feature maps, and iteratively refine the box coordinates to form the final prediction. Experimental results on Chula-ParasiteEgg-11 dataset demonstrate that the C2BNet not only performs satisfactorily compared with state-of-the-art methods, but also can focus more on learning detailed morphology features and abundant semantic features, resulting in more precise detection for parasitic eggs located in the 2D microscopic image.
Efektifitas penyaringan Air Bersih Terhadap Kuliatas Air Sumur Gali di Kelurahan Patingalloang, Kecamatan Ujung Tanah, Kota Makassar Program Corporate Social Responsibility PT Pertamina Patra Niaga IT Makassar Tahun 2023

Introduction: site requirements for the construction of a dug well, this is necessary so that the quality of dug well water is safe in accordance with the established rules, Pollution caused by bacteria to water in the ground widens to ± 2 meters at a distance of 5 meters from the source of pollution 3 and narrows to a distance of 11 meters in the direction of the soil flow, before there was a CSR Program PT Pertamina Patra Niaga IT Makassar water quality of Dug Wells in Pattingalloang Village is very risky for environment-based diseases so it is necessary to hold clean water filtration.&#x0D; Research Method: This type of research is descriptive research, namely to determine the use of silica sand filter media – activated carbon in filter reactors to reduce TDS, Nitrite (NO2), Dissolved Manganese and MPN Coliform with a comparison of the composition of filter media with references to clean water quality standards Permenkes No. 2 of 2023, The sample in this study is 1 dug well located in Pattingaloang Village, Ujung Tanah District, Makaasar City in 2023.&#x0D; Results: this study after the filtration intervention of manganese media, activated charcoal, and silica sand CSR Program of PT Pertamina Patra Niaga IT Makassar water quality of Wells, The results of this study after filtration interventions of manganese media, activated carbon, and silica sand found that TDS, color, Manganese Nitrite (NO3) has decreased environmental pollution levels in accordance with the Regulation of the Minister of Health of the Republic of Indonesia Number 2 of 2023.&#x0D; Conclusion: The conclusion of the research results is that silica, manganese and activated carbon sand filtration media, TDS, manganese (NO2) nitrate and MPN Coliform can effectively reduce water pollution levels in dug wells in accordance with Minister of Health Regulation No. 2 of 2023. So that the risk of infectious and non-communicable diseases can be overcome by the intervention of filtration media filtration of silica sand, manganese and activated carbon.
Everything is a prototype, but not at all in the same way

In this article I reflect on discussions from the 2022 DASTS conference around the shifting nature of prototyping in design research. Specifically, I reflect on Ruth Neubauer and colleagues’ work on ‘prototyping living spaces’ (2022, and this volume), and Simy Gahoonia and Christopher Gad’s (Forthcoming; 2022) study of the Danish Technical Comprehension experiment. I also reflect on an element of my own design-research that has involved developing speculative prototypes, namely a tandem bicycle that was designed as an ‘interview machine’ for gathering research on the Calais Jungle refugee camp in France (Healy, 2021). I go on to develop what I refer to as a prototyping ecology to underpin the above and to consider the different modes and methods involved in different kinds of prototyping practices, and the ways they stabilise and destabilise the situations they enter into. Finally, I ‘test’ this ecology by revisiting the aforementioned examples to explore the forms of ‘stirring’ that prototypes participate in, how they straddle the ecology, and the ways they produce and encourage research events where I argue for the importance of how one might become attuned to the ways they encourage the unexpected by forming new relations and events through material intervention.