Reviewerly/exHarmony huggingface dataset:
Handling concept drift in deep learning applications for process monitoring

As machine learning applications transcend from laboratories to real-world operations in manufacturing use cases such as intelligent maintenance and quality control, questions regarding their continuous reliability and robustness arise. Static datasets used to develop machine learning models can only capture a subset of possible real-world conditions. Instances of concept drift, such as changing environmental-, equipment- and operating conditions may, over time, significantly degrade the performance of machine learning models, compromising safety, acceptance, and economics if not properly addressed. It is thus necessary to (1) detect drifts and (2) efficiently adapt the model to dynamically changing conditions. In this work, we propose a framework to tackle the issue of drift detection by utilizing the underlying neural network's uncertainty. We demonstrate the framework's effectiveness in a case study involving process monitoring of a real-world CNC milling process. The dataset includes accelerometer data and multiple types of realistic concept drifts. In addition, we introduce a methodology for inducing controllable, synthetic concept drift through a simulated sensor rotation. Our experiments show that the introduced drifts lead to a strong performance degradation of the model, which highlights the relevance for practitioners. All drifts are detected using the proposed framework, enabling continuously reliable applications.
Long-Term Extrinsic Uniformity Analysis of Gamma Cameras

Purpose: The long-term trend of extrinsic uniformity by type of gamma camera was analyzed, and the factors affecting uniformity were investigated. Based on this, the purpose was to predict the life of the gamma camera, pay attention to factors that affect uniformity, and obtain better quality images. Materials and methods: Four of the gamma cameras in operation at a senior general hospital in Seoul were selected and the trend of extrinsic uniformity from the first operation date to the present was analyzed. In order to minimize various factors affecting uniformity, a detailed analysis was conducted by calculating the monthly and annual average of the uniformity values. Results: Two Symbia E gamma cameras from SIEMENS, one Symbia Evo Excel gamma camera, and one Symbia Intevo16 gamma camera were selected and analyzed. The uniformity of Symbia E1 (2012 warehousing) changed unevenly, and the uniformity of Symbia E2 (2014 warehousing) changed according to the replacement cycle of 57Co sheet sources. The uniformity of Symbia Evo Excel (received in 2017) and Symbia Intevo 16 (received in 2017) was constant compared to Symbia E. Conclusion: The extrinsic uniformity of the gamma camera gradually increased over time. However, there was a difference in uniformity for each type of gamma camera, and there was a change in uniformity in which the cause could not be accurately identified. In order to improve the quality of the image, it is necessary to periodically check changes in uniformity and minimize factors that affect uniformity.
ANALISIS MULTIVARIAT UNTUK PEMETAAN KARAKTERISTIK MUTU PENDIDIKAN SEKOLAH MENENGAH KEJURUAN (SMK) DI INDONESIA

Evaluation of the education quality in Indonesia, especially in Vocational High Schools (VHS), carried out through the Presidential Instruction 2016. The implementation of these instructions is not easy because of the different characteristics of each region both geographically and socio-culturally. This study aims to analyze related to the mapping of the characteristics of the quality of vocational education from 34 provinces in Indonesia. The methods used are correspondence analysis, biplot analysis, and cluster analysis which are multivariate statistical analysis methods. The correspondence analysis results show that there are differences in the characteristics of the quality of vocational education from each province based on school accreditation. The biplot analysis show that Teacher Quality is greatest diversity variable compared to other variables. In addition, the provinces of Aceh, East Nusa Tenggara, West Sulawesi and Central Kalimantan have a quality score below the average and are dominated by non-accredited VHSs based on the results of the analysis. The k-means cluster analysis with 4 optimal clusters gives the result that cluster 2 is the cluster with the best quality value while cluster 4 is the cluster with the lowest quality value. Three analytical concluded that provinces with very good quality scores such as DKI Jakarta, Bali and DI Yogyakarta can be used as pilot provinces for other provinces. Meanwhile, provinces with VHS quality scores below the provincial average as a whole should receive attention in order to further improve the quality of their VHSs
Media Advocacy for Legal Reforms in Nigeria: A Study of Communication Strategies in Promoting Access to Justice

This study explores the evolving landscape of media engagement in legal reforms and its impact on shaping public perceptions, influencing policy decisions, and ultimately enhancing access to justice in the Nigerian context. Through an in-depth analysis of existing literature, policy documents, and case studies, this research aims to uncover the diverse communication strategies utilized by media advocates to address legal challenges and contribute to the reform agenda. The study examines the role of traditional and new media platforms, including social media, in disseminating information, fostering public awareness, and mobilizing support for legal reforms. Furthermore, the research investigates the challenges and opportunities associated with media advocacy for legal reforms in Nigeria, considering factors such as cultural nuances, political dynamics, and the evolving nature of communication technologies. It identifies successful case studies where media advocacy has played a pivotal role in driving legal reforms and expanding access to justice for marginalized communities. By synthesizing insights from various sources, this conceptual research aims to provide a comprehensive understanding of the communication strategies employed in media advocacy for legal reforms in Nigeria. The findings will contribute to the existing knowledge base on the symbiotic relationship between media engagement and legal advancements, offering valuable insights for policymakers, media practitioners, and advocates working towards a more accessible and equitable legal system in Nigeria.
Bias Mitigation in Federated Learning for Edge Computing

Federated learning (FL) is a distributed machine learning paradigm that enables data owners to collaborate on training models while preserving data privacy. As FL effectively leverages decentralized and sensitive data sources, it is increasingly used in ubiquitous computing including remote healthcare, activity recognition, and mobile applications. However, FL raises ethical and social concerns as it may introduce bias with regard to sensitive attributes such as race, gender, and location. Mitigating FL bias is thus a major research challenge. In this paper, we propose Astral, a novel bias mitigation system for FL. Astral provides a novel model aggregation approach to select the most effective aggregation weights to combine FL clients' models. It guarantees a predefined fairness objective by constraining bias below a given threshold while keeping model accuracy as high as possible. Astral handles the bias of single and multiple sensitive attributes and supports all bias metrics. Our comprehensive evaluation on seven real-world datasets with three popular bias metrics shows that Astral outperforms state-of-the-art FL bias mitigation techniques in terms of bias mitigation and model accuracy. Moreover, we show that Astral is robust against data heterogeneity and scalable in terms of data size and number of FL clients. Astral's code base is publicly available.
IIOT visualization applications based on augmented reality – practical approach for easy implementation

Connecting machines and systems is becoming increasingly important, especially in technology-based companies. The use of Augmented Reality (AR) based systems can bring significant advantages, such as improved quality or increased productivity. However, the implementation barriers often seem to outweigh the benefits for many companies. This is particularly true for small and medium-sized enterprises (SMEs), which leads to important questions: To what extent can AR applications be efficiently implemented using standard Industrial Internet of Things (IIoT) software? Is it possible for SMEs without specialized knowledge to implement AR applications with appropriate effort by utilizing an IIoT software? How would a software and hardware architecture for this look like? This paper presents an approach - based on commercially available standard software – to outline the necessary framework and demonstrating the benefits of this technology through practical use cases. The results show that useful applications can be implemented quickly and require comparatively little expertise when using the right toolchain.
A new Instrument for Production Control: The Smart Order Concept

Production control is an essential component of a production system used to monitor and manage production orders. Centrally controlled production systems rely on basic data management, such as storing the bill of materials and required work schedules. Components of customer orders are added to generate the corresponding production orders. However, this type of production control can pose serious problems in case of unforeseen events such as machine failure or changing customer requirements during production. In such scenarios, costly and time-consuming re-scheduling becomes necessary, which endangers the company's competitive position and leads to data retention issues. This paper introduces a new method of production control based on the concept of smart orders. Smart orders use smart contracts for autonomous routing based on programmed information in the source code, enabling self-control through the production system. Smart contracts are transaction programs that work with ‘if-then logic”, allowing for partially flexible process schedules and reducing the need for human intervention. The smart order uses the functionalities of blockchain technology, such as security, transparency, and immutability, to ensure the integrity of production data. The paper concludes by presenting the expected value propositions of smart order-based production control on the production system. Depending on the production control system used, companies can integrate the results presented in this paper into their own blockchain implementation strategy.
MIRROR

We present MIRROR, an on-device video virtual try-on (VTO) system that provides realistic, private, and rapid experiences in mobile clothes shopping. Despite recent advancements in generative adversarial networks (GANs) for VTO, designing MIRROR involves two challenges: (1) data discrepancy due to restricted training data that miss various poses, body sizes, and backgrounds and (2) local computation overhead that uses up 24% of battery for converting only a single video. To alleviate the problems, we propose a generalizable VTO GAN that not only discerns intricate human body semantics but also captures domain-invariant features without requiring additional training data. In addition, we craft lightweight, reliable clothes/pose-tracking that generates refined pixel-wise warping flow without neural-net computation. As a holistic system, MIRROR integrates the new VTO GAN and tracking method with meticulous pre/post-processing, operating in two distinct phases (on/offline). Our results on Android smartphones and real-world user videos show that compared to a cutting-edge VTO GAN, MIRROR achieves 6.5× better accuracy with 20.1× faster video conversion and 16.9× less energy consumption.
RimSense

Smart eyewear's interaction mode has attracted significant research attention. While most commercial devices have adopted touch panels situated on the temple front of eyeglasses for interaction, this paper identifies a drawback stemming from the unparalleled plane between the touch panel and the display, which disrupts the direct mapping between gestures and the manipulated objects on display. Therefore, this paper proposes RimSense, a proof-of-concept design for smart eyewear, to introduce an alternative realm for interaction - touch gestures on eyewear rim. RimSense leverages piezoelectric (PZT) transducers to convert the eyeglass rim into a touch-sensitive surface. When users touch the rim, the alteration in the eyeglass's structural signal manifests its effect into a channel frequency response (CFR). This allows RimSense to recognize the executed touch gestures based on the collected CFR patterns. Technically, we employ a buffered chirp as the probe signal to fulfil the sensing granularity and noise resistance requirements. Additionally, we present a deep learning-based gesture recognition framework tailored for fine-grained time sequence prediction and further integrated with a Finite-State Machine (FSM) algorithm for event-level prediction to suit the interaction experience for gestures of varying durations. We implement a functional eyewear prototype with two commercial PZT transducers. RimSense can recognize eight touch gestures on the eyeglass rim and estimate gesture durations simultaneously, allowing gestures of varying lengths to serve as distinct inputs. We evaluate the performance of RimSense on 30 subjects and show that it can sense eight gestures and an additional negative class with an F1-score of 0.95 and a relative duration estimation error of 11%. We further make the system work in real-time and conduct a user study on 14 subjects to assess the practicability of RimSense through interactions with two demo applications. The user study demonstrates RimSense's good performance, high usability, learnability and enjoyability. Additionally, we conduct interviews with the subjects, and their comments provide valuable insight for future eyewear design.
How to provide work instructions to reduce the workers’ physical and mental workload

The transition to Industry 5.0 emphasizes the human-centric perspective and pushes for ergonomic risks prevention and mitigation. The diffusion of the production paradigms of mass customization and mass personalization also increases the need for methods and tools to support workers in manufacturing activities. The growing number of product variants requires a higher cognitive load and increases the likelihood of human errors. Work instructions become fundamental, as well as their digitalization. The technologies enabling industry 4.0 have opened up new scenarios for the use of this information, from extended reality to digital assistance systems. This work deals with the human-centered redesign of a workstation for the wire harness assembly with the aim of mitigating the ergonomic risks for operators, both physical and cognitive. Four different scenarios for using the work instructions were implemented and tested: paper-based instructions, workstation display, mixed reality application usable with a head-mounted display, and augmented reality application that only requires a projection system. The experimentation involved user experience evaluation, performance assessment, and objective postural analysis through inertial sensors.
The Man’s Struggle In Dealing With Life’s Conflict as Seen in John Green’s The Fault in Our Stars

The title of this research is "The Man's Struggle in Dealing With Life's Conflict As Seen in John Green's The Fault In Our Stars". The problem of this research is focused on the problems of internal and external conflicts experienced by woman and man as the main characters. The purpose of this research is to find out the struggles of woman and man as the main characters in dealing with life conflicts described in the novel The Fault in Our Stars by John Green. This research is descriptive qualitative research. The technique used in this research is literature to obtain certain data by searching and collecting several books related to conflict analysis in literature, by quoting sentences or words that indicate conflict, and then describing them. In the main character conflict, there are two characters who are considered as the main characters because they dominate the whole story, they are Hazel and Augustus. The research uses the theory of conflict by Wallek and Warren (1956). Conflict into two parts, namely external conflict and internal conflict. Conflict with other characters in a story or conflict with social environment which is called external conflict. While the conflict that occurs in the heart and mind, in the soul of a character or character in a story is called internal conflict. The results of this research indicate that the development of the main character in the novel is influenced by situations, people, and feelings. At first, Hazel is a girl who likes to be alone, spending her time only at home, reading books and watching tv. She then becomes a girl who wants to share with others and have friends. Augustus at first is a boy who thinks and tries to handle everything on his own, he does not want to be pitied, eventually he realizes that he needs other people in his life and is grateful for what others do to him.
Young Girl as Seen in Anne Frank’s The Diary of A Young Girl

This research is motivated by the existence of personality forms and cool rooms that are hiding places where Anne's maturity grows, which is realized by the mob in Anne Frank's novel The Diary of a Young Girl. The purpose of this study is to identify Anne's personality form, analyze the shape of the foreign space where Anne's maturity grows in Anne Frank's novel The Diary of a Young Girl. The method with the research is descriptive qualitative research and the approach used is literary psychology with psychology literature Sigmund Freud theory. The data in this novel are excerpts from words, sentences related to the personality of Anne's character and forms of foreign space that affect Anne's maturity growth. The data collection techniques in this study were reading, marking the data, recording and distinguishing the data collected based on Anne’s personality and foreign space that influenced Anne’s maturity in Anne Frank novel The Diary of a Young Girl. The result of this study indicate that in the novel The Diary of a Young Girl by Anne Frank, the personality experienced by Anne Frank by is more Id than the Ego and Superego, this is due to the difference in her Mother’s love between herself and older sister, and the form of foreign space that affects Anne Frank maturity is a room called the Secret Annex.
A privacy-preserving robust and efficient homomorphic signcryption system tailored for smart agriculture

Diagnosing plant diseases is a challenging task due to the complex nature of plants and the visual similarities among different species. Timely identification and classification of these diseases are crucial to prevent their spread in crops. Convolutional Neural Networks (CNN) have emerged as an advanced technology for image identification in this domain. This study explores deep neural networks and machine learning techniques to diagnose plant diseases using images of affected plants, with a specific emphasis on developing a CNN model and highlighting the importance of hyperparameters for precise results. The research involves processes such as image preprocessing, feature extraction, and classification, along with a manual exploration of diverse hyperparameter settings to evaluate the performance of the proposed CNN model trained on an openly accessible dataset. The study compares customized CNN models for the classification of plant diseases, demonstrating the feasibility of disease classification and automatic identification through machine learning-based approaches. It specifically presents a CNN model and traditional machine learning methodologies for categorizing diseases in apple and maize leaves, utilizing a dataset comprising 7023 images divided into 8 categories. The evaluation criteria indicate that the CNN achieves an impressive accuracy of approximately 98.02%.
Classification and Prioritization of Tasks in Public Administration

A large volume of data is being generated in public administration and it is necessary to develop new computational methodologies to classify and analyze it to do a better analysis and decision making. For this reason, the goal of this paper is to present a computational methodology that allows classifying and prioritizing a series of complaints using Artificial Intelligence techniques. To test this model, we generate 600 complaints in four sectors of the public administration to prove the concept. Later, we calculated the tree decision with the help of the Confusion Matrix, and finally the Priority Matrix (based on the Eisenhower model) allows setting priorities on the complaints, and offers the possibility of delegating and even postponing the response to them. In this way, it is possible to prioritize the complaints made in the public administration.
Clustering objects for spatial data mining: a comparative study

Spatial data mining (SDM) is searching important relationships and characteristics that can clearly exist in spatial databases. This content aims to compare object clustering algorithms for spatial data mining, before identifying the most efficient algorithm. To this end, this paper compare k-means, Partionning Around Medoids (PAM) and Clustering Large Applications based on RANdomized Search (CLARANS) algorithms based on computing time. Experimental results indicate that, CLARANS is very efficient and effective.
Wykorzystywanie emoji i emotikonów przez nauczycieli – jako łącznik ze światem uczniowskim (?)

The article concerns the use of emojis and emoticons by teachers in the classroom and was written to draw attention to the educational use of these signs and symbols and to encourage wider possibilities of using them in education. In the first part of the text, considerations will be presented on the importance of emojis and emoticons in modern communication and the origins of the appearance of these characters in our society. In the second part, the results of a research survey conducted among teachers and students at Kielce schools and their parents in 2022 will be presented. An element of the research reconnaissance was also the analysis of websites devoted to emojis and emoticons used in the school classroom. In the last part of the article, conclusions will be presented that teachers most often use emojis and emoticons for educational tasks, as an element of assessment, as an “addition” to the assessment, as a separate assessment and a reward for completing the task and or a motivational element. Emojis and emoticons are mainly used in early childhood education, as they are positively received by younger students. Older students reserve these symbols rather for their peers, gradually distance themselves from the older generation (to which the teachers belong) and mock attempts by teachers to introduce these elements in the older classes. Parents mostly understand the use of these signs by teachers in the classroom. However, there are those who criticize the possibility of their use in schools.