Reviewerly/exHarmony huggingface dataset:
Resilience in design: The use of user persona to enhance co-working spaces design

Persona is a design methodology that exemplifies targets users as fictional, detailed archetypical characters that represent distinct groupings of behaviours, goals and motivations observed and identified by the designer during the research phase of a design project. The persona method has developed from being a user cantered design methodology for IT systems to be applied in many other contexts, including the development of products, marketing, communication strategy, and service design. The method is used to communicate targeted customers' needs, allow the designer to switch between the user's perspective and the designer role and orient design teams to user experience. Despite the importance of understanding users in the interior design field, minimal research about using persona for interior designers has been recorded. This research aims to present the value of using user persona maps in designing resilience interiors, looking at a co-working space as an example. The research methodology developed persona maps as a diagnostic tool for interior designers to help design resilience interiors by understanding space users. The paper goal will be achieved by investigating persona maps as a tool for interior designers to help them understand, analyse and communicate users' needs in designing interior spaces. The customised persona model is modified to include a narrative, picture, and name to provide interior designers with a vivid and detailed representation of the design target. The results of this research are based on three qualitative phases with the participation of 46 designers; through the continuous analysis of persona maps and design implementation, this research documents the benefits of incorporating personas into different design processes. Then elaborates on difficulties experienced within the suggested model and contribute to the body of knowledge by producing a practical model that can be the starting point of a successful project brief, client's ambitions and users' requirements realisation in a project space. The findings of persona usages in the interior design of the suggested co-working space showed how persona enriched various design process phases as interior designers continuously referred to persona analysis during the conceptualisation and schematic design phases. Additionally, persona helped designers design a resilient interior by demonstrating ways of understanding and interpreting user needs and helped filter user information to develop guidelines and design components that responded to users' hidden needs. Moreover, the user's persona helped the Interior Designers bridge the gap between a theoretical understanding of users in space and design for them. The findings revealed that persona could be in the aesthetical form of the resulting space design and the long-term value and functionality of space, making it a more resilient interior aid.
The Invisible Message

The number and variety of messages conveyed by an instructional experience is astonishing, but most designers are unaware of their number, subtilty, and impact. Many of those messages they would not choose to send if they recognized their existence in practice. The design of invisible and abstract message structures receives less attention from designers today than those parts of the design given to more vivid, colorful, and showy surface structures. Invisible message structures work behind the scenes to produce the smooth surface performances in front of the curtain; they are seldom seen directly, but their power is indisputable. The purpose of this chapter is to shed light on the message construct—a structure implicit in the writings of instructional theorists and design psychologists for decades and across multiple epochs of psychological theory. Without realizing the values conveyed by message design—or the lack of it—designers miss one of their most useful tools for disciplining the everyday design of more interactive, adaptive, generative, and scalable instruction experiences: instructional conversations.
INTEGRASI NETWORK MONITORING SYSTEM BEBRBASIS NAGIOS DENGAN TICKETING SYSTEM BERBASIS OS TICKET

INTEGRASI NETWORK MONITORING SYSTEM BEBRBASIS NAGIOS DENGAN TICKETING SYSTEM BERBASIS OS TICKET
Semi Automatic Hand Pose Annotation Using a Single Depth Camera

This paper addresses the problem of 3D hand pose annotation using a single depth camera. While hand pose annotations are critically important for training deep neural networks, creating such reliable training data is challenging and manual labor intensive. We propose a semi-automatic pipeline for efficiently and accurately labeling the 3D hand poses in a depth video containing single hand images. The process starts by selecting a subset of frames that are representative of all the frames in the dataset and the user only provides an estimate of the 2D hand key-points in these selected frames. We use this information to infer the 3D location of the hand joints for all the frames by enforcing appearance, temporal and distance constraints. Finally, we demonstrate that our method can generate 3D hand pose training data more accurately using less manual intervention and offering more flexibility in comparison to other state-of-the-art methods.
Permissioned blockchain for business process visibility: A case of expenditure cycle

Blockchain has been perceived to address the information integrity challenge of today's complex environment. While this technology posits huge potentials, current literature appears to run into shortness in study of blockchain use for expenditure process by design. Following the design science methodology, this study aims to fill the void through an instantiation of Hyperledger Fabric-based permissioned blockchain and Attribute-based Access Control method. We apply the blockchain feasibility on a procure-to-pay business process as the common example of accounting cycle. The test shows that the blockchain-driven expenditure can improve the procure-to-pay process in data integrity, end-to-end visibility, and confidentiality.
Bilingual IT Service Desk Ticket Classification Using Language Model Pre-training Techniques

Language model pre-training techniques have been successfully applied to several natural language processing and text-mining tasks. However, existing published studies regarding automatic IT service desk ticket categorization were mostly conducted using the traditional bag-of-words (BoW) model and focused on the tickets that contain only one language. Therefore, this paper presents an examination of applying the state-of-the-art language model pre-training approaches to automatically determine the service category of bilingual IT service desk tickets, particularly for those tickets that contain Thai and/or English texts. Three well-known algorithms, mBERT, ULMFiT, and XLM-R, are investigated in this study using an in-house real-world dataset. Three Ensemble methods with bag-of-words text representation are used as performance evaluation baselines. According to our experimental results, language model pre-training techniques are superior to the BoW-based Ensemble methods for bilingual IT ticket categorization tasks. XLM-R gives the highest overall performance at 87.02% accuracy and 86.96% F1-score on the test dataset, followed by ULMFiT, mBERT and Ensemble methods, respectively
A perspective depiction of heuristics in virtual reality

Virtual Reality (VR) can also be referred to as virtual environments. The recent extensive media coverage has helped VR grab audience attention rapidly. VR, also known as Simulated Reality, has the inherent potential to convince people to be in a physical world with a 3D interface that is superlative. With a broad range of applications, it is now a booming sector. With the rise in technology, heuristics can be applied to this virtual environment. This chapter deals with the role of heuristics in VR application domain. In the first case study, evaluation of a crime scene is done using VR concept. Second case study highlights the role of VR in game of chess. In the third case study, we discuss the issue of client assignment and present two algorithms that more easily assign clients to servers.
A Hybrid Deep Learning Model for Emotion Detection in Emotion-sensitive Robo-advisors

The rise of human-like conversational agents such as chatbots and robo-advisors have motivated researchers to exploit the property of “anthropomorphizing” in conversational agents who can facilitate an array of real-world applications. Since human can naturally detect respondents’ emotions embedded in their responses and constantly adapt to varying conversational contexts, it is desirable to equip chatbots or robo-advisors with the corresponding emotion-sensitive conversational generation capabilities to better mimic human-like intelligence (i.e., anthropomorphizing). However, the design and development of emotion-sensitive conversational agents are still in its infant stage. To fill the aforementioned research gap, we propose a deep learning-based emotion detection method for the development of emotion-sensitive conversational agents such as robo-advisors in this paper. In particular, we propose a new hybrid deep learning approach which combines the merits of pre-trained deep learning models such as BERT and Bi-LSTM by using a residual connection method which supports a layer-wise connection approach to stabilize the fine-tuning process and bootstrap the overall emotion classification performance. Based on two well-known benchmark emotion corpora, namely IEMOCAP and FRIENDS, our rigorous experiments reveal that the proposed hybrid deep learning model with the residual connection method achieves promising emotion classification performance, which lays a solid foundation for the development of emotion-sensitive conversational agents such as chatbots and robo-advisors.
CRIMES ASSOCIATED WITH THE USE OF ELECTRONIC PAYMENT MEANS: METHODS AND TRACE INFORMATION

The results of a forensic analysis of typical methods of committing crimes involving the use of electronic payment methods are presented. It is shown that the basis of this criminal activity are the methods of social engineering based on the application of the achievements of modern information and communication technologies, as well as on actions and approaches through which offenders gain unauthorized access to the personal data of the victim. Taking into account the study of the stages of the mechanism of the considered criminal acts, the sources of trace information are formulated, reflecting the processes of interaction of participants in a criminal event with each other and with the environment.
Multiuser Behavior Recognition Module Based on DC-DMN

The multiuser behavior recognition task based on environmental sensors can provide reliable health monitoring, suspicious person identification and behavior correction. Compared with camera equipment and wearable sensors, the task can achieve acquisition of binary data from the environmental sensors without requiring wearable sensors. Therefore, privacy protection of users and use burden can be improved. However, there are still challenges in this behavior recognition scenario: First, the data consistency shown by the different behaviors of a single user in the same scenario need to be guaranteed. Second, the interactive behavior of multiusers may cause a data association problem. Therefore, the multiuser behavior recognition task based on environmental sensors has, apart from application value, important research challenges. In response, we propose the divide and conquer dynamic memory network model (DC-DMN). Based on the periodicity of user behavior, personal habits, time and spatial characteristics, the multiuser behavior recognition ability of the model can be enhanced. First, the GRU model is used to solve the consistency problem of different behaviors at the data level. Then, we expand the model memory based on the idea of a dynamic memory network. In addition, two sections of memory are designed to integrate and store data more effectively. In this way, the data association and support problem can be solved. Finally, we use three standard datasets to conduct experiments and compare them with the existing benchmark methods in two dimensions of accuracy and recall. Experiments show that DC-DMN performs well in three different datasets. It can effectively solve the problems of data consistency and data association, thereby improving the recognition accuracy.
Impact of a science and technology strategy on the learning of physics

Abstract The use of smartphones and some applications for educational purposes are valuable tools in the laboratory since they are motivating for students and the teacher can take advantage of this advantage for the teaching of physics. The experience is based on the anthropological theory of didactics and the teaching approach in science, technology, engineering, and mathematics. It is proposed to investigate a trigger question in physics. To respond, an application is used that uses the smartphone’s sensors to record the simulation data. The experience is described, and results of its implementation are presented. Methodologically, a qualitative descriptive approach was used in a group of tenth grade students taking the physics course. Finally, it is concluded that the students felt motivated since they felt they participated in the construction of their own learning, supported using technologies that facilitate the integration of knowledge in physics.
Auto-Generated Code Detector - A Preprocessor to Enhance Security, Quality, Maintenance and Accurate Productivity Calculation of Code

Auto-generated code detection is emerging fast as a must-have pre-processing activity for software companies. If unchecked, auto-generated code can trigger security issues and incorrect quality and productivity calculations. Besides, devising a generic algorithm is difficult because of the colossal size of repositories and diversified technology stack. That is why researchers are trying to detect, scrutinize, verify, and validate code in terms of security, copyrights, and code licenses to exclude auto-generated code during quality and productivity analysis. It will improve the software reliability and integrity, code quality, maintainability of deliverable, correct productivity calculation and ensure code’s adherence to copyrights and licenses.Auto-generated code detection detects machine-generated code, external libraries, minified files/packages/bundles files, and anomaly files [1]. Among these, the anomaly files have one-time large commits and code from other repositories. Such files force the quality analysis tools to slow down or hang or produce incorrect quality reports causing major bottlenecks. Also, the manual identification of auto-generated code is time-consuming and not sustainable. These acted as a catalyst for our extensive research to detect auto-generated code with high accuracy. We used our domain expertise and machine learning skills to write algorithms for better detection. As its outcome, our proposed technique can identify whether the source code is auto-generated or not by mining the code and collecting data from version control. We have run our technology-agnostic algorithm across various repositories within Talentica as well as for external repositories belonging to Google, Facebook, Twitter, Thoughtworks, Netflix and have achieved an average of 96.85% accuracy.
A Hybrid Method for Day-Ahead Photovoltaic Power Forecasting Based on Generative Adversarial Network Combined with Convolutional Autoencoder

Photovoltaic (PV) has high impact on the decarbonization pathways of power systems. The accuracy of PV power forecasting becomes crucial in operation and control of power system with a high share of PV penetration. This paper develops a hybrid method based on deep learning method of generative adversarial network (GAN) combined with convolutional autoencoder (CAE) to improve day-ahead PV power forecasting accuracy. First, a data pre-processing method of self-organizing map is utilized to classify the target days based on solar irradiance commonly known as weather type indicator. Next, with the ability of adversarial GAN to reduce the burden of loss and the advantages of CAE to extract multi-scale effective features from the weather information and PV power, the PV power forecasting model based on GAN and CAE is proposed. The developed model has been tested on a real dataset in a Chinese PV station, and the forecasting results demonstrate high accuracy of developed model compared with other deep learning models.
Bangladeshi Currency Notes (Bdt) Recognition Using Logistic Regression

Money is an important element for all the people in the world, including blind people. Blind people cannot recognize notes like any human can do, and often they are cheated by wrong people on various money issues. Therefore, they have to get assistance from technology. To solve this problem, deep learning techniques, such as Convolutional Neural Network (CNN), have been approached, which surpasses human visual accuracy noticeably. But there is another drawback, this approach requires intensive training, which is not possible for all scenarios. On the other hand, logistic regression, although being limited compared to CNN, is less intensive and can be used for similar applications as well. It has been discovered that by applying adequate tests and training, this method can achieve an accuracy rate almost equivalent to state-of-the-art. This study attempts to apply a combination of logistic regression and image classification into recognizing a specific kind of currency, the Bangladeshi Taka (BDT) and classifying them based on its different notes, aiming to reach an accuracy level equivalent to CNN.
Quantifying graphemic variation via large text corpora

Abstract The use of some basic computer science concepts could expand the possibilities of (manual) graphematic text corpus analysis. With these it can be shown that graphematic variation decreases constantly in printed German texts from 1600 to 1900. While the variability is continuously lesser on a text-internal level, it decreases faster for the whole available writing system of individual decades. But which changes took place exactly? Which types of variation went away more quickly, which ones persisted? How do we deal with large amounts of data which cannot be processed manually anymore? Which aspects are of special importance or go missing while working with a large textual base? The use of a measurement called entropy quantifies the variability of the spellings of a given word form, lemma, text or subcorpus, with few restrictions but also less details in the results. The difference between two spellings can be measured via Damerau-Levenshtein distance . To a certain degree, automated data handling can also determine the exact changes that took place. Afterwards, these differences can be counted and ranked. As data source the German Text Archive of the Berlin-Brandenburg Academy of Sciences and Humanities is used. It offers for example orthographic normalization – which is extremely useful –, preprocessing of parts of speech and lemmatization. As opposed to many other approaches the establishment of today’s normed spellings is not seen as the aim of the developments and is therefore not the focus of the research. Instead, the differences between individual spellings are of interest. Afterwards intra- and extralinguistic factors which caused these developments should be determined. These methodological findings could subsequently be used for improving research methods in other graphematic fields of interest, e. g. computer-mediated communication.
Analysis and Evaluation on Practical Teaching of Space Transformation Design of Old Buildings Based on Place Perception

With the development of urban construction, the requirements of old building transformation are improved, and the teaching demand of old building space transformation is also gradually strengthened. This paper analyzes the significance and function of the characteristic analysis of old building space transformation and architectural decoration patterns, discusses the teaching practice of old building transformation design based on place perception from the aspects of the activity organization of field investigation, “place perception” field teaching method and the arrangement of architectural decoration patterns, and makes a comprehensive evaluation of “place perception” field teaching method by using analytic hierarchy process, The improvement direction of practical teaching in the space transformation design of old buildings is clarified.
ME-DARTS: Introduce Multi-stage Evolution to Improve Differentiable Architecture Search

Neural Network Architecture Search (NAS) can automatically learn to obtain deep neural networks for specific scenarios. Differentiable Architecture Search (DARTS) makes the NAS process more efficient. However, its performance is limited by the tendency of DARTS to fall into architectural local optima. In this study, we introduce evolutionary algorithm (EA) into DARTS and propose multi-stage evolution to improve the performance of DARTS, named ME-DARTS. We construct a population containing multiple networks and divide the search process into three stages. An inadequate-to-adequate search strategy and datasets approximation computation are used to accelerate the search process. The networks in the population will be promoted by the evolutionary operators, and finally a network architecture with good performance will be selected from the population. Our algorithm achieves a test error of 2.6% on CIFAR10 and 15.68% on CIFAR100, demonstrating the effectiveness of ME-DARTS.
Efficient Models for Real-Time Person Segmentation on Mobile Phones

Despite constantly evolving mobile hardware, realtime person segmentation on mobile phones is challenging due to the limited computational resources. To address this problem, we introduce a novel UNet-like network architecture based on MobileNetV3, which enables the segmentation of persons in images and videos on mobile phones. Our model, which is not limited to a specific shot type, outperforms specialized models in their respective domains and runs with 35 fps on a Google Pixel 4 mobile phone. Moreover, we demonstrate how the segmentation accuracy can be further improved by exploiting the temporal coherence of consecutive frames in videos.