Reviewerly/exHarmony huggingface dataset:
Visual Information Processing in Virtual Reality: Merging Theory and Practice

Understanding visual information processing is becoming more important as technologies like virtual reality (VR) are being integrated into our training and learning experiences. A dissection between reception from perception is necessary to accommodate researching these visually intensive technologies. For instance, visual reception is the act of encoding information from the outside world, whereas perception is the act of interpreting that information internally. Theories regarding cue location, hierarchy of features, perceptional salience, postattentive amnesia, inattentional blindness, change blindness, and attentional blink will be discussed in this paper to focus on the separation of visual reception and perception. This paper will also highlight human thresholds of visual interpretations and the reliability of immersive technologies like VR for researching and understanding visual information processing. Concluding, this paper will discuss the need for iSchool communities to collaborate on this type of research. Specifically, educators, developers, and researchers working with VR contexts to better understand human behavior and software design.
Pooling Graph Convolutional Networks for Structural Performance Prediction

Neural Architecture Search can help in finding high-performance task specific neural network architectures. However, the training of architectures that is required for fitness computation can be prohibitively expensive. Employing surrogate models as performance predictors can reduce or remove the need for these costly evaluations. We present a deep graph learning approach that achieves state-of-the-art performance in multiple NAS performance prediction benchmarks. In contrast to other methods, this model is purely supervised, which can be a methodologic advantage, as it does not rely on unlabeled instances sampled from complex search spaces.
A custom recovery approach for physical forensic imaging of android device

Physical forensics imaging is part of the acquisition process. It captures all the data on the drive. The problem of forensics investigation on the android device is the acquisition process. It is because the device needs to be rooted for the acquisition. The root process on Android is usually done so that exploration can be carried out on the system. However, it can risk damaging the system itself if done incorrectly. In this paper, we proposed how to do physical forensics imaging on an android device by using the custom recovery (CR) approach. Custom recovery is a recovery environment in Android that can be used to restore the default setting of a system. We developed an application that performs the custom recovery approach. We use the computational thinking method to develop the application. The result of the paper is an application that enables to perform the physical forensics imaging on an android device by generating the hash checksum to ensure the integrity of the result from the physical forensics imaging process.
Automatic Alert and Triggering System to Detect Persons’ Fall Off the Wheelchair

Sensor-based human activities need a lot of attention in the driving technologies of the Internet of Things (IoT) because it should set the human activities (make and perform personal assisting tasks) that should be performed well. Taking care of physically disabled persons and preventing them from falling from a wheelchair are vital tasks to be performed. Scrutinizing these kinds of accidents via surveillance systems based on CCTV cameras would not prevent them from falling from the wheelchair. In the present work, a solution to detect and signal the situation, an intelligent and cost-effective fall detection system is presented. This module utilizes the benefits of the advanced technology, i.e., the Internet of Things (IoT). Here, an ultrasonic sensor is fixed on to the wheelchair to detect the obstacles such as pits, stairs, and an accelerometer to detect human movements. These sensors are connected through a microcontroller to transmit acceleration data continuously. The system monitors the falls and abrupt changes in the person's movement. A sudden jerky change in the system is treated as a crash or fall. The system senses these changes and automatically triggers a warning via the Wi-Fi connection and sends information to the near ones about the situation via the Blynk application.
Bit-Depth Enhancement with Distortion Sensitivity

Displaying low bit-depth (LBD) images in high bit-depth (HBD) monitors will cause structural distortions, resulting in the explosion of advanced bit-depth enhancement (BDE) methods. Despite the recent progress, current BDE works cannot entirely remove false contours and color shifts as they focus on the overall image quality while leaving these visually sensitive distorted regions uncared. Thus, in this paper, we propose a bit-depth enhancement algorithm with distortion sensitivity to explicitly pay extra attention to the visually sensitive distorted regions indicated by the predicted quantization distortion. Concretely, we propose a Distortion Sensitivity Network (DSN) working in multiple recurrent stages with shared parameters to extract the distortion-guided features from the input LBD image. A Distortion-Guided (DG) module and a Distortion-Guided Mutual Attention (DGMA) module are designed to leverage the predicted distortion as well as its gradient at different scales. Experimental results on two widely used datasets demonstrate the superiority of the proposed methods compared with state-of-the-art methods.
Distributed Multi-level Hierarchical Cross-Chain Power Trading Model

The inherent financial attribute of blockchain technology can be well applied to power transactions. However, due to the throughput constraints and network delay of blockchain, it is difficult to improve the transaction confirmation throughput while ensuring the delay. This paper proposes a blockchain power trading model with hierarchical governance and layer by layer confirmation, which provides a reference for the State Grid Corporation of China to use blockchain technology in power transaction, as well as for distributed high-throughput blockchain application scenarios.
EfficientNet-Transformer for image captioning in Bahasa

The role of image captioning task that gives visual understanding of images enables semantic-based information retrieval. The research on image captioning always aims to produce better descriptions of images. There is only a little research found regarding image captioning in Bahasa Indonesia. All the studies use sequence-to-sequence (seq2seq) models alongside attention mechanisms. The model gives good results. However, there are crucial drawbacks. The seq2seq model gives poor performance when dealing with long sentences, while the attention mechanism consumes a lot of resources because it relies on Recurrent Neural Network (RNN). Inspired by the success of the Transformer architecture in machine translation, this research focuses on developing the image captioning model using Transformer architecture in Bahasa. The Transformer architecture accelerates the learning process since it only uses attention mechanisms without relying on RNN. Moreover, we use EfficientNet, one of the state-of-the-art architectures to extract features of the images. We use the MS COCO 2014 dataset with translated captions (in Bahasa). We did several experiments through hyperparameter tuning and selected the best model. The best model in our study gives a BLEU- {1,2,3,4} score of {77.42, 67.11, 60.52, 50.46}. The evaluation score indication and the inference result of the model, developed by EfficientNet-Transformer architecture, gives a very good, generated caption result.
Design and Implementation of an Automatic Vehicle Based on Machine Vision Technologies

Machine vision technologies play a pivotal role in autonomous driving. It is very important for a vehicle to perceive the external environment and to make independent judgment according to environmental perception. At the same time, in the face of the increasingly complex road traffic, the road traffic signs are increasing and complex today. It is particularly important for the driving vehicles to accurately identify the traffic signs on the road. This paper has proposed a scheme of automatic trolley based on identification guidance. The scheme uses a crawler model car with a power unit carrying a vision module for simulation. The vision module OpenMV shoots is to compare and identify the traffic signs in front of the road. A NCC algorithm is to improve the automatic identification on the icon so as to identify the information contained in the traffic logo. Through the serial port with communication, the master controller manage the power unit of the vehicle and define different power output, so as to realize car driving and realize the purpose of automatic driving.
Research on Intelligent Energy Management System for Differential Pressure Power Generation

With the rapid economic development, the world is looking for renewable energy to replace traditional energy. Natural gas is a clean renewable energy source, and building a hybrid power generation system with natural gas pressure power generation and energy storage devices will effectively improve the utilization rate of renewable energy. This thesis proposes to use the deep reinforcement learning (DRL) algorithm to optimize the scheduling of the differential pressure power generation energy management system. A data-driven approach is proposed to train the energy management system offline, and test the trained model online. Through the experimental verification and the comparison of different algorithms, the effectiveness of the energy management system based on the Double deep Q-Network (DDQN) algorithm for differential pressure power generation is verified.
Fixing Bugs – Planning Doomed to Fail

An interesting issue that we face in IT projects is planning how long it will take to fix the bugs reported either after internal tests or user acceptance tests. Most managers, especially executives, on the client’s or supplier’s side expect a specific date (mostly related to a milestone) by which all identified issues will be fixed. They assume that the process of achieving a desired quality state is no different from any other work, that it can be planned.
Participatory Web Archiving: Multifaceted Challenges

There has been increasing interest in participatory web archiving in recent years. Indeed, it is widely regarded as a necessary step in the development of web archives. From a theoretical point of view, it has been seen as driven by the “archival turn” in which the origin of archives is critically analysed, and it becomes clear that established power dominates archives while marginalized voices are absent. Web archiving benefits from this “archival turn”, not only in addressing limitations inherited from conventional archives, but also in challenging embedded systemic and selection biases when choosing what to archive from the Web. Through a critical literature review, this paper addresses the need to analyse participatory web archiving practices, the mechanisms and power relations within them through political theories of power and participation.
Similarity Visualizer Using Natural Language Processing in Academic Documents of the DSpace in Ecuador

Due to the widespread use of the Internet, users have the ease of accessing collections of university academic documents stored in virtual libraries whose information is of an unstructured type. In recent years, the production and publication of scientific documents in Ecuador have increased considerably, so the search and classification of documents is a fundamental task within information retrieval computer systems. Intelligent search systems allow found information with a high degree of accuracy and similarity. For the development of this project, academic documents from the Ecuador Network of Open Access Repositories (RRAAE) were retrieved using a glossary of terms in the area of science and technology. For the recovery of documents, the web scraping technique was used and its results were stored in a cloud database in JSON format. In the recovered documents, NLP techniques were applied to clean and homogenize the unstructured information. Two similarity metrics were used to measure the divergence between the retrieved documents, and similarity matrices were generated based on the title, keywords, and abstract, which were then unified into a weighted matrix. The results of the system are displayed in a web interface that, through the use of graphs, shows the relationship between the linked documents. The operation of the similarity system was validated through functional tests through experimentation with a collection of 30 queries with indexed and non-indexed terms in the input of the information retrieval system. The experiments showed that for indexed terms, the system performs better.
Managing Delays

It’s more than certain that delays will happen at some stage of the project. Shorter or longer, sooner or later, but they will definitely happen. To a large extent, managing a project is organizing the team’s work in such a way that blockers are avoided or minimized if they do happen, in order to create an environment that will enable the team to work effectively. Delays happen and they always will, especially at the level of single, individual tasks done by specific people. It’s a consequence of the following:
Evaluating Tools for Data Management Plans: A Comparative Study of the DART Rubric and the Belmont Scorecard

Data management plans (DMPs) are required from researchers seeking funding from federal agencies in the United States. Ideally, DMPs disclose how research outputs will be managed and shared. How well DMPs communicate those plans is less understood. Evaluation tools such as the DART rubric and the Belmont scorecard assess the completeness of DMPs and offer one view into what DMPs communicate. This paper compares the evaluation criteria of the two tools by applying them to the same corpus of 150 DMPs from five different NSF programs. Findings suggest that the DART rubric and the Belmont score overlap significantly, but the Belmont scorecard provides a better method to assess completeness. We find that most DMPs fail to address many of the best practices that are articulated by librarians and information professionals in the different evaluation tools. However, the evaluation methodology of both tools relies on a rating scale that does not account for the interaction of key areas of data management. This work contributes to the improvement of evaluation tools for data management planning.
How Much Context Do Users Provide in App Reviews? Implications for Requirements Elicitation

People post millions of app reviews on Google Play and Apple’s App Store, but developers can struggle to incorporate this feedback in human-centered design processes. Although researchers have developed automated techniques to gather requirement information, including bug reports and feature requests, for developers tasked with app updates, these methods overlook contextual details in app reviews that explain why users encounter problems and offer insight into new design possibilities. However, prior research has not described the relative availability and characteristics of requirement and context information provided by users in app reviews. To address this gap in the literature, this study performs a content analysis of reviews of Citizen, a personal safety app, to show that users often include rich, contextual details about where, why, and how they use Citizen, but rarely discuss explicit requirements that most automated requirements elicitation techniques attempt to gather from app reviews. These findings suggest opportunities to scale human-centered design processes by collecting and classifying contextual details in app reviews to summarize use case scenarios that can provide rationales for app updates and inspire ideas for the design of new features and products.
Towards a Useful Chinese Annotation Tool: An Examination of Annotators’ Practice and Needs

With the rapid development of digital humanities research and the digitization of ancient Chinese books, annotation has become a critical pre-processing activity to extract information for quantitative analysis and to prepare training corpus for natural language processing models. Since manual annotation is a repetitive and time-consuming task that occupies the majority of humanities scholars’ time, humanities scholars are looking for tools to facilitate annotation. However, existing tools cannot fulfill the annotation needs of humanities scholars. Few studies have investigated current annotation practices and requirements of humanities scholars, not to mention Chinese annotators. This paper explores Chinese humanities scholars’ annotation practice, needs, and expectations of annotation tools based on interviews with 12 participants from different humanities disciplines. The interview transcripts are coded in three steps: open coding, axial coding, and selective coding. Six main categories are identified. We described the analysis results of three categories: annotation practice, feature requirements, and user experience. We further discussed the design implications and requirements of the research environment and the limitations of the work and future work.
“Design, Design, and Design Again”: An Information-Architecture Redesign Workflow from Case Studies of a Government Portal and a Learning-Management System

While heuristics are useful resources for designing the web’s information architecture (IA) from scratch today, IA practitioners occasionally receive requests to redesign established products, and guidelines are also needed to address such “redesign” requests. Past studies on IA design tend to focus on prototyping and how iterations contribute to final products, but such iterations have more to do with how users interact with the prototype than with its IA per se. This commentary paper reports a workflow for re-designing and optimizing two websites’ information architecture (IA). Based on two case studies, we explored a redesigned workflow of IA, which contains five stages: 1) screening, 2) synergizing, 3) synchronizing, 4) IA development 5) evaluation & execution. Compared to designing an IA from scratch, a team who redesigns an IA may communicate with more stakeholders and consider internal politics’ impact. Our proposed IA redesign workflow helps web designers allocate resources and prioritize work when given redesign tasks.
A Surprise Birthday Party in VR: Leveraging Social Virtual Reality to Maintain Existing Close Ties over Distance

This paper explores how social virtual reality, a novel 3D virtual social interaction space that provides embodied and immersive experiences, can be leveraged to maintain existing close interpersonal relationships over distance. Based on 672 Reddit posts and comments, our findings show that in addition to initiating and building new relationships with online strangers, social VR platforms’ unique features, including physicality, the enhanced sense of presence, and the broad range of shared embodied activities, can also help maintain various types of established close ties (e.g., parent-child, friends, siblings, and romantic partners) over distance in a nuanced way. This work contributes to understanding the increasingly important role of social VR in innovating modern computer-mediated relationships and can inform the future design of social VR to better support people’s social needs and interpersonal connections.
Nine Challenges for Immersive Entertainment

Immersive entertainment is an important driver of mixed reality platforms. As technology becomes better and cheaper, this will spark the next major shift in digital entertainment. We identify nine research topics on immersive entertainment across theoretical-scientific, creative, systems, and social dimensions. For each topic, we discuss possible research directions that can lead to advances in the field. This chapter is an extended version of a paper originally published in the proceedings of the XX Brazilian Symposium on Computer Games and Digital Entertainment. Each of the topics is discussed in more depth and a separate challenge for input devices and body tracking was added.
Lex Informatica and Freedom of Expression: Reflections on the Regulation of Internet Trolling Behaviors

This paper revisits the concepts of lex informatica (Reidenberg) and code is law (Lessig), both early theories related to how regulation of behaviors on the internet might be managed, By focusing on the context of internet trolling in the United Kingdom, we consider the nature of trolling, how it has manifested in the UK in terms of actual notable incidences, and reflect on whether the law of the state within the regulatory regimes envisioned by Reidenberg and Lessig are in fact the best fit for managing these behaviors. Abusive behavior online can have profound impacts on people, causing significant fear and leading to an impact on private and family life. In balancing the qualified nature of freedom of expression rights versus other human rights we consider whether the laws of peoples, the regulatory capabilities of social media companies, and the wider culture of digital society are best placed collectively to create respectful norms on the Internet.
Characterizing Data Practices in Research Papers Across Four Disciplines

Research Data Practices (RDP) refer to research activities conducted across the lifespan of data. Characterizing RDP in disciplinary contexts is beneficial for providing data stakeholders with practical understanding of RDP necessary to design data curation services which are tailored to researchers’ need. In this paper, we focus on the five most common types of RDP – collecting data, processing data, analyzing data, representing data, and publishing or citing data. First, we compared the distributions of the five types of RDP across disciplines and observed noticeable differences between disciplines. In addition, we examined the characteristics of each type of RDP under different disciplinary contexts, by developing discipline-specific RDP vocabulary employing the tf-idf approach. Based on the common terms as well as the discipline-specific ones, we found that the five types of RDP can be distinctly conceptualized, while each type of RDP varies by disciplines in terms of their action, object, and instrument.
MSPP-IQA: Adaptive Blind Image Quality Assessment Based on Multi-level Spatial Pyramid Pooling

The main reason why image quality assessment (IQA) for real distortion has not been well solved is that, first, after the training of CNN is completed, the parameters of the convolution kernel are fixed, but the image content is changeable, which limits the generalization performance of the model. Second, the real distortion often exists in the local area of the image, which puts higher requirements on the ability of the model to perceive local distortion. In this paper, a multi-level spatial pyramid pooling-based blind image quality assessment method (MSPP-IQA) for real distortion is proposed. First, ResNet50 is used as the backbone network. Next, a feature pyramid structure is used to combine features, and then the proposed adaptive local distortion perception (ALDP) module is used to improve the semantic features. The ALDP module is based on conditional parameter convolution to make sure that the model’s scoring ability can handle different images in large datasets. Residual connectivity is used to improve the model’s ability to learn. At the same time, the model uses the semantic feature understanding module for image content understanding and provides prediction guidance for the scoring module. The proposed model improved the generalization power as well as the evaluation accuracy of the quality assessment compared to the current more advanced models. The final experimental resulted demonstrate that the model can quickly and accurately evaluate the real image quality without reference.