Reviewerly/exHarmony huggingface dataset:
Image manipulation in scholarly publications: are there ways to an automated solution?

Purpose This paper provides an introduction to research in the field of image forensics and asks whether advances in the field of algorithm development and digital forensics will facilitate the examination of images in the scientific publication process in the near future. Design/methodology/approach This study looks at the status quo of image analysis in the peer review process and evaluates selected articles from the field of Digital Image and Signal Processing that have addressed the discovery of copy-move, cut-paste and erase-fill manipulations. Findings The article focuses on forensic research and shows that, despite numerous efforts, there is still no applicable tool for the automated detection of image manipulation. Nonetheless, the status quo for examining images in scientific publications remains visual inspection and will likely remain so for the foreseeable future. This study summarizes aspects that make automated detection of image manipulation difficult from a forensic research perspective. Research limitations/implications Results of this study underscore the need for a conceptual reconsideration of the problems involving image manipulation with a view toward the need for interdisciplinary collaboration in conjunction with library and information science (LIS) expertise on information integrity. Practical implications This study not only identifies a number of conceptual challenges but also suggests areas of action that the scientific community can address in the future. Originality/value Image manipulation is often discussed in isolation as a technical challenge. This study takes a more holistic view of the topic and demonstrates the necessity for a multidisciplinary approach.
Application of Contemporary Architecture in the Transfer Hub High Land Borobudur Building

This study aims to provide a supporting facility for the Borobudur Highland region in the Nglinggo-Sedayu Gate portion that will serve as a public transportation/transfer hub for the Borobudur Highland tourism area. This phase of research employs a combination of methods: site survey, primary and secondary data gathering, review of literature, precedents, and current comparative studies, data processing, and idea generation. According to the findings, the Transfer Hub building idea is Contemporary Architecture, which blends two architectural styles: modern architecture and Neo Vernacular, in which modern architecture incorporates contemporary features. In contrast, Neo Vernacular is more cultural, as shown by the Transfer Hub building's façade featuring the
Analysis of User Personalized Retrieval of Multimedia Digital Archives Dependent on BP Neural Network Algorithm

This paper briefly introduces the characteristics of content-based multimedia retrieval under the information background, analyzes the implementation process of these technologies in the multimedia archives retrieval system including video and image information of digital archives, and points out that the content-based multimedia retrieval technology is bound to be organically combined with the traditional text retrieval methods. The information retrieval technologies in the past can only comply with the specific requirements of customers. Due to their characteristics of universality, they can hardly meet the demands of different environments, various purposes, and different times at the same time yet. Researchers have put forward personalized retrieval of multimedia files based on the BP neural network computing. In this way, the interest model of customers can be analyzed based on the characteristics of the different classification areas of users. Subsequently, the corresponding calculations are carried out, and the model is updated accordingly. Through the experiments, it is verified that the probability model put forward in this paper is the optimal solution to express the interest of customers and its changes.
Leveraging medical context to recommend semantically similar terms for chart reviews

Information retrieval (IR) help clinicians answer questions posed to large collections of electronic medical records (EMRs), such as how best to identify a patient's cancer stage. One of the more promising approaches to IR for EMRs is to expand a keyword query with similar terms (e.g., augmenting cancer with mets). However, there is a large range of clinical chart review tasks, such that fixed sets of similar terms is insufficient. Current language models, such as Bidirectional Encoder Representations from Transformers (BERT) embeddings, do not capture the full non-textual context of a task. In this study, we present new methods that provide similar terms dynamically by adjusting with the context of the chart review task.We introduce a vector space for medical-context in which each word is represented by a vector that captures the word's usage in different medical contexts (e.g., how frequently cancer is used when ordering a prescription versus describing family history) beyond the context learned from the surrounding text. These vectors are transformed into a vector space for customizing the set of similar terms selected for different chart review tasks. We evaluate the vector space model with multiple chart review tasks, in which supervised machine learning models learn to predict the preferred terms of clinically knowledgeable reviewers. To quantify the usefulness of the predicted similar terms to a baseline of standard word2vec embeddings, we measure (1) the prediction performance of the medical-context vector space model using the area under the receiver operating characteristic curve (AUROC) and (2) the labeling effort required to train the models.The vector space outperformed the baseline word2vec embeddings in all three chart review tasks with an average AUROC of 0.80 versus 0.66, respectively. Additionally, the medical-context vector space significantly reduced the number of labels required to learn and predict the preferred similar terms of reviewers. Specifically, the labeling effort was reduced to 10% of the entire dataset in all three tasks.The set of preferred similar terms that are relevant to a chart review task can be learned by leveraging the medical context of the task.
The concept of information architecture in the context of enterprise architecture

Purpose Information architecture (IA) is often understood as a comprehensive master plan for organizational data assets and is widely considered as an essential component of broader enterprise architecture (EA). However, the status and practical operationalization of IA still remain largely unclear. In order to clarify these questions, this paper investigates what instruments related to IA are actually employed in organizations. Design/methodology/approach This study builds on the analysis of architecture practices in 27 diverse organizations. Based on the semi-structured interviews with architects and the examination of utilized architectural documents, we explore IA-related instruments with their usage scenarios that have been adopted in the studied organizations. Findings The authors identify 12 distinct instruments used in the industry and analyze in detail their features, properties and relationships. This paper analysis shows that these instruments are rather diverse and largely inconsistent across different organizations. The study findings also suggest that IA cannot be considered as a comprehensive plan for information, but rather as a variable set of loosely related instruments and practices that help organizations manage information. Originality/value The study offers a unique perspective on the concept of IA, as it is practiced in the industry today, as well as a critical scrutiny of the respective prescriptions abundant in the existing literature. Although this study does not attempt to theorize on the findings, it makes a significant empirical contribution by offering a solid evidence-based view of IA and its key instruments currently missing in the available literature.
Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.
Probing Linguistic Information for Logical Inference in Pre-trained Language Models

Progress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language models uncovered a wide range of linguistic properties encoded in their contextualized representations. However, it is unclear whether they encode semantic knowledge that is crucial to symbolic inference methods. We propose a methodology for probing knowledge for inference that logical systems require but often lack in pre-trained language model representations. Our probing datasets cover a list of key types of knowledge used by many symbolic inference systems. We find that (i) pre-trained language models do encode several types of knowledge for inference, but there are also some types of knowledge for inference that are not encoded, (ii) language models can effectively learn missing knowledge for inference through fine-tuning. Overall, our findings provide insights into which aspects of knowledge for inference language models and their pre-training procedures capture. Moreover, we have demonstrated language models' potential as semantic and background knowledge bases for supporting symbolic inference methods.
Research on Pedestrian Detection Technology Based on SSD Algorithm

Artificial intelligence has experienced two cold winters. As algorithms based on deep learning have entered people's sight, target detection technology has also ushered in a leaping development. Compared with other algorithms, the SSD algorithm has very obvious advantages in detection speed and accuracy. This algorithm only runs detection at the top level, which has very high feasibility. Background interference often occurs in pedestrian detection, and there is also the problem of missed detection of small targets. The main research content of the thesis has three parts: the improvement of the original SSD algorithm and the discussion of the basic composition and characteristics of the neural network; changing the basic vgg16 of the original SSD algorithm to resnet50 can improve the detection speed and accuracy.
Effects of Image Processing Operations on Adversarial Noise and Their Use in Detecting and Correcting Adversarial Images

Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry. However, DNNs are vulnerable to adversarial machine learning attacks in which noise is added to the input to change the networks' output. Consequently, DNN-based mission-critical applications such as those used in self-driving vehicles have reduced reliability and could cause severe accidents and damage. Moreover, adversarial examples could be used to poison DNN training data, resulting in corruptions of trained models. Besides the need for detecting adversarial examples, correcting them is important for restoring data and system functionality to normal. We have developed methods for detecting and correcting adversarial images that use multiple image processing operations with multiple parameter values. For detection, we devised a statistical-based method that outperforms the feature squeezing method. For correction, we devised a method that uses for the first time two levels of correction. The first level is label correction, with the focus on restoring the adversarial images' original predicted labels (for use in the current task). The second level is image correction, with the focus on both the correctness and quality of the corrected images (for use in the current and other tasks). Our experiments demonstrated that the correction method could correct nearly 90% of the adversarial images created by classical adversarial attacks and affected only about 2% of the normal images.
Detection and Identification of Animals in Wild Life Sanctuaries using Convolutional Neural Network

Comprehensive, Precise and real time data regarding the position and characteristics of animals is necessary for safeguarding visitors inside a wildlife sanctuary. Investigations are made on the capability for automated, unambiguous and economical collection of data that are useful to perform rescue operations within the sanctuary because of the absence of other communicational sources. Web camera enables collection of photos relating to wildlife economically, conservatively as well as regularly. Extraction of information from such photos is costly, slow and requires human intervention. The proposed system demonstrates the automatic extraction of such data using Convolutional Neural Network (CNN). Deep CNN is trained for a set of images available in a wildlife dataset.
A Successive Epidemic Prevention Infrastructure Using Mobile Robots and Smart Devices in Intelligent Public Area

Advanced intelligent paradigm allows critical systems to be expanded into various systems and applicable areas consisting of heterogeneous devices including smart devices and mobile robots. As one of those areas, intelligent public area can be considered, which provides intelligent services and solutions in public area where all citizens are accessible. Also, due to recent pandemic disease such as covid-19, the infrastructure for epidemic prevention is highly necessary. In this letter, we design a successive epidemic prevention framework using mobile robots and smart devices which are located at intelligent public area. Also, we formally define a problem whose goal is to maximize a series of detection accuracy with moving range for epidemic prevention such that multiple number of epidemic prevention barriers are built. To solve the problem, a novel method using weigh activation is proposed. Then, the proposed approach is implemented by expanded experiments using various settings and the obtained result is discussed.
Toward Ubimus Philosophical Frameworks

Abstract We tackle the philosophical implications of post-2020 music practices. To situate our discussion, we address pending issues in current definitions of music-making. Our analysis indicates that post-2020 definitions of music should feature sonic information and events, framed through social interactions and through the material grounding of the musical activity. Ubiquitous music (ubimus) furnishes a promising playing field for the emerging aspects of creative music-thinking. New frameworks that encompass the dynamic, multimodal and situated characteristics of music while skewing an anthropocentric perspective on creativity may provide meaningful targets for ubimus research toward a new notion of musicality. Three artistic projects serve to exemplify key aspects of this proposal: Atravessamentos, Memory Tree and Lyapunov Time. We address the philosophical implications of these artistic endeavors toward the construction of ubimus philosophical frameworks.
Decision-Focused Summarization

Relevance in summarization is typically defined based on textual information alone, without incorporating insights about a particular decision.As a result, to support risk analysis of pancreatic cancer, summaries of medical notes may include irrelevant information such as a knee injury.We propose a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision.We leverage a predictive model that makes the decision based on the full text to provide valuable insights on how a decision can be inferred from text.To build a summary, we then select representative sentences that lead to similar model decisions as using the full text while accounting for textual non-redundancy.To evaluate our method (DecSum), we build a testbed where the task is to summarize the first ten reviews of a restaurant in support of predicting its future rating on Yelp.DecSum substantially outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness.We further demonstrate that DecSum is the only method that enables humans to outperform random chance in predicting which restaurant will be better rated in the future.
Explainable artificial intelligence and social science: Further insights for qualitative investigation

Abstract We present a scoping review of user studies in explainable artificial intelligence (XAI) entailing qualitative investigation. We draw on social science corpora to suggest ways for improving the rigor of studies where XAI researchers use observations, interviews, focus groups, and/or questionnaire tasks to collect qualitative data. We contextualize the presentation of the XAI papers included in our review according to the components of rigor discussed in the qualitative research literature: (a) underlying theories or frameworks; (b) methodological approaches; (c) data collection methods; and (d) data analysis processes. The results of our review dovetail with calls made by others in the XAI community advocating for collaboration with experts from social disciplines toward bolstering rigor and effectiveness in user studies.
Performance Evaluation of the Codec Agnostic Approach in MPEG-I Video-Based Point Cloud Compression

In this study, we evaluated the codec agnostic approach of video-based point cloud compression (V-PCC) by applying several video codecs to V-PCC. The main concept of V-PCC is to use a video codec to compress the 2D patch images generated from the 3D point cloud. As a new immersive media standard of the Moving Picture Experts Group (MPEG), V-PCC is designed to support the codec agnostic approach, which can be employed to compress point cloud data by using any video codec. At present, the V-PCC reference software is designed by using MPEG High-Efficiency Video Coding. We extended the evaluation of the video codec applicability for PCC with well-known MPEG video coding standards, such as Advanced Video Coding, Essential Video Coding, and Versatile Video Coding. During the evaluation, we identified several key strategies for applying a video codec to V-PCC to maximize compression efficiency or computational complexity. Furthermore, the coding efficiency and time complexity of each codec were tested. The evaluation tests revealed that V-PCC supports the codec agnostic approach and that the performance of the video codec has a positive correlation with the V-PCC final coding efficiency. Reviewing these key strategies would help to develop V-PCC with different video codecs based on their profiles and levels.
Geochemistry of Sub-Depositional Environments in Estuarine Sediments: Development of an Approach to Predict Palaeo-Environments from Holocene Cores

In the quest to use modern analogues to understand clay mineral distribution patterns to better predict clay mineral occurrence in ancient and deeply buried sandstones, it has been necessary to define palaeo sub-environments from cores through modern sediment successions. Holocene cores from Ravenglass in the NW of England, United Kingdom, contained metre-thick successions of massive sand that could not be unequivocally interpreted in terms of palaeo sub-environments using conventional descriptive logging facies analysis. We have therefore explored the use of geochemical data from portable X-ray fluorescence analyses, from whole-sediment samples, to develop a tool to uniquely define the palaeo sub-environment based on geochemical data. This work was carried out through mapping and defining sub-depositional environments in the Ravenglass Estuary and collecting 497 surface samples for analysis. Using R statistical software, we produced a classification tree based on surface geochemical data from Ravenglass that can take compositional data for any sediment sample from the core or the surface and define the sub-depositional environment. The classification tree allowed us to geochemically define ten out of eleven of the sub-depositional environments from the Ravenglass Estuary surface sediments. We applied the classification tree to a core drilled through the Holocene succession at Ravenglass, which allowed us to identify the dominant paleo sub-depositional environments. A texturally featureless (massive) metre-thick succession, that had defied interpretation based on core description, was successfully related to a palaeo sub-depositional environment using the geochemical classification approach. Calibrated geochemical classification models may prove to be widely applicable to the interpretation of sub-depositional environments from other marginal marine environments and even from ancient and deeply buried estuarine sandstones.
Modulation Format Recognition Based on CNN in Satellite Communication System

Modulation format recognition (MFR) has become a challenging topic with the development of multi-format hybrid transmission. Although MFR in communication systems has been widely studied, it is still not well developed for satellite communication systems. Deep learning (DL) has been demonstrated outstanding performance in quite a few fields. This paper studied the use of DL in the MFR for satellite communication systems. For research and application, DL relies on a large amount of data, which can be collected from communication systems. In addition, DL has the advantage of not requiring manual feature selection, which significantly decreases the complexity of MFR task. In this article, we used a DL model based on convolutional neural network (CNN) to test the accuracy of MFR. In addition, we also conducted comparative measurements with traditional ML-based algorithms. The simulation results showed remarkable performance of DL in MFR task of the satellite communication system, which proves the feasibility of its application.
Facial Expression Recognition Based on Deep Learning and Attention Mechanism

Facial expression recognition has always been a challenging task. With the development of deep learning theory, facial expression recognition has brought new breakthroughs and development trends. This paper proposes a network based on attention mechanism. A mask block is designed to extract facial expression feature information, the improved residual network is used to obtain multi-scale feature information, and the convolutional block attention module (CBAM) is added to the network to pay attention to image detail features. The experimental results show that the recognition rate of the proposed network reaches 72.84% and 85.43% of the public data sets of FER2013 and RAF-DB, which effectively improves the accuracy of expression recognition.