Reviewerly/exHarmony huggingface dataset:
Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals

In a disaster site, terrestrial communication infrastructures are often destroyed or malfunctioning, and hence it is very difficult to detect the existence of survivors in the site. At such sites, UAVs are rapidly emerging as an alternative to mobile base stations to establish temporary infrastructure. In this paper, a novel deep-learning-based multi-source detection scheme is proposed for the scenario in which an UAV wants to estimate the number of survivors sending rescue signals within its coverage in a disaster site. For practicality, survivors are assumed to use off-the-shelf smartphones to send rescue signals, and hence the transmitted signals are orthogonal frequency division multiplexing (OFDM)-modulated. Since the line of sight between the UAV and survivors cannot be generally secured, the sensing performance of existing radar techniques significantly deteriorates. Furthermore, we discover that transmitted signals of survivors are unavoidably aysnchronized to each other, and thus existing frequency-domain multi-source classification approaches cannot work. To overcome the limitations of these existing technologies, we propose a lightweight deep-learning-based multi-source detection scheme by carefully designing neural network architecture, input and output signals, and a training method. Extensive numerical simulations show that the proposed scheme outperforms existing methods for various SNRs under the scenario where synchronous and asynchronous transmission is mixed in a received signal. For almost all cases, the precision and recall of the proposed scheme is nearly one, even when users’ signal-to-noise ratios (SNRs) are randomly changing within a certain range. The precision and recall are improved up to 100% compared to existing methods, confirming that the proposal overcomes the limitation of the existing works due to the asynchronicity. Moreover, for Intel(R) Core(TM) i7-6900K CPU, the processing time of our proposal for a case is 31.8 milliseconds. As a result, the proposed scheme provides a robust and reliable detection performance with fast processing time. This proposal can also be applied to any field that needs to detect the number of wireless signals in a scenario where synchronization between wireless signals is not guaranteed.
Experimental research on occlusion face detection method based on attention mechanism

Abstract Face occlusion is a common problem in real life. Aiming at the problem that the face detection effect is poor when there is occlusion in the natural environment, this paper proposes the RetinaNet occlusion face detection network based on the fusion image enhancement network and the attention mechanism. Traditional RetinaNet, as a typical single-stage detector, does not explicitly give the process of extracting candidate regions and directly obtains the results of object detection. Compared with the two-stage detector, the processing speed is faster, but the accuracy will also be reduced. In this paper, the attention mechanism and image enhancement strategy are introduced to improve the detection accuracy while ensuring the detection speed. The experimental results on the MAFA dataset show that the RetinaNet model based on the attention mechanism proposed in this paper achieves 89.7% accuracy in detecting occluded faces. The feasibility and effectiveness of the model are verified.
Inclusion Techniques for Team Practices in HCI

In tech, women and people of color consistently report being ignored, devalued, and perceived as less competent than men. But if all members of diverse teams do not feel valued and connected, team performance and innovation are undermined. This course introduces practices that interrupt bias and negative interactions while ensuring all are heard during working meetings. We cover effective techniques to structure participation in working team meetings and guide decisions and feedback so all are heard. These techniques arm HCI professionals who are leaders or facilitators, and academics guiding student teams, with ways to ensure inclusion.
Research Methods for People in a Hurry

Research methods are a foundational part of an education in human-computer interaction. This education is not always provided, and what training is available may not always be focused on the most relevant topics for this diverse field. This course aims to survey research methods relevant to psychology, human factors, computer science, and engineering. This course is suitable for those with limited background in research, and can benefit those with substantial experience. This course aims to provide exposure to many relevant topics and inspire attendees to delve deeper and hone their craft with this course as an introduction.
Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model

In this paper, an in-depth study of cross-media semantic matching and user adaptive satisfaction analysis model is carried out based on the convolutional neural network. Based on the existing convolutional neural network, this paper uses rich information. The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity. Aiming at the problem that it is difficult for the current hyperspectral image classification method based on convolutional neural network to capture the spatial pose characteristics of objects, the problem is that principal component analysis ignores some vital information when retaining a few components. This paper proposes a polymorphism based on extension Attribute Profile Feature (EMAP) Stereo Capsule Network Model for Hyperspectral Image Classification. To ensure the model has good generalization performance, a new remote sensing image Pan sharpening algorithm based on convolutional neural network is proposed, which increases the model's width to extract the feature information of the image and uses dilated instead of traditional convolution. The experimental results show that the algorithm has good generalization while ensuring self-adaptive satisfaction.
Thermal entanglement of two atoms with dipole-dipole and Ising interactions

In this paper, we investigated the entanglement dynamics between two two-level natural or artificial atoms interacting with a thermal one-mode field of lossless cavity taking into account the direct dipole-dipole and Ising coupling. We obtained the exact solution for time-dependent density matrix and calculated atom-atom negativity. The results showed that for dipole-uncoupled atoms and resonant atoms-field interaction the entanglement greatly enhances with Ising coupling increasing. In contrast, for model with non-zero detuning the Ising coupling has almost no effect on the degree of entanglement. We also derived that for resonant interaction and dipole-coupled atoms the anti-Ferromagnetic coupling produces a slightly higher degree of entanglement in comparison with the Ferromagnetic coupling. For entangled initial atomic states we obtained that Ising coupling interaction has effect on entanglement behavior only for a model with large detuning values.
Playing games through interactive data comics to explore water quality in a lake

This case study explores the use of an existing data-driven storytelling method, called data comic, within a co-design process, to improve the sense-making of data. Data can often support a co-design process by providing additional insight towards a problem that is being solved. A large number of methods are available to facilitate different aspects of a co-design process, but when it comes to embedding curated data, alternatives are limited. Not everyone has the expertise to understand raw data and co-design scenarios are typically time-limited meaning that learning new data skills is not the focus. Therefore, appropriate data curation can help to bring data into a design process while reducing time spent on data manipulation or upskilling. At the same time, it is important that participants are encouraged to think critically about the data and not simply accept a pre-determined viewpoint. We have adapted an existing technique called Data Comic for curating data for use in a co-design situation by turning the comic panels into a card game so that they can be used interactively and collaboratively. In this paper, we describe and reflect on its use within a workshop with participants who were mostly teenagers. We led them through a process of engaging with curated data to make sense of water pollution in a Finnish lake since the 1970s and raise awareness towards environmental pollution. We present our results from the participant's feedback and workshop to reflect on how data comics played a role in sense-making.
Anti-Racism in Action: A Speculative Design Approach to Reimagining SIGCHI

In this interactive panel, a brief introduction by the panelists regarding their stances on race and HCI will be followed by breakout group discussion in which participants will be prompted to ask themselves what anti-racist actions they can take in their workplaces and in HCI work.
Deep Learning in Data Mining Management of Industrial and Commercial Enterprises

Due to the frequent use of the Internet and the generation of massive amounts of data, people have entered the era of big data. A large amount of data is generated in people’s daily life. Because these data contain a large amount of information and more complicated content, the process of data analysis and utilization must account for an increasing number of issues. As the problem becomes more complex, the use of deep learning in data mining becomes increasingly important. This article gives a certain introduction and understanding of deep learning and data mining and analyzes and summarizes the application of deep learning in data mining. It introduces a data mining approach for industrial and commercial companies, as well as the requirements of the classic CRISP-DM data mining method. It also exhibits the application of deep learning in industrial time-series data classification in the content of industrial and commercial companies.
Connecting the dots in clinical document understanding with Relation Extraction at scale

<h2>Abstract</h2> We present a text mining framework based on top of the Spark NLP library - comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models, which expands on previous work in three main ways. First, we release new RE model architectures that obtain state-of-the-art F1 scores on 5 out of 7 benchmark datasets. Second, we introduce a modular approach to train and stack multiple models in a single nlp pipeline in a production grade library with little coding. Third, we apply these models in practical applications including knowledge graph generation, prescription parsing, and robust ontology mapping.
Methodological Reflections on Ways of Seeing

The computer as a creative medium has largely influenced how we make, use, and share images. This study investigates how similarly or distinctively human designers and the computers see (i.e., process visual information from the activity of seeing) in creative practice. Based on the review of the perspectives relevant to seeing (e.g., visual experience, visual creativity, visual computing), we practiced a visual inquiry by marking how we see photographic images in comparison to what computer vision processes from the same images. Taking a researcher-introspective approach, two authors reflectively analyzed the processes and outcomes of the inquiry. The findings revealed that during the creative practice, the activity of seeing is a meaning-making process that is guided, shifted, and diffracted through visual conversations between visible and invisible qualities suggested by compositional rules and computer vision. We discuss potentials of computational visual intelligence as creative agents and conclude with methodological and practical implications.
Content-Based Image Retrieval Using DNA Transcription and Translation

DNA carries the genetic information of almost all the living beings on the earth. The flow of genetic information takes place by a series of transcription and translation reactions in which the DNA gets converted into amino-acid sequences which determine the phenotype of an organism. This property of DNA has been used in the proposed CBIR technique in which the images are first stored in DNA sequences and then their corresponding amino-acid sequences are extracted which are used to form the feature-vectors. This not only ensures the reduction of the dimension of the feature-vectors but also the preservation of the necessary information. These feature-vectors are then given as input to various classifiers for training and testing purpose. Ensemble learning is then applied to enhance the retrieval efficiency of the algorithm. The proposed algorithm is a novel approach that uses the efficiency of DNA-based computing to increase the efficiency of classifiers for image retrieval. Experimental results show that the proposed method is more efficient than the existing state-of-the-art algorithms.
The who, what, why and when of data protection impact assessments

Despite their importance, misunderstandings about Data Protection Impact Assessments (DPIAs) are a common stumbling block for many businesses. So what do you need to know about them?
An Image Compression and Encryption Scheme for Retrieval on Compressed Domain Ciphertext

With the development of cloud computing, people usually outsource encrypted images for saving storage and protecting privacy. However, traditional image encryption methods not only hinder the availability of images such as similarity retrieval, but also degrade the compression performance. To address this issue, we propose a retrievable image compression and encryption method(RICE). RICE takes into account the contradiction of image compression, availability and security, then propose a cascaded information bottleneck model, which includes the compression information bottleneck and the security and available information bottleneck. The compressed information bottleneck is converted into a rate distortion problem and its optimal solution is sought by a convolutional neural network(CNN)-based compression network which includes channel space attention module and discrete wavelet transform(DWT) module. To solve the security and available information bottleneck, we propose a feature partition method to find a retrieval subset that balances the contradiction between security and availability, and design a DNA-based deterministic encryption method for this subset to support ciphertext retrieval. The ciphertext of the retrieved subset is sent to the proposed similarity search fully connected network(SimFcNet) to improve the retrieval accuracy. The remaining subset is encrypted by Non-deterministic encryption to further improve security. Experimental results show that our method is 36.56% higher than JPEG2000 at compression ratio of 60:1 in MS-SSIM, the accuracy of ciphertext retrieval can reach 0.828, and the security of ciphertext is close to that of traditional encryption methods.
Multi-level Prediction for Overlapped Parcel Segmentation

In this paper, we propose a new instance segmentation framework based on a multi-level prediction mechanism, aiming at segmenting overlapped parcels. In this framework, one location on FPN’s feature maps can predict a set of overlapped instances through a multi-level head architecture according to their overlapping order. Besides, to avoid the inherent limit of bounding boxes in object overlapping scenes, our approach is bounding-box free. And we also provide a dataset for overlapped parcel instance segmentation named OLParcel. On a Mask RCNN baseline, our network can improve 4.25% AP, 4.26% recall, and 7.11% MR $$^{-2}$$ on our dataset.
Compression of thermal images for machine vision based on objectness measure

Recent development of intelligent object detection systems requires high-definition images for reliable detection accuracy performance, which can cause a high occupation problem of network bandwidth as well as archiving storage capacity. In this paper, we propose an objectness measure-based image compression method of thermal images for machine vision. Based on the objectness of a certain area, bounding box for the area with high objectness is adjusted in order not to affect the possible object detection performance and the image is compressed in a way that the area having a high objectness is compressed with lower compression ratio than other area. The experiments indicate that superior object detection accuracy at comparable BPP is accomplished using the proposed scheme to that of the state-of-the-art video compression method.
Template matching via search history driven genetic algorithm

Pixel-based template matching suffers from computational cost by increasing potential solutions. Genetic algorithms has been adopted to search hopeful solutions, whereas there is a demand for more accurate matching. In this paper, we propose to employ a modified real-coded genetic algorithm to solve the template matching problem. Specifically, individuals sampled during the exploration process are stored in an archive and spatially clustered in the search space. An enhanced crossover (abbreviated as SHX) exploits the extra cluster information to generate new individuals in more promising regions. To solve the matching problem, this algorithm searches for suitable geometric parameters using a pixel-level dense similarity measure. Experimental results show the effectiveness of SHX for solving the template matching problem.
Depth image restoration algorithm using graph signal processing based image colorization

This paper deals with a depth image restoration algorithm. Depth images for the purpose of visualizing stereoscopic information of an object are generally obtained from a depth sensor with low resolution compared with an RGB sensor. Therefore it is required to realize super-resolution of the depth image. This paper proposes the depth image restoration algorithm using the color guided image based on the graph signal processing. A graph is generated from a color image, and the graph signal value is considered as a depth value. Then the depth value is restored using the technique of graph signal value restoration. Numerical experiments show that it has higher restoration performance than several previous methods.
An interactive shadow generation system for spatial augmented reality

A shadow implicitly represents the existence of a human or object for various applications in interaction and media art designs. However, it is challenging to generate a natural shadow in artificial for a spatial augmented reality where the conventional approaches ignore the object dynamics and automatic control. In this work, we propose an interactive shadow generation system that creates the interactive shadow of users using a projector-camera system. With the offline processes of human mesh modeling and virtual environment registration, the proposed system rigs the 3D model created by scanning the user to generate the shadow. Finally, the generated shadow is projected into the real environment. We verify the usability of the proposed system and the impression of the generated shadow from our user study.
Swimmer position estimation and stroke analysis based on head recognition

In the field of competitive swimming, performance investigation on official games is important and useful for performance development. We have been working on swimmer position estimation from a wide video view of official games. The challenges are water splash and complicated reflection of light that may hide swimmers from the camera. To overcome these problems, we utilize YOLOv3 and prepare a dedicated dataset of swimmers' heads in real games. We have trained the YOLOv3, and the trained YOLOv3 can detect heads by 48.1% mAP. In addition to the position estimation, we also propose a new method to investigate the status of the strokes along the time by detecting two head-classes: over the water and under the water. We also prepare another dedicated dataset for this two-class training. With the trained YOLOv3, we successfully visualize the status change of a swimmer over a whole game.
Dynamic photometric stereo for flat bas-relief surfaces

Photometric stereo recovers surface shape for well details but fails when the images are captured with relative motion between camera and object. Therefore, we propose a dynamic photometric stereo method for 3D reconstruction of flat bas-relief objects. The key contributions of our work are to build a unified world coordinate system between multiview images by structure from motion with eliminating the mismatching points caused by the shadow, and to establish the pixel-level dense match, utilizing the homography between the flat object in two views. Finally, we can use the classic photometric stereo to obtain a high-quality 3D reconstruction result. The effectiveness of our method is verified on the real datasets.
Enhancing angular resolution using layers obtained from light field superpixel segmentation

In order to alleviate the inherent trade-off relation between spatial and angular resolutions of light field (LF) images, many experiments have been carried out to enhance the angular resolution of LFs by creating novel views. In this paper, we investigate a method to enhance the angular resolution of LF image by first grouping the pixels within and across the multiple views into LF superpixels using existing LF segmentation method, then generating novel views by shifting and overlaying the layers containing the LF superpixels having similar per-view disparity values. Experimental results with synthetic and real-scene datasets show that our method achieves good quality of reconstruction.
A method of reflection representation in mixed reality using light source estimation

In recent years, research has been conducted on the reflection of real space onto virtual objects. However, conventional methods have not been able to achieve accurate reflection due to problems such as the direction and angle of the reflected object not matching. In addition, since the light source is not estimated, the color of the virtual object floats away from the surroundings and shadows are not generated. In this study, we propose a method to integrate virtual objects and real space without any discomfort by using light source estimation.
Design of a SSIM-optimal 2D post filter with symmetric coefficients for coding artifacts reduction

We previously proposed a method of designing a 2D FIR filter that can maximize the well-known objective quality index called SSIM. The designed filter can be used as a post processing tool for lossy image coding methods to reduce coding artifacts. In this scenario, there is a trade-off between the amount of side information on filter coefficients and the obtained gain in image quality. In this paper, effectiveness of the designed filters on the rate-SSIM based coding performance is evaluated under different settings of the size and quantization precision of the filter coefficients. Moreover, we introduce symmetric constraints on the filter coefficients to reduce the side information.
Does modern facial feature extraction network need face normalization? A study on K-Face dataset

We analyzed the effect of face normalization function on face recognition. In detail, the effect of face normalization on identification accuracy was analyzed for two different feature extraction networks. Identification protocol using K-Face dataset was designed to train and test. The positive effect of face normalization on large pose was examined. However, normalization had a negative impact on modern feature extraction network. In conclusion, it was expected that the performance improvement could be achieved only when more precise face normalization modules were added to the advanced feature extraction network.