Reviewerly/exHarmony huggingface dataset:
VISTA: A 704mW 4K-UHD CNN Processor for Video and Image Spatial/Temporal Interpolation Acceleration

Video convolutional neural networks (CNNs) have achieved great success in high-resolution imaging applications, such as video super-resolution (VSR) and demonstrated superior quality and temporal consistency by leveraging time information. In particular, as shown in Fig. 2.6.1, video CNNs can also support applications like video-frame interpolation (VFI) which is difficult to achieve by single-image CNNs. Therefore, video CNNs have enormous potential for next-generation imaging/display technology. However, there are three design challenges while inferencing high-throughput video CNNs. Firstly, massive external memory access (EMA) and computation complexity are induced since they both grow accordingly as the number of input frames (N) increases. Secondly, tremendous memory usage of feature maps (FMs) is required for supporting cross-frame alignment with in-order frame scheduling. Thirdly, supporting deformable convolution (DC) for alignment costs extra line buffers for irregular samples and computation for bilinear interpolation (BI). In this work, we present a video CNN processor supporting diverse-application video CNN inference at 4K-UHD resolution and address the challenges through three key features: 1) a cuboid-based layer-fusion (CBLF) inference flow to reduce EMA and computation complexity; 2) an alignment-aware memory optimization technique to save the FM memory size; 3) a hardware-model co-design of tile-based offset-confined deformable convolution (TODC) to alleviate the overheads of induced FM line buffers and computation logics for DC.
Remote sensing image destriping based on variable weight and group sparse regularization

Striping effects are common phenomena in remote sensing images, and they significantly limit subsequent applications. Although many destriping approaches have been developed, there aren’t many that can completely eliminate complex stripes with varying levels of strength. To address this issue, we propose a stripe removal model based on a variable weight coefficient and group sparse regularization. Specifically, rather than a single scalar for the stripes in most approaches, different weights are set for different stripe rows to estimate the stripes with varying intensities. An adaptive method to estimate the weight matrix is proposed. On the other hand, group sparsity regularization is employed to constrain the entire stripe. In addition, region weights are designed for regions with different stripe characteristics. The alternating direction multiplier method is employed to solve the proposed model by alternating minimization. Experimental results based on simulation and real data demonstrate that the proposed model outperforms other advanced methods in terms of stripe noise removal and image detail preservation.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF BERBASIS ANIMASI PADA KELISTRIKAN BODI KENDARAAN

The purpose of this study is to develop and determine the feasibility of animation-based interactive learning media on the vehicle body electrical system. The research method used is Research and Development (R&amp;D) using the Four D (4D) Define, Design, Development, and Dissemination model. The data analysis technique used is quantitative and qualitative analysis techniques. The location of this research is at Gorontalo State University and the research subjects are students who have completed or are currently taking Automotive Electrical courses. The data collection technique is a questionnaire to test the feasibility of the media. The results of this study produced a product in the form of interactive learning media based on animation on the body's electrical system. The designed learning media is declared valid because it has been tested for its validity by media experts at 81.7%, material experts at 86.6%, and students at 90%. Based on the data above, it can be concluded that the media developed is very feasible and can be used as a learning tool in the Mechanical Engineering Education Study Program, State University of Gorontalo.
Public verifiable measurement-only blind quantum computation based on entanglement witnesses

Recently, Sato et al. proposed an public verifiable blind quantum computation (BQC) protocol by inserting a third-party arbiter. However, it is not true public verifiable in a sense, because the arbiter is determined in advance and participates in the whole process. In this paper, a public verifiable protocol for measurement-only BQC is proposed. The fidelity between arbitrary states and the graph states of 2-colorable graphs is estimated by measuring the entanglement witnesses of the graph states,so as to verify the correctness of the prepared graph states. Compared with the previous protocol, our protocol is public verifiable in the true sense by allowing other random clients to execute the public verification. It also has greater advantages in the efficiency, where the number of local measurements is O(n^3*log {n}) and graph states' copies is O(n^2*log{n}).
DEVELOPMENT OF HARD SKILLS FOR THE PROCESS OF DIGITAL ENTREPRENEURIAL EDUCATION IN HIGHER EDUCATION INSTITUTIONS

DEVELOPMENT OF HARD SKILLS FOR THE PROCESS OF DIGITAL ENTREPRENEURIAL EDUCATION IN HIGHER EDUCATION INSTITUTIONS
EPLL image denoising with multi-feature dictionaries

The expected patch log likelihood (EPLL) is a patch-prior based denoising model that has become a popular tool in image restoration for its outstanding edge preservation and noise reduction abilities. However, without considering the geometrical feature of patches, the EPLL removes numerous important details and frequently suffers from the staircase effect, residual noise and artifacts. To preserve more detailed information and suppress the generation of artifacts, we propose a novel EPLL denoising model with multi-feature dictionaries (MFDs) and adaptive regularization parameters in this paper. In the proposed method, to exploit the statistical and geometrical characteristics of patches, two feature layers are designed before training the Gaussian mixture model (GMM). In the first layer, the within-class variance minimum (WCVM) method is used to learn the statistical features of patches. In the second layer, two geometrical characteristic dictionaries (GCDs) are trained. In the denoising phase, we select the corresponding dictionary for each patch. Then, each patch is denoised independently. Moreover, the mean localized spectral response (MLSR) based regularization parameters are employed in the proposed method, which is spatial self-adaptive and robust to noise. Extensive experiments demonstrate that our approach achieves desired performance in both detail preservation and artifact suppression compared to many existing denoising methods.
Image-based Visual-Servoing for Air-to-Air Drone Tracking &amp; Following with Model Predictive Control

This paper proposes an algorithm for three dimensional image-based visual servo (IBVS) control of airto-air drone tracking and following applications. The control algorithm is based on Model Predictive Control (MPC). Using video feed from a monocular camera, the controller estimates the position and orientation of a target drone. The controller ensures that the target drone is always within the Field of View (FOV) of the camera by controlling the relative positions between the target drone and the observer drone. The challenge is to ensure that the velocity input to the observer drone is feasible. This objective is achieved by developing a model predictive controller, where the control inputs are bounded while keeping the target drone within the camera FOV. The model predictive control (MPC) performed better than PID controller, providing feedback to keep target drone within the field of view (FOV) by controlling the relative yaw positions between the target and observer drones. We have achieved visual tracking by MPC for the challenging curvy trajectories at 100% (0% of readings outside 80% tolerance) and 33% (67% of readings outside 20% tolerance).
Identity theft and financial loss

Traditionally, identity theft has been seen as a means for obtaining access to a victim's financial information for monetary gain by a perpetrator. The most frequent targets were banking accounts and credit card information that could then be accessed, used to make purchases, and become the basis for opening new, fraudulent lines of credits. The movement of all or most of our financial information to a digital setting has increased the opportunity for identity theft and the possible losses associated with one's identity being coopted by criminal offenders. This chapter looks at the various methods used by offenders to commit identity fraud, the spectrum of crimes that are subsequently perpetrated once an identity is stolen, and governmental/justice system responses to identity fraud crimes.
The prosecution, conviction, and sentencing of techno-criminals: the limits of international cooperation

The global reach of the internet has allowed criminals to operate across international borders and, often, without penalty. The purpose of this chapter is to provide an overview of the issues involved in bringing a cybercriminal to justice, both within the United States and internationally. With that goal in mind, we begin with a discussion of jurisdictional and definitional challenges. We then examine the investigation, prosecution, and sentencing of cybercriminals in the United States. From there, we discuss the difficulties with international prosecution and the steps that have been taken by global leaders to address some of these challenges. We end the chapter with a call for global leaders to put geopolitical differences aside in order to strategically address the problem multilaterally.
Attitudes towards digital citizenship in university students from southern Sonora, Mexico

This study analyzed the attitudes of university students towards digital citizenship to improve practices when using technologies. The methodological design was mixed with a qualitative phenomenological approach and the quantitative part was correlational-causal with a non-experimental cross-sectional design. 84 students participated, of which 45 were studying engineering and 39 undergraduates. To obtain data, a semi-structured survey was applied individually to 72 participants, a focus group of three key informants and nine in an interview. The findings show that the process of appropriation of digital citizenship in university students still has a long way to go, given the limited acquisition of behaviors inherent to the proper exercise of digital citizenship. In this sense, the results are expressed in two aspects; on the one hand, the positive attitude where technology is expressed as an important part of society and its development, on the other hand, there is the negative attitude that perceives technology as a mere expendable tool. Finally, it is concluded that students must still improve the use of technologies since this use can negatively affect their training process.
When hearts speak

This article gathers and analyses research on biosensing user-interface design strategies and empirical research approaches in human-computer interaction (HCI). Placing human experience at the core of the primary investigation, this research article will explore Soma Design strategies developed by researcher and computer scientist Kristina Höök and comparable approaches in human computer mediations. In addition, this article investigates the peculiarities of creating a container and expressive model for architectonic media based on WorldMaker Universe (WMU) schematic, a software framework for the development of computational artworks, created by scholar and artist Mark-David Hosale and explores works and research that intersect the art and science domains. This research document also offers a unique angle on the creative and technical processes of creating a bio-art installation and virtual sculpture called Somatic Interventions , developed as a group assignment for the Vertical Studio Lab course taught by Professor Mark-David Hosale at York University, Toronto, Canada. It will critically examine and question the sculpture design’s architectural choices and evaluate the biosignal feedback system that connects human participants to internally built artificial chemistry and multiple layers of unique state machines.
Performer/audience experience, performer perception and audience immersion

This article considers dance performances that utilize immersive technologies, in particular those that result in comparatively different experiences for the performer and the audience. The article proposes that with the incorporation of technology, creators and audiences may become so engrossed by the effects of technology and what can be achieved that we perhaps overlook what is omitted when technology takes centre stage. By analysing three aspects of technology-mediated dance performances, this article highlights how technology alters and extrapolates the dance experience for performers and audiences. The differing experiences of performer and audience, the dancer’s perception of their environment and the audience’s immersion are in some ways enhanced, but also arguably hindered, by the mediation of technology. The article aims to provoke questions about technology-mediated performances and proposes more theoretical discussions on the communication of dance through technology.
Comparative Study of Image Encryption and Image Steganography Using Cryptographic Algorithms and Image Evaluation Metrics

In this paper, Steganography is carried out by converting the message hidden into ciphertext using renowned cryptographic algorithms like Advanced Encryption Standard (AES) and Triple Data Encryption Algorithm (TDEA or Triple DEA). The embedding technique produces a Stego image. Considering this as the first level of encryption, we then encrypted the whole stego image using AES and TDEA algorithms, giving us the second layer of encryption. The encrypted stego image will be returned in enc format, unreadable without the proper key. We have good resistance to brute-force, statistical, and differential attacks due to the experimental results of two tiers of protection. In addition to the encryption and decryption process, this paper also talks about Image evaluation metrics like PSNR value and SSIM value, performed on stego images, which use ciphertext from AES and TDEA algorithms that provides the statistical information.
Neighborhood Contrastive Transformer for Change Captioning

Change captioning is to describe the semantic change between a pair of similar images in natural language. It is more challenging than general image captioning, because it requires capturing fine-grained change information while being immune to irrelevant viewpoint changes, and solving syntax ambiguity in change descriptions. In this paper, we propose a neighborhood contrastive transformer to improve the model's perceiving ability for various changes under different scenes and cognition ability for complex syntax structure. Concretely, we first design a neighboring feature aggregating to integrate neighboring context into each feature, which helps quickly locate the inconspicuous changes under the guidance of conspicuous referents. Then, we devise a common feature distilling to compare two images at neighborhood level and extract common properties from each image, so as to learn effective contrastive information between them. Finally, we introduce the explicit dependencies between words to calibrate the transformer decoder, which helps better understand complex syntax structure during training. Extensive experimental results demonstrate that the proposed method achieves the state-of-the-art performance on three public datasets with different change scenarios. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tuyunbin/NCT</uri> .
Object Detector Differences when Using Synthetic and Real Training Data

To train well-performing generalizing neural networks, sufficiently large and diverse datasets are needed. Collecting data while adhering to privacy legislation becomes increasingly difficult and annotating these large datasets is both a resource-heavy and time-consuming task. An approach to overcome these difficulties is to use synthetic data since it is inherently scalable and can be automatically annotated. However, how training on synthetic data affects the layers of a neural network is still unclear. In this paper, we train the YOLOv3 object detector on real and synthetic images from city environments. We perform a similarity analysis using Centered Kernel Alignment (CKA) to explore the effects of training on synthetic data on a layer-wise basis. The analysis captures the architecture of the detector while showing both different and similar patterns between different models. With this similarity analysis we want to give insights on how training synthetic data affects each layer and to give a better understanding of the inner workings of complex neural networks. The results show that the largest similarity between a detector trained on real data and a detector trained on synthetic data was in the early layers, and the largest difference was in the head part. The results also show that no major difference in performance or similarity could be seen between frozen and unfrozen backbone.
Towards the Evolution of Prompts with MetaPrompter

The dissemination of open-source text-to-image generative models and the increasing quality of their output has led to a growth in interest in the field. The quality of the images greatly depends on the prompt used, i.e. a phrase that includes descriptive terms to be used as input on text-to-image model. However, choosing the right prompt is a complex task, often relying on a trial-and-error approach. In this paper, we introduce an evolutionary approach to prompt generation where users begin by creating a blueprint for what might be a candidate prompt and then initiate an evolutionary process to interactively explore the space of prompts encoded by the initial blueprint and according to their preferences. Our work is a step towards a more dynamic and interactive way to generate prompts that lead to a wide variety of visual outputs, with which users can easily obtain prompts that match their goals.
Cerebrovascular segmentation in phase-contrast magnetic resonance angiography by a Radon projection composition network

Cerebrovascular segmentation based on phase-contrast magnetic resonance angiography (PC-MRA) provides patient-specific intracranial vascular structures for neurosurgery planning. However, the vascular complex topology and spatial sparsity make the task challenging. Inspired by the computed tomography reconstruction, this paper proposes a Radon Projection Composition Network (RPC-Net) for cerebrovascular segmentation in PC-MRA, aiming to enhance distribution probability of vessels and fully obtain the vascular topological information. Multi-directional Radon projections of the images are introduced and a two-stream network is used to learn the features of the 3D images and projections. The projection domain features are remapped to the 3D image domain by filtered back-projection transform to obtain the image-projection joint features for predicting vessel voxels. A four-fold cross-validation experiment was performed on a local dataset containing 128 PC-MRA scans. The average Dice similarity coefficient, precision and recall of the RPC-Net achieved 86.12%, 85.91% and 86.50%, respectively, while the average completeness and validity of the vessel structure were 85.50% and 92.38%, respectively. The proposed method outperformed the existing methods, especially with significant improvement on the extraction of small and low-intensity vessels. Moreover, the applicability of the segmentation for electrode trajectory planning was also validated. The results demonstrate that the RPC-Net realizes an accurate and complete cerebrovascular segmentation and has potential applications in assisting neurosurgery preoperative planning.
TAG: Teacher-Advice Mechanism With Gaussian Process for Reinforcement Learning

Reinforcement learning (RL) still suffers from the problem of sample inefficiency and struggles with the exploration issue, particularly in situations with long-delayed rewards, sparse rewards, and deep local optimum. Recently, learning from demonstration (LfD) paradigm was proposed to tackle this problem. However, these methods usually require a large number of demonstrations. In this study, we present a sample efficient teacher-advice mechanism with Gaussian process (TAG) by leveraging a few expert demonstrations. In TAG, a teacher model is built to provide both an advice action and its associated confidence value. Then, a guided policy is formulated to guide the agent in the exploration phase via the defined criteria. Through the TAG mechanism, the agent is capable of exploring the environment more intentionally. Moreover, with the confidence value, the guided policy can guide the agent precisely. Also, due to the strong generalization ability of Gaussian process, the teacher model can utilize the demonstrations more effectively. Therefore, substantial improvement in performance and sample efficiency can be attained. Considerable experiments on sparse reward environments demonstrate that the TAG mechanism can help typical RL algorithms achieve significant performance gains. In addition, the TAG mechanism with soft actor-critic algorithm (TAG-SAC) attains the state-of-the-art performance over other LfD counterparts on several delayed reward and complicated continuous control environments.
Accurate segmentation of nuclear instances using a double-stage neural network

Automatic nuclear instance segmentation is a crucial task in computational pathology as this information is required for extracting cell-based features in down-stream analysis. However, instance segmentation is a challenging task due to the nature of histology images which show large variations and irregularities in nuclei appearances and arrangements. Various deep learning-based methods have tried to tackle these challenges, however, most of these methods fail to segment the nuclei instances in crowded scenes accurately, or they are not fast enough for practical usage. In this paper, we propose a double-stage neural network for nuclear instance segmentation which leverages the power of an interactive model, NuClick, for accurate instance segmentation by replacing the user input with a nuclei detection module, YOLOv5. We optimized the proposed method to be lightweight and fast and show that it can achieve promising results when tested on the largest publicly available nuclei dataset.
Boosting Image Emotion Analysis via Coupling Dependent Attentive Model

Image emotion analysis aims to determine an author's attitude toward the overall contextual polarity of a topic or document. It is an instrumental but challenging task, especially when analyzing fine-grained emotions. Existing techniques based on convolutional neural networks learn affective representations from local visual features while ignoring that both the global semantics of images and objects impact emotional representation learning. To address this limitation, we propose a unique Coupling Dependent Attentive Model (CDA) for image emotion analysis. The model extracts image semantics through several residual blocks and transforms the semantic feature maps obtained from fully convolutional networks through spatial attention. It then implants a self-attention mechanism to mine the correlations between local visual features and achieve the final affective representation. Finally, considering the affective image, we proposed an automated image augmentation proxy to increase model generalization. Extensive experiments on six benchmark datasets show that the proposed model outperforms state-of-the-art methods in image emotion analysis.