Reviewerly/exHarmony huggingface dataset:
Relay-UNet: Reduce Semantic Gap for Glomerular Image Segmentation

With the increasing number of patients with chronic kidney disease (CDK), the workload of pathologists is heavy and the diagnostic efficiency is low. Therefore, the use of computer technology to assist the diagnosis of nephropathy becomes the trend of future development with a great application space. In recent years, UNet network has been widely used in medical image segmentation, and many improved algorithms have appeared. Most of existing methods focus on adding new modules or incorporating other design concepts on the basis of UNet, however, the structure of UNet network has not been fully analyzed. This paper points out two problems existing in UNet network: insufficient feature extraction in encoder interferes with the accuracy of image segmentation, and the fixed mode of skip connection in each layer leads to information redundancy in the network. To solve these problems, we improve the encoder and feature fusion method of UNet and named the new network as Relay-UNet. Experiment results show that under the condition of almost no change in consumption time, the Dice coefficient of Relay UNet on the glomerular dataset reaches 97.5%, and the training process is more stable than that of UNet.
Robust Weakly Supervised Semantic Segmentation via Dual Domain Cascaded Decision and Annotation Bootstrap

The performance of weakly supervised semantic segmentation (WSSS) can be effectively improved by introducing Web images. In order to achieve knowledge transfer using external data robustly, a robust knowledge transfer learning method of dual domain collaborative bootstrap is proposed. Firstly, the knowledge transfer from Web domain to target domain is effectively achieved through dual domain cascaded decision-making strategy, which highly promotes the robustness of WSSS. In addition, the reliability is intensively enhanced due to reducing the noise of images and improving the quality of annotation by dual domain collaborative learning. The method achieves 65.4% and 65.9% mIoU on benchmark dataset PASCAL VOC 2012 validation and test sets, respectively. The experimental results outperforming the most of WSSS methods could show the effectiveness of the proposed method.
Towards Understanding and Improving Handwriting with AI

What makes a handwriting good? If the aesthetic judgment of handwriting follows implicit rules, can those rules be recovered by observing good and bad examples? To answer these questions, we apply explainability techniques to the classification of good and bad handwriting. We show that it is indeed possible to recover these inherent rules. We develop an AI system that uses a modified version of LIME Image Explainer and generates images containing suggestions for improvement. We use single-character and word-level datasets labelled with binary labels generated via accepted rules for handwriting classification. We discuss the possible improvements to the current system as well as where this research could be applied, such as user-specific auto-suggestions.
AI-Based Tracking System from Real-Time CCTV Captures

Real-time tracking of criminals and missing people is still a challenge in surveillance systems as police are still searching the criminals in the CCTV footages manually without any automated system. Moreover, police lookup criminals and missing people manually only after a search warrant is issued to them. The usage of fuel and time over searching a criminal who would have easily managed to change his country goes completely in vain. Even though there exists some similar system to recognize faces from CCTV cameras, there are lot of challenges faced by those systems which are resolved mostly in this paper. Hence, we suggest an AI-based tracking system which uses improved versions of face recognition algorithms, thereby considering the challenges faced by CCTV cameras and multiple generated face detection under all possible lighting conditions and all possible facial features to identify the target.
A Methodology for Formalizing Different Types of Norms

In a world where many activities are carried out digitally, it is increasingly urgent to be able to formally represent the rules, norms, and policies that regulate these activities. In multi-agent systems, formalizing policies written in a natural language into a formal model, making them machine-readable, is a demanding task. In this paper, we introduce a methodology to help people to understand the fundamental elements that they should consider for this transformation. In this paper we will focus mainly on a methodology for formalizing norms using the T-Norm norm model, this because it allows us to express a rich set of different types of norms. In any case, the proposed methodology is general enough to also be used, in some of its steps, to formalize norms using other formal languages. This is an important issue because since there is not yet a set of different types of norms that is sufficiently expressive and is recognized as valid by the NorMAS community, papers presenting a given model usually do not explicitly state which types of norms can be expressed with that model and which cannot. Therefore, the second goal of this paper is to propose and discuss a rich set of norm types that could be used to study the expressive power of different formal models of norms and to compare them.
A Comprehensive Evaluation of Convolutional Hardware Accelerators

Industry increasingly adopts Convolutional Neural Networks (CNNs) in applications ranging from IoT to autonomous driving. Convolutional hardware accelerators for the inference phase are an option for CPUs and GPUs due to the smaller power consumption and improved performance. The literature presents hardware accelerators using different 2D architectures, including weight stationary (WS), input stationary (IS), and output stationary (OS). The main differentiation between these architectures is how accelerators access data (input feature map and weight tensors) and compute the output (output feature map tensor). There is a gap in the literature related to a comprehensive evaluation of such architectures. This brief aims to answer the following question: “which accelerator type should I use according to my design constraints and memory type (SRAM or DRAM)”. Experiments show that when using SRAM as external memory to the accelerator, WS presents the smallest area and energy consumption, while IS presents the best performance. On the other hand, the IS accelerator stands out when using DRAM because it has a reduced performance sensitivity to memory latency.
Insulators Detection with High Resolution Images

The potential safety hazards for the power grid caused by explosion of insulators occur again and again. Thus, the detecting and monitoring of insulators on the transmission towers is vital. In the paper, a novel method was proposed to detect insulators with high resolution satellites images. First, the SuperView-1 (0.5 m) and WorldView-3 (0.3 m) scenes of Yunnan were gathered, and then gram-schmidt method was used to fusion the original images. Second, a wide deep super resolution network (WDSR) is used to enhance the images resolution by 4 times. Third, fake color output and 1% linear stretched were applied to enhance image detail. Then, an object detection neural network based on feature pyramid networks (FPN) was used to detect transmission tower. Finally, a high-resolution network (HR-Net) was used to detect insulators on the tower. For comparison, three different class weight calculation methods and online hard example mining (OHEM) training methods of HR-Net were also proposed. HR-Net-c2-ohem final achieved highest 0.8001 of F1-Score. Therefore, our proposed method is robust to detect the insulators of transmission line tower with high resolution satellites images.
Gradient-Domain Path Tracing with Gain Control Strategy

Uniform sampling on the pixel plane makes the rendering quality of gradient domain path tracing differ greatly in different frequency regions. Aiming at this problem, gradient-domain path tracing with gain control strategy is proposed. The average gradient is introduced to calculate the average gradient value of partial areas, to filter and to mark the high-frequency areas and low-frequency ones, then to enhance the sampling of the high-frequency areas by samples in the low-frequency ones. With the continuous update of the high and low-frequency areas at the end of each round of rendering, the samples are allocated adaptively, so as to realize the gain control of the pixel plane sampling. On the one hand, the experiment compares the improved algorithm and the gradient-domain path tracing algorithm under different materials and lighting scenes, the results show that the improved algorithm makes the rendering of the high-frequency areas sensitive to the human eye more delicate; on the other hand, the experiment introduces the mean square error, peak signal to noise ratio and structural similarity to evaluate the rendered image, and compare the trends of the above three indicators during the sampling process, the results show that the improved algorithm can accelerate the convergence speed of rendering and improve the rendering efficiency under the condition of ensuring the same number of samples.
Reduced-Reference Image Quality Assessment for Single-Image Super-Resolution by Convolutional Neural Network

Single-Image Super-Resolution (SISR) aims to improve the image resolution with good visual quality, which is a classical problem in the field of image processing. How to assess the SISR image quality is still a challenging problem, although many SISR algorithms have been proposed. In this paper, we design a convolutional neural network (CNN) to predict the image quality of SISR by taking the low resolution (LR) image as the reference image. The proposed network consists of six convolution layers, four fully connected layers and one regression layer. This reduced-reference method uses CNN to extract the features of LR and SR image patches, and predict the quality of super-resolution reconstruction image patches by random forest regression. Experimental results show that the proposed method can provide evaluation that is more consistent with the subjective assessment score, and outperforms other image quality assessment methods.
Breast Cancer Image Pre-Processing With Convolutional Neural Network For Detection and Classification

Breast Cancer is one of the most common types of cancer. This research was conducted with the purpose of developing a Computer-Aided Diagnosis to detect breast cancers from mammogram images. The mammogram images were obtained from the INbreast Dataset and Husada Hospital in Jakarta. The program was developed with the usage of pre-processing which includes Median Filtering, Otsu thresholding, Truncation Normalization, and Contrast Limited Adaptive Histogram Equalization to manipulate the images and Convolutional Neural Network to classify the images into either mass or normal, or either benign or malignant. The pre-processing pipeline have provided enhanced images to be used to train and test the Convolutional Neural Network. The best model achieved reached an accuracy, precision and sensitivity of 94.1%, 100% and 85.7% in classifying the mammogram images into benign or malignant, and 88.3%, 92.6% and 83.3% in classifying the mammogram images into mass or normal. In conclusion, the algorithm was able to classify mammogram images and has provided results as high as other related researches.
Improving Person Re-identification with Semantically Aligned Appearance Transformer

Person re-identification (re-id) has achieved significant improvement under the convolution neural network (CNN)-based methods. But it suffers from information loss on details caused by convolution and downsampling operators. Recently, there has been a growing interest in conducting image classification tasks by applying transformer-based methods to overcome these limitations. However, it is still limited by the body misalignment problem caused by pose/viewpoint variations, imperfect person detection, occlusion, etc. To address these problems, we leverage the estimation of the dense semantics of a person image to construct a set of densely human appearance semantically aligned images (DHASA-images), where the same spatial positions have the same semantics across different images. In this paper, we take both original images and DHASA-images into consideration to obtain the discriminative representation of pedestrians. We propose a novel approach named appearance semantically aligned guiding network (ASAG-Net) for person reid based on the transformer. The network is composed of two subnetworks: one for feature extraction from two kinds of images and another for feature fusion and obtain the final feature final feature generation. To the best of our knowledge, we are the first to make use of densely human appearance semantically aligned to strengthening the transformer-based person re-id method. We demonstrate the proposed method through extensive experiments and achieve superior results on three large-scale person re-id datasets, Market1501, DukeMTMC-reID, and MSMT17.
Light-Weight Permutation Generator for Efficient Convolutional Neural Network Data Augmentation

Permutation is a fundamental way of data augmentation. However, it is not commonly used in image based systems with hardware acceleration due to distortion of spatial correlation and generation complexity. This paper proposes Restricted Permutation Network (RPN), a scalable architecture to automatically generate a restricted subset of local permutation, preserving the features of the dataset while simplifying the generation to improve scalability. RPN reduces the spatial complexity from $$\textit{O}(Nlog(N))$$ to $$\textit{O}(N)$$ , making it easily scalable to 64 inputs and beyond, with 21 times speed up in generation and significantly reducing data storage and transfer, while maintaining the same level of accuracy as the original dataset for deep learning training. Experiments show Convolutional Neural Networks (CNNs) trained by the augmented dataset can be as accurate as the original one. Combining three to five networks in general improves the network accuracy by 5%. Network training can be accelerated by training multiple sub-networks in parallel with a reduced training data set and epochs, resulting in up to 5 times speed up with a negligible loss in accuracy. This opens up the opportunity to easily split long iterative training process into independent parallelizable processes, facilitating the trade off between resources and run time.
ROS-based simulation environment for obstacle avoidance in autonomous navigation

In this paper, we present a Robot Operating System (ROS) based simulation environment for obstacle avoidance in maritime operations, suitable for different planning algorithms. The chosen algorithm in the current implementation is the Timed Elastic Band (TEB) available in the TEB_local_planner package of ROS. It allows to plan paths that are consistent with the kinematic constraints of an Autonomous Surface Vehicle (ASV) and to optimize the planning according to several parameters such as the navigation time and the Cross Track Error. The proposed framework has been validated and its performance assessed via Monte Carlo simulations.
3CPS: The Design of an Environment-Focussed Intermediate Representation

We describe the design of 3CPS, a compiler intermediate representation (IR) we have developed for use in compiling call-by-value functional languages such as SML, OCaml, Scheme, and Lisp. The language is a low-level form designed in tandem with a matching suite of static analyses. It reflects our belief that the core task of an optimising compiler for a functional language is to reason about the environment structure of the program. Our IR is distinguished by the presence of extent annotations, added to all variables (and verified by static analysis). These annotations are defined in terms of the semantics of the IR, but they directly tell the compiler what machine resources are needed to implement the environment structure of each annotated variable.
Architecture Colorization via Self-supervised Learning and Instance Segmentation

In recent years, building coloring has become one of the hot spots in computer vision. In this paper, we design an instance segmentation module consisting of a region proposal network (RPN) and a backbone network, which can effectively avoid the effect of coloring between multiple objects. At the same time, we combine the feature map and the weights of the separated parts for fusion to avoid the coloring position error caused by segmentation. Meanwhile, we propose a self-supervised architectural coloring model that can construct positive and negative samples to achieve contrast constraints to solve the problem of unlabeled datasets. The model filters out unnecessary image noise based on architectural features and improves the effectiveness of the loss function by obtaining more accurate structural details. We provide a dataset and perform segmentation to help train the coloring weights for all seasons. We conducted extensive ablation experiments with multiple datasets. The experimental results visually demonstrate the excellent performance of our model.
“All the Things that Come and Go, Stop and Say Hello": Towards an ontological account of how participants enter and exit events

This paper presents an ontological account for how participants enter or exit events, based on a view of events as delimited by systems. With that, we introduce the notions of engaging and disengaging event as the events through which (respectively) an object becomes or ceases to be a participant of a main, external event by becoming or ceasing to be a component of the system that delimits this external event. We argue that it is a general type of event with many practical examples, which reveals a contextual facet of events and may shed light on the notion of roles for events.
Adaptive Context Modeling for Arithmetic Coding Using Perceptrons

Arithmetic coding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context sizes is often infeasible. Recently, neural networks have been used to model probabilities of large contexts in order to drive arithmetic coders. These neural networks have been trained offline. We introduce an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> method for training a perceptron-based context-adaptive arithmetic coder on-the-fly, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive perceptron coding</i> , which continuously learns the context probabilities and quickly converges to the signal statistics. We test adaptive perceptron coding over a binary image database, with results always exceeding the performance of LUT-based methods for large context sizes and of recurrent neural networks. We also compare the method to a version requiring offline training, which leads to equally satisfactory results.
Smart Meter Intrusion Detection Based on APSO-DBN Model

With the continuous development of network technology and information technology, more and more attention has been paid to the network security of smart meters. When facing the network intrusion data of smart meters, the data has large-scale, high-dimensional and non-linear intrusion detection problems. This paper proposes a model about the network intrusion model, which is based on Adaptive Particle Optimization (APSO) Deep Belief Network (DBN). First, construct a classification model based on DBN algorithm. Next, accelerate the optimization of DBN by using APSO algorithm, and calculate the number of hidden layer nodes to obtain the DBN model structure of the best hidden layer. Finally, construct the intrusion detection model of smart meter based on APSO-DBN.
The Analysis of the Effectiveness of Online Learning Media for the Students During the Covid-19 Pandemic

This study discusses the analysis of the effectiveness of online learning media for the students during the covid-19 pandemic. This research is to know the effectiveness of online media for the students during the covid 19 pandemic. The method used is quantitative descriptive research with a total sampling of students of Stikes St. Elisabeth Semarang during the online learning process by giving questionnaires related to online learning media during the covid-19 period. The results obtained that most of the students at Stikes St. Elisabeth often uses Google Meet (GM) learning media and likes online learning through Google Meet (GM). The most frequently used feature of GM is the presentation form. The conclusion is that GM is an effective and efficient learning media during the covid-19 pandemic.
Critical Condition

For the past several decades, academics from all disciplines have condemned and lamented the transformation of universities into neoliberal institutions. This chapter draws from these texts to examine the environment within which academic workers are supposed to write critical theory. In doing so, this chapter asks a simple question: is it possible to write critical theory while working in the neoliberal university? Just as the conditions that produce hunger do not produce the possibility of providing food, the conditions of the neoliberal university may well attest to the need for critical theory, but the circumstances that give rise to this need do not necessarily provide any solution.
Mobile technology as a tool to support the enhancement of students' communicative competence

Mobile technology is an integral part of today's modern world. In developed countries people use mobile technology (smartphones or tablets) every day. This fact is reflected in education where mobile technology has become a widely used digital tool. Much of the existing research has focused on the relationship between mobile technology and knowledge acquisition, specifically the effect mobile technology has on knowledge retention, students' imagination and motivation or the comprehensibility of the subject matter. Considering the scope of the existing studies, a question arises as to how mobile technology helps improve key competencies. Key competencies form the basis of modern education, and their development prepares the students for their professional and social life. Communicative competence is one of the main key competencies. The paper presents the design of a research project aimed at determining the effect of the targeted use of mobile technology on the development of students' communicative competence. The first part of the paper introduces the topic and provides theoretical background on key competencies and mobile technology in education. The next part of the paper focuses on the research outcomes. Research will be conducted through an educational experiment where the authors will be comparing the control and experimental group outputs. The control group will be developing its communicative competence through activities that have already been used in class. On the other hand, the experimental group will be using alternative methods where mobile technology plays a key role in developing communicative competence. In this paper the authors define the research questions, research objective and research hypotheses. It also focuses on the target group and the research time frame. Furthermore, the authors introduce the research tools that will be used to determine the level of students' communicative competence. The third chapter focuses on proposed activities for the control and experimental groups. In the final part of the paper, the authors introduce possible research risks and limitations, as well as future research possibilities.