Reviewerly/exHarmony huggingface dataset:
PRO-TIP: Phantom for RObust Automatic Ultrasound Calibration by TIP Detection

We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between multiple sweeps. We extract them using a convolutional neural network to segment the cones in every ultrasound frame and then track them across the sweep. The calibration is robustly estimated using RANSAC and later refined employing image based techniques. Our phantom can be 3D-printed and offers many advantages over state-of-the-art methods. The phantom design and algorithm code are freely available online. Since our phantom does not require a tracking target on itself, ease of use is improved over currently used techniques. The fully automatic method generalizes to new probes and different vendors, as shown in our experiments. Our approach produces results comparable to calibrations obtained by a domain expert.
Bringing Linguistics to a Programming Class: A Problem of Automatic Text Generation for Describing Data Series

This study focuses on a variety of aspects of teaching programming which can connect the agenda of programming classes to foreign language learning and the broader scope of applied linguistics. In the domain of developing computer-assisted interactive tools for language learning, we can discover a number of interesting and non-trivial problems that can be suggested to students participating in programming classes. Specifically, this study reports an example from our project on the automatic generation of data series trend descriptions to accompany presentation graphs and charts. Originally designed for language learning purposes, this project can be revisited for possible applications as programming exercises that serve as vehicles for a problem-based learning approach to class organization. In general, we believe that through working on, and hopefully, solving practical problems students can apply the content studied in other classes. This application can significantly enhance the knowledge and skills students obtain in programming classes; and thus, improve their professional skill set.
Electron Microscope Image Registration Using Laplacian Sharpening Transformer U-Net

Image registration is an essential task in electron microscope (EM) image analysis, which aims to accurately warp the moving image to align with the fixed image, to reduce the spatial deformations across serial slices resulted during image acquisition. Existing learning-based registration approaches are primarily based on Convolution Neural Networks (CNNs). However, for the requirements of EM image registration, CNN-based methods lack the capability of learning global and long-term semantic information. In this work, we propose a new framework, Cascaded LST-UNet, which integrates a sharpening skip-connection layer with the Swin Transformer based U-Net structure in a cascaded manner for unsupervised EM image registration. Our experimental results on a public dataset show that our method consistently outperforms the baseline approaches.
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans

In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost. Nevertheless, the coarse resolution may lead to difficulties in medical diagnosis by either physicians or computer-aided diagnosis algorithms. Deep learning-based volumetric super-resolution (SR) methods are feasible ways to improve resolution, with convolutional neural networks (CNN) at their core. Despite recent progress, these methods are limited by inherent properties of convolution operators, which ignore content relevance and cannot effectively model long-range dependencies. In addition, most of the existing methods use pseudo-paired volumes for training and evaluation, where pseudo low-resolution (LR) volumes are generated by a simple degradation of their high-resolution (HR) counterparts. However, the domain gap between pseudo- and real-LR volumes leads to the poor performance of these methods in practice. In this paper, we build the first public real-paired dataset RPLHR-CT as a benchmark for volumetric SR, and provide baseline results by re-implementing four state-of-the-art CNN-based methods. Considering the inherent shortcoming of CNN, we also propose a transformer volumetric super-resolution network (TVSRN) based on attention mechanisms, dispensing with convolutions entirely. This is the first research to use a pure transformer for CT volumetric SR. The experimental results show that TVSRN significantly outperforms all baselines on both PSNR and SSIM. Moreover, the TVSRN method achieves a better trade-off between the image quality, the number of parameters, and the running time. Data and code are available at https://github.com/smilenaxx/RPLHR-CT.
ANF preserves dependent types up to extensional equality

Abstract Many programmers use dependently typed languages such as Coq to machine-verify high-assurance software. However, existing compilers for these languages provide no guarantees after compiling, nor when linking after compilation. Type-preserving compilers preserve guarantees encoded in types and then use type checking to verify compiled code and ensure safe linking with external code. Unfortunately, standard compiler passes do not preserve the dependent typing of commonly used (intensional) type theories. This is because assumptions valid in simpler type systems no longer hold, and intensional dependent type systems are highly sensitive to syntactic changes, including compilation. We develop an A-normal form (ANF) translation with join-point optimization—a standard translation for making control flow explicit in functional languages—from the Extended Calculus of Constructions (ECC) with dependent elimination of booleans and natural numbers (a representative subset of Coq). Our dependently typed target language has equality reflection, allowing the type system to encode semantic equality of terms. This is key to proving type preservation and correctness of separate compilation for this translation. This is the first ANF translation for dependent types. Unlike related translations, it supports the universe hierarchy, and does not rely on parametricity or impredicativity.
Maximum-confidence measurement for qubit states

In quantum state discrimination, one aims to identify unknown states from a given ensemble by performing measurements. Different strategies such as minimum-error discrimination or unambiguous state identification find different optimal measurements. Maximum-confidence measurements (MCMs) maximise the confidence with which inputs can be identified given the measurement outcomes. This unifies a range of discrimination strategies including minimum-error and unambiguous state identification, which can be understood as limiting cases of MCM. In this work, we investigate MCMs for general ensembles of qubit states. We present a method for finding MCMs for qubit-state ensembles by exploiting their geometry and apply it to several interesting cases, including ensembles two and four mixed states and ensembles of an arbitrary number of pure states. We also compare MCMs to minimum-error and unambiguous discrimination for qubits. Our results provide interpretations of various qubit measurements in terms of MCM and can be used to devise qubit protocols.
Supporting Resilient Operator 5.0: An Augmented Softbot Approach

Industry 4.0 and 5.0 have been posing new challenges to industries. From a focus on supporting resilience at a corporate level, there is a need to do it at a more operational level, enabling people to act with resilience as well. The resilient operator 5.0 is a new concept emerged from this need. It has the aim of providing more intuitive, symbiotic, human-centered, and cognitive working computing environments to enhance human adaptation capabilities, productivity, and mental health. In this direction, this paper presents an approach that combines softbots and augmented reality, called ‘augmented softbot’. Looking at a specific company, a software prototype has been implemented to evaluate how this approach can be useful and feasible for preventive maintenance. Three scenarios have been devised for that, and they are summarized in the paper. The achieved results are discussed, showing the high potential of the approach.
The Value of Communication Management in Agile Project Environments

In today's business environment, a less agile way of working is no longer a business choice. Implementing the agile method is not for fashion and not for keeping up with the “newest and greatest” trend. Without the agile method, it is difficult to implement any other strategic initiatives, such as digital transformation. Agile development focuses on the evolution of user needs and then uses a step-by-step approach as well as an iterative approach to software development. This paper finds that communication is vital in agile project management. The author puts forward some opinions on the communication management of agile projects, including the establishment of the information portal and agile communication matrix. Additionally, a literature review is performed to illustrate how certain variables, concepts, and models can assist in project management. This study takes a design-science-investigate approach to assess pre-existing literature.
DISTRIBUTED INFORMATION SYSTEM FOR WATER SUPPLY USING PREDICTION METHODS TO PREVENT ACCIDENTS

The purpose of the study is the development and implementation of an information alarm system for the water supply system. The article describes the developed information system for efficient water supply based on the parameters obtained from devices located on pipes. A conceptual, logical and physical model based on the description of the subject area has been developed. A water supply network database was developed using the MySQL Workbench tool. An interface has been created for monitoring the state of pipelines using the Angular framework and the programming languages TypeScript and JavaScript. The information system allows the user to view information from humidity and flow sensors. The application predicts the humidity around the pipes and the water flow for the subsequent period and receives the appropriate message if an accident is possible. The program provides the user with a convenient graphical interface for tracking possible accidents in the water supply network with reference to the coordinates of the pipes, so that the user can make a timely decision to carry out repairs and prevent such a situation.
SIHG4SR: Side Information Heterogeneous Graph for Session Recommender

In this paper we present Side Information Heterogeneous Graph for Session Recommender – SIHG4SR, our solution for RecSys Challenge 2022[3], a competition organized by Dressipi for fashion recommendation. Dressipi provides data about user session, purchased items and content features to predict which fashion item will be bought. Our solution leverages side information and heterogeneous graph, deep dives into the data and engineers new features, employs two-stage training and multi-level ensemble strategy, and enhances the performance with fine tuning and hyper-parameter tuning. Finally SIHG4SR outperforms the state-of-art baselines, getting an MRR score 0.20762 and ranked 4th position on final leaderboard(team name ”MooreWins”). We published our solution at github1.
A Survey of Digital Image Watermarking Techniques in Spatial, Transform, and Hybrid Domains

In this digital era, the vast development of multimedia environments and information technology creates a need for protecting digital content from unauthorized access. Digital watermarking is a universally accepted technique for tackling the issues in the field of multimedia security and information rights management. Several appropriate image watermarking processes have been designed to secure the digital content. An efficient watermarking technique should maintain the tradeoff among imperceptibility, robustness, embedding capacity, and security. Primarily, this paper presents the classifications and applications of digital image watermarking techniques. The discussion comprises the spatial, transform, and hybrid image watermarking methods as well as its comparison with the different schemes. The contribution of this article will be beneficial to the academicians for gaining deep knowledge about image watermarking schemes.
Reversible data hiding with enhancing contrast and preserving brightness in medical image

With the development of telemedicine and medical sharing, private information which stored in the cloud may be disclosed. In order to protect the private information of patients and improve the quality of medical image further, there are some researches of reversible data hiding (RDH) based on contrast enhancement in medical image. However, the existing methods may cause the embedded medical image to be over-enhanced when increasing the embedding capacity. With the intention of protecting the medical image stored on a semi-trusted server, a novel reversible data hiding with enhancing contrast and preserving brightness is proposed in this paper. The proposed algorithm separates medical image into regions of interest (ROI) and regions of non-interest (NROI) firstly. Then through pre-processing the bins in the histogram of ROI regions to get better contrast stretching effect. Thereafter, stretch the contrast of ROI region and embed secret message with brightness preservation. Experimental results show that the proposed method can not only enhance the ROI region’s contrast but also preserve the brightness of the embedded images when compared with traditional methods.
Creating a stop word dictionary in Serbian

By using natural language processing techniques, it is possible to get a lot of information from the extraction of document topics through mapping of document key words or content-based classification of documents, etc. To get this information, an important step is to separate words that carries informative value in a sentence from those words that do not affect its meaning. By using dictionaries of stop words specific to each natural language, the marking of words that do not carry meaning in the sentence is achieved. This paper presents creating a stop word dictionary in Serbian. The influence of stop words to the text processing is presented on three different data set. It is shown that by using proposed dictionary of Serbian stop words the data set dimension is reduced from 15% to 39%, while the quality of the obtained n-gram language models is improved.
Classification of Earthquake Vibrations Using the ANN (Artificial Neural Network) Algorithm

The Indo-Australian Plate, the Eurasian Plate, and the Pacific Plate all converge where Indonesia is situated. As a result, Indonesia is a nation where earthquakes occur frequently. Some researchers have studied machine learning algorithms for categorizing earthquake vibrations. In this experiment, earthquake vibrations are categorized using the Artificial Neural Network method. We need appropriate datasets to obtain the maximum accuracy from the artificial neural network technique. The findings of this experiment show that feature extraction is required for the datasets to be trained to obtain a high accuracy value. The mean, median, maximum, minimum, skew, and kurtosis values are the feature that are extracted. In addition to employing feature extraction, it is crucial to modify the algorithm model. The experimental setup that uses “sigmoid” activation on the input layer, the three hidden layers, and the output layer yields the best accuracy when all feature are extracted, with training to test ratio of 90% to 10%. This is demonstrated by the exceptional training accuracy and testing accuracy values, which are 99.85 percent for training accuracy and 99.12 percent for validation accuracy. The mean value yields the highest accuracy result compared to employing just one feature extraction. Only 90.97 and 90.37 percent of training and validation accuracy are obtained when the mean is used alone for feature extraction.
Data-Driven Estimation of Capacity Upper Bounds

We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm is proposed that exploits the dual representation of capacity where the maximization over the input distribution is replaced with a minimization over a reference distribution on the channel output. To efficiently compute the required divergence maximization between the conditional channel and the reference distribution, we use a modified mutual information neural estimator that takes the channel input as an additional parameter. We numerically evaluate our approach on different memoryless channels and show empirically that the estimated upper bounds closely converge either to the channel capacity or to best-known lower bounds.
Turbidity Underwater Image Enhancement based on Generative Adversarial Network

In recent years, with the continuous development of underwater object detection and recognition applications, the requirements for underwater image quality are getting higher and higher. However the quality of underwater image is always degraded seriously due to the light absorption, scattering of water itself and the suspended particles in water. As a result, the underwater images always suffer from noise pollution, reduced contrast, color distortion and blurred texture, etc. With the development of neural networks, they are applied to solve the problem of underwater enhancement as well. Due to the limited learning ability of classical deep networks, the distortion of underwater images cannot be removed thoroughly. Therefore a generative adversarial network is proposed in this paper for underwater image enhancement. In the experiments, the underwater images of different states were widely tested, and the proposed generative adversarial network improved the image quality and texture details better compared with the traditional enhancement method and classical residual networks.
Artificial Intelligence-Based Breast Cancer Detection Using WPSO

To detect breast cancer in the early stages, microcalcifications are considered a key symptom. Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used. Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment. This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying microcalcifications as normal, benign, or malignant, thereby improving the accuracy. In the breast region, tumor part is extracted using optimization methods. Here, artificial intelligence (AI) is proposed for detecting breast cancer, which reduces the manual overheads. AI framework is constructed for extracting features efficiently. This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal. This model uses MG images obtained from hospitals.
Graph Neural Networks for Cross-Camera Data Association

Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose estimation, etc. This association task is typically modeled as a bipartite graph matching problem and often solved by applying minimum-cost flow techniques, which may be computationally demanding for large data. Furthermore, cameras are usually treated by pairs, obtaining local solutions, rather than finding a global solution at once for all multiple cameras. Other key issue is that of the affinity function: the widespread usage of non-learnable pre-defined distances, such as the Euclidean and Cosine ones. This paper proposes an effective approach for cross-camera data-association focused on a global solution, instead of processing cameras by pairs. To avoid the usage of fixed distances and thresholds, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity functions. We validate the proposal for pedestrian cross-camera association, showing results over the EPFL multi-camera pedestrian dataset. Our approach considerably outperforms the literature data association techniques, without requiring to be trained in the same scenario in which it is tested. Our code is available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://www-vpu.eps.uam.es/publications/gnn</uri></monospace>
Natural Sign Language Interfaces for Deaf Users: Rationale and Design Guidelines

This paper introduces an approach to design user interfaces for people who are deaf, which prioritizes the natural language used by a significant proportion of this community, i. e., sign language. To date, the design of most interfaces assumes that users are literate in a spoken language, such as Chinese, French, or English. Thus, interfaces rely mostly on text elements in one of those languages, in addition to graphical and other multimedia components. These interfaces pose accessibility barriers for many deaf users whose first language is one of the various existing sign languages. Our approach gives sign language visibility and considers it should be the main access means to functionality of any application intended for deaf users. Key contributions of the paper are a rationale for natural sign language interfaces and a set of related design guidelines. We illustrate the application of the proposed guidelines to the design of a prototypical environment for assisting users in translating between LSM, the official Mexican sign language, and Libras, the official Brazilian sign language.
Software Effort Estimation for Agile Software Development Using a Strategy Based on k-Nearest Neighbors Algorithm

Agile development adoption in organizations is a trend that continues to accelerate according to the 15th State of Agile Report. Enterprises need to respond quickly to the needs of their customers and stakeholders and by adopting agile practices in IT teams, business value is raised in both performance and quality, so it is important to adopt practices and models that ensure the time, scope and cost of a project are achieved successfully. This paper presents a hybrid effort estimation model that uses a story point approach with machine learning techniques to estimate completion time and total cost of a project that is developed with agile methods like Scrum. The main machine learning technique used to implement the project is the k-Nearest Neighbors algorithm (KNN), its learning capabilities are assessed through 10-Fold cross validation and the estimates are compared with the original dataset and the results obtained from literature to show that estimates are competitive. The proposed approach uses category size labels that improve the original estimation model based on linear regression. The research uses 21 projects developed by six software houses, and training is done on a set created from a technique called data augmentation that generates 42 projects with a small amount of noise. Completion time is measured in days and total cost is valued in Pakistan rupees. All the results are evaluated through accuracy, Mean Squared Error, Mean Relative Error, variance and coefficient of determination.