Reviewerly/exHarmony huggingface dataset:
A fully automatic framework for evaluating cosmetic results of breast conserving therapy

The breast cosmetic outcome after breast conserving therapy is essential for evaluating breast treatment and determining patient's remedy selection. This prompts the need of objective and efficient methods for breast cosmesis evaluations. However, current evaluation methods rely on ratings from a small group of physicians or semi-automated pipelines, making the processes time-consuming and their results inconsistent. To solve the problem, in this study, we proposed: 1. a fully-automatic Machine Learning Breast Cosmetic evaluation algorithm leveraging the state-of-the-art Deep Learning algorithms for breast detection and contour annotation, 2. a novel set of Breast Cosmesis features, 3. a new Breast Cosmetic dataset consisting 3k+ images from three clinical trials with human annotations on both breast components and their cosmesis scores. We show our fully-automatic framework can achieve comparable performance to state-of-the-art without the need of human inputs, leading to a more objective, low-cost and scalable solution for breast cosmetic evaluation in breast cancer treatment.
Digital infrared chromatic aberration correction algorithm for a membrane diffractive lens based on coherent imaging

Aiming at the wide-spectrum chromatic aberration problem of membrane diffractive lenses, the concept of the digital chromatic aberration correction infrared imaging system was proposed. The principle of digital chromatic aberration correction was given, and the chromatic aberration correction of this system was completed on a computer, which is based on a wavelength-tunable laser local oscillator coherent detection detector. Compared with the chromatic aberration correction method for traditional lenses, this membrane diffractive optical system exhibited characteristics of small size, low weight, and low complexity. The digital chromatic aberration correction application conditions of this membrane diffractive optical system were analyzed. In addition, the main parameters and imaging simulation results were given. The center wavelength of this membrane diffractive optical system is 1.55 µm, and its spectral range is 1.50-1.60 µm. The application of autocorrelation processing to infrared complex images after digital chromatic aberration correction followed by incoherent accumulation of the received corrected complex images based on multiple stepping wavelengths could significantly improve the imaging signal-to-noise ratio.
The Usage of Internet of Things in Agriculture: The Role of Size and Perceived Value

The application of internet of things (IoT) has reached all fields and industries with variation among countries. One of the industries that received less attention is the agriculture. This research intended to identify the factors that affect the intention to use IoT (IUIoT) among farmers in developing countries such as Iraq. Based on technology acceptance model (TAM) and theory of planned behaviour (TPB), this study proposed that perceived usefulness (PU), perceived complexity (PC), subjective norms (SN), reliability (RE) and cost saving (CS) will affect the IUIoT. Perceived value (PV) is proposed as a mediator while land size is proposed as a moderator. The data of this study was collected from 223 farmers in Iraq using purposive sampling. The analysis of Smart PLS showed that the effect of PU, PC, RE, SN, and CS on IUIoT are significant. PV mediated fully the effect of PC and partially the effect of other variables on IUIoT. Land size did not moderate the effect of PV on IUIoT. Decision makers are recommended to ease the process of using the IoT and to enlighten farmers about the benefits of using the IoT.
Digital Quality Resources Resulting from Standardized Program for Rubric Training in Medical Residents

Introduction: In the health care area, tuition is an essential part to provide the instrument that proves the graduates have acquired the necessary skills in their specialties. Objective: Evaluation of the improvement in quality of resources for residents after standardized digital training program with rubrics. Methods: Prospective observational study of first year medical residents in seven medical specialties in four different training centers. Five dimensions were considered to scale the quality of medical resident research: Validation of rubric in investigation methodology topics for each block in b-learning mode; initial and ending evaluation; colloquium investigation rubric; results of final investigation; satisfaction survey of 360 degrees. The instruments were validated using the delphi method with a minimum agreement of 0.8. We considered global values greater than 80 points as good quality. Results: 85 medical residents participated and obtained a final average of 80.62 (±9.59), and the satisfaction of the course was qualified as excellent/good in 82.5%. A positive relation was observed between the scope of the evaluation and the level of satisfaction. Mean quality score for the course was good. There is no relationship between the research experience of the students and the final average r = 0.123 (p = 0.291). Conclusions: The implementation of research seminars in b-learning mode results in improving the education program for health residents after a training program with a rubric system and their acquiring research skills, and, as a consequence, the final product also showed better quality, even when the student did not have any experience in a scientific publication.
New Generation Federated Learning

With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup in which the task size is fixed and the storage space is unlimited, which is impossible in the real world. In fact, new classes of these participating clients always emerge over time, and some samples are overwritten or discarded due to storage limitations. We urgently need a new framework to adapt to the dynamic task sequences and strict storage constraints in the real world. Continuous learning or incremental learning is the ultimate goal of deep learning, and we introduce incremental learning into FL to describe a new federated learning framework. New generation federated learning (NGFL) is probably the most desirable framework for FL, in which, in addition to the basic task of training the server, each client needs to learn its private tasks, which arrive continuously independent of communication with the server. We give a rigorous mathematical representation of this framework, detail several major challenges faced under this framework, and address the main challenges of combining incremental learning with federated learning (aggregation of heterogeneous output layers and the task transformation mutual knowledge problem), and show the lower and upper baselines of the framework.
Boundary Corrected Multi-Scale Fusion Network for Real-Time Semantic Segmentation

Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application. Existing semantic segmentation methods mainly rely on the high-resolution input to achieve high accuracy and do not meet the requirements of inference time. Although some methods focus on high-speed scene parsing with lightweight architectures, they can not fully mine semantic features under low computation with relatively low performance. To realize the real-time and high-precision segmentation, we propose a new method named Boundary Corrected Multi-scale Fusion Network, which uses the designed Low-resolution Multi-scale Fusion Module to extract semantic information. Moreover, to deal with boundary errors caused by low-resolution feature map fusion, we further design an additional Boundary Corrected Loss to constrain overly smooth features. Extensive experiments show that our method achieves a state-of-the-art balance of accuracy and speed for the real-time semantic segmentation.
Information-Growth Swin Transformer Network for Image Super-Resolution

Super-resolution (SR) reconstruction is a typical ill-posed problem and therefore can be considered as an information-growth process. The regions with dramatic information increase in the stage of extracting depth features often contain more high-frequency details. So giving more attention to these regions will improve the performance of super-resolution reconstruction. Recently, Transformer-based models have shown remarkable performance in SR. However, current Transformer-based models focus on processing for the features of the current layer input and cannot capture the degree of informational growth crossing successive layers. For this reason, we propose an information-growth Swin Transformer network (IGSTN) for single image super-resolution. The IGSTN can adaptively extract information-growth global dependencies to generate spatial attention, and then this spatial attention will be fused with the feature self-attention in the Transformer to produce the final attention, which allows the model to focus more on high-frequency regions and learn more high-frequency details from them. Extensive experimental results on publicly benchmark datasets show the effectiveness of our IGSTN.
Face Photo Synthesis Via Intermediate Semantic Enhancement Generative Adversarial Network

Face sketch-photo synthesis is an important task in computer vision now. Recently, researchers have introduced face parsing to further improve the quality of synthesized face images. However, the semantic difference between face sketch parsing and photo parsing is usually ignored, leading to deformations and aliasing on synthesized face images. To solve these problems, we propose an intermediate face parsing to enhance the semantic information of the input face parsing. According to this intermediate face parsing, we propose an Intermediate Semantic Enhancement Generative Adversarial Network (ISEGAN) to generate high-quality realistic face photos. Furthermore, a Parsing Matching Loss (PM Loss) is proposed to encourage the intermediate face parsing to be more semantically accurate. Extensive comparison experiments demonstrate that our ISEGAN significantly out-performs the state-of-the-art methods.
Designing Hardware for Cryptography and Cryptography for Hardware

There have been few high-impact deployments of hardware implementations of cryptographic primitives. We present the benefits and challenges of hardware acceleration of sophisticated cryptographic primitives and protocols, and briefly describe our recent work. We argue the significant potential for synergistic codesign of cryptography and hardware, where customized hardware accelerates cryptographic protocols that are designed with hardware acceleration in mind.
Serving unseen deep learning models with near-optimal configurations

Public clouds provide a bewildering choice of configurations for Deep Learning (DL) models, and the choice of configuration will significantly impact the performance and budget. However, it is an obvious challenge to recommend a near-optimal configuration for a particular DL model from a wide range of candidates. The huge search overhead of finding such a configuration is the notorious cold start problem in state-of-the-art efforts, and this problem becomes more severe when they are faced with unseen DL models.
The assessment of class relations importance using enumeration in a graph representation model for key classes detection based on PageRank

Key classes are the most important classes of an object-oriented software system. They are usually described in project documentations or executive summaries representing the backbone of the software system. Key classes are vital in software reengineering tasks especially in the context where poor or no documentation is maintained with the project. Thus, detecting key classes becomes an important issue. Several methods for key classes detection were reported: static code analysis, dynamic code analysis, machine learning. In a static code analysis approach the model of a weighted graph is used to represent the relations between classes like: inheritance, interface realization, field usage, return type usage etc. On the weighted graph model we run a link analysis algorithm to rank the classes in their order of importance, thus detecting the top 20 most important ones. We intend to assess the importance of class relations by generating all possible weights combinations of 0 and 1 for each class relation in the weighted graph construction. We experiment our approach on 14 analyzed systems of different sizes regarding the number of classes and from domains like: games, web servers, utilities, etc.
A mediation model of the relationship between university students’ news media literacy and xenophobia: The role of intellectual humility, perceived threat, and blind patriotism

The dissemination of misinformation and disinformation has increased the need for news media literacy. This study administered a self-developed questionnaire to measure the relationship between news media literacy and xenophobia among college students in China (N = 430). The questionnaire measured five variables: news media literacy, xenophobia, blind patriotism, perceived threat, and intellectual humility. Confirmatory factor analysis (CFA) was used to validate this five-variable survey, and the analyses indicated satisfactory construct validity. Results from structural equation modeling indicated that intellectual humility was a significant predictor of news media literacy, and blind patriotism and perceived threat mediated the relationship between news media literacy and xenophobia. This study provides insights for researchers and instructors who are promoting news media literacy education in schools.
Effect of <i>M</i>th coherent state on the interaction between two two-level atoms and two-mode quantized field

The objective of this work is to measure the influence of the Mth coherent state on the optics model of two atoms interacting with two-mode quantized field. Also, two atoms interacting together is included in this optics model. The time-dependent wave function is computed analytically. The two-mode quantized field is in the Mth coherent state while the two atoms have two levels. The influence of the Mth coherent state on the quantum model is measured via studying the atomic inversion, von Neumann entropy, variance squeezing, correlation function and Husimi function. The influence of each mode of the quantized field separately and both modes together are discussed. The statistical measures indicate that the nonclassical quantized features increase in the presence of the Mth coherent state, and it features a controlling mechanism. This study opens up a wide range of a large number of serious applications for the quantum optics models.
Quantum Algorithm for Variant Maximum Satisfiability

In this paper, we proposed a novel quantum algorithm for the maximum satisfiability problem. Satisfiability (SAT) is to find the set of assignment values of input variables for the given Boolean function that evaluates this function as TRUE or prove that such satisfying values do not exist. For a POS SAT problem, we proposed a novel quantum algorithm for the maximum satisfiability (MAX-SAT), which returns the maximum number of OR terms that are satisfied for the SAT-unsatisfiable function, providing us with information on how far the given Boolean function is from the SAT satisfaction. We used Grover’s algorithm with a new block called quantum counter in the oracle circuit. The proposed circuit can be adapted for various forms of satisfiability expressions and several satisfiability-like problems. Using the quantum counter and mirrors for SAT terms reduces the need for ancilla qubits and realizes a large Toffoli gate that is then not needed. Our circuit reduces the number of ancilla qubits for the terms T of the Boolean function from T of ancilla qubits to ≈log2T+1. We analyzed and compared the quantum cost of the traditional oracle design with our design which gives a low quantum cost.
Application of Singularity Theory to the Distribution of Heavy Metals in Surface Sediments of the Zhongsha Islands

This research aimed to use nonlinear theory and technology to describe the spatial distribution of heavy metals in the surface sediments of the Zhongsha Islands Sea region. The goal of this study is to explore the spatial distribution characteristics of heavy metals in the surface sediments of the Zhongsha Islands. The singularity theory and method were used to delineate heavy metal geochemical anomalies and the generalized self-similarity analysis method was used to decompose heavy metal geochemical anomalies and background concentrations. The results showed that there were abnormally high concentrations of heavy metals in the deep-sea plain area and in the western central sand trough area. The results of this study can inform priority areas for environmental monitoring. The element anomalies extracted by the singularity analysis method in this paper can be a guide to the next step in the investigation and provide the basis for the regional environmental assessment.
Multi-wave tabu search for the boolean quadratic programming problem with generalized upper bound constraints

The boolean quadratic programming problem with generalized upper bound constraints (BQP-GUB) is an NP-hard problem with many practical applications. In this study, we propose an effective multi-wave tabu search algorithm for solving BQP-GUB. The algorithm performs a sequence of search waves, where each wave alternates between the forward and reverse phases, and the transition between two adjacent waves depends on a hybrid perturbation phase. The forward phase employs tabu search to reach a critical solution and the reverse phase follows to reverse previously performed moves and perform an equal number of moves by referring to the search information gathered from the latest search process. The hybrid perturbation phase randomly chooses a directed strategy, a frequency guided strategy and a recency guided strategy to achieve search diversification. Experimental results on 78 standard instances indicate that the proposed algorithm is able to improve the lower bounds for 6 instances and match the best solutions in the literature for most instances within competitive time. • Propose a multi-wave tabu search algorithm for solving BQP-GUB. • Design a tabu search based forward phase for search intensification. • Design an information guided reverse phase for solution adjustments. • Design a hybrid perturbation phase for search diversification. • Employ multiple memory based strategies to guide the search process. • Find new lower bounds for 6 instances.
Classification feature sets for source code plagiarism detection in Java

Abstract In programming learning environments, the pressure of delivering many programming assignments makes plagiarism the easiest solution. This highly threatens the learning process; therefore, the need of an automatic, fast, and accurate detection of source code plagiarism becomes essential. To detect whether a pair of Java files is plagiarized, this paper proposes four classification feature sets: (i) structural histogram features, histogram-based features for summarizing similarity matrices; (ii) lexical per-class features, extracted from a lexical similarity matrix between the classes of the two compared files based on character 3-grams; (iii) structural counting features, twelve counting features representing the code structure; and (iv) modified original features: a set of modifications on the features of the used baseline. The results show that the best feature sets in F-measure are the structural histogram features and the lexical per-class features combined, which improve the F-measure by 4% compared to the baseline. The added features slow down the execution time. However, it is still efficient, given that it can classify 70k pairs in 23 min. In addition, we partially re-annotated the SOurce COde Re-use dataset. After the re-annotation, the F-measure of both the baseline and our work is improved, and our work achieves an F-measure of 93.6%, which is 7.5% higher than the new F-measure of the baseline. In addition, some remarks and recommendations are provided for using the SOurce COde Re-use dataset as a benchmark.
Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble

In many real-world binary classification problems, one class tends to be heavily underrepresented when it consists of far fewer observations than the other class. This results in creating a biased model with undesirable performance. Different techniques, such as undersampling, have been proposed to fix this issue. Ensemble methods have also been proven to be a good strategy to improve the performance of the resulting model in the case of class imbalance. In this paper, we propose an evidential undersampling-based ensemble approach. To alleviate the issue of losing important data, our undersampling technique assigns soft evidential labels to each majority instance, which are later used to discard only the unwanted observations, such as noisy and ambiguous examples. Finally, to improve the final results, the proposed undersampling approach is incorporated into an evidential classifier fusion-based ensemble. The comparative study against well-known ensemble methods reveal that our method is efficient according to the G-Mean and F-Score measures.
Automatic generation of test circuits for the verification of Quantum deterministic algorithms

Quantum computing will make it possible to exponentially accelerate the performance of a wide range of computational problems, such as cryptography, machine learning or chemical simulation. However, the quantum potential is not only a matter of hardware, but also of software. Therefore, this new paradigm has an impact yet to be explored on software development processes and techniques, and the adaptation of classical software engineering to the new classical/quantum hybrid systems raises a number of important challenges: a new Quantum Software Engineering is therefore needed. Specifically, and focusing on quantum software quality, software verification remains an open research question, as its novelty and complexity make quantum software development a particularly error-prone process. Most current approaches to test-driven verification rely heavily on simulations, which is a problem due to the lack of scalability of simulators running on classical computers. To address this shortcoming, we define the concept of a "Quantum Test Case", and then present a method to test quantum circuits on real machines, without using simulation test functionalities such as amplitude calculation or non-destructive measurement. This is achieved by automatically generating a Quantum Test Case, which wraps the circuit under test and performs the verification. We also present the process to run a set of tests on a circuit with this method, along with an example to illustrate the technique.