Reviewerly/exHarmony huggingface dataset:
Hybrid UNET Model Segmentation for an Early Breast Cancer Detection Using Ultrasound Images

In recent years, progress in breast cancer mortality reduction has slowed. The mortality rate was increasing and Breast cancer became the first cause of death for women. The early diagnosis is crucial in a treatment process as it may avoid the complications as well as the heavy treatment of the pathology. For this purpose, a lot of CAD systems were established. However, to provide more precise results, the later still needs to improve by incorporating new techniques. In this paper, we present a deep learning framework built on the U-Net architecture. A MobileNetV2 and a VGG16 model encoder have been used to handle the semantic segmentation of a biomedical image effectively. This approach is based on the integration of these pre-trained models with the UNet and having an efficient network architecture. By transfer learning, these CNNs are fine-tuned to segment Breast Ultrasound images in normal and tumoral pixels. An extensive experiment of our proposed architecture has been done using Breast Ultrasound Dataset B. Quantitative metrics for evaluation of segmentation results including Dice coefficient, Precision, Recall, and, all reached over 80%, which proves that the method proposed has the capacity to distinguish functional tissues in breast ultrasound images. Thus, our proposed method might have the potential to provide the segmentation necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical ultrasound.
A Reinforcement Learning and Transformers Based Intelligent System for the Support of Alzheimer’s Patients in Daily Life Activities

Alzheimer’s disease is one of the most well-known diseases among the elderly. It is a neuro-degenerative and irreversible brain disease that gradually erodes memory, thinking skills, and, eventually, the capacity to do even basic daily activities. Therefore, patients should be aided at all times in carrying out their daily tasks. In this study, we propose a new assistance system for Alzheimer’s patients to help them performing daily life activities independently. The proposed assistance system is composed of a human activity recognition module, based on 3D skeletons data and transformer encoder, in order to monitor the patient behavior and an assistance module, based on reinforcement learning (RL), to detect anomalies in the patient behavior and provide alerts accordingly. Experiments carried out on the DemCare dataset proved the effectiveness of our RL-based assistance system compared to state-of-the-art methods.
From Scalability to Subsidiarity in Addressing Online Harm

Large social media platforms are generally designed for scalability—the ambition to increase in size without a fundamental change in form. This means that to address harm among users, they favor automated moderation wherever possible and typically apply a uniform set of rules. This article contrasts scalability with restorative and transformative justice approaches to harm, which are usually context-sensitive, relational, and individualized. We argue that subsidiarity—the principle that local social units should have meaningful autonomy within larger systems—might foster the balance between context and scale that is needed for improving responses to harm.
MOBA Game Item Recommendation via Relation-aware Graph Attention Network

Recommender systems based on graph attention networks have received increasing attention due to their excellent ability to learn various side information. However, previous work usually focused on game character recommendation without paying much attention to items. In addition, as the team of the match changes, the items used by the characters may also change. To overcome these limitations, we propose a relation-aware graph attention item recommendation method. It considers the relationship between characters and items. Furthermore, the graph attention mechanism aggregates the embeddings of items and analyzes the effects of items on related characters while assigning attention weights between characters and items. Extensive experiments on the kaggle public game dataset show that our method significantly outperforms previous methods in terms of Precision, F1 and MAP compared to other existing methods.
Multi-embedding space set-kernel and its application to multi-instance learning

Set-level problems become critical when we are interested in animals in pictures, links in web pages, and components in drugs. The key issue is to measure the similarity between two sets. This paper develops a data-dependent multi-embedding space set-kernel (MSK) with close to linear time complexity and applies it to multi-instance learning (MIL), which is a typical set-level problem. The majority of current set-kernels are independent of the underlying data distribution. In contrast, MSK indirectly measures set similarity by determining the relationship between embedding vectors. Each set's embedding vectors are new representations with controlled dimensionality in the multi-embedding space. Multi-embedding space is described here as a set containing multiple subspaces based on the distribution of the data set. In addition, the MSK feature map is used to speed up the computation of similarity over the entire data set. Extensive experiments were done on 46 MIL data sets across five application domains. The results demonstrate that MSK has the lowest average classification loss and the highest stability compared with the rival set-kernels. The linear time complexity is also verified. Source codes are available at https://github.com/InkiInki/MSK.
D-NMS: A dynamic NMS network for general object detection

Non-maximum Suppression (NMS), which is used to find the optimal inferences among all candidate bounding boxes, is a significant post-processing step in most state-of-the-art object detectors. The fixed threshold scheme in the standard NMS equally treats each input image, which leads to the neglect of uniqueness. Recently, several adaptive NMS methods have been proposed and demonstrated to be superior to the standard NMS with a fixed threshold. However, the adaptability performance of these methods is limited due to the deficiency of measuring the complexity of the input image. In this paper, we propose a dynamic NMS network (D-NMS net) to predict the best NMS threshold for each input image, which can be embedded into most state-of-the-art single-stage object detectors. Concretely, we first propose a unified scene complexity definition for a single image according to the relationship between the P-R curve and the changing NMS threshold. Secondly, we calculate the optimal NMS threshold for each image according to the proposed definition, which is then applied as the supervision label in the training stage. Lastly, we embed the lightweight regression network, D-NMS net, into the mainstream object detectors. Extensive experiments are conducted on challenging datasets. With the help of our D-NMS net, the accuracy and efficiency of detectors have achieved obvious improvements. On Pascal VOC, the mean Average Precision (mAP) of RetinaNet is boosted from 81.60% to 84.74%, and the mAP of FCOS is improved from 79.12% to 84.20%. On MS-COCO, the Average Precision(AP) of RetinaNet is boosted from 36.4% to 38.5%, and the AP of FCOS is improved from 37.2% to 39.1%. Meanwhile, the inference speed of our method is increased by 62% at most.
A novel hierarchical iterative hypothesis strategy for intrinsic parameters calibration of laser structured-light weld vision sensor

In automatic welding, multi-line laser structured-light vision sensors provide more adequate information to compared with traditional single-line structured light and are gradually being widely designed. However, when calibrating the sensor, a global calibration approach should be adopted in order that the laser intrinsic parameters follow the inherent constraints. In this paper, a Lagrange solution is shown and its unsuitability for general cases is qualitatively analyzed. We then propose a novel hierarchical iterative hypothesis (HIH) strategy to deal with issues with the following conditions. First, the configuration and pose (including rotation and translation) of a 3D model need to be solved. Second, the model can be obtained by scaling a known model in several dimensions. Third, part of sampling points of the model can be acquired. The proposed hypothesis is introduced based on the coordinate transformation theory. Hierarchical iteration, including global and local iteration, is elaborated mathematically, which utilizes singular value decomposition (SVD) and gradient descent technology. The process of applying hierarchical iterative hypothesis strategy HIH is illustrated, and a reconfirmation for transformation is introduced. The steps in the intrinsic parameters calibration for weld vision sensor are described. A calibration experiment is witnessed that the ambiguity on the intersection issue is reduced to compared with ordinary least square method for structured light calibration. Several quality measures were introduced that allow us to judge the performance of our method. Two experiments for verifying calibration results reveal that the proposed strategy earns a great calibration performance that can meet weld seam tracking needs.
Integration of multi-sensor remote sensing, geological and geochemical data for delineation of Pb–Zn bearing carbonates of Middle Aravalli group in Zawar–Dungarpur Belt, NW India

The rocks of the Aravalli Protocontinent of NW India are enriched in lead–zinc bearing deposits amongst which the Zawar mineralized belt is one of the famous for base metal deposits and was mined since ancient times. In the present study, an attempt has been made to identify and map the extension of the mineralized belt and base metal prognostic zones using the integration of multi-sensor remote sensing, geological and geochemical data. Remote Sensing studies were carried out using ASTER, AVIRIS-NG, and ASAR datasets to understand the extension and associated structural features of host rocks (dolomite in the present case) of lead–zinc mineralization from the Middle Aravalli Group. Relative band depth (B6 + B9/B8) was used to delineate the dolomite of the region using the ASTER imagery. Mineral map was derived using the AVIRIS-NG dataset with the help of the MTMF algorithm. Multifrequency and multipolarization ASAR datasets demarcated the structural features in the complexly deformed rocks of the extended belt. The obtained results from remote sensing were validated with the help of geological and geochemical studies. Geological studies (field surveys and petrographic studies) confirmed the presence of dolomites and associative mafics. Mineralogical, major oxides and trace elements data further substantiated the presence of dolomite, associated sulfides such as galena, sphalerite, pyrite, and chalcopyrite, and lead, zinc and copper in the Zawar–Dungarpur Belt. Presence of chromium and nickel were observed through the trace element studies of dolomite belt. The trace elements interpolated maps were superimposed by traced structural maps using ASAR datasets. The densely populated E-W lineaments are considered the suitable zones for base metal accumulation. These lineaments carried the base metal bearing fluid along with a low concentration of Chromium and Nickel. Under the finding of this study, the northern head of Hati Magra and near Keekawat are the suitable zones for Pb–Zn sulfide mineralization.
Validating an Interactive Ranking Operator for NSGA-II to Support the Optimization of Software Engineering Problems

Search-Based Software Engineering (SBSE) has been beneficial for optimizing the solution of several Software Engineering (SE) problems. The incorporation of Decision Makers (DM) preferences during the search process may help the algorithms to find more adequate solutions for their profiles. Some interactive approaches allow the DM to evaluate solutions, rating them with scores during the search process. These scores represent the adequacy level of the solutions in relation to the DM preferences and should influence the evolution of the search algorithm. In previous work, we proposed an interactive ranking operator for NSGA-II to support the complete prioritization of solutions for any SE problem domain. Although this operator worked satisfactorily in an application example, its validation is required so that it can be used in real application contexts. In this sense, we instantiated the interactive ranking operator for NSGA-II presented in previous work, and we conducted an exploratory study with a twofold goal: (i) validate the impact in the ranking of solutions, and (ii) check the diversity of them. To accomplish such goals, we made statistical tests such as correlation and regression analysis using quality metrics for Product Line Architecture (PLA) Design. The results pointed out that the interactive ranking operator can properly deal with the DM preferences, giving a greater chance of surviving to those solutions with higher scores, without compromising their diversity.
A Technique to Test Refactoring Detection Tools

Refactoring detection tools, such as RefactoringMiner and RefDiff, are helpful to study refactorings applied to software repositories. To evaluate them, the tools’ authors study software repositories and manually classify transformations as refactorings. However, this is a time-consuming and an error-prone activity. It is unclear to what extent the refactoring mechanics is consistent with refactoring implementations available in IDEs. In this paper, we propose a technique to test refactoring detection tools. In our technique, we apply a single refactoring using a popular IDE, and then we run the refactoring detection tool to check whether it detects the transformation applied by the IDE. We evaluate our technique by automatically performing 9,885 transformations on four real open-source projects using eight Eclipse IDE refactorings. RefactoringMiner and RefDiff detect more refactorings in 20.41% and 14.11% of the analyzed transformations, respectively. In the remaining cases, RefactoringMiner and RefDiff either do not detect the refactoring or classify it as other types of refactorings. We report 34 issues to refactoring detection tools, and developers fixed 16 bugs, and 3 bugs are duplicated. In other cases, 3 issues are not accepted. This study brings evidence for the need of a shared understanding of refactoring mechanics.
PriorTD: A Method for Prioritization Technical Debt

Advances in Technical Debt (TD) have enabled the identification of numerous immature artifacts and their impacts on software maintenance and evolution. However, organizations can identify and understand the impacts of TD, but challenges arise when deciding which TD item should be paid for and which should be deferred. These decisions are influenced by many contextual factors that cannot be determined by considering source code alone. Prioritization strategies must consider the business objectives, the scope of the projects, and the needs of the development teams. This paper presents the PriorTD method, which prioritizes TD from the most relevant source code for the project and provides scenarios to meet business needs. It was built throughout two cycles of action research, over two years and eight months, in a software development company. PriorTD analyzes the source code and TD from the perspective of the importance of the source code for the project, team, and managers and provides guidance and support for all involved to understand the decisions related to the prioritization of TD.
Energy-saving optimization of application server clusters based on mixed integer linear programming

The issue of how to dynamically optimize the deployment of an application server cluster according to the changing load to reduce energy consumption is an important problem that must be urgently solved. In this paper, we propose an energy-saving optimization strategy for application server clusters, whose optimization content includes the on/off state, CPU frequency, and load size of each server. Compared with existing research, our strategy is not only more accurate in power and load models but also considers the switching cost of servers to avoid server switching jitter. The strategy includes two schemes, which both formulate the cluster energy-saving optimization as a mixed integer linear programming (MILP) problem and then adopt a toolkit to solve the problem. One scheme defines variables for each server, and the resulting programming problem is called the MILP4PH problem. The other scheme defines variables for each server type, resulting in a programming problem called the MILP4GH problem. The experimental results reveal that for clusters with poor homogeneity, the MILP4PH problem has fewer variables and can be solved in real time, while for clusters with good homogeneity, the MILP4GH problem has fewer variables and can be solved in real time.
SNSPD Array with Single-Channel Readout Based on Compressive Sensing

Superconducting nanowire single-photon detectors (SNSPDs) play an important role in lidar and quantum information due to their excellent comprehensive performance. However, it is a major challenge to read out the SNSPD arrays for the high heat load and cost of the readout circuit in a cryocooler. Here, we propose a single-channel readout and imaging scheme based on compressed sensing (CS), which synergistically samples, combines, and reconstructs the response signals from all of the pixels of SNSPD arrays. Based on the row–column (RC) multiplexing SNSPD array, the compressed sampling of each pixel is realized by randomly switching the bias current of rows and columns, and the counts of each pixel are reconstructed according to the sampling matrices and combined readout signals. This scheme shifts the system complexity to the bias end, eliminates the need for expensive multichannel readout equipment, and enables multiphoton coincidence detection. We demonstrate spot imaging with a 16-pixel SNSPD array and find that the peak signal-to-noise ratio of the CS readout signal is 10 dB higher than that of the RC readout scheme at −100 dBm light intensity and 1000 fps.
Traffic classification in an increasingly encrypted web

Traffic classification is essential in network management for a wide range of operations. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, for example, as a de facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using deep learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations are commonly ignored. In this paper, we design a novel feature engineering approach used for encrypted Web protocols, and develop a neural network architecture based on stacked long short-term memory layers and convolutional neural networks. We evaluate our approach on a real-world Web traffic dataset from a major Internet service provider and mobile network operator. We achieve an accuracy of 95% in service classification with less raw traffic and a smaller number of parameters, outperforming a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted Web protocols. We also evaluate our approach on a public QUIC dataset with finer application-level granularity in labeling, achieving an overall accuracy of 99%.
The spatial and temporal evolution of mineral discoveries and their impact on mineral rarity

Abstract This paper presents the proceedings of the data analysis of the year and country of mineral discoveries with their Nickel-Strunz classes and rarity to enrich our knowledge of the evolution of mineral discoveries and their spatial distribution during different periods. Based on the dynamic of mineral discovery, three principal periods were identified: (1) Ancient period (up to 1800) of irregular mineral records; (2) Sustainable development period (1800–1949) with regular records and a moderate increase in the total number of minerals; and (3) Modern period (1950–present) of rapid development. It is pointed out that the timeline of mineral discoveries exhibits local anomalies. The positive anomalies were linked to the publications of mineralogical encyclopedias and classifications, while the negative ones were caused mainly by historical events, suppressing scientific activity. The majority of rock-forming and widespread minerals were discovered before the 1980s, while the discovery rate of rare and endemic species still progresses due to the study of hard-to-reach locations and the introduction of high-resolution analytical methods. A comparison of Nickel-Strunz class counts throughout mineral history revealed that the fraction of carbonates, oxides, and elements have drastically decreased during the Sustainable development period and the Modern period with a minor increase of elements during the last period. However, opposite behavior is observed for the phosphates, sulfates, and sulfides, with a sudden decrease in sulfates during the Modern period. On the other hand, the fraction of borates, halides, and silicates remained unchanged during all periods. Spatial analysis of the data showed that the distribution of mineral discoveries on the world map depends not only on the country’s geology but also on the area, population, economic development, and general interest in science.