Reviewerly/exHarmony huggingface dataset:
Interpretation of Machine Learning Model Using Medical Record Visual Analytics

The state of the art of medical application that being implemented are mostly based on common machine learning model. Nevertheless, one of the drawbacks of the practice of medical diagnosis is the lack of explanation on the proposed solution, which is also known as a black box, without knowing the internal decision process between the input and output. It will lead to untrustworthiness and difficult to understand by the medical expert. They are questioning how the complexity of machine learning methods decide on the output without clear and understandable explanations. Moreover, in machine learning field the characteristic of a black box model may lead to biased data analysis and incorrect output decisions. There is work that uses visual analytics techniques to interpret the machine learning output to ease the understanding of medical experts. However, the functionality of existed and combined visual analytics techniques is not sufficient to visualized and interpreted the output of machine learning operation. Other visual analytic techniques faced the same problem, unreliability to produce strong reason on the output when working with complex machine learning models. This paper analyzed several visual analytics approaches instantiated in machine learning algorithm for medical record analytics. The motivation of this paper is to allow medical experts to understand the interpretation of a black box machine learning model in predicting medical outcome. This paper studied on the effectiveness of visual analytics techniques to identify the appropriate technique to be instantiated to the machine learning algorithm to further elaborate the results obtained by demonstrating transparency, interpretability and explainability of the machine learning algorithm. The visual analytics that are been studied are Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP). Based on the comparison of LIME and SHAP methods, this paper found that SHAP has consistent interpretability as compared to LIME.
Comparative Analysis of Chaotic Image Encryption Using Improved 2D Gingerbreadman Map and Chirikov Standard Map

Securing the digital images floated over an unsecure communication channel is a difficult task. In this paper a comparative analysis of the image encryption algorithms based on a novel 2 D Gingerbreadman map and chaotic Chirikov Standard map is presented. The Pseudo Random Bit Generators are constructed from novel models of 2 D Gingerbreadman map and Chirikov Standard chaotic map. To eliminate the drawback of the traditional Gingerbreadman map a perturbation function is inserted in the novel Gingerbreadman map thus discarding the presence of fixed and periodic points making the system highly integrable for the cryptographic applications. The strength of the proposed Gingerbreadman map is compared with the chaotic standard map with the help of standard evaluation measures available in the literature thus focusing the superiorities and limitations of each method of encryption.
Understanding Refactoring Tactics and their Effect on Software Quality

Refactoring is a widely used technique that removes the flaws of the system and improves its quality. It is often practiced to change the inner framework of the code while preserving its functionality. Software metrics represent the quantitative aspect of software quality. They play a pivotal role in understanding the structural design quality of the code. In this study, different releases of two open-source projects, i. e., Java-apns and Thumbnailator have been explored to investigate the effect of refactoring tactics-root canal and floss refactorings, on the system&#x2019;s quality. Root canal refactorings are pure refactorings that aim to remove the defects from the code. Whereas, floss refactorings intend to maintain the code&#x2019;s health and are applied in conjunction with other program changes. Six software metrics belonging to the widely used CK metric suite and five prominent refactoring types were selected for the evaluation purpose. The findings of the study show that developers perform floss refactoring more than root canal refactorings. Nearly, 73&#x00025; of the total refactoring operations were categorized as floss. Also, root canal refactorings perform better in improving the software quality than floss refactorings. Nearly, 93.33&#x00025; of the root canal refactorings improved the quality of the selected metrics. The current study helps in exploring the association between the two refactoring tactics and the design quality of the system.
A method for synthesis of S-boxes with good avalanche characteristics of component Boolean and quaternary functions

The widespread use of block symmetric ciphers in modern information security systems makes the task of increasing their efficiency urgent, as well as the task of synthesizing high-quality cryptographic primitives used in modern block symmetric ciphers, primarily S-boxes. In particular, the S-boxes must have good avalanche characteristics not only of their component Boolean functions, but also of the component functions of many-valued logic. In this paper, we propose a recursive method for the synthesis of large lengths S-boxes that satisfy the strict avalanche criterion of component 4-functions. As a raw material for the construction of such an S-boxes, small-length S-boxes are used that satisfy the strict avalanche criterion of component 4-functions. In the constructed set of S-boxes that satisfy the strict avalanche criterion of component 4-functions, we found those that simultaneously satisfy the criterion of the maximum avalanche effect of component Boolean functions. The avalanche characteristics of these S-boxes are of high quality both from the point of view of their representation with help of Boolean functions, and from the point of view of their representation using 4-functions. These S-boxes can be recommended for practical use to modernize existing symmetric cryptographic algorithms, as well as to build new perspective ciphers.
Chunk Grouping Method for Low-Latency HTTP-Based Live Streaming

The common media application format (CMAF) provides low-latency, HTTP-based live streaming. However, conventional bandwidth prediction for adaptive bit rate streaming is inaccurate because download time is affected not only by network bandwidth but also by the idle times between chunks in the same segment. Inaccurate bandwidth estimation decreases the viewer&#x0027;s quality of experience. In this paper, we propose a chunk-grouping method for low-latency, HTTP-based live streaming. The proposed method introduces delayed transmissions of HTTP requests for grouping the first of multiple chunks to accurately estimate bandwidth. From the results of the simulations, we show that, compared to the conventional CMAF, the proposed method could obtain higher video quality without excessive degradation in terms of re-buffering time, bit rate variation, or delay.
Face Recognition Based Automated Recognition System

In this digital time, face recognition system is one the challenging area of pattern recognition. Face recognition has wide scope for security, verification, recognizable proof, and has got numerous more points of interest. Face recognition system has more enhanced as compared to biometric based one. It has low exactness when compared to iris recognition and unique mark acknowledgment, hence utilized for participation checking in schools, colleges, workplaces, etc. This framework aims to construct a course participation system which employments the concept of face recognition as the existing manual participation framework. This framework comprises of four stages-database creation, confront discovery, and confront acknowledgment and participation. A standard database has been harnessed.Confront discovery and acknowledgment is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm separately. Faces are recognized and recognized from live spilling video of the classroom and reported individual workforce at the conclusion of the session.
3D geochemical modeling of the Qujia gold deposit, China: Implications for ore genesis and geochemical exploration of deep concealed ore bodies

Phase separation is a crucial controlling factor of gold deposition in shallow and low-medium temperature orogenic gold systems in the Jiaodong gold province. In this study, spatial distributions by three-dimensional (3D) modeling and correlation analysis of Au, S, Cl, Hg, Fe2O3, Cu, Pb, and Bi were conducted to demonstrate the occurrence of phase separation and its significance in geochemical exploration. The modeling and analysis were based on a geochemical dataset of 1130 borehole samples from the Qujia gold deposit. The ore bodies developed in deep traps (e.g., fault intersections and dilational cracks). Gold and S are positively correlated and mainly distributed along the Jiaojia Fault (JJF). Scatter plots show little correlation between Au and Cl. However, the Au and Cl distributions presented by the ore-bearing boreholes and 3D modeling suggest that Cl escaped from the ore-forming fluid and formed enrichment zones above the ore bodies during the Au mineralization which is consistent with the salinity decrease from the early and main ore-forming stage to the late ore-forming stage of many gold deposits in the JJF and other areas of the Jiaodong gold province. The spatial distribution of Au and Hg are indicative and compelling although they have little correlation in the scatter analysis. Mercury depletion zones and subsequent funnel-like Hg anomalies are present above the ore bodies and strong Hg anomalies appear in the deep traps. The results suggest that phase separation is responsible for Au precipitation in the Qujia gold deposit. Mercury, Cl, and Pb partitioned into a gas phase forming medium-to-high anomalies along the secondary faults whereas Au, S, Fe2O3, Cu, and Bi partitioned into a liquid phase forming ore bodies and medium-to-high anomalies along the JJF during the phase separation. Specifically, the pH increase induced by the partition of CO2, HCl and minor amounts of H2S into the gas phase, and S consumption by the abundant metals (Fe2O3, Cu, Bi, etc.) in the ore-forming fluid, rather than H2S escape, are the dominant factors governing Au precipitation. Finally, potential indicators of geochemical exploration are proposed including composite anomalies of Au and Hg in the near surface medium, high Hg anomaly accompanied by no or low Au anomaly in the fault zones, and extremely high Cl or S anomalies in deep depths. Given the similarity of the ore-forming fluid and metallogenic mechanism at the shallow depths, these indicators are potentially useful for Au exploration in the Jiaodong gold province.
Perception and Recognition of Upper Limb Movement Trajectory of Aerobics Based on Multi-Intelligent Sensors

Aerobics is an indispensable part of school teaching. Aerobics can exercise the human body’s flexibility, muscle extension, and cardiopulmonary function. However, there are many details in the aerobics movements that need attention, so this article mainly studies the perception and recognition of the upper limb movement trajectory of aerobics. This article is mainly based on multi-intelligent sensors to perform motion capture and recognition of upper limb movements in aerobics. Therefore, in order to design a multismart sensor-based aerobics upper limb movement perception recognition system, this paper proposes to combine the VM-i three-axis magnetic sensor and MEMS smart sensor to capture the upper limb movement trajectory. Then, the aerobics upper limb movement trajectory and posture mathematical description method to obtain the model of the action perception recognition system are written. In order to verify the system, this paper also designed the actual effect of attitude calculation and A-RRM algorithm verification experiment. Finally, it is optimized with the data obtained from the experiment, and the accuracy and efficiency comparison experiment with the traditional motion perception recognition system is carried out. Experimental results show that the motion recognition accuracy of the aerobics upper limb movement trajectory recognition system based on multi-smart sensors is improved by 13%-23% compared with the traditional motion recognition system. The recognition efficiency of the action perception recognition system based on multismart sensors is 9%-15% higher than that of the traditional action recognition system.
Fast and reliable probabilistic face embeddings based on constrained data uncertainty estimation

Probabilistic Face Embeddings (PFE) can improve face recognition performance in unconstrained scenarios by integrating data uncertainty into the feature representation. However, the matching speed of PFE is too slow to be applied to large-scale face recognition or retrieval applications. Moreover, since deep learning-based data uncertainty estimation tends to be over-confident, the recognition performance of PFE is unstable. This paper proposes a probabilistic face embedding method to improve the robustness and speed of PFE. Specifically, the mutual likelihood score (MLS) metric used in PFE is simplified by using a one-dimensional variance to approximate the data uncertainty of face feature, to speed up the matching of probabilistic embedding pairs. Then, a unilateral constraint loss is proposed to limit the variation range of the lower part of the estimated data uncertainties, which can solve the problem of accuracy degradation in high-quality images. In addition, a feature fusion method based on temperature scaling is proposed, which can adjust the fusion weights of different quality images to improve the performance of video face recognition. Comprehensive experiments show that the proposed method can achieve comparable or better results in 6 benchmarks than the state-of-the-art methods with a less computational cost at the matching process. The code of our work is publicly available in GitHub (https://github.com/KaenChan/ProbFace).
Performance of PV systems in Slovenia with the help of typical daily profiles and automatic detection of orientation and inclination angles

Operators of photovoltaic (PV) systems, especially the small ones, monitor only the produced energy output, since they are not equipped with a meteorological station, or there is none in the neighbourhood. When evaluating a large number of PV systems across a region or country, access to the data due to privacy restrictions is usually very limited. To resolve both aspects, we report on a methodology to evaluate the performance of photovoltaic systems where only produced energy data and rated power of the PV system are available and all other data, including orientation and inclination angles, and the exact location are missing. We developed a novel algorithm for automatic detection of orientation/inclination angles and used the recently developed Typical Daily Profiles (TDP) methodology for a fast and accurate evaluation of PV systems. TDPs are representative hourly profiles of the observed parameter (e.g., power, irradiance and temperature) on a monthly basis. For the PV system degradation rate extraction, year-to-year TDPs are used. The case study of 957 PV systems in Slovenia in the period 2015–2019 reveals an average PV system performance ratio exceeding 85% and an average PV system rated power degradation rate of −0.7% per year.
Targeted deformable motion compensation for vascular interventional cone-beam CT imaging

Purpose: Cone-beam CT has become commonplace for 3D guidance in interventional radiology (IR), especially for vascular procedures in which identification of small vascular structures is crucial. However, its long image acquisition time poses a limit to image quality due to soft-tissue deformable motion that hampers visibility of small vessels. Autofocus motion compensation has shown promising potential for soft-tissue deformable motion compensation, but it lacks specific target to the imaging task. This work presents an approach for deformable motion compensation targeted at imaging of vascular structures. Methods: The proposed method consists on a two-stage framework for: i) identification of contrast-enhanced blood vessels in 2D projection data and delineation of an approximate region covering the vascular target in the volume space, and, ii) a novel autofocus approach including a metric designed to promote the presence of vascular structures acting solely in the region of interest. The vesselness of the image is quantified via evaluation of the properties of the 3D image Hessian, yielding a vesselness filter that gives larger values to voxels candidate to be part of a tubular structure. A cost metric is designed to promote large vesselness values and spatial sparsity, as expected in regions of fine vascularity. A targeted autofocus method was designed by combining the presented metric with a conventional autofocus term acting outside of the region of interest. The resulting method was evaluated on simulated data including synthetic vascularity merged with real anatomical features obtained from MDCT data. Further evaluation was obtained in two clinical datasets obtained during TACE procedures with a robotic C-arm (Artis Zeego, Siemens Healthineers). Results: The targeted vascular autofocus effectively restored the shape and contrast of the contrast-enhanced vascularity in the simulation cases, resulting in improved visibility and reduced artifacts. Segmentations performed with a single threshold value on the target vascular regions yielded a net increase of up to 42% in DICE coefficient computed against the static reference. Motion compensation in clinical datasets resulted in improved visibility of vascular structures, observed in maximum intensity projections of the contrast-enhanced liver vessel tree. Conclusion: Targeted motion compensation for vascular imaging showed promising performance for increased identification of small vascular structures in presence of motion. The development of autofocus metrics and methods tailored to vascular imaging opens the way for reliable compensation of deformable motion while preserving the integrity of anatomical structures in the image.
An accurate soft diagnosis method of breast cancer using the operative fusion of derived features and classification approaches

Abstract Breast cancer is one of the most common types of cancer around the world. The early‐stage recognition of breast cancer is favourable for the diagnosis and treatment of the affecting patient. Data mining approaches can ease the diagnosis of breast cancer by analysis of the associated dataset for real‐time decision‐making. The present study proposes an effective transformation approach of experimental attributes of a breast cancer dataset using the latent semantic analysis and the fusion of derived features with classification methods for accurate recognition of breast cancer. The proposed approach is validated using the most widely used benchmark and open‐access breast cancer dataset. The transformed features of the original dataset result in 100% recognition efficiency using a multilayer perceptron, support vector machine, multi‐class classifier and functional tree classifiers. Other classifiers, like naïve Bayes, rotation forest, simple linear logistic regression and logistic model tree result in recognition accuracy between 96.85% and 99.30% using a similar feature subset. Besides, the optimal subset of derived features has been affirmed based on the evaluation metrics of classification approaches.
Efficient search termination without task experience.

As a general rule, if it is easy to detect a target in a visual scene, it is also easy to detect its absence. To account for this, models of visual search explain search termination as resulting either from counterfactual reasoning over second-order representations of search efficiency, automatic extraction of ensemble statistics of a display, or heuristic adjustment of a search termination strategy based on previous trials. Traditional few-subjects/many-trials lab-based experiments render it impossible to disentangle the unique contribution of these different processes to absence pop-out - the immediate recognition that a feature is missing from a display. In 2 preregistered large-scale online experiments (N1 = 1187; N2 = 887) we show that search termination times are already aligned with target identification times in the very first trials of the experiment before any experience with target presence. Exploratory analysis reveals that explicit metacognitive knowledge about search efficiency is not necessary for efficient search termination. We conclude that for basic stimulus properties, efficient inference about absence is independent of task experience and of explicit metacognitive knowledge about visual search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Development of a Rotary Cleaning Mechanism Using Planetary Gears for Removing Grease Deposited in Kitchen Ventilation Ducts

In this paper, it is proposed a grease dust cleaning mechanism using a planetary gear mechanism to remove accumulated grease in ventilation ducts.In existing research, cleaning robots have been developed to remove dust from air-conditioning ducts. The robot successfully removed dust from the ducts by rotating and scraping the brushes and using air pressure. However, no cleaning mechanism has been developed to remove the highly viscous grease that adheres to the ventilation ducts installed in restaurants. Thus, it is difficult to clean the oil and dust ducts, which are impenetrable.Therefore, in this paper, the authors propose a grease-cleaning mechanism using a planetary gear mechanism for cleaning grease from round ducts. This mechanism cleans the inside of the pipe by scrubbing off the grease from the walls by the rotation and revolution of the brush. This paper describes the selection of the cleaning mechanism, the modeling of the brush trajectory, the control method, and the evaluation experiment of the control method. The evaluation experiment of the control method confirmed the validity of the model and the fact that the brushes made contact with the entire inside of the pipe. This shows the possibility that the inside of the pipe could be efficiently cleaned by the intermittent collisions of the dispersed brushes.
Model Requirements for the Development of Extended Reality Applications in Construction Projects: A Literature Review

The Architecture, Engineering, and Construction (AEC) industry has explored, implemented, and reaped benefits from various Extended Reality (XR) applications. However, a large portion of these applications was developed in an ad-hoc approach for different technologies and different stages of a project. Sharing digital content among these applications is challenging, although many of them have the same development platform and original 3D model. This paper reviewed XR applications in the lifecycle of a construction project and summarized key information requirements for 3D models, development software systems, and processes for information transfer. Results show that a generic and adaptable model that satisfies these requirements can streamline the development process of different XR applications in the same project. Creating a generic model does not require additional activities. Instead, it relocates model pre-processing activities to the beginning of the XR development and precludes repetitive activities when possible. This paper contributes to the body of knowledge by specifying the requirements of a generic model that enhanced the interoperability of digital assets among XR technologies and applications.