Reviewerly/exHarmony huggingface dataset:
Revisiting the Identification of the Co-evolution of Production and Test Code

Many software processes advocate that the test code should co-evolve with the production code. Prior work usually studies such co-evolution based on production-test co-evolution samples mined from software repositories. A production-test co-evolution sample refers to a pair of a test code change and a production code change where the test code change triggers or is triggered by the production code change. The quality of the mined samples is critical to the reliability of research conclusions. Existing studies mined production-test co-evolution samples based on the following assumption: if a test class and its associated production class change together in one commit, or a test class changes immediately after the changes of the associated production class within a short time interval, this change pair should be a production-test co-evolution sample . However, the validity of this assumption has never been investigated. To fill this gap, we present an empirical study, investigating the reasons for test code updates occurring after the associated production code changes, and revealing the pervasive existence of noise in the production-test co-evolution samples identified based on the aforementioned assumption by existing works. We define a taxonomy of such noise, including six categories (i.e., adaptive maintenance, perfective maintenance, corrective maintenance, indirectly related production code update, indirectly related test code update, and other reasons). Guided by the empirical findings, we propose CHOSEN (an identifi C ation met H od O f production-te S t co- E volutio N ) based on a two-stage strategy. CHOSEN takes a test code change and its associated production code change as input, aiming to determine whether the production-test change pair is a production-test co-evolution sample. Such identified samples are the basis of or are useful for various downstream tasks. We conduct a series of experiments to evaluate our method. Results show that (1) CHOSEN achieves an AUC of 0.931 and an F1-score of 0.928, significantly outperforming existing identification methods, and (2) CHOSEN can help researchers and practitioners draw more accurate conclusions on studies related to the co-evolution of production and test code. For the task of Just-In-Time (JIT) obsolete test code detection, which can help detect whether a piece of test code should be updated when developers modify the production code, the test set constructed by CHOSEN can help measure the detection method’s performance more accurately, only leading to 0.76% of average error compared with ground truth. In addition, the dataset constructed by CHOSEN can be used to train a better obsolete test code detection model, of which the average improvements on accuracy, precision, recall, and F1-score are 12.00%, 17.35%, 8.75%, and 13.50% respectively.
On the structure of mirrored operators obtained from optimal entanglement witnesses

Abstract Entanglement witnesses (EWs) are a versatile tool in the verification of entangled states. The framework of mirrored EW doubles the power of a given EW by introducing its twin—a mirrored EW—whereby two EWs related by mirroring can bound the set of separable states more efficiently. In this work, we investigate the relation between the EWs and its mirrored ones, and present a conjecture which claims that the mirrored operator obtained from an optimal EW is either a positive operator or a decomposable EW, which implies that positive-partial-transpose entangled states, also known as the bound entangled states, cannot be detected. This conjecture is reached by studying numerous known examples of optimal EWs. However, the mirrored EWs obtained from the non-optimal ones can be non-decomposable as well. We also show that mirrored operators obtained from the extremal decomposable witnesses are positive semi-definite. Interestingly, the witnesses that violate the well known conjecture of Structural Physical Approximation, do satisfy our conjecture. The intricate relation between these two conjectures is discussed and it reveals a novel structure of the separability problem.
Analyzing Bibliometrics of AI and Smart Vehicles

This chapter presents a concise bibliometric analysis of the intersection between artificial intelligence (AI) and smart vehicles. Through systematic literature review and analysis, key research trends and themes were identified, including AI-driven autonomous driving, intelligent transportation systems, machine learning algorithms, and human-machine interaction. The analysis highlights the growing interest in the field and identifies influential research works, contributors, and collaborative networks. This bibliometric analysis provides valuable insights into the research landscape and offers directions for future studies in AI and smart vehicles.
Estimación de la Irradiación Solar Difusa en el Plano Inclinado de Paneles Solares Fotovoltaicos

Solar energy, in the recent decades has gained a great attention due to its clean, simple and easy adaptable process. The effective utilization of solar energy is possible only if proper data of incident solar radiation is available. There are number of solar models based on different climatical factors, for the assessment of radiation on plane and sloped surfaces are available in the literature but the selection of best possible model is a challenging task. Here, different isotropic and anisotropic solar models have been utilized for the estimation of overall radiation incident on the sloped surface in Karachi then estimation was compared with the experimental values. The models selected for this study includes Liu and Jordan, Koronakis, Badescue, Hay and Davies, Temps and Coulson and HDKR. The predicted values and measured values are compared by using different statistical techniques like Mean Absolute Percentage Error (MAPE), Mean Biased Error (MBE), Root Mean Square Error (RMSE) and t-stats. The outcomes revealed that isotropic models are more suitable model than the anisotropic models. Among all models, Badescue is the best suitable model for the estimation of radiations on sloped surfaces with lowest value of MBE, RMSE and t-stats while Temps and Coulson model, on the basis of MAPE, MBE, RMSE and t-stats is the most inappropriate correlation for the assessment of solar radiations on sloped surface. Overall, for the solar radiation estimation on any sloped plane in Karachi isotropic models have shown a good agreement.
Insulator Fault Detection From UAV Images Using YOLOv5

Identification of insulator defects is one of the most important goals of an intelligent examination of high-voltage transmission lines. Because they provide mechanical support for electric transmission lines as well as electrical insulation, insulators are essential to the secure and reliable operation of power networks. A fresh dataset is first built by collecting aerial pictures in various scenes that have one or more defects. A feature pyramid network and an enhanced loss function are used by the CSPD-YOLO model to increase the precision of insulator failure detection. The insulator defective data set, which has two classes (insulator, defect), is used by the suggested technique to train and test the model using the YOLOv5 object detection algorithm. The authors evaluate how well the YOLOv3, YOLOv5, and related families perform when trained on the insulator defective dataset. Practitioners can use this information to choose the appropriate technique based on the insulator defective dataset.
Experimental Verification of a One-dimensional Diffraction-limit Coronagraph

We performed an experimental verification of a coronagraph. As a result, we confirmed that, at the focal region where the planetary point spread function exists, the coronagraph system mitigates the raw contrast of a star-planet system by at least $1\times10^{-5}$ even for the 1-$\lambda/D$ star-planet separation. In addition, the verified coronagraph keeps the shapes of the off-axis point spread functions when the setup has the source angular separation of 1$\lambda/D$. The low-order wavefront error and the non-zero extinction ratio of the linear polarizer may affect the currently confirmed contrast. The sharpness of the off-axis point spread function generated by the sub-$\lambda/D$ separated sources is promising for the fiber-based observation of exoplanets. The coupling efficiency with a single mode fiber exceeds 50% when the angular separation is greater than 3--4$\times 10^{-1}\lambda/D$. For sub-$\lambda/D$ separated sources, the peak positions (obtained with Gaussian fitting) of the output point spread functions are different from the angular positions of sources; the peak position moved from about $0.8\lambda/D$ to $1.0\lambda/D$ as the angular separation of the light source varies from $0.1\lambda/D$ to $1.0\lambda/D$. The off-axis throughput including the fiber-coupling efficiency (with respect to no focal plane mask) is about 40% for 1-$\lambda/D$ separated sources and 10% for 0.5-$\lambda/D$ separated ones (excluding the factor of the ratio of pupil aperture width and Lyot stop width), where we assumed a linear-polarized-light injection. In addition, because this coronagraph can remove point sources on a line in the sky, it has another promising application for high-contrast imaging of exoplanets in binary systems.
Plasma Cell Detection in Multiple Myeloma Cases Using Mask Region Based Convolutional Neural Network Method (Mask R-CNN)

Multiple myeloma cancer is the third major of hematologic malignancy after lymphoma and leukemia, which is about 1% of 13% of hematologic malignancies. Unlike other cancers, myeloma does not form a tumor or lump, but rather causes an accumulation of abnormal plasma cells in the bone marrow which is more than 10% and paraprotein in the body. One of the first steps in diagnosing Multiple Myeloma cancer is by detecting plasma cells in a bone marrow sample taken from the patient's body. Blood samples are taken on several preparations, and the number of plasma cells will be counted from the entire sample. If the number of plasma cells is more than 30% of all cells that have nuclei, then the patient is diagnosed with Multiple Myeloma cancer. The process of detecting plasma cells and calculating the entire sample takes quite a long time and can lead to misdiagnosis due to inaccuracy in the calculation process and the fatigue factor of the medical personnel who check it. In this study, a model was developed to detect Plasma Cells in Multiple Myeloma Cases Using the Mask Region Based Convolutional Neural Network (Mask R-CNN) method, which is expected to speed up the diagnosis process. The use of the Mask Region Based Convolutional Neural Network (Mask R-CNN) method is implemented using the SegPC-2021-dataset for the model training process, and data from the Kepanjen general hospital for the testing process. Using this dataset, the mAp value is 75.94%, the mean precision is 73.93%, and the mean recall is 53.9%.
Anomaly detection using autoencoders with network analysis features

Fraudulent activity within a financial ecosystem often involves the coordinated efforts of several bad actors. Expressing the interactions between participants in a system as a mathematical graph allows researchers to apply social network analysis to understand the nature of these relationships better. This article proposes and extends a unified approach using an autoencoder to detect anomalies in a transactional setting. The methodology begins with a neural architecture search to determine a best autoencoder model architecture configuration. This is followed by a threshold optimisation process to find a reconstruction error that best discriminates between normal and anomalous classes. Gaussian scaling is applied to the raw anomaly scores in order to represent the output in an interpretable and universally transferable form. The unified approach is extended by selecting and including network metrics as features, for the purposes of producing a model that can detect anomalies from both standard transactional data and network data representing the relationships between users within a financial system. Applying SHAP on the model output highlighted the strongest contributing or offsetting network metric features for all anomalies detected. The PageRank and degree centrality network metrics were most significant in detecting anomalous instances within the data. Including network metrics in the feature space generated encouraging model performance results, leading to a potential low operational cost of fraud.
Mediação de leitura com leitores digitais em hospitais: um estudo na pediatria do Hospital das Clínicas/UFMG

This work presents partial results of research that investigates the impact of the use of e-readers on literary reading practices carried out by children and adolescents admitted to the Pediatrics Department of the Hospital de Clínicas of the Federal University of Minas Gerais (HC-UFMG), in Brazil. This is a qualitative research, whose data were generated through semi-structured interviews carried out during the first contact of three participants with electronic devices. After a bibliographical review of scientific productions on reading in hospitals, some excerpts from the interviews with the participants are presented, which are analyzed from the perspective of the theoretical review. The results indicate a positive evaluation of the e-readers, due to the features presented that facilitate reading and due to the possibility of providing well-being and postural comfort, especially when compared to the requirements of printed texts. The plot, however, continues to be the triggering element of the action of reading and seems to be independent of the choice of the electronic device as an instrument of access to the book.
ANALISIS KEMAMPUAN NUMERASI SISWA SMA DITINJAU DARI TIPE KEPRIBADIAN EXTROVERT DAN INTROVERT

The purpose of this study is to analyze the numeracy skills of high school students in solving AKM questions in terms of the extrovert-introvert personality type. The method in this study is a descriptive method with a qualitative approach. The subjects of this study were 6 students from class X IPA in one of the public high schools in North Jakarta who were taken using a purposive random sampling technique with three students each from the extroverted personality type and three other students from the introverted personality type, which was measured using the theory of Eysenck's personality is the Eysenck Personality Inventory (EPI). There are 3 indicators that meet numeracy abilities. The results of this study indicate that Extrovert students have lower numeracy skills than Introvert students. Extrovert students can meet 2 ability indicators while Introvert students can meet 3 ability indicators.
Design of a Novel Signed Binary Subtractor Using Quantum Gates

In this paper, focus has been given to design and implement signed binary subtraction in quantum logic. Since the type of operand may be positive or negative, therefore a novel algorithm has been developed to detect the type of operand and as per the selection of the type of operands, separate design techniques have been developed to make the circuit compact and work very efficiently. Two separate methods have been shown in the paper to perform the signed subtraction. The results show promising for the second method in respect of ancillary input count and garbage output count but at the cost of quantum cost.
A critical review of auditing at the time of blockchain technology – a bibliometric analysis

Purpose This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better understanding of the field. Design/methodology/approach This study makes use of the Bibliometric Analysis method and gathered 725 papers from the Web of Science and Scopus databases in the management and accounting, business, financial, economic and social science, as well as decision sciences fields from 2017 to 2021 using the R-Package Bibliometrix Analysis “biblioshiny”. Findings The findings revealed that blockchain research in terms of auditing has already increased and started to spark a quick rise in popularity, but is still in its initial phases with important quality though less in quantity. Moreover, the Journal of Emerging Technologies in Accounting is the most prolific journal with 2019 as the highest publication year, with the United States and China as the most cited countries in this field. Furthermore, in this field, there are much research topics involving blockchain, audit and smart contracts; and there is less involving data analytics, governance, hyperledger, distributed ledger and financial reporting. Additionally, Sheldon (2019) and Smith and Castonguay (2020) are the most productive authors in the field in terms of the H-index. Research limitations/implications This study has certain limitations such as the fact that it only looked at 105 papers in the domains of finance, business, economics, accounting, management as well as multidisciplinary science. Moreover, the research’s data and dates have an impact on the results dependability. As this is an original topic, fresh studies are anticipated to remain to shine a spotlight on and suggest answers to blockchain’s implications on auditing. Additionally, the period of time was limited to only the last five years, from 2017 to 2021. As a result, extensive study into the topic is required since there is currently a research deficit in the blockchain field in the setting of auditing. So, new research is required to offer new frameworks and understandings for describing the blockchain function in auditing, including processes, techniques, security, as well as timeliness. Investigations in unique circumstances and research employing innovative research methodologies for discovering the new issue would be valuable in acquiring a higher grasp of the complexities faced. Originality/value This research contributed to the field by assessing the present state of the art of research on the usage and use of BT in finding research gaps, the audit profession and, most importantly, recommending a future direction for researchers in the subject.
Real-Time Computer Vision Based Hand Gesture Recognition

The ability to recognize the shape and movement of hands can help improve the user experience in a wide range of technical domains and platforms. It can help you understand sign language and move your hands in the right way, for example. It can also make it possible for digital information and materials to be added on top of the real world in augmented reality. Here, I talk about a real-time, on-device hand gesture recognition solution that lets us control our system’s graphical user interface (GUI) with static and dynamic hand gestures that can be trained to do a set of actions that are similar to what we do with our mouse and keyboard. It is built with MediaPipe-Hands, which finds the palm landmark point. The data is then sent through a pipeline of data-preprocessing functions and trained with two models: one for static gesture recognition and one for dynamic gesture recognition. In real-time, the models are then used to detect similar gestures on-device from a video-capturing device like a webcam.
ANALISIS FAKTOR YANG MEMPENGARUHI MINAT BACA SISWA KELAS 2 SDN 02 GAYAMSARI SEMARANG

Interest in reading is someone interested in reading and has a strong desire to read, evaluate, and understand the material. Thus, various factors support the low enthusiasm of students in reading. The purpose of this study was to find out how the reading interest of grade 2 students at SDN 02 Gayamsari Semarang. This study aims to determine students' reading interest in grade 2 SDN 02 Gayamsari. The research method used is the descriptive qualitative method. The findings show that some students' interest in reading is lacking, but most students' interest in reading is positive. There are 2 internal and external factors identified as factors of students' interest in reading based on the findings of data analysis. Internal elements come from students, such as a lack of motivation to read, students' lack of feelings, attention to books and the benefits of reading, and reading books at the request of the teacher. there is an impact of smartphone use. The steps taken are by giving motivation, continuous attention to grade 2 students, and attention to increasing interest in reading. Factors that influence interest in reading outside are the role of the teacher, environment, family, and facilities. Teachers must use theory or learning strategy components as learning principles so that learning is well received by students and becomes easier.
Improving Indic code-mixed to monolingual translation using Mixed Script Augmentation, Generation &amp; Transfer Learning

The use of code-mixed languages (written in Roman character) on social media platforms is prevalent in multilingual nations. Translation from code-mixed to monolingual is necessary for social media analysis, content filtering, and targeted advertising. Training translation models from scratch is difficult due to the scarcity of available code-mixed resources and the extremely noisy nature of real-time code-mixed sentences. At the moment, multilingual state-of-the-art language models are routinely used for multilingual applications. However, multilingual models are ineffective in handling code-mixed sentences as it is usually written in Roman script but contain words from at least two languages. In the paper, two data augmentation techniques are proposed to improve code-mixed to monolingual translation, one based on script augmentation and the other on code-mixed sentence generation. The proposed approach converts the code-mixed sentences into ‘Mixed Script form’ that restore the native language words in the sentences with corresponding native language scripts. The novelty of the work is that the multilingual language models include each language’s linguistic competence, preserving context in the monolingual sentences, not possible in the earlier models. Using an mT5 model, denoising and mixed-script switching are performed, followed by monolingual translation with another mT5 model. Code-mixed sentences are generated by employing a simple code-mixed sentence generating technique using monolingual parallel inputs. Two different Indic language sets, namely Hindi-English and Bengali-English are applied and in each case, the proposed approach outperforms straight uni-script (Roman) code-mixed to monolingual translation.