Reviewerly/exHarmony huggingface dataset:
Wait Statistics Internals

SQL Server wait statistics are an important tool for analyzing performance-related problems and optimizing SQL Server performance. However, they are not always known to many database administrators or developers. I believe this has to do with their relatively complex nature, the sheer volume of different types available, and the lack of documentation for many types of wait statistics. Wait statistics are also directly tied to the specific SQL Server on which they occur, meaning it is impossible to compare wait statistics of Server A to wait statistics of Server B, even with identical hardware and database configuration. Every configuration option, from the hardware firmware level to the configuration of the SQL Server Native Client on the client computers, will impact the wait statistics!
Physics-driven mode decomposition based on a training-free shallow neural network

A mode decomposition method based on a training-free shallow neural network (TFSNN-MD) is proposed. The TFSNN-MD underlies the interaction between the eigenmode superposition laws and the neural network (NN), eliminating the massive beam pattern datasets and training process. It has obvious advantages with high accuracy, fast operation and great versatility. In the case of using only a shallow NN with only two convolutional layers, the errors of modal weights and modal relative phases are lower than 0.50% and 0.60%, theoretically. Furthermore, it performs well in generalization regardless of the variation of the FMF’s core radius and NA. In experiment, the correlation between the real and reconstructed beam patterns is higher than 95%.
Primary Care Physicians’ and Patients’ Perspectives on Equity and Health Security of Infectious Disease Digital Surveillance

The coronavirus disease 2019 (COVID-19) pandemic facilitated the rapid development of digital detection surveillance (DDS) for outbreaks. This qualitative study examined how DDS for infectious diseases (ID) was perceived and experienced by primary care physicians and patients in order to highlight ethical considerations for promoting patients' autonomy and health care rights.In-depth interviews were conducted with a purposefully selected group of 16 primary care physicians and 24 of their patients. The group was reflective of a range of ages, educational attainment, and clinical experiences from urban areas in northern and southern China. Interviews were audio recorded, transcribed, and translated. Two researchers coded data and organized it into themes. A third researcher reviewed 15% of the data and discussed findings with the other researchers to assure accuracy.Five themes were identified: ambiguity around the need for informed consent with usage of DDS; importance of autonomous decision making; potential for discrimination against vulnerable users of DDS for ID; risk of social inequity and disparate care outcomes; and authoritarian institutions' responsibility for maintaining health data security. The adoption of DDS meant some patients would be reluctant to go to the hospital for fear of either being discriminated against or forced into quarantine. Certain groups (older people and children) were thought to be vulnerable to DDS misappropriation.These findings indicate the paramount importance of establishing national and international ethical frameworks for DDS implementation. Frameworks should guide all aspects of ID surveillance, addressing privacy protection and health security, and underscored by principles of social equity and accountability.Annals "Online First" article.
Improved YOLOv5 for skeleton-based classroom behavior recognition

Classroom behavior is an important criterion for evaluating instructional efficacy. In comparison to other behaviors, the challenge of classroom behavior detection is primarily influenced by ambient light variables and the presence of too many targets to recognize, resulting in missed detection. Recent research has demonstrated that information about the human skeleton can be used to identify classroom conduct. As a result, we present an enhanced yolov5-based skeletal recognition system for detecting classroom behavior in this paper. First, the YOLOv5 detection algorithm is improved to extract target prospects for the problem of missed detection; then, the human skeleton information is obtained using the Alphapose framework; finally, the skeletal data is sent into a two-stream adaptive graph convolution network to allow for the accurate recognition of various classroom behaviors. According to extensive tests, the detection algorithm based on bone recognition improves detection accuracy and lowers the false detection rate.
Learning Models for Bone Marrow Edema Detection in Magnetic Resonance Imaging

Bone marrow edema (BME) is the term given to the abnormal fluid signal seen within the bone marrow on magnetic resonance imaging (MRI). It usually indicates the presence of underlying pathology and is associated with a myriad of conditions/causes. However, it can be misleading, as in some cases, it may be associated with normal changes in the bone, especially during the growth period of childhood, and objective methods for assessment are lacking. In this work, learning models for BME detection were developed. Transfer learning was used to overcome the size limitations of the dataset, and two different regions of interest (ROI) were defined and compared to evaluate their impact on the performance of the model: bone segmention and intensity mask. The best model was obtained for the high intensity masking technique, which achieved a balanced accuracy of 0.792 ± 0.034. This study represents a comparison of different models and data regularization techniques for BME detection and showed promising results, even in the most difficult range of ages: children and adolescents. The application of machine learning methods will help to decrease the dependence on the clinicians, providing an initial stratification of the patients based on the probability of edema presence and supporting their decisions on the diagnosis.
An Immigration Strategy-based Spherical Search Algorithm

The spherical search algorithm (SS) is a novel and competitive algorithm applied to real-world problems. However, the population of SS algorithm is divided equally, which requires a large number of computation resources for different problems. To alleviate the issues, we propose an immigration strategy-based spherical search algorithm, namely ISS. ISS adaptively selects individuals that are successfully updated in each generation and replaces the operator in the next iteration. The experiments were conducted on the 30 benchmark functions from the IEEE CEC2017. ISS is compared with SS to verify the effectiveness of the proposed adaptive immigration strategy. Additionally, the classical differential evolutionary algorithm (DE) and a state-of-the-art triple archive particle swarm optimization (TAPSO) are compared to test its performance further. The population diversity is analyzed to discuss the effect of ISS. The experimental results demonstrate that the proposed immigration strategy is quite effective, and ISS is significantly better than its peer's algorithms.
A Population Resource Allocation-based Adaptive Spherical Search Algorithm

The spherical search (SS) algorithm is a meta-heuristic algorithm which aims to solve the global optimization problems that is non-linear and bound-constrained. Due to the balance between the exploration and exploitation capabilities of the algorithm by utilizing two mechanisms, namely, toward-random and toward-best, the SS algorithm shows superior performance compared to some other meta-heuristic techniques. However, the algorithm still suffers from the drawback of being easily trapped in local optima. In this paper, we propose a population resource allocation-based adaptive spherical search algorithm that adaptively adjusts the ratio of population resource allocation. The algorithm evaluates the performance of different operators at the current search stage in real-time during the iteration, and continuously adjusts the resource allocation ratio based on the performance until the end of the iteration. Experimental results are obtained on the basis of IEEE Congress on Evolutionary Computation (CEC) 2017 problem set to verify the effectiveness and the efficiency of the proposed method.
An optimization Strategy for Deep Neural Networks Training

Learning rate is one of the essential hyperparameters influencing the training process and the accuracy of deep neural networks. However, until now, it is challenging to determine an optimal learning rate. A large learning rate can accelerate the training process but may bring instability in the training process and miss the global optimum. In contrast, a small learning rate would be in a stable training process, but the training speed would be slow, and the training process is easy to fall into local optimum. In this paper, first, the impact of the learning rate is analyzed. It is found that a learning rate schedule should consist of two stages to take into account the speed and accuracy of the training process simultaneously. Based on this consideration, this paper proposes an improvement strategy of learning rate schedules: a two-stage integration strategy of a large fixed learning rate and a rapid decay learning rate. Second, the proposed strategy is applied to optimize a series of widely used learning rate settings. Extensive experiments on CIFAR-10 and CIFAR-100 datasets with VGG19, ResNets, ResNext, DenseNets, SeNet, and some other models demonstrate that the proposed strategy optimizes the learning rate settings, the performance of trained models is enhanced.
Long-Range Bell States from Local Measurements and Many-Body Teleportation without Time Reversal

In this Letter, we study quantum many-body teleportation, where a single qubit is teleported through a strongly interacting quantum system, as a result of a scrambling unitary and local measurements on a few qubits. Usual many-body teleportation protocols require a double copy of the system, and backward time evolution, we demonstrate that teleportation is possible in the 2D spin-$1/2$ $XY$ model, without these constraints. The necessary long-range entanglement for teleportation is generated from the model hosting special eigenstates known as rainbow scars. We outline a specific protocol for preparing this highly entangled state by evolving a product state and performing iterative measurements on only two qubits with feedback control.
Tractable Mode-Finding in Sum-Product Networks with Gaussian Leaves

In this work, we leverage the relation between Sum-Product Networks (SPNs) and Gaussian mixtures to propose an algorithm that adapts the Expectation-Maximization method to efficiently find the modes of SPNs with Gaussian leaves. We discuss how the algorithm can be used to perform Maximum-A-Posteriori inference in SPNs learned from continuous data with theoretical advantages over the existing methods in the literature, and how it can be used to shrink the size of learned models. As an additional example of the use of the algorithm, we perform an SPN-based hierarchical clustering of digit images. Thus, our proposed algorithm can be used for model analysis, model compression, and exploratory data analysis.
Automatic Classification of Bug Reports for Mobile Devices: An Industrial Case Study

When a failure is found during software testing activities, a bug report (BR) is written and stored in product management tools. In order to prioritize the errors to fix, a BR triage process is performed to identify the most critical errors. This is specifically relevant in the context of mobile applications due to the fast development cycle, which results on a high number of BRs to evaluate daily. In this paper, Machine Learning (ML) and Natural Language Processing (NLP) techniques are investigated to automatically classify the criticality of BRs in the context of a real mobile environment, and a prototype was developed. Results on a corpus of 9,785 BRs were very satisfactory, reaching up to 0.79 of AUC and meeting the performance level required by the considered application.
Impact of Employee Engagement on Employee Performance with Mediating Effect of Employee Retention

A formal study determines the impact of varied approaches and perspectives on entrepreneurial behavior. Entrepreneurs are necessary in today's competitive business environment because they stay abreast of the most recent technological developments, which aid in expanding their enterprises. Statistical methods known as structured equation modelling (SEM) quantify and examine the connections between latent and observable variables. To determine the relationship between the study's independent, mediating, moderating, and reliant variables, researchers employed innovative PLS software to do structural equation modeling. Using a questionnaire, data is gathered from a random population sample using the SEM approach. The obtained information is subsequently examined using Smart PLS's analysis and testing capabilities. Overall, results from the majority hypothesis indicate that entrepreneurship is significantly affected. The R2 value is 0.560, which suggests that the factors affecting entrepreneurship account for 56% of the variance. As per the results, the significant influence of inspiring motivators on new business ventures, innovativeness, and responsiveness should urge managers to involve employees in formulating common strategic visions to encourage them to launch new internal creative projects. Additionally, managers should develop training programs to increase employee enthusiasm and creativity, improving their ability to think creatively and act in a self-renewing manner.
Virtual Hands-On Workshops to Bring Teenage Girls to Computer Science in Uruguay: An Experience

During the last decades the presence of women in the area of Computer Science has decreased in most countries. In the last years, at the Facultad de Ingeniería (School of Engineering), Universidad de la República, Uruguay, we have carried out several activities with the goal of bringing girls closer to Computer Science area, on the occasion of Girls in ICT Day. Through these activities, we intend to eliminate certain preconceived ideas and negative stereotypes that promote girls’ distancing from careers of this area. This paper presents the experience of the three virtual workshops carried out during 2021 (in virtual modality, due to the COVID-19 pandemic), which focused on programming, data, and geographic information systems, and the analysis of a set of virtual platforms considered for each workshop.
Cyber Crime, Security and Regulation in India

In India every single moment one individual become internet users. Its union with carefully upheld stages and devices, shielding the guardians as well as understudies from the cybercrimes is turning into a challenging task. Notwithstanding, the squeezing the truth is that the internet user are not getting refreshed on the helpless Digital dangers and security issues, at the speed they are getting refreshed with the utilization of web empowered instruments Also applications. In this manner the momentum research paper centres in Discovering the responses to disturbing inquiries – “Is the Netizen truly mindful that he/she is defenceless against different Digital wrongdoings?”; “Assuming netizen knows, how much?”. “If Not mindful of cybercrimes, what measures can be taken on To make the citizens more mindful and refreshed. The paper Recommended a theoretical model disclosing how to maintain What’s more execute the mindfulness programs among internet user in regards to cybercrimes.
The cloud forensics frameworks and tools: A brief review

A Cloud is a platform that allows for quick application deployment and dynamic scaling. The cloud differs from on-premise software and data storage in terms of cost, security, scalability, recovery, and mobility which makes more businesses are switching from on-premise to cloud solutions every year. Although cloud computing models have several benefits over on-site models, they are nonetheless vulnerable to both internal and external threats. Even malicious operations can be carried out on the cloud with ease because of the flexible environment. Forensic investigations require the extraction of evidence, and analysis of a cloud system after an intrusion or break-in. It enables investigators to find and retrieve data from a variety of sources in the cloud environment. It is very challenging to find proof of a crime since the distributed nature of the cloud prevents evidence from being retained on a specific physical machine and instead disperses the data over various regions. This paper focuses on current forensic investigation tools used in the Cloud environment and highlights the need for the development of efficient cloud forensic tools.
An Experimental Study on Web Interface Design Optimization Based on User Cognitive Load

The complexity of information structure in the digital age has resulted in an imbalance between Web interface design and user cognition. Based on the perspective of cognitive load, this study uses two experimental methods, eye-tracking and user behavior analysis, to explore experiments on two iterations of the DIC (Design Intelligence Center) Cultural and Creative Design Manufacturing Collaborative Innovation Center website, presenting the preliminary validation results are presented to demonstrate its effectiveness. To a certain extent, this experiment has achieved subjective and objective unity in digital interface research, and has improved the visualization and readable presentation of the pages while ensuring the basic functionality and aesthetics, At the same time, it forms a good human-computer interaction experience, providing new design optimization for web interface design, and further improve the scientific and rational design scheme.
Systems as an effort to guarantee the safety and security of guests study of hotel building fire protection

&lt;p&gt;Hotel fires can occur at any time without knowing the season for which the hotel building must have a fire extinguishing system that can guarantee the safety of guests, the purpose of this study is to find out the equipment used in the hotel building fire extinguishing system, to assess the completeness of facilities and infrastructure of the hotel building fire protection system, know the precautions against hotel fires in an effort to guarantee the safety and security of guests in hotel. The research methods used in this study are library study methods, interview methods, observation methods, documentation methods. Assessment of the completeness and reliability of building fire protection systems against fire hazards is used the parameters of tread completeness, means of rectification, active and passive protection systems. In data collection, it was carried out on the object of the study, namely at the Sahid Jaya Solo Hotel. The results of the discussion in this study, the equipment in the research object is complete and functions well and the results of the assessment of the reliability of the fire protection system obtained results of 90.63 which means that the reliability of the building is very good. From these results, it can be used as a consideration for the hotel in improving the right fire extinguishing system in an effort to guarantee the safety and security of guests.&lt;/p&gt;
How are software datasets constructed in Empirical Software Engineering studies? A systematic mapping study

Context: Software projects are common inputs in Empirical Software Engineering (ESE) studies, although they are often selected with ad-hoc strategies that reduce the generalizability of the results. An alternative is the usage of available datasets of software projects, which should be current and follow explicit rules for ensuring their validity over time. Goal: In this context, it is important to assess the general state of software datasets in terms of purpose, last update, project characterization, source code metrics, and tools to extract source-code-related artifacts. Method: We conducted a systematic mapping study retrieving software datasets used in ESE studies published from January 2013 to December 2021. Results: We selected 74 datasets created mainly for software defects, software estimation, and software maintainability studies. The majority of these datasets (64%) explicitly stated the characteristics to select the projects, and the most common programming languages were Java and C. Conclusions: Our study identified scarce efforts to keep datasets updated over time and also provides recommendations to support their construction and consumption for ESE studies.
Design and Development of Smart Wheelchair System Using Hand Gesture Control

According to the data provided by WHO, about 15% of the world population i.e. about 1 billion people experience disability with various impairments. Studies reveal that of these, 10% of people have motor disability requiring a wheelchair. Individuals with severe motor impairment face difficulty with the existing wheelchair using joystick control in steering it. Hence the proposed system aims at designing and developing a hand gesture controlled semi-autonomous electric wheelchair that is adaptable both indoors and outdoors. The system is designed in such a way the user can operate the wheelchair using hand gestures and android application. Hand gesture control is employed through the MEMS sensor. It is a three-axis accelerometer sensor that measures the hand orientation of the user and by analysing the motion, Arduino microcontroller processes the data and helps in propelling the wheelchair accordingly. An android application with directional buttons where the user can operate and control the wheelchair by pressing the specific button as a command. Bluetooth module interfaced with Arduino is integrated with the android application to recognize the command and process the data respectively. The developed system incorporating the user interface helps to easily navigate and reduces the effort in manually propelling the wheelchair. Experimental results demonstrate the viability of the proposed system. The future development as speed control system, and ergonomic design with safety belts make it highly advanced mobility solution and improves the reliability of the system.
End-to-end Image Compression with Swin-Transformer

In this paper, we propose an end-to-end image compression framework, which cooperates with the swin-transformer modules to capture the localized and non-localized similarities in image compression. In particular, the swin-transformer modules are deployed in the analysis and synthesis stages, interleaving with convolution layers. The transformer layers are expected to perceive more flexible receptive fields, such that the spatially localized and non-localized redundancies could be more effectively eliminated. The proposed method reveals the excellent capability of signal conjunction and prediction, leading to the improvement of the rate and distortion performance. Experimental results show that the proposed method is superior to the existing methods on both natural scene and screen content images, where 22.46% BD-Rate savings are achieved when compared with the BPG. Over 30% BD-Rate gains could be observed with screen content images when compared with the classical hyper-prior end-to-end coding method.
Doing for Data What the Internet Did for Networking

The acronym FAIR, (findable, accessible, interoperable, and reusable), refers to an international movement to effectively manage scientific data. The FAIR digital object (FDO) is an approach to implement FAIR data.1,2