Reviewerly/exHarmony huggingface dataset:
Palm Oil Employee Performance: Are Knowledge Sharing, Learning Organization, And Work-Life Balance The Issues?

The purpose of this study was to find out the impact of knowledge sharing, learning organization and work life balance toward employee performance. This study was associative research and using quantitative method. The population in this study was the employees of palm oil industry, in which 110 of them are re-spondents and sample was taken by using simple random sampling. The data was collected through ques-tionnaire between July-December 2020 and we did validity test, reliability test, and classic assumption test before being analyzed by using multiple regression analysis. The result shows that knowledge sharing, learning organization and work life balance has significant impact, partially and simultaneously, toward employee performance. Managerial implication of this study was asking employees to be more proactive in doing task or project and also making collaborative job in order to make employee cooperate with their coworker well.
Analysis on the latest research hotspots of computer deep learning optimization algorithms

Deep learning can effectively learn the characteristics of data, which is of great significance to the development of artificial intelligence. This article first outlines the development of deep learning, then describes four typical deep learning algorithms, and then presents the progress of the current deep learning in the optimization of learning algorithms After a review, it finally discusses the advantages and disadvantages of deep learning in data analysis, model building, and algorithm optimization, as well as the problems that need to be further studied and resolved.
A Computer Vision Sensor for AI‐Accelerated Detection and Tracking of Occluded Objects

A prominent problem in computer vision is occlusion, which occurs when an object's key features temporarily disappear behind another crossing body, causing the computer to struggle with image detection. While the human brain is capable of compensating for the invisible parts of the blocked object, computers lack such scene interpretation skills. Cloud computing using convolutional neural networks is typically the method of choice for handling such a scenario. However, for mobile applications where energy consumption and computational costs are critical, cloud computing should be minimized. In this regard, a computer vision sensor capable of efficiently detecting and tracking covered objects without heavy reliance on occlusion‐handling software is proposed. The edge‐computing sensor accomplishes this task by self‐learning the object prior to the moment of occlusion and uses this information to “reconstruct” the blocked invisible features. Furthermore, the sensor is capable of tracking a moving object by predicting the path it will most likely take while traveling out of sight behind an obstructing body. Finally, sensor operation is demonstrated by exposing the device to various simulated occlusion events. An interactive preprint version of the article can be found at DOI https://www.authorea.com/doi/full/10.22541/au.164192000.02603887/ .
JPL/Caltech ArtCenter

Abstract Data visualization frequently provides audiences with novel semantic and computational presentations. How does a multifaceted team expand this scope by harnessing the power of visualization as a tool to think with? The NASA JPL/Caltech/ ArtCenter data visualization program demonstrates how scientific knowledge, shaped from data and theory, is equally co-constructed from diverse human perspectives. We will share case studies from Mars Rover Path planning and PIXLISE, a visual reasoning tool for understanding planetary geology. Working from source data through mixed media artifacts, these projects explore co-design methods for complex scientific domains with real-world applications. Our methodology emphasizes that all participants in the co-design process are both learners and experts. In this dynamic, the design and coding process are unique modes of critical discovery.
Analysis of the influence of simultaneous phase-shifting shearing interference polarizer

The aspheric surface testing system based on the shearing interference principle is used to better analyze how the installation and fabrication errors of linear polarizer affects the precision of the aspheric surface. The effects of the angle error of the linear polarizer and the phase-shifting error of the wave plate on the reconstruction of the aspheric surface are studied. Constructing the model of the Jones matrix with the error term system, the reconstruction surface and residual after the corresponding error system are obtained using the four-step phase-shifting and differential Zernike method. The polarizer array of an angle of θ was varied from ( θ − 1 deg ) to ( θ + 2 deg ) every 0.2 deg, and the changes of the fitting surface and the residual surface were simulated. The phase-shifting error of the simulated one-fourth wave plate was <λ / 300. Then, the change of the fitting surface and residual surface when these two errors exist at the same time were simulated. The simulation results show that the influence of the phase-shifting error of the wave plate on the fitting surface is much greater than the angle error of the polarizer array. When assembling the interference system, the phase-shifting error of the wave plate should be <λ / 300 when the light transmission angle error of the polarizer array is between −0.2 deg and 0.2 deg, which will ensure the testing precision of the aspheric surface testing system.
Outcomes of a Community Engagement and Information Gathering Program to Support Telephone-Based COVID-19 Contact Tracing: Descriptive Analysis

Contact tracing is an important public health tool for curbing the spread of infectious diseases. Effective and efficient contact tracing involves the rapid identification of individuals with infection and their exposed contacts and ensuring their isolation or quarantine, respectively. Manual contact tracing via telephone call and digital proximity app technology have been key strategies in mitigating the spread of COVID-19. However, many people are not reached for COVID-19 contact tracing due to missing telephone numbers or nonresponse to telephone calls. The New York City COVID-19 Trace program augmented the efforts of telephone-based contact tracers with information gatherers (IGs) to search and obtain telephone numbers or residential addresses, and community engagement specialists (CESs) made home visits to individuals that were not contacted via telephone calls.The aim of this study was to assess the contribution of information gathering and home visits to the yields of COVID-19 contact tracing in New York City.IGs looked for phone numbers or addresses when records were missing phone numbers to locate case-patients or contacts. CESs made home visits to case-patients and contacts with no phone numbers or those who were not reached by telephone-based tracers. Contact tracing management software was used to triage and queue assignments for the telephone-based tracers, IGs, and CESs. We measured the outcomes of contact tracing-related tasks performed by the IGs and CESs from July 2020 to June 2021.Of 659,484 cases and 861,566 contact records in the Trace system, 28% (185,485) of cases and 35% (303,550) of contacts were referred to IGs. IGs obtained new phone numbers for 33% (61,804) of case-patients and 11% (31,951) of contacts; 50% (31,019) of the case-patients and 46% (14,604) of the contacts with new phone numbers completed interviews; 25% (167,815) of case-patients and 8% (72,437) of contacts were referred to CESs. CESs attempted 80% (132,781) of case and 69% (49,846) of contact investigations, of which 47% (62,733) and 50% (25,015) respectively, completed interviews. An additional 12,192 contacts were identified following IG investigations and 13,507 following CES interventions.Gathering new or missing locating information and making home visits increased the number of case-patients and contacts interviewed for contact tracing and resulted in additional contacts. When possible, contact tracing programs should add information gathering and home visiting strategies to increase COVID-19 contact tracing coverage and yields as well as promote equity in the delivery of this public health intervention.
Three Product Implementations for Improving “Just-In-Time” Delivery of Library Resources

The academic library plays a pivotal role within the organizational framework of higher education, supporting teaching, learning, and research with robust collections of materials in an array of formats. The value and ease of access to academic library collections within the higher education organizational framework cannot be understated. Accordingly, neither can the importance of interlibrary loan services from which users may gain access to materials beyond the local collection. In an effort to enhance access and discovery of materials, promote interlibrary loan services, and meet the goals and objectives of a new strategic plan. The University of Alabama Libraries implemented three new tools over the course of a few short months: Lean Library, Article Galaxy Scholar, and EBSCO custom linking. This article presents a case study detailing the implementation of these tools at a major research library.
Tensor Affinity Learning for Hyperorder Graph Matching

Hypergraph matching has been attractive in the application of computer vision in recent years. The interference of external factors, such as squeezing, pulling, occlusion, and noise, results in the same target displaying different image characteristics under different influencing factors. After extracting the image feature point description, the traditional method directly measures the feature description using distance measurement methods such as Euclidean distance, cosine distance, and Manhattan distance, which lack a sufficient generalization ability and negatively impact the accuracy and effectiveness of matching. This paper proposes a metric-learning-based hypergraph matching (MLGM) approach that employs metric learning to express the similarity relationship between high-order image descriptors and learns a new metric function based on scene requirements and target characteristics. The experimental results show that our proposed method performs better than state-of-the-art algorithms on both synthetic and natural images.
Promotion of Electronic Information Resources: How to Help Users to Find what is Available in the Library

Promotion of e-resources in library settings remains to be an obstacle for its efficient use. There are three main reasons of insufficient use of e-resources: majority of users remain to be unaware of availability of e-collections in their institutions, lack of searching skills among end-users, low level of competency among librarians on e-resources and e-platforms. The lack of support and training of end-users results in poor usage of research oriented e-resources, especially foreign ones. Recommendations for librarians on promotion of e-resources include redesign of library website as main entry-point to e-collections, correct URLs to publisher platforms and their correct description, information on access options, availability of end-user emails for notification purposes — these and many other tips are important for efficient usage of acquired e-resources for research and educational purposes.
ReAFFPN: Rotation-Equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detetcion

This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN. ReAFFPN aims at improving the effect of rotation-equivariant features fusion between adjacent layers which suffers from the semantic and scale discontinuity. Due to the particularity of rotational equivariant convolution, general methods are unable to achieve their original effect while ensuring rotation equivariance of the network. To solve this problem, we design a new Rotation-equivariant Channel Attention which has the ability to both generate channel attention and keep rotation equivariance. Then we embed a new channel attention function into Iterative Attentional Feature Fusion (iAFF) module to realize Rotation-equivariant Attention Feature Fusion. Experimental results demonstrate that ReAFFPN achieves a better rotation-equivariant feature fusion ability and significantly improve the accuracy of the Rotation-equivariant Convolutional Networks.
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors

The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge the gap, we present mRI, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 160k synchronized frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality. We hope that the release of mRI can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly facilitate the applications of home-based health monitoring.
Anomaly detection for remote maintenance control system of natural gas pipeline based on nearest neighbor clustering

Accurate anomaly detection of remote maintenance control system of natural gas pipeline is of great significance in ensuring the safe and stable operation of gas pipeline network. A supervised anomaly detection method based on k-nearest neighbors searching and clustering is proposed. Firstly, the labels of neighbor samples are used to determine whether the sample is noise or belongs to an anomaly cluster, and the iterative search is conducted in the neighbor samples until no more abnormal samples belonging to this cluster are found. Then, the noise samples are filtered and the numbers of new abnormal samples to be generated in each cluster are calculated. Finally, SMOTE is used to generate the artificial samples in each cluster to balance the data. The experiment results on public datasets and the remote maintenance control system monitoring dataset show that the proposed method outperforms the compared methods in terms of F-measure and G-mean.
Exploring the Intersection between Databases and Digital Forensics

Digital forensics has attracted attention from assorted researchers, who primarily work on predicting and solving digital hacks and crimes. In turn, the number and types of digital crimes have increased considerably, mainly due to the growing use of digital media to perform daily personal and professional tasks. Like most computer-related activities, data is at the center of such hacks and crimes. Hence, this work presents a systematic literature review of publications at the intersection between Digital Forensics and Databases. We discuss problems and trends of two main categories: Data Building and Database Management Systems. Overall, this research opens the doors for the communication between databases and an area with several exciting and concrete challenges, with great potential for social, economic, and technical-scientific contributions.
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning

Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. As a result, the existing paradigm limits the diversity of knowledge selection and generation. To this end, we establish a multi-reference KGC dataset and propose a series of metrics to systematically assess the one-to-many efficacy of existing KGC models. Furthermore, to extend the hypothesis space of knowledge selection to enhance the mapping relationship between multiple knowledge and multiple responses, we devise a span-based variational model and optimize the model in a wake-sleep style with an ameliorated evidence lower bound objective to learn the one-to-many generalization. Both automatic and human evaluations demonstrate the efficacy of our approach.
Information retrieval linguistic instruments in e-catalogs of Russian large libraries

The article is based on the findings of the study of linguistic instruments of information search used in e-catalogs of Russian largest libraries. For the study, the libraries with the collections of over 4 million entries were sele cted. The authors analyze search types applicable to e-catalog search interface. They specify the following search types applied in the above libraries: single-line (simple) search, standard (basic), advanced and professional search. The advanced search options are discussed, namely refinement systems, ranking and sorting of search results; at least one of these options is used in the e-catalogs under study. The authors also analyze the systems of search dictionaries, guidelines and use characteristics. The popular information retrieval languages are discussed with the conclusion made that the e-catalogs under study offer search options for various sets of bibliographic elements, among them author, title, year of publication, and subject search based on verbal languages (every library under study) and classification languages (majority of the libraries). The authors formulate the common features inherent in the libraries' e-catalogs and information retrieval linguistic instruments. They also offer recommendations on how to develop and efficiently use these instruments for upgrading library e-catalogs.
Problems and Countermeasures of Educational Management of Postgraduates in Colleges and Universities

With the rapid development of postgraduate education in China, the number of postgraduate students in school shows a trend of continuous growth. The postgraduate education management system in most of the colleges and universities has been gradually improved, and it is the primary task of contemporary postgraduate education administrators to cultivate high-level innovative talents who are suitable for the country’s social and economic development. This study analyzed the existing problems in the management of postgraduate education in colleges and universities, and the relevant solutions of the problems were also proposed in this paper.
Lightweight mobile network for real-time violence recognition

Most existing violence recognition methods have complex network structures and high cost of computation and cannot meet the requirements of large-scale deployment. The purpose of this paper is to reduce the complexity of the model to realize the application of violence recognition on mobile intelligent terminals. To solve this problem, we propose MobileNet-TSM, a lightweight network, which uses MobileNet-V2 as main structure. By incorporating temporal shift modules (TSM), which can exchange information between frames, the capability of extracting dynamic characteristics between consecutive frames is strengthened. Extensive experiments are conducted to prove the validity of this method. Our proposed model has only 8.49MB parameters and 175.86MB estimated total size. Compared with the existing methods, this method greatly reduced the model size, at the cost of an accuracy gap of about 3%. The proposed model has achieved accuracy of 97.959%, 97.5% and 87.75% on three public datasets (Crowd Violence, Hockey Fights, and RWF-2000), respectively. Based on this, we also build a real-time violence recognition application on the Android terminal. The source code and trained models are available on https://github.com/1840210289/MobileNet-TSM.git .
Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI)

The deficiency of oxygen in the brain will cause the cells to die, and the body parts controlled by the brain cells will become dysfunctional. Damage or rupture of blood vessels in the brain is better known as a stroke. Many factors affect stroke. These factors certainly need to be observed and alerted to prevent the high number of stroke sufferers. Therefore, this study aims to analyze the variables that influence stroke in medical records using statistical analysis (correlation) and stroke prediction using the XAI algorithm. Factors analyzed included gender, age, hypertension, heart disease, marital status, residence type, occupation, glucose level, BMI, and smoking. Based on the study results, we found that women have a higher risk of stroke than men, and even people who do not have hypertension and heart disease (hypertension and heart disease are not detected early) still have a high risk of stroke. Married people also have a higher risk of stroke than unmarried people. In addition, bad habits such as smoking, working with very intense thoughts and activities, and the type of living environment that is not conducive can also trigger a stroke. Increasing age, BMI, and glucose levels certainly affect a person's stroke risk. We have also succeeded in predicting stroke using the EMR data with high accuracy, sensitivity, and precision. Based on the performance matrix, PNN has the highest accuracy, sensitivity, and F-measure levels of 95%, 100%, and 97% compared to other algorithms, such as RF, NB, SVM, and KNN.
Optimal heap limits for reducing browser memory use

Garbage-collected language runtimes carefully tune heap limits to reduce garbage collection time and memory usage. However, there's a trade-off: a lower heap limit reduces memory use but increases garbage collection time. Classic methods for setting heap limits include manually tuned heap limits and multiple-of-live-size rules of thumb, but it is not clear when one rule is better than another or how to compare them. We address this problem with a new framework where heap limits are set for multiple heaps at once. Our key insight is that every heap limit rule induces a particular allocation of memory across multiple processes, and this allocation can be sub-optimal. We use our framework to derive an optimal "square-root" heap limit rule, which minimizes total memory usage for any amount of total garbage collection time. Paradoxically, the square-root heap limit rule achieves this coordination without communication: it allocates memory optimally across multiple heaps without requiring any communication between those heaps. To demonstrate that this heap limit rule is effective, we prototype it for V8, the JavaScript runtime used in Google Chrome, Microsoft Edge, and other browsers, as well as in server-side frameworks like node.js and Deno. On real-world web pages, our prototype achieves reductions of approximately 16.0% of memory usage while keeping garbage collection time constant. On memory-intensive benchmarks, reductions of up to 30.0% of garbage collection time are possible with no change in total memory usage.