Reviewerly/exHarmony huggingface dataset:
SelB-k-NN: A Mini-Batch K-Nearest Neighbors Algorithm on AI Processors

The popularity of Artificial Intelligence (AI) motivates novel domain-specific hardware named AI processors. With a design trade-off, the AI processors feature incredible computation power for matrix multiplications and activations, while some leave other operations less powerful, e.g., scalar operations and vectorized comparisons & selections. For k-nearest neighbors (k-NN) algorithm, consisting of distance computation phase and k-selection phase, while the former is naturally accelerated, the previous efficient k-selection becomes problematic. Moreover, limited memory forces k-NN to adopt a mini-batch manner with tiling technique. As the distance computation’s results are the k-selection’s inputs, the former’s tiling shape determines that of the latter. Since the two phases execute on separate hardware units requiring different performance analyses, whether the former’s tiling strategies benefit the latter and entire k-NN is doubtful.To address the new challenges brought by the AI processors, this paper proposes SelB-k-NN (Selection-Bitonic-k-NN), a mini-batch algorithm inspired by selection sort and bitonic k-selection. SelB-k-NN avoids the expansion of the weakly-supported operations on the huge scale of datasets. To apply SelB-k-NN to various AI processors, we propose two algorithms to reduce the hardware support requirements. Since the matrix multiplication operates data with the specifically-designed memory hierarchy which k-selection does not share, the tiling shape of the former cannot guarantee the best execution of the latter and vice versa. By quantifying the runtime workload variations of k-selection, we formulate an optimization problem to search for the optimal tiling shapes of both phases with an offline pruning method, which reduces the search space in the preprocessing stage. Evaluations show that on Huawei Ascend 310 AI processor, SelB-k-NN achieves 2.01× speedup of the bitonic k-selection, 23.93× of the heap approach, 78.52× of the CPU approach. For mini-batch SelB-k-NN, the optimal tiling shapes for two phases respectively achieve 1.48× acceleration compared with the matrix multiplication tiling shapes and 1.14× with the k-selection tiling shapes, with 72.80% of the search space pruned.
Improving the Ability to Recognize Number Concepts through Number Card Media in Children Aged 4-5 Years

This study aims to investigate the improvement in number concept recognition ability using number cards in 4–5-year-old children in kindergarten. In this study, the action method was used. This form of study is a collaborative study of classroom activities. The study subjects were one teacher and 15 children aged 4 to 5. The data collection methods of this study are observation, interview, and documentation. This study was conducted in two cycles, each cycle consisted of two meetings, and each cycle consisted of four stages: planning, execution, observation, and reflection. The results of this study indicated that learning to recognize the concept of numbers through the media of number cards in children aged 4-5 years at Kindergarten has increased. According to the study results, the child's ability to recognize the numbers 1 to 10 symbols increased with each cycle. Based on the data of this study, the child's number concept recognition ability using number card carriers increased by 18.2% in cycle I and cycle II, while second meeting out of 33% in cycle I and cycle II. It increased by 3% per period. It indicates that 4-5-year-old children in kindergarten can develop their ability to recognize numbers through playing number cards. Keywords: Number Card Media, Kindergarten, Ability to Recognize Number
IMPLEMENTATION OF THE THREE READING SCHOOL LITERATION MOVEMENT MODEL TEN MINUTES FOR STUDENTS OF SMPN 14 PONTIANAK TIMUR

This research on the Application of the School Literacy Movement Model at SMPN 14 Pontianak Timur aims to describe the process of implementing the school literacy movement model, the constraints, and the supporting factors encountered at SMPN 14 Pontianak Timur. The research method used is descriptive qualitative with observation, interview and documentation techniques. The informants of this study consisted of 3 people, namely the school principal, deputy student affairs, and the coordinator of Al-Qur'an literacy activities. The results of this study are that there are 3 stages in the implementation of the School Literacy Movement activities at SMPN 14 Pontianak Timur. The first stage is habituation, students carry out reading activities of folklore books 30 minutes before learning begins and make the habit of reading the Qur'an for 15 minutes every 4th week for Muslim students. The second stage is learning, the teacher applies a discussion and question and answer system every hour the lesson takes place. The third stage is development, the school collaborates with the FIM organization (Indonesian Writing Forum) so that students can make short stories entitled Anthology of Pirate Ships, Anthology of Two Pairs of Sandals, Anthology of Small Notes from Finger Friends, and Anthology of Glasses published by Pustaka One . The obstacle to implementing the School Literacy Movement activities was that schools did not provide reading books so that students were asked to buy story books outside, even though not all students wanted or could afford to buy story books. The obstacle to implementing AlQur'an literacy activities is that there are still students who are not fluent and cannot read the Al-Qur'an. The supporting factors for the implementation of reading literacy activities and the implementation of Al-Qur'an literacy are the presence of teachers who support and assist schools in carrying out these activities.Keywords: application, model, school literacy movement
EVALUATION OF DRAINAGE SYSTEM IN SIMOREJO SETTLEMENT AREA, SIMOMULYO VILLAGE, SUKOMANUNGGAL SUB-DISTRICT, SURABAYA CITY

Flooding frequently occurs in the Simorejo Settlement Area, Simomulyo Village, Sukomanunggal Sub-District, Surabaya City, Indonesia. Recent flooding revealed that the existing channel's capacity was insufficient to handle flood discharge. Therefore, an evaluation of the drainage channels was carried out to determine design and existing flood discharges and channel re-designing measures. Rain data from 2012 to 2021, for the last decade, was used in the evaluation. Maximum rainfall was calculated using the arithmetic method, while design and existing channel discharges were calculated based on the rational method with a maximum rainfall of 2 years, 5 years, and 10 years. The calculation results showed that the channel condition was unable to accommodate the planned flood discharge with a 2-year, 5-year, and 10-year return period. Consequently, re-designing was carried out using a trial and error method until the channel was able to accommodate the design of flood discharge. The re-designing process resulted in an enlarged drainage channel cross-section with dimensions of 1.2 m x 1 m x 1 m.
GENERATION OF SYNTHETIC RAINFALL DATA IN SEKAYAM SUB-WATERSHED BASED ON TRMM SATELLITE RAINFALL DATA CORRECTION EQUATION

The hydrological analysis is the first stage of the review in waterworks planning. Hydrological analysis necessitates the availability of sufficient data. Data availability tends to have several problems, including a lack of availability, incomplete/empty data, a smaller number of stations, observers, and observation systems, data entry that is still manual, and slow data collection. One possible solution is to use rain satellites. However, TRMM data must be evaluated for field suitability. TRMM (Tropical Rainfall Measurement Mission) rainfall data can help overcome this. TRMM is a NASA mission that uses weather monitoring satellite technology to monitor tropical rainfall. This is also the case for the Sekayam Subwatershed, part of the Kapuas River Basin.In the Sekayam sub-watershed, there are 14 (fourteen) rainfall observation stations managed by the Balai Wilayah Sungai Kalimantan (BWSK) I, but currently only 5 observation stations are still active, namely the SGU-01 Sanggau, SGU-03 Balai Karangan, SGU-06 Entikong, SGU-19 Semuntai and SC-01 Kembayan observation stations with data recorded up to 2019, while the other 9 (nine) observation stations do not have long continuous data, because there are years where rainfall data is not recorded. This is because the recording of rainfall on average stopped until 2005, or even some have stopped operating since the 1990s or early 2000s. This study aims to generate representative synthetic TRMM daily rainfall amount data for the Sekayam sub-watershed based on the correction equations obtained in a series of TRMM rainfall data validation analyses, so that it can be used as alternative daily rainfall data in water resources planning and management in the Sekayam sub-watershed. From the analysis, it can be seen that synthetic rainfall data in the Sekayam sub-watershed will be valid if it is generated with the linear model correction equation Y = 0.6708 X + 139.123 or can be interpreted as TRMM' = 0.6708 TRMM + 139.123.
Rule-based Engine for Position Data: Low-code/No-code Approach to Process Logic

Real-time location systems (RTLS) are becoming a standard part of modern digitized industrial facilities, enabling acquistition of position data of objects or people tracked in a given monitored area.These systems can utilize the virtualized coordinates to divide the real space into user-defined digital zones and give them meaning within a process.This paper describes the design and implementation of a commercial RTLS system extension, intended to eliminate the need for programming any complex position data processing by leveraging Low-code/Nocode approach to creating process logic in a user friendly way.
A Data Distillation Enhanced Autoencoder for Detecting Anomalous Gas Consumption

The number of natural gas users has been growing rapidly in China due to the promotion of clean energy and the economic benefits of natural gas, especially in businesses and industries. Though the infrastructures for gas supplies have been highly improved, gas providers are still suffering from various problems such as malfunctioning gas meters, gas leakage, gas theft etc. With the development of the Internet of Things, smart gas meters have been widely adopted by gas providers to collect real-time gas consumption data for billing purposes which can also serve as a basis for anomaly detection. One challenge of using such data for anomaly detection is that it is difficult to obtain sufficient labelled data for model training. To address this challenge, we propose DAE, a data distillation enhanced autoencoder for detecting anomalous gas consumption, which consists of three modules. The first module preprocesses the raw meter readings and carries out a rule-based anomaly detection. The second module extracts the normal gas usage patterns via an integration of correlation and clustering based consistency evaluation methods. The extracted normal usage patterns are then used in the third module to train an autoencoder for anomaly detection. DAE intends to provide a method to detect anomalous gas consumption induced by various causes such that manual inspection can be largely reduced. Moreover, DAE does not require user-specific information and can be applied to different types of gas users. Based on a real-world gas consumption dataset, we carry out a set of experiments and show that DAE outperforms the existing and improves the F1 score by an average of 7.4% for restaurant users and 5.7% for canteen users.
Gaussian Process Regression under Location Uncertainty using Monte Carlo Approximation

Gaussian Process Regression (GPR) is a common statistical framework for spatial function estimation.While its flexibility and availability of closed-form estimation solution after training are its advantages, it suffers on applicability constraints in scenarios with uncertain training positions.This paper presents the derivation of the exact GPR operating on uncertain training positions along with approximation of the resulting terms using Monte Carlo (MC) sampling.This method is then implemented in a simulation environment and shown to improve the estimation quality over the standard GPR approach with uncertain training positions.
A Comparison Particle Filter for Searching a Radiation Source in Real and Simulated World

In this paper, we are focusing on comparing solutions for localizing an unknown radiation source in both a Gazebo simulator and the real world.A proper simulation of the environment, sensors, and radiation source can significantly reduce the development time of robotic algorithms.We proposed a simple sampling importance resampling (SIR) particle filter.To verify its effectiveness and similarities, we first tested the algorithm's performance in the real world and then in the Gazebo simulator.In experiment, we used a 2-inch NaI(Tl) radiation detector and radiation source Cesium 137 with an activity of 330 Mbq.We compared the algorithm process using the evolution of information entropy, variance, and Kullback-Leibler divergence.The proposed metrics demonstrated the similarity between the simulator and the real world, providing valuable insights to improve and facilitate further development of radiation search and mapping algorithms.
THE INFLUENCE OF PROBLEM BASED LEARNING AND MIND MAPPING MODELS ON LEARNING OUTCOMES INDONESIAN LANGUAGE STUDENTS OF CLASS X AT SMAN PAKUSARI

Learning needs to be emphasized on process and quality to realize maximum learning outcomes. Learning Indonesian in class X is still not getting optimal results, so there is a need for a new model to be implemented, namely by using a problem based learning model and a mind mapping model. Learning with this observation report material is appropriate to use the problem based learning model because students are directly involved with what they see. Meanwhile, the concept map model makes it easier for students to design texts.The purpose of this study was to determine: (1) there is an effect of the Problem Basic Learning (PBL) Model on Indonesian Language Learning Outcomes for Class X Students at SMAN Pakusari Jember Regency, (2) there is an influence of the Mind Mapping Model (MM) on Language Learning Outcomes. Indonesia Class X Students at SMAN Pakusari Jember Regency, and (3) Is there an effect of the Problem Basic Learning (PBL) Model with the MIND MAPPING (MM) Model on the Indonesian Language Learning Outcomes of Class X Students at SMAN Pakusari Jember Regency. This quantitative research method is experimental. Due to the large population, the sub- population of the study was taken as many as 35 students. The school is SMA Negeri Pakusari class X 1 which uses 2 models, namely the Problem Basic Learning Model (PBL) and the Mind Mapping Model (MM). The data analysis technique in this study is to use two-way analysis. Based on the hypothesis test, it is proven that: (1) there is an effect of the Problem Based Learning model on the Indonesian Language Learning Outcomes of Class X Students on the LHO learning outcomes in the comparison of learning outcomes before and after treatment, the tcount value is 33.4356 with a significance value of 0.110. Because the value of tcount &gt; ttable or the significance value is smaller than the 5% significance level. (2) There is an effect of the Mind Mapping model on Indonesian Language Learning Outcomes of Class X Students on learning outcomes in the comparison of Learning Outcomes before and after treatment, the tcount value is 32.569 with a significance value of .000 Because the tcount &gt; ttable or the significance value is more smaller than the 5% level of significance. (3) There is an Influence of Problem Based Learning Model and Mind Mapping Model Together on Learning Outcomes for class X LHO in comparison of learning outcomes before and after treatment, the tcount value is 6.377 with a significance value of 0.206. Because the value of tcount &gt; ttable or the significance value is smaller than the 5% significance level
IMPACT OF EFFECTIVENESS OF THE EARLY CHILDHOOD EDUCATION (ECE) CLASSROOM LEARNING ENVIRONMENT IN GOVERNMENT GIRLS’ SCHOOLS OF NASEERABAD ZONE

The early childhood education is the period of human life (first eight years) is important for emotional growth, cognitive and physical development of child. The main objectives of the study to check the effectiveness of the early childhood education early childhood education of classroom learning environment is provided that students learning. The population of the study, there were total (428 schools in 6 districts) UNICEF started the practicing early childhood education but researcher selected Naseerabad zone for this purpose. There were 30 schools of Government girls Naseerabad zone. Data collected from semi-structured interview and observations, data was analyzed through thematic analysis, the findings of research. There were very less facilitates for the early childhood education teachers, there was no specialized two-year training offered, according to 2009 National Education Policy. It was also noticed that more extra rooms required for early childhood education. There was only one classroom in schools that has been designated as child-friendly and set aside for early childhood education classrooms, so that government must start the early childhood education trainings on regular bases and arrange the activities corners in the classrooms for the students.
Retracted: Optimal Control of Whole Network Control System Using Improved Genetic Algorithm and Information Integrity Scale

[This retracts the article DOI: 10.1155/2022/9897894.].
Quantum secure metrology for network sensing-based applications

Abstract Quantum secure metrology protocols harness quantum effects to probe remote systems with enhanced precision and security. Traditional QSM protocols require multi-partite entanglement, which limits its near-term implementation due to technological constraints. This paper proposes a QSM scheme that employs Bell pairs to provide unconditional security while offering precision scaling beyond the standard quantum limit. We provide a detailed comparative performance analysis of our proposal under multiple attacks. We found that the employed controlled encoding strategy is far better than the parallel encoding of multi-partite entangled states with regard to the secrecy of the parameter. We also identify and characterize an intrinsic trade-off relationship between the maximum achievable precision and security under the limited availability of resources. The dynamic scalability of the proposed protocol makes it suitable for large-scale network sensing scenarios.
Double-shot N-step phase measuring profilometry

Recently, a single-shot N-step phase measuring profilometry was proposed by our research group. It not only maintains the single-shot real-time measuring characteristics but also makes its measuring accuracy selectable as appropriate N. But when the spectral aliasing in the captured deformed pattern is severe, the alternative current (AC) component may be extracted imprecisely or even failed. So, a double-shot N-step phase measuring profilometry (double-shot N-PMP) is proposed. While two complementary sinusoidal gratings are projected onto the measured object, the AC component of the captured deformed patterns can be extracted precisely even if spectrum aliasing is very serious. If the AC component multiplies with N frames of the AC components of the N-step phase-shifting fringe patterns captured from the reference plane in advance, a new N-step phase-shifting algorithm for the phase difference between the measured object and the reference plane is accomplished to reconstruct the measured object. The experimental results show the possibility and effectiveness of the proposed method. It can either improve the measuring accuracy or expand application scope. Though the double-frame gratings are needed, the real-time measuring characteristics can still maintain with the time-division multiplexing method.
Improved Skip-Gram Based on Graph Structure Information

Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially applied to word embedding, is left insufficiently explored. To compensate for the shortcoming, we analyze the difference between word embedding and graph embedding and reveal the principle of graph representation learning through a case study to explain the essential idea of graph embedding intuitively. Through the case study and in-depth understanding of graph embeddings, we propose Graph Skip-gram, an extension of the Skip-gram model using graph structure information. Graph Skip-gram can be combined with a variety of algorithms for excellent adaptability. Inspired by word embeddings in natural language processing, we design a novel feature fusion algorithm to fuse node vectors based on node vector similarity. We fully articulate the ideas of our approach on a small network and provide extensive experimental comparisons, including multiple classification tasks and link prediction tasks, demonstrating that our proposed approach is more applicable to graph representation learning.
Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

Context awareness is a field in pervasive computing, which has begun to impact medical systems via an increasing number of healthcare applications that are starting to use context awareness. The present work seeks to determine which contexts are important for medical applications and which domains of context-aware applications exist in healthcare. A systematic scoping review of context-aware medical systems currently used by patients or healthcare providers (inclusion criteria) was conducted between April 2021 and June 2023. A search strategy was designed and applied to Pub Med, EBSCO, IEEE Explore, Wiley, Science Direct, Springer Link, and ACM, articles from the databases were then filtered based on their abstract, and relevant articles were screened using a questionnaire applied to their full texts prior to data extraction. Applications were grouped into context-aware healthcare application domains based on past reviews and screening results. A total of 25 articles passed all screening levels and underwent data extraction. The most common contexts used were user location (8 out of 25 studies), demographic information (6 out of 25 studies), movement status/activity level (7 out of 25 studies), time of day (5 out of 25 studies), phone usage patterns (5 out of 25 studies), lab/vitals (7 out of 25 studies), and patient history data (8 out of 23 studies). Through a systematic review process, the current study determined the key contexts within context-aware healthcare applications that have reached healthcare providers and patients. The present work has illuminated many of the early successful context-aware healthcare applications. Additionally, the primary contexts leveraged by these systems have been identified, allowing future systems to focus on prioritizing the integration of these key contexts.