Reviewerly/exHarmony huggingface dataset:
PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE STUDENT TEAMS ACHIEVEMENT DEVISIONS (STAD) TERHADAP HASIL BELAJAR KOGNITIF PESERTA DIDIK KELAS X SMA YPK OIKOUMENE MANOKWARI PADA REAKSI REDOKS

Abstact YPK Oikoumene Manokwari High School is one of the private schools located in Manokwari. This high school has implemented the 2013 curriculum with adequate facilities and infrastructure. The learning model used is still using conventional teacher-centered learning models so that students feel bored with the learning process that affects student learning outcomes. Cooperative learning model type Student Teams Achievement Divisions (STAD) which involves many students in studying the material covered in a lesson and checking their understanding of the content of the lesson. Based on the results of the research obtained on the cooperative learning model of the Student Teams Achievement Divisions (STAD) type on the cognitive learning outcomes of students, the Standard Gain results obtained with a value of 0.64 so that it can be said to have a moderate effect. The percentage of the effect obtained is 50% which shows that the cooperative learning model of the Student Teams Achievement Divisions (STAD) type has a fairly good influence on the learning outcomes of students in class X IPS SMA YPK Oikoumene Manokwari. Keywords: Cognitive Learning, Redox Reaction, Student Teams Achievement Divisions (STAD
Empirical demonstration of achieving image resolution below the optical diffraction limit with Image Phase Alignment Super-Sampling

A super-sampling algorithm is applied to empirical sets of translated images taken in a laboratory setting. Measurements applied to processed images reveal that image resolution subceeds the system diffraction limit for range of aperture settings.
Wide angle imaging system image simulation for accurate neural network performances prediction.

We developed a simulation pipeline of realistic wide-angle systems and compare the relative influence of optical characteristics used for design optimization (distortion, PSF) on single-image depth estimation accuracy using neural networks.
Linked Data Tools : semantic web for the masses

Semantic Web technologies have immense potential to transform the Internet into a distributed reasoning machine that will not only execute extremely precise searches, but will also have the ability to analyze the data it finds to create new knowledge. This paper examines the state of Semantic Web (also known as Linked Data) tools and infrastructure to determine whether semantic technologies are sufficiently mature for non–expert use, and to identify some of the obstacles to global Linked Data implementation.
Multiple-Stage Knowledge Distillation

Knowledge distillation (KD) is a method in which a teacher network guides the learning of a student network, thereby resulting in an improvement in the performance of the student network. Recent research in this area has concentrated on developing effective definitions of knowledge and efficient methods of knowledge transfer while ignoring the learning ability of the student network. To fully utilize this potential learning ability and improve learning efficiency, this study proposes a multiple-stage KD (MSKD) method that allows students to learn the knowledge delivered by the teacher network in multiple stages. The student network in this method consists of a multi-exit architecture, and the students imitate the output of the teacher network at each exit. The final classification by the student network is achieved through ensemble learning. However, because this results in an unreasonable gap between the number of parameters in the student branch network and those in the teacher branch network, as well as a mismatch in learning capacity between these two networks, we extend the MSKD method to a one-to-one multiple-stage KD method. The experimental results reveal that the proposed method applied to the CIFAR100 and Tiny ImageNet datasets exhibits good performance gain. The proposed method of enhancing KD by changing the style of student learning provides new insight into KD.
Analysis and Correlation between a Non-Invasive Sensor Network System in the Room and the Improvement of Sleep Quality

Good sleep quality is essential in human life due to its impact on health. Currently, technology has focused on providing specific features for quality sleep monitoring in people. This work represents a contribution to state of the art on non-invasive technologies that can help improve the quality of people’s sleep at a low cost. We reviewed the sleep quality of a group of people by analyzing their good and bad sleeping habits. We take that information to feed a proposed algorithm for a non-invasive sensor network in the person’s room for monitoring factors that help them fall asleep. We analyze vital signs and health conditions in order to be able to relate these parameters to the person’s way of sleeping. We help people get valuable information about their sleep with technology to live a healthy life, and we get about a 15% improvement in sleep quality. Finally, we compare the implementations given by the network with wearables to show the improvement in the behavior of the person’s sleep.
"The Role of Technology Forecasting on Unemployment: A Panacea to Insecurity in North Central Nigeria"

The rising rate of insecurity in Nigeria is extremely detrimental to the growth and the economy of the Nation.It is therefore imperative that solutions are found to this issue.This paper posits that a direct relationship exists between unemployment and insecurity and that by tackling the issue of unemployment in Nigeria, the insecurity threatening to topple the Nigerian nation will be lessened.This study with the topic The Role of Technology Forecasting on Unemployment; A Panacea to Insecurity in North Central Nigeria proposes that by using Technology Forecasting to predict the future employment trends, specialized institutions or skill centers can be created that will help in preparing the youth for jobs of the future, this will have direct ramifications in reducing unemployment and will lead to a reduction in insecurity.The study sort to discover if a direct relationship exists between unemployment and crime/insecurity and the role of Technology forecasting in predicting future employment opportunities?Findings from the secondary data analyzed show that there exists a direct relationship between unemployment and insecurity.The primary data obtained through structured questionnaires from a sample population in the region of Abuja showed that there is a strong interest in enrolling in specialized institutions and skill centers that will teach specialized skills in preparation for future jobs.And thus concluded that Technology forecasting is useful in solving unemployment which will in turn resolve insecurity issues.
The Law Applicable to P2P Networks on National and International Bases for Violating Intellectual Property Rights

To provide a path to the copyright holders for filing a lawsuit against the infringers and violators through a legitimate process and applying the law that applies to that particular situation that keeps a balance between the peer-to-peer network technology and copyright holders this research has presented four different law strategies that can reduce the violation of the rights of the intellectual properties on national and international bases and advance the P2P network file-sharing procedure. The authors include the incorporation of the proper law for any illegal activity regarding copyrights via peer-to-peer networks on national and international bases with modification in the security system of file-sharing. It caters to the filing of the case for the violation of any copywritten works regardless of where the cases have happened, and the law should be standard for all the intellectual properties and is acceptable by other countries as well.
Deep Learning Model for Dynamic Hand Gesture Recognition for Natural Human-Machine Interface on End Devices

As end devices have become ubiquitous in daily life, the use of natural human-machine interfaces has become an important topic. Many researchers have proposed the frameworks to improve the performance of dynamic hand gesture recognition. Some CNN models are widely used to increase the accuracy of dynamic hand gesture recognition. However, most CNN models are not suitable for end devices. This is because image frames are captured continuously and result in lower hand gesture recognition accuracy. In addition, the trained models need to be efficiently deployed on end devices. To solve the problems, the study proposes a dynamic hand gesture recognition framework on end devices. The authors provide a method (i.e., ModelOps) to deploy the trained model on end devices, by building an edge computing architecture using Kubernetes. The research provides developers with a real-time gesture recognition component. The experimental results show that the framework is suitable on end devices.
CCESHP: Causal Consistency Model of Edge Storage Based on Hash Ring and Partial Geo-Replication

Abstract At present, most of the causal consistency models that rely on cloud storage have problems such as high operation delays and large metadata overhead. To solve these problems, this paper proposes a causal consistency model for edge storage based on hash rings, CCESHP. The proposed model uses two hashes to map the keys and servers on the hash ring for grouping and stores a subset of the complete data set in a replica node located at the edge of the network, thereby realizing a partial geographic replication strategy in the edge storage environment. Operation latency will be reduced since the edge replica is closer to the client. At the same time, it also generates and maintains a combined timestamp to capture causality according to the update type, which can keep the amount of managed metadata in a relatively stable and low state, reduce the overhead of system management metadata, and improve system throughput. The experimental evaluation results under different workloads show that the model has better performance in throughput and operation delay when compared with the existing causal consistency model.
Establishment of Review Criteria Coordinating With the Automated Digital Cell Morphology Identification System in a Specialized Women’s and Children’s Hospital

We aimed to establish appropriate review criteria for blood cell analysis in a specialized women's and children's hospital. Also, the CellaVision DI-60, was developed as one of the automated digital cell morphology analyzer, we evaluated if it was shown to be most effective under the certain review criteria.A total of 2890 blood samples were detected to optimize the previously established review criteria for women and children with the Sysmex XE-2100. A total of 623 samples were used to validate the criteria.The microscopic-review rate based on the initial review criteria was 51.0%. After optimization, it was reduced to 17.3% and the false-negative rate was 3.85%. There was > 80% consistency between manual review results and CellaVision DI-60 preclassification when samples triggered the platelet- or red cell-related rules. The sensitivity for abnormalities (immature granulocytes, nucleated red blood cells) of reclassification was 90% to 100% and the false-negative rate was < 5%. However, direct microscopic review was required when the "Blasts/AbnLympho?" and "Atypical Lympho?" flags were triggered.Specialized review criteria are needed for women and children. An automated morphology identification system might help to improve the review criteria.
On geometrically uniform codes and topological quantum MDS codes

In this paper, we present generalized edge-pairings for the family of hyperbolic tessellations {4λ, 4} with the purpose of obtaining the corresponding discrete group of isometries. These tessellations have greater density packing than the self-dual tessellations {4λ, 4λ}, implying that the associated codes achieve the least error probability or, equivalently, that these codes are optimum codes. We also consider the topological quantum error-correcting codes on surfaces with genus g ≥ 2, as proposed in other papers. From that, we focus on the class of topological quantum maximum distance separable codes, showing that the only such codes are precisely the ones with minimum distance d ≤ 2.
Combining bounded solving and controllable randomization for approximate model counting

Propositional model counting is the problem of computing the satisfying assignments count of a given CNF formula. Due to the weak solving ability of the existing exact model counters in large-scale problems, approximate model counting has been proposed as a practical alternative to exact model counting. Most of the best current approaches are based on XOR constraints for approximate model counting, also known as the hashing-based method. In this paper, a new approximate model counter using short XOR constraints is proposed, which integrates bounded solving and controllable randomisation. By constantly adding short XOR constants, the solution space has been reduced to a smaller one. However, a smaller scale of solution space may result in worse result accuracy. Therefore, by limiting the scale of solution space that applies XOR constraints, bounded solving effectively increases the accuracy of a model counter. Controllable randomisation makes use of backbone variables and the constraint level among variables, such that the short XOR constraints can reach the same reduction effect on solution space as the long ones. It improves the quality of XOR constraints as well as the SAT solving efficiency. Experimentally, we demonstrate that in almost every benchmark used in well-known ApproxMC2 and STAC_CNF, our approach outperforms the existing approximate model counters in both accuracy and efficiency.
Comparisons of five least-squares adaptive matched filtering methods in multiple suppression

Abstract In this paper, we comprehensively compare the application effects of five least-squares adaptive matched filtering methods (the single-channel, the multichannel, the equipoise multichannel, the pseudo multichannel and the equipoise pseudo multichannel) for multiple suppression in three representative datasets with different degrees of orthogonality. By introducing an error function, we can quantitatively analyse the influence of the five methods for multiple suppression in terms of the filter length, the normalized regularization factor, the number of matched channels, the iteration number, the amplitude ratio and noise immunity. In addition, we provide the corresponding optimal parameters or their selection principles. The comparison results show that: (i) the dependence on orthogonality is not the same for these five methods; only the equipoise multichannel and the equipoise pseudo multichannel methods can effectively reduce the dependence on orthogonality; (ii) the single-channel method is relatively balanced in all aspects; (iii) the pseudo multichannel and the equipoise pseudo multichannel methods have a stronger shaping ability but generate larger errors; (iv) the multichannel method requires a higher degree of orthogonality and (v) the optimal parameters derived from the three datasets will be better reference values for complex models or field data for the multiple suppression.
The following articles for this Special Issue were published in different Issues

Abstract S. Sharma, L.M. Pandey. Integration of biosorption and biodegradation in a fed-batch mode for the enhanced crude oil remediation. 2021;73(4):471-476. https://doi.org/10.1111/lam.13535 https://onlinelibrary.wiley.com/doi/10.1111/lam.13535 R.K. Gupta, B.J. Poddar, S.P. Nakhate, A.R. Chavan, A.K. Singh, H.J. Purohit, A.A. Khardenavis. Role of heterotrophic nitrifiers and aerobic denitrifiers in simultaneous nitrification and denitrification process: a nonconventional nitrogen removal pathway in wastewater treatment. 2022;74(2):159-184. https://doi.org/10.1111/lam.13553 https://onlinelibrary.wiley.com/doi/10.1111/lam.13553
Exploring the Impact of Balanced and Imbalanced Learning in Source Code Suggestion

Studies have confirmed the robust performance of machine learning classifiers for various source code modeling tasks. In general, machine learning approaches are incapable of handling imbalanced datasets, since they are sensitive to the choice of diverse classes. Therefore, these approaches may lean towards the classes with a large percentage of observations. In this work, we investigate and explore the impact of balanced and imbalanced learning on source code suggestion task otherwise known as code completion, covering a large number of imbalanced classes. We further explore the impact of vocabulary size on modeling performance. First, we provide the essentials to formulate the problem of source code suggestion as a classification task and investigate the level of imbalanced classes. Second, we train the four most adapted neural language models as a baseline to assess the modeling performance. Third, we impose two diverse class balancing techniques, TomekLinks and AllKNN, to balance the datasets and evaluate their impact on the modeling performance. Finally, we trained these models with a weighted imbalanced learning approach and compared the performance with balanced learning approaches. Additionally, we train models by varying the vocabulary size to study their impact. In total, we trained 230 models on 10 real-world software projects and extensively evaluated these models with widely used performance metrics such as Precision, Recall, FScore, mean reciprocal rank (MRR), and Receiver operating characteristics (ROC). Additionally, we employed ANOVA statistical analysis to study the statistical significance and differences between these approaches. This study has demonstrated that the modeling performance decreases during balanced model training, whereas the weighted imbalance training produces comparable results and is more efficient in terms of time cost. Additionally, this study exhibits that a large size of vocabulary does not necessarily improve the modeling performance when out-of-vocabulary predictions are disregarded.
Prediction Model of Ischemic Stroke Based on Machine Learning

Machine learning (ML) can be used for deep mining and analysis of multidimensional medical data. At present, it has been widely used in medical diagnosis and prognosis prediction. This paper aims to make the existing research no longer focus on identifying key risk factors of stroke, and predict stroke risk more accurately. We collected the data of 3,962 cerebral apoplexy patients from 2019 to 2020, according to gender (male: 2,613; female: 1,349) and age (16–40 years old; 41–54 years old; 55–69 years old; 70 years old and above) layered. After data preprocessing, a stroke risk prediction model was built using principal component analysis (PCA) and extreme learning institutions (ELM). The prediction accuracy of PCA-ELM was as high as 97%. In this model, total cholesterol and high density lipoprotein are taken as 10 important factors that affect the incidence of stroke. This method can timely and efficiently mine the factors influencing the incidence of cerebral apoplexy from the data, and can predict the incidence of cerebral apoplexy. It has high value in practical application. This paper has great reference value in the research of brain death.
Spatial Model Personalization in Gboard

We introduce a framework for adapting a virtual keyboard to individual user behavior by modifying a Gaussian spatial model to use personalized key center offset means and, optionally, learned covariances. Through numerous real-world studies, we determine the importance of training data quantity and weights, as well as the number of clusters into which to group keys to avoid overfitting. While past research has shown potential of this technique using artificially-simple virtual keyboards and games or fixed typing prompts, we demonstrate effectiveness using the highly-tuned Gboard app with a representative set of users and their real typing behaviors. Across a variety of top languages, we achieve small-but-significant improvements in both typing speed and decoder accuracy.
Interacting with Rigid and Soft Surfaces for Smart-Home Control

For an improved user experience in smart homes, there is a need to enhance the current interaction methods by embedding control interfaces in everyday household objects. Embedded interfaces will allow smart home inhabitants to control their surroundings using more familiar interaction concepts and techniques than those imposed by appliance manufacturers and vendors. However, to achieve this state, technology designers need to understand the behaviours and attitudes of end users when they interact with different elements of a smart home using everyday objects as control media. We present a set of exploratory investigations focused on understanding the behaviours and attitudes of end-users as they control elements of a fictional smart home. Accordingly, we investigate how two highly different locations and form factors -- a table surface and a couch pillow -- impact user expectations about gestures used to control various smart home features. To this end, we conducted explorative elicitation with 14 participants. Our results highlight that an object's physicality significantly impacts the types of gestures users perceive as intuitive and that deforming an interface has the potential to bring playfulness to interaction. Following our observations, we formulated four design implications for conceiving interaction modalities with deformable interfaces for domestic environments.
TrackballWatch

A common problem of touch-based smartwatch interaction is the occlusion of the display. Although some models provide solutions like the Apple "digital crown" or the Samsung rotatable bezel, these are limited to only one degree of freedom (DOF). Performing complex tasks like navigating on a map is still problematic as the additional input option helps to zoom, but touching the screen to pan the map is still required. In this work, we propose using a trackball as an additional input device that adds two DOFs to prevent the occlusion of the screen. We created several prototypes to find a suitable placement and evaluated them in a typical map navigation scenario. Our results show that the participants were significantly faster (15.7%) with one of the trackball setups compared to touch input. The results also show that the idle times are significantly higher with touch input than with all trackball prototypes, presumably because users have to reorient themselves after panning with finger occlusion.
Prashant Pardeshi and Kaoru Horie (eds.): <i>Nihongo to sekai no gengo no meisisyuusyoku-hyoogen</i>

Article Prashant Pardeshi and Kaoru Horie (eds.): Nihongo to sekai no gengo no meisisyuusyoku-hyoogen was published on November 1, 2022 in the journal Journal of Japanese Linguistics (volume 38, issue 2).