Reviewerly/exHarmony huggingface dataset:
Personalized Computer Support of Performance Rates and Education Process in High School: Case Study of Engineering Students

To date, the existing principles of a unified approach to schooling do not meet the requirements of a fundamental socio-economic transition characterized by personalization and customization of products and services that concern all areas, including education. The paradigm of education systems is being restructured in many countries towards a new approach of personalized learning that is adapted to students' needs, allows them to choose individual educational trajectories, and respects unique skills and qualities. The study aimed to determine the impact of personalization on the academic performance of engineering students in physics. The experiment on personalized student support was conducted during the academic year 2018-2019 at I.T. Trubilin Kuban State Agrarian University. The sample covered 78 students. The starting point of the research was the assessment of students' skills in mathematics, as the study of physics is based on mathematical analysis and interpretation. Test in physics and the criterion of alpha Kronbach (test reliability is determined with a coefficient of 0.87) were used as a toolkit for research. Besides, the statistical tools and covariance analysis of data processing were employed. The research was supported by the SPSS Statistics software package. According to the results obtained, higher post-test scores were recorded for students in the experimental group. The personalized learning has been found to have a significant impact on the academic performance of students. Those with low and medium ability in mathematics demonstrated higher growth in academic achievement than students with recorded high aptitude scores. A correlation between learning methods and mathematical skills to achieve high academic performance has been established. The influence of personalization in the learning process was determined to be moderately high (determinacy coefficient of 72.6%). The study is relevant for the development of constructive approaches that could help teachers improve their approach to learning and students improve academic achievements. The results may be of interest to researchers of educational technology, educators, and university administration management.
The Nigerian population: A treasure for national development or an unsurmountable national challenge

Nigeria is the most populous black nation in the world. It is equally one of the Less Developed Countries (LDCs) with very high population. Population growth is a very important element and a challenge in the development process in LDCs. The population of Nigeria is expected to continue to grow up to 239 million by 2025 and 440 million by 2050, thereby ranking it to 4th position among countries of the World with high population. This without doubt will place Nigeria in a position of major player in the global system, and more importantly in the African region. It is against this background that this study examines Nigeria’s population composition by poising the following questions; will Nigeria’s present and future population structure be a benefit or a burden? How can Nigeria’s relative share of working-age composition (15- 64) and dependents (under 15 and 65 and over) contribute to long term economic growth and development of the country? The findings of the study reveals that population growth is a critical factor in the development of any economy, providing workforce for production of goods and services to boost economic development and a critical determinant of the potentials of a country’s investment. The study findings also show that continuous population growth militates against economic growth through inducement of poverty, falling medical care/services and environmental degradation, worsen resource scarcity in areas where a large proportion of the population already relies on natural resource-based livelihoods. The study argued that population increase is not a problem in itself to any nation, and that there are some impeding factors associated with population growth such as corruption, inadequate planning, inappropriate implementation of development plans, poor budget/implementation and complacency in developing human capital. These are issues that the Nigerian state since independence have continued to battle with which has invariably made it a seemingly failed state. The study concludes that how much any country can benefit from its population size is dependent on the quality of human capital. Based on the findings, the study recommends economic diversification, government empowerment of Small and Medium scale Enterprises, paying attention to human capital development and target-oriented education.
A hybrid scheme for prime factorization and its experimental implementation using IBM quantum processor

We report a quantum–classical hybrid scheme for factorization of bi-prime numbers (which are odd and square-free) using IBM’s quantum processors. The hybrid scheme proposed here involves both classical optimization techniques and adiabatic quantum optimization techniques, and is build by extending a previous scheme of hybrid factorization [Pal et al., Pramana 92, 26 (2019) and Xu et al., Phys. Rev. Lett. 108, 130501 (2012)]. The quantum part of the scheme is very general in the sense that it can be implemented using any quantum computing architecture. Here, as an example, we experimentally implement our scheme for prime factorization using IBM’s QX4 quantum processor and have factorized 35.
Analytic Filter-Function Derivatives for Quantum Optimal Control

Autocorrelated noise appears in many solid-state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of noise is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows the computation of gate fidelities in the presence of autocorrelated classical noise. Hence, this formalism can be combined with optimal control algorithms to design control pulses, which optimally implement quantum gates. To enable the use of gradient-based algorithms with fast convergence, we present analytically derived filter function gradients with respect to control pulse amplitudes, and analyze the computational complexity of our results. When comparing pulse optimization using our derivatives to a gradient-free approach, we find that the gradient-based method is roughly 2 orders of magnitude faster for our test cases. We also provide a modular computational implementation compatible with quantum optimal control packages.
A New Project-based Learning Concept in English Writing

At present, more international talents are needed. English writing ability is an essential quality for such talents. However, by far the teaching of English writing is faced with some problems, that is, students lack interest and learn passively, teachers’ teaching methods are not diversified enough, and there are insufficient interactions between teachers and students in the classroom. How to improve the effectiveness of the teaching of English writing has become a top priority in the current teaching reform of English writing. By taking advantage of Internet information technology, this study establishes an online teaching mode for English writing. In view of the characteristics of the teaching of English writing, based on the classroom design concept of ADDIE (Analysis, Design, Development, Implementation and Evaluation). In the meantime, by integrating the project-based learning mode, we assign writing project questions and incorporate such steps as information collection, knowledge acquisition, solution discussion, peer collaboration, hands-on practice, etc. On the other hand, the neural network algorithm is also used to classify texts of electronic lecture notes. It is found through teaching practice, that the project-based learning based on the concept of ADDIE is an effective teaching way, which can significantly improve the effect of classroom teaching and enhance students’ learning initiative.
VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.
Anomaly Detection for Raw Water Quality – A Comparative Analysis of the Local Outlier Factor Algorithm and the Random Forest Algorithms

The increased use of real-time water quality monitoring using automated systems with sensors demands and makes it possible to identify unexpected values in time.Anomalies are brought by technical issues that are likely to prevent detection of problematic data manually at the incoming data rate.Use of machine learning approaches to detect anomalies for water quality data is the main focus of this article.There is analysis of four time series machine learning anomaly detection techniques: the local outlier factor, the isolation forest, the extended isolation forest and robust random cut forest.A subset data collected from deployment of sensors in a water treatment plant (Nyeri-Kenya) was used to carry out extensive analysis of experiments of the afore-mentioned techniques; for turbidity and pH parameters.There was successful correct detection of all outliers for both subsets by the local outlier factor algorithm, contrary to the rest of the other algorithms considered.As per the primary experiment, the local outlier factor emerged the fastest.Also, it was easier use as long as there was selection of optimum parameters.Moreover, analysis of the four techniques demonstrated that with or without training, it is a powerful tool for water quality anomaly detection and hence a feasible approach.
The Cyborg Philharmonic: Synchronizing interactive musical performances between humans and machines

Abstract Music offers a uniquely abstract way for the expression of human emotions and moods, wherein melodic harmony is achieved through a succinct blend of pitch, rhythm, tempo, texture, and other sonic qualities. The emerging field of “ Robotic Musicianship ” focuses on developing machine intelligence, in terms of algorithms and cognitive models, to capture the underlying principles of musical perception, composition, and performance. The capability of new-generation robots to manifest music in a human-like artistically expressive manner lies at the intersection of engineering, computers, music, and psychology; promising to offer new forms of creativity, sharing, and interpreting musical impulses. This manuscript explores how real-time collaborations between humans and machines might be achieved by the integration of technological and mathematical models from Synchronization and Learning , with precise configuration for the seamless generation of melody in tandem, towards the vision of human–robot symphonic orchestra . To explicitly capture the key ingredients of a good symphony—synchronization and anticipation—this work discusses a possible approach based on the joint strategy of: (i) Mapping — wherein mathematical models for oscillator coupling like Kuramoto could be used for establishing and maintaining synchronization, and (ii) Modelling —employing modern deep learning predictive models like Neural Network architectures to anticipate (or predict) future state changes in the sequence of music generation and pre-empt transitions in the coupled oscillator sequence. It is hoped that this discussion will foster new insights and research for better “real-time synchronized human-computer collaborative interfaces and interactions”.
Towards Green Query Processing - Auditing Power Before Deploying

Nowadays, energy reduction has become a critical and urgent issue for the database community. A lot of initiatives have been launched on energy-efficiency for intensive-workload computation covering individual hardware components, system software, to applications. This computation is mainly ensured by query optimizers. Their current versions minimize inputs-outputs operations and try to exploit RAM as much as possible, by ignoring energy. A couple of studies proposed the integration of energy into query optimizers that can be classified into hardware and software solutions. Several researchers have the idea that the operating systems and firmware manage energy and put software solutions in the second plan. This does not distinguish between tasks of operating systems and DBMSs. In this paper, we claim that building from scratch a green query processors and revisiting existing ones pass through 4-steps procedure: (1) establishment of a deep audit that allows understanding the query processor functioning, (2) identification of relevant energy-sensitive parameters belonging to hardware and software components, (3) elaboration of mathematical cost models estimating consumed energy when executing a query on a target DBMS and (4) setting of values of the energy-sensitive parameters using a nonlinear regression technique. To show the effectiveness of this procedure, we apply it on two open-source DBMSs with different functioning policies: PostgreSQL and MonetDB and compared them using the dataset and the workload of the TPC-H benchmark.
OblivSketch: Oblivious Network Measurement as a Cloud Service

Network function virtualisation enables versatile network functions as cloud services with reduced cost.Specifically, network measurement tasks such as heavy-hitter detection and flow distribution estimation serve many core network functions for improved performance and security of enterprise networks.However, deploying network measurement services in third-party multi-tenant cloud service providers raises critical privacy and security concerns.Recent studies demonstrate that leaking and abusing flow statistics can lead to severe network attacks such as DDoS, network topology manipulation and poisoning, etc.In this paper, we propose OblivSketch, an oblivious network measurement service using Intel SGX.It employs hardware enclave for secure network statistics generation and queries.The statistics are maintained in newly designed oblivious data structures inside the SGX enclave and queried by data-oblivious algorithms to prevent data leakage caused by access patterns to the memory of SGX.To demonstrate the practicality, we implement OblivSketch as a full-fledge service integrated with the off-the-shelf SDN framework.The evaluations demonstrate that OblivSketch consumes a constant and small memory space (6MB) to track a massive amount of flows (from 30k to 1.45m), and it takes no more than 15ms to respond six widely adopted measurement queries for a 5s-trace with 70k flows.
Disinfectant Spraying System with Quadcopter Type Unmanned Aerial Vehicle (UAV) Technology as an Effort to Break the Chain of the COVID-19 Virus

Spraying disinfectants as a preventive measure in preventing the transmission of COVID-19 is still being carried out by officers manually by surrounding all parts of the building. Technological developments in the modern era can help and simplify this job. One way to take advantage of technology during the COVID-19 pandemic is to use a drone or an Unmanned Aerial Vehicle (UAV) to spray disinfectants indoors. An unmanned Aerial Vehicle (UAV) is an unmanned aircraft whose flight can be controlled remotely. In this applied research the researchers used a quadcopter UAV which consists of four propellers, each of which is mounted on an 11.1 Volt brushless motor. This motor functions as a propeller drive that uses a current source from LiPo 3S. Based on the research that has been done, the quadcopter is designed using a 2200KV BLDC motor controlled by the SP Racing F3 flight controller. Has been able to fly a quadcopter along with 200ml of disinfectant. However, in order for the quadcopter to be able to lift even greater loads the specifications of the BLDC motor need to be improved
Industrial Machine Identification Using Augmented Reality

Augmented Reality is an interactive experience of a real world environment.It has been used in the following fields Medical, Education, Mechanical Industries, Food Industries, Real Estate and so on.Since October 2020 the industrial growth is nearly about 26.18%, thereby it is seen that there is a gradual development for the manufacturing models.In current scenario, the freshers of mechanical industries are knowing the working of machineries by the user guide or trainers.As a fresher to the industry, they might have communication struggles to ask for the working of the machine with supervisors or experts.The normal learning process from the user guide by the freshers may take a long period of time and also for training the freshers by the experts may cost high.To overcome the issue, we propose an augmented reality application which can help the freshers to identify the industrial machines and also know the working of machine in three dimensional views.Our application is a interactive platform which will be handy for the freshers and it could be helpful for better understanding of a machine in short period of time in low cost.
Multivariate‐bounded Gaussian mixture model with minimum message length criterion for model selection

Abstract Bounded support Gaussian mixture model (BGMM) has been proposed for data modelling as an alternative to unbounded support mixture models for the cases when the data lies in bounded support. In this paper, we propose applications of multivariate BGMM in data clustering for more insightful analysis of the model. We also propose minimum message length (MML) criterion for model selection in data clustering using multivariate BGMM. The presented model is applied to data clustering in several speech (TSP and Spoken Digits) and image databases (MNIST and Fashion MNIST). We also propose the application of BGMM in code‐book generation at feature extraction phase. Inspired by the success of bag of visual words approach in computer vision, it is also introduced in speech data representation and validated through experiments presented in this paper. For validation of model selection criterion, MML is applied to different medical, speech and image datasets. Experimental results obtained during the model selection through MML are further compared with seven different model selection criteria. The results presented in the paper demonstrate the effectiveness of BGMM for clustering speech and image databases, code‐book generation through clustering for feature representation and model selection.
Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.
Hidden qubit cluster states

Continuous-variable cluster states (CVCSs) can be supplemented with Gottesman-Kitaev-Preskill (GKP) states to form a hybrid cluster state with the power to execute universal, fault-tolerant quantum computing in a measurement-based fashion. As the resource states that comprise a hybrid cluster state are of a very different nature, a natural question arises: Why do GKP states interface so well with CVCSs? To answer this question, we apply the recently introduced subsystem decomposition of a bosonic mode, which divides a mode into logical and gauge-mode subsystems, to three types of cluster state: CVCSs, GKP cluster states, and hybrid continuous-variable (CV)-GKP cluster states. We find that each of these contains a ``hidden'' qubit cluster state across their logical subsystems, which lies at the heart of their utility for measurement-based quantum computing. To complement the analytical approach, we introduce a simple graphical description of these CV-mode cluster states that depicts precisely how the hidden qubit cluster states are entangled with the gauge modes, and we outline how these results would extend to the case of finitely squeezed states. This work provides important insight that is both conceptually satisfying and helps to address important practical issues like when a simpler resource (such as a Gaussian state) can stand in for a more complex one (like a GKP state), leading to more efficient use of the resources available for CV quantum computing.
Augmented Reality Technology as a Complement on Graphic Design to Face Revolution Industry 4.0 Learning and Competence: The Development and Validity

This study aims at developing the augmented reality application technology, a complementary learning on graphic design to face the skills and learning of the industrial revolution 4.0. This study uses a 4D (four-D) development method for the development of augmented reality application technology and uses a survey method for validity. This AR application will be validated by 12 experts, while the respondents used in filling out the needs analysis questionnaire at the define stage (four-D) are students and lecturers of Informatics Engineering Education, Padang State University. The results of this study indicate that on the analysis of the achievement of the learning process, it can be seen that there are still expectations and advantages in the achievement of the learning process desired by lecturers and students, where the learning process is still in the sufficient category and 80%, students need development of technology as complement reality to be used as learning media in the learning process. The validation test also shows that the augmented reality application technology is included in the valid category with an average value of V value of 0.933. So, it cannot be denied that the augmented reality application technology is valid to be used in the learning process.