Reviewerly/exHarmony huggingface dataset:
Prediction of Student Graduation with Naive Bayes Algorithm

The research carried out in this study is the development and analysis of student performance in the academic field using the Naive Bayes algorithm so that it can help agencies and students see early graduation predictions, and help managers to see the progress and predictions of active student graduation. The purpose of this research is to study student achievement prediction models that have model values. Referring to previous research in reference that getting student prediction results in the form of semester GPA 1,2,3,4, in this study make predictions based on training data and variables that affect the model. The prediction model optimization step by selecting the variable used in the prediction model development is IPS1,2,3,4. The data used in this study are the results of observations from universities. The result of this research is the prediction of Student Achievement Development with Naive Bayes Algorithm based on Ip semester 1.2,3,4 variable and added value are UN rate, Gender, and status stay. From the results of research conducted from the initial stage up to the testing stage the application of the naïve Bayes method for the prediction process of graduate students, it was found that: the application of the naïve Bayes algorithm for model 1 is a model prediction using variable IP students result in an accuracy of 75% and R2 = 68,2%. Model 2 for prediction are used 8 variables namely Nim, Gender, Residence, IPS 1, IPS2, IPS3, IPS4, study period and student status result accuracy of prediction 89% and with a prediction level of R2 = 71,4 %. This certainly improves the performance of the training data efficiency prediction model.
Optimizing an Image Coding Framework with Deep Learning-based Pre- and Post-Processing

Convolutional neural networks (CNN) are a popular machine learning architecture used to address multiple image-based tasks from understanding to coding. This paper targets improving image compression efficiency by designing and optimizing an image coding framework where a standard image codec, e.g. JPEG, is combined with deep neural network based pre- and post-processing. While the pre-processing CNN targets simplifying the image to make it more amenable to compression, notably involving its down-sampling, the post-processing CNN targets enhancing the decoded image, also involving its up-sampling. To optimize the compression performance, the processing CNNs are trained involving a third CNN, so-called CNN-FakeCodec, which targets modeling the image codec output, since the encoder-decoder pair is not differentiable, thus not allowing any training. Since the available alternative coding solutions focus on minimizing the image distortion, this paper proposes a new loss function which also considers a rate component, thus allowing to jointly minimize the rate and distortion. The performance results show that the proposed coding solutions can outperform the selected benchmarks, both classical and CNN-based.
Towards application level testing of ROS networks

Robotics is a highly interdisciplinary field where applications of complex architectures are built to solve challenging problems. Integration of multiple components in this environment continues to be a time-consuming, tedious and error-prone activity. Typical approaches in parallel component development or composition from re-usable components focus on structural and static aspects like ROS message and topic definitions. However, the behavioural part of robot software components is hardly ever specified in sufficient detail. In this work, we tackle this issue by proposing an application-level testing framework for robot software applications that uses a fluent API to describe the expected behaviour of an application or its components. This is a first step towards test-first based development of robot software in order to increase robot software quality.
Early Detection of Mild Cognitive Impairment Progression Using Non-Wearable Sensor Data – a Deep Learning Approach

Mild Cognitive Impairment (MCI) is an early indication of progression into Alzheimer disease (AD). Early detection of MCI can help older adults receive appropriate interventions and thus enhance quality of life and the capacity for independent living. Trends in daily routines/activities provide measurement of cognitive and functional status, particularly in older adults. This paper proposes a model to predict the AD progression at a future time point, through leveraging sensor-captured older adult's activity trend data along with health assessment details recorded at various historical time points. The proposed work aims to leverage inherent temporal nature of this disease in predicting the progression. Despite several studies have proposed various approaches for early detection of MCI utilizing activity data, they fail to leverage this temporal nature in predicting disease's future status. Long short-term memory recurrent neural networks (RNN) based prediction model is adopted in the proposed work. RNN models recognize temporal pattern in longitudinal data and hence RNN models are natural choice in predicting AD progression.
Secure Data Hiding Technique using Secret Image Scrambling

Today secure data hiding is remaining as the demanding topic because of its advantages. Most of the things are delivering electronically during these days, so steganography fulfills this requirement by providing safe data hiding. In this paper, wavelet based secure data hiding is proposed. Initially, the secrete image is encrypted by using pixel scrambling operation with circular shift and swapping operation then the resultant image is added to the cover image. Scrambling process is applied up to N round. The resultant image picture quality has been improved by applying pixel-based correlation operation. The results show the strength of proposed technique. The proposed technique is more robust because the secret image is initially scrambled before embedding.
Improved multiple watermarking algorithm for Medical Images

At present, most watermarking algorithms use linear correlation method to detect watermarks. However, when the original media signal does not obey the Gaussian distribution, or the watermark is not embedded into the media object to be protected, this method has certain problems. The imperceptibility constraint of digital watermark determines that watermark detection is a weak signal detection problem. Using this feature, firstly, based on the statistical characteristics of DCT (discrete cosine transform) and DWT (discrete wavelet transform), the generalized Gaussian distribution is used to establish its statistical distribution model. Then, the watermark detection problem is transformed into a binary hypothesis test problem. The basic theory of weak signal detection in non-Gaussian noise is used as the theoretical detection model of multiplication watermarking, and the optimized multiply embedded watermark detection algorithm is derived. The algorithm is tested. The results show that the proposed watermark detector has good detection performance for the blind detection of watermarking with unknown embedding strength. Therefore, the detector can be applied in the copyright protection of digital media data.
Learning Control Policy with Previous Experiences from Robot Simulator

Advances in deep reinforcement learning enabled cost-efficient training of control policy of physical robot actions from robot simulators. Learning control policy in a simulated environment is cost-efficient over learning in a real environment. Reward engineering is one of the key components to train efficient control policy. For tasks with long horizons such as navigation and manipulation, a sparse reward is providing limited information. The robot simulator for a physical engine of physical robot manipulation has made it easy for researchers in the field of deep reinforcement learning to simulate complicated robot manipulation environments. In this paper, A robot manipulation simulator and a deep RL framework are utilized for implement a training control policy by utilizing previous experiences. For implementation, Recent innovation Hindsight Experience Replay (HER) algorithms with previous experiences to calculate dense rewards from a sparse reward is leveraged . Proposed implementation showed an approach to investigate the reward engineering method to formulate dense reward in robot manipulator tasks.
Usability Considerations of Hand Held Augmented Reality Wiring Tutors

Electrical repair tasks across domains use a common set of skills that combine problem solving, fine motor and spatial skills. Augmented Reality (AR) helps develop these skills by overlaying virtual objects on the real-world. So we designed a hand held AR-based wiring tutor which incorporates Constraint-Based Modelling (CBM) paradigms to detect learner errors in an electrical wiring task. We compared the performance and usability of our prototype with a state of the art hand held AR-based training system with a consistent user interface (UI) design, which lacks CBM approaches. Although, the CBM condition had significantly lower usability scores than the state of the art, participants using the CBM approach reported higher practical scores. We discuss reasons for the usability differences, including potential for positive perceptions of the system to be distorted by critical feedback needed to regulate learning in the electrical wiring domain. Next steps would include using the same system to evaluate the theoretical and practical learning outcomes.
Research on college physical education model based on virtual crowd simulation and digital media

At present, the traditional sports teaching model has been unable to meet the needs of modern diversified talents. Based on the actual needs of physical education, this article reforms traditional physical education methods, proposes a university physical education model based on virtual crowd simulation and digital media, and builds a corresponding system. Moreover, this paper uses feature identification to monitor classroom teaching and counts multiple parameters to assist the effective teaching activities. In model tracking, the DMS motion trajectory is obtained, and then the center points of all grid sequences are calculated to generate a center point matrix. Considering the difference in motion between adjacent frames of DMS, the difference is quantified by the significance value, so that the sequence of adjacent frames with small differences is organized into clusters. In addition, this paper builds a computer system model based on actual needs and evaluates model performance through actual teaching. The research results show that the proposed model has good performance.
ICT Integration in Education

Information and communication technologies (ICT) are influencing every discipline under the sun including education. It is affecting every aspect of education from teaching-learning to assessment and evaluation. It improves the effectiveness of education. It aids literacy movements. It enhances scope of education by facilitating mobile learning and inclusive education. It facilitates research and scholarly communication. Impact of ICT and its potential for the education field is manifold. It positively affects all the stakeholders of the education field. The chapter discusses the same along with the various projects and schemes adopted by the country to spread ICT literacy and competency.
Video Analysis on Handwashing Movement for the Completeness Evaluation

The pandemic situation of COVID-19 is still ongoing in every part of the world countries. Up to now, the medicine and the vaccination for curing the COVID-19 are not yet available. Since the vaccine and medication of COVID-19 are not yet available, the government in many countries are strongly socialising three kinds of new practices. The new habits are physical distancing, wearing a mask, and handwashing frequently. This paper focuses on assessing the completeness of handwashing. The handwashing frames are extracted from a video clip. The frames are then categorised into six-movement steps by using a deep learning-based algorithm. The frame of hand object is separated from the video by applying skin colour differentiation. Two kinds of experiments based on two different video sources are performed in the paper. Scoring simulation is also conducted in the research. The results show that the proposed method can give an excellent performance with accuracy values not less than 90%.
SoC based IoT sensor network hub for activity recognition using ML.net framework

Nowadays single-board computers - SBC and System on a Chip SoC's get smaller and provide more processing power, yielding perfect alternatives to low-cost local hubs for small sensor networks. Even though sensors can now be connected directly to the cloud, for example, the ESP32 SoC, a local hub can provide a simple interface for multiple sensors to reach the IoT cloud. One of the best options for a local hub is the Raspberry Pi SBC, based on the Broadcom BCM2837 SoC, due to its small form factor and power consumption. We propose a system based on the Raspberry Pi SBC that interconnects connects two SensorTag CC2650 Bluetooth modules to the cloud, a MongoDB database. The obtained data is then processed offline using Microsoft's ML.NET machine learning framework to detect the user's activity. This framework was chosen as it's free, cross-platform, and open-source.
An energy efficient multipliers using reversible gates

Abstract The known advantages of Reversible gates namely reduced power consumption and low latency found useful in Quantum computing. Its low power consumption and quicker operation makes them to be useful in applications Microprocessors, DSP processors and Quantum computers. Numerous reversible gates have been found in literature. This article focuses the implementation of 4 bit multiplier with the help of reversible logic gates. Array multiplier is chosen for our choice. Toffoli gate is chosen for partial product generation. The accumulation of partial products is carried using three topologies. The first topology uses reversible carry look ahead adder. The second topology uses parity preserving reversible carry look ahead adders. The third topology uses modified parity preserving reversible carry look ahead adders. The energy efficient multiplier’s topology proposed in this article implementation are done using Cadence 180nm technology. Performance evaluation reveals that the proposed reversible gate based multiplier results lesser power consumption and also a quicker one. It results that the proposed topology consumes 52.03% lesser power, topology 2 consumes 63.02% lesser power and topology 3 consumes 63.79% lesser power when compared with an existing reversible multiplier design. As the power consumption is the dominating quantity in VLSI design, the proposed topologies of multiplier design results better performance. The achieved area occupation of the proposed topology 1, topology and topology 3 are also 58.44%, 61.90% and 69.26% lesser respectively when compared with that existing reversible multiplier. Its quicker operation is also achieved by the reduction in propagation delay. Hence the proposed multiplier topologies named as an energy efficient multipliers.
Evaluation of students’ IELTS writing ability based on machine learning and neural network algorithm

With the increase of social development and international communication, more and more Chinese candidates take IELTS test as the basis of study and work. IELTS is more difficult than national CET-4 and CET-6, listening and reading are the process of students “entering” language, they accept it passively; words, writing and translation are students”’ exit “language process, which lays the foundation for” writing “. Writing is a second language and one of the main indicators of students’ ability to use English. It is not only an important way to communicate ideas, emotional expressions and cultural exchanges, but also reflects the language ability, communication ability and thinking level of English. Therefore, English writing ability is an important index of students’ English professional ability and an important aspect of students’ English professional training. This study identified the following research tasks: according to the established measurement and evaluation indicators, the students’ writing ability was evaluated and the students’ writing ability was evaluated.
MiXR: A Hybrid AR Sheet Music Interface for Live Performance

Musicians face a number of issues when performing live, including organizing and annotating sheet music. This can be an unwieldy process, as musicians need to simultaneously read and manipulate sheet music and interact with the conductor and other musicians. Augmented Reality can provide a way to ease some of the more cumbersome aspects of live performance and practice. We present MiXR, a novel interactive system that combines an AR headset, a smartphone, and a tablet to allow performers to intuitively and efficiently manage and annotate virtual sheet music in their physical environment. We discuss our underlying motivation, the interaction techniques supported, and the system architecture.
Major choosing decision support system with talent interest approach using SWRL and SAW method

Students who are majoring in college that are not following their interests will have difficulty in adapting. Psychologically, to learn things that are not by your interests and abilities will lead to emotion blockage, which then blocks the effectiveness of brain performance. This issue will have an impact on the inhibition of learning motivation and achievement of academic performance. To avoid these problems, a diagnosis of career interests, including interests and needs to be done as an initial process of further study planning interventions to support career decisions making in individuals. Therefore, this research builds a Decision Support System with the Talent Interest Test approach to help users find college majors that suit their interests and abilities. This study uses the SAW method and SWRL reasoning. The results of this study indicate that the implementation of this approach in a Department Selection Decision Support System with Talent Interest Approach can provide an alternative that is in line with the interest of undergraduate student candidates with the compatibility level of Agree (95.31%) and Disagree (4.69%).
Visual learning curves for American sign language (ASL) alphabet

The study objectives include: 1) to identify the visual key areas and the learning curves when learning the ASL alphabet, 2) to identify the impact of practice on key visual areas, and 3) to identify differences in visual learning curves between signs. An empirical behavioral study with twenty ASL untrained hearing participants performing the ASL alphabet for eight consecutive trials using virtual reality and eye-tracking was conducted. After completing the experiment, participants reduced the fixation duration by 51.90% and the observation count by 45.69%. The representations of the fingers acquired 66% of the fixation duration. In addition, the observation proportion for the visual instructions for the fingers decreased from 59% to 41% after 8 trials. An ANOVA analysis showed unequal variance for ASL signs, and a power regression showed different learning rates for all signs. Our experimental setting successfully tracked and identified the key visual areas when learning the ASL alphabet.
Design of an IoT-based smart incubator that listens to the baby

Abstract In the industrial era 4.0, domestic baby incubator producers are facing the challenge of free trade of foreign products that will compete in innovation with the application of IoT technology. One of the opportunities that arise consciously or unconsciously at the NICU (Neonatal Intensive Care Unit) unit in Hospitals, in general, is that there are no facilities for parents to monitor the baby’s condition inside the incubator directly. The purpose of this research project is to build a prototype of an internet-based baby incubator monitoring system based on things equipped with various sensors that will send data to the server in real-time and mobile apps for monitoring facilities for parents, including the voice of the baby. This study focused on how to develop baby incubators that can listen to baby’s crying, capture the voice, and interpret it using artificial intelligence. More than 40 (forty) voice datasets were used and successfully classified into five possible terms of the baby’s condition: burping, sleepy, hungry, uncomfortable, and pain by energy signal and spectrum analysis. The benefit of this research is as a driver of innovation for baby incubator products that will support the national medical devices manufacturing company.
Inverse Design of Nanophotonic Devices using Deep Neural Networks

We present three different approaches to apply deep learning to inverse design for nanophotonic devices. The forward and inverse regression models use device parameters as inputs and device responses as outputs, and vice versa. The generative model to create a series of improved designs. We demonstrate them to design nanophotonic power splitters with multiple splitting ratios.
Reinforcement Learning Driven Physical Synthesis : (Invited Paper)

Physical synthesis has emerged as a core component in a modern circuit design flow. Large-scale optimization problem is often involved in the process, which requires substantial efforts to solve and no optimality is guaranteed. Reinforcement learning provides one direction to deal with the above issue by automatically acquiring knowledge through experience, which has shown great success in various applications. In this paper, we introduce the foundation of and the progress in reinforcement learning, and review some recent approaches in applying reinforcement learning to physical synthesis. We hope to inspire more work and to see more talented ideas in this field.
AUGGO: Augmented Reality and Marker-based Application for Learning Geometry in Elementary Schools

Suitable learning media can be used to achieve learning goals. The more interactive the learning media, the learning process will be more interactive. This paper proposes a learning media named Auggo. Auggo is an Augmented Reality (AR) and marker-based application as an interactive media to learn geometry in Elementary School. To show the impact of Auggo, this research conducts a test to 14 students in elementary school as participants. The test was carried out using the Two-Group Paired Sample T-Test method with those 14 elementary school students. Participants were divided into 2 groups: 7 participants as Group A (experimental class) and 7 participants as Group B (control class). Assessment of participants showed the results of the pretest to posttest in Group A increased by 26%, while Group B only increased by 10.29%. These results indicate that the AR-based AUGGO application can help students in improving the understanding of space concept.
A Customized Data Recovery Tool

Abstract Data recovery is the process of salvaging deleted, formatted, corrupted, damaged or inaccessible data from the storage media as it cannot be accessed through normal way. There are two types of data recovery which is physical recovery and logical recovery. Physical recovery needs special equipment to repair the damaged parts of storage device like scratched plates, stalled spindle, fried chip, etc. and this process need to be undertaken in clean and controlled environment to extract data from storage device. Logical recovery is a software-based solution that recovers data from storage device which faced logical error in operating system or accidental user deletion. This paper will discuss on logical recovery and it is divided into few sections which is public awareness on data recovery, literature review on existing data recovery tool, analysis on user requirements for data recovery tool, proposed data recovery tool functionality and proposed data recovery tool framework.