Reviewerly/exHarmony huggingface dataset:
A Comparison between Slimed Network and Pruned Network for Head Pose Estimation

Estimating the position of someone's head is a difficult problem with many potential uses. Many methods almost solved head pose estimation problems but They are not suitable for edge devices and embedded systems because they are computationally expensive. In this paper, a deep learning network based on a modified MobileNetV3 architecture is proposed to reduce the computational cost with results comparable to heavy methods. The proposed method is pruned to achieve even less computational cost and results in a network that is ideal for edge devices and smartphones. The architecture used is MobileNetV3Small which has more inverted residual blocks, making it able to inherit MobileNetV3Large performance but with less width, followed by dense layers. Pruning is enhanced by estimating layer importance and resource reallocation, for the informative layers to be less affected by pruning and also to improve performance. In the experiments, the proposed model performs better than many existing heavies with 3.46 MAE before the pruning and 3.61 MAE after the pruning, even though the model has six times fewer parameters than the others and its inference time is about 7ms.
Bitland-A Decentralized Commercial Real Estate Platform

Real estate has always been a significant investment sector, but it has a high barrier to entry. Bitland Project aims to use blockchain technology in the real estate market, which could have an impact on market inefficiencies. Bitland is a platform that uses smart contracts to provide services for selling fractional ownership, registering real estate in the property registration and investors to buy the commercial property. This paper discusses existing blockchain application use cases in the real estate market. The study discusses current problems and roadblocks that must be overcome before blockchain technology can completely mature in this business. The goal is to create a blockchain-based digital real estate network for the management of investment properties and to handle the distribution of real estate smart contracts.
Part-Based Convolutional Neural Network and Dual Interactive Wasserstein Generative Adversarial Networks for Land Mark Detection and Localization of Autonomous Robots in Outdoor Environment

Robot localization is a fundamental competency required by an autonomous robot because the robot's location knowledge is an essential precursor to making decisions about future actions. Accurate localization of robots or autonomous vehicles is the most important requirement for autonomous applications. In this manuscript, a part-based convolutional neural network and dual interactive Wasserstein generative adversarial networks for landmark detection and localization of autonomous robots in an outdoor environment are proposed (P-CNN-DIWGAN-LMD-LZ). This research contains two phases landmark detection phase and the localization phase. In the landmark detection phase, the part-based convolutional neural network (P-CNN) is proposed to detect landmarks in capturing images. This landmark detection process creates 3 categories of responses for every detected landmarks instance: bounding box, label, and score. The bounding box has positioning with the sizing of detected landmarks at the input imagery. Label implicates detected landmark's class name. A score signifies an abjectness score that scales bounding box membership for landmarks or background classes. In the localization phase, a dual interactive Wasserstein generative adversarial network (DIWGAN) is proposed to determine robot location coordinates. Finally, the proposed method attains high robot localization recall at high accuracy in the real-world environment. Here, the outdoor robot localization dataset is taken from the KITTI dataset. The proposed method is implemented in Python; its performance is estimated under certain performance metrics, like mean absolute error (MAE), cosine proximity (CP), and accuracy. The performance of the proposed method shows higher accuracy compared with existing approaches, like DQP-ODA-LMD-LZ, TDL-LMD-LZ, and 3D-RISS-MLEVD- LMD-LZ.
Verification of Job Authenticity using Prediction of Online Employment Scam Model (POESM)

In the recent times it is found to that there is a growing interest in Internet of Things (IoT) and its respective sophisticated cloud architectures. However, such development does not ensure the confidentiality, integrity due to its security vulnerabilities in its improvements. Therefore data breaching occurs rapidly in all sectors. Nowadays, several job-seekers are becoming the prime victim for such digital loophole fraudsters, when there is an advertisement for bulk requirement. In order to reduce frauds and scams that are designed to take advantage of people who seek jobs, a model to predict the genuineness of posting digital vacancies for job is real or fake. This paper presents the implementation of Prediction of Employment Scam Model (POESM), intending for the classification of fraudulent and non-fraudulent digital job posting advertisements. In our work, we used eight techniques such as Logistic Regression, Naive Bayes, Multiple Layer Perceptron, K-Nearest Neighbor, Decision Tree, Random Forests, Adaboost, Gradient Boosting classifiers. To analyze the dataset, supervised machine learning techniques is used to capture several essential information. Empirical results are found based on Accuracy, F1-Score, Cohen-Kappa Score, and MSE declares that Random Forest classifier is best suited for the prediction of online fraudulent requirements. The proposed POESM strategy can be a promising solution for identifying the genuineness of vacancies for job posted via internet.
Multi-type and Multi-level Feature Fusion Network for RGBD Indoor Semantic Segmentation

RGBD images, which refer to the three-channel color images augmented with depth channel, have been greatly exploited to improve the performance of scene semantic segmentation. RGBD semantic segmentation task is of great significance to help the development of robot navigation and grasping. However, most existing methods addressing RGBD semantic segmentation lack effective feature fusion modules. To address the aforementioned problem, we propose a novel network with two novel modules for feature fusion, namely Channel-Attention based Complementary Feature Fusion Module (CAC-FFM) and Cross-Layer Feature Fusion Module (CL-FFM). Specifically, CAC-FFM, which is essentially based on the channel-attention mechanism, effectively utilizes the complementary information of RGB and depth to generate fusion features, and CL-FFM captures patch-wise features from low-level feature maps to assist the training of high-level features so as to further optimize the segmentation results. Experimental results on the publicly available NYUDv2 dataset validate the effectiveness and superiority of our proposed method.
Research on the Taxiing Control Method of a Small Full-Wing UAV under the Sensor Constraints

In order to solve the problem that the yaw angle measured by the small full-wing UAV during the taxiing process is susceptible and the course angle is unreliable at low speeds, a control approach which uses both yaw angle and course angle to control the taxiing process is proposed.Firstly, a dynamic model of the full-wing UAV during taxiing is established. Secondly, by improving the vector field trajectory tracking method, a linear trajectory tracking method is designed. Finally, based on the active disturbance rejection control method(ADRC), a taxiing direction controller is designed which uses the corrected course angle as the controlled state. Simulation and experimental results show that the designed controller has a good control performance, and can control the full-wing UAV taxiing along the correct trajectory under the condition of inaccurate yaw angle measurement.
Robust Principal Component Analysis based on Purity

Robust principal component analysis has attracted widespread attention in recent years due to its good dimensionality reduction performance. However, most of the current researches identify and process noise by the statistical characteristics of the reconstruction error, while not considering the relationship between the reconstruction error and the projection variance of a data in a principal component space, moreover, they don’t consider the overall distribution characteristics of the data. To solve these problems, in this paper, we proposed the robust principal component analysis based on Purity(PRPCA). First, we construct the Cauchy weighted minimization model by reconstruction error and consider the prior probability of the original data based on reconstruction error. Then, the relationship between the reconstruction error of each sample point and the sample projection variance is analyzed based on Purity. Purity describes the membership degree of sample points in principal component space and error reconstruction space so that outliers and sample points can be correctly separated based on Purity. Experimental results illustrate that the PRPCA is more effective than the other robust PCA algorithm.
A New Method to Evaluate the Engineering Practice Ability of Young University Teachers

The engineering practice ability of young teachers directly affects the quality of cultivation of applied talents, so it becomes especially important to objectively evaluate the engineering practice ability of teachers and adopt targeted teacher training methods based on the evaluation results. This paper establishes an index system with subject teaching, practical ability, scientific research and innovation, communication and expression and personality quality as the first-level indicators, uses the improved AHP method to determine the weights of the indicators, and establishes the IF-TOPSIS model to evaluate the engineering practice ability of young teachers. The cases show that the model can effectively and reasonably evaluate the engineering practice ability of young teachers in colleges and universities, and provides a basis for scientific evaluation of the engineering practice ability of young teachers.
DESIGNING RO-LEX APPLICATION FOR DYSLEXIC STUDENTS

This research aims at designing learning media named RO-LEX for Dyslexic Students and ease the students in learning English. It focuses only on reading skill. The research method used is Research and Development (RD) using Borg and Gall model that consists of ten procedures, they are: 1) Research and Information Collecting, 2) Planning, 3) Develop Preliminary Form of Product, 4) Preliminary Field Testing, 5) Main Product Revision, 6) Main Field Testing, 7) Operational Product Revision, 8) Operational Field Testing, 9) Final Product Revision, and 10) Dissemination and Implementation. From the processes conducted, the researcher got the results of the research. 1) The result of material validation is 80 (very good category), 2) The result of media validation is 69 (good category), 3) The result of dyslexic expert was 59 (good category). Based on the results, the researcher concluded that this RO-LEX application is deserved to be used for dyslexic students as an English learning media.
Boundary Experience and “Transcendentals”in the Philosophy of Мerab Mamardashviliand Ludwig Wittgenstein

Merab Mamardashvili repeatedly mentions Ludwig Wittgenstein in his writings. Even when Mamardashvili does not refer to Wittgenstein directly, there is a recognizable echo of Wittgenstein in Mamardashvili’s thinking. This article follows the closeness of their approaches to determine in what sense the school of transcendentalism is relevant to both philosophers, and to see how Mamardashvili displays his Wittgensteinian critique of the idea that consciousness is “put forward” to the world. We encounter a similar idea in Mamardashvili: “speak of consciousness and not of anything else, we are speaking of such things for which it is impossible to show (substitute) their empirical and, by a finite number of operations, controllable equivalents.” In turn, Wittgenstein compares consciousness to an eye that cannot see itself in its field of vision. Mamardashvili calls the same phenomenon “minimum-transcensus” and states that “it is impossible to pass continuously from empirical facts explained by theoretical concepts to those concepts by which they are explained, that is, to deduce them in a purely logical way.” “Minimum-transcensus” belongs to the boundary of experience, so it cannot be found within experience itself, “put forward” within that which constitutes the objectivity and objective manifestation of our experience.
THE CORRELATION BETWEEN WRITING SELF-PERCEPTION AND WRITING SKILL AT THE TENTH GRADE STUDENTS OF SMA INTEGRAL HIDAYATULLAH BATAM IN THE ACADEMIC YEARS 2020/2021

The aim of this study was to find out whether there is a correlation between students' writing self-perception and students' writing skill. The research design used in this research was quantitative correlational research. The populations in this study were 25 students of tenth grade students of SMA Integral Hidayatullah Batam in academic years 2020/2021. The population was selected by using cluster random sampling technique. The sample in this research was 14 students. Writing Self-Perception Scale (WSPS) and Writing Test were used as instruments of this study after the validity and reliability tests of the two instruments were conducted. The data normality test used in this study was Saphiro Wilk. The researcher analyzed the correlation using the Pearson Product Moment formula to find out correlation between Students’ Writing Self-Perception and Students’ Writing Skill at tenth grade students of SMA Integral Hidayatullah Batam in academic years 2020/2021. The research found out that there was a significant correlation between Students' Writing Self-Perception and Students' Writing Skill at tenth grade students of SMA Integral Hidayatullah Batam in the Academic Year 2020/2021. Suggestions were taken by researcher, namely that teachers can find writing activities to support the students to have a positive writing self-perception.
Variational Quantum Search with Shallow Depth for Unstructured Database Search

With the advent of powerful quantum computers, the quest for more efficient quantum algorithms becomes crucial in attaining quantum supremacy over classical counterparts in the noisy intermediate-scale quantum era. While Grover's search algorithm and its generalization, quantum amplitude amplification, offer quadratic speedup in solving various important scientific problems, their exponential time complexity limits scalability as the quantum circuit depths grow exponentially with the number of qubits. To overcome this challenge, we propose Variational Quantum Search (VQS), a novel algorithm based on variational quantum algorithms and parameterized quantum circuits. We show that a depth-10 Ansatz can amplify the total probability of $k$ ($k \geq 1$) good elements, out of $2^n$ elements represented by $n$+1 qubits, from $k/2^n$ to nearly 1, as verified for $n$ up to 26, and that the maximum depth of quantum circuits in the VQS increases linearly with the number of qubits. Our experimental results have validated the efficacy of VQS and its exponential advantage over Grover's algorithm in circuit depth for up to 26 qubits. We demonstrate that a depth-56 circuit in VQS can replace a depth-270,989 circuit in Grover's algorithm. Envisioning its potential, VQS holds promise to accelerate solutions to critical problems.
Self-supervised learning of Split Invariant Equivariant representations

Recent progress has been made towards learning invariant or equivariant representations with self-supervised learning. While invariant methods are evaluated on large scale datasets, equivariant ones are evaluated in smaller, more controlled, settings. We aim at bridging the gap between the two in order to learn more diverse representations that are suitable for a wide range of tasks. We start by introducing a dataset called 3DIEBench, consisting of renderings from 3D models over 55 classes and more than 2.5 million images where we have full control on the transformations applied to the objects. We further introduce a predictor architecture based on hypernetworks to learn equivariant representations with no possible collapse to invariance. We introduce SIE (Split Invariant-Equivariant) which combines the hypernetwork-based predictor with representations split in two parts, one invariant, the other equivariant, to learn richer representations. We demonstrate significant performance gains over existing methods on equivariance related tasks from both a qualitative and quantitative point of view. We further analyze our introduced predictor and show how it steers the learned latent space. We hope that both our introduced dataset and approach will enable learning richer representations without supervision in more complex scenarios. Code and data are available at https://github.com/facebookresearch/SIE.
Review of Extreme Multilabel Classification

Extreme multilabel classification or XML, is an active area of interest in machine learning. Compared to traditional multilabel classification, here the number of labels is extremely large, hence, the name extreme multilabel classification. Using classical one versus all classification wont scale in this case due to large number of labels, same is true for any other classifiers. Embedding of labels as well as features into smaller label space is an essential first step. Moreover, other issues include existence of head and tail labels, where tail labels are labels which exist in relatively smaller number of given samples. The existence of tail labels creates issues during embedding. This area has invited application of wide range of approaches ranging from bit compression motivated from compressed sensing, tree based embeddings, deep learning based latent space embedding including using attention weights, linear algebra based embeddings such as SVD, clustering, hashing, to name a few. The community has come up with a useful set of metrics to identify correctly the prediction for head or tail labels.
Optimization of Convolutional Neural Network Using the Linearly  Decreasing Weight Particle Swarm Optimization

Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. Various forms of models have been proposed and im-proved for learning at CNN. When learning with CNN, it is necessary to determine the optimal hyperparameters. However, the number of hyperparameters is so large that it is difficult to do it manually, so much research has been done on automation. A method that uses metaheuristic algorithms is attracting attention in research on hyperparameter optimization. Metaheuristic algorithms are naturally inspired and include evolution strategies, genetic algorithms, antcolony optimization and particle swarm optimization. In particular, particle swarm optimization converges faster than genetic algorithms, and various models have been proposed. In this paper, we pro-pose CNN hyperparameter optimization with linearly decreasing weight particle swarm optimization (LDWPSO). In the experiment, the MNIST data set and CIFAR-10 data set, which are often used as benchmark data sets, are used. By opti-mizing CNN hyperparameters with LDWPSO, learning the MNIST and CIFAR-10 datasets, we compare the accuracy with a standard CNN based on LeNet-5. As a result, when using the MNIST dataset, the baseline CNN is 94.02% at the 5th epoch, compared to 98.95% for LDWPSO CNN, which improves accuracy. When using the CIFAR-10 dataset, the Baseline CNN is 28.07% at the 10th epoch, compared to 69.37% for the LDWPSO CNN, which greatly improves accuracy. This paper is presented at the 36th Annual Conference of the Japanese Society for Artificial In-telligence. The final version is available at the following URL: https://doi.org/10.11517/pjsai.JSAI2022.0_2S4IS2b03
Three Actors of Entrepreneur Mentoring and Their Impact on Perceived Mentoring Effectiveness: The Korean Formal Mentoring Context

Purpose: This study investigates the impact of three actors that influence entrepreneur mentoring—mentor, mentee, and operating agency—on mentoring effectiveness within the context of formal mentoring in Korea. Specifically, the study considers the characteristics of the mentee, mentoring functions provided by the mentor, and support from the operating agency as antecedents to explain the perceived mentoring effectiveness of the mentee.&#x0D; Design/methodology/approach: I conducted two studies. Study 1 involves data from mentees participating in the entrepreneur mentoring program hosted by the Korea Venture Business Association from March to June 2018. Data for Study 2 was collected from mentees participating in the mentoring program hosted by incubating center of university located in South Korea from November to October 2022. Hierarchical regression analysis was used to examine the hypotheses.&#x0D; Findings: First, Study 1 shows that willingness to receive mentoring positively affects entrepreneurial competence improvement, but this impact was insignificant in Study 2. Second, the problem-solving function provided by the mentor presents a significant positive effect on both mentoring satisfaction and entrepreneurial competence improvement. Third, the motivating function undertaken by a mentor only has a significant positive effect on improving entrepreneurial competence in Study 1. This motivating function positively affects mentoring satisfaction and entrepreneur competence improvement in Study 2. Fourth, support from the operating agency has a significant positive impact on both mentoring satisfaction and improving entrepreneurial competence after controlling for the mentor and mentee effects.&#x0D; Research limitations/implications: This study provides a theoretical contribution to the research on entrepreneur mentoring. In this regard, it suggests three actors-based models of mentoring effectiveness and practical implications for organizations executing entrepreneurship mentoring programs. Accordingly, it confirms that the operating agency’s managerial role is necessary for enhancing entrepreneur mentoring effectiveness.&#x0D; Originality/value: Entrepreneur mentoring is implemented via formal mentoring in many developing countries. However, little attention has been paid to the operating agency as the primary determinant affecting mentoring effectiveness. The current study addresses this gap by examining the effects of three actors in formal mentoring— mentee, mentor, and operating agency—on the mentee’s perceived mentoring effectiveness.
Secure virtual learning using blockchain technology

A blockchain is a data structure composed of sequential blocks of data. It is characterized by its modularity, dependability, data sharing, security, and other features. It’s been used in a wide range of fields, including digital currency, smart contracts, credit encryption, and so on. Due to the obvious advancement of internet technology, virtual learning, a novel educational modality, has risen in popularity. Educators’ data protection is still of concern in this educational format, as is the credibility of the courses, and as are credit and credential authenticity. This article highlights the major technological ideas and functionality of blockchain technology, drawing on recent literature and case studies, and recommends a blockchain-based remedy to virtual academia’s problems. Learning records can be spread and trusted, digital certificates can be trusted, smart contracts can be used to share learning resources, and data encryption can be deployed to conform with intellectual property laws. Incorporating blockchain technology into the growth of online education, according to the research, is a promising trend.