Reviewerly/exHarmony huggingface dataset:
Talk, Smartphones, Notebooks, and Brown Paper

This chapter discusses orality and literacy as cultural constructs enabled in different ways by technology in traditional village environments in Bangladesh. The villages have been part of an international information and communication technologies for development (ICT4D) project between Monash University in Australia and Oxfam, an international non-governmental organization (NGO). There is a continuum of different practices involving oral, textual, and online knowledge around the creation, preservation, and pluralization of traditional information and knowledge involving not just traditional villagers, but other players, such as researchers and developers. Innovation involving ICT needs to take account of this ‘information ecology’ and its impacts on the development of sustainable solutions. In fact, the preferred medium for information and knowledge sharing is not necessarily the most technically sophisticated one, highlighting the importance of working at the grass roots in social-technical projects in international development. These issues are considered within a theory and teaching model known as the Monash Information Continuum.
Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Multi-cloud is a vendor-based heterogeneous cloud paradigm in recent era of computing with dynamic infrastructural deployment.Multi-cloud provides all the essential and on-demand requirements of a virtual environment from various domains under a single service level agreement (SLA).Consumers from multitier domains can access all the available resources placed in a shared pool on service provider's side, as per their requirement.The shared pool of resources creates complexity in assigning the best and suitable resource to a particular virtual instance under the same services provider end.The complexity of resources in terms of accessibility from the various domains, dynamic allocation, security, and quality of services (QoS) raises concerns in the multi-cloud infrastructure.This complexity raise concern relates to optimal provisioning and cost management.In the proposed work a hybrid technique with a shuffled leapfrog algorithm and ubiquitous binary search (SLFA-UBS) to resolve these issues with optimal provisioning, dynamic allocation and better resource selection.The proposed work will help to create a need-based and demand-based resource pool with the appropriate selection of each resource.The proposed model also supports resource optimization with dynamic provisioning, cost-effective solution to achieve QoS in multi-cloud deployment on service provider end.
Design of intelligent invigilator system based on artificial vision

Abstract Aiming at the problems of high packet loss rate and low intelligent degree in the current video monitoring system for examination invigilation, an intelligent invigilator system design method based on artificial vision is proposed in this paper. The system design is divided into hardware and software parts, including a / D circuit, clock circuit, video frame cyclic error correction and coding circuit, program loading circuit and output interface circuit. The experimental results show that the system can effectively reduce the video packet loss in the process of invigilation, improve the visual coverage of the invigilator area, and the system is highly integrated and intelligent.
A Study of the effectiveness Fin Stabilizer on Unsada Water Tour Bus to Comfort the Rolling Period to Support Toba Lake Tourism

Abstract Lake Toba is one of the five super priority national tourist destinations. To support Lake Toba tourism. a suitable and safe mode of transportation is needed and to be able to provide an attraction for tourists. See this opportunity. we plan to make this mode of transportation. The Air Unsada Tourism Bus is one of the modes of transportation that is a pioneer of new modes of tourism transportation in Indonesia. especially on Lake Toba. North Sumatra. The Unsada Water Tourism Bus is an amphibious bus that can move both on land and in water. The Unsada Air Tour Bus is specially designed using fin stabilizers on the bus body. like the hull of a ship in general. The purpose of this research is to design a Water Tourism Bus that can provide comfort and safety for tourists. in this case that is how to minimize the rolling time on the Air Unsada Tourism Bus to pay attention to the condition of Lake Toba’s waters which is quite extreme. In designing this bus using the dimensions of a conventional bus with a length of 13.115 meters. 2.5 meters wide. As high as 3.8 meters. and a double decker used for the roof so that tourists can enjoy views of Lake Toba. The method used for the calculation is IMO stability level 2 and also uses Indonesian land transportation regulations contained in government regulation no 55 chapter 3 part 3d 2012. For calculations and analysis using the Maxsurf software. Meet the IMO standard parametric rolling level 2. The results obtained are 0,000239 below the 0.06 standard.
Crowdsourced Data Stream Mining for Tourism Recommendation

Crowdsourced data streams are continuous flows of data generated at high rate by users, also known as the crowd. These data streams are popular and extremely valuable in several domains. This is the case of tourism, where crowdsourcing platforms rely on tourist and business inputs to provide tailored recommendations to future tourists in real time. The continuous, open and non-curated nature of the crowd-originated data requires robust data stream mining techniques for on-line profiling, recommendation and evaluation. The sought techniques need, not only, to continuously improve profiles and learn models, but also be transparent, overcome biases, prioritise preferences, and master huge data volumes; all in real time. This article surveys the state-of-art in this field, and identifies future research opportunities.
Robust and Private Learning of Halfspaces

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces. We prove nearly tight bounds on the sample complexity of robust private learning of halfspaces for a large regime of parameters. A highlight of our results is that robust and private learning is harder than robust or private learning alone. We complement our theoretical analysis with experimental results on the MNIST and USPS datasets, for a learning algorithm that is both differentially private and adversarially robust.
Face Recognition Using Sf3CNN with Higher Feature Discrimination

With the advent of 2-dimensional Convolution Neural Networks (2D CNNs), the face recognition accuracy has reached above 99%. However, face recognition is still a challenge in real world conditions. A video, instead of an image, as an input can be more useful to solve the challenges of face recognition in real world conditions. This is because a video provides more features than an image. However, 2D CNNs cannot take advantage of the temporal features present in the video. We therefore, propose a framework called $$Sf_{3}CNN$$ for face recognition in videos. The $$Sf_{3}CNN$$ framework uses 3-dimensional Residual Network (3D Resnet) and A-Softmax loss for face recognition in videos. The use of 3D ResNet helps to capture both spatial and temporal features into one compact feature map. However, the 3D CNN features must be highly discriminative for efficient face recognition. The use of A-Softmax loss helps to extract highly discriminative features from the video for face recognition. $$Sf_{3}CNN$$ framework gives an increased accuracy of 99.10% on CVBL video database in comparison to the previous 97% on the same database using 3D ResNets.
Distance learning of students through informational and educational blogs

The present study aims to disclose the structure, content, and stages of creating a teacher’s informational and educational blog designed to serve the cognitive component of professional and communicative culture during distance learning of students of psychological and social university faculties. Different stages of the present study involved deploying the following methods: in addition to the scientific literature, a questionnaire allowing the authors to determine the degree of formation of the cognitive component of the professional and communicative culture in future specialists; in addition to other procedural axes. The objectives and functions of a said blog, as well as the stages of creating one, are determined and its structure is developed. The requirements for the content of a blog and the format of content delivery are established. Information should be presented in the form of clips; vivid visual presentations with clear and figurative language are to be used; visual images in the form of slides of animated videos are also recommended for use.
Construction of modular teaching system of human resource management specialty

With the development of economy, society and science and technology, the disadvantages of the traditional “subject based” curriculum system construction mode are increasingly apparent. Graduates go to the society, what they learn is not what they use. There is a phenomenon that education and employment deviate from each other. Even if they have a job, they have to learn again, which requires a longer adaptation period. According to the skills and abilities that human resource management (HRM) students may enter into after graduation, a targeted modular teaching system is constructed. The modular teaching system can carry out customized learning in advance according to the students’ work goals, and grasp the required employability in advance. At the same time, the reform of the system also enriches the teaching methods of teachers. Through the questionnaire and experimental test, this paper finds that: modular teaching breaks the traditional curriculum design method under the framework of the discipline theoretical system, aims to cultivate students’ professional ability, improve their adaptability to the job, and further enhance their practical employability. Experimental results: the relationship between the quality characteristics and academic performance of HRM students was verified, and the relationship between them and the influence model of causality were clarified. The results of the two classes are similar in the pre-test; and from the back results, we can see that the scores of the experimental class after the use of modular teaching are significantly improved, while the comparison class is not significantly improved compared with before the experiment, which fully shows that the implementation of modular teaching can help students improve their comprehensive performance. The path coefficient of growth driven to academic performance is 0.428, the path coefficient of learning development to academic performance is 0.122, and interpersonal skills affect academic performance indirectly. It fully verifies the relationship between the quality characteristic model and academic performance, and shows that the quality characteristic model constructed is relatively perfect and can be used as a reference for students’ evaluation.
Sequential linked data: The state of affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.
Identifikasi Acute Lymphoblastic Leukemia pada Citra Mikroskopis Menggunakan Algoritma Naïve Bayes

Leukemia is a type of blood cancer that occurs when the body overproduces abnormal white blood cells. Acute Lymphoblastic Leukemia (ALL) is a type of acute leukemia. ALL occurs when the spinal cord is excessively producing young lymphocytes, known as lymphoblasts. Leukemia is difficult to detect because it has the same symptoms as other diseases. One way to detect leukemia is to use a complete blood count test. Blood count test is done by calculating the population of red blood cells, white blood cells and platelets. The health condition of the body is indicated by the number of each blood cell. The small number of erythrocyte and abnormal cell shape is indicative of leukemia. How to identify leukemia still using a microscope. In this study the researchers made a way of identifying acute lymphoblastic leukemia cells by image processing, include cropping, segmentation, feature extraction and identification. The method used in image identification is Naïve Bayes Classifier (NBC). NBC is a classification method which applies simple probability calculations using B ayes theorem. The white blood cell image tested using this application will be evaluated with accurations. The greatest accuracy results from several test scenarios obtained 80% accuracy.
Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach.

We propose a novel estimator of the mutual information between two ordinal vectors $x$ and $y$. Our approach is inductive (as opposed to deductive) in that it depends on the data generating distribution solely through some nonparametric properties revealing associations in the data, and does not require having enough data to fully characterize the true joint distributions $P_{x, y}$. Specifically, our approach consists of (i) noting that $I\left(y; x\right) = I\left(u_y; u_x\right)$ where $u_y$ and $u_x$ are the copula-uniform dual representations of $y$ and $x$ (i.e. their images under the probability integral transform), and (ii) estimating the copula entropies $h\left(u_y\right)$, $h\left(u_x\right)$ and $h\left(u_y, u_x\right)$ by solving a maximum-entropy problem over the space of copula densities under a constraint of the type $\alpha_m = E\left[\phi_m(u_y, u_x)\right]$. We prove that, so long as the constraint is feasible, this problem admits a unique solution, it is in the exponential family, and it can be learned by solving a convex optimization problem. The resulting estimator, which we denote MIND, is marginal-invariant, always non-negative, unbounded for any sample size $n$, consistent, has MSE rate $O(1/n)$, and is more data-efficient than competing approaches. Beyond mutual information estimation, we illustrate that our approach may be used to mitigate mode collapse in GANs by maximizing the entropy of the copula of fake samples, a model we refer to as Copula Entropy Regularized GAN (CER-GAN).
Breast Self Examination and Cancer Awareness among Female Staff in Federal Medical Center, Yenagoa, Bayelsa State (Knowledge and Practice)

Breast self-examination (BSE) is a recommended technique in medical science for early detection of breast cancer. It is a simple technique with no specific equipment and no cost implication. It has been shown to improve breast health awareness and thus detect potential abnormalities in the breast. It should be practiced by all women (or females known to be more at risk) from the moment the breast is developed.&#x0D; The aim of this study was to assess the knowledge and practice of BSE in relation to breast cancer awareness among female staff in Federal Medical Center, Yenagoa, Bayelsa State.&#x0D; Methods: A simple random technique was used to select a total of 411 female staff from different departments of the hospital. The data were collected using structured pretested questionnaire. Descriptive statistical method and correlation method was employed to analyze the variables.&#x0D; Results: The findings of this study revealed that 369(89.8%) had knowledge of BSE. It also revealed that 210 (51.1%) about half of the total number practice BSE regularly. Majority of the respondents, 187 (45.5%) heard of BSE through awareness programme. There was a statistical significant association between knowledge of breast self- examination and its practice.&#x0D; Conclusion: The study revealed that majority of the respondents had knowledge of BSE but the practice of BSE was poor. Knowing when to perform, how to perform, position to perform and confidence of what to look for during BSE were seen as a major issue or objective determinant of successful examination, in this study.&#x0D; Therefore, there is need for intensified sensitization programmes to address knowledge and the practice of BSE among females in Yenagoa, Bayelsa State.
Relevance of Teaching of Isms in the B.Ed Curriculum

The study of isms (mainly naturalism, idealism, pragmatism and realism) gives a sense of ‘meaning’ to the educational practitioner for his work and its place in the general scheme of life. It enables him to see clearly the relationship between his day-to-day, so called the routine work and the goals of an individual, social and national life. He will not feel that he is lost in the wilderness counting the trees without having an idea of the horizons. It would help him to adjust himself to his work to fit in a square peg and if necessary to try to change in his own possible ways the status quo. This paper tells how teaching of isms gives a purpose and a direction to life which he would be able to infuse in children or teachers as the case may be.
A Collaborative VR Murder Mystery using Photorealistic User Representations

The VRTogether project has developed a Social VR platform for remote communication and collaboration. The hyper-realistic representation of users, as volumetric video, allows for natural interaction in a virtual environment with others. This video shows one of the use cases, an escape room style, where remote users need to collaboratively resolve a murder mystery. The experience takes place in the victim's apartment where the police team (avatars) together with up to four real-time captured users (point clouds), work as a team to find clues and come up with a conclusion about what happened to the victim and who was the criminal. This experience includes a layer of interaction, enabling the users to interact with the environment, by touching objects, and to talk to the characters. It also allows for navigating between the rooms of the apartment. The experience provides immersion and social connectedness, where users are protagonists of the story, sharing the virtual environment and following the narrative. The combination of virtual reality environments (space and characters) with novel technologies for real-time volumetric video conferencing enables unique new experiences in a number of areas such as healthcare, broadcasting, and gaming.
HMM-based phoneme speech recognition system for the control and command of industrial robots

In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (roboticarm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.
A Novel Hybrid Recommendation Approach Based on Correlation and Co-occurrence Between Activities Within Social Learning Network

Social learning is considered one of the most prevalent disciplines within e-learning. To handle learning resources within social environments, recommendation systems are gaining tremendous prominence based on a series of criteria such as the rate of learner interaction with the learning environment. On the basis of this, we highlight an overriding issue focusing on integrating the variety of activities carried out by learners within the learning environment. The challenge with most recommendation systems is that they do not address multiple activities performed by learners which may significantly affect recommendations. In our study, we focus on this point while proposing a hybrid system combining the two parameters of correlation and co-occurrence. The objective is to pinpoint the degree of connectivity between activities and their influence on recommendations. We carried out our study on an available database carried out by a Chinese team. We thus compare the results of the hybrid system with the non-hybrid system based solely on co-occurrence by measuring the performance of the two approaches. It is demonstrated that the proposed hybrid system produces highly gratifying results compared to the non-hybrid system.
No-regret Algorithms for Multi-task Bayesian Optimization

We consider multi-objective optimization (MOO) of an unknown vector-valued function in the non-parametric Bayesian optimization (BO) setting, with the aim being to learn points on the Pareto front of the objectives. Most existing BO algorithms do not model the fact that the multiple objectives, or equivalently, tasks can share similarities, and even the few that do lack rigorous, finite-time regret guarantees that capture explicitly inter-task structure. In this work, we address this problem by modelling inter-task dependencies using a multi-task kernel and develop two novel BO algorithms based on random scalarizations of the objectives. Our algorithms employ vector-valued kernel regression as a stepping stone and belong to the upper confidence bound class of algorithms. Under a smoothness assumption that the unknown vector-valued function is an element of the reproducing kernel Hilbert space associated with the multi-task kernel, we derive worst-case regret bounds for our algorithms that explicitly capture the similarities between tasks. We numerically benchmark our algorithms on both synthetic and real-life MOO problems, and show the advantages offered by learning with multi-task kernels.