Reviewerly/exHarmony huggingface dataset:
Efficiency Comparison of Conventional Septic Tank and Septic Tank Modification In Reduce Level Of Bod, TSS coliform and Household Waste

Background. Septic tanks that are commonly used in the community are conventional septic tanks. The use of this septic tank is still not optimal because the processing efficiency has only reached 65%, causing only 22.5% of the total organic pollutants that can be processed (Said, 2005). In addition, low efficiency causes rapid accumulation of sludge which reduces the service life of the septic tank. Aim. This study aims to determine the differences in the efficiency of conventional septic tanks and modified septic tanks in reducing levels of BOD, TSS, and Coliform in household wastewater. Method. This research is an experimental study using a one group pretest-posttest research design. Results. The results showed that conventional septic tanks were not efficient in reducing BOD (-38.21%) and Coliform (0%), as well as very small efficiency in reducing TSS content (5.09%) in wastewater, modified septic tanks had little efficiency for reducing BOD content (2.4%), but it is very efficient for reducing TSS content (89.44%) and inefficiency to reduce coliform content (0%) in wastewater. Conclusion. There was no significant efficiency difference between conventional septic tanks and modified septic tanks in reducing BOD and Coliform content, but there were significant efficiency differences between conventional septic tanks and modified septic tanks in reducing TSS content in the wastewater.
Parameterized Complexity of Dynamic Belief Updates

Dynamic Belief Update (DBU) is a model checking problem in Dynamic Epistemic Logic (DEL) concerning the effect of applying a number of epistemic actions on an initial epistemic model. It can also be considered as a plan verification problem in epistemic planning. The problem is known to be PSPACE-hard. To better understand the source of complexity of the problem, previous research has investigated the complexity of 128 parameterized versions of the problem with parameters such as number of agents and size of actions. The complexity of many parameter combinations has been determined, but previous research left a few combinations as open problems. In this paper, we solve most of the remaining open problems by proving all of them to be fixed-parameter intractable. Only two parameter combinations are still left as open problem for future research.
Efficient Zooming Without Resolution Loss in Streaming Video Using 8K Video Sources

We report a novel approach for achieving efficient zooming/panning without resolution loss in streaming videos for mobile devices such as smartphones and tablets. It is in principle possible to zoom without losing video quality by playing videos with significantly higher resolutions than the display resolution. However, this can drastically increase network traffic and the processing burden of terminal devices. To address this problem, we propose an efficient distribution of a region of interest in video frames with the appropriate resolution based on the zoom factor. We implement the proposed architecture as a web application based on HTTP adaptive streaming and demonstrate its feasibility.
Project-based method in the organization of educational activities

Abstract In recent decades, there has been a surge in interest in the design method. In many countries, it is now seen as an innovative approach to science and technology education. The article investigates a model based on the projects method that extend the capabilities of the theory in the successful fostering of students’ self-improvement and self-development; determines the main conditions of its implementation; selection criteria and its effectiveness indicators; the necessity and possibility to modernize the educational training of future specialists. The relevance of the research problem is conditional upon the fact that the transition to new educational standards, which poses new challenges for the graduate school, that provides cognitive activity and independent thinking of students and overcoming the reproductive style of learning requires an appropriate method. The project method is becoming more widespread in teaching practice, involving the solution of relevant and interesting problem for the trainees. The article identified socio-economic background of using the project method in the training system of highly qualified specialists. Basic criteria (valued attitude to the project activity; creativity in design and the performance of project activities) and indicators estimated the level in professional competence of the project activities. Identified and justified by organizational and educational conditions of using the project method, training specialist at the University. Since the traditional education system, which is based on the assignment of ready-made knowledge, reduces to the solution of theoretical and practical problems, specific algorithms and schemes, little oriented to the students self-improvement and self-development, the task of finding the effective ways to solve problems of the qualitative training of highly qualified specialists goes to a new level.
Technology of establishment of terrestrial objects aerospace survey-based object-oriented monitoring systems

The paper studies a technology for the establishment of Earth remote sensing systems based on the object-oriented approach to the complex system analysis and design. The technology relies upon the system of knowledge combining the knowledge of a certain subject domain, the knowledge of image processing algorithms and the typology of the problems in question. Another important element of the technology is a set of human-computer interaction methods that make it possible to arrange a task setting and solving cycle without or with little involvement of image processing experts. The paper presents the input conditions and the main stages of the technology, as well as examples of possible use of the system in agricultural monitoring.
Self-Paced Learning for Automatic Prostate Segmentation on MR Images with Hierarchical Boundary Sensitive Network

Accurate segmentation of Magnetic Resonance (MR) on prostate is an essential step for robotics surgery in prostate cancer treatment planning. This paper proposes a Hierarchical Boundary Sensitive Residual U-net (HBS-RUnet) model with self-paced learning strategy for prostate segmentation in MR image. Instead of regarding the segmentation task independently, our network consists of two branches: one segmentation branch detects the prostate region and the boundary branch finds prostate shape. The outputs of boundary branch are employed to refine the HBS-RUnet model by adding a boundary regularization, which helps to find desirable and spatially consistent prostate region. Moreover, a hierarchical dynamic self-paced learning strategy is proposed to measure the difficulty for each prostate image and gradually select the relatively simpler samples for model training. Such a simple-to-complex learning strategy could robustly learn image features and enable the robust prostate segmentation. We applied 66 cases from the PROSTATEx Challenge to evaluate the robustness and effectiveness of the proposed HBS-RUnet, and our fully automatic segmentation results demonstrate high consistency (DSC 87.1%) with the manual segmentation results by experienced physicians.
Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems

The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.
Generator from Edges: Reconstruction of Facial Images

Applications that involve supervised training require paired images. Researchers of single image super-resolution (SISR) create such images by artificially generating blurry input images from the corresponding ground truth. Similarly we can create paired images with the canny edge. We propose Generator From Edges (GFE) [Fig. 1]. Our aim is to determine the best architecture for GFE, along with reviews of perceptual loss [1, 2]. To this end, we conducted three experiments. First, we explored the effects of the adversarial loss often used in SISR. In particular, we uncovered that it is not an essential component to form a perceptual loss. Eliminating adversarial loss will lead to a more effective architecture from the perspective of hardware resource. It also means that considerations for the problems pertaining to generative adversarial network (GAN) [3], such as mode collapse, are not necessary. Second, we reexamined VGG loss and found that the mid-layers yield the best results. By extracting the full potential of VGG loss, the overall performance of perceptual loss improves significantly. Third, based on the findings of the first two experiments, we reevaluated the dense network to construct GFE. Using GFE as an intermediate process, reconstructing a facial image from a pencil sketch can become an easy task.
International Journal of Research in Entrepreneurship and Business Studies

IJREBS aims to publish articles, original research results, reviews, and case studies on recent trends in the field of Entrepreneurship, Commerce, and Business Studies.The scope is to create an easy avenue and platform to the budding academicians in the middle east region and the developing countries by enhancing research facilities through support and assistance and extending the same to the interested researchers all over the world.
An Exploratory Study for Designing Social Experience of Watching VR Movies Based on Audience’s Voice Comments

Social experience is important when audience are watching movies. Virtual reality (VR) movies engage audience through immersive environment and interactive narrative. However, VR headsets restrict audience to an individual experience, which disrupt the potential for shared social realities. In our study, we propose an approach to design an asynchronous social experience that allows the participant to receive other audiences' voice comments (such as their opinions, impressions or emotional reactions) in VR movies. We measured the participants' feedback on their engagement levels, recall abilities and social presence. The results showed that in VR-Voice Comment (VR-VC) movie, the audience's voice comments could affect participant's engagement and the recall of information in the scenes. The participants obtained social awareness and enjoyment at the same time. A few of them were worried mainly because of the potential auditory clutter that resulted from unpredictable voice comments. We discuss the design implications for this and directions for future research. Overall, we observe a positive tendency in watching VR-VC movie, which could be adapted for future VR movie experience.
Open Set Deep Networks Based on Extreme Value Theory (EVT) for Open Set Recognition in Skin Disease Classification

A computerized skin disease classification system generally works on closed-set data, meaning images from unknown classes will still be classified as one of the known classes. In the Teledermatology system, skin disease classes are usually defined before the training process. However, in the real world application, it may receive images that belong to a new class or disease. To avoid misclassification, we have implemented the Extreme Value Theory of Weibull distribution function for out of distribution detection and incorporated the OpenMax layer to the deep networks for open-set recognition in skin disease classification. The system can classify seven classes of common skin disease in Indonesia with an accuracy of 71.64% using Inception v3 for closed-set data, while it achieved an accuracy of 83.33% for open-set recognition. The result indicates that the proposed method in this study has reached the purpose of recognizing open-set data in skin disease classification.
Exploiting Cognitive Architectures to Design Storytelling Activities for NarRob

In this work, we exploited the potential of a cognitive architecture to model the characters of a story in an interactive storytelling system. The system is accessible through NarRob, a humanoid storyteller robot. Our main goal was to implement the cognitive processes of the agents played by the robot within a narrative context environment.
Knowledge-Driven Autonomous Robotic Action Planning for Industry 4.0

Autonomous robots are being increasingly integrated into manufacturing, supply chain, and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases that may be queried during planning and execution. In this chapter, the authors propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, they model the knowledge of world models, robotic capabilities, and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. They demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses.
Advanced Homological Analysis for Biometric Identification Using Accelerometer

Biometric information acquired by mobile devices has been explored for various emerging applications in recent years. One of these new applications is advanced authentication. On-line user-identification based on biometrics collected by smartphones has been drawing tremendous research interest lately. The accelerometer data are available pervasively on cellular phones and therefore there incurs no extra cost to facilitate accelerometer-based biometric systems. Individual time-series constituted by accelerometer data are quite complex and therefore impose challenges to the user-identification research. In this work, we propose a novel on-line user-identification approach using accelerometer data by exploiting new homological analysis. The inherent walking patterns of different users in the accelerometer data are monitored in the embedded phase space. Then the expected persistence diagrams (EPDs) are formed and transformed into the corresponding probability distribution functions. Thus, the discrepancy in walking patterns can be measured by the Kullback-Leibler divergence (KLD) of those ultimate distribution functions. Users can be identified according to the KLDs resulting from the sampled data and pre-stored training data. Experimental results from the real-world accelerometer data have demonstrated that the accuracy of our proposed new scheme reaches up to 94.5% and it greatly outperforms other existing methods.
Bi-Lattice LSTM Model with Self-Attention for Chinese NER

In this paper, we investigate a bi-lattice-structured LSTM model for Chinese NER based lattice LSTM model, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with lattice LSTM model, our model fully combines the forward and backward information of each character in the word. And we also introduce the highway model to solve the gradient problem to some extent. In order to capture the global sequence information from multiple subspaces, we introduce the self-attention mechanism. Experiments on various datasets show that our model outperforms both original model and other advanced models without relying on external resources such as dictionaries and multi-task joint training.
ICAQ: Adaptive QoS System for 5G and Beyond Applications

Management of network becomes increasingly difficult due to the rapid proliferation of the Internet of Things (IoT) and heterogeneous demands of applications. To mitigate congestion and maintain a high quality of service (QoS) for application users, we propose an importance-oriented clustering-based QoS system named ICAQ. The system uses unsupervised machine learning to determine and differentiate different application flow priority. The system is proposed to be a generic to both network core features and the application features. An environment monitoring IoT application is implemented as an example to demonstrate the expressive power of ICAQ. Experimental results have verified the effectiveness and efficiency of ICAQ and show a promising potential for using ICAQ for the QoS management of IoT applications in 5G and beyond networks.
Design and Implementation of Data Communication and Compression Methods in SIGMA Framework

This paper introduces data communication and flexible data compression methods in SIGMA, the application framework we are developing. Recently, various kinds of wireless communication technologies and sensing devices have been developed. Hence, the opportunity of transmitting images and other large-volume data via the Internet is expected to increase. We consider specifying a region of interest (ROI) on spatiotemporal axes to reduce data size of unnecessary areas. This can be achieved by projecting a three-dimensional space which represents a ROI onto an image. To confirm the usefulness of this mechanism, we prototyped the ROI function in the WebP image format.
Modeling of Low-Resolution Face Imaging

The image captured by the surveillance camera at a large distance yields a low-resolution image. Commonly, researchers recognize a face from a distance by improving the quality of the low-resolution image. After the image has been upgraded, it will be compared with the high-resolution face image saved on the database. But in this paper, the inverse is applied. The model is made by downgrading the quality of the high-resolution image in order to make it similar to the low- resolution image. Some methods in digital image formation are used to make the model. Some experiments also conducted to compare the model with images obtained by the real cameras at various distances. In order to optimize the model, some parameters were used to tune some steps of low-resolution modeling, i.e., scaling, kernel size of the filter, gamma, and compression quality. The result shows that the proposed model can improve the recognition performance on SC face.
Learning without Forgetting for Decentralized Neural Nets with Low Communication Overhead

We consider the problem of training a neural net over a decentralized scenario with a low communication over-head. The problem is addressed by adapting a recently proposed incremental learning approach, called `learning without forgetting'. While an incremental learning approach assumes data availability in a sequence, nodes of the decentralized scenario can not share data between them and there is no master node. Nodes can communicate information about model parameters among neighbors. Communication of model parameters is the key to adapt the `learning without forgetting' approach to the decentralized scenario. We use random walk based communication to handle a highly limited communication resource.
Review of Luca Tateo, A theory of Imagining, Knowing and Understanding, Cham (Switzerland), Springer, 2020, pp. 1-97

Review of the book A theory of Imagining, Knowing and Understanding by Luca Tateo. In this book, Tateo describes, argues and defends the central role of imaginative processes in the construction of everyday and scientific knowledge, and, especially, in human development.
A framework to support multi-cloud collaboration

With the rapid development of cloud computing, major cloud providers have launched various cloud services with different functions to meet customer's needs. Therefore, flexibility is extremely important when developers use these cloud services. However, APIs of cloud services change dozens of times annually without backward compatibility. It means developers have to adapt these clouds with manual efforts. Such efforts make the multi-cloud collaboration extremely complex and cannot meet the demand of flexibility. This paper describes a configuration-based multi-cloud collaboration framework, which can support new clouds with comprehensible configurations. Meanwhile, if cloud APIs are updated without backward compatibility, it can restore services during runtime with minimized configuration. The main technologies used in this article include automatic discovery, unified abstraction, dynamic mapping and incremental update. We tested the virtual machine and container services of seven well-known cloud providers. The system can support heterogeneous clouds well. When the APIs are updated, the system can restore services in less than 200 milliseconds. At the same time, the extra cost of our framework is acceptable to cloud users.
Creating Navigable Competency Maps from Learning Resource Corpora

This paper addresses the problem of organising large collections of learning resources in a way that is conducive to learners to get a semantic overview of the subject area. We propose mechanisms for automatic creation of a “competency map” that organises a learning space in terms of basic units of learning, each of which is called a competency. The competency map is organised as a 2-dimensional progression space. A progression space not only has a concept of distance between any pairs of competencies, but also a partial ordering that indicates progress made by a learner on reaching some competency. In addition to the idea of progression, a competency map needs to be organised such that, learning resources mapped onto the space can create coherent sequences of learning pathways that can be traversed on the space. To address such requirements, we introduce the concept of topic and resource “volumes” which are used as an indicator of the position of any competency in the progression space. Competency maps are created over several learning corpora, containing hand-curated learning pathways. These pathways were then used as ground truth to evaluate the soundness of the organisation of competencies.