Reviewerly/exHarmony huggingface dataset:
On the Concept of Correlationism: Meillassoux Q., Harman G., Brassier R.

Speculative realism is often associated with a group of thinkers who have consciously united around the idea of fighting a common enemy – correlationism. The article substantiates the thesis that there is no agreement among speculative realists both about correlationism and the ways to overcome it. Based on the works of Meillassoux, Harman and Brassier, the author demonstrates that their interpretations of correlationism and programs for its refutation are incompatible. It is suggested that the result of such incongruence of the concept of correlationism is the conviction of the philosophers of this direction in correlationism of each other. The author considers Meillassoux’s concept of the “Era of Correlation”. From the point of view of Brassier, Meillassoux fails to substantiate the anti-correlationist thesis that human thinking is capable of cognizing non-subjective being. Rather, on the contrary, Meillassoux once again subordinates this being to thinking, and therefore remains a correlationist. Harman also accuses Meillassoux of correlationism for anthropocentrism. The author also clarifies Harman’s concept of the “philosophy of access”. It is established that the concepts of correlationism in the interpretation of Harman and Meillassoux cannot be completely compatible. It is revealed that Harman remains a philosopher of the era of correlation for Meillassoux, because, firstly, he illegitimately attributes human characteristics to non-human beings. Secondly, it does not accept the correlationist argument of the circle and preserves the Kantian metaphysical dichotomy of the noumenal/phenomenal. Brassier also considers Harman a correlationist, since he ontologically equates natural science knowledge and scientific rationality with other types of knowledge. The paper reveals the main features of correlationism in the interpretation of Brassier. It is established that for Meillassoux, the Brassier approach is correlationist on the same basis as the Harman approach. Harman, in turn, considers Brassier as a correlationist, since the latter postulates various ontological taxonomies, the most obvious among which are the priority of scientific rationality and anthropocentrism. According to the results of the work done, it is once again emphasized that speculative realists are by no means united in their understanding of correlationism and ways to overthrow this philosophical program.
An Modern Approach to Detect Person Wearing Mask Using Deep Learning

In this paper, we are developing a system to constantly monitor people if they are wearing a mask and maintain social distancing. The human detection and mask verification from the live streaming is done using an object detection algorithm mobilenet SSD. The distance between two humans is calculated using the eucleidian distance between two bounding boxes of the humans to verify if they are maintaining social distancing. If two humans are not following social distancing or even if they are not wearing any mask the bounding boxes are marked red alerting them. Thus, helps to effectively monitor social distancing norms among the general public.
Beast: Behavior as a Service for Trust Management in Iot Devices

As the internet becomes intertwined into every aspect of human lives, security of the Internet of Things (IoT) becomes increasingly critical. IoT devices become the data source for variety of smart cities applications, where critical decisions are based on this collected data. If malicious actors control or tampered with the the data being transmitted, the integrity of an entire smart city will be compromised. Therefore, through monitoring the IoT devices’ behavior, anomalies can be detected and isolated to avoid any negative impact on decision making. This behavioral monitoring process will complement traditional trust management approaches, as more accurate trust value can be calculated without the need to rely on a majority consensus. In this work we present a behavior as a service methodology that implements a deep learning based behavioral model to accurately classify IoT devices’ interactions in the system. Through the Elo rating system, these classifications will be presented as a vector of behaviors per device, which dynamically reflects on the device’s trust in the system. This work presents an analysis of our methodology as well as a threat model. Using simulation, a real-world use-case is presented showing the interactions between IoT based devices. Our results show that our behavior as a service model is able to dynamically evaluate each IoT device’s trust as well as capture and mitigate multiple threats targeting the trust in the system.
Setting Up a Pseudo-Body

In the last two chapters on seamless locomotion and arm-swinging movement, you probably noticed that you could easily pass through the solid containers, barrels, ladder, and table available in the Demo scene. In most cases, you wouldn’t want such behavior to happen though. Ideally, you want yourself to be stopped the moment you collide with a solid object. All barrels, containers, ladder, and tables have been fitted with colliders that aren’t Triggers. However, you can still pass through these objects. To ensure that this doesn’t happen, you will need to set up a pseudo-body. This chapter is dedicated to setting up this pseudo-body to enforce the Plausibility Illusion, wherein you cannot pass through solid objects. We will also review a commonly used technique wherein the player’s headset fades to black upon their head colliding with a solid object.
Learning Social Influence from Network Structure for Recommender Systems

The purchase decision of users is influenced by their basic preference of items, as well as the social influence of peers. Most social recommendation methods focus on incorporating the semantic collaborative information of social friends. In this paper, we argue that the semantic strength of their friends is also influenced by the subnetwork structure of friendship groups, which had not been well addressed in social recommendation literature. We propose a deep adversarial social model (SoGAN) that can automatically integrate the subnetwork structure of social groups and their semantic information into a unified recommendation framework. Specifically, we first align users in two different views, i.e., the “social-friend” view and “co-purchase” view. Then a generative adversarial network is used to learn the structure information of social groups to enhance the performance of recommender systems. We utilize the structural similarity between two views to produce true samples in SoGAN, and generate the mimic data based on the similarity between the semantic representations of users in two views. By discriminating the true instances based on structure similarity, we naturally inject the structure information into semantic learning of users. Extensive experiments on three real-world datasets, show the superiority of incorporating the social structure impact in recommender systems.
Development of Smartphone Application-Based Learning Devices to Improve Learning Outcomes and Independent Learning of Students at Nurul Amaliyah High School Tanjung Morawa

This study aims to describe: 1) Validity, Practicality, and Effectiveness of Learning Devices based smartphone application in Mathematics Learning on Matrix Subject; 2) Improving student learning outcomes on the Matrix Subject after using the developed learning devices 3) Increasing the student learning independence in Matrix subjects after using learning devices developed.This study is development research with 4D Model in XI class Senior High School Of Nurul Amaliyah Tanjung Morawa.From the first trial and second trial obtained: 1) Learning Devices of Smartphone Application to improve Learning Outcomes and Independent Learning is Valid, Practice, and Effective.2) In the first and second trials, there was an increase in learning outcomes from an average score of 60 increasing to an average score of 78.7. 3) In the first and second trials there was an increase in learning independence from 3,36 in the first trial and increasing to 3,9 in the second trial.Thus, the development of smartphone application-based learning devices can improve student learning outcomes and independent learning in XI Class IPA SMA S Nurul Amaliyah Tanjung Morawa.
Traffic Sign Detection for Green Smart Public Transportation Vehicles Based on Light Neural Network Model

Aiming to rise the security degree and the safety level of drivers, vehicles, and pedestrians, a traffic sign detection system is proposed in this work based on deep learning technologies. By developing the proposed assisting system, we contribute to build a new public smart transportation system used in smart cities and smart environments. Traffic sign detection presents one of the most important parts in an ADAS system due to its safety reasons. Detecting road sign can widely prevent people from accidents by respecting the traffic rules. Ensuring a reliable implementation on edge devices as field programmable gate arrays (FPGAs) presents an increasing challenge. To address this problem, we propose to build in this paper a new traffic sign detection system based on deep convolutional neural networks (DCNNs). The proposed detection system has been built based on YOLO as an objects detectors model in combination with SqueezeNet model which was used as lightweight backbone for features extraction. The use of SqueezeNet has been proposed to ensure a lightweight implementation on FPGA. In order to ensure the model implementation on FPGA, different optimizations techniques have been proposed. The proposed lightweight implementation of the traffic sign detection system has been performed on the pynq z1 platform. Training and testing experiments have been performed using the Chinese traffic sign detection (CTSD) dataset. Based on the experiments results, the proposed detection system achieved very interesting results in terms of detection accuracy and processing time. It achieves 96% mAP as a detection accuracy with 16 FPS as a processing time.
ELECTIVE COURSE "INTRODUCTION TO THE WORLD OF RANDOMNESS AND STATISTICAL LAWS" IN THE DIGITAL ENVIRONMENT AS A MEANS OF DEVELOPING THE STOCHASTIC WORLDVIEW OF HIGH SCHOOL STUDENTS

The article considers the main provisions describing the features of the introduction and implementation of the elective course on stochasticity for high school students of secondary schools. The stochastic line of the school mathematics course for 10-11 grades is aimed at identifying general patterns, assessing the probabilistic nature of real-world dependences, defining phenomena of random nature, processing statistical data, constructing distributions of random variables, using the law of large numbers and probabilistic sampling method of measurement. The expediency of developing this elective course is due to the importance of knowledge of probability theory for both completing the Unified State Exam in Mathematics and preparing for university studies. The article argues that students who have mastered the methods of measuring and evaluating the phenomena associated with the concept of chance achieve high individual results. As part of the elective course and working in the digital environment, students learn how to use probabilistic and statistical methods to solve problems effectively, the focus of which is conditioned by the establishment of interdisciplinary links with biology, physics, chemistry, astronomy, etc. (for example, the study of Mendel's laws of heredity, Brownian motion (random motion of physical particles), Gibbs' rule, etc.). Skills and abilities obtained help students better understand variability and uncertainty inherent in social and natural processes, transfer their knowledge to new conditions and to new tasks, accumulate personal worldview experience. Thanks to this, high school students have a positive attitude towards a chance and its manifestations, perceive the surrounding reality more objectively. According to the results of the elective course implementation pupils have stable cognitive motives for the cognition of the stochastic component in the environment, deep subject knowledge, which conditions the development of motivational, value, cognitive and activity constructs of stochastic world-view.
Stock Markets as a Lever for Growth in Central Africa: The Case of 'Central African Capital Market' (BVMAC)

The Central African region comprises 11 countries, namely Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of the Congo, the Republic of the Congo, Equatorial Guinea, Gabon, Rwanda, and São Tomé and Príncipe. The Gross Domestic Product per person ranges from $492 for the Central African Republic to $7,766 and $8,419 for Gabon and Equatorial Guinea (World Bank, 2022). However, high rates of per capita GDP do not immediately translate to economic prosperity, a well-functioning financial sector, and the effective conversion of savings into investments. There are several stock markets in Central Africa, but one of particular importance is the BVMAC, the Central African Securities Exchange. Unlike the Rwandan Stock Exchange, the BVMAC forms a merger between the Exchanges of Cameroon (Former: DSX) and the Libreville stock exchange (LBVSE) (Ocamer.org 2020). Stock markets are likely for businesses to search for funding, especially for larger companies that employ thousands of people and make productive investments (Pan and Mishra, 2016). Furthermore, policymakers, businesses and civil society are wary of finance and decry the lack of funding opportunities to support economic development and create employment. The stock market could facilitate the conversion of savings into investments and ensure that companies pool capital from a range of investors across Cameroon, central Africa, and beyond. BVMAC is still at a nascent stage, but it has progressed slowly. While there are no official statistics on the number of investors, the regulatory and licensing requirements are not readily accessible to the public just yet. This article discusses why capital markets are essential and their role in generating economic growth. It then outlines developments in the BVMAC and listing requirements before providing recommendations to attract foreign investors and effectively mobilises domestic investment.
Blind identification of MIMO Space-Time Block Codes Based on Convolutional Neural Network

Aiming at the blind identification of space-time block codes (STBC) in multiple input multiple output (MIMO) system, this paper proposes a new convolutional neural network (CNN-N) to realize the blind identification. Compared to traditional algorithms using statistical features of received signal, CNN-N can reduce the computation with a higher correct identification rate. Consist of multiple layers with special functions, CNN-N has good generalization ability in complex MIMO channels. The network in this paper can recognize 6 STBC codes include spatial multiplexing signal (SM) and some OSTBC codes. The simulation result shows that this new convolutional neural network can finish STBC identification with a high correct rate even in low SNR by consuming moderate amounts of time.
Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation

In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem.In the existing works, the unified representation is usually constructed in the original data space.However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss.To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain.In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency.In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function.Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.
Reusability Estimation of Object-Oriented (O–O) Systems Using SOM Technique

Due to recent changes and advancements in software sector, there was an alarming need to cut down time as well as efforts required during complete software development process. While constructing and designing product and services, assuring the quality of software product is very important aspect in order to boost market value of that product. To attain both quality as well as productivity objectives, there is an urge for software reusability. Reusability is an essential measure that can be used to improve overall software quality with lesser cost and efforts. Data Mining is a process to extract useful data from a larger set containing raw data. It also means analyzing patterns from large collection of data. In Object-Oriented (O–O) Software Systems, class reusability can be easily obtained by inspecting metrics values. Inspecting metric values would save time to develop software components from initial phase. In this paper various software metrics related to software reusability of Object-oriented (O–O) software systems has been outlined with the help of MATLAB environment. SOM Technique was applied to create clusters of metrics values that were derived from two Java based system that were a part of COMET dataset that was easily available online. The goal of this study was to identify relationship among metrics values and different level of reusability of particular class. This study shows how SOM technique can be applied on metrics with SOM grids of different sizes. The study depicts elimination of two of the software metrics i.e. NOC and DIT due to poor distribution depicted by both of them.
An Efficient Hybrid Textual Similarity Technique for Analyzing COVID-19 Dataset

The biomedical information retrieval systems utilize NLP techniques to gather essential information and play a pivotal role in the diagnosis and rapid response of Covid-19. The domain-independent and individual syntactic similarity performance can be enhanced for effective representation of biomedical concepts in the literature. A selective combination of syntactic measures and semantic word embeddings are applied to derive hybrid textual similarity techniques HTS-1 and HTS-2. The fine-tuned semantic BERT language model with optimal weighting mechanism is used to ascertain the similarity scores between biomedical sentence sets. Each sentence pair was annotated by ten medical field specialists ranging from 0 to 4, denoting un-related to fully-related sentences. The Pearson coefficient correlation between the golden annotations and proposed model is empirically evaluated as 0.802 (HTS-1) and 0.819 (HTS-2), reflecting performance excellence of 39.5% when compared with other state-of-the-art methods used for the biomedical domain.
Online Learning Contract: A Way Disciplining Students

The contract between teacher and student or what is often called as teacher-student agreement is one of the class management in applying student discipline. Good class management, especially in student discipline, is considered important in creating a conducive learning atmosphere. In conventional learning, teacher and student contracts regarding student discipline are related to how students' attitudes in participating in learning, such as students not being allowed to go around the class while learning is taking place, students are not allowed to be busy with other things besides those related to learning and others. At this time, when online learning or distance learning is applied, of course this contract is also undergoing changes. There is a disciplinary agreement between the teacher and students that adapts to distance learning conditions. This study aims to identify the learning discipline contract between students and teachers in distance learning. Questionnaires and interviews were used to collect data. The data were analysed descriptively to get a description of the agreement between teachers and students in online learning and the type of agreement that commonly used. The findings show that the most widely regulation is related to student’s obedience in following teacher’s instruction in online class. This study also revealed that learning contract is an effective way to create a successful online learning based on teacher’s perspectives.
An Analytical Framework Considering Factors Affecting the Change of Learning Methods at Universities in Vietnam in the Context of Digital Transformation

Digital transformation is a process whose results can give a completely new look to education, with new methods, ways, techniques, tools and media.This study aims to review the factors affecting the change of learning methods at universities in Vietnam in the context of digital transformation.Through applying the TAM model and TRA model to eveluate the factors affecting the change of learning methods at universities in Vietnam in the context of transformation, it can be expected to get better the learning methods at universities in Vietnam.
Creating Spatial 3D User Interface Game Objects

In this chapter, we’ll learn to create User Interface game objects that accept input and communicate information. The UI is an integral part of any game. It informs players about their health and score, and helps control the game’s workflow via menus and pause screens. In a non-VR game, the UI used is a Heads-Up Display, or HUD, which is placed over the top of the main screen and displays information to the player. This type of UI is known as a nondiegetic UI, meaning it doesn’t exist in the game world. Nevertheless, it directly relates to what the player sees or interacts with inside the game. Unfortunately, this nondiegetic UI approach doesn’t work well in VR, as the player can’t focus on something so close to their eyes while trying to focus on the game. It’s very distracting and uncomfortable, as it moves with the player’s head as if it has been glued on. A better and more intuitive way to display the UI is to have it positioned in the game environment. This can be achieved by using Unity’s UI and basing it on a real UI canvas. This approach is referred to as Spatial UI. However, with version 4 of the VRTK, you can create 3D spatial UIs using spatial buttons that can be interacted with via Interactors or pointers. In this chapter, we’ll try out spatial 3D buttons, which exist as game objects in your VR world, using a spatial pointer. We’ve already encountered a straight object pointer when setting up our distance grab mechanic. We’ll start out this chapter by adding a straight spatial pointer that can interact with spatial buttons made available to you in VRTK 4.
Connectivity and Connected Components in the Number-in-Hand Computation Model

We study the multiparty communication model where players are the nodes of a graph G and each of these nodes knows his/her own identifier together with the identifiers of his/her neighbors. The nodes simultaneously send a unique message to a referee who must give the output (which is a fiunction of G). In this paper we prove that counting the numbers of connected components of G and deciding the connectivity of G are equivalent problems (in terms of the size of the messages).