Reviewerly/exHarmony huggingface dataset:
ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces

In recent years, neural implicit surface reconstruction has emerged as a popular paradigm for multi-view 3D reconstruction. Unlike traditional multi-view stereo approaches, the neural implicit surface-based methods leverage neural networks to represent 3D scenes as signed distance functions (SDFs). However, they tend to disregard the reconstruction of individual objects within the scene, which limits their performance and practical applications. To address this issue, previous work ObjectSDF introduced a nice framework of object-composition neural implicit surfaces, which utilizes 2D instance masks to supervise individual object SDFs. In this paper, we propose a new framework called ObjectSDF++ to overcome the limitations of ObjectSDF. First, in contrast to ObjectSDF whose performance is primarily restricted by its converted semantic field, the core component of our model is an occlusion-aware object opacity rendering formulation that directly volume-renders object opacity to be supervised with instance masks. Second, we design a novel regularization term for object distinction, which can effectively mitigate the issue that ObjectSDF may result in unexpected reconstruction in invisible regions due to the lack of constraint to prevent collisions. Our extensive experiments demonstrate that our novel framework not only produces superior object reconstruction results but also significantly improves the quality of scene reconstruction. Code and more resources can be found in \url{https://qianyiwu.github.io/objectsdf++}
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns are an important factor in the performance of KGE models. Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern. To address this challenge, we evaluate the performance of 7 KGE models over 4 common relational patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency, and part-to-whole three aspects and get some counterintuitive conclusions. Finally, we introduce a training-free method Score-based Patterns Adaptation (SPA) to enhance KGE models' performance over various relational patterns. This approach is simple yet effective and can be applied to KGE models without additional training. Our experimental results demonstrate that our method generally enhances performance over specific relational patterns. Our source code is available from GitHub at https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns.
Fuzzy Cognitive Maps for Algorithmic Decision Making in eMental Health

Abstract Background : eMental health is on the rise to meet growing global mental healthcare needs and so is the need for applications with automatic decision-making capabilities to support clinical practitioners and to personalize eMental health interventions to their users. However, due to unresolved challenges and clinicians’ reluctance to adopt artificial intelligence in mental healthcare, the technology’s current state in mental health is still in the phase of proof-of-concepts rather than providing decision making algorithms that are ready to create clinical impact. Fuzzy cognitive maps (FCMs), a soft computing technique that leverages expert knowledge by combining cognitive maps with fuzzy logic, can be an alternative approach to equipping eMental health with automatic decision-making capabilities. Method : This article provides an introduction to FCMs for developing decision algorithms for eMental health. It demonstrates step by step how FCMs are developed, based on an example from mental health, while discussing challenges and future directions for research. Results : FCMs are a viable option at the disposal of developers and researchers in the field of eMental health that come with their own set of advantages and challenges: (1) they allow the construction of initial mental health decision models without depending on the availability of large amounts of historical client data, (2) they are explainable, and (3) they involve clinicians in the construction process, inherently promoting professionals’ acceptance of the resulting algorithm. Regarding challenges, (4) there is a pronounced trade-off between the simplicity of FCMs and the accurate representation of mental health constructs since their approach offers limited ways for modelling beyond direct causality and for including temporal aspects of mental health problems, and (5) FCM decision models are not usually on par with data-driven black box AI models in terms of classification accuracy. Conclusion : FCMs are an interesting and promising alternative modelling approach for developing decision-making algorithms in eMental health. Their inherent expert-basis, explainability and independence of large clinical data sets to arrive at initial usable decision models makes them especially attractive for the mental health domain.
CaC. Circuits as Code, a Typed Lambda formulation for Circuit Theory, and Design Patterns in the architecture of Analog Electronics.

In the IaC, CaC and MaC formulation in MFA II design, 1this paper delineated CaC as typed Lambda Calculus in Lambda for Fields using Real Calculus of Constructions , R.C.C and Lambda Calculus with Types for Fields (LCTF), amongst several other formal verification typed lambda calculus for higher order function theory.CaC is essential in Biomorph and Neuromorph computing, towards a field of Biological Cybernetics as Biomimetic design. MFA II is from functional semantics inspired by biological design in multiple functional semantic maps to genome definitions in transcription. Code Units are defined on the Iac, CaC and MaC definitions verifiable in theoretical synthetic biology experiments.Keywords: Analog electronics, Circuits as code, Typed lambdas, Design patterns, Biomorph computing, Neuromorph computing, Machine learning, Optimization, Control systems, Robotics.
Harnessing Self-Supervision in Unlabelled Data for Effective World Representation Learning in AI Models

Artificial intelligence (AI) models rely on large, labelled datasets to learn effective representations of the world. However, labelled data can be scarce, biased, and expensive to obtain. Self-supervised learning offers a promising solution by enabling models to learn from unlabelled data through pre-training tasks that involve predicting masked or distorted portions of the data. This allows the model to learn powerful representations without explicit human labelling. This conceptual research paper examines how self-supervision from unlabelled data can be harnessed to train AI models capable of learning richer, more meaningful representations of the world. A detailed methodology utilizing contrastive self-supervised learning on unlabelled images is proposed. Quantitative results demonstrate the proposed approach enables models to learn superior representations compared to supervised learning, particularly when labelled data is scarce. The research provides critical insights into the future promise of self-supervised learning in developing AI systems that better perceive and understand the complexity of the real world.
Performativity and the domestic space Practices of embodied dwelling through enactivism, participation, and auto-construction

&#x0D; &#x0D; &#x0D; This paper examines the transformative potential of performativity in reimagining the concept of dwelling and of domestic spaces. Drawing from enactivist and embodied cognition perspectives, we delve into the dynamic relationship between individuals, their bodies, and the architectural environment. Emphasising the role of bodily engagement, sensorimotor experiences, and interoceptive awareness, we explore how individuals actively participate in and shape their architectural surroundings. Moving beyond individual interactions, we also highlight the social and collective dynamics influenced by the built environment, underscoring the impact of cultural conventions and societal norms.&#x0D; This paper investigates some contributions that advocate for a reappropriation of institutionalised domestic spaces through imaginative interventions that challenge conventional norms and envision future-oriented dwelling practices. Within the context of institutionalised domestic spaces, we investigate the role of the fantastical and the monstrous as disordered qualities that challenge traditional boundaries and offer opportunities for transformation. Through case studies, we examine projects that blur the lines between public and private realms, enabling participatory practices and urban influences to reshape functional space utilisation.&#x0D; In conclusion, this paper underscores the importance of a dialogue between embodied performativity and the concept of dwelling. It proposes a re-evaluation of our relationship with space that is not just functional but enactive, and it champions the transformative potential of the arts in conceiving our future homes. With a renewed focus on sustainability, participation, and the interplay between the human body and space, we can begin to imagine a future of dwelling that is as dynamic, inclusive, and vibrant as the lives we wish to live within these spaces.&#x0D; &#x0D; &#x0D;
Online classroom : Bibliometric analysis

Advances in information and communication technology (ICT) have profoundly transformed organizational processes, educational institutions have not been left out of these changes either. Moreover in the beginning of 2020 the Covid-19 pandemic came to extend the use of ICT tools in education through the educational design of emergency remote teaching (ERE). It was sought to understand the evolution of teaching in academic production, for this a bibliometric study was carried out between 2004 and 2021 on the subject "online teaching" comparing the Scopus base and Web of Science (WoS) that according to Marín-Velásquez and Arrojas-Tocuyo (2021) are worldwide recognized and accepted bases of academic search. In this way, the evolution of publications, the areas, the authors, the most cited documents, as well as the key words that appear the most, are highlighted in this work. We noticed as main findings the dominance of the number of Scopus publications and while Scopus is dominated by the area of computing and WoS is led by the area of education.
SAFEGUARDING MOBILE NETWORKS AGAINST EMERGING THREATS &amp; ENSURING USER CONFIDENCE IN THE FACE OF PHISHING ATTACKS

Mobile networks, which connect billions of devices and allow for seamless communication on the go, have become an essential part of our daily lives. However, in order to maintain the privacy of mobile communications and data, there are substantial security issues that must be resolved.The increased reliance on mobile networks has made them attractive targets for hackers. A breach in the security of a mobile network can have serious repercussions, such as unauthorized access to confidential data, financial loss. The aim of this research paper is to explore the various aspects of mobile network security and the specific threat landscape posed by phishing attacks. The paper will give insights about the techniques employed by attackers, the vulnerabilities in mobile networks that make them susceptible to phishing attacks and the potential consequences for individuals and organizations. By understanding the nature of phishing threats in mobile networks and adopting effective preventive measures, organizations and individuals can protect themselves against the ever-evolving landscape of cyber threats. KEYWORDS: Network Security, Data Breach, Phishing, Detection, Tools.
Research on multi-dimensional disaster information perception and fusion service technology in underground and shelter space

Abstract. The disaster information of human casualties and infrastructure damage in underground and sheltered spaces usually demands to be acquired timely during natural disasters such as earthquakes. Therefore, to address the last link problem in the field of disaster relief, the paper investigates the service technology of multi-dimensional disaster information perception and fusion in underground and sheltered space. In this paper, the classification and characteristics of disaster information in underground space are summarized. The key technologies for the extraction and identification of geo-environmental information, the detection methods and sensors used for the data of real-time environmental parameters, and the means of multi-source disaster information fusion under a unified spatio-temporal framework are discussed respectively in the underground space. Finally, a method of dynamic visualization of underground space disaster information is proposed to visualize the underground space scene and environmental parameters. It is achieved the visualization of 2-3D linkage and the simulation of environmental parameters.
Cloud-Based Dynamic Task Scheduling and Load Balancing Using a Variety of Intelligent Schemes

One of the main problems with cloud computing is load balancing, which divides tasks into virtual machines (VMs) with varying lengths and completion times. As a result, keeping VMs loaded up during job scheduling and allocation is difficult. In this research, we suggested secure load balancing in a cloud context and resource-aware dynamic job scheduling utilizing intelligent algorithms. The process for the planned work is as follows: (1). The Human Mental Search (HMS) method is used to cluster Real-Time and Non-Real Time jobs into four categories: Real-Time and Short, Real-Time and Long, Non-Real Time and Short, and Non-Real Time and Long. (2). Knowledge of Energy and Delay Combined Tasks Tasks are planned based on task clustering, and the fuzzy VIKOR method is used to schedule them in the order of priority. (3). Security Risks aware VM Clustering, which employs the Hierarchical Agglomerative Clustering algorithm, consists of three distinct entities: a VM's information collector, which compiles data about VMs; a load monitor, which oversees the entire datacenter; and a decision maker, which assigns tasks by choosing the best VM. (4). Co-Resident assaults were stopped by the continuous-Actor-Critical-Algorithm (C- ACAL), which is used to pick the best VM. Using CloudSim, which measures performance in terms of response time, latency, resource utilization, throughput, energy consumption, and task execution success rate, the simulation is carried out.
A Model for Migrating Relational Database to NoSQL Database

Relational Database Management Systems (RDBMS) is widely used to store the Data and Information. As we already know RDBMS can store the data in the form of table but if we want to know the complex data relationships and interconnected information, we have to convert the relational database to graph database. Professionals working with airline databases will be able to quickly query highly connected data thanks to graph databases' ability to quickly uncover and manage new and beneficial associations. Relational databases use expensive huge joins to query related data, whereas graph data-stores contain direct references to their neighboring nodes. In order to handle the enormous quantity of airline data being created at a very high pace, scalability was therefore greatly needed. Additionally, the majority of airline data is semi- or un-structured, necessitating a schema-less database. In this paper a methodology to issues that occurs while converting an Airline relational to a graph database. By utilizing the source's restrictions and schema. Conjunctive SQL searches over the source are supported by the method and converted into graphs. procedures that traverse the target. To demonstrate the viability of the solution and the effectiveness of query response across the target database, the experimental outcomes are shown. Nodes are mapped to tuples, while edges are mapped to foreign keys. Constraints were maintained during the change. In order to build the suggested solution, SQL2Neo, popular graph database Neo4j and MySQL as relational databases were both used. Index Terms—Database migration, Graph, data, attributes, Relationships, tables.
CYBER THREATS IMPLICATIONS ON SOCIAL NETWORKING SITES

Abstract—Social networkingservices have revolutionized how individuals connect, communicate, and exchange information online since their introduction and popular use. However, this digital revolution has also opened the door to an array of cyber threats that pose significant challenges to the security and privacy of users and platforms alike. The goal of this study is to investigate the complex effects that cyber attacks have on social networking sites. The research delves into various types of cyber threats, including but not limited to malware, phishing, social engineering, data breaches, and identity theft, that target social networking platforms and their users. By analyzing real-world case studies and relevant statistics, the paper identifies the most prevalent cyber threats affecting these platforms and their consequences for individuals, businesses, and the overall online community. Keywords— cyber threats, social networking sites, cybersecurity, privacy, data breaches, online community
PROJECT ELL (Enhancing Literacy Skills of Learners through Audio – Visual Delivery) and Reading Comprehension Among Grade 5 Pupils

This study focused on the PROJECT ELL (enhancing learners’ literacy skills through audio-visual delivery) and reading comprehension among grade 5 pupils of San Roque Elementary School. Specifically, it aimed to determine the effect of the PROJECT ELL in improving reading comprehension, the pre-and post-test results of the grade 5 learners before and after implementing the intervention, and the intervention’s effect size on reading comprehension. This study utilized 13 participants using a single shoot test utilizing total enumeration, particularly frustrated readers. The researcher used a pre-post-test-only design, wherein the learners were identified through the purpose of the study. From the findings, PROJECT ELL greatly assisted the frustrated leaders in improving their reading comprehension skills, specifically in locating and explaining their answers with pertinent information. To reach the highest possible reading comprehension level among frustrated readers, it is necessary to employ various strategies and techniques.
EFIKASI DIRI GURU DAN LITERASI DIGITAL SERTA KONTRIBUSINYA TERHADAP EFEKTIVITAS PROSES PEMBELAJARAN DALAM JARINGAN

The effectiveness of the learning process in the network is one that needs attention, especially after the 2019 pandemic where the use of the network in the learning process is a must. With the effectiveness of the learning process in a good network, students can get maximum education in the learning process. Based on the results of initial observations made by researchers, it was found that the effectiveness of the learning process in the network was not optimal. This is thought to be caused by low teacher self-efficacy and teacher digital literacy. Therefore, it is necessary to do research to test the truth. This study uses a survey method with a quantitative approach, as well as correlational and regression techniques. Data collection technique through a Likert scale questionnaire to 53 respondents who are elementary school teachers. The results of the data analysis show that (1) Teacher self-efficacy contributes to the effectiveness of the learning process in the network, (2) Digital literacy contributes to the effectiveness of the learning process in the network, (3) Teacher self-efficacy and digital literacy jointly contribute to the effectiveness of the learning process . Principals can provide special training related to the use of digital products or media, especially regarding the use of software. Additional knowledge about software is no longer an added value but has become a necessity in today's information technology era. The school principal is deemed necessary to provide training with the aim of making teachers accustomed to choosing digital literacy. With the circulation of various sources of information, ideally teachers can find sources of information that have high credibility.
The Implementation Of Listening While Reading (LWR) To Enhance Student’s Reading Flueency And Comprehension

The aim of this research was to find out the effect implementation listening while reading (LWR) strategy to enhance student’s reading fluency and comprehension of the seventh grade students at SMP Y.P Keluarga Pematangsiantar in the academic year 2021/2022. This research used quantitative approach with true experimental design. The data were obtained by using pre-test and post-test. The data of the pre-test and post- test of both groups were analyzed by using descriptive and inferential statistics (SPSS 26.0). the data to be analyzed are frequency, statistic, normality, homogeneity and hypothesis testing. The result of the research shows that the testing hypothesis of post-test scores, it is found that Tcount =5.663 &gt; Ttable = 1.699 at the significance level a = 0.05 and degrees of freedom (df) = 29. So, H0 is rejected and Ha is accepted. It means that, the implementation of LWR effect significantly on students' reading fluency and comprehension abilities at seventh grade of Y.P. Keluarga Pematangsiantar in academic year 2021/2022
The Effect of Using Question-Answer Relationship (QAR) Strategy on Students’ Reading Comprehension in Descriptive Text of XII Grade at SMA Negeri 2 Perbaungan

&#x0D; &#x0D; &#x0D; &#x0D; The objective of this research was to obtain evidence of the effectiveness of the use of question-and-answer strategy relationships to students' reading ability in the description text This research uses quantitative research methods with experimental designs. The sample from this research was selected using a random sampling technique with a total sample of 70 students divided into two classes, an experimental class and a control class. The population of this research is class XII students of SMA Negeri 2 Perbaungan. The instrument used is a reading ability test. Before giving treatment, the students of both classes were given preliminary tests to find out what still exists in their ability to read explanatory texts. The test consists of 20 multiple-choice questions. At the end of the treatment, the students were given a post-test. Data from the two classes is processed using t-test. The average result from the post-test shows an improvement from the pre-test result to the post-test result. The results of the t-test show that the t-observed is greater than the t-table, namely t-observed 16.15 and t-table 2.00. The point is that the t-observed is greater than the t-table (16.15&gt;2.00) with df 68 at a significant level of ? 0.025. After analyzing the data, it can be stated that Ho (Null Hypothesis) has been rejected and Ha (Alternative Hypothesis) has been accepted. Thus, the question-and-answer relationship strategy has an effect on students' reading comprehension in the description text.&#x0D; &#x0D; &#x0D; &#x0D;