Reviewerly/exHarmony huggingface dataset:
The challenges and Opportunities for proptech adoption in Nigeria: an exploration of the factors that influence the adoption and diffusion of Proptech innovation in the Nigerian Real Estate industry

This research paper examines the challenges and opportunities for proptech adoption in the Nigerian real estate industry. Proptech, a convergence of property and technology, has the potential to revolutionize the way real estate businesses are conducted by enabling more efficient, quick, and cost-effective operations. However, proptech adoption in developing countries like Nigeria faces unique challenges, including inadequate infrastructure, limited access to finance, and lack of awareness and trust in modern technologies. This study aims to explore the factors that influence the adoption and diffusion of proptech innovation in Nigeria. A descriptive survey will be used to gather data; a structured questionnaire will be distributed to real estate professionals, proptech companies, and potential users of proptech solutions in Nigeria. The findings of this study will shed light on the current challenges facing proptech adoption in Nigeria and the opportunities it presents for the real estate industry. The results indicate that about 71% of the respondents are currently using proptech solutions; with increased efficiency, cost reduction, and better decision-making identified as the main benefits. Lack of awareness was cited as the primary reason for not adopting proptech solutions while concerns regarding data privacy and security were noted as potential risks. Factors influencing adoption include ease of use and compatibility with existing systems. The study's significance lies in providing insights into the state of proptech adoption in Nigeria and identifying key challenges and opportunities. This information will be valuable for real estate managers, developers, investors, brokers, and technology providers, helping them make informed decisions regarding the adoption of proptech solutions. By improving operational efficiency, enhancing customer experience, and driving business growth, proptech adoption can contribute to the growth and development of the Nigerian real estate industry.
Influence of Reading Emotions on the Reading Comprehension of Elementary Pupils

The disproportionately high percentage of underachievers in the Philippines, students fell short of the required reading proficiency and the reports that most of students have lack reading comprehension for both English and Filipino are among the problems faced by educators. This study investigated the influence of reading emotion on reading comprehension in the intermediate grade school students. The respondents to this study were the 100 students from grades 4, 5 and 6 of Tagas Elementary School. The data was obtained using the descriptive survey method, a questionnaire with a rating sheet, checklist and a reading test adapted from Phil IRI. The data were analyzed through weighted mean, and correlation. Which results that most of the pupils possess frustration level on reading comprehension and minimal fall under independent reader. Also the high levels of enjoyment are beneficial for reading comprehension as they typically indicate engagement, motivation, and a willingness to immerse oneself in the reading material. It is encouraging to note that the learners derive enjoyment from their reading experiences, as it may contribute to their overall reading comprehension abilities and foster a love for reading. While moderate levels of anxiety may not significantly hinder reading comprehension, it is important to address and support learners who experience higher levels of anxiety. Nonetheless, ceating a supportive and low-stress reading environment, providing students with strategies to manage anxiety, and addressing individual needs are important considerations to mitigate anxiety-related obstacles to reading comprehension. Educators must ensure that reading materials are diverse, relevant, and cater to the learners’ interests and reading levels, thus reducing boredom and promoting active engagement during reading sessions.
Dynamic Relation Graph Learning for Time-aware Service Recommendation

Driven by Service-Oriented Computing, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users’ dynamic preferences. Recently, users’ social connections have shown significant benefits to time-aware service recommendation, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm always presumes a given social network, which is not necessarily consistent with the similarities of service preferences among users and is expensive to collect for most service platforms. We propose a novel idea to learn the graph structure among historical mashups and make time-aware service recommendation for dynamic mashup creation collectively in a coupled framework. This idea raises two challenges, i.e., scalability and accuracy. To solve both challenges simultaneously, we introduce the Dynamic Relation Graph Learning (DRGL) framework for time-aware service recommendation. For scalability, our framework has a coarse-to-fine recalling strategy to learn the graph structure among the mashups, which enables the exploration of potential links among all historical mashups while maintaining a tractable amount of computation. For accuracy, we leverage recent advances in self-attention mechanisms to the mashup modeling and propose a transformer-based mashup encoder, which considers long-range dependencies in dense mashups for more accurate mashup representations. Extensive experiments show that the DRGL model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.
The Fall of the Lottery

Many lotteries use rotating drums with numbered balls falling over each other until one of them drops through an opening. They always follow an identical procedure though the time of rotation can differ slightly. The case of the Belgian lottery is used here to find out if this repetitive method really results in a random set of numbers.
Hardware-Based Lane Detection System Architecture for Autonomous Vehicles

Hough Transform (HT) algorithm is a method for extracting straight lines from an edge image. In the Hough Transform, the parameters of edge pixels, i.e., points in an image with sharp intensity changes, are treated as “votes”. Then, they will be accumulated in Hough Space (HS) variables ( $$\rho $$ , $$\theta $$ ) to find the votes which are maximum values, in other words, pixels that have the same value as $$\theta $$ , and the $$\rho $$ distance will be the same. However, this algorithm requires huge memory and computational complexity. In this paper, we propose an HT architecture that uses a Look Up Table (LUT) to store trigonometric values and use the value of orientation $$\theta $$ calculated in the Sobel Edge Detection algorithm instead of rotating small angles as the HT standard. We will then reduce the processing time for each image frame so that it can be applied in real-time processing. Our work has been processed at 170 MHz, and the processing time per 1024 $$\,\times \,$$ 1024 image resolution frame is 6.17 ms with an accuracy of 94 %. This design is synthesized on the FPGA Virtex-7 VC707.
Architecture Optimization of a CNN Media Noise Estimator for TDMR

We investigate several architectures for a convolutional neural network (CNN) media noise estimator (MNE) that sends media noise estimates to a three-track Bahl-Cocke-Jelenik-Raviv (BCJR) TDMR detector. While novel CNN architectures have previously been proposed for image classification problems, here the CNN estimates media noise based on inputs from the BCJR detector and from a linear partial response equalizer. The CNN architecture used in our previous work on CNN-based TDMR detection is used as a comparison baseline. Experiments show that an optimized CNN MNE with three residual path connections achieves up to a 3.2% reduction in mean squared estimation error, a 3.7% detector bit error rate (BER) improvement, and a 71.9% reduction in computational complexity compared to the baseline.
Member-Augmented Group Recommendation With Multi-Interest Framework and Knowledge Graph Embeddings

People consume items not only by themselves but also in groups that include their family, friends, coworkers, and online social groups. Different from individual recommendation systems, group recommendation systems first consider group member’s preference separately. The impacts among members are considered, and then, the final group preference and decision would be generated. However, existing group recommendation models suffer severer data sparsity problems than traditional recommendation systems. There is currently lack of a systematic approach to properly address the above issue. What is worse, previous works less consider situations that users may have diverse interests, which means that users may change their preferences by considering the preferences of other group members. Here, we propose a member-augmented multi-interest model with knowledge graph (KG) embeddings to overcome the aforementioned drawbacks. Because only positive labels of groups can be identified in a dataset, precisely predicting a group’s opinion on items that members have not been exposed to is difficult. Accordingly, our model applies the member augmentation (MA) technique to precisely predict a group’s opinion on items. In addition, we leverage multi-interest framework to model the change of diverse user preferences. The framework can know which interests of the user will affect the decision of the group, and interests will vary with different group members. Experiments indicated that the proposed model improves the performance by around 20%, 10%, and 2% in MaFengWo, Yelp, and Meetup-NYC, respectively.
SecureBlock: An ML-Blockchain Consumer-Centric Sustainable Solution for Industry 5.0

Consumer Electronics (CE) are defined as one of the emerging trends where smart devices, such as refrigerators, smart phones, or house hold devices, generate a huge amount of information by communicating among each other without any human intervention. Yet, the speed of producing such products greatly differs from what would be an optimal utilization and integration of such devices in the environment. In addition, generating and transmitting huge amounts of data within heterogeneous networks produces e-wastage (excessive storage, energy and computational requirements) that further leads to environmental crises, and that can raise severe security problems in terms of network trustworthiness, as the information is conveyed using an open channel, i.e., the Internet. In this regard, most of the solutions reported in the literature are inadequate to handle the aforementioned issues. Hence, in this paper, we propose a lightweight solution to secure the information transmission in one of the most relevant applications of CE, i.e. IIoT networks, using an ML-based Hidden Markov Model (HMM) whereby we compute the degree of trust associated to the different devices based on a blockchain mechanism. In our proposal, an Intrusion Detection System (IDS) is used to inspect the continuous behaviour of each node, and a blockchain network ensures the transparency and security in the network. The proposed technique is validated against existing mechanisms using various security metrics. Extensive simulation results demonstrate that the proposed technique is more effective in terms of malicious node identification in comparison to existing schemes.
PROSPECTS FOR THE USE OF ADAPTIVE HUMAN RESOURCE MANAGEMENT TECHNOLOGY IN HIGHER EDUCATION

In this scientific work, the description and prospects of the application of adaptive human resource management technology in the higher education system are carried out. The authors analyze such adaptive methods as adaptive recruitment management, adaptive assessment methods, adaptive development, adaptive performance management, adaptive incentive system, adaptive organizational culture, adaptive motivation mechanisms, adaptive career management, adaptive conflict management. The integration of all these technologies into a single whole will allow a comprehensive approach to prospective human resource management in a higher educational institution.
An Improved Hardware Architecture of Ethereum Blockchain Hashing System

Blockchain technology has become extremely popular, especially in recent years. It has been successfully applied in the field of cryptocurrencies. Prominent is Ethereum, the most popular and highly valued cryptocurrency on the market. It is used in many aspects of society, including healthcare, education, and the economy. Ethereum mining is the process of adding a block of transactions to the Ethereum Blockchain. This process generates a new block, which processes, stores, and secures the transaction information created by the user’s transaction processes. Ethash is Ethereum’s current mining algorithm. The primary cryptographic hash functions of Ethash are Keccak512 and Keccak256. This paper proposes a 2-stages pipelined architecture with high throughput and high efficiency for the Keccak hash function. Then we applied this architecture to implement the Ethash algorithm on hardware. This design will be implemented on Xilinx Virtex-7 VC707 FPGA. The results of our proposed Keccak architecture achieve a Frequency of 816.742 MHz, Throughput and Efficiency reach 35.545 Gbps and 36.196 Mbps/Slices, respectively. The Hashrate of the proposed system achieves 414.14 KH/s with an operating Frequency of 66.263 MHz.
Detecting Phishing Using a Multi-Layered Social Engineering Framework

As businesses develop and expand with a significant volume of data, data protection and privacy become increasingly important. Research has shown a tremendous increase in phishing activities during and after COVID-19. This research aimed to improve the existing approaches to detecting phishing activities on the internet. We designed a multi-layered phish detection algorithm to detect and prevent phishing applications on the internet using URLs. In the algorithm, we considered technical dimensions of phishing attack prevention and mitigation on the internet. In our approach, we merge, Phishtank, Blacklist, Blocklist, and Whitelist to form our framework. A web application system and browser extension were developed to implement the algorithm. The multi-layer phish detector evaluated ten thousand URLs gathered randomly from the internet (five thousand phishing and five thousand legitimate URLs). The system was estimated to detect levels of accuracy, true-positive and false-positive values. The system level accuracy was recorded to be 98.16%. Approximately 49.6% of the websites were detected as illegitimate, whilst 49.8% were seen as legitimate.
Comparative Study: Algorithms for Short Message Service Classification

This research aims to classify Short Message Service (SMS) data by applying classification models that have studied SMS data to classify SMS data into SMS spam and SMS ham. The classification model is made from data mining algorithms: Naive Bayes and support vector machine. Before implementing the two algorithms, the SMS data will go through a text preprocessing stage, including data cleaning (whitespace removal, removal of punctuation, and removal of numbers), case folding, stemming, tokenizing, and stop word removal. In this research, a comparison of the accuracy of the two data mining methods will be carried out to see and get the best classification algorithm. Researchers also implemented several experiments by comparing the use of testing data by 20 and 30% and comparing the application of preprocessing stemming and without stemming. This study found that the support vector machine algorithm using testing data of 20% by applying the stemming stage had the highest accuracy rate, 97.5%.
Are We Heading towards a Dynamic Language Winter? (Invited Talk)

Though the 2010s saw many research publications about languages such as JavaScript and Python, there currently appears to be a general loss of interest in dynamic languages, with popular new languages such as Rust and Zig being statically typed, and AOT compilation often being viewed as a preferable option to JIT compilation. There is a legitimate question as to whether we are headed towards, or already in, a dynamic language “winter”, with reduced interest from industry and reduced access to funding for dynamic language research. However, despite this, many of the most popular languages (Python, JS, Julia, etc.) are still dynamically typed. In this talk, we discuss questions such as potential causes for a dynamic language winter, what makes statically typed languages so attractive at this time, the major strengths of dynamic languages that could help turn the tide, and what may come after.
Programming Languages for AI Programing Agents (Invited Talk)

Over the past decade software development has shifted from a process centered around writing code to a process that increasingly involves composition of external packages and managing the integration of code from other team members. The next decade-plus will be defined by the shift from a process where humans are the central developers of code into one where AI agents, likely Large Language Model (LLM) based, will be the major creators of code and humans will shift to a supervisory role as curators, integrating rich framework-functionality and code developed by AI programming agents.
Adaptive Structural Operational Semantics

Software systems evolve more and more in complex and changing environments, often requiring runtime adaptation to best deliver their services. When self-adaptation is the main concern of the system, a manual implementation of the underlying feedback loop and trade-off analysis may be desirable. However, the required expertise and substantial development effort make such implementations prohibitively difficult when it is only a secondary concern for the given domain. In this paper, we present ASOS, a metalanguage abstracting the runtime adaptation concern of a given domain in the behavioral semantics of a domain-specific language (DSL), freeing the language user from implementing it from scratch for each system in the domain. We demonstrate our approach on RobLANG, a procedural DSL for robotics, where we abstract a recurrent energy-saving behavior depending on the context. We provide formal semantics for ASOS and pave the way for checking properties such as determinism, completeness, and termination of the resulting self-adaptable language. We provide first results on the performance of our approach compared to a manual implementation of this self-adaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient.
Analyzing the Educational Content of Play-Based Early Childhood Education Curriculum: From the Perspective of Powerful Knowledge and Relational Epistemology.

This study purpose to examine the educational content that emerges from a unique play-based early childhood education curriculum, which involves elements such as children, materials, teachers, play, interest, and experiences. The researcher interprets this content through a relational-epistemology by the perspective of 'Powerful Knowledge' from a critical and social realism, as well as the features of 'Neo-materialism' presented in the context of posthumanism. To explore educational content in the field, three teachers participated, engaging in an inquiry community and stimulated recall interviews. Data collection occurred from January to June of the 22nd year, with analysis completed by December of the same year. The research findings reveal that educational content is generated through intricate entanglements among open-elements involving children, materials, and teachers. The analysis results First, the study examines the knowledge embedded in play and life, observing educational content that transcends the boundaries of educational processes and curriculum-specific components. Second, the uniqueness of the early childhood education environment results in evolving educational content over time and space, shaped by the relationship between continuity and materials. Third, play opens the door to generative educational content, with teachers and young children creating and experimenting within play, resulting in educational outcomes. This research was possible to recognize that the implementation of early childhood education should start from how teachers perceive educational content, emphasizing the uniqueness of early childhood education. The educational content can encompass “moments of life, play, learning, immersion, joy, entanglement, rawness, respect, and care, as well as communication” that occur within the world of materials and the lives of children.
Preoperative Education Against Anxiety on Patient Pre Retinal Ablasio Surgery at The Eye Hospital Invitation Surabaya

Retinal detachment is a disease of decreased vision due to the separation of the retinal cone cells and rod cells from the retinal epithelial cells. The most common response in preoperative patients is anxiety. Preoperative anxiety can cause palpitations, palpitations, feeling like fainting, increased blood pressure, and increased pulse pressure. The purpose of this study was to determine the effectiveness of providing preoperative education on anxiety in preoperative retinal detachment patients at Undaan Eye Hospital, Surabaya. The research design was a quasi-experimental study with a one group pre-test – post-test design approach. The results showed that the patient's anxiety level before being educated was half of the respondents with severe anxiety, the anxiety level after being educated was almost half of the respondents who had no anxiety and mild anxiety. Based on SPSS calculations using the dependent sample t-test, the value of p = 0.000 (&lt;0.05) is obtained at the sig.(2.tailed) value. So, there is a difference in the level of anxiety before and after being given education to preoperative retinal detachment patients at the Undaan Eye Hospital, Surabaya. This means that the education given to preoperative retinal detachment patients is effective in reducing anxiety levels. The conclusion of this research is that there is effectiveness in providing education on anxiety levels at the Mata Undaan Hospital, Surabaya.
THE FUTURE OF INSTRUMENTAL SOUND AND INSTRUMENTALISTS

The accelerated development of technology and climatic changes, which is progressively interwoven with each other, will unavoidably lead to changes in the production and use of musical instruments. It is time to investigate into these upcoming changes and their impact on many features of social life, with the views on past issues included. In this regard, the aim of this paper is to give a first overview on how practices with musical instruments can be continued on different levels of production and use through a historically informed kind of musician and instrument producer. Insofar, this overview can be seen as a beginning of diverting from a physical fixing in ethnic belongings and financial approaches widely requested among musicologists of the 21st century. ‘The global perspective cannot be the end of musical instruments’ is one of the theses being discussed with the help of most recent literature on the topic. It is dedicated to the second main topic of the symposium. The structure of this paper follows the classical three-folded principle: Introduction and Background, Analysis and Findings, and Conclusions for each section, followed by a list of references that were used. This order is extremely boring, and I do not welcome the stubborn keeping to indiscriminate structures. Yet, in this case, these boring structures have to be filled with some life following the facts found.