Reviewerly/exHarmony huggingface dataset:
Lightweight Cryptography based Deep Learning Techniques for Securing IoT Based E-Healthcare System

The integration of deep learning algorithms with lightweight cryptography for boosting the security and effectiveness of an Internet of Things (IoT) based E-Healthcare system is likely discussed in the paper’s abstract. The article might describe the difficulties in protecting private healthcare data sent via IoT networks and suggest a remedy that blends deep learning algorithms with simple encryption techniques. The E-Healthcare system may benefit from increased data privacy, authentication, and integrity as a result of this integration, ensuring that patient information is kept secure during transmission and processing. The potential advantages of this strategy, such as decreased processing overhead, energy usage, and latency, which are essential elements for IoT applications, might be highlighted in the abstract. Overall, by utilizing the synergy of deep learning and lightweight cryptography in the context of IoT, the research hopes to develop secure and effective EHealthcare systems.
INVENTIONSHIP TEACHING: UKRAINIAN EXPERIENCE AND ACHIEVEMENTS

Kyiv society of inventors recruited chief of technical creation`s laboratory of Ukraine children’s technical school for move study of theory of inventions. He developed new program and reached children to this program. He trained them in the methods of creation of inventors in groups of young inventors, and organized a system to promote learning methods of technical creativity of young technicians of Ukraine. I have developed a training program for groups of young inventors “Basic technical creativity” for training students for 2 years with a volume of 200 hours. In 1993, the team developed the concept of the basic educational discipline “Basic Technical Creativity”: from kindergaden to university to Mimicry of Adulation. On the order and grant of the State Committee for Science and Technology, the collective development of teaching methods and computer programs based on them in preschool, secondary, special secondary and higher education institutions. The school of methodological means of the theory of invention — TRIVZ allowed to gain full experience in learning how to use all TRIVZ, and in particular the computer program “Invention Machine”, even to use them for improvement of the principles of building computer programs. Also, the team developed the innovative technology “Evronika”, and the corresponding educational and methodological kits for preschoolers, students of secondary schools, vocational schools, colleges and higher educational institutions, and gave students the opportunity to master it.
Secure Video loT Monitoring System Based on ESP32

Monitoring systems have become an integral part of modern households and offices. Recent advancements in loT technologies have made highly sophisticated systems affordable. The use of Artificial Intelligence in monitoring systems, especially with cameras, has become more accessible and sought-after. Currently, there are systems available for performing people-counting tasks in buildings, but they often come with a high cost and high power usage. Images of people as well as the count of people in a room is sensitive data in many occasions. One of potential solutions could be usage of esp32 based cameras due to low cost, small power consumption and compact design. Important to note that esp32 has limited computing resources and is not able to process images locally with AI tools. At the same time, secure data transfer also requires resources to encrypt data due to that available resolution from camera could be reduced. In this paper, the authors investigate the limitations of ESP32based cameras for people counting through a series of tests involving different ESP32-based cameras and configurations. To demonstrate the proof-of-concept, a central computing module was employed in conjunction with MQTT-based TLS secured communication.The results indicate that this approach could achieve a maximum resolution of up to 1600x1200, with a potential image transmission frequency of up to 20 images per minute in some cases. Nevertheless, it is crucial to consider that such high frequency may lead to temperature-related issues
Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high-success rates and real-time performance. Code, video: https://github.com/tud-amr/multi-robot-fabrics
Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection

The goal of Feature Selection - comprising filter, wrapper, and embedded approaches - is to find the optimal feature subset for designated downstream tasks. Nevertheless, current feature selection methods are limited by: 1) the selection criteria of these methods are varied for different domains, leading them hard to be generalized; 2) the selection performance of these approaches drops significantly when processing high-dimensional feature space coupled with small sample size. In light of these challenges, we pose the question: can selected feature subsets be more robust, accurate, and input dimensionality agnostic? In this paper, we reformulate the feature selection problem as a deep differentiable optimization task and propose a new research perspective: conceptualizing discrete feature subsetting as continuous embedding space optimization. We introduce a novel and principled framework that encompasses a sequential encoder, an accuracy evaluator, a sequential decoder, and a gradient ascent optimizer. This comprehensive framework includes four important steps: preparation of features-accuracy training data, deep feature subset embedding, gradient-optimized search, and feature subset reconstruction. Specifically, we utilize reinforcement feature selection learning to generate diverse and high-quality training data and enhance generalization. By optimizing reconstruction and accuracy losses, we embed feature selection knowledge into a continuous space using an encodere-valuator-decoder model structure. We employ a gradient ascent search algorithm to find better embeddings in the learned embedding space. Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset. Finally, extensive experimental results demonstrate the effectiveness of our proposed method, showcasing significant enhancements in feature selection robustness and accuracy. To improve the reproducibility of our research, we have released accompanying code and datasets by Dropbox. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
SKGHOI: Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is a challenging computer vision task that aims to recognize and understand the interactions between humans and objects in images or videos. Existing techniques heavily rely on appearance-based features and computationally expensive transformer models for semantic representation. In this paper, we propose SKGHOI (Spatial-Semantic Knowledge Graph for HOI), a novel graph-based approach that efficiently captures semantic representation by integrating spatial and semantic knowledge. SKGHOI constructs a graph with interaction components as nodes and spatial relationships as edges. Our approach leverages spatial and semantic encoders to extract spatial and semantic information, which are then fused to create a knowledge graph that captures the semantic representation of HOIs. Compared to existing methods, SKGHOI offers computational efficiency and the incorporation of prior knowledge, making it practical for real-world applications. Experimental evaluations on the widely-used HICO-DET datasets demonstrate that SKGHOI outperforms state-of-the-art graph-based methods by a significant margin, showcasing its effectiveness and potential for improving the accuracy and efficiency of HOI detection.
CAMPUR KODE BAHASA GAUL MAHASISWA DALAM PEMBELAJARAN BAHASA INDONESIA

The purpose of this study was to describe the code mixing of student slang at universities in Indonesian language courses, especially Esa Unggul University. This type of qualitative research and descriptive method of analysis. The process of collecting basic and advanced technical data is insertion, intrusion, or insertion techniques. The data before being analyzed is verified first, then analyzed. Validation is done by triangulation technique through internal and external validation. The results found that many students use mixed language, especially writing. It is better for lecturers to give warnings both directly in class, and in writing through scientific writing, and provide intensive counseling so that the younger generation does not lose their national identity. Keywords: Code Mixing, Student Language, Language Learning
Mobile Disinfectant Spraying Robot and its Implementation Components for Virus Outbreak: Case Study of COVID-19

The virus pandemic COVID-19 outbreak brought a huge pressure to the public healthcare system worldwide, especially in developing African countries like Uganda. The Educational system and institutions were put on a standstill due to no quick countermeasures to make the environment clean and safe for normal activities to continue. This paper successfully and comprehensively reviewed the Bluetooth and smart disinfectant spraying robot that successfully controlled the spread of the deadly virus. It also detailed different components that made up the complete spraying robot systems and from this it was observed that spraying robot systems are made up of almost the same components for implementations but differs on program that is embedded on the microcontroller due to different functions. This programing differs based on the functions that the designer/programmer wants the robot to do despite using almost the same components. This research review paper will act as guide for future researchers when designing and implementing a mobile spraying robot.
Query Classification

Query classification, which is to assign a search query into a given target taxonomy, has been recognized as an important technique that can bring improvements in both efficiency and effectiveness of general Web search. Various classification taxonomies have been proposed in order to understand users’ search query from different views, including intent taxonomy, topic taxonomy, performance taxonomy, and so on. Unlike traditional document classification tasks, query classification is much more difficult due to the short and ambiguous nature of queries as well as the demanding online computation requirement. In this chapter, we aim to provide a comprehensive review of the query classification methods in the literature to help readers have an idea of the development of this technique up till now.
Optical Qubits

Photons are the only viable approach for long distance transmission of quantum information. A capability that has been impressively demonstrated with the distribution of quantum keys and entanglement via satellite. It is therefore natural to ask if qubits encoded in photons could also be used for quantum computation.
Random Forest based Fuzzy Feature Weighing model for Imbalance Class Distribution towards Preterm-birth Classification

In medical domain, dataset are found to have imbalanced distribution across the available classes. Classification results on imbalance datasets are found to be biased towards majority class, for the same reasons it is important to realize features by focusing upon the capability to identify the minority class. We propose a Fuzzy Features weighing model named RFFWCI (Random forest based Fuzzy Feature Weighing for Class Imbalance), based on mutual information and F-score. Sigmoid and linear fuzzy membership function are generated which penalize features for false negative prediction and reward for true positive predictions. Experiments are performed using different classifiers on mother physical feature dataset with 903 records, towards preterm birth prediction and it has been observed that proposed feature weighing model has shown significant improvement in classification results. Random Forest classifier with sigmoid function has come up with the best AUC value of 0.65 and prediction accuracy of 86% using linear function.
Aspects and the Context for the Research

The literacy portal was created by the international and interdisciplinary team with the financial support of the European Union in the context of ICT Program, 7th framework – ICT-2011.5.5 (288596). The project aimed at the creation of a modern online portal for training of the reading technique and understanding, the effective usage of the internet environment in daily life, and the creation of the social communities of the users. The researchers used the mixed strategy of the research side by side with the triangulation methods from the analysis of documents, web pages content through the focus groups, user testing to the surveys. The portal was created using the rules of the human-centered design (HCD) approach. The initial steps in the HCD process specify that you need to start with the analysis of future users through article research, analysis of the current web application for the same target audience, and through personal interviews with people with dyslexia. The authors extracted and described the possible tasks for the literacy portal based on the information.
Collaborative Filtering Based on a New Matrix Factorization Algorithm

Abstract Online platforms such as Facebook, Netflix, and Amazon, provide users with information on products or services that matches their interests or backgrounds. As one of the most popular services over online platforms, recommendation system has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is item recommendation over high spectral data. Existing recommendation techniques are not effective for handling users with diverse interests. In this paper, the sparsity problem of traditional recommendation algorithms, a new recommendation algorithm based on spectral clustering is proposed. The spectral clustering process can improve the efficiency of spectral clustering algorithm. Spectral clustering can be performed off-line, which will accelerate the speed of online recommendation. The experimental results on Netflix show that the new algorithm improves recommendation quality in MAE and coverage.
Markov Logic meets Graph Neural Networks: A Study for Situational Awareness

Situational awareness requires continual learning from observations and adaptive reasoning from domain and contextual knowledge. The integration of reasoning and learning has been a standing goal of machine learning and AI in general, and a pressing need for real-world situational awareness in particular. Representative techniques among the numerous methods proposed include integrating logics with learning formalisms, whether probabilistic graphical models or neural methods. These techniques are motivated by the need to model and exploit the symmetry, regularities and complex relations between entities exhibited in real world scenarios (in the form of relational or graph data) for effective reasoning and learning. In this work, we investigate the benefits of integrating two prominent methods for reasoning and learning with relational/graph data, Markov Logic Networks (or simply Markov Logic) and Graph Neural Networks. The former is well-recognised for its powerful representation and uncertainty handling, while the latter have gained much attention due to their efficiency in handling large-scale graph datasets. This paper reports on the potential benefits of combining their respective strengths and applying them to a use case illustration in the maritime domain, together with empirical results.
Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.
Welcome to the Third International Workshop on Crowd-Based Requirements Engineering (CrowdRE'19)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
SoC for real - time object tracking in 3D space

With the rapid growth of workplaces, there is also an increase of the risk employees are exposed to. A high percentage of the injuries suffered annually are work related and many of those are due to the decrease in alertness of employees as they get tired. As a result, many of the injuries are fall related, touching a hot object etc. Therefore, safety in the workplace can be increased if employees can be monitored continuously and warning them when they come close to a restricted (hazardous) area. The current paper presents an in-depth analysis of existing approaches to 3D object tracking which can be deployed to monitor workers and try to prevent injury. The final goal is to design, implement and test an SoC capable of performing real time object tracking in 3D which can send the coordinates to a standalone computer to be displayed and processed.
Automatic Lecture Summarizer

Due to the advance of web technologies and the popularity of video capture devices in the past few decades, the amount of video data is dramatically increasing. Videos are usually analysed by humans which demands immense manpower . Thus, automatic video summarization is an important and growing research area. By watching the summary , users can make quick decisions on the usefulness of the video. Students often feel the need to revise live class lectures for the subsequent test and examinations. With the advent advance NLP and machine learning we can now summarize the Lecture and create lecture notes for students Automatically .Our Solution will be a plugin which can be enabled by the lecturer. The plugin will record the lecture and convert it to text using available speech to text Api solutions. We will then extract the context in the lecture and apply topic modelling, extract semantics and build a summary and lecture notes for the students. Text Summarization as advanced dramatically over the last five years. Libraries and material for support are available in various academic publications’ Aim is to take this technology and apply it to a real-world problem.
Perancangan Aplikasi Penyandian Pesan Teks dengan Menggunakan Algoritma Digraph Cipher

Humans interact with fellow humans using communication. Not everything that is communicated contains a message so that it can be known by many people, sometimes the communication is confidential so that only certain people can know it. The occurrence of data tapping submitted is certainly a problem if the data is confidential, therefore a data security system is needed so that the data cannot be stolen or changed by tappers or crackers. One solution to secure data is to use cryptography. Cryptography is the science and art of maintaining message security when messages are sent from one media to another without experiencing interference from third parties. Through cryptographic techniques, data can be changed into passwords that are not understood and return them back to the original, this process is called data encryption and decryption. This data security process uses the Digraph Cipher method. Some facilities in the Digraph Cipher method are the keys used in the encryption and decryption process are the same (symmetrical) and the decryption process is the opposite of the encryption process.
CIS Society Officers

I t is my great pleasure to announce that the IEEE Compu- tational Intelligence Society is leading the creation of the first comprehensive IEEE journal dedicated to all aspects of Artificial Intelligence.The IEEE Transactions on Artificial Intelligence (TAI) has just been launched and is already accepting submissions.I am very grateful to the three key persons who contributed to this historical moment in IEEE CIS.The IEEE CIS Past President, Nikhil Pal, established a strong partnership with the IEEE Computer Society and the IEEE Systems, Man and Cybernetics Society in 2019, laying out the foundations for TAI.The current CIS Vice-President for Publications, James Keller, and the former CIS Vice-President for Technical Activities, Hussein Abbass, actively and efficiently prepared the case
Three-Phase Inverter Fault Diagnosis Strategy Based on Compressed Sensing and Wavelet Packet Decomposition

To solve the open circuit fault problem in the inverter system. a three-phase inverter fault feature extraction method based on compressed sensing and wavelet packet decomposition (CS-WPD) is proposed. Take the open circuit fault diagnosis of insulated gate bipolar type (IGBT) in three-phase inverter as an example. Firstly, a simulation model of the three-phase inverter topology is established, the phase voltage is adopted as the fault signal, and convert the three-phase voltage signal to two-phase through Clark transformation, and then use the CS algorithm to sample the data. Compression is used to improve the signal processing speed. Secondly, the WPD of energy entropy is used to extract the characteristics of the voltage signal. Finally, the BP neural network is optimized by the beetle antennae search algorithm (BAS) to implement fault diagnosis and compare it with the traditional BP neural network. Network identification methods are compared. The simulation results show that the accuracy and robustness of the system are effectively improved. The accuracy of IGBT tube fault diagnosis reaches 98.29%, the results show that the proposed method is feasible and effective.
Musical Mathematics

It’s often claimed that music and mathematics are closely connected, although given their different cultural associations many people find this hard to believe. In this chapter, we look at a couple of ways music really can be considered mathematical. First there’s the connection, known since ancient times, between musical pitch intervals and the ratios of acoustic frequencies. As interesting as this is to scientists, it’s little help to musicians because intervals are perceived as differences, not ratios. An alternative formulation, musical set theory, is more useful in this respect. Among other things, it helps to explain why the “sci-fi” sounds described in the previous chapter sound as alien as they do.