Reviewerly/exHarmony huggingface dataset:
Mall Customer Segmentation Engine Through Clustering Analysis

Finding related information within a cluster is done using a technique called clustering. The dataset cluster uses the data's maximum and minimum values to group together similar data. Clustering is a process in which matter has been split into groups and grouped based on a rule to maximize within-group similarity and minimize between-group difference likeness. In this chapter, the authors examine and contrast the various group analysis techniques and algorithms employed by Rapid Miner. Multiple clustering methods have been developed. In the chapter, two types of clustering for algorithms are analyzed. One area of mall patrons was evaluated. The data set is used with Rapid Miner tools to determine the proper cluster.
Noise-aware Local Model Training Mechanism for Federated Learning

As a new paradigm in training intelligent models, federated learning is widely used to train a global model without requiring local data to be uploaded from end devices. However, there are often mislabeled samples (i.e., noisy samples) in the dataset, which will cause the model update to deviate from the correct direction during the training process, thus reducing the convergence accuracy of the global model. Existing works employ noisy label correction techniques to reduce the impact of noisy samples on model updates by correcting labels; however, such methods necessitate the use of prior knowledge and additional communication costs, which cannot be directly applied to federated learning due to data privacy concerns and limited communication resources. Therefore, this paper proposes a noise-aware local model training method that corrects the noisy labels directly at the end device under the constraints of federated learning. By constructing a label correction model, a joint optimization problem is formally defined for optimizing both the label correction model and the client-side local training model (e.g., classification model). As a solution to this optimization problem, we propose a robustness training algorithm using label correction, along with a cross-validation data sampling algorithm that updates both models simultaneously. It is verified through experiments that the mechanism can effectively improve the model convergence accuracy on noisy datasets in federated learning scenarios.
Building infrastructure analysis using total station and unmanned aerial vehicle drone for surveying and modelling

Abstract Unmanned Aerial Vehicle (UAV) drones and Total Stations are two of some methods and tools used to conduct surveys on areas or building buildings. A UAV is a flying machine that functions with remote control by the pilot or can control itself, uses aerodynamic laws to lift itself, can reusable and can carry certain payloads. A total station is a device that is a combination of electronic theodolite, Electronic Distance Meter, and software that serves as a data collector and processor. The implementation of land surveying is one of the stages of the implementation of infrastructure development. Land survey itself is a way or method to evaluate land to get data directly from the field. Survey activities consist of field activities, making data analysis, interpretation of objectives and making survey reports. In this modern period, the technology to conduct land / land surveys often develops, namely with the total station and the use of UAV drone technology. The study aims to analyse and compare the capabilities of UAV Drones and Total Stations.
Quantum Computing with Differentiable Quantum Transforms

We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.
power of eLearning from promises to practices applied in engineering

Significant strides are being made in the development of Information and Communication Technology (ICT) by infusing ICT as a tool for learning and education. Expansive and visionary programs are initiated meant to transform university education. The latter have jointly contributed to the considerable progress of the renewed curricula development and implementation, including the hybrid learning model for a number of key courses such as mathematics, science, computer networks, programming and ICT. Sectoral digitalization is implemented due to the digitalization strategies and initiatives. Computer and science labs support e-learning and of course the training and retraining of the teaching staff for new curricula (study-programmes), new pedagogical methodologies and assessment strategies. In general terms, the study-programmes have been renewed/redesigned to become more relevant fulfilling students’ needs, while the specific outcomes are based less on knowledge and content and more on learning skills and competences, i.e. real-world applicable tasks/activities. The paper describes the study case of the presentation format for the Computer Networks course, which can be found in the study plan/syllabus of several study-programmes at the Faculty of Computers, Informatics and Microelectronics (FCIM) and the Faculty of Electronics and Telecommunications (FET) of the Technical University of Moldova (TUM).
An integrated framework of mobile crowd estimation for the 2019, July 1st rally in Hong Kong

Traditional approach of mobile crowd estimation involves counting a group of individuals at a specific place, manually, in real-time. It is a laborious exercise that can be physically and mentally demanding. In Hong Kong, a large rally can last more than six hours, making the manual count method susceptible to human errors. While crowd counting using object detection and tracking has been well-established in computer vision, such application has remained relatively small scale within a controlled indoor setting (e.g. counting people at fixed gateways in a mall). No attempt to date has applied the automatic crowd counting method to count hundreds of thousands of people along an open stretch of rally route within the complex urban outdoor landscape. This research proposed an integrated approach that combines the capture-recapture method in statistics and a Convolutional Neural Network (CNN) method in computer vision to count the mobile crowd. The research teams implemented the integrative approach and counted 276,970 people with a 95% confidence interval of 263,663 to 290,276 in the 2019, July 1st Rally in Hong Kong. This work counted the attendance of a large-scale rally as a proof of concept to fill in a gap in the empirical studies. The intellectual merits and research findings shed useful insights to improve mobile population estimation and leverage alternative data sources to support related scientific applications.
The future of acupuncture education: 3D Augmented Reality (AR) acupuncture model for Chinese Medicine

Acupuncture is a therapeutic modality in Traditional Chinese Medicine which aims to improve the flow of Qi (chi) or energy around the body by applying needles to specific points on the skin. Traditional Chinese Medicine teaches that Qi must flow freely through the body’s meridians, or channels, to ensure health and well-being. Traditionally, Chinese Medicine practitioners used a wooden, copper or bronze acupuncture model to show the points where needles should be applied to the skin and the meridians through which Qi flows around the body. However, as traditional models depict acupuncture points which are too rigid for teaching purposes, understanding the descriptions of acupuncture points from textbooks in 2D is also another hurdle for beginners to apply them to the human body in 3D. With modern computing and 3D-printing technologies, this article introduces a realistic human model in the form of a 6-foot copper acupuncture sculpture with over 370 virtual acupuncture points using state-of-the-art Augmented Reality (AR) and metal 3D-printing techniques for enhanced teaching, learning, and demonstration of acupuncture principles. It will be a new training model for Western and Chinese medical practitioners as well as for the public to gain interactive acupuncture experience that is beneficial to their own health.
An information theoretic score for learning hierarchical concepts

How do humans learn the regularities of their complex noisy world in a robust manner? There is ample evidence that much of this learning and development occurs in an unsupervised fashion via interactions with the environment. Both the structure of the world as well as the brain appear hierarchical in a number of ways, and structured hierarchical representations offer potential benefits for efficient learning and organization of knowledge, such as concepts (patterns) sharing parts (subpatterns), and for providing a foundation for symbolic computation and language. A major question arises: what drives the processes behind acquiring such hierarchical spatiotemporal concepts? We posit that the goal of advancing one's predictions is a major driver for learning such hierarchies and introduce an information-theoretic score that shows promise in guiding the processes, and, in particular, motivating the learner to build larger concepts. We have been exploring the challenges of building an integrated learning and developing system within the framework of prediction games, wherein concepts serve as (1) predictors, (2) targets of prediction, and (3) building blocks for future higher-level concepts. Our current implementation works on raw text: it begins at a low level, such as characters, which are the hardwired or primitive concepts, and grows its vocabulary of networked hierarchical concepts over time. Concepts are strings or n-grams in our current realization, but we hope to relax this limitation, e.g., to a larger subclass of finite automata. After an overview of the current system, we focus on the score, named CORE. CORE is based on comparing the prediction performance of the system with a simple baseline system that is limited to predicting with the primitives. CORE incorporates a tradeoff between how strongly a concept is predicted (or how well it fits its context, i.e., nearby predicted concepts) vs. how well it matches the (ground) "reality," i.e., the lowest level observations (the characters in the input episode). CORE is applicable to generative models such as probabilistic finite state machines (beyond strings). We highlight a few properties of CORE with examples. The learning is scalable and open-ended. For instance, thousands of concepts are learned after hundreds of thousands of episodes. We give examples of what is learned, and we also empirically compare with transformer neural networks and n-gram language models to situate the current implementation with respect to state-of-the-art and to further illustrate the similarities and differences with existing techniques. We touch on a variety of challenges and promising future directions in advancing the approach, in particular, the challenge of learning concepts with a more sophisticated structure.
<scp>XML‐LightGBMDroid</scp>: A self‐driven interactive mobile application utilizing explainable machine learning for breast cancer diagnosis

Abstract Nowadays, breast cancer detection and diagnosis are done using machine learning algorithms. It can enhance cancer understanding and help in treatment selection and diagnosis. But many reliable decision assistance systems have been developed as “black boxes,” or devices that conceal their internal workings from the user. In fact, this method's output is difficult to understand, which makes it difficult for doctors to use it. This study uses explainable machine learning to investigate a technique for more promptly and accurately predicting breast cancer. The data is obtained from Kaggle to generate a machine learning (ML) model that forecasts the occurrence of breast cancer and Shapley Additive exPlanations (SHAP) are used to interpret the model's forecasts. To forecast the development of this disease, explainable machine learning (XML) model based on gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM) is built. The investigation's findings show that the LightGBM is capable of a maximum accuracy of 99%. An explainable ML has been demonstrated here which may produce an explicit understanding of how models generate their predictions, which is critical in boosting the confidence and acceptance of cutting‐edge ML methods in oncology and healthcare in general. Finally, a mobile app is also developed, integrating the best model.
Dense video captioning based on local attention

Abstract Dense video captioning aims to locate multiple events in an untrimmed video and generate captions for each event. Previous methods experienced difficulties in establishing the multimodal feature relationship between frames and captions, resulting in low accuracy of the generated captions. To address this problem, a novel Dense Video Captioning Model Based on Local Attention (DVCL) is proposed. DVCL employs a 2D temporal differential CNN to extract video features, followed by feature encoding using a deformable transformer that establishes the global feature dependence of the input sequence. Then DIoU and TIoU are incorporated into the event proposal match algorithm and evaluation algorithm during training, to yield more accurate event proposals and hence increase the quality of the captions. Furthermore, an LSTM based on local attention is designed to generate captions, enabling each word in the captions to correspond to the relevant frame. Extensive experimental results demonstrate the effectiveness of DVCL. On the ActivityNet Captions dataset, DVCL performs significantly better than other baselines, with improvements of 5.6%, 8.2%, and 15.8% over the best baseline in BLEU4, METEOR, and CIDEr, respectively.
Unsupervised soft-to-hard hashing with contrastive learning

In order to optimize a binary hash code with deep neural networks, greedy optimization with the discrete constraint relaxation is widely used due to its efficiency. However, noises from skipping gradients may destabilize the optimization in contrastive learning since the noises are superimposed on embedding from two distorted views. To reduce such undesirable impact from the noises, we propose a novel soft-to-hard hashing method by introducing an auxiliary loss in the penultimate block of the model based on the information bottleneck principle, which not only propagates the correlations between positives but also makes the embedding to be non-redundant to the samples in a high-dimensional continuous space. Our method beats the state-of-the-art result on three public datasets and demonstrates how soft code can help greedy back-propagation find a better solution during optimization. Another benefit of our method is that it enables a joint training of unsupervised representational learning and hash code generation, and obtains 5.4% mAP gain over conventional step-by-step training on the CIFAR-10 dataset.
Sampling-based fuzzy speech clustering systems for faster communication with virtual robotics toward social applications

For social applications, virtual robotics technologies require the Internet of Things (IoT) and cloud services. IoT based-speaker identification is required for virtual social robotics to operate in the most secure manner possible. Moreover, humans communicate using only natural speech to virtual robotics through IoT devices; Virtual robotics are the most emerging and which are required developing the speech-based identification models in the virtual cloud platforms. State of the art of speech models defines the composite technique with the combination of k-means clustering and Gaussian mixture model (GMM) for speaker identification problems. Every speaker has their own annotation, acoustic, and utterance. The Gaussian mixture model (GMM) describes the modeling of features in the speech clustering system for speaker identification applications. These traditional systems are expensive for speech clustering of large datasets when considering the time and space parameters. Sampling-based-fuzzy-spherical k-means and sampling-based-fuzzy-mini-batch k-means (mbkm) are proposed to overcome the problem of scalability issue in the implementation of speech-based virtual cloud-based robotic systems. For demonstrating the effectiveness of the proposed fuzzy-based speaker models, extensive experimental research is conducted using a variety of benchmark speech datasets.
Image recognition of sports dance teaching and auxiliary function data verification based on neural network algorithm

Nowadays, physical dance is widely spread in the society as an emerging sport. Dance movement is favored by people because of its unique social function and fitness effect. For dance teaching, dance movement analysis can help optimize and improve the existing dance movements and the understanding and inheritance of traditional dance movements. With the rise of online teaching, intelligent identification and analysis of dance movements can promote the better development of sports dance teaching. However, the relevant research in this area is still very scarce. As the basis of this kind of research, there is an urgent need for dance motion recognition technology. Based on this background, this paper by introducing neural network algorithm for dance teaching sports image special recognition design, the algorithm can combine feature extraction technology to process video, extract the dance movements in the target data set, then for the extraction of cumulative feature extraction operation, in order to accumulate all the collected target features, so as to further complete the gradient histogram acquisition. Through the design experimental test, the cumulative feature image extraction results obtained through the algorithm are obviously better than the traditional image recognition results, so the design rationality and effectiveness of the algorithm are proved, and the sports dance teaching can be specially assisted. This paper designs an effective auxiliary image recognition algorithm by introducing the neural network algorithm into the field of sports dance teaching.
A Maturity Model for Content Strategy Development and Technical Communicator Leadership

While technical communication consultants and researchers agree that content strategy requires attention to both customer needs and business goals, we found no evidence that technical communication educators promote an accurate understanding of business goals among their content strategy students. Through industry–academia collaboration, we integrate two existing models, using content tactics within organizational characteristics that define the maturity level of an organization's content operations. Analyzing the current state of maturity for each characteristic highlights gaps that can define a content strategy with prioritized tactics and, ultimately, encourages the growth of technical communicator leadership and the empowerment of our profession.
Combining GitHub, Chat, and Peer Evaluation Data to Assess Individual Contributions to Team Software Development Projects

Assessing team software development projects is notoriously difficult and typically based on subjective metrics. To help make assessments more rigorous, we conducted an empirical study to explore relationships between subjective metrics based on peer and instructor assessments, and objective metrics based on GitHub and chat data. We studied 23 undergraduate software teams ( n = 117 students) from two undergraduate computing courses at two North American research universities. We collected data on teams’ (a) commits and issues from their GitHub code repositories, (b) chat messages from their Slack and Microsoft Teams channels, (c) peer evaluation ratings from the CATME peer evaluation system, and (d) individual assignment grades from the courses. We derived metrics from (a) and (b) to measure both individual team members’ contributions to the team, and the equality of team members’ contributions. We then performed Pearson analyses to identify correlations among the metrics, peer evaluation ratings, and individual grades. We found significant positive correlations between team members’ GitHub contributions, chat contributions, and peer evaluation ratings. In addition, the equality of teams’ GitHub contributions was positively correlated with teams’ average peer evaluation ratings and negatively correlated with the variance in those ratings. However, no such positive correlations were detected between the equality of teams’ chat contributions and their peer evaluation ratings. Our study extends previous research results by providing evidence that (a) team members’ chat contributions, like their GitHub contributions, are positively correlated with their peer evaluation ratings; (b) team members’ chat contributions are positively correlated with their GitHub contributions; and (c) the equality of team’ GitHub contributions is positively correlated with their peer evaluation ratings. These results lend further support to the idea that combining objective and subjective metrics can make the assessment of team software projects more comprehensive and rigorous.
Theil-Sen Regressive Miyaguchi–Preneel-based Cryptographic Hash Blockchain for Secure Data Transmission Using Remote Sensing Data in IoT

Based on yours suggestion, we have shortened the abstract below 250 words and is mentioned as follows, “Internet of things (IoT) is the most advanced version of the internet to maintain a number of applications that combines different intelligent devices used by management or individuals, such as remote sensing data processing, smart health with environmental monitoring, and other related fields. IoT devices interact and interfere with one another, posing a variety of security risks. Prior industrial IoT blockchain data transmission methods suffer from lower security, higher management costs for trading centres, and significant supervision challenges. To deal with this issue, Theil-Sen Regressive Miyaguchi-Preneel-based Cryptographic Hash Blockchain approach is proposed in this manuscript for securing data transmission utilizing remote sensing data in IoT (TSR-MPCHB-SDT-RSD). To enhance data transfer security, TSR-MPCHB is proposed. Remote sensing data are gathered using IoT devices. Miyaguchi-Preneel cryptographic function generates every data confusing value. It aids to enhance data privacy with an integrity ratio. The simulation of the proposed approach is done in Java. The performance metrics, namely data privacy ratio, data dependability ratio, process time average delay, average power consumption, and blockchain data transfer efficacy, is examined. The proposed approach attains higher data confidentiality rate of 31.06%, 12.24%, 15.64%, and 11.66% compared to existing such as Blockchain-Envisioned Secure Data Delivery along Collection Mode for 5G-Base IoT-Enabled Internet of Drones Context (BESDDC-IOD-RSD), BIoTHR: Electronic Health Record Service System in IoT-Blockchain Ecosystem (BIoTHR-SDT-RSD), Blockchain- LamportMerkle Digital Signature: Authentication tool in IoT healthcare (LMDSG-SDT-RSD), and Privacy Protection Blockchain Attain Safe and Dependable Sharing of IoT Data (ACOMKSVM-SDT-RSD).