Reviewerly/exHarmony huggingface dataset:
AR Furniture

Augmented reality (AR) technology has potential to revolutionize the way we interact with and visualize the world around us. One area where this technology has great potential is in the field of furniture design and home decor. The goal of the Augmented Reality Furniture project is to develop a platform which allows users to virtually preview and interact with furniture in their own home environments using AR technology. Using a smartphone or other AR-enabled device, users will be able to see how different furniture pieces will look and fit in their own living spaces before making a purchase. The project will involve the use of 3D models of a variety of furniture items, as well as the development of AR software that can accurately place and scale these models in the user's environment. The platform will also include a user-friendly interface for browsing and selecting different furniture items, as well as tools for customizing and arranging the items in the virtual space.
“Just Like Blooming Fireworks, And Match With Function Perfectly”: Explore and Evaluate User-Defined One-Handed Gestures of Smartwatch

One-handed gesture interaction is a more convenient input interaction method on smartwatches for some special scenarios, e.g. wearing a smartwatch when running or biking. To explore user-friendly one-handed gestures, what users are thinking when using the gesture, and what characteristic would make the user feel this one-handed gesture is friendly, we developed a series of one-handed gestures for 6 basic functions of the smartwatch. The end-user elicitation method resulted in 12 new one-hand gestures. We compared these 12 user-defined one-handed gestures with the Apple Watch one-handed gestures. We developed a Wizard of Oz model and evaluated these gestures by using qualitative and quantitative approaches. The results show that we generated a set of one-handed gestures that are more friendly than the existing Apple Watch one-handed gestures. Also, during the evaluation process, we collected quantitative data and interesting user perspectives. We also gave some design recommendations for one-handed gestures.
An Intelligent Approach for Automating Data Inputs on Web Portals for Several Apex / Statutory Bodies in the Field Education using Robotics and Machine Learning

ABSTRACT-World is upgrading day by day and education system is going side by side using that technologies. Automatic data inputs is a data entry process which means taking information from different sites and sorting them out in a particular section. This is very important part for any business and automating process helps this work easy as it is quite tough to do all the work for a single person. It is very essential for one student to get knowledge about machine language as it will be helpful for them in the future. Machine learning technology helps to keep all the data up to date and also for cyber security. Children are always finding something which is easy to remember and funny, hence learning new thing using robotic function helps them a lot. After COVID-19, most of the education filed has started online classes and using MI and robotic things, it is easy for them to gaining knowledge that are not able to attend schools. Keywords: Robots, machine language, data, education, methods, upgrade
A Relevance-theoretic Approach to Multimodal Discourse Analysis: A Case Study of Emoji Advertising

With the development of the internet and mass media, emojis are not only frequently used in daily internet-based communication but have become important marketing tools. Based on the relevance theory, this paper will take emojis in advertising as the research object. From the perspective of implicit/explicit information, contextual construction, and implicated conclusion, it aims to explore how this multimodal resource in advertisement helps the audience to get the advertiser’s communicative intentions and obtain optimal relevance. This study concludes that emojis serve as a tool of ostensive communication, conveying the message with the presumption of relevance; emojis help consumers construct new cognitive context; and they are complementary with other modalities, which increase the ostension of information and save cognitive effort, and promote the audience find the optimal relevance path.
KOMBİNATORYAL ENİYİLEME PROBLEMLERİNİN ÇÖZÜMÜ İÇİN PARAMETRESİZ VE METAFORSUZ METASEZGİSEL ALGORİTMA ÖNERİSİ

Many optimization problems are complex, challenging and take a significant amount of computational effort to solve. These problems have gained the attention of researchers and they have developed lots of metaheuristic algorithms to use for solving these problems. Most of the developed metaheuristic algorithms are based on some metaphors. For this reason, these algorithms have algorithm-specific parameters to reflect the nature of the inspired metaphor. This violates the algorithm's simplicity and brings extra workload to execute the algorithm. However, the optimization problems can also be solved with simple, useful, metaphor-less and algorithm-specific parameter-less metaheuristic algorithms. So, it is the essential motivation behind this study. We present a novel metaheuristic algorithm called Discrete Rao Algorithm (DRA) by updating some components of the generic Rao algorithm to solve the combinatorial optimization problems. To evaluate the performance of the DRA, we perform experiments on Traveling Salesman Problem (TSP) which is the well-known combinatorial optimization problem. The experiments are performed on different sized benchmark problems in the literature. The computational results show that the developed algorithm has obtained high quality solutions in a reasonable computation time and it is competitive with other algorithms in the literature for solving the TSP.
Comparative study of two commercial stoneware pastes for plastic shaping by pottery wheel

Two commercial stoneware pastes traditionally used to produce ceramic objects were evaluated.The objective was the comparative characterization of the pastes and to find the most suitable to obtain fine ceramic objects using the pottery wheel.Both ceramic pastes were characterized by X-ray powder diffraction (XRPD), particle size analyses (laser diffraction) and thermal analyses (thermogravimetry-TG, its derivative-DTG, and differential thermal analysis-DTA).The technological properties like linear drying and firing shrinkage, moisture content and plasticity were also determined.The results showed that both pastes have appropriate properties making them suitable for plastic shaping by pottery wheel.In addition, the stoneware paper clay paste is "greener" due to its content of cellulose fibers which can come even from recycled paper.
JoVE Video Dataset

To grasp an object successfully, we must select appropriate contact regions for our hands on the surface of the object. However, identifying such regions is challenging. This paper describes a workflow to estimate the contact regions from marker-based tracking data. Participants grasp real objects, while we track the 3D position of both the objects and the hand, including the fingers' joints. We first determine the joint Euler angles from a selection of tracked markers positioned on the back of the hand. Then, we use state-of-the-art hand mesh reconstruction algorithms to generate a mesh model of the participant's hand in the current pose and the 3D position. Using objects that were either 3D printed or 3D scanned-and are, thus, available as both real objects and mesh data-allows the hand and object meshes to be co-registered. In turn, this allows the estimation of approximate contact regions by calculating the intersections between the hand mesh and the co-registered 3D object mesh. The method may be used to estimate where and how humans grasp objects under a variety of conditions. Therefore, the method could be of interest to researchers studying visual and haptic perception, motor control, human-computer interaction in virtual and augmented reality, and robotics.
Research on the Multilabel Data Stream Classification Method Based on Fuzzy Complex Set-Valued Measure Learning

In the process of modern industrial production equipment developing towards the direction of structure, automation, and intelligence, motor is still the main power output equipment. If the data flow classification occurs during the operation of the motor, it will lead to problems such as the reduction of its operation efficiency and the increase in system energy consumption. In serious cases, it will even cause motor damage, and the overall system equipment will be shut down for maintenance for a long time, resulting in serious economic losses. Therefore, the research on intelligent multilabel data stream classification technology of motor is of great significance to ensure the stability and reliability of efficient operation of production equipment. In order to improve the recognition efficiency and accuracy of ms-1dcnn’s multilabel data stream classification method in the environment of variable motor conditions and strong noise interference, a multiscale feature fusion framework is constructed based on the residual network structure. The implementation principles of two kinds of attention mechanism algorithms, squeeze and excitation module and convolution attention module, are studied, respectively. The attention module suitable for one-dimensional residual network is designed and embedded into the residual module to build a multiscale attention residual network model. Finally, the effectiveness and superiority of the proposed model are verified by using the experimental platform data.
Skin Burn Detection using Image Processing

Skin cancer at its early stages can be cured. But when it is not recognized at its early stages, it begins to spread to other parts of the body and can be deadly. Benign Melanoma is simply appearance of moles on skin. A normal mole is usually an evenly coloured brown, tan, or black spot on the skin. It can be either flat or raised. Skin burns are the deadly form of cancers in humans. If skin burns are detected at early stages, it can be cured completely. So, an early detection of skin cancer can save the patients. Skin burns are of two types- Benign and Malignant Melanoma. Benign melanoma is not a deadly condition, but malignant melanoma is a deadly form. Both resemble same in appearance at the initial stages. Only an expert dermatologist can classify which one is benign and which one is malignant. The CNN based Classification methodology uses Image processing techniques. Main advantage of this computer-based CNN classification is that patient does not need to go to hospitals and undergo various painful diagnosing techniques like Biopsy.
Development of a Lightning Prediction Model Using Machine Learning Algorithm: Survey.

This research is aimed at preventing broadcast equipment from lightning damage. Inview of the location in which my broadcast outfit is located (located in a valley; some few meters above sea level in the Confluence of Lokoja Kogi State Nigeria). Several improvement of earthing and installation of lightning arresting facilities, there has not been significant change protecting broadcast equipment from lightning. The solution I proffered is to isolate all electrical connection from equipment. Lightning as a natural phenomenon is very unpredictive and destructive which can occur during transmission. How do we know the day and time distructive lightning will come? The answer is to develop a lightning prediction system that is accurate. When lightning lead time is known, personnel on duty will be alerted to isolate all broadcast equipment from the mains and central earth connection. Since the lightning prediction system has to be localized. Deployment of machine learning algorithm is most appropriate. The use of ten(10) years Weather Numerical Values(2012-2022) such as rainfall, atmospheric pressure, relative humidity, temperature and lightning records which are gotten from Nigeria Meteorological Agency (NIMET), Lokoja Area Office, Kogi State. This parametization are factors that are used to work on the lightning forecast system as lightning will occur at their certain threshold values. The model is intended to be deployed on web application. Prediction model can be localized to support Numerical Weather Prediction in any environment in Nigeria.
Deep Learning vs. Traditional 3d Registration: A Featureless 3d Registration Baseline

Recent 3D registration methods are mostly learning-based that either find correspondences in feature space and match them, or directly estimate the registration transformation from the given point cloud features. Therefore, these feature based methods have difficulties with generalizing onto point clouds that differ substantially from their training data. We propose a simple featureless traditional 3D registration baseline method based on the weighted cross-correlation between two given point clouds. Our method achieves strong results on current benchmarking datasets, outperforming most deep-learning methods. We further propose a methodology to create a 3D registration benchmark, given a point cloud dataset, that provides a more informative evaluation of a method w.r.t. other benchmarks. We also propose to use the 3D registration parameter range of a benchmark as an indicator of its difficulty. Using this methodology, we create a novel FAUST-partial (FP) benchmark, based on the FAUST dataset, with several difficulty levels. The FP benchmark addresses the limitations of the current benchmarks: lack of data and parameter range variability, and allows to evaluate the strengths and weaknesses of a 3D registration method w.r.t. a single registration parameter. Using the new FP benchmark, we provide a thorough analysis of the current state-of-the-art methods. Our source code is available on GitHub.
Computational Principles Manifesting in Learning Symmetric Equilibria by Exponentiated Dynamics

In this paper, we consider the problem of computing a Nash equilibrium in symmetric bimatrix games whose payoff matrix is either symmetric or antisymmetric. To that end, we employ an exponentiated dynamic that has a parameter called the learning rate. Our main results are the discovery and proof of computational principles manifesting in these classes of games. If the payoff matrix is symmetric, we call a sequence of iterates monotone if the payoff function increases. Our most compelling principle is that in this class of games under any bounded sequence of learning rates (giving a monotone sequence of iterates) whose sum diverges, every limit point of the weighted (by the learning rates) arithmetic mean of the iterates is an equilibrium. In the class of antisymmetric games, our principle is modest as in addition to requiring the sum of the learning rates to diverge, the sequence of learning rates must vanish.
A Self-Adaptive Image Encryption Scheme Based on Chaos and Gravitation Model

In order to improve the security of image transmission, this paper proposes a self-adaptive image encryption scheme based on chaos and gravitation model.The proposed scheme consists of three stages: key generation stage, double scrambling stage, and double diffusion stage.In the key generation stage, the plaintext-associated key generation mechanism (BPPAKG) based on the bit plane is used to obtain the key associated with the plaintext.Since the key ultimately depends on the plaintext image itself, this scheme has self-adaptability.The first stage of scrambling uses mixed chaotic transform and shift row transform to disturb pixel position, then changes the pixel value of the image based on the concept of gravitation to achieve the first stage of diffusion.To enhance the security performance of the scheme, the traditional encryption structure is extended, and the second stage of scrambling and diffusion is quickly realized by using diagonal scanning transform and XOR operation.Simulation and experimental results show that the scheme has good encryption and decryption performance and strong resistance to attack.In addition, compared with some latest proposed algorithms, it has better security.
Multitask Particle Swarm Optimization With Dynamic Transformation

Multitask optimization (MTO) mainly utilizes knowledge transfer among tasks to address multiple optimization problems in parallel. However, the decision space dimensions of different tasks often differ, which leads to the failure of knowledge transfer. Therefore, it is a challenging problem to transfer knowledge among tasks with different dimensions to achieve parallel optimization of multiple tasks. To address this problem, multitask particle swarm optimization with a dynamic transformation strategy (MTPSO-DTS) is proposed to improve the performance of MTO. First, an intertask similarity index based on gradient information and location information is designed to dynamically assess the degree of similarity among different tasks. Then, the intertask similarity is determined to assist the transformation of dimensions. Second, a dynamic transformation strategy based on intertask similarity is developed to achieve a uniform representation of different dimension knowledge, including dimension supplementation and dimension reduction. Then, the MTPSO-DTS algorithm, which takes advantage of the search characteristics of particle swarm optimization, can facilitate knowledge transfer among tasks with different dimensions. Third, the convergence of the MTPSO-DTS algorithm is analyzed to verify the validity theoretically. Finally, numerical comparison experiments on benchmark problems and a practical application are carried out to demonstrate the effectiveness of the MTPSO-DTS algorithm. The results indicate that the proposed MTPSO-DTS algorithm can facilitate knowledge transfer among tasks of different dimensions to promote parallel optimization of multiple tasks.
Nigeria Startup Act 2022: Impact on Technology Development in Nigeria

The Nigeria Startup Act project is a joint initiative by Nigeria’s tech startup ecosystem and the Presidency to harness the potential of our digital economy through co-created regulations. The Act is aimed at ensuring that Nigeria's laws and regulations are clear, planned, and work for the tech ecosystem. This paper is a conscientious attempt at examining the potential impact of the Act on technology development in Nigeria. While the crux of this paper is sustained, it also discusses the state of affairs in the Nigerian technology space before the enactment of the Nigeria Startup Act.
Actual trends in teachers’ professional development in the context of modern educational practices

The modern sphere of education is continuously being transformed. Rapid digitalization, the introduction of innovative educational practices, as well as a characteristic portrait of a modern student who grew up in a digital environment, lead to a change in the nature of teaching. The position of the teacher in the educational process is also changing, acquiring the features of an organizer, tutor, coach, etc. The article provides a comprehensive analysis of the currently relevant concepts in teaching- the components of a teacher&amp;apos;s professiogram and professional mobility. The review and analysis of scientific works of recent years in the fields of professiology of education and new educational practices has been carried out. Based on the results, professional and personal qualities relevant for a teacher and the problems actual for teachers nowadays are highlighted. The actual trends of advanced training of specialists are indicated - teacher mobility in the digital environment and in the field of educational technologies.
The Joint Response of Currency and Stock Markets to IMF News During the Pandemic Evidence from Emerging Countries

This article examines the influence of IMF announcements as a source of news on currency and stock markets in the wake of the coronavirus pandemic. We use a set of IMF-related news for a sample of 14 emerging countries from December, 2019 to December, 2020. Our central finding is that IMF news exerts a significant impact that is predominantly related to the mean, rather than the variance, of low corruption emerging countries’ underlying currency and stock markets, and the direction of IMF-impact is dependent on the cross-market spillover effects in the two markets.
Body and Mind: Decoding the Dynamics of Pedestrians and `Smartphone Zombies' by Coupling Mechanical and Decisional Processes

Pedestrians are able to anticipate, which gives them an edge in avoiding collisions and navigating in cluttered spaces. However, these capabilities are impaired by digital distraction through smartphones, a growing safety concern. To capture these features, we put forward a continuous agent-based model (dubbed ANDA) hinging on a transparent delineation of a decision-making process, wherein a desired velocity is selected as the optimum of a perceived cost, and a mechanical layer that handles contacts and collisions. Altogether, the model includes less than a dozen parameters, many of which are fit using independent experimental data. The versatility of ANDA is demonstrated by numerical simulations that successfully replicate empirical observations in a very wide range of scenarios. These scenarios vary from collision avoidance involving one, two, or more agents, to collective flow properties in unidirectional and bidirectional settings, and to the dynamics of evacuation through a bottleneck, where contact forces are directly accessible. Remarkably, the model was able to replicate the enhanced chaoticity of the flow observed experimentally in ’smartphone-walking’ pedestrians, by reducing the frequency of decisional updates, replicating the digital distraction effect. The conceptual transparency of the model makes it easy to pinpoint the origin of its current limitations and to clarify the singular position of pedestrian crowds amid active-matter systems.
Novel Cross-Domain Fault Diagnosis Method Based on Model-Agnostic Meta-Learning Embedded in Adaptive Threshold Network

Fault diagnosis of rotating machinery based on deep learning requires many identical labeled data to achieve satisfactory results. However, in engineering application scenarios, few typical fault data have been manually marked, resulting in a poor on-site diagnosis consequence. Thus training an efficient cross-domain diagnosis model under limited data is extremely challenging. This paper proposes a novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in Adaptive threshold network (ATN-MAML). The threshold acquisition module is built to determine the signal's irrelevant information threshold. A threshold function is designed to filter out invalid information effectively and improve the accuracy of cross-domain fault diagnosis. Then, MAML's training strategy is optimized to prevent overfitting. Finally, many cross-domain comparison experiments are conducted to verify the effectiveness of the proposed method on two datasets. The results show the model can maintain high accuracy even when the source and target domain data are from different devices.