Reviewerly/exHarmony huggingface dataset:
Implementation of an Authoring Tool for Wheelchair Simulation with Visual and Vestibular Feedback

In this study, we develop a wheelchair simulator that provides visual and vestibular feedback at a low cost by leveraging the combination of vection-inducing movies displayed on a head-mounted display and vestibular feedback provided by an electric-powered wheelchair. However, this simulator requires users to manually create a syn-chronized pair of a VR movie and a motion scenario (chronological control commands for the wheelchair), which necessitates consid-erable effort and experience. In this paper, we introduce a novel authoring tool that generates a pair of a VR movie and a motion scenario based on a few parameters entered by the user.
Blink Detection to Control Display Light

A cross-platform application to control screen light to turn on/off based on whether his eyes are open or not. People who spend a lot of time in front of screens, there&#x0027;s a high chance they&#x0027;ve computer vision syndrome. They suffer from various eye problems, the major one being dry eyes cause dropped blinking rate. This is a background running application useful to automatically turn off-screen while the user will close his eyes, immediately turn on-screen while he opens his eyes and keep the screen-on while no eyes are detected. It ignores the casual blinks without resulting in screen flickering.
The Mechanism of Blockchain Technology Influencing Management Accounting

Blockchain technology is reconstructing and optimizing the management mode and transaction mode of enterprises. As an important part of the management mode and transaction mode of enterprises, management accounting is increasingly being reshaped by blockchain technology. This paper first analyzes the relationship between block chain technology and management accounting, then discusses the impact mechanism of block chain technology on management accounting innovation, and finally explores the impact mechanism of block chain technology on enterprise strategic management accounting, budget management, cost management and performance evaluation. This paper is conducive to promoting the application of block chain technology in the internal management accounting of enterprises, and it also enricifies the research literature on the application of block chain in the field of management accounting.
Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning

Unlike previous dialogue-based question-answering (QA) datasets, DREAM, multiple-choice Dialogue-based REAding comprehension exaMination dataset, requires a deep understanding of dialogue. Many problems require multi-sentence reasoning, whereas some require commonsense reasoning. However, most pre-trained language models (PTLMs) do not consider commonsense. In addition, because the maximum number of tokens that a language model (LM) can deal with is limited, the entire dialogue history cannot be included. The resulting information loss has an adverse effect on performance. To address these problems, we propose a Dialogue-based QA model with Common-sense Reasoning (DQACR), a language model that exploits Semantic Search and continual learning. We used Semantic Search to complement information loss from truncated dialogue. In addition, we used Semantic Search and continual learning to improve the PTLM’s commonsense reasoning. Our model achieves an improvement of approximately 1.5% over the baseline method and can thus facilitate QA-related tasks. It contributes toward not only dialogue-based QA tasks but also another form of QA datasets for future tasks.
The analysis of artificial intelligence technology: based on neural network

Contemporarily, deep Learning has achieved remarkable achievement in the field of artificial intelligence technology. Comparing with traditional machine learning methods, deep learning creates its model by constructing the neural network. This investigation reviews the neural network's development history and describes classical neural network methods, e.g., convolutional neural networks and recurrent neural networks. Besides, the shortcomings and limitations that the neural network is currently facing, including aspects in accuracy, stability, and robustness, are discussed. Meanwhile, the solutions towards these limitations are also mentioned, e.g., capsule network and adversarial attack. These results shed light for the future developments of Neural Network.
Modelling a Swarm of Delivery Drones for Disaster Relief Utilizing an Organization Approach

Abstract This paper describes an organization-based approach to modelling an autonomous distributed system. This approach simplifies design of such systems and allows for a better understanding of the system overall. Such an approach is applicable for modelling a variety of distributed systems. We demonstrated it for a swarm system of autonomous drones used for disaster relief aid delivery.
A New Insight on the Morphology of Web Mining

Recent surveys have revealed that about 199 million of active and over 1.2 Billion of inactive websites exist across the globe. The categories of websites have also increased beyond espionage networks of spies, computer networks for corporate organizations, networks for governments' agencies, networks for social interactions, search engines and networks for religious bodies, etc. These diversities have generated complex issues regarding the morphology and classification of webs and web mining. Thus, the validity of the generic web classification, web mining taxonomy, and contemporary studies on the regularities of web usage, web content, web semantic, web structures, and the process of extracting useful information and interesting patterns from the intricate of the Internet are frequently questionable. The existing web mining taxonomy can also lead to misinformation, misclassification, and crisscrossed issues such that numerous webs' patterns could be marked with crossing and inexplicable lines. By using qualitative virtual interviews of 26 skilled web-designers and a focus group-conference of 7 experts in web-usage to brainstorm on the above issues, this chapter comprehensively discusses the above concepts and how they relate to web classification and web mining taxonomy. The themes obtained elucidate the techniques that commonly underpin basic web mining taxonomy. New concepts like existence of esoteric web data, exoteric web data; mysterious, inexplicable, and mystifying patterns; and cryptic vocabularies are discussed to assist web analytics. Finally, the author suggests eight classification attributes for web mining patterns (illustrative, expositive, educative, advisory, interpretative, demonstrative, revealing, and informatory) and proposes a new web mining taxonomy to minimize the impacts of the above concerns on global settings.
Correlation and Subtraction

SummaryA more intuitive explanation and definition of correlation is given in the sense that it relates a primary comparison method (i.e., subtraction) with the conventional definition of the correlation. The emphasis is on explaining correlation as a unitless measure of comparison without worrying about scaling and the offset factor.
‘An Element of Perfection’: The Transductive Art of Robert Mallary

In 1969, American artist Robert Mallary (1917–1997) coined the term ‘transductive art’ to describe an approach to art based on the notion of receiving energy from one system and retransmitting it, often in a different form, to another. Long before the realm of techno-art became a recognizable construct, Mallary was interested in a system of relationships, seeking in his words, ‘an element of perfection’ in combinations of materials and technologies to make ‘a beautiful whole’. From his experiments with the Mexican Muralists to assemblage and Neo-Dada sculpture, and finally, his synergistic relationship with the computer, Mallary’s work addressed the place of the human in a technological world. He instigated one of the first American computer art curriculums within a fine art department, developing early examples of software created by artists for use by artists. His espousal of the digital to become a ‘Supermedium’, led him to conceptualise a ‘spatial-synesthetic art’, a multi-media immersive environment combining three-dimensional projected visual elements, motion, and sound. Although unrealised, this system anticipated future VR/virtual reality developments such as the ‘Cave Automatic Virtual Environment’ (CAVE™) system developed at the University of Illinois, Chicago, in 1992. The current review will therefore argue, by example, that Mallary deserves a prominent position in the history of techno-art, and by virtue of both the several emerging influences he had the insight to recognise and bring together and his numerous subsequent contributions as simultaneously an artist, a theorist, and an educator.
An efficient approach for discriminant analysis based on adaptive feature augmentation

Effective discriminant analysis is of great practical importance, as demonstrated by economic and genetic applications. Feature augmentation via nonparametric and selection (FANS) is an efficient approach that has been widely used in classification. However, FANS may impair efficiency when a linear decision boundary separates data reasonably well. The available remedy is to use both the transformed features and original ones, which may increase computational cost and model complexity. Motivated by these concerns, this paper proposes an efficient nonparametric approach for binary discriminant analysis, called adaptive FANS, integrating augmentation and nonparametric tests. In this procedure, the original features or transformed ones are used selectively to keep the number of features constant. Thus, this procedure avoids the ergodic transformation and reduces error caused by nonparametric estimation and computational complexity. Simulation and real data analysis demonstrate its competitiveness and significant adaptability. Moreover, our approach can be easily extended to other linear frameworks.
Machine Learning Based VoxelNet and LUNET architectures for Object Detection using LiDAR Cloud Points

Autonomous vehicles are the epitome of technological transformation in this decade. Object detection is a primary task in an autonomous vehicle, is an important issue to evaluate to bring autonomous vehicles into reality. This paper examines 3D object detection using VoxelNet architecture and LUNet architecture from LiDAR-based cloud points. The aforementioned architecture addresses the problem of manual feature extraction, high computational costs, and memory constraints. The datasets that were utilized for training, as well as testing, have been taken out of the KITTI vehicle detection benchmarking software. Post-training and testing of the model, the results obtained have been used to evaluate the performance of this implementation. The LUNET fares better in terms of low loss whereas VoxelNet fares better with regard to low computational costs and ease of implementation.
Geomorphology of the Beqaa Valley, Lebanon and Anti-Lebanon Mountains

Abstract Geomorphology of Lebanon presents a unique pattern of contrasting landforms. These include two notable mountain ranges, the Lebanon and Anti-Lebanon Mountains, the Beqaa Valley, the elongated coastal area and a significant amount of karst relief forms. This study focuses on the investigation of the topographic and geologic setting of Lebanon by visualizing datasets covering Lebanon and Anti-Lebanon mountains and the Beqaa Valley. Data were collected using the open source repositories of the high-resolution data (GEBCO, ETOPO1, DEM embedded in R). Three 3D models of the relief of the country are presented based on the ‘grdview’ package of GMT with azimuth rotations of the view point at 205°/30° and 165°/30°. The geologic map is based on the compiled datasets of the USGS. The R based modelling allowed division of the raster grid into several geomorphological zones according to the slope steepness and aspect orientation. The extreme elevations of the study area range from -2007 m and 2973 m. The key contribution of this work is the topographic and geologic data synthesis for 2D and 3D modelling of Lebanon. Another aspect concerns technical integration of GMT and R scripting approaches with QGIS mapping into the cartographic framework for visualizing of the Lebanese topography as a multi-tool approach. For the future similar studies on Lebanon this paper can serve as a guide for completing a project on the multi-source 2D and 3D data mapping as a conceptual foundation for research on Lebanese environment.
A deep learning model for long-tail visual recognition

Deep learning has a wide range of applications and far-reaching influence in our production and life, but the training of models requires a lot of data. In the real world, data acquisition requires a lot of costs, and the distribution of data is often uneven, with a small number of categories occupying a large number of samples, making the overall data present a long-tailed distribution. This makes convolutional neural network often perform poorly when training data are heavily class-imbalanced. In this work, enhance the feature extraction capabilities of the base model by add attention mechanism, and use the regularization technology mix-up algorithm to enhance the long-tail data. compared several state-of-the-arts techniques on the benchmark datasets imbalanced CIFAR10 and CIFAR100, that our method provides consistent and significant improvements over previous models.
Survey on challenges of federated learning in edge computing scenarios

More and more edge devices are linked with the network along with the digital transformation of society and the rapid development of the Internet of things. A large number of edge devices produce a large amount of data which challenges the IOT network with cloud computing as the core computing power. The edge computing can save network bandwidth and reduce delay as the extension and supplement of cloud computing on the one hand; on the other hand, the characteristics of multiple mobile devices based on edge computing make it very suitable to realize big data fusion with the Federated Learning framework, the privacy and security of users can be greatly improved by edge computing based on Federated Learning, the data silos can be broken and a more intelligent Internet of things can be achieved. This paper summarizes the common algorithms of Federated Learning based on edge computing, analyzes the existing challenges, and summarizes the corresponding algorithms.
Impact of Data Quality on Training Neural Networks in Text Categorization Problems

The question of the influence of the quality of a dataset for training neural networks for text categorization problems is considered. Methods for improving the quality and "purity" of data are considered. Examples with the final indicators of training are given. Conclusions are made about the influence of methods for improving the quality of data on the final learning outcome.
Day Ahead Hybrid Forecasting of Global Horizontal Irradiance using Machine Learning (Random Forest Algorithm) and Time-Series Model (SARIMAX)

This paper aims to propose and analyze a method to combine the Machine learning model with the Time-series model for hybrid forecasting of Global Horizontal Irradiance (GHI). This hybrid model exploits the performance of the Time-series model and Machine learning model, which perform differently on a different set of weather conditions, to give a more accurate result. For this research, Random Forest has been used as a machine learning model, and for the Time-series model, Seasonal Autoregressive Integrated Moving Average with exogenous regressors (SARIMAX) model has been used. The machine learning model considers weather conditions such as humidity, cloud cover temp, etc., to predict GHI. The time series model only depends on past data values, which makes it independent of weather conditions. A hybrid forecast tends to exploit the advantages of both models and overcome limitations. The final estimates from the Hybrid model contain the weight of each model, which is calculated during the validation period using a regression algorithm.
GraphDEAR: An Accelerator Architecture for Exploiting Cache Locality in Graph Analytics Applications

Data structure is the key in Edge Computing where various types of data are continuously generated by ubiquitous devices. Within all common data structures, graphs are used to express relationships and dependencies among human identities, objects, and locations; and they are expected to become one of the most important data infrastructure in the near future. Furthermore, as graph processing often requires random accesses to vast memory spaces, conventional memory hierarchies with caches cannot perform efficiently. To alleviate such memory access bottlenecks in graph processing, we present a solution through vertex accesses scheduling and edge array re-ordering, in parallel with the execution of graph processing application to improve both temporal and spatial locality of memory accesses, especially for edge-centric graphs which are popular means in handling dynamic graphs. Our proposed architecture is evaluated and tested through both trace-based cache simulations and cycle-accurate FPGA-based prototyping. Evaluation results show that our proposal has a potential of significantly reducing the quantity of Miss-Per-Kilo-Instructions (MPKI) for Last Level Cache (LLC) by 56.27% on average.
Quantum AI and IoT Cognitive Disease Data Security to Evade Quantum Computing Attacks

The aim of the chapter is to provide the enormous profits of the IoT systems (or) devices vulnerable to highly capable intrusions of different attackers. The vital security necessities such as authorization and authentication do not satisfactorily meet the requirements, and prevailing events are not capable of securing the IoT innovative healthcare environment from data gaps of the system security. With the number of IoT application domains growing to incorporate smart homes, mobile healthcare, autonomous intelligent healthcare communication, and smart cities in day-to-day human life, the significance of an attack in the IoT intelligent healthcare networks will become serious. As there are many issues in applying present cryptographic principles to resource-constrained IoT sensor devices, the recommended new security features solutions come with a compromise between security, feasibility, and performance. These research topics focus on evolving lightweight cryptographic results that specifically implement the post-quantum McEliece cryptography algorithm to encrypt the IoT intelligent healthcare device data, which is integrated into the classical blockchain with hashing function SHA-512. The evolving quantum computing integration with AI is together a transformational technology. The AI requires quantum computing power to attain substantial advancement to analyze the enormous data set faster, specifically the mental medical images or patient data.
AIR-range: Arranging optical systems to present mid-AIR images with continuous luminance on and above a tabletop

We propose &#x201C;AIR-range&#x201D;- a system that seamlessly connects mid-air images from the surface of a table to mid-air space. This system can display tall mid-air images in the three-dimensional (3D) space beyond the screen. AIR-range is implemented using a symmetrical mirror structure that displays a large image by integrating multiple imaging paths. The mirror arrangement in previous research had a problem in that the luminance was discontinuous. In this study, we theorize the relationship between the parameters of optical elements and the appearance of mid-air images and optimize an optical system to minimize the difference in luminance between image paths.
A Precise Flow Representation for Autonomous IoT-Devices Reconnaissance

Devices from the Internet of Things increasingly mediate a significant number of essential everyday activities. IoT devices empower homes, industries, and offices, monitoring, sensing, and acting ubiquitously and stealthily. However, each device produces a network fingerprint that leaks information about users&#x0027; behaviors and routines. This paper proposes a flow representation method for precise recognition of different types of IoT Devices. Our proposal relies on a tensor representation of the network flows to retrieve spatial and temporal correlation of flows. We show that our proposal achieves up to 99&#x0025; precision on classifying IoT network flows using machine learning algorithms, such as Convolution Neural Networks, Recurrent Neural Networks and boosted decision trees.
Constructing Features Using a Hybrid Genetic Algorithm

A hybrid procedure that incorporates grammatical evolution and a weight decaying technique is proposed here for various classification and regression problems. The proposed method has two main phases: the creation of features and the evaluation of these features. During the first phase, using grammatical evolution, new features are created as non-linear combinations of the original features of the datasets. In the second phase, based on the characteristics of the first phase, the original dataset is modified and a neural network trained with a genetic algorithm is applied to this dataset. The proposed method was applied to an extremely wide set of datasets from the relevant literature and the experimental results were compared with four other techniques.
Multi-scale cooperative search simplified particle swarm optimization algorithm and performance analysis

Abstract A multi-scale cooperative searching simplified PSO (MSPSO) was put forward to avoid the standard PSO (SPSO) premature, which was put forward by simulating ants working cooperatively in nature. Its particles trajectories were analyzed and its convergence was proved. The numerical experiment indicates that the MSPSO has better searching performance, especially in solving the higher dimensional optimization problems.
A survey on style transfer using generative adversarial networks

Developing and investigating recently, a new type of model appeared to greatly help the human with generating models. It can be trained with both supervised or unsupervised learning and contain both generative and discriminative models. Style transfer is one of the functions the GAN can be trained to produce, that it can synthesis two images together to get a new result by having one of the pictures as subject and the other one as style. In this paper, the work will introduce GAN and style transfer in detail and the application of style transfer in the real world.
Interferometric grazing incidence test of rough steep convex aspherics: full 3D reconstruction of the object

A grazing incidence interferometric measurement procedure is applied to test rough convex steep rotationally symmetric aspherics. The measurement of rough surfaces is possible; i.e., without the need to polish the surfaces, due to the large effective wavelength (λeff=10µm) of the test. One measurement step using diffractive beam splitters and phase-shifting techniques delivers the surface information along one meridian. The full surface description can be stitched together from several phase results combined with appropriate object rotations. This publication includes, besides the short recapitulation of the measurement principle and experimental setup, a presentation of the simulated and measured data of an aspherical object under test. The data analysis of each meridian is focused on the elimination of the misalignment aberrations caused by specimen displacements. Finally, the stitching of multiple meridian regions to a 3D surface map of the specimen is shown.