Reviewerly/exHarmony huggingface dataset:
Low-level Voice and Hand-Tracking Interaction Actions: Explorations with Let's Go There

Hand-tracking allows users to engage with a virtual environment with their own hands, rather than the more traditional method of using accompanying controllers in order to operate the device they are using and interact with the virtual world. We seek to explore the range of low-level interaction actions and high-level interaction tasks and domains can be associated with the multimodal hand-tracking and voice input in VR. Thus, we created Let's Go There, which explores this joint-input method. So far, we have identified four low-level interaction actions which are exemplified by this demo: positioning oneself, positioning others, selection, and information assignment. We anticipate potential high-level interaction tasks and domains to include customer service training, social skills training, and cultural competency training (e.g. when interacting with older adults). Let's Go There, the system described in this paper, had been previously demonstrated at CUI 2020 and MobileHCI 2021. We have since updated our approach to its development to separate it into low- and high-level interactions. Thus, we believe there is value in bringing it to MobileHCI again to highlight these different types of interactions for further showcase and discussion.
Mining Correlated High Utility Itemsets from MOOC Data

Affected by the COVID-19 epidemic, massive open online courses (MOOCs) have gained unprecedented attention as an alternative education. A huge number of registration data has been accumulated on various platforms. It may improve learning experience by using high utility itemset mining to discover knowledge from MOOC data. However, the traditional high utility itemset mining only considers the utility measure, which results in weak correlation of most itemsets. To address the issue, this paper defines correlated high utility itemsets and proposes a correlated high utility mining algorithm called ULB-CHMiner. The algorithm puts forward two pruning methods, namely correlation-utility upper bound and ECUCP (Estimated Correlation-Utility Cooccurrence Pruning). The calculation of course utility in transaction is redefined by considering user's preference. Experimental results show that ULB-CHMiner can prune numerous weakly correlated high utility itemsets and is better than the state-of-the-art algorithms in time and space performance.
Medical Records Management Using Cloud Technology

Medical Health Records are being used in healthcare services to reduce the issues and limitations of paper-based approaches, but their adoption has been slow due to high investment and storage files. Many hospitals have been using paper-based methodologies because they are unfamiliar with electronic medical systems. Confidential data, data sharing, and delegation of authority are mostly issues. Medical Records Management Implementation Using cloud technology in healthcare systems is now a distinctive trend. Because of benefits such as reduced price and efficiency, cutting-cloud cloud in order to reduce local storage, warehousing has become a common way of storing a high proportion of EHRs. Further, the proposed model has included an OTP authentication mechanism to the patient data, which gives a high level of patient data confidentiality that is not present in existing applications. Also, a different key will be generated for encryption and decryption each time when a user log in, which is more useful for protecting user's data from manipulators.
Satisfiability, Lattices, Temporal Logic and Constraint Logic Programming on Intervals

This essay narrates some of the influences that Alasdair Urquhart has had on computer science at the intersection of automated theorem proving, temporal logic and lattice theory—topics that have no obvious relationship to one another. I illustrate this by showing how Allen’s temporal relations are represented in a system for constraint logic programming over intervals and how the combination of a linear-resolution theorem prover and an interval constraint satisfaction system are connected via a lattice-theoretical semantic model. I also speculate on whether some of Thich Nhat Hanh’s meditation teachings might have been one of the sources of Alasdair’s creative inspirations and preternatural capacity for lateral thinking.
Automatic Image Colorization with Convolutional Neural Networks

Image colorization is the procedure of adding RGB color space value to the original greyscale image. Greyscale Image carries only the intensity information at each pixel value. This intensity mostly varies from 0 to 256 which is just enough to observe a gradual change in intensity. Image colorization is a very crucial process and has many benefits of its own for example in medical science, the entertainment industry, and the study of scientific and astrological images etc. We have developed a CNN model in which we designed our own CNN layers that takes a greyscale image as input and returns a colored image as output. The model proposed here does not require any human interaction in the process of colorization of the image once it is fed to the neural network.
Performance Evaluation and Comparison of Various Personal Cloud Storage Services for Healthcare Images

In recent times, usage of personal cloud storage services for storing e-health records in on a rise. This is due to the constant accessibility, easy sharing, and safe storage of the data at a nominal cost. In this paper, we have analyzed the performance of four personal cloud storage services: Google Drive, Dropbox, Sync.com, and Icedrive using medical image data files of various sizes. The parameters checked were number of packets transmitted during file upload and duration of time to upload, download, and delete the files. The results show us a comparative analysis of the personal cloud storage services based on the parameters and also help us identify certain gaps for the future.
CSRDNN: An Integrated Scheme for Single Satellite Image Colorization and Super-Resolution Using Deep Neural Networks

Deep convolutional neural networks have respectively achieved significant success in image super-resolution and colorization. The DNN has a strong capability to generate high quality images. Both colorization and super-resolution (SR) can be regarded as an independent pixel mapping problem, and this work combines these two visual problems into an integrated task. In this work, we propose an end-to-end model for accomplishing single satellite image colorization and SR simultaneously. Our model comprises two phases: features extraction network and recovery network. First, the residual receptive field block structure is introduced in features extraction network to learn better feature representations for image colorization and SR. Residual Receptive Field Block(RRFB) is improved by expanding the receptive field and enhancing the context connection from inception model. Second, the extracted features are transformed to a color high-resolution image by a recovery architecture. In this work, U-net is employed as the key structure of the recovery architecture. Besides, the squeeze-and-excitation blocks and complex residual blocks are incorporated into the proposed model to increase the reconstruction performance. To verify the performance, our method is compared with the state-of-the-art methods of SR and colorization. The experiments show that proposed method can get competitive in visual effect and evaluation index compared with the existing methods. In the end, the panchromatic dataset is also used to validate our model, and a good color high-resolution image can be obtained by giving a gray and low-resolution panchromatic image.
Observer-Based Fuzzy Adaptive Command Filtering Finite-Time Control of Stochastic Nonlinear Systems

The output feedback problem of finite-time command filtering for nonlinear systems with random disturbance is addressed in this paper. This is the first time that command filtering and output feedback are integrated so that a nonlinear system with random disturbance converge rapidly in finite time. The uncertain functions and unmeasured states are estimated by the fuzzy logic system (FLS) and nonlinear state observer, respectively. Based on the adaptive framework, command filtering technology is applied to mitigate the problem of “term explosion” inherent in traditional methods, and error compensation mechanism is considered to improve the control performance of the system. The developed output feedback controller ensures the boundedness of all signals in the stochastic system within a finite time, and the convergence residual can converge to a small region. The validity of this scheme is well verified in a numerical example.
An efficient smartphone based Parasite Malaria Detection with Deep Neural Networks

Malaria is a serious infection caused by a blood parasite called Plasmodiums pp. Every year, the World Health Organization [WHO] estimates 300–500 million malaria cases and over one deaths worldwide. Manually counting and arranging epithetical contaminated erythrocytes is a time-consuming and exhausting operation. Computerized parasite detection using mobile phones is a potential alternative to manual parasite meaning intestinal illness assessment, especially in remote areas without expert parasitologists. As a result, the relevance of developing novel devices to facilitate quick and simple detection of epithetical malaria in areas with limited access to social insurance administrations cannot be overstated. The preceding study investigates the possibility of epithetical mechanised intestinal illness parasite recognition trig thick blood distributes around cell phones. We have developed a primary deep learning approach that can recognize malaria parasites, generate dense blood smear images, and can run forth cell phones. Along with the aforementioned research, we created a dataset of 1819 thick smear images from 150 patients that is publicly accessible via examination network.
Existence of solutions for neural networks with impulsive mechanisms involving general linear boundary conditions

The existence of solutions for neural networks with impulsive mechanisms involving general linear boundary conditions on Banach spaces has been investigated. By constructing an appropriate operator, the existence problem of the solution is transformed into a fixed point problem of the constructed operator, and some theorems of solutions are proposed.
An Empirical Comparison of Handwritten Character Recognition Using Machine Learning

A Strong means of communication even with the existence of evolved technologies are Handwritten notes. Making a computer to recognize these handwritten notes has major applications in today's world like postal address reading, recognizing pin codes, reading cheques electronically, crime proof, etc. But this field has major challenges as handwritten characters vary from person to person based on the style of handwriting, inclination of characters, clarity, neatness. To address these challenges and recognize handwritten characters of different languages and scripts different research have worked and deduced results. Few of them are reviewed, analyzed, and briefed in this paper. Different scope of future work concerning each work is also discussed here.
A Novel Text Ensemble Clustering Based on Weighted Entropy Filtering Model

Abstract Text clustering is one of the important technical bases of natural language processing, and ensemble clustering improves the robustness of text clustering. According to the existing research of scholars and experts, the quality and diversity of basic clustering have a great influence on consensus clustering, and it has a particularly significant effect on text clustering. However, there are a few pieces of research aiming at reducing the number of low-quality clustering in ensembles. This paper proposes a novel clustering filtering model based on entropy criteria. The entropy criterion is used to evaluate the uncertainty of each cluster w.r.t. the ensemble. Two indexes are proposed on the basis of the uncertainty of cluster, namely, Clustering Trend Index (CTI) which indicates the contribution of each cluster w.r.t. basic clustering, and Cluster Consistency Index(CCI) which indicates the degree of cluster dispersion in the basic clustering. The proposed clustering filtering model is built on the basis of new weight using two proposed indexes. Thereby, by dropping the low-quality clustering, the percentage of high-quality clustering will increase. A large number of experiments on various real text data sets using optimal thresholds show that the proposed method has greatly improved accuracy and robustness, and is superior to existing ensemble clustering algorithms.
Towards a Versatile Intelligent Conversational Agent as Personal Assistant for Migrants

We present a knowledge-driven multilingual conversational agent (referred to as “MyWelcome Agent”) that acts as personal assistant for migrants in the contexts of their reception and integration. In order to also account for tasks that go beyond communication and require advanced service coordination skills, the architecture of the proposed platform separates the dialogue management service from the agent behavior including the service selection and planning. The involvement of genuine agent planning strategies in the design of personal assistants, which tend to be limited to dialogue management tasks, makes the proposed agent significantly more versatile and intelligent. To ensure high quality verbal interaction, we draw upon state-of-the-art multilingual spoken language understanding and generation technologies.
Data Engineering and Exploratory Data Analysis Techniques

This chapter introduces data sources and data engineering attributes for handling agricultural datasets. The chapter starts with Knowledge Discovery in Databases (KDD) framework, the reference architecture for data mining applications, feature engineering techniques, and exploratory data analysis (EDA) process. Next, the chapter introduces feature reduction techniques such as principal component analysis (PCA). In agriculture data, most of the time, data for all important features may not be available and predictor class may not be fully populated. The chapter introduces imbalanced class techniques and demonstrates the workings. Finally, the chapter concludes with explainability of ML model and the need for democratizing the artificial intelligence.
Security Awareness Level Evaluation of Healthcare Participants Through Educational Games

The purpose of this paper consists of implementing an educational board game to evaluate the information security awareness level of healthcare personnel. The National Health Service Greater Glasgow and Clyde (NHSGGC) Information Security Acceptable Use Policy was used as a basis to generate the educational content of the board game and Lev Vygotsky’s social development theory was followed for the learning process of the participants. Two evaluations were carried out during this study. The results obtained during the first evaluation showed that it is fundamental to design the board game based on a set of rules in information security enacted by an organization to properly guide the participants with the knowledge they need to counter security incidents. The second evaluation showed that redesigning the content of the board game based on the information security policies of the NHSGGC, resulted in a more effective way of guiding participants on the procedures required for compliance with the policies of this health institution and offer them an understanding of the risks behind security incidents. This was demonstrated during this evaluation since the results obtained gave an approximation that it is possible to increase the level of awareness of information security in people regardless of their area of work or studies.
Computer Games Based on Artificial Intelligence

Chinese chess has a long history, but research on Chinese chess computer game lags behind that on International chess. In this paper, we apply Min-Max algorithm as a basis to implement artificial intelligence of chess. After generating moves, execution of alternate recursive search in finite depth and situation assessment in the searched leaf nodes is performed and execution of Alpha-Beta pruning judgment is obtained. Finally, we implement a chess game application program on the basis of application program framework of chess intelligent algorithm and Direct2D. The practice indicates that the program has better intelligent effect.
On the use of boundary gradient for the analysis of MR wrist bones volumes segmentation

Image segmentation partitions a digital image into regions of interest, by taking into account the information content and local properties of homogeneity and topological connectivity. The evaluation of segmentation quality is usually based on supervised ground-truth images, which are provided by experts and used for comparison with segmented images. Indeed, intra-rater and inter-rater reliability affect the accuracy of such reference information. In addition to subjectivity, the skill of the experts and their knowledge could lead to bias. To achieve an objective performance measure, an automatic method for unsupervised evaluation of volume segmentation is proposed, which aims at finding a repetitive and user-independent evaluation of segmented results. The proposed approach leverages on the integration of volume- and boundary-based analysis. The quality of the segmentation is estimated on the basis of gradient properties, measured in the volume and focused on the voxels of the border. The segmentation is evaluated through a simple fuzzy index based on the resulting gradient. The experimental phase is conducted on a set of Magnetic Resonance volumes of the wrist district.
Improved Kerberos protocol based on sliding window and its formal analysis

Aiming at the problem that the time stamp mechanism in the Kerberos protocol cannot effectively resist replay attacks, this paper proposes an improved kerberos protocol based on the sequence number and sliding window mechanism. The authentication server and the application server maintain a sliding window with a sequence number to determine the replay of the client's request message. Considering the impact of message reordering and long jump rearrangement, a fault-tolerant shift mechanism is added to the server to increase the window Flexibility. We give the specific process of the improved kerberos protocol, and use the BAN logic to formally analyze the improved protocol to verify the security and reliability of the protocol.
Large-Scale Target Detection and Classification Based on Improved Candidate Regions

Target detection of aerial images has become a frontier subject of concern in the image processing field. Using existing method to detect and classify large-scale building objects in aerial images, the accuracy is still a little low. This is mainly because the current method does not make full use of the prior information of the target to be detected, so there are too much redundant information in the candidate box. In this paper, our own dataset were built and then utilize the Hough transform to filter out the images that may exist in the sequence image. For images with dense lines or circles, it is possible that there is an artificial building target which will be detected, otherwise it is excluded directly. Besides, this paper exploits significance analysis from the filtered image and then extract the area of interest where the potential target is located. The results of the above-mentioned processing lay a good foundation for the subsequent detection and classification which can help improve the accuracy.
ImageCube: A Low-Cost 6D Controller with Smooth Tracking for Mobile Augmented Reality

With the development of mobile computing powers, augmented reality has reached to common users. The users manipulate virtual contents with the help of tracking techniques. While image markers are affordable for common mobile users, the tracking space is limited due to occlusion. To better democratize augmented reality, we propose ImageCube, a simple and effective solution to enlarge the tracking space of image markers for mobile augmented reality. We design a 3D model and combine image tracking techniques to obtain full 6D motion tracking. We also propose a weighting scheme for smoothly tracking the controller. The controller is inexpensive, stable and easy to deploy. We show some applications to demonstrate the efficiency of the technique.
FORMATION OF DIGITAL COMPETENCIES IN THE GLOBAL EDUCATIONAL ENVIRONMENT

The article deals with the current issues of the formation of digital competencies of students as a stabilizing factor in ensuring the sustainability of learning outcomes in the context of the globalization of the educational environment, considers the model approach and system criteria for ensuring the development of digital competencies of students.
Next Step to the Future of Restaurants Through Artificial Intelligence and Facial Recognition

In the previous generation, the resources we had for our technology were limited. The development was not enough. There were big dreams to be fulfilled in technology by only imagining our favorite books and watching our favorite sci-fi movies. Through the years, as our resources expanded, so did our technology. Artificial Intelligence technology has developed a lot through the years and is still developing. As our resources grow, we all are surrounded by technology in this age and time. It helps to make our lives a little easier than before, and Artificial Intelligence (AI) is a technology used to an industrial level to make things easier. Restaurants are commonly visited places by any middle-class man; let it be for lunch or Birthday Party. It is very common to visit a restaurant. There are issues regarding the restaurants’ hygiene, from the kitchen countertop to the waiter's hygiene serving you food. During the period of normalizing going out in the Covid-19 pandemic, there were many issues regarding this? In this paper, the core point is to avoid physical contact and enhance restaurants’ future with AI and its upcoming potential to deliver a new and safer experience to the customer. This will help prevent contact with the waiter, create a unique customer experience, and create a hygiene environment while implementing newer things.
Bitmap Vectorization Conversion Method in Graphic Design with OpenGL Framework

Bitmap vectorization conversion methodology in graphic design with OpenGL framework is designed in this paper. As noise and edges are both high-frequency components in image processing, figuring out how to minimize noise while boosting edges is a crucial challenge for edge extraction. Generally, the results of the source coordinates and the corresponding target coordinates of more than two points are required to obtain the transformation parameters between the two coordinate systems. With this model, the OpenGL is combined for the analysis with the graphic model. The author uses the difference between the coordinates of the obvious feature points such as features on the vectorized digital map and the coordinates of the actual location to evaluate the accuracy of the image geometric correction results. The data is collected from the Internet, and the proposed model is validated when compared with the other methods.
Adaptive Feature Normalization Network for Single Image Dehazing

In this paper, we design an end-to-end adaptive feature normalization network (AFN-Net) for single image dehazing. In order to fit the function that can recover haze-free images from haze images more effectively, an Adaptive Normalization module (AN) is designed, which can unify the input into the same feature space. A large number of experimental evaluations show that the proposed method is superior to the state-of-the-art (SOTA) method on the benchmark datasets.