Reviewerly/exHarmony huggingface dataset:
Deep Semantic Manipulation of Facial Videos

Editing and manipulating facial features in videos is an interesting and important field of research with a plethora of applications, ranging from movie post-production and visual effects to realistic avatars for video games and virtual assistants. Our method supports semantic video manipulation based on neural rendering and 3D-based facial expression modelling. We focus on interactive manipulation of the videos by altering and controlling the facial expressions, achieving promising photorealistic results. The proposed method is based on a disentangled representation and estimation of the 3D facial shape and activity, providing the user with intuitive and easy-to-use control of the facial expressions in the input video. We also introduce a user-friendly, interactive AI tool that processes human-readable semantic labels about the desired expression manipulations in specific parts of the input video and synthesizes photorealistic manipulated videos. We achieve that by mapping the emotion labels to points on the Valence-Arousal space (where Valence quantifies how positive or negative is an emotion and Arousal quantifies the power of the emotion activation), which in turn are mapped to disentangled 3D facial expressions through an especially-designed and trained expression decoder network. The paper presents detailed qualitative and quantitative experiments, which demonstrate the effectiveness of our system and the promising results it achieves.
Implementation of Geofencing for Monitoring People under Home Quarantine

Coronavirus Disease (COVID-19) is an infectious disease caused by SARS-CoV-2, a newly discovered coronavirus. Infected people experience mild symptoms such as colds, which would lead to severe respiratory-related diseases that weaken the entire body. As the virus can be transmitted primarily through direct contact with an infected person or by touching surfaces and objects contaminated by respiratory droplets, it is critical to control and cut the transmission routes by exposing individuals under quarantine. In this study, the researchers developed an Arduino-based ankle tracker, associated with an application as the GUI, to implement geofencing to monitor people under home quarantine. The ankle tracker acts as the trigger being monitored within a geofence, a circular virtual perimeter. The system was designed to have a 95% confidence level to filter out inaccurate GNSS location data. It will only accept data that is highly accurate which depends on the PDOP value. The Geo-mapping accuracy of the tracker device was tested and presented in this study.
A Feasible Design of Ballet Learning Support System with Automated Feedback

Online courses have become increasingly common in recent years. Many people have also begun to exercise and learn new activities, including dancing, at home by watching online videos and lessons, particularly during the COVID-19 epidemic. Classical ballet is one of those physical activities that conventionally requires visits to a studio, not only to dance but to receive feedback to improve poses. Since guidance is needed, it is difficult for the at-home learner to learn new dance poses and movements simply by watching videos. As a result of this problem, this late-breaking paper propose a support system that can assist at-home ballet learners in providing feedback of their dance poses.
Image Feature Matching of the Moon Surface Based on Multi-task Learning

Accurate and effective lunar surface feature matching is a key technology in lunar exploration projects and precise positioning of lunar surface targets. The traditional feature matching algorithm extracts and matches the feature points of the lunar surface image, because of its complicated calculation, single application, large descriptor dimension, long matching time, poor robustness to local similar regions, etc. There are certain limitations in the application scenarios in real life. Based on deep learning, this paper proposes an image feature matching based on multi-task learning. The method is based on the improved HF-Net network, adopting the lightweight MobileNetv3 network, and combining the transfer learning knowledge distillation method to achieve end-to-end line Feature point matching. Experiments show that this method can effectively reduce the complexity of calculation, improve the accuracy of feature matching, and has good robustness under different lighting conditions.
An Empirical Study of Size Discrimination in Augmented Reality

Existing psychophysical experiments show that size perception can influence the human identification of object properties (e.g., shape or weight) in augmented reality (AR). Some recent studies have revealed the detection threshold of object size in real physical objects. However, the users’ absolute detection threshold of object size augmentation is not clear, which limits the further evaluation of AR design. In this paper, we present two two-alternative forced-choice-based experiments on size perception of virtual objects in AR to explore the detection threshold of size difference in object augmentation. Our experimental results demonstrate that the user’s point of subjective equality (PSE) is 4.00%, and the size difference could be easily detected when the virtual object is larger than 5.18%.
FBR-CNN: A Feedback Recurrent Network for Video Saliency Detection

Different from the saliency detection on static images, the context and dynamic information from video sequences play an important role in saliency prediction on dynamic scenes. In this work, we propose a novel feedback recurrent network (FBR-CNN) to simultaneously learn the abundant contextual and dynamic features for video saliency detection. In order to learn the dynamic relationship from video frames, we incorporate the recurrent convolutional layers into the standard feed-forward CNN model. With multiple video frames as inputs, the long-term dependence and contextual relevance over time could be strengthen due to the powerful recurrent units. Unlike the feed-forward only CNN models, we propose to feed back the learned CNN features from high-level feedback recurrent blocks (FBR-block) to low-level layers to further enhance the the contextual representations. Experiments on the public video saliency benchmarks demonstrate that the model with feedback connections and recurrent units can dramatically improve the performance of the baseline feedforward structure. Moreover, although the proposed model has few parameters (~6.5 MB), it achieves comparable performance against the existing video saliency approaches.
Modeling the information activity of the human brain based on quantum field theory and virtual particles

This paper discusses the possible approaches to modeling the cognitive activity of the human brain using the mathematical apparatus of quantum field theory (primarily – potential wells) in terms of the theory of information representations. The article briefly describes the proposed theory of information representations, draws analogies, and identifies common features of information representations of the human mind and Feynman's virtual particles. The human mind is represented as a one-dimensional potential well with finite walls of different sizes and internal potential barrier simulating the boundary between consciousness and subconsciousness. This creates a foundation for a mathematical apparatus that can make it possible to forecast particular cognitive functions of the human brain.
State Space Models

This chapter introduces state space models and provides some motivating examples. Linear Gaussian and non-linear, non-Gaussian models are introduced. Examples include linear trend and seasonal time series, time-varying regression, bearings-only tracking, financial time series and systems identification state space models. The chapter sets the stage for the book and provides a chapter-by-chapter description of the book. The chapter includes a brief historical overview of the developments that led to the discovery of the Kalman filter and the research directions in the wider field of state space modelling.
5G-Connected Drone for Public Road Safety-Research Challenges and Future Research Roadmap

5G mobile communication infrastructure attracts increasing attention of stakeholders, going beyond only interconnection of people and increasingly serve to connect and manage 5G-enabled IoT devices. Among others, unmanned aerial vehicles (UAVs) or systems (UAS) already rely on 5G communication infrastructure and in the near future are envisioned to use it even more. 5G systems already support a wide-variety of different applications, such as remote healthcare, self-driving ground vehicles, virtual or augmented reality, drones, surveillance and many more. Among these is the high-resolution video surveillance using drones for different purposes. In road traffic analysis, one of the most important public safety applications, the low compressed or uncompressed video stream can greatly improve the analysis and traffic incident detection performance. On the other hand it is challenging to transmit high-resolution video stream in real time due to its data size. This paper provides an overview of the current research in the area of UAV command and control using 5G systems describing basic concepts and challenges. We review some of the latest research in regard to real-time high-resolution video transfer. A brief discussion of experiments on the 5G private campus network communication between a drone and a ground analytical system is presented.
Scientific Expertise of Profiling Programs: Algorithmization of Students' Success

The goal of modern specialized training is to increase the human potential – the sustainable development of schoolchildren in the process of becoming professional adaptability – the ability to cope with the challenges of civilization and globalization in production activities, the active introduction of students into socio-economic life, the formation of satisfaction with the process and results of training that create a positive attitude as a factor for ensuring life success both during school education and in later life.&#x0D; Profiling of training is the development of programs aimed at meeting the needs of students in professional self-determination.&#x0D; Algorithmization of training profiling (building of educational material in a scientifically based sequence) it helps to increase the effectiveness of the educational process aimed at the formation of social and professional success of students.&#x0D; Scientific expertise of specialized training programs is a mechanism for managing the personal success of students in a specialized school. During the examination, an active qualitative analysis of the didactic materials and educational technologies proposed by the developer takes place, methods of their improvement are recommended. This is the activity essence of pedagogical expertise.
Performance Comparative Analysis of Heuristic based Independent Task Scheduling Techniques on Standard and Real Datasets

Allocating user cloudlets under heterogeneous environment like Cloud is a tiresome process. It’s one of core steps that efficiently hunt for capable yet diverse resources. Therefore, researchers around the world have given birth to several algorithms under heuristic and meta-heuristic categories. Such mapping techniques are proposed by keeping different performance parameters. Different metrics are used for evaluating the efficiency of scheduling algorithms like: resource utilization rate, makespan, waiting time, fitness, flow time and machines variation etc. This paper has compared eleven heuristic techniques based on the above parameters on standard, real and artificial dataset to yield their performance in heterogeneous scenarios.
Amplifying Realities: Gradual and Seamless Scaling of Visual and Auditory Stimuli in Extended Reality

Existing literature in the field of extended reality has demonstrated that visual/auditory manipulations can change a person’s perception and behavior. For example, a mismatch between the physical self and the virtual self can have psychological and behavioral implications. There are many different approaches that can incur a perceptual change. An under-explored field of research are gradual and subtle manipulations, such as scaling the food one eats or scaling the heads of people one sees. In this position paper, I provide an overview of my prior PhD research focusing on means to gradually and seamlessly scale visual and auditory stimuli in extended reality, and investigations of the corresponding changes in human perception.
A Color Image Quantization Method Considering Chromatic Visual Impression

In recent years, many high-precision color image quantization (CIQ) methods have been proposed. However, they do not consider human visual impression, exposing the image to the possibility of altering its visual impression. In this paper, we propose a novel CIQ method that considers human visual impression. An impression is calculated by using a saliency map of an original image and the frequency of each of its colors to prevents CIQ from changing the impression of the image. Quantitative and qualitative evaluations of the experimental results demonstrate that the proposed method preserves the impressionistic colors better than the general CIQ methods.
Creating multisensory learning experiences that go beyond the limitations of traditional media

Digital tools can be used in various interesting and creative ways to deliver content and/or as a proxy for open-ended students projects. However, they are all bound by the limitations of physics. Augmented Reality paired with gamification becomes a powerful tool for motivation and engagement. It also unlocks the ability to create multisensory learning experiences that go beyond the limitations of traditional media. These experiences make the teaching subject more tangible. Some of the experiences give students the ability to launch the content at any place while others need a trigger. In a few examples, the author will show how these experiences can be created, using various apps and tools based on trigger images, surface scanning, or service location. Furthermore, these examples will demonstrate curriculum-based integration possibilities and also their impact on student engagement while in a remote and non-remote teaching environment.
Improvement of the Processing Speed of The Robot’s Vision System Using Robot Operating System

This Research presents the improvement of the processing speed of the vision system at Robot ERSOW using Robot Operating System. In Mobile robot Soccer ERSOW to perform the ability to find the ball, kick the ball, avoid the obstacles, and shooting the ball into the goal, accurate supporting data is required. There are 3 objects that have been detected by this vision system, that is ball detection, robot detection, and obstacles detection. Based on the number of objects detected, vision systems in ERSOW robot require large computations and processing times. Therefore, by using Robot Operating System (ROS), the speed of the object detection process in the ERSOW robot vision system can increase rapidly. In addition, using ROS and the upgraded detection concept will consume less memory computing. In this research, the result of ball detection average FPS will increase by ±17.19% and the average computation of the ball detection will decrease by ±16.67% with the average speed processing ± 18ms. For obstacle detection using ROS, the average FPS will increase by ±32.92% and the average computation will decrease by ±16.10% with the average speed processing ± 13ms. And for the detection of robots using ROS, the average FPS will increase by ±19.07% and the average computation will decrease by ±13.78% with the average speed processing ± 26ms.
Secure Data Deduplication And Sharing Method Based On UMLE And CP-ABE

In the era of big data, more and more users store data in the cloud. Massive amounts of data have brought huge storage costs to cloud storage providers, and data deduplication technology has emerged. In order to protect the confidentiality of user data, user data should be encrypted and stored in the cloud. Therefore, deduplication of encrypted data has become a research hotspot. Cloud storage provides users with data sharing services, and the sharing of encrypted data is another research hotspot. The combination of encrypted data deduplication and sharing will inevitably become a future trend. The current better-performing updateable block-level message-locked encryption (UMLE) deduplication scheme does not support data sharing, and the performance of the encrypted data de-duplication scheme that introduces data sharing is not as good as that of UMLE. This paper introduces the ciphertext policy attribute based encryption (CP-ABE) system sharing mechanism on the basis of UMLE, applies the CP-ABE method to encrypt the master key generated by UMLE, to achieve secure and efficient data deduplication and sharing. In this paper, we propose a permission verification method based on bilinear mapping, and according to the definition of the security model proposed in the security analysis phase, we prove this permission verification method, showing that our scheme is secure. The comparison of theoretical analysis and simulation experiment results shows that this scheme has more complete functions and better performance than existing schemes, and the proposed authorization verification method is also secure.
Addressing Academic Administration Challenges in Higher Educational Institutions through Mobile Technologies and Cloud Computing

The mushrooming of mobile technologies and the need to support academic administration whilst mobile call for a revolution. A new term has been coined in this research is Mobile Administration which refers to the use of mobile technologies in administration processes. A Mobile Administration model has been proposed in this paper which integrates the core elements of mobile technologies, applications or systems, lecturers' administrative duties, users of the mobile administration platform, theories of administration, and micro-administration. The way mobile technologies are and its uses are growing there is a need to address the demand and issue of cloud computing as well. Organizations rely on Information Technologies to complete all sorts of tasks and projects. But, it is also important to make sure that their progress is saved and stored securely and efficiently as well. This is where the cloud services come into play. Cloud computing provides on demand computing services such as storage and processing power over the internet. The research concluded that Higher Institutes of Education are not yet fully-fledged to make use of mobile administration due to lack of mobile technologies resources, lack of skills, support, and so on hence policies need to be enacted to support mobile administration. Theories of administration must be blended within mobile micro-administration processes.
Skeleton-based Fall Events Classification with Data Fusion

Human fall detection aims to classify falls and normal activities. It can improve the speed of rescue for the elderly after a fall occurs, it can also efficiently prevent the elderly from suffering secondary injuries due to untimely or inaccurate fall detection. This technology is widely used in hospitals, smart homes and nursing homes. The challenges are that the injured parts of the elderly can vary due to different types of fall events, such as falling sideways and falling backwards. In this paper, we propose a data fusion method which combine the skeleton keypoints captured from RGB images into fused keypoints to improve the performance of fall events classification. Four well known classification methods, Random Forest, Support Vector Machine, Multi-layer Perceptron, AdaBoost are used in the proposed framework. Meanwhile, the impact on fall detection results due to missing data caused by occlusion and or privacy protection is also analyzed through ablation study. The experimental results confirm that the proposed framework outperformed the state-of-the-art with reduced computational cost.
Decentralised and Privacy Preserving Machine Learning for Multiple Distributed Data Resources

There are many issues related to distributed data resources, such as data transfer restrictions, privacy, and computation and communication costs. It brings challenges for developing a distributed machine learning approach that can overcome these issues without using complicated machine learning methods, sharing, or centralising the distributed data resources. To address these issues, we proposed a decentralised machine learning approach that allows distributed sites to develop classification models at global and local levels without sharing or moving distributed data to a centralised database or using a central server for iterative computation or communication. Such a decentralised approach restricts the exchanged information between distributed sites with only the trained local models and minimal information. The experimental evaluation conducted on different datasets reported that our approach performed better or close to the centralised or other existing distributed machine learning approaches. Also, we applied a model selection strategy based on F-measure and different combination methods. That is, the proposed method can be used to distributed data resources efficiently and preserve data privacy without sacrificing the predictive performance.
Review of Protocol Security and Cloud Model in Cloud Computing Environment

Cloud Computing is a scalable, cost-effective and validated IT infrastructure company network over the Web. The inclusion of danger of cloud computing, though, is that critical resources are frequently outsourced to third parties, making it difficult for data protection and safety, data support and functionality quality, and enforcement to be demonstrated. Cloud Technology utilizes multiple innovations and often makes use of the safety issues that we address here to recognize core weaknesses in certain forms in systems and major challenges to cloud infrastructure and the community in the literature as well as to define and link weaknesses and risks to potential solutions. The new development of centralized and concurrent computing setting is that cloud computing is rising for everybody. It is the largest category of the linked client or network servers that are found in public, proprietary or hybrid clouds. Specific resources are offered by cloud storage. A company-focused program is one Software as a Service (SaaS). Shared technologies and services and shared access to details on request devices allow service providers to connect and maintenance costs at any level and anywhere in the world. Another support is an IaaS that defends against the awareness of protection clients and also guarantees works. Additional Platform templates for Platforms as a Software (PaaS). PaaS cloud protection shapes a multi-controller connection. Its protection has secured resources and customer health, challenges, security and risks together.
The Use of Text Mining to Investigate Undergraduate Research Topic Clusters from 2015 to 2019

This study investigated the clusters of research topics written by undergraduate students of Informatics Dept., Satya Wacana Christian University (SWCU) from 2015 to 2019 using text mining techniques. Eight hundred and twenty-seven (827) final year project (FYP) reports' abstracts were retrieved from the University Library database. Text-mining techniques were used for identifying topics of these reports, and developed a hierarchal connection among these topics. Student reports were grouped into two domains with four groups containing nine clusters based on abstracts analysis. The clusters obtained are web & mobile applications, desktop applications, network security, network infrastructure, random generators, implementation of web service-based cryptography, cryptography in institutions, general block chippers, and block chipper with pattern. Distributions of the research topics were discussed in relation to Friedman's tower of achievement, and its implications on the curriculum.