Reviewerly/exHarmony huggingface dataset:
Confidential Computing as an Attempt to Secure Service Provider's Confidential Client Data in a Multi-Tenant Cloud Environment

Cloud-oriented infrastructure posed itself as a predominant deployment paradigm in the recent decade due to its ease of provisioning and relatively low cost. However, entrusting a third party with sensitive data in a multi-tenant environment brings about increased data breach risks. The aim of this paper is to give an insight into challenges and threats encountered in mitigating data breach and repudiation risks for service providers utilizing cloud-based environments. Through constructing and studying a possible cloud-based service, we form a corresponding threat model with a special focus on risks originating from internal actors. We explore confidential computing as a possible solution for data confidentiality and mitigation of internal data breach risks for Software as a Service (SaaS) providers.
High-Rate Damage Classification and Lifecycle Prediction via Deep Learning

While the lack of high-fidelity physics in high-rate dynamic events has posed as a challenge for many years, machine learning (ML) is making significant breakthroughs in numerous practical engineering applications. The ability to classify precise levels of damage and predict component-level failure becomes increasingly important with high-rate applications. Structural health monitoring (SHM) benefits from ML algorithms, which extract features containing a component’s damage information, thereby reflecting a system’s overall health status. This research presents a deep convolutional variational auto-encoder (CVAE) that classifies damage levels of electronic assemblies subjected to high-acceleration mechanical shock. These electronic assemblies were shocked using a drop tower, damaging individual components, and causing overall system failure. Wavelet transformations are used to maintain adequate time-frequency information of the shock response and augment the presentation of its high-rate dynamics. Given only six training examples containing sparse data, the CVAE used in this paper classifies the severity of an electronic package’s damage, which could extend to predicting useful remaining lifetime.
Stereo-Visual-LiDAR Sensor Fusion Using Set-Membership Methods

Taking advantage of the complementary error characteristics of Light Detection and Ranging (LiDAR) and stereo camera reconstruction, we propose a set-membership-based method for fusing LiDAR information with dense stereo data under consideration of interval uncertainty of all measurements and calibration parameters. Employing interval analysis, we can propagate the uncertainties to the extraction of distinct features in a straightforward manner. To show the applicability of our approach, we use the fused information for dead reckoning. In contrast to other works, we can consistently propagate the sensor uncertainties to the localization of the robot. Further, we can provide guaranteed bounds for the relative motion between consecutive frames. Using real data we validate that our approach is indeed able to always enclose the true pose of the robot.
An Improved Non-local Mean Filtering Algorithm Based on Medical Image Restoration

In the process of image imaging, replication, scanning, transmission and display, image degradation will inevitably occur, such as image blurring, noise interference, etc. In the field of medical image application, clear and high-quality images are needed. Therefore, medical image restoration is of great significance. In view of the fact that the current popular image restoration algorithms are less than ideal, this paper proposes an improved non-local mean filtering algorithm based on medical image restoration. This algorithm firstly adopts the norm LO gradient minimization restoration algorithm to restore the smooth part of the image, then adopts the wave-atom transformation to restore the detail part of the image, and finally adopts the improved non-local mean filtering to deal with the ringing effect and false edge generated by wave-atom transformation. The algorithm experiment was carried out on MATLAB R2009a platform. The experimental results show that the restoration algorithm has certain improvement in both subjective and objective effects of image restoration compared with the current popular image restoration algorithm.
Lifting Scheme and Schur Decomposition based Robust Watermarking for Copyright Protection

The widespread of multimedia big data in the recent scenario increases the demand for copyright protection of color images. This paper proposes a blind watermarking scheme using Lifting Wavelet Transform (LWT) and Schur decomposition for accomplishing copyright protection. LWT is the latest, efficient, and fast computing transform. Initially, divide the cover image into RGB channels and then perform the LWT technique using Haar to the B channel to get the corresponding lifting scheme. Subsequently, partition the LL subband of LWT into non-overlapping image blocks of 4 × 4 after that perform Schur decomposition to the particular subband. The scrambling of RGB channels of the original watermark image is performed by Arnold transform to achieve security and robustness. The embedding of the binary watermark sequence is done into the unitary matrix of Schur decomposition using the proposed scheme. The experimental analysis confirms the proposed method robustness and good imperceptibility by achieving normalized cross-correlation (NCC) nearer to 1 and peak signal to noise ratio (PSNR) of 41.1241 dB.
Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning

Autonomous robots are capable of making decisions based on the information they can obtain from their environments, as opposed to simply following a program. Reinforcement learning is a machine learning paradigm that is widely used in autonomous robotics, since their principles are very similar. Reinforcement learning is based on how humans and animals perceive, reason and act on their environments by trial-and-error, learning which actions are better and which are worse depending on the reward they produce. Likewise, robots interact with their environments using their sensors to perceive and their actuators, motors and end effectors to actuate. The main drawback of reinforcement learning is the steep computational cost in processes that involve large state and action spaces. However, this problem has been mitigated by the use of deep learning, which has proved to be an outstanding solution by automatically discovering relevant features and representations in raw and high-dimensional data. This combination results in a new paradigm known as deep reinforcement learning, that has been successfully employed in robotic tasks such as navigation and manipulation. Developments in robotics have enabled the presence of robots in increasingly dynamic, unpredictable, unstructured and partially observable environments, wherein uncertainty is a key element that stems from the lack of information. Thus, the ability to cope with uncertainty is crucial for autonomous robots. However, this scarcity of information has been widely ignored in most robotics works for the sake of simplicity, leading to inaccurate, approximate and unsafe solutions. In order to improve the navigation as well as other robotic tasks and, therefore, the safety of the robot itself and all the objects and beings that surround it, more research is needed on the different approaches and methods for dealing with uncertain information. This chapter aims to provide an overview of deep reinforcement learning algorithms and strategies for mobile robot navigation with handling of uncertainty.
A Rubric to Identify Misogynistic and Sexist Texts from Software Developer Communications

Background: As contemporary software development organizations are dominated by males, occurrences of misogynistic and sexist remarks are abundant in many communities. Such remarks are barriers to promoting diversity and inclusion in the software engineering (SE) domain.
How Neural Processes Improve Graph Link Prediction

We propose a meta-learning approach with graph neural networks for link prediction: Neural Processes for Graph Neural Networks (NPGNN), which can not only perform both transductive and inductive learning tasks, but also generalize well when only training on a small subgraph. The key idea is to assume the node embeddings follow a Gaussian Process parameterised by graph neural networks and then use a meta-learning framework to pass information from the subgraph to the complete graph. Experiments on real-world citation networks are conducted to validate our model, where the results suggest that the proposed method achieves stronger performance compared to other state-of-the-art models.
Lane Changing Using Multi-Agent DQN

This study explores the feasibility of autonomous lane changing using a novel approach of multi-agent Deep Q-Network. Most existing algorithms that use Deep Reinforcement Learning adopt a single-agent approach, with the assumption of only ego-agent changing lanes. We argue that such an approach is merely a simplification of the real-world without considering multi-agent negotiations. In this work, we model the lane changing problem as a multi-agent system and develop a decision-making policy using Deep Q-Network. We address the non-stationarity problem caused by our multi-agent setup which includes an Experience Replay. While prior research recommends avoiding the Experience Replay under such conditions, we report for the first time that an Experience Replay can help yield a robust negotiation policy in our lane changing experiment, without impairing the training of the Deep Q-Network. We show that our approach enables the model to learn negotiating-behaviors like overtaking, yielding, lane interchanging, and lane merging.
Annealing in the Noisy Intermediate-Scale Quantum Era: Key concepts and approaches

In the noisy intermediate-scale quantum (NISQ) era, annealing-based computing is emerging as a new type of high-performance computing (HPC) technology for solving computationally intractable combinatorial optimization problems (COPs). Inspired by thermal annealing in metallurgy, the cost function of a COP is encoded in an energy function (Hamiltonian), with its lowest energy state being searched using an annealer and finally transformed to the global or global approximate optimal solution to a target problem. We address the key technology underlying annealing-based methods along with an experimental study with a commercial digital annealer (DA). We hope to shed light on how industries could benefit from this emerging computing technology to explore the challenges and opportunities in the quantum computing age.
Regulation Mechanism of Spatial Capacity of Tourist Resources in Scenic Spots Based on Internet of Things Technology

At present, it is urgent to solve the problem of scenic area tourism environmental capacity. This paper analyzes the causes of congestion and overload in scenic spots and then points out that the methods to solve the problem are capacity restriction and flow control. This paper first calculates the spatial capacity of various tourism resources in a scenic spot by using mathematical model and then studies the intelligent spatial management model of tourist flow in the scenic spot based on the Internet of Things technology. Based on the Internet of Things technology, the spatial capacity control platform of tourist resources in scenic spots built in this paper can realize the structure analysis of peak forest of tourist flow in scenic spots and the fractal and fractal dimension analysis of tourist flow and determine the spatial and temporal threshold of tourist flow control in tourist destinations. Through capacity restriction and flow regulation, tourists can be scientifically channelled and distributed, which can greatly reduce congestion and overload, enhance tourist experience, and improve tourist satisfaction, so as to promote the sustainable development of the scenic spot.
Deep Learning with Optimization Techniques for the Classification of Spoken English Digit

Multiclass classification is a fundamental problem for many speech recognition systems. A typical example of multiclass classification in speech recognition is spoken digit classification. This type of classification is generally a challenging task since the signals last for a short period and often some digits are acoustically very similar to each other. To resolve this challenge, an intelligent based system is proposed using an enhanced deep feedforward network technique for the spoken digit classification. In the proposed method, Short Time Fourier Transform (STFT) features were first extracted from audio data and one hot encoding was performed on the audio data to generate the target audio class label. This was then used as input into the deep learning model. The model performance was evaluated using hyper-parameter optimization techniques such as Adam optimization algorithm and Stochastic Gradient Descent (SGD) optimization algorithm to reduce losses and to provide the most accurate results possible. The experimental results show that the system when used with Adam optimization algorithm outperformed the classical SGD optimization algorithm on the well-established public benchmark spoken English digit dataset, PCM and achieved an overall accuracy of 99.65%. The results show that the choice of a good hyper-parameter optimizer like Adam optimization algorithm can achieve optimal accuracy.
The Impact of Threats on the Cybersecurity System of Public Administration in the Context of the Development of Financial Technologies

The article analyzes the main essential characteristics of the concept of "cybersecurity of the public administration system" in the context of the development of financial technologies. It was determined that the cybersecurity of the public administration system today acts as the basis of national security, which forms and ensures full protection of society and the public administration system of the state in the cyber space through the formation of optimal mechanisms to counter threats to the cybersecurity of the public administration system. The main external threats that affect the cybersecurity of the public administration system are highlighted, namely: pandemic impact, targeted attacks, cyber warfare, attacks on banking systems, attacks on electronic systems of government, hatcism, cyberterrorism. To determine the degree of influence of each threat to the public administration cybersecurity system, we used graph theory. In the course of the research, we identified the threats that have the greatest impact on cybersecurity system of public administration in the context of the development of financial technologies
Cast suppression in radiographs by generative adversarial networks

Injured extremities commonly need to be immobilized by casts to allow proper healing. We propose a method to suppress cast superimpositions in pediatric wrist radiographs based on the cycle generative adversarial network (CycleGAN) model. We retrospectively reviewed unpaired pediatric wrist radiographs (n = 9672) and sampled them into 2 equal groups, with and without cast. The test subset consisted of 718 radiographs with cast. We evaluated different quadratic input sizes (256, 512, and 1024 pixels) for U-Net and ResNet-based CycleGAN architectures in cast suppression, quantitatively and qualitatively. The mean age was 11 ± 3 years in images containing cast (n = 4836), and 11 ± 4 years in castless samples (n = 4836). A total of 5956 X-rays had been done in males and 3716 in females. A U-Net 512 CycleGAN performed best (P ≤ .001). CycleGAN models successfully suppressed casts in pediatric wrist radiographs, allowing the development of a related software tool for radiology image viewers.
A Computational Intelligent Cognition System Under Uncertainty

It has been explored the conception of implementation the system of the most important systemic psychological function of the Computational Brain (CB)—the System of Computational Situational Reasonable Cognition and Understanding of the reality Under Uncertainty with applying the Fuzzy Logic, Fuzzy Control, Computational Linguistics, Cognitive Psychology, Data Science, Computer Science at whole, oriented on introduction in the Artificial Super Intelligence Self-X system as one of the main components. Computational psychology is investigated and implemented on basis of the following CB’s computational systemic mental situational functional processes of self-perception, self-inference, self-decision making, self-control, self-developing, intuition, self-awareness, self-consciousness, and self-understanding of reality. These processes are implemented on basis of the self-developing memory and modules, that use the self-computing computational models, computational mathematical modeling psychological situations under their time changes. The computed and identified psychological categories, properties, features, and essences of objects of reality are correlated with the corresponding subject area and are used by the mentioned processes for the intellectual analysis and modeling of the systemic situational reasonable cognition.
Demand Analysis Based on SWOT ’s Intelligent Unmanned Aerial Vehicle Swarm

With the gradual maturity and development of intelligent technology, its application has covered all aspects of various fields. In modern war, intelligent and swarm combination has emerged more advanced integrity. Therefore, the application and development of Intelligent UAV swarm (unmanned aerial vehicle swarm, UAV swarm) have become one of the hot researches. The UAV swarm have been coordinated with each other, and more advanced group intelligence characteristics have emerged, showing more advanced self-healing and robust and intelligent. intelligent systems that are integrated, systematic autonomous, information sharing and autonomous decision-making through different loads. Although the UAV swarm. has many advantages, how to apply the UAV swarm to the most suitable field, and the relevant demand demonstration research is still less. SWOT is a classic method of strategic target analysis, which has the characteristics of structured, systematic, and clearing. Analysis of UAV swarm. through SWOT, further clarifying the need for its development and the challenges currently needed.
A Competence-Based Three-Layer Cybersecurity Education Framework and Its Application

The Computing Curricula 2020 (CC2020) competence model raises a big challenge to cybersecurity education in terms of knowledge, skill and disposition. In this paper, we propose a competence-based three-layer cybersecurity education framework using an ensembling learning strategy. Typically, the proposed framework contains the following three parts: core requirements of competence for cybersecurity, a learning ecosystem, and a unified learning platform. After that, we use the framework to reform a course named Network Security. In the case study, we present the implementing details of the proposed framework followed by extensive data analysis and teaching reflections. Statistical results suggest that the proposed framework is powerful to raise the level of learning achievements that are grounded in CC2020 and course learning objectives.
SensiBlend: Sensing Blended Experiences in Professional and Social Contexts

Unlike traditional workshops, SensiBlend is a living experiment about the future of remote, hybrid, and blended experiences within professional and other social contexts. The interplay of interpersonal relationships with tools and spaces—digital and physical—has been abruptly challenged and fundamentally altered as a result of the COVID-19 pandemic. With this meta-workshop, we seek to scrutinize and advance the role and impact of Ubiquitous Computing in the new "blended" social reality, and raise questions relating to the specific attributes of socio-technical experiences in the future organization of interpersonal relationships. How do we better equip people to deal with blended experiences? What dimensions of socio-technical experiences are at stake? To this end, we will utilize the occasion of a virtual UbiComp in combination with novel remote-working tools and participatory sensing with attendees to collectively examine, discuss, and elicit the potential routes of augmenting social practices in a discourse about the future of blended working, socializing, and living.
A Digital Future of Diversity, Connectivity, and Sustainability

In a mobile society where information and knowledge become accessible to anyone, anywhere, and anytime on the internet, online education has been transformed fundamentally. By the end of 2020, Mandarin Chinese was taught to over 20 million people in over 180 countries around the world. By critically reviewing 15 recent studies from 2013 to 2021 on mobile-assisted language learning (MALL) in teaching and learning Chinese as a second or foreign language (CSL/CFL), this chapter emphasizes the need for pedagogical diversity in goals, practices, and context. In the second theme of ‘connectivity', the new theory of online education connectivism is applied to evaluate the official Chinese language learning mobile app named ‘e-Learn Chinese'. In the final theme of ‘sustainability', it demonstrates an ecosystem of MALL through an ecological perspective based on a critical discussion of the mostly reviewed ten mobile language learning apps, which could innovate sustainable pedagogies in a life-long learning society.
Latent Transformations via NeuralODEs for GAN-based Image Editing

Recent advances in high-fidelity semantic image editing heavily rely on the presumably disentangled latent spaces of the state-of-the-art generative models, such as Style-GAN. Specifically, recent works show that it is possible to achieve decent controllability of attributes in face images via linear shifts along with latent directions. Several recent methods address the discovery of such directions, implicitly assuming that the state-of-the-art GANs learn the latent spaces with inherently linearly separable attribute distributions and semantic vector arithmetic properties.In our work, we show that nonlinear latent code manipulations realized as flows of a trainable Neural ODE are beneficial for many practical non-face image domains with more complex non-textured factors of variation. In particular, we investigate a large number of datasets with known attributes and demonstrate that certain attribute manipulations are challenging to obtain with linear shifts only.