Reviewerly/exHarmony huggingface dataset:
Reference-Free DIBR-Synthesized Video Quality Metric in Spatial and Temporal Domains

Depth image-based rendering (DIBR) techniques play an important role in free viewpoint videos (FVVs), which have a wide range of applications including immersive entertainment, remote monitoring, education, etc. FVVs are usually synthesized by DIBR techniques in a &#x201C;blind&#x201D; environment (without a reference video). Thus, an effective reference-free synthesized video quality assessment (VQA) metric is vital. At present, many image quality assessment (IQA) algorithms for DIBR-synthesized images have been proposed, but limited researches have been concerned about the quality assessment of DIBR-synthesized videos. To this end, this paper proposes a novel reference-free VQA method for synthesized videos, which operates in Spatial and Temporal Domains, dubbed as STD. The design fundamental of the proposed STD metric considers the effects of two major distortions introduced by DIBR techniques on the visual quality of synthesized videos. First, considering the geometric distortion introduced by DIBR technologies can increase high-frequency contents of the synthesized frame, the influence of the geometric distortion on the visual quality of a synthesized video can be effectively evaluated by estimating high-frequency energies of each synthesized frame in spatial domain. Second, temporal inconsistency caused by DIBR techniques brings the temporal flicker distortion, which is one of the most annoying artifacts in DIBR-synthesized videos. In temporal domain, we quantify temporal inconsistency by measuring motion differences between consecutive frames. Specifically, optical flow method is first used to estimate the motion field between adjacent frames. Then, we calculate the structural similarity of adjacent optical flow fields and further adopt the structural similarity value to weight the pixel differences of adjacent optical flow fields. Experiments show that the above two features are able to well perceive the visual quality of DIBR-synthesized videos. Furthermore, since the two features are extracted from spatial and temporal domains, respectively, we integrate them using a linear weighting strategy to obtain our STD metric, which proves advantageous over two components and the competing state-of-the-art I/VQA methods. The source code is available at <uri>https://github.com/wgc-vsfm/DIBR-video-quality-assessment</uri>.
An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

This paper introduces new learning to the prediction model to enhance the prediction algorithms’ performance in dynamic circumstances. We have proposed a novel technique based on the alpha-beta filter and deep extreme learning machine (DELM) algorithm named as learning to alpha-beta filter. The proposed method has two main components, namely the prediction unit and the learning unit. We have used the alpha-beta filter in the prediction unit, and the learning unit uses a DELM. The main problem with the conventional alpha-beta filter is that the values are generally selected via the trial-and-error technique. Once the alpha-beta values are chosen for a specific problem, they remain fixed for the entire data. It has been observed that different alpha-beta values for the same problem give different results. Hence it is essential to tune the alpha-beta values according to their historical behavior for certain values. Therefore, in the proposed method, we have addressed this problem and added the learning module to the conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> filter to improve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> filter’s performance. The DELM algorithm has been used to enhance the conventional alpha-beta filter algorithm’s performance in dynamically changing conditions. The model performance has been measured using indoor environmental values of temperature and humidity. The relative improvement in the proposed learning prediction model’s accuracy was 7.72% and 16.47% in RMSE and MSE metrics. The results show that the proposed model outperforms in terms of the result as compared to the conventional alpha-beta filter.
Trust in blockchain-based systems

Trust can best be understood as a relational attribute between (1) a social actor and other actor(s) (interpersonal trust) and / or (2) actors and institutions (institutional or systemic trust) and (3) institutions and (trusting) actors (trust as shared expectations), where institutional frameworks define the nature and strength of trust relationships between different actors.
Blockchain For Education Purpose: Essential Topology

Blockchain technology is known for its large-scale digitisation trends that play an important role in various organisations. Because it is distributed and decentralized from blockchain, this feature makes the role of blockchain technologies based on products and services significantly different from previous technology offerings. The value proposition offered by this blockchain is very suitable to be applied in education. After a few years this caused disruption from various industry circles, current educational areas including those that are very perceived by the Indonesian people there is no progress and road in place. It is possible that in the next ten years, the educational actors who are less sensitive will one by one fall because of the storm-cast disruption technology. The educational institution is one of which is aware of the advantages and extraordinary potential of blockchain technology. Among other things, a digital signature certificate is important in the context of digital archiving to verify the authenticity of a document in an educational institution. But the lack of education from the community makes ignorance about the point of the advantage of blockchain technology that can be applied in the field of education. Therefore, a public understanding of the blockchain is packaged in the form of ontology on the education shutter. This research in particular is exploring the ontology approach of using root merkle methods in providing solutions to the application of blockchain, including slow process and small storage space. This issue causes it to be very difficult for educational institutions to store processes and stages of learning into the blockchain. This research can be the cornerstone of identifying solutions from certificates of digital signatures in terms of archiving, authentication and digital verification on the design of a new breakthrough framework with an ontology approach to blockchain educational design.
Mutual authentication of nodes using session token with fingerprint and MAC address validation

Security has become an important issue during communication among mobile nodes in an unfavorable condition. The mobile node's property is dynamic, so it isn't easy to manage security policies. These difficulties present a barrier to building multigene security arrangements that accomplish both assurance and attractive network execution. The proposed work suggests the mutual authentication-based protocol, helping in the handshake between two nodes. Once they connected securely through this mechanism, they can interchange the required information. We have a monitoring node that will maintain the access list of the authorized nodes. The monitoring node will keep the nodes' list based on the MAC address of nodes and digital signature key. The key is generated by combining the hash code of the MAC address with the variety of the user's fingerprint file acting as a node. The combination of both will verify the entity. It involves the various subsections that include registering the user, token generation, sending and receiving messages through multiple algorithms. The proposed work is implemented using MATLAB. The analysis of the base work and the proposed work is visualized by structuring the GUI. Due to all the strength of the pattern of session token is further validated using the various online password checking tools, and the results obtained are quite impressive. Secure Hash Algorithm (SHA) is a cryptographic hashing algorithm used to decide a specific bit of information's integrity. In the proposed method, we used different types of keys with additional entropy to check the trust level of password security. The result has been discussed using various entropy graphs that prove improvement over the existing approach.
Contextual Subspace Variational Quantum Eigensolver

We describe the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">contextual subspace variational quantum eigensolver</mml:mtext></mml:mrow></mml:math> (CS-VQE), a hybrid quantum-classical algorithm for approximating the ground state energy of a Hamiltonian. The approximation to the ground state energy is obtained as the sum of two contributions. The first contribution comes from a noncontextual approximation to the Hamiltonian, and is computed classically. The second contribution is obtained by using the variational quantum eigensolver (VQE) technique to compute a contextual correction on a quantum processor. In general the VQE computation of the contextual correction uses fewer qubits and measurements than the VQE computation of the original problem. Varying the number of qubits used for the contextual correction adjusts the quality of the approximation. We simulate CS-VQE on tapered Hamiltonians for small molecules, and find that the number of qubits required to reach chemical accuracy can be reduced by more than a factor of two. The number of terms required to compute the contextual correction can be reduced by more than a factor of ten, without the use of other measurement reduction schemes. This indicates that CS-VQE is a promising approach for eigenvalue computations on noisy intermediate-scale quantum devices.
Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.
Attack on Practical Speaker Verification System Using Universal Adversarial Perturbations

In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text. Previous studies played an audio adversarial example as a digital signal to perform physical attacks, which would be easily rejected by audio replay detection modules. This work shows that by playing our crafted adversarial perturbation as a separate source when the adversary is speaking, the practical speaker verification system will misjudge the adversary as a target speaker. A two-step algorithm is proposed to optimize the universal adversarial perturbation to be text-independent and has little effect on the authentication text recognition. We also estimated room impulse response (RIR) in the algorithm which allowed the perturbation to be effective after being played over the air. In the physical experiment, we achieved targeted attacks with success rate of 100%, while the word error rate (WER) on speech recognition was only increased by 3.55%. And recorded audios could pass replay detection for the live person speaking.
Towards “Avatar-Friendly” 3D Manipulation Techniques: Bridging the Gap Between Sense of Embodiment and Interaction in Virtual Reality

Avatars, the users' virtual representations, are becoming ubiquitous in virtual reality applications. In this context, the avatar becomes the medium which enables users to manipulate objects in the virtual environment. It also becomes the users' main spatial reference, which can not only alter their interaction with the virtual environment, but also the perception of themselves. In this paper, we review and analyse the current state-of-the-art for 3D object manipulation and the sense of embodiment. Our analysis is twofold. First, we discuss the impact that the avatar can have on object manipulation. Second, we discuss how the different components of a manipulation technique (i.e. input, control and feedback) can influence the user's sense of embodiment. Throughout the analysis, we crystallise our discussion with practical guidelines for VR application designers and we propose several research topics towards "avatar-friendly'' manipulation techniques.
Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning

Abstract Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effects, and even death. The prediction of breast cancer recurrence is crucial for reducing mortality. This paper proposes a prediction model for the recurrence of breast cancer based on clinical nominal and numeric features. In this study, our data consist of 1,061 patients from Breast Cancer Registry from Shin Kong Wu Ho-Su Memorial Hospital between 2011 and 2016, in which 37 records are denoted as breast cancer recurrence. Each record has 85 features. Our approach consists of three stages. First, we perform data preprocessing and feature selection techniques to consolidate the dataset. Among all features, six features are identified for further processing in the following stages. Next, we apply resampling techniques to resolve the issue of class imbalance. Finally, we construct two classifiers, AdaBoost and cost-sensitive learning, to predict the risk of recurrence and carry out the performance evaluation in three-fold cross-validation. By applying the AdaBoost method, we achieve accuracy of 0.973 and sensitivity of 0.675. By combining the AdaBoost and cost-sensitive method of our model, we achieve a reasonable accuracy of 0.468 and substantially high sensitivity of 0.947 which guarantee almost no false dismissal. Our model can be used as a supporting tool in the setting and evaluation of the follow-up visit for early intervention and more advanced treatments to lower cancer mortality.
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text

Logical reasoning of text requires identifying critical logical structures in the text and performing inference over them.Existing methods for logical reasoning mainly focus on contextual semantics of text while struggling to explicitly model the logical inference process.In this paper, we not only put forward a logic-driven context extension framework but also propose a logic-driven data augmentation algorithm.The former follows a three-step reasoning paradigm, and each step is respectively to extract logical expressions as elementary reasoning units, symbolically infer the implicit expressions following equivalence laws and extend the context to validate the options.The latter augments literally similar but logically different instances and incorporates contrastive learning to better capture logical information, especially logical negative and conditional relationships.We conduct experiments on two benchmark datasets, ReClor and LogiQA.The results show that our method achieves state-of-the-art performance on both datasets, and even surpasses human performance on the ReClor dataset.
Drones as internet of video things front-end sensors: challenges and opportunities

Abstract Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.
Comparison of spatial orientation skill between real and virtual environment

Abstract Virtual reality (VR) is a promising tool and is increasingly used in many different fields, in which virtual walking can be generalized through detailed modeling of the physical environment such as in sports science, medicine and furthermore. However, the visualization of a virtual environment using a head-mounted display (HMD) differs compared to reality, and it is still not clear whether the visual perception works equally within VR. The purpose of the current study is to compare the spatial orientation between real world (RW) and VR. Therefore, the participants had to walk blindfolded to different placed objects in a real and virtual environment, which did not differ in physical properties. They were equipped with passive markers to track the position of the back of their hand, which was used to specify each object’s location. The first task was to walk blindfolded from one starting position to different placed sport-specific objects requiring different degrees of rotation after observing them for 15 s (0°, 45°, 180°, and 225°). The three-way ANOVA with repeated measurements indicated no significant difference between RW and VR within the different degrees of rotation ( p &gt; 0.05). In addition, the participants were asked to walk blindfolded three times from a new starting position to two objects, which were ordered differently during the conditions. Except for one case, no significant differences in the pathways between RW and VR were found ( p &gt; 0.05). This study supports that the use of VR ensures similar behavior of the participants compared to real-world interactions and its authorization of use.
UDALM: Unsupervised Domain Adaptation through Language Modeling

In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks.We introduce UDALM, a fine-tuning procedure, using a mixed classification and Masked Language Model loss, that can adapt to the target domain distribution in a robust and sample efficient manner.Our experiments show that performance of models trained with the mixed loss scales with the amount of available target data and the mixed loss can be effectively used as a stopping criterion during UDA training.Furthermore, we discuss the relationship between A-distance and the target error and explore some limitations of the Domain Adversarial Training approach.Our method is evaluated on twelve domain pairs of the Amazon Reviews Sentiment dataset, yielding 91.74% accuracy, which is an 1.11% absolute improvement over the state-of-the-art.
Emergence of transient chaos and intermittency in machine learning

Abstract An emerging paradigm for predicting the state evolution of chaotic systems is machine learning with reservoir computing, the core of which is a dynamical network of artificial neurons. Through training with measured time series, a reservoir machine can be harnessed to replicate the evolution of the target chaotic system for some amount of time, typically about half dozen Lyapunov times. Recently, we developed a reservoir computing framework with an additional parameter channel for predicting system collapse and chaotic transients associated with crisis. It was found that the crisis point after which transient chaos emerges can be accurately predicted. The idea of adding a parameter channel to reservoir computing has also been used by others to predict bifurcation points and distinct asymptotic behaviors. In this paper, we address three issues associated with machine-generated transient chaos. First, we report the results from a detailed study of the statistical behaviors of transient chaos generated by our parameter-aware reservoir computing machine. When multiple time series from a small number of distinct values of the bifurcation parameter, all in the regime of attracting chaos, are deployed to train the reservoir machine, it can generate the correct dynamical behavior in the regime of transient chaos of the target system in the sense that the basic statistical features of the machine generated transient chaos agree with those of the real system. Second, we demonstrate that our machine learning framework can reproduce intermittency of the target system. Third, we consider a system for which the known methods of sparse optimization fail to predict crisis and demonstrate that our reservoir computing scheme can solve this problem. These findings have potential applications in anticipating system collapse as induced by, e.g., a parameter drift that places the system in a transient regime.