Reviewerly/exHarmony huggingface dataset:
Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an underlying, yet hidden, geometric structure of the data. The proposed approach augments the DIFFRAC method with a representation learning capability, using a gradient-based stochastic training algorithm and an optimal transport algorithm with entropic regularization to perform the cluster assignment step. The resulting method is evaluated on several real datasets when varying the ratio of labeled data to unlabeled data and thereby interpolating between the fully unsupervised regime and the fully supervised regime. The experimental results suggest that the proposed method can learn powerful feature representations even in the fully unsupervised regime and can leverage even small amounts of labeled data to improve the feature representations and to obtain better clusterings of complex datasets.
The Antecedents to the Actual Use of Digital Currencies in Ghana

This study investigates the antecedents to the use of digital currencies in a developing country. In the digital currency literature, behavioral and technological antecedents that affect individuals' behavior towards the use of digital currencies remain scanty. Drawing on Bitcoin, mobile money, and perfect money as some types of digital currencies, this research adopts the technology organization environment (TOE) framework and the prospect theory. Based on 520 questionnaire responses, this research employs the partial least square structural equation modeling (PLS-SEM) technique. Results from the analysis reveal that technology competence, service, and agent trust, perceived risk, and cost have positive influences on the intention to use digital currencies. Behavioral intention influences the actual use of the technology. However, a country's regulatory environment does not influence the intention to adopt digital currencies. The research provides important analytical advances and presents practical and economic consequences for the implementation of digital currencies.
Research on Data Mining Algorithm of Power Grid Based on Cloud Computing

There are a large amount of complex data in the power grid database. How to use these data reasonably and efficiently and solve the problem of power grid operation safety has become an urgent need. Based on the principle of association rule mining technology, this paper realizes the mining of the potential information of the massive data of the power grid database through the improved Apriori algorithm., The improved algorithm innovation introduces two factors of efficiency and interest, so that strong association rules can be screened more accurately. In addition, in view of the large number of candidate sets of the Apriori algorithm, which is not conducive to the shortcomings of later convergence, an early pruning mechanism is introduced. Thereby improving the efficiency of the algorithm for mining information. Finally, the effectiveness and accuracy of the method are verified through experiments.
The Use of Virtual Reality to Drive Innovations. VRE-IP Experiment

In the context of widespread digitalization of businesses and academia due to COVID-19, the new effective tool has become necessary to drive innovations. This paper observes the international scientific experiment VRE-IP that tested the hypothesis of the advantages of using virtual reality for teamwork, creativity, and innovation. VRE-IP experiment was based on design thinking methodology and supported SAP innovation process. The analysis of the trends of using virtual reality for educational and business development goals, and VRE-IP experiment revealed that employing virtual reality technology to organize workshops, distant meetings, plenary reports, and other tasks is exceptionally efficient, improves the engagement rate and creativity and contributes to the process of innovative ideas stimulation. The experiment also demonstrated the need for building a hybrid model for teamwork. The combination of traditional teamwork instruments and virtual technologies is proposed as the most sustainable and systematic model for online collaboration at the current stage of technological development.
Sample Complexity of Model-Based Robust Reinforcement Learning

We consider the problem of learning the optimal robust value function and the optimal robust policy in discounted-reward Robust Markov Decision Process (RMDP). The goal of the RMDP framework is to find a policy that is robust to the parameter uncertainties due to the mismatch between the simulator model and real-world settings. While the optimal robust value function and policy can be computed using robust dynamic programming, it requires the exact knowledge of the nominal simulator model and the uncertainty set around it. This paper proposes a model-based robust reinforcement learning algorithm that learns an -optimal robust value function and policy in a finite state and action space setting when the exact knowledge of the nominal simulator model is not known. We assume access to a standard generative sampling model, which can generate next-state samples for all state-action pairs of the nominal simulator model. We give a precise characterization of the sample complexity of obtaining an ϵ-optimal robust value function and policy using our algorithm. Finally, we demonstrate the performance of our algorithm on some benchmark problems.
An Efficient Hashing Method for Exact String Matching Problems

String matching is a crucial part of various important areas of computer science, and the aim is to find out every occurrence of a string within another string. String matching problems play an energetic role in many different areas such as intrusion detection within a network, identifying plagiarism, pattern recognition, data security, document searching, data mining, bioinformatics, speech exploration, and other various fields. There are two significant components of the string coordinating with calculations, which are the number of attempts and the number of character comparisons. The fundamental objective of our proposed method is to minimize both components. With these challenges, we have mobilized the good properties of Rabin Karp algorithm with our new searching technique which is named the EHM (Efficient Hashing Method) algorithm. At the analysis of performance, our algorithm shows a satisfactory result which is better than the BFM (Back and Forth Matching), MAC (Minimum Attempts and Comparisons), MS (Maximum Shift), and QS (Quick Search) algorithm. We have used different types of datasets such as DNA sequence, protein sequence, English text, etc., to analyze the performance.
CTRW modeling of quantum measurement and fractional equations of quantum stochastic filtering and control

Abstract Initially developed in the framework of quantum stochastic calculus, the main equations of quantum stochastic filtering were later on derived as the limits of Markov models of discrete measurements under appropriate scaling. In many branches of modern physics it became popular to extend random walk modeling to the continuous time random walk (CTRW) modeling, where the time between discrete events is taken to be non-exponential. In the present paper we apply the CTRW modeling to the continuous quantum measurements yielding the new fractional in time evolution equations of quantum filtering and thus new fractional equations of quantum mechanics of open systems. The related quantum control problems and games turn out to be described by the fractional Hamilton-Jacobi-Bellman (HJB) equations on Riemannian manifolds. By-passing we provide a full derivation of the standard quantum filtering equations, in a modified way as compared with existing texts, which (i) provides explicit rates of convergence (that are not available via the tightness of martingales approach developed previously) and (ii) allows for the direct applications of the basic results of CTRWs to deduce the final fractional filtering equations.
A Pairing-Free Signature Scheme from Correlation Intractable Hash Function and Strong Diffie-Hellman Assumption

Goh and Jarecki (Eurocrypt 2003) showed how to get a signature scheme from the computational Diffie-Hellman assumption, and they introduced the name EDL for signatures of this type. The corresponding EDL family of signature schemes is remarkable for several reasons: elegance, simplicity and tight security. However, EDL security proofs stand in the random oracle model, and, to the best of our knowledge, extending this family without using an idealization of hash functions has never been successful. In this paper, we propose a new signature scheme belonging to the EDL family, which is simple, natural and efficient, without using the random oracle model. Our scheme is based on the very same assumption than the Boneh-Boyen scheme, namely the strong Diffie-Hellman assumption, with the precision that our groups are not bound to being bilinear. We also make use of a correlation-intractable hash function, for a particular relation related to discrete-logarithm. In addition to the theoretical interest of extending the EDL family without the random oracle model, our scheme is also one of the very few schemes which achieve discrete-log security properties without relying on pairings.
English Teachers' Perception and Strategies Towards the Use of Code-Switching in Teaching EFL Young Learners

The study was aimed to investigate the English teachers’ perception of the effectiveness of using code-switching to the EFL young learner at SD Lab Undiksha Singaraja, Bali and to find out the strategy used by the teachers in using code-switching to teach the EFL young learners. The study used mixed method design with 5 English teachers of SD Lab Undiksha as the participant. A questionnaire with a total of 18 items was distributed and an interview was conducted to collect the data related to the teachers’ perception, which covered the teachers’ perception related to the teachers’ persona, subject access, classroom management, and interpersonal relations. Then, to answer the second research question related to the strategy of code-switching used by the teachers, an interview was conducted. The result showed that there were 50% teachers had positive perception in relation to teachers’ persona, 70% teachers had positive perception in relation to subject access, 84% teachers had positive perception in relation to classroom management, and 80% teachers had positive perception in relation to interpersonal relations. Relating to the strategy, the results revealed that the teachers usually did code-switching whenever they introduced or say English vocabulary that were considered new or difficult for the students. It was done by either inserting the English or Bahasa Indonesia words in between their utterance. This indicated that the perception towards code-switching in teaching English was perceived positively and was implemented in order to ease the students in understanding the target language.
Construction and Research of Recommendation Model Based on Deep Learning

In order to further improve the recommendation accuracy, a recommendation model is built by combining deep learning and cosine similarity. Simulation results show that the proposed personalized recommendation model based on deep learning has lower deviation value and higher recommendation accuracy, which can effectively improve the personalized recommendation effect of users.
Face Manipulation Detection in Remote Operational Systems

Abstract In this chapter, we present the various categories of Face Manipulation and their use within different remote operational systems. We then use the example of remote identity document onboarding systems to illustrate how each category can be used in practice to compromise such a system. After a definition of the different Face Manipulation categories and the common algorithms used to produce them, we go through the various manipulation detection algorithms and common image and video forgery datasets. We then introduce some known counter-forensics methods that can be used by an attacker to avoid detection. Knowing the detection methods and the counter-forensic, we present how we can build up a safer system by using the correct methods at the correct time. But also how knowledge about the tampering process could be used to design the user experience to make the systems harder to compromise. We complete this review by the standardisation effort and legal aspect on the matter. And we conclude by discussing the remaining challenges and perspectives for better use of nowadays detection methods in practical usage.
Image Texture Removal by Total Variantional Rolling Guidance

The image texture removal is a solution for the image content separation problem, which aim at preserving image edges and removing uninterested textures. This problem has wide applications in image feature extractions, such as texture extraction, detail enhancement and so on. A new image content decomposition approach is introduced with a minimization refinement architecture in this paper, which contains the guidance component and the total varation component. The guidance component introduces a rolling guidance filtering to iteratively update the more and more smoothing images. The total varation component uses a new total variation regularization method to remove the image texture and preserve the structural contents. The non-convex objective function is simplified into these two sub-problems, which yield a linear solution. Then the experiments demonstrate the prior performance of our method and its potential for many image processing applications.
Diagnosis Of Anemia In Expert System Programming With Certainty Factor

Lack of blood or anemia is a condition when the body lacks healthy red blood cells or when red blood cells do not function properly. As a result, the organs of the body do not get enough oxygen, making people with anemia pale and tired easily. Anemia is often ignored because it is considered just an ordinary disease. But if left untreated, this disease can be dangerous. So that people are expected to also have knowledge about this disease and its symptoms and how to handle and prevent it so that the risk can be reduced. An expert system is a form of utilizing computer technology that adopts human knowledge to computers, so that computers can solve problems as is usually done by experts. An expert system is a computer system that is able to imitate the reasoning of an expert with expertise in a particular knowledge. An expert system can replace the role of an expert by utilizing a computer which in the end can provide a definite solution as done by an expert. The expert system for diagnosing anemia is built using the certainty factor method which can provide results in the form of the possibility of anemia suffered, the percentage of belief, symptoms, and treatment. The results of this study indicate that the expert system that was built can determine the value of the degree of confidence or the certainty factor of the data obtained from the results of consultations with patients through the certainty factor method.
A Novel Multi-Modal Network-Based Dynamic Scene Understanding

In recent years, dynamic scene understanding has gained attention from researchers because of its widespread applications. The main important factor in successfully understanding the dynamic scenes lies in jointly representing the appearance and motion features to obtain an informative description. Numerous methods have been introduced to solve dynamic scene recognition problem, nevertheless, a few concerns still need to be investigated. In this article, we introduce a novel multi-modal network for dynamic scene understanding from video data, which captures both spatial appearance and temporal dynamics effectively. Furthermore, two-level joint tuning layers are proposed to integrate the global and local spatial features as well as spatial and temporal stream deep features. In order to extract the temporal information, we present a novel dynamic descriptor, namely, Volume Symmetric Gradient Local Graph Structure ( VSGLGS ), which generates temporal feature maps similar to optical flow maps. However, this approach overcomes the issues of optical flow maps. Additionally, Volume Local Directional Transition Pattern ( VLDTP ) based handcrafted spatiotemporal feature descriptor is also introduced, which extracts the directional information through exploiting edge responses. Lastly, a stacked Bidirectional Long Short-Term Memory ( Bi-LSTM ) network along with a temporal mixed pooling scheme is designed to achieve the dynamic information without noise interference. The extensive experimental investigation proves that the proposed multi-modal network outperforms most of the state-of-the-art approaches for dynamic scene understanding.
Quality Assessment of Deep Learning Based Super Resolution Techniques on Thermal Images

This paper presents a new quality assessment parameter for the evaluation of deep learning based super resolution techniques applied on thermal images. Three widely used deep learning-based models namely Super-Resolution Convolutional Neural Network (SRCNN), Thermal Enhancement Network (TEN) and Very Deep Super Resolution (VDSR) have been implemented for achieving super resolution of thermal images. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) have been the most widely used conventional evaluation metrics for performance measurement of super resolution algorithms. Since these parameters require a reference image for the evaluation of the resultant images, we propose a new quality assessment parameter based on strength of the edges. Edge detection of the super resoluted image is performed utilizing Canny Edge Detection method and the entropy of the edge detection image is computed to provide a new parameter, Edge Detection Entropy Score (EDES). For the comparison and validation of the proposed image quality assessment techniques, Mean Opinion Score (MOS) of the target images have been obtained to be used as a benchmark. The obtained results indicate that the proposed EDES of the super resoluted images has high degree of correlation with the MOS as well as PSNR and SSIM.
Background

Generally speaking, robots are composed of three parts: the mechanical subsystem, the sensing subsystem and the control subsystem. With effective design, these three subsystems can work collectively for specified tasks. However, it is worth mentioning that with the rapid development of advanced manufacturing industry, there are urgent need for high-precision, compliant robotic manipulations in unstructured and flexible working environments or human-robot interactions, which give rises to the researches from traditional industrial robots to intelligent robots with various application areas.
Probabilistic Multi-knowledge Transfer in Reinforcement Learning

Transfer in Reinforcement Learning (RL) aims to remedy the problem of learning complex RL tasks from scratch, which is impractical in most of the cases due to the huge sample requirements. To overcome this problem, transferring the knowledge acquired from a set of source tasks to a new target task is a core idea. This knowledge can be the policy, the model (state transition and/or reward function), or the value function learned in the source tasks. However, algorithms in transfer learning focus on transferring a single type of knowledge at a time, although intuitively it might be interesting to reuse several types of this knowledge. For this reason, in this paper we propose a multi-knowledge transfer RL algorithm which we call Probabilistic Transfer of Policies and Models (PTPM). PTPM, unlike single-knowledge transfer approaches, combines the transfer of two types of knowledge: policies and models. We show through different experiments on two well-known domains (Grid World and Mountain Car) how this novel multi-knowledge transfer algorithm improves the results of the two methods in which it is inspired separately. As an additional result, we show that sequential learning of multiple tasks is generally better than learning from a library of previously learned tasks from scratch.
COVID-19 Face Mask Restoration Using Pix2pix model

Accurate facial recognition can effectively help the population combat the disease by offering risk-free phone usage, access controls, etc. In the era of COVID-19, a mask has become a necessity. However, masks may reduce the accuracy of face recognition to some degree. Thus, it is necessary to use deep learning to increase face recognition accuracy by recovering the face with a mask. For this purpose, this study proposed an AIbased model based on Pix2pix and U-net generator for restoring face mask images using the paired image database. In the training step, we used two adversarial models, including one generator and one discriminator. Then they are extended to a conditional model, which will be piped to the Pix2pix algorithm once again. U-Net was built in the training of the generator. The loss curves of generator and discriminators show that as iteration time increases, the loss of fake discriminator becomes lower stably. In contrast, the loss of real discriminator has the same tendency. In the meantime, the loss of generator shows an increased tendency. The result indicates that our model can help build reliable face mask restoration for daily use, which helps to improve the recognition accuracy of the face with a mask.
A Multi-Objective Neural Network for SAR Despeckling based on an Hybrid Training Approach

Severa1 deep learning based methods have been proposed in the last years for SAR image despeckling. Due to the lack of the noise-free reference, the main issue shared by all the proposed methods is the construction of a reliable dataset, leading many methods on resorting on optical images and on fully developed speckle. In this work, MONet, a recently deep learning based solution, is retrained with a different training approach in which real SAR images are directly involved in place of optical images used in its original version. Assessment on both simulated and real data, and a comparison with other methods show the improvement in filtering different SAR scenarios.
Trade-offs in large blockchain-based IoT system design

The well known Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is not well suited to blockchain-based Internet of Things (IoT) systems which cover large geographical areas. To reduce queuing delays and eliminates a permanent leader as a single point of failure, we use a multiple entry, multi-tier PBFT architecture and investigate the distribution of orderers that will lead to minimization of the total delay from the reception of a block of IoT data to the moment it is linked to the global blockchain. Our results indicate that the total number of orderers for given system coverage and total load are main determinants of the block linking time. We show that, given the dimensions of an area and the number of orderers, partitioning the orderers into a smaller number of tiers with more clusters will lead to lower block linking time. These observations may be used in the process of planning and dimensioning of multi-tier cluster architectures for blockchain-enabled IoT systems.