Reviewerly/exHarmony huggingface dataset:
A Comparative Evaluation of Feature Detectors and Descriptors for Monocular Endoscopic Sparse Depth Reconstruction

This paper provides a comparative performance evaluation of well-known feature detection and description algorithms. Although numerous comparisons of features have been studied for natural visual images, their performance on surgical endoscopic images is less discussed so far. We perform a thorough comparison of various feature algorithms for structure from motion to sparsely reconstruct monocular endoscopic video images. Our contribution lies in two aspects: (1) thoroughly investigate and evaluate the performance of most well-known local feature detection and descriptor methods for structure from motion and (2) systematically compare their performance for sparse depth estimation and reconstruction. According to our investigation, we achieve novel and useful insights on applying current local features to monocular endoscopic image sparse depth estimation that will be used for self-supervised learning based dense depth recovery.
Overview of Underwater Image Enhancement and Restoration Methods

Marine resources are rich and diverse, which has great value and research significance. In recent years, underwater exploration and research has become a hot direction. As the carrier of information, underwater image plays a vital role in underwater exploration and research. However, due to the influence of unclear underwater image and low contrast, it has caused great obstacles to the exploration of underwater resources. This paper summarizes the traditional image enhancement methods and image enhancement methods under deep learning for the past few years, and makes a comprehensive introduction to several algorithms and models. With the rapid and comprehensive development of deep learning for the past few years, this paper focuses on the image enhancement and restoration algorithm based on deep learning, summarizes the present public data sets in detail, introduces two mainstream objective evaluation systems, and finally looks forward to the future research.
KONFLIK BATIN PARA TOKOH DALAM NOVEL MARYAM KARYA OKKY MADASARI: SUATU TINJAUAN PSIKOLOGI SASTRA

AbstractThe background of this research is based on the researcher’s interest about the characters’ internal conflict in the novel Maryam By Okky Madasari. Internal conflict occurs because of the individuals are unable or untrained in controlling their emotions, common sense, or mind and conscience toward their problems in life. As for the research problem, it is the internal conflict in the novel Maryam by Okky Madasari that every character experiences. The research method used is descriptive with qualitative design and behavioristic approach. The research data are words or phrases, sentences and paragraphs that depict the formulation of problems in this research. The source of data is the novel Maryam and the research tool which is the researcher as main instrument. The result of this research is that internal conflict of the main and side characters that comprises of shame, sadness, hatred, fear, regret, hope, anxiety, upset, heartache, jealousy, trauma, heartbreak, dislikeness and anger. This research may be used as a reference to teaching material for Indonesian language teacher at the second semester in twelfth grade foor KD 3.9 and KD 4.9. The suggestion for next research is to analyze other aspects such as educational values in this novel.Keywords: Analysis, Internal Conflict, Novel
ACA: Anonymous, Confidential and Auditable Transaction Systems for Blockchain

The rapid development and wide application of blockchain not only highlight the significance of privacy protection (including anonymity and confidentiality) but also the necessity of auditability. While several ingenious schemes such as MiniLedger and traceable Monero supporting both privacy protection and auditability have been proposed, they either provide incomplete privacy protection (only achieving anonymity within a small set or only providing confidentiality but not anonymity), or involve additional auditing conditions such as reaching threshold transaction volume or requiring permissioned nodes to serve as the manager, or restrict to specific blockchain types such as Monero. To mitigate these issues, this article proposes a generic anonymous, confidential, and auditable transaction system (named ACA), which is compatible with both UTXO-based permissionless and permissioned blockchains. Core technologies of ACA include designed traceable anonymous key generation and publicly verifiable authorization mechanisms from existing cryptographic tools (i.e., public key encryption, partially homomorphic encryption, and accumulator) as well as the meticulous designed signatures of knowledge and smart contract. To demonstrate the entity of our proposal, we first prove its security including authenticity, anonymity, confidentiality and soundness, and then provide an instantiation to evaluate its performance. The final implementation and benchmarks show that our proposal can still gain performance advantage even adding more functionalities.
Towards a Bayesian-Neural-Networks Framework for Scaling Posterior Distributions over Different-Curation Datasets

The idea of Bayesian inference is used by Bayesian neural networks to make approximations of difficult posterior distributions. In fact, this approach seems to be very helpful in contemporary big data analytics. Through the use of a “temperature” hyper-parameter to scale posterior distributions, recent studies propose novel methods to enhance the performance of models. With the Bayesian approach, however, no additional manipulation of the posterior should be required with a proper prior initialization and an appropriate model configuration. We test numerous hypotheses about the subject and extend the range to non-curated datasets, where data uncertainty is more pronounced, in order to quantify the impact of this scaling technique. The findings demonstrate that, given the significant complexity, over-parametrized models typically gain more from this scaling.
Data-Driven Recommendation Model with Meta-learning Autoencoder for Algorithm Selection

To improve the efficiency of problem-solving for complex optimization problems, meta-learning was applied in algorithm selection to choose the most appropriate algorithm recently. However, the common meta-learners are feature-sensitive, where the selection and extraction of meta-features impact the quality of algorithm recommendations. In this study, we propose a data-driven recommendation model to implement the intelligent algorithm selection based on deep meta-features. A new kind of supervised stacked Autoencoder, named meta-learning Autoencoder, has been designed to process the deep meta-feature which is suitable both for instance-based and model-based meta-learners. To evaluate the performance of the proposed model, experiments have been conducted on some benchmark problems. Experimental results show that the recommendation accuracy of the model achieves nearly 100% in the seen problems and more than 80% in the unseen problems.
Bi-Modal Meta-Classification of Tweet Spamicity Using Machine Learning Approach

Nowadays, social media plays a centric role in sharing and disseminating information on products and services. Social network users post messages for attracting followers and increasing popularity. This attracts spammers to post fake and untruthful opinions on products. So, a strong and dependable framework for distinguishing spam users is important. Most of the research on detecting spam focuses on either tweets or metadata from Twitter profiles. Generally, spam tweets are generated using automated tools. The existence and credibility of the users in the social network is an important parameter in deciding the tweet is spam or not. In this work, a meta-classification approach is used with dynamic features extracted from the tweets, and tweeter behavior. The proposed work confirms spamicity in tweets using an aggregated model with spamicity inference generated by LSTM and machine learning models. Machine learning models are selected as it learns to extract patterns and interpret results from large dataset efficiently. The identification of distinct behavioral patterns from tweets is potent in various practical applications such as automatic knowledge extraction fields and large-scale human content generated platforms. The performance of the bi-modal meta-classifier is tested with benchmark datasets, and the accuracy of 99.76% achieved is found to be better than some recent related works.
Design of augmented reality-based visualization system for server room operation and maintenance

In traditional communication room operation and maintenance operations, due to incomplete communication equipment operation and maintenance information and inexperienced technical skills personnel, problem solving often requires consulting operation manuals or inviting experts to the site for diagnosis and guidance, which greatly limits the efficiency of operation and maintenance. The paper combines augmented reality (AR) technology to design an operation and maintenance visualization system for server rooms. When the operation and maintenance personnel wear AR devices to recognize the images of the signage of the server room, they can interact with the virtual components and achieve the functions of the operation and maintenance inspection assistance. The system can run smoothly with low hardware requirement and simple deployment, and in the verification experiment of augmented reality hologram stability, the system can keep the error at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0=5{\%}$</tex> when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0^{\circ}\sim 360^{\circ}$</tex> and distance within 2m, which proves the practicality of the method in the maintenance process of the server room for operators.
Improving Performance Analysis in Classification with Accuracy of Adult Income Salary using Novel Gated Residual Neural Network in Comparison with Logistic Regression Algorithm

Aim: Prediction of adult income by getting accuracy using Novel Gated Residual Neural Network (NGRNN) as the difference from Logistic Regression. Materials and Methods: Accuracy is performed with employee data as adults with samples of 32561 samples. Classification of adult income is performed by the novel gated residual network with a total number which is measured as samples (N=94) and logistic regression algorithm with a total number which is measured as samples (N=94), the proposed algorithm novel gated residual neural network (NGRNN) implies salary prediction that can be compared with Logistic Regression algorithm which is the second technique. Results: Novel Gated residual neural network (NGRNN) from the dataset Adult, produces an accuracy of 95% which can be comparatively higher than Logistic Regression accuracy of 74%. A significance T-test calculation based on statistics values as 0.006 from the Independent input sample. It shows that the p-value is around 0.05. Conclusion: Prediction accuracy of NGRNN techniques shows better when compared to existing techniques in income prediction. Novel Gated Residual Neural Networks have accuracy higher compared with Logistic Regression.
Chat Application Using Homomorphic Encryption

Chat applications nowadays have evolved into one of the most significant and widely used applications on smart devices. They are capable of sending and receiving messages, documents, and images with zero cost to any part of the world. All the messages should be protected. All the chat applications today are used to send messages quickly and securely. The actuality of the situation is that the messages sent are not as secure as they claim to be. So, to bridge this gap, in this paper Homomorphic Encryption is used to secure the messages further while also not reducing the speed of the transaction. This paper aims to implement a Chat application using Homomorphic Encryption which adds a layer of security over end-to-end encryption.
Secure and Transparent Supply Chain Management to Prevent Counterfeit Drugs

The production and distribution of counterfeit drugs are critical and progressively becoming a major issue in the world. One of the main reasons behind drug counterfeiting is the imperfect supply chain. It is difficult to track over the drugs from manufacturing to distribution. The major requirement of the drug supply chain is the assurance of drug quality, together with efficacy and safety. In this paper, we propose a blockchain-based platform of drug supply chain management from manufacturer to pharmacist. We provide tracking over drugs as well as visibility and the safety of sensitive drug information. The existing drug supply chain manufacturers don’t know what happens with their drugs or which pharmacy is taking their drugs. But our proposed model ensures traceability so that manufacturers will be able to know which pharmacy is taking their drugs through the distributed network provided by blockchain. Our platform will provide a secure network where all participants can see all the transactions between other participants. All transactions between participants are recorded in the blockchain and other participants will be able to view the transactions history of participants. All blockchain transactions information is kept in encrypted form in a database. Also, encrypted QR code will ensure that illegitimate participants will not be able to make a copy of the information given by the manufacturer, and will not be able to produce fake drugs by using the copied information. So, it will ensure security of drug information. We have built a prototype of the platform over the Ethereum network.
A hybrid algorithm for fuzzy clustering based on global and local membership degree

The clustering task has challenges that change according to the data, thus different algorithms have been proposed where each one has a bias on the data. In the fuzzy clustering approach, the most popular algorithm is the Fuzzy C-Means (FCM), which uses a global view of variables to calculate the degree of membership. On the other hand, the Multivariate Fuzzy C-Means (MFCM) uses a local view of variables to calculate the degree of membership. In this work, we proposed a new hybrid algorithm to use a combined local and global view approaches. For this, a new objective function based on the hybridization parameter is introduced. The experiments show the robustness and superiority of the proposed algorithm in real and synthetic datasets in most of the analyzed scenarios.
Compound State, Its Conditionality and Quantum Mutual Information

It is shown that quantum states on composite system may be represented by a composition of two types of complete positive (CP) maps, especially one of them is called by quantum conditionality, and such construction can be regarded as the quantum analogue of Bayes formula between a classical joint probability and a conditional probability. Then the mutual information of quantum state (the quantum analogue of Shannon’s mutual information) may be related to the channel given by the dual map of quantum conditionality.
Contrastive Learning of Handwritten Signature Representations for Writer-Independent Verification

In writer-independent verification systems, a single model is trained for all users of the system using dissimilarity vectors obtained through a dichotomy transformation that converts a multi-class problem into a 2-class problem comprising: (i) the intra-class dissimilarity vectors computed from samples of the same user, (ii) the inter-class dissimilarity vectors computed from samples of different users. When mapping handwritten signature representations, it is desired to obtain well-separated dense clusters of signature representations for each user, in such a way that transformed intra-class dissimilarity vectors tend to be separated from the inter-class dissimilarity vectors. Moreover, since skilled forgeries resemble reference signatures, it is also desired to obtain skilled forgery dissimilarity vectors that are further away from the region of the intra-class dissimilarity vectors. In this work, it is hypothesized that an improved dissimilarity space can be achieved through a multi-task framework for learning handwritten signature feature representations based on deep contrastive learning. The proposed framework is composed of two objective-specific tasks; it does not use skilled forgeries for training. The first task aims to map signature examples of a given user in a dense cluster, while linearly separating the signature representations of different users. The second task aims to adjust forgery representations by adopting a contrastive loss with the ability to perform hard negative mining. Hard negatives are similar examples but from different classes that can be seen as artificially generated skilled forgeries for training. In a writer-independent verification approach, the model obtained with the proposed framework is evaluated in terms of the equal error rate on GPDS-300, CEDAR and MCYT-75 datasets. Experiments demonstrated a statistically significant improvement in signature verification compared to the state-of-the-art SigNet feature extraction method.
Distributed Sorting in Complex Environments

We introduce an algorithm allowing a robot swarm to sort objects in complex environments. In this task, objects of different types are scattered around the environment and there is a specific goal area for each object type. The robots orbit the environment looking for objects and gather each class of objects into their designated area by physically pushing them around obstacles and towards their goals. The robots utilize a distributed collision avoidance algorithm for avoiding collisions with obstacles and among themselves based on the concept of buffered Voronoi cells. The robots decide which objects to target based on buffered Voronoi cell occupancy, thus preventing contention between robots. Global planning to determine the direction to move an object towards its goal along the shortest path is performed using goal maps generated from the distance transforms of these goal areas. The proposed algorithm is fully distributed and requires no central control or communication between robots. We evaluate the performance of our algorithm using an open-source web-based simulator and validate the real-world performance of the proposed algorithm in live experiments.
Facial Landmarks and Generative Priors Guided Blind Face Restoration

Blind face restoration (BFR) from severely degraded face images is important in face image processing, and has attracted increasing attention due to its wide applications. How-ever, due to the complex unknown degradations in real-world scenarios, existing priors-based methods tend to restore faces with unstable quality. In this paper, we propose a Facial Landmarks and Generative Priors Guided Blind Face Restoration Network (FGPNet) to seamlessly integrate the advantages of generative priors and face-specific geometry priors. Specifically, we pretrain a high-quality (HQ) face synthesis generative adversarial network (GAN) and a landmarks prediction network, and then embed them into a U-shaped deep neural network (DNN) as decoder priors to guide face restoration, during which the generative priors can provide adequate details and the landmarks priors provide geometry and semantic information. Furthermore, we design facial priors fusion (FPF) blocks to incorporate the prior features from pretrained face synthesis GAN and landmarks prediction network in an adaptive and progressive manner, making our FGPNet exhibits good generalization in real-world application. Experiments demonstrate the superiority of our FGPNet in comparison to state-of-the-arts, and also show its potential in handling real-world low-quality images from several practical applications.
Overcoming Data Sparsity in Automatic Transcription of Dictated Medical Findings

This paper presents a method for introducing class n-gram language models as a means for overcoming data sparsity in the training of an automatic speech recognition (ASR) system aimed at transcription of dictated medical findings composed predominantly in the Serbian language, including occasional phrases in Latin. The classes used by the model are defined with the specific aim of avoiding the need of identifying an appropriate orthographic expansion of each abbreviation, number or other non-orthographic element in a particular context. Generated language models are decoded in Kaldi using token passing, and lattices generated in this way are rescored using recurrent neural network language models (RNNLM). Although the proposed approach requires extensive effort for initial definition of classes based on existing text corpora of medical findings, it improves the quality of the model and increases the degree of automation in the processing of future training corpora. As such, the proposed method is particularly suitable for training on noisy data, full of misspel-lings and other errors, such as medical findings. The feasibility of the approach has been tested on a corpus of medical findings in the domain of radiology, where a perplexity score of 59.55 and word error rate of 1.4% have been achieved.
Advanced Learning Rate-Free Latent Factor Analysis via P2SO

Numerous entities are frequently encountered in various big data-related applications like wireless sensor networks [1–3], bioinformatics applications [4–9], social networks [10–13], user-service QoS [14, 15] and electronic commerce systems [16–18]. With the increasing number of involved entities, it becomes impossible to observe their whole interaction mapping. Hence, a resultant interaction mapping can be denoted by a high-dimensional and sparse (HiDS) matrix [19–23] with a few entries known (describing the observed interactions) while the most others unknown (describing the unobserved ones).
Low-Cost Training of Speech Recognition System for Hindi ASR Challenge 2022

This paper describes the speech recognition system, developed for Gram Vaani ASR Challenge 2022. The acoustic modeling techniques included i-vectors-based speaker adaptation and a combination of convolutional and factored time-delayed neural networks, fine-tuned with state-level Minimum Bayes Risk criteria. Experiments with text data augmentation and separation of different domains in test data are discussed. Proposed system is quite competitive, as it was among the top four participants in the evaluation, and show best result among individual participants. Yet, it requires very low computation resources to build it, which can be important for developing countries.
Examination of reducing VR sickness by displayed fixed grid in wheelchair simulator

This paper describes a method for reducing VR sickness in a wheelchair simulator. Our previous study developed a wheelchair simulator that can evaluate and examine barrier-free houses for wheelchair users at the design phase. However, this simulator had a problem of VR sickness caused by the discrepancy between the sensation obtained from the virtual space and the sensation predicted from past experiences. In particular, it was known that VR sickness occurred frequently and strongly during rotational motion. Furthermore, among rotational motions, VR sickness occurs most easily during yaw rotation. Therefore, this paper proposes two methods to reduce VR sickness by focusing on the yaw rotation. To verify the effectiveness of the methods, an experiment was conducted on a subject.