Reviewerly/exHarmony huggingface dataset:
Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration

Multimodal medical image registration has an important role in monitoring tumor growth, radiotherapy, and disease diagnosis. Deep-learning-based methods have made great progress in the past few years. However, its success depends on large training datasets, and the performance of the model decreases due to overfitting and poor generalization when only limited data are available. In this paper, a multimodal medical image registration framework based on few-shot learning is proposed, named reverse-net, which can improve the accuracy and generalization ability of the network by using a few segmentation labels. Firstly, we used the border enhancement network to enhance the ROI (region of interest) boundaries of T1 images to provide high-quality data for the subsequent pixel alignment stage. Secondly, through a coarse registration network, the T1 image and T2 image were roughly aligned. Then, the pixel alignment network generated more smooth deformation fields. Finally, the reverse teaching network used the warped T1 segmentation labels and warped images generated by the deformation field to teach the border enhancement network more structural knowledge. The performance and generalizability of our model have been evaluated on publicly available brain datasets including the MRBrainS13DataNii-Pro, SRI24, CIT168, and OASIS datasets. Compared with VoxelMorph, the reverse-net obtained performance improvements of 4.36% in DSC on the publicly available MRBrainS13DataNii-Pro dataset. On the unseen dataset OASIS, the reverse-net obtained performance improvements of 4.2% in DSC compared with VoxelMorph, which shows that the model can obtain better generalizability. The promising performance on dataset CIT168 indicates that the model is practicable.
Grammar Induction - Experimental Results

The OpenCog Learning project explores novel techniques for the induction of symbolic structure from environmental sources. As a first application of these ideas, the automated learning of the grammatical structure of English is explored. This document provides a summary of results observed to date. The symbolic structure manifests itself as large, complex graphs tying together words and grammatical structure. The induction process provides probability distributions on the graphs. The primary task is to describe and understand the nature of those graphs.
Toward Generating Natural-Language Explanations of Modal-Logic Proofs

As we work toward artificial general intelligence, it is clear that we must try to imbue agents with faculties which ensure they are trustworthy. We firmly believe that an AGI agent must be able to explain its decision-making in order for it to be considered trustworthy. More specifically, agents must be able to explain themselves in a way that is both logically correct and understandable to humans. We take a first step toward a system that can generate explanations which satisfy this pair of conditions. We created the first model that can produce summaries of modal-logic proofs using a transformer language model. We qualitatively evaluated the model’s outputs on a held-out test set and found that the logical content of the model’s explanations precisely matched the input proofs in 60% of cases.
A General-Purpose Machine Reasoning Engine

We are developing a machine reasoning engine that can learn arbitrary concepts from small number of training samples, and generate explainable models which can be visualized graphically. In this article, we present our intermediate results by experimenting with problems that require reasoning with simple arithmetic, geometric shapes, logical operators, simple program syntax, and regular and context-free languages.
Dynamics of Quantum Networks in Noisy Environments

Noise exists inherently in realistic quantum systems and affects the evolution of quantum systems. We investigate the dynamics of quantum networks in noisy environments by using the fidelity of the quantum evolved states and the classical percolation theory. We propose an analytical framework that allows us to characterize the stability of quantum networks in terms of quantum noises and network topologies. The calculation results of the framework determine the maximal time that quantum networks with different network topologies can maintain the ability to communicate under noise. We demonstrate the results of the framework through examples of specific graphs under amplitude damping and phase damping noises. We further consider the capacity of the quantum network in a noisy environment according to the proposed framework. The analytical framework helps us better understand the evolution time of a quantum network and provides a reference for designing large quantum networks.
Hu-bot: promoting the cooperation between humans and mobile robots

Abstract This paper investigates human–robot collaboration in a novel setup: a human helps a mobile robot that can move and navigate freely in an environment. Specifically, the human helps by remotely taking over control during the learning of a task. The task is to find and collect several items in a walled arena, and Reinforcement Learning is used to seek a suitable controller. If the human observes undesired robot behavior, they can directly issue commands for the wheels through a game joystick. Experiments in a simulator showed that human assistance improved robot behavior efficacy by 30% and efficiency by 12%. The best policies were also tested in real life, using physical robots. Hardware experiments showed no significant difference concerning the simulations, providing empirical validation of our approach in practice.
Exact Inference in a Tetranomial Distribution

Sequential sampling plans for unbiased estimation of the Bernoulli parameter ‘p’ and its functions have been studied almost 70 years back. An extension of the idea to parametric function estimation in a tetranomial distribution has been considered in this paper. The cell probabilities are taken to be p2, q2, r2 and 2(pq+pr+qr), satisfying p, q, r > 0, p +q +r = 1. Some illustrative examples have been studied to demonstrate the underlying concepts and the computational procedure. The emphasis is on unbiased estimation in the exact sense which makes it difficult and challenging.
A dynamic block reward approach to improve the performance of blockchain systems

In Ethereum, miners are responsible for expanding the blockchain ledger by appending new blocks of transactions in exchange for incentives. Within the current Ethereum incentive mechanism, miners can still receive a significant amount of reward when creating non-full or even empty blocks, despite their negative impact on the system performance. We provide an extensive data-driven analysis of the impact of non-full blocks on the system performance, with the help of the BlockSim simulation tool. We collect the data for 500,000 Ethereum blocks and fit the appropriate probability distributions to the data to provide input suitable for the simulator. We show that the performance of Ethereum can be improved by over 50% if all blocks were filled with transactions. We propose an adjustment to the current Ethereum incentive model to assure the received incentive is always proportional to the block utilization level. Using our proposed approach, the incentive for non-full blocks is significantly reduced, making this behavior less attractive for miners. This implies that miners would be enforced to fill their blocks with transactions, and thus the performance is pushed to its optimal level. We show that our approach can work in practice without any crucial security issues.
Precision inspection of transparent component quality

In this work, a method using transmission interferometry is proposed to detect micro defects (streaks, inclusions, air bubbles, grooves, cracks, etc.) present in transparent materials surface. This technique is non-destructive and non-contact for the analysis of transparent and optical components whose surfaces vary from a few mm2 to larger sizes. The purpose of this method is to provide a means as simple as possible and to identify defects with low contrast, and in particular barely visible defects, and to differentiate between the defects. The transmission system generates interference fringes by the superposition of two microscopic periodic structures. According to the principle of the method, the image of the periodic microscopic structure, transmitted by the laser beam, traverses the sample. It then superposed on the reference microscopic structure; to generate the interference fringes, which materialize the presence of defects in the material. Changes in the shape of interference fringes inform the presence and dimensions of defects. This technique makes it possible to clearly identify microscopic and submicroscopic defects, thanks to the high resolution of the system. The optical device used allows high defect magnification of up to 1000 times. This control and measurement method allows for real-time inspection. It provides high detection resolution, allowing better observation of defects, which facilitates the automation of measurements and controls. Therefore, the proposed method can be suitable for the detection of surface defects in transparent optical objects such as optical films, lenses, and prisms.
Multitemporal SAR image despeckling based on non-local theory

In this paper, a multitemporal SAR image despeckling based on non-local theory (NLG-MulSAR) algorithm is proposed, which is improved based on the basic framework of the ratio-based multitemporal SAR image denoising (RABASAR). The temporal and spatial information of a multitemporal SAR image is integrated. The super image and the ratio image acquisition part of the RABASAR algorithm are optimized by the NLG filtering algorithm. The NLG algorithm does not need to transform multiplicative noise into additive noise on a synthetic aperture radar (SAR) image and then filter it. The NLG algorithm uses the nonlinear method to eliminate the influence of strong noise points while preserving image edge features. Based on the number of pixels in the non-local image block, the NLG algorithm avoids the generation of fuzzy noise on the filtered image. In this study, we use seven Gaofen three SAR images captured at different times in the Beijing area as experimental data to evaluate the effect of filtering methods in terms of five objective parameters: signal-to-noise ratio, standard deviation, equivalent number of looks, radiative resolution, and speckle noise index. In addition, based on the ratio image, we propose an index, namely the filtering edge coefficient of a multitemporal SAR image, to evaluate the filtering edge retention characteristics of a multitemporal SAR image. The results show that compared with the RABASAR filtering algorithm, the proposed NLG-MulSAR filtering algorithm can better balance the relationship between multiplicative noise and texture detail information and attenuate the speckle while protecting the texture detail information on the SAR image.
On security aspects of the ciphers T-310 and SKS with approved long-term keys

The cipher T-310 was developed by the Central Cipher Authority of the German Democratic Republic in the 1970s and widely used for protection of teletype communication up to security level secret. After publication of the cipher algorithm in 2006, several articles appeared aiming at the cryptographic properties of the cipher T-310. The cryptographic strength of the cipher T-310 must be assessed as a combination of both the cipher algorithm and the approved long-term keys. This article provides a rationale of the design decisions and the selection of approved long-term keys for T-310. We demonstrate that the attacks involving fundamental equations, linear characteristics, and some of their generalizations do not work if historical approved long-term keys are used. The results are also valid for the cipher SKS and block ciphers, whose groups of round functions are the alternating groups over the blocks.
A survey on Virtual Reality for Medical Applications

The evolution of low-cost Virtual Reality (VR) technology is captivating the attention of researchers and users around the globe implying that it may be the next remarkable feat in technological innovation. Healthcare is among the top 3 industries that are the leading adapters of VR technology. Extreme demand for the high quality healthcare services, increased use of connected devices in the medical field and the need to make healthcare cost-effective are the key drivers of the adoption of VR in medicine. Both medical professionals and patients benefit from the VR as it allows them to work in the computer-simulated environment explicitly devised for practices and various medical procedures. This paper intends to provide comprehensive information about the application of VR in diverse disciplines of the medical field. This review aims at investigating the past studies on VR to improvise the traditional method of treating various medical conditions.
Can Technology Replace the Teachers’ Role in Higher Education Settings? A Systematic Literature Review

As technologies continue to advance and become more complex, they will have significant ramifications for formal education contexts. It is mentioned that digital technologies will remain in our virtual and physical classrooms for the foreseeable future. Moreover, when it comes to the direct contact between students and their teachers, it is challenging to simulate a teacher’s ability to read and respond to students’ emotions. Therefore, the purpose of this study is to obtain data from a variety of sources to determine whether technology can replace teachers using PRISMA Protocol as the research methodology with 39 previous studies from the SCOPUS journal. According to the findings of this study, it can be concluded that technology cannot replace the rules and positions of teachers, particularly when it comes to psychological and value issues, because ICTs cannot make every potential decision about other people.
COVID-19 Vaccines in Indonesia: A Public Opinion Study Based on Twitter Data Multidimensional Sentiment Analysis

Vaccination is one of several solutions to control the spread of Coronavirus disease (COVID-19). However, it turns out that vaccination is not well received by the community, resulting in public disputes across the country. This study analyzes the relevance of vaccines to several points of view, such as political, economic, and spiritual, which aims to confirm the effectiveness of mass vaccination in reducing the spread of COVID-19. We used the Indo-BERT (Bahasa Indonesia of Bidirectional Encoder Representations from Transformers) method to see public sentiment towards vaccinations. The result of this research is that people’s sentiments tend to be more political, economic, social, and spiritual.
Android-Based Chatbot Application Using Back Propagation Neural Network to Help the First Treatment of Children’s Diseases

The level of children’s health is one of the problems that the Indonesian government pays attention to, especially in the health sector. Data from the Indonesian Demographic and Health Survey (IDHS) in 2017 showed that the infant mortality rate (IMR) reached 24 deaths out of 1000 live births. Diseases and health problems that cause child mortality such as perinatal problems, infectious disorders, and malnutrition problems are difficult problems to handle at the health care center level in remote areas due to the lack of diagnostic equipment and medicines. Along with the rapid development of technology today, there are many applications that function to simplify the process of managing and finding information, one of which is chatbot technology. Like a pediatrician, later the chatbot will diagnose the disease from a series of questions submitted by the child’s parents. This final project research aims to develop an android-based chatbot application to help identify diseases in children using a back-propagation artificial neural network. The data used in this research is based on the Integrated Management of Sick Toddler (IMCI) chart book. Based on the results obtained in this final project, the best BPNN model for children’s disease problems in a chatbot application with the number of neurons (128, 64), epochs as many as 800, dropout rate 0.5, optimizer Adam, getting training loss values of 0.11, training accuracy of 96%, validation loss of 1.05 and has a validation accuracy of 64%.
Hybrid Filtering Algorithm in Event Manager Partner Recommendation System

One of the social media-based event manager applications that have been developed on the client side is MeetingYuk, and the application on the partner side is known as MerchantYuk. The development of these two applications (MeetingYuk and MerchantYuk) increased the number of user and partner data. Currently, a list of partners is shown to the user without any filtering option against user preferences. The more list of partners provided, the more options for users will increase, thus making users confused about choosing a partner. Based on this problem, it is necessary to develop a recommendation system to help meet users' needs in selecting partners. The recommendation system utilizes a hybrid filtering method, combining collaborative and content-based filtering methods. The collaborative filtering method analyzes the relationship between the ratings given by the MeetingYuk application users, and the content-based filtering method analyzes the similarity of services at each merchant. The two methods were combined using social aperture to get the prediction results. The experiment results show that the hybrid filtering method provides the best results for predicting suitable merchants, with an MAE value of 0.2019 and an RMSE of 0.5161.
A Re-rank Algorithm for Online Hotel Search

Abstract Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by several factors in their decision-making. We analysed a historical dataset from a meta-search booking platform with the aim of exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on the influence of (i) ranking position, (ii) number of reviews, (iii) average ratings and (iv) price when analysing users’ click behaviour. Our results confirmed conventional wisdom that position and price were the “two elephants in the room” heavily influencing user decision-making. Thus, they need to be taken into account when, for instance, trying to learn user preferences from clickstream data. Using the results coming from this analysis, we performed an online A/B test on this meta-search booking platform comparing the current policy with a price-based re-rank policy. Our online experiments suggested that, although in offline experiments items with lower prices tend to have a higher Click-Through Rate, in an online context a price-based re-rank was only capable to improve the Click-Through Rate metric for the first positions of the recommended lists.
A Framework for Assessing Motivational Methods Towards Incentivizing Cybersecurity Practice in Healthcare

Data breaches in healthcare have become common in recent times due to the weakness of the human element. As a result, intrinsic and extrinsic motivations were identified, analyzed, and assessed through a literature survey. After a critical gap analysis of the related studies, a framework was designed. This can be used to practically assess the effectiveness of various motivational methods for incentivizing healthcare staff toward strengthening the human aspect of security practice.
Implementation of the SPECK Cipher Algorithm on Patient Blood Pressure Data Using the Internet of Things (IoT)

Internet of Things technology allows humans to collect information about daily activities, for example to find out someone's health information, but at the time of data transmission it has an impact on one's privacy. Sniffing can record data sent from the user to the database server, this results in a leak of individual privacy. The SPECK algorithm is one of the cryptographic algorithms that guarantees confidentiality because the SPECK algorithm has varying key lengths and block sizes. Adding the SPECK cipher Algorithm to the node that holds sensor data can minimize privacy data leaks caused by data transmission on the Internet of Things. The SPECK algorithm has the same results as the test vectors in published journals and the resulting ciphertext can be returned to its original form by decryption. The encryption time is 0.089354 seconds and the decryption time on the server is 0.00610295 seconds.
IDENTIFICATION OF INDONESIAN PHONEMES IN VIDEOS USING A NEURAL NETWORK BACKPROPAGATION

Visual communication is a vital component in everyday life in the understanding speech by looking at the movements of the lips. Lip movements while pronouncing syllables or words will form a specific pattern. This pattern lets us get information about what other people say without audio or sound. A deaf person is a person who has a minimal level of hearing / cannot hear, so there are obstacles in the process of language information through the sense of hearing with or without hearing aids. The shortcomings of the previous journal were that it only detected syllable Indonesians. The research we did will detect phonemes that come out of an image. This research is a small part of an identification application that will make. This application is used for visual-to-text transformation to help translate visuals into a word the deaf can read. This research focuses on image processing and pattern recognition fields of lip movement, which allows us to create a system for automatic lip reading that can help the deaf. We use the neural network method, which begins with a data prefix, namely data collection for each phoneme and data sharing (test data and train data). In the preprocessing stage, we do data cropping and resizing and then proceed with the RGB to gray conversion process. In the Data processing stage, we perform feature extraction, transposing, and initialization code for each phoneme. We perform the classification process and produce accurate results at the result stage. The stages of this method will produce the final result in the form of a phoneme identification application. From the research we carried out, we got good results, 90% for the train data and 96% for the test data. In this study, we use the NN (Neural Network) Backpropagation method for lip pattern recognition when pronouncing phonemes in Indonesian and compare the accuracy results with the Naïve Bayes method. The test results from this study indicate that the NN Backpropagation method can recognize lip movement patterns with the highest accuracy achieved than Naïve Bayes.