Reviewerly/exHarmony huggingface dataset:
Recurrent Scattering Network Detects Metastable Behavior in Polyphonic Seismo-Volcanic Signals for Volcano Eruption Forecasting

We introduce an end-to-end (E2E) deep neural network architecture designed to perform seismo-volcanic monitoring focused on detecting change. Due to the complexity of volcanic processes, this requires a polyphonic detection, segmentation, and classification approach. Through evolving epistemic uncertainty, invoking a Bayesian network strategy, we detect change and demonstrate its significance as an indicator for possible forecasting of eruptions using data from the Bezymianny and Etna volcanoes. Specifically, we propose morphing the scattering transform from previous work into a novel E2E hybrid and recurrent learnable deep scattering network to adapt to multi-scale temporal dependencies from streaming data. The time-dependent scattering is in some sense physics informed, namely, through time&#x2013;frequency representation (TFR) of the data. At the same time, with a carefully designed deep convolutional LSTM (ConvLSTM) architecture, we learn intra-event, temporal dynamics from the scattering coefficients or features. We verify the effectiveness of transfer learning switching between volcanoes. Our experimental results set a new norm for semi-supervised seismo-volcanic monitoring.
Truss Decomposition on Multilayer Graphs

Multilayer graphs are very powerful in representing the multiplex relationships among entities. The truss decomposition on single-layer graphs is a well-studied problem which divides a graph into a hierarchy structure, and has a series of applications. However, little attention has been paid to the truss decomposition on multilayer graphs. In addition, truss decomposition on multilayer graphs derives new challenges compared to the scenario on single-layer graphs. In this paper, we devise an efficient algorithm to compute the truss decomposition on multilayer graphs. Extensive experiments on 5 real-life datasets validate the effectiveness and efficiency of our methods.
A Model for Car Registration System based on Blockchain Technology

Blockchain has become the most promising technology in recent years that raised significant interest among researchers, developers, and industry practitioners due to its unique trustworthiness and security characteristics. In this research, we propose a model for a car registration system based on blockchain technology. We use the Hyperledger Fabric as the blockchain platform of the system. The DApp is implemented using ReactJS, JSX, NodeJS, and JWT. The model prototype provides preliminary features of a car registration system. The testing result shows that all developed features work correctly, and blockchain is a promising technology for such a type of application.
Minable Data Publication Based on Sensitive Association Rule Hiding

Minable data publication can promote data sharing among commercial companies and further facilitate the development of data-driven services. However, these commercial companies are often reluctant to publish their data due to security concerns. The published data may contain some sensitive information that is minable by malicious entities, leading to data privacy leakage. Therefore, it is highly demanded to develop the technologies supporting minable data publication with privacy protection. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> rivacy-preserved <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> inable <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> ublication scheme (PMDP). PMDP enables selective sensitive association rules hiding while supporting the association rule mining. In PMDP, how to balance the trade-off between data privacy and data utility is the major problem, which can be formulated as a multi-objective optimization problem. To tackle this multi-objective optimization problem, we develop a customized multi-objective evolutionary algorithm (MOEA). In the customized MOEA, the local optimum trapping issue and slow convergence speed issue are hard to be addressed. First, to avoid being trapped into the local optimum, we carefully design a novel mutation method to guarantee the diversity of solutions. Second, to accelerate the convergence speed, we present a preprocessing method before the evolution process of the MOEA. In addition, we introduce the elite learning strategy into the MOEA, so the convergence speed can be further accelerated. At last, experiments are conducted over several datasets to demonstrate the effectiveness of PMDP.
Trustable Policy Collaboration Scheme for Multi-Agent Stigmergic Reinforcement Learning

In this letter, we propose a trustable policy collaboration scheme in the paradigm of multi-agent independent reinforcement learning (MAIRL). This trustable policy collaboration scheme is realized by directly mixing the policy parameters of homogeneous agents, for which an upper bound of the mixture metric is derived to guarantee the policy improvement. This trustable policy collaboration scheme can update the behavioral policies of agents distributedly and further improve the performance of MAIRL. In addition, we develop a practical implementation of this trustable policy collaboration scheme, and verify its effectiveness in a mixed-autonomy traffic control simulation scenario through the performance comparison with other typical methods.
Autonomous Navigation of Drones Using Reinforcement Learning

An UAV is a small hovering machine that can be remotely guided or flown autonomously through software-controlled flight plans in its embedded systems, operating in combination with onboard sensors and GPS. UAVs have demonstrated their versatility during the COVID-19 pandemic. Medicines and personal protection equipment were airlifted to the remote locations using UAVs. In future, transportation of drugs using UAV will be cost-effective and efficient. However, using human resources to operate these UAVs need a lot of time and investment in training. Most UAVs use GPS technology to travel to their destination from the start point. Many UAVs in the airspace generate a need for a drone traffic management system to mitigate collision risk. The drone traffic management system again demands human experts and massive expenditures. To overlook this challenge, we propose to model UAVs' autonomous navigation using the already available infrastructures present in highways like bike lanes and walking lanes. This research suggests a framework by using reinforcement learning and GPS way-points to allow the UAV to operate successfully from the origin location to the end location by following the bike lanes present on the roads.
An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method.
COVID-19 Contact Tracing Using BLE and RFID for Data Protection and Integrity

Responding to the rapid spread of the COVID-19 virus, the need for contact tracing and self-isolation has become the focus of many health experts and governments as being the primary option for containing the spread of the disease. The utilization of the smartphone has been the focus of many efforts by creating mobile applications that harness the potential of GPS and BLE technologies to make contact tracing as efficient and effective as possible. The prevailing issue with this system is the concern of privacy and data protection of app users. In order to address this problem, through this paper, we are suggesting the use of passive RFID technology similar to that of most public transit systems. This system contains a client-held RFID card and a receiver, and a server that processes the data. Through this paper, we hope to present an alternative, less invasive system that will help governments and health officials prevent the spread of COVID-19 in their communities.
A Literature Review on Bidirectional Encoder Representations from Transformers

Transfer learning is a technique of training a model for a specific problem and using it as a base for training another related problem. It has been proved to be very effective and has two phases: the pre-training phase (generation of pre-trained models) and the adaptive phase (reuse of pre-trained models). Auto-encoding pre-trained learning model is one type of pre-trained model, which uses the transformer model's encoder component to perform natural language understanding. This work discusses the bidirectional encoder representations from transformers (BERT) and its variants and relative performances. BERTs are transformer-based models developed for pre-training unlabeled texts, bidirectional, by considering the semantics of texts from both sides of the word being processed. The model implements the above function using two specific functions: masked language modeling (MLM) and next sequence prediction (NSP). The robustly optimized BERT (RoBERTa) variant of BERT with few modifications has significant improvements in removing NSP loss function due to its inefficiency. SpanBERT is another variant that modifies MLM tasks by masking contagious random spans and also uses the span-boundary objective (SBO) loss function. A lite BERT (ALBERT) is another variant with two-parameter reduction techniques: factorized embedding parameterization and cross-layer parameter sharing. It also uses inter-sentence coherence loss instead of NSP. The performance of the BERT's variants is found to be better than BERT, with few modifications as per the available literature.
Modeling Without Sharing Privacy: Federated Neural Machine Translation

Training neural machine translation models requires large amount of diverse training corpora. It poses a challenge for collecting sufficient data. In addition, labeling monolingual corpus demands professional knowledge in certain domain. Building collaboration between different institutes produces other problems such as legality of data exchange and commercial data leakage.In this paper, we proposed a federated neural machine translation model FedNMT to train a robust machine translation system without sharing raw data from participants. By applying FedNMT, neural machine translation (NMT) systems can be ameliorated from the corpus held by different contributors without directly exposing them to one another. This approach preserves the user privacy by utilizing the federated learning framework, encryption techniques. In the federated learning paradigms, a global model is distributed to user clients, and a central server is built to aggregate the learning parameters and update the gradients. Experimental results show the effectiveness of our model in comparison with the data-centralized model.
Federated Learning Protocols for IoT Edge Computing

In this article, we provide a set of federated learning (FL) protocols for future Internet architectures, which integrate the edge computing with the Internet of Things (IoT) known as “IoT edge computing.” The proposed protocols aim to the efficient implementation of the FL, i.e., distributed intelligence, in future IoT networks, where edge computing will leverage the overall procedure at the edge of the network. We first provide a list of application requirements for such an FL implementation, which result in the architecture of constrained and nonconstrained IoT devices based on a set of Internet engineering task force (IETF) standards. The FL protocols consist of three stages as follows: 1) initial configuration; 2) distributed training; and 3) cloud updates. The specified FL protocols are tested using an experimental IoT platform, which is used to obtain experimental results that provide the performance evaluation of the FL protocols in terms of accuracy, time, and latency. We propose those FL protocols for the next-generation Internet (NGI), where IoT, edge computing, and FL will be blended efficiently for future Internet applications.
Research on the Construction of Classroom Teaching Evaluation System Based on Artificial Intelligence

Human beings are accelerating into the era of artificial intelligence. In all areas of society, artificial intelligence is playing an irreplaceable and huge role. The development of artificial intelligence in the field of education is accelerating. The development of artificial intelligence in the field of education is accelerating. It promotes the development of teaching and makes classroom teaching more lively and interesting. Classroom teaching evaluation occupies a very important position in education and teaching, and it is a ruler to measure the quality of classroom teaching. Based on the study of traditional classroom teaching evaluation, this paper analyzes the problems in traditional teaching evaluation from three aspects: single teaching evaluation method, untimely feedback of evaluation results, and inability to achieve comprehensive evaluation. An artificial intelligence-based classroom teaching evaluation system is constructed from three aspects: system structure, procedural evaluation of students' listening to lectures, and procedural evaluation of teachers' teaching. On this basis, it analyzes the technical, ethical, and systematic problems of artificial intelligence in classroom teaching evaluation. In response to these problems, the development strategy of artificial intelligence in classroom teaching evaluation is proposed from several aspects, such as breaking through the technical bottleneck and establishing a teaching evaluation simulation system. Finally, the prospects for the development of artificial intelligence in teaching evaluation are prospected.
The Aspects of Smart Education in The World

The author examines smart education and its basic principles in this article. The author also shows that smart education is a learning process in which technology innovations and Internet resources are used globally. After all, it allows students to gain professional competence based on a systematic multivariate vision and study of disciplines, taking into account their multidimensional and continuous content updating. The author shows the concept of smart education. It is the creation of an intellectual environment, the continuous development of competencies of participants in the educational process, including formal and informal learning processes based on innovative technologies. Thus, the introduction of smart technologies in education will lead to the transition from the old system of reproductive knowledge to a new, creative form of learning using innovative methods and will provide the necessary knowledge and skills which will be necessary for the student in the future. The author also notes that education based on three educational activities, namely, arithmetic, reading, and writing, transforms into education which improves the following seven skills: creativity and innovation; communication; cross-cultural understanding; collaboration and leadership; critical thinking and problem solving; ICT literacy; career and life skills. These are seven skills and abilities of smart education in the 21st century. In this case, smart education assumes that the teacher should use technological innovations and Internet resources. Thus, smart education provides new opportunities for teachers to share experiences and ideas, do more research activities, personalize the course of study depending on its tasks and the competence of the listener, and save time.
Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

It's even one year since the COVID-19 pandemic hit Indonesia, to anticipate it, the government brought in a COVID-19 vaccine. Various types of COVID-19 vaccine have been introduced to Indonesia, including which ones will be considered the best according to the community through the Twitter platform. One of the venues that creates the most public sentiment is Twitter. It can be determined whether the public fully approves or rejects the existence of vaccination in Indonesia by analyzing public sentiment surrounding the COVID-19 vaccine. Data acquisition using a crawling procedure by connecting the Twitter API, pre-processing, sentiment categorization, and sentiment analysis outcomes are the stages of the sentiment analysis process to become a sentiment analysis application. The PHP and MySQL programming languages are used to create the database for the sentiment analysis application. After the application has been fully implemented, it can do sentiment analysis from each dictionary probability using the Naive Bayes Classifier approach. The study of the two keywords "vaksin covid" and "vaksin corona" yielded the following results. It has 93% positive sentiment results, 72% negative sentiment results, and 35% neutral sentiment outcomes, with an accuracy of 94.74% and 75.47% per keyword. Meanwhile, the Sinopharm vaccine, which has the most positive attitude with the terms "vaksin sinovac," "vaksin astrazeneca," "vaksin sinopharm," and "vaksin nusantara," has 84 percent tweets with a 74.23% accuracy rate.
A Model for Understanding Project Requirements based on CMMI Specifications

The main objective is to make sure that all the stakeholders understand the issues in the development of software. Knowing the needs ensures the success of the project. CAMI Level 2, therefore, helps organizations improve processes of software by providing them with important special practices to improve their software. Level 2 (in other words, SP 1.1 &#x201C;understand requirements&#x201D;). This practice helps developers to understand the problem and the needs of the domain in full. In this paper, we have visited three companies and conducted three detailed interviews. We have also developed a model of SP 1.1 by taking advantage of the different opinions and experiences of practices on the implementation of SP 1.1. This model comprises four key stages: preparation, analysis, validation, and updating. It will provide practitioners with key advice on how to implement SP 1.1 effectively.
PIVOT: Private and Effective Contact Tracing

We propose, design, and evaluate PIVOT, a privacy-enhancing and effective contact tracing solution that aims to strike a balance between utility and privacy: one that does not collect sensitive information yet allowing effective tracing and notifying the close contacts of diagnosed users. PIVOT requires a considerably low degree of trust in the entities involved compared to centralized alternatives while retaining the necessary utility. To protect users’ privacy, it uses local proximity tracing based on broadcasting and recording constantly changing anonymous public keys via short-range communication. These public keys are used to establish a shared secret key between two people in close contact. The three keys (i.e., the two public keys and the established shared key) are then used to generate two unique per-user-per-contact hashes: one for infection registration and one for exposure score query. These hashes are never revealed to the public. To improve utility, user exposure score computation is performed centrally, which provides health authorities with minimal, yet insightful and actionable data. Data minimization is achieved by the use of per-user-per-contact hashes and by enforcing role separation: the health authority act as a mixing node, while the matching between reported and queried hashes is outsourced to a third entity, an independent matching service (MS). This separation ensures that out-of-scope information, such as users’ social interactions, is hidden from the health authorities, whereas the MS does not learn users’ sensitive information. To sustain our claims, we conduct a practical evaluation that encompasses anonymity guarantees and energy requirements.
Nonclassicality criteria for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -dimensional optical fields detected by quadratic detectors

Non-classicality criteria for general N-dimensional optical fields are derived. They involve intensity moments, the probabilities of photon-number distributions or combinations of both. The Hillery criteria for the sums of the probabilities of even or odd photon numbers are generalized to N-dimensional fields. As an example, the derived non-classicality criteria are applied to an experimental 3-mode optical field containing two types of photon-pair contributions. The accompanying non-classicality depths are used to mutually compare their performance.
Distributed Active Learning for Semantic Segmentation on Walking Robots

Quickly adapting to new and unknown environments is a vital functionality for autonomous robots. To increase their capabilities, they need a sophisticated self- and environmental awareness. Understanding the surroundings can be aided by a pixelwise semantic segmentation of the images taken by the robot. However, to achieve viable results a large database of annotated images is needed in advance. To aid in a quick understanding of the surroundings a distributed active learning approach for semantic segmentation is presented. The robot evaluates and selects images where it thinks annotation would lead to a better overall segmentation results. Since the robot is potentially on an autonomous remote mission, the communication might be limited. Therefore, the robot selects images which are stored in a buffer before they are transmitted in batches to the base station. The annotation of the images and training of a new model for the segmentation can be performed asynchronously to the robot's mission. The model of the robot is then updated once ready. Four different stream-based query metrics are deployed and tested on a dataset and on ANYmal in a real scenario. The proposed approach allows a robot to be deployed in an unknown surrounding and through quick and continuous learning it gains an understanding of the area.
Robotic Weight-based Object Relocation in Clutter via Tree-based Q-learning Approach using Breadth and Depth Search Techniques

In this paper, the problem of retrieving a target object from a cluttered environment through a mobile manipulator is considered. The task is solved by combining Task and Motion Planning; in detail, at a higher level, the task planner is in charge of planning the sequence of objects to relocate while, at a lower level, the motion planner is in charge of planning the robot movements taking into consideration robot and environment constraints. In particular, the latter provides feedback to the former about the feasibility of object sequences; this information is exploited to train a Reinforcement Learning agent that, according to an objects-weight based metrics, builds a dynamic decision-tree where each node represents a sequence of relocated objects, and edge values are weights updated via Q-learning-inspired algorithm. Three learning strategies differing in how the tree is explored are analysed. Moreover, each exploration approach is performed using two different tree-search methods: the Breadth first and Depth first techniques. Finally, the proposed learning strategies are numerically validated and compared in three scenarios of growing-complexity.