Reviewerly/exHarmony huggingface dataset:
BP neural Network-Kalman filter fusion method for unmanned aerial vehicle target tracking

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.
Overview of the Causality-driven Adhoc Information Retrieval (CAIR) task at FIRE-2020

This paper describes an overview of the findings of the track named ‘Causality-driven Ad hoc Information Retrieval’ (abbv. CAIR) at the Forum for Information Retrieval Evaluation (FIRE) 2020. The purpose of the track was to investigate how effectively can search systems retrieve documents that are causally related to a specified query event. Different from standard information retrieval (IR), the criteria of relevance in this search scenario is stricter in the sense that the retrieved documents at the top ranks should provide information on the potentially relevant causes that might have caused a given query event, e.g. retrieve documents on political situations that might have led to ‘Brexit’. We released a dataset comprised of a set of 25 queries split into train and test sets. We received submissions from two participating groups. The two main observations from the best performing runs from the two participating groups are that longer queries showed a general trend to yield more causally relevant documents towards top ranks as seen from the results obtained from the first participating group, whereas it turned out that sequence-based text representation for semantically matching the documents with queries did not yield effective retrieval results, thus leaving the scope to develop supervised or semi-supervised methods to address causality-based retrieval.
An Anti-Quantum Signature Scheme over Ideal Lattice in Blockchain

Blockchain is a decentralized technology that provides untampered and anonymous security service to users. Without relying on trusted third party, it can establish the value transfer between nodes and reduce the transaction costs. Mature public key cryptosystem and signature scheme are important basis of blockchain security. Currently, most of the public key cryptosystems are based on classic difficult problems such as RSA and ECC. However, the above asymmetric cryptosystems are no longer secure with the development of quantum computing technology. To resist quantum attacks, researchers have proposed encryption schemes based on lattice recently. Although existing schemes have theoretical significance in blockchain, they are not suitable for the practical application due to the large size of key and signature. To tackle the above issues, this paper proposes an anti-quantum signature scheme over ideal lattice in blockchain. First, we transfer the signature scheme from the standard lattice to the ideal lattice, which reduces the size of public key. Afterwards, a novel signature scheme is proposed to reduce both the size of the private and public key significantly. Finally, we theoretically prove the security of our ideal lattice-based signature scheme with a reduction to the hardness assumption of Ideal Small Integer Sulotion problem which can resist quantum attacks. The efficiency analysis demonstrates that our signature scheme can be practically used in blockchain.
Text Recognition System for Visually Impaired using Portable Camera

Text detection in natural and complex images play an important role in analysis of image. Text information appears everywhere like product labels, documents, scene images etc. It will be very difficult for visually impaired person to find text region in an image. This problem should be addressed because it cannot assume that the captured image contains only text. This paper presents camera-based text reading framework which helps visually impaired people to read texts in natural scenes, product labels etc. Here, task is divided into two steps i.e. text detection and text recognition. Text detection involves text localization phase. Firstly, we binarize the image. Geometric and stroke width filtering removes non text regions an image. Segmented text region in a cluttered scene is binarized and recognized by optical character recognition. Recognized texts are output to the visually impaired people in speech. Experimental result shows that proposed method offers better performance in text recognition.
Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin types, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.
Privacy-Preserving Public Key Infrastructure for Vehicular Networks

Cooperative intelligent transport systems promise considerable improvements on road safety and the utilization of transport infrastructures. Current approaches, however, build upon policies to protect privacy, which raise serious concerns. In this paper, we propose a privacy-preserving public key infrastructure (PKI) for vehicle-to-everything communication. We use zero-knowledge proofs to authenticate, while still being able to hide identities. In order to exclude malicious actors, we integrate an anonymous reputation-based blacklisting scheme. Our benchmarks on an on-board connectivity unit with resource-constrained hardware confirms the feasibility of the approach. Specifically, we expect approximately 67 kB payload and 35 minutes computation time per day to authenticate.
Information Hygiene as Prevention of Destructive Impacts of Digital Environment

Today a completely new type of reality is being constituted – «digital reality», which entails new challenges both for an individual and society. Deep involvement into the digital reality and the active application of its technical mediators put a strain on the individual’s psychophysics that, especially at the stage of a brisk growth, not only malforms behavioral patterns but can exercise influence on shaping brain structures. Secondary practices by means of which society used to help an individual to forge his or her identity are losing their former certainty in digital dimension. The present article treats the notion of information hygiene as a set of physical, intellectual and spiritual practices suitable for fighting the frustrating impact of digital reality and forming destructive dependencies of its various forms. The authors asked a group of the first year university students to describe their experience of one - day abstinence from the Internet. The analysis of impromptu ways of overcoming deep involvement into digital reality makes it possible to single out the most appropriate forms of escaping from its destructive impacts. It shows as well the internal consonance of these forms with ancient practices of self-care, which paves the way for the thematization of a complex approach towards the issue of human survival in front of digital reality.
Teaching algorithms and data structures with a proof assistant (invited talk)

We report on a new course Verified Functional Data Structures and Algorithms taught at the Technical University of Munich. The course first introduces students to interactive theorem proving with the Isabelle proof assistant. Then it covers a range of standard data structures, in particular search trees and priority queues: it is shown how to express these data structures functionally and how to reason about their correctness and running time in Isabelle.
Unpacking Content Moderation: The Rise of Social Media Platforms as Online Civil Courts

Social media platforms create and amplify conflicts, but fail to ensure adequate access to justice through content moderation when harms arise. This chapter focuses on a gap in current scholarship on platform governance, by addressing content moderation from the procedural perspective of dispute resolution. We trace the emergence of content moderation as a form of digital dispute resolution, proposing a theoretical framework for the understanding of social media platforms as private adjudicators, and illustrating how platforms have progressively embraced this role. This framework is further complemented by an empirical overview of the content reporting mechanisms of four social media platforms (Facebook, TikTok, Twitch and Twitter). Subsequently, we explore the current shortcomings of content moderation, pointing out three problems which currently affect the transparency and legitimacy of the dispute resolution activities carried out by platforms through content moderation. How can these shortcomings be corrected? The chapter identifies two complementary avenues, through which legitimacy and due process guarantees could potentially be injected into content moderation. On the one hand, social media platforms themselves could “judicialize” themselves, setting up structures and procedures that enhance the legitimacy of content moderation, and afford sufficient procedural rights to those affected by content moderation decisions. From this point of view, we analyze Facebook’s recent decision to institute a quasi-judicial body, the Oversight Board, to deal with delicate and significant content moderation disputes. On the other hand, legitimacy could be enhanced “from without”, by way of regulation. In order to explore this avenue, we conclude the chapter with some normative reflections relating to the possible future regulation of content moderation, with specific reference to the European Union and the proposal for a Digital Services Act.
Coupled-dynamic learning for vision and language: Exploring Interaction between different tasks

Intensive research interests have been paid for the vision and language communities. Especially, image captioning task aims to generate natural language descriptions from the image content. Oppositely, image synthesis task aims to generate realistic images from natural language descriptions. Moreover, both of them can achieve promising results by using Long Short-Term Memory (LSTM), which models the sequence dynamics at each time step as hidden state. Nevertheless, the research on dynamics is often limited in the individual task, while there is no progress exploring the mutual relationship between dynamics in different tasks. In this work, we present a novel coupled-dynamic formulation that can iteratively reduce the distance between task-dependent dynamics in the training process. To embed adverse information into individual network, we construct dual-loss architectures to interactively align dynamics. We evaluate the proposed framework on Flickr8k, Flickr30k and MSCOCO datasets. Experimental results show that our approach can boost dual tasks together and achieve competing performances against state-of-the-art methods.
Hierarchy analysis for monitoring the uniformity of measurements system

Abstract An approach to monitoring the state of the system for ensuring the uniformity of measurements and forecasting its state, based on the method of hierarchy analysis, is proposed. A decomposition of the system for ensuring the uniformity of measurements is proposed, the further processing of the judgments sequence for decision-making based on paired comparisons, is discussed. The processing of experts judgments in order to obtain a generalized judgment is described.
Technology Enhanced Learning Using Humanoid Robots

In this paper we present a mixture of technologies tailored for e-learning related to the Deep Learning, Sentiment Analysis, and Semantic Web domains, which we have employed to show four different use cases that we have validated in the field of Human-Robot Interaction. The approach has been designed using Zora, a humanoid robot that can be easily extended with new software behaviors. The goal is to make the robot able to engage users through natural language for different tasks. Using our software the robot can (i) talk to the user and understand their sentiments through a dedicated Semantic Sentiment Analysis engine; (ii) answer to open-dialog natural language utterances by means of a Generative Conversational Agent; (iii) perform action commands leveraging a defined Robot Action ontology and open-dialog natural language utterances; and (iv) detect which objects the user is handing by using convolutional neural networks trained on a huge collection of annotated objects. Each module can be extended with more data and information and the overall architectural design is general, flexible, and scalable and can be expanded with other components, thus enriching the interaction with the human. Different applications within the e-learning domains are foreseen: The robot can either be a trainer and autonomously perform physical actions (e.g., in rehabilitation centers) or it can interact with the users (performing simple tests or even identifying emotions) according to the program developed by the teachers.
A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs

In data clustering, the measured data are usually regarded as uncertain data. As a probability-based clustering technique, possible world can easily cluster the uncertain data. However, the method of possible world needs to satisfy two conditions: determine the data of different possible worlds and determine the corresponding probability of occurrence. The existing methods mostly make multiple measurements and treat each measurement as deterministic data of a possible world. In this paper, a possible world-based fusion estimation model is proposed, which changes the deterministic data into probability distribution according to the estimation algorithm, and the corresponding probability can be confirmed naturally. Further, in the clustering stage, the Kullback–Leibler divergence is introduced to describe the relationships of probability distributions among different possible worlds. Then, an application in wearable body networks (WBNs) is given, and some interesting conclusions are shown. Finally, simulations show better performance when the relationships between features in measured data are more complex.
Autonomous Drone Using ROS for Surveillance and 3D Mapping Using Satellite Map

Robot Operating System (ROS) is a framework for Linux which provides ample resources and development frameworks within it that makes robot development easy, flexible, robust and faster. With the use of this technology, this paper basically focuses on development of an autonomous robotic drone using ROS which have two functionalities; one is mapping a 3D view of an environment of a provided satellite map and another is surveillancing over an environment of a provided satellite map. This paper describes the idea and methodology of development of such devices. Such robotic drones can have ample applications which can be re-modified for efficiency in future. ROS provides a greater flexibility of establishing communication and synchronization between various components of a drone whether hardware or software.
Decentralized Incentive Mechanism for Cooperative Content Dissemination in Vehicular Networks

Cooperative content dissemination allows vehicles to directly retrieve content from each other by vehicle-to-vehicle (V2V) communications. It can release the downlink traffic pressure caused by repetitive of popular content downloads. A critical but open issue is how to motivate vehicles to participate in content dissemination. Most of the existing incentive mechanisms are proposed under a centralized architecture that suffers from the single point attack and trustless third-platform. In this paper, we propose a decentralized incentive mechanism for cooperative content dissemination in vehicular networks. By introducing the Directed Acyclic Graph (DAG) based blockchain technology, a decentralized architecture is proposed, which can improve incentive efficiency and reliability by removing the third-platform. Next, using the contract theory, the vehicles are divided into different types according to their route feature, and a series of contracts are designed for different types of vehicles to provide suitable incentives, as well as maximize the content generators' profit. Simulation results demonstrate the effectiveness and efficiency of our solution.
Intelligent Mentoring Bots in Learning Management Systems

Mentoring students in online courses is an essential task to keep students on track. Often, they ask simple questions or have repetitive issues. However, due to a high resource demand, this task is difficult to achieve on a massive scale. Digitizing mentoring processes has a high potential to fill this resource gap. In this paper, we present the concept, implementation and evaluation of intelligent mentoring bots. These bots are implemented as chatbots that can be integrated into learning management systems. With these bots, we enable 24/7 mentoring support for students. Students can ask Frequently Asked Questions (FAQs) regarding their general studies or about one of their current classes. Further, we integrate external learning applications with the bots to provide personalized feedback to students. The realization of the chatbots is based on Natural Language Understanding (NLU) to identify the respective intent of the student. We apply those chatbots as an additional offer in university courses and evaluate them in this context. We see the potential of bots as a scalable solution for mentoring in online courses.
An Intelligent Procedure for Dynamic Evaluation in a Scientific Learning Session

Abstract In this research, we are interested in the evaluation of scientific subjects’ teaching components such as course questions, examples, application and deepening exercises. Thereby, we have presented a method that allows the teacher to make decisions using a system that implements interpretations based on the decision tree. The proposed system does not only propose assessing students’ learning, but it also offers the teacher intervention phases to reduce the difficulties encountered by students. This evaluation approach offers the opportunity for each student to acquire the necessary skills needed in their learning according to their abilities. In our research, we are interested in an evaluation that is based on an intelligent strategy to adjust the students learning during the classroom session. We present the interaction strategy between the teacher, the intelligent system and the students. Last but not least, we build the decision tree and interpret the results obtained, its use by the teacher for any further improvement. Consequently, this indicates the importance of this evaluation conception through artificial intelligence and its impact on machine learning by offering it an interesting database.
Reinforcement Learning with Randomized Physical Parameters for Fault-Tolerant Robots

In reinforcement learning, for cost and safety reasons, the policy is usually learned in simulation environments, after which it is applied to the real world. However, the learned policy cannot often adapt because real world disturbances and robot failures lead to gaps between the two environments. To narrow such gaps, policies that can adapt to various scenarios are needed. In this study, we propose a reinforcement learning method for acquiring a robust policy against robot failures. In the proposed method, failure is represented by adjusting the physical parameters of the robot. Reinforcement learning under various faults takes place by randomizing the physical parameters during learning. In the experiments, we demonstrate that the robot that learned using the proposed method has higher average rewards than a conventional robot for quadruped walking tasks in a simulation environment with/without robot failures.
“He was poking holes …” A case study on figuring out metadiscursive rules through primary sources

An important aspect of participation in a new academic discourse pertains to the metadiscursive rules which govern that discourse. Researchers have documented the viability of using primary sources in undergraduate mathematics education for scaffolding students' recognition of those rules. Our research explores the related question of whether the use of primary sources can support students' learning of metadiscursive rules in a way that goes beyond mere recognition. We present a case study of one student's "figuring out" of metadiscursive rules in a university Analysis course as a result of her experience with a Primary Source Project, illustrate evidence for three dimensions of "figuring out" (adoption, acceptance, awareness) that emerged from that case study, and discuss the implications of our findings for classroom instruction and future research.