Reviewerly/exHarmony huggingface dataset:
ATM: Attribute-Based Privacy-Preserving Task Assignment and Incentive Mechanism for Crowdsensing

Crowdsensing is a practical component in Internet-of-Things, in which task requesters outsource sensing tasks to workers by a crowdsensing server (CS). Task assignment and incentive design are two essential parts of crowdsensing. However, due to the semi-honest CS and malicious workers, there exist several security and fairness issues. First, during task assigning and reward distributing, CS can get the private information of both task requester and workers such as identity, attributes. Second, malicious workers who are unable to execute tasks can get the task content, which leaks task privacy. Third, most auction-based incentive mechanism only considers the biddings of workers and ignores the real capabilities of workers. To this end, we propose a privacy-preserving fine-grained task assignment scheme for the crowdsensing system. Moreover, we also design a novel incentive mechanism based on the capabilities of workers. To be specific, a ciphertext-policy attribute-based encryption (CP-ABE) scheme with the characteristics of hidden policy is adopted to select workers and protect the privacy that of both requirements and workers’ attributes. We quantify the capabilities of workers based on the workers’ attributes and further determine the workers’ reward according to their capabilities. Detailed security analysis demonstrates that our proposed scheme can protect workers’ identity privacy, attribute privacy, and data confidentiality. Besides, the extensive experiments show that our incentive mechanism can effectively motivate workers.
Big Data Processing Technology in Film and Television Post Production

Abstract Nowadays, the wave of digitization, networking and informatization is sweeping the world, which makes the visual image become an important way of communication and transmission of global culture. The purpose of this paper is to analyze the application of big data processing technology in film and television post production. The distributed computing power of MapReduce is used to extract the content features of video images, construct corresponding indexes, and store and retrieve them in HBase database according to the indexes to form a big data processing platform for video images. This paper deeply discusses the framework related theory as the support of big data processing technology in film and television post production, analyzes the common algorithms in nonlinear editing system, discusses the mirror effect monorail and double track special effects in video special effects, and makes the realization effect of each kind of special effects in the space domain, and makes some special effects among them, The implementation of compressed domain is discussed. In this paper, through the application of experimental video big data processing technology to the film and television post production of various possibilities, and draw the following conclusions: in extreme cases, the stunt tree becomes a one-dimensional linear structure, using traditional parallel topology sorting, the device parallelism is 0, and using pipeline scheduling, because of breaking the sequence between frames, can play a relatively large parallelism.
Harnessing Ensemble of Data Preprocessing and Hand-crafted Features for Irony Detection in Tweets

In the decade of microblogging, people are more inclined to use figurative languages such as irony to express their opinions towards various issues. Determining the ironic orientation of tweets is crucial for various natural language processing (NLP) tasks especially in opinion mining due to its twisted nature. However, the noisy and informal characteristics of tweets demand a formidable system to address the challenges of detecting ironic tweets. In this paper, we propose an approach for irony detection in tweets. We focus on an ensemble of preprocessing techniques to address the noisy tweet characteristics and idiosyncratic natures. Besides, we utilize an effective combination of n-gram features and various hand-crafted features in a unified supervised classification model. We leverage the lexical syntax, parts-of-speech (POS), and opinionated lexicons to extract the hand-crafted features and employ a feature selection technique to select the best combination. We present a thorough analysis of the contribution of various features and preprocessing techniques using the SemEva1-2018 benchmark irony detection dataset in tweets. Experimental findings demonstrate the efficacy of our method over several state-of-the-art methods.
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization

The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions, each collecting evidence from a different modality, followed by a pointer network that selects frames to include in the summary. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.
Efficient Path Tracking Control for Autonomous Driving of Tracked Emergency Rescue Robot under 6G Network

This paper proposes a path tracking control algorithm of tracked mobile robots based on Preview Linear Model Predictive Control (MPC), which is used to achieve autonomous driving in the unstructured environment under an emergency rescue scenario. It is the future trend to realize the communication and control of rescue equipment with 6G and edge cloud cooperation. In this framework, linear MPC (LMPC) is suitable for the path tracking control of rescue robots due to its advantages of less computing resources and good real-time performance. However, in such a scene, the driving environment is complex and the path curvature changes greatly. Since LMPC can only introduce linearized feedforward information, the tracking accuracy of the path with large curvature changes is low. To overcome this issue, combined with the idea of preview control, preview-linear MPC is designed in this paper. The controller is verified by MATLAB/Simulink simulation and prototype experiment. The results show that the proposed method can improve the tracking accuracy while ensuring real-time performance and has better tracking performance for the path with large curvature variation.
Autonomous Reinforcement Control of Underwater Vehicles based on Monocular Depth Vision

In this paper, a monocular depth prediction based end-to-end reinforcement control framework is proposed for autonomous control of underwater vehicles in the unknown environment. In the control framework, with the input of camera sensor RGB videos, a monocular depth prediction network is proposed to generate underwater depth images and a sequential reinforcement learning controller is also developed for autonomous obstacle-avoiding navigation and movement control. Simulated and experimental results demonstrate that the proposed control scheme can achieve remarkable performance on collision-avoidance navigation and autonomous control in the unknown environment.
Literature Survey: Application of Machine Learning Techniques on Static Sign Language Recognition

Sign Language Recognition systems have been thoroughly researched in recent times to bridge the gap between the signer and non-signer community. This paper focuses on fulfilling the need for a thorough review of the works done in building such systems. The systems discussed herein for static Sign Language Recognition are broadly classified on Support Vector Machine, Convolutional Neural Network as classifiers. The identified phases for the reviewed systems include preprocessing, feature extraction and classification. The methods discussed in these phases include Principal Component Analysis, Histograms of Orientations Gradient, Hybrid Sign Invariant Feature Transformation. Further the limitations and improvements in approaches are relatively portrayed to give a better analysis for future work. The overall aim for this paper is to provide insight into making effective systems for Sign Language Recognition. This will make these systems easily available for educating the masses about static Sign Language. Thus, sign language will no longer remain a barrier.
Distributed quantum computation for near-term quantum environments

A scalable model for a distributed quantum computation is a challenging problem due to the complexity of the problem space provided by the diversity of possible quantum systems, from smaller quantum devices to large-scale quantum computers. Here, we define a scalable distributed model of gate-model quantum computation in near-term quantum systems.
Counter-Collusion Smart Contracts for Watchtowers in Payment Channel Networks

Payment channel networks (PCNs) are proposed to improve the cryptocurrency scalability by settling off-chain transactions. However, PCN introduces an undesirable assumption that a channel participant must stay online and be synchronized with the blockchain to defend against frauds. To alleviate this issue, watchtowers have been introduced, such that a hiring party can employ a watchtower to monitor the channel for fraud. However, a watchtower might profit from colluding with a cheating counterparty and fail to perform this job. Existing solutions either focus on heavy cryptographic techniques or require a large collateral. In this work, we leverage smart contracts through economic approaches to counter collusions for watchtowers in PCNs. This brings distrust between the watchtower and the counterparty, so that rational parties do not collude or cheat. We provide detailed analyses on the contracts and rigorously prove that the contracts are effective to counter collusions with minimal on-chain operations. In particular, a watchtower only needs to lock a small collateral, which incentivizes participation of watchtowers and users. We also provide an implementation of the contracts in Solidity and execute them on Ethereum to demonstrate the scalability and efficiency of the contracts.
Capability comparison of quantum sensors of single or two qubits for a spin chain system

Quantum sensing, utilizing quantum techniques to extract key information of a quantum (or classical) system, is a fundamental area in quantum science and technology. For quantum sensors, a basic capability is to uniquely infer unknown parameters in a system based on measurement data from the sensors. In this paper, we investigate the capability of a class of quantum sensors for a spin-12 chain system with unknown parameters. The sensors are composed of qubits which are coupled to the object system and can be initialized and measured. We consider the capability of the single- and two-qubit sensors and show that the capability of single-qubit quantum sensors can be enhanced by adding an extra qubit into the sensor under a certain initialization and measurement setting.
Multiscale fully convolutional network‐based approach for multilingual character segmentation

Character segmentation is a challenging task for optical character recognition systems. Traditional methods usually utilize rule-based algorithms but most of them are not applicable in modern intelligent recognition applications that require high accuracy. It is especially the case for text containing Eastern Asian language characters with complex pictograph structures, such as Chinese. To alleviate this problem, this study proposes an encoder–decoder structure-based multiscale fully convolutional network (MSFCN) model for optical character segmentation. Comparing with other methods, MSFCN can not only effectively extract semantic details from images but also exploit boundary information of intervals between characters, thereby distinguishing characters from a background in pixel level. Extensive experiments have been conducted on two benchmark data sets of ICDAR2013 and MLCS. Obtained results prove that MSFCN achieves state-of-the-art segmentation performance and indicated its practical application value.
Spatial distribution of multielements including lanthanides in sediments of Iron Gate I Reservoir in the Danube River

Recent studies show that lanthanides (Ln) are becoming emerging pollutants due to their wide application in new technologies, but their environmental fate, transport, and possible accumulation are still relatively unknown. This study aims to determine major and trace elements including Ln in the Danube River sediment which either belong or close to the Iron Gate Reservoir. The Iron Gate Reservoir is characterized by accumulation of sediments as an effect of building hydropower dam Iron Gate I. The surface sediments were collected on the Danube River-1141 to 864 km and three tributaries along this waterway. Two samples of deep sediments were used for comparison. The results indicate the significant upward enrichment of Zn, Sb, Cr, Nd, and Dy in sediments belongs to the Iron Gate Reservoir. The sample 4-Smed is labelled as a hot spot of contamination with Zn, Cr, As, Sb, Nd, and Dy. Also, a trend of increasing concentration in the time period from 1995 to 2016 was found for elements Zn, Cr, and Ni in sediment samples in the Iron Gate Reservoir. Chemometric analysis shows the grouping of sample sites into clusters characterized by the following properties: (i) increased concentration of all measured elements (samples within the Iron Gate Reservoir); (ii) increased Cu concentration (11-Pek); and (iii) lower concentrations of the measured elements (deep sediments). The data presented hereby contribute to the monitoring of pollution of the River Danube sediments and give the first view of Ln profile in the studied sediments.
HAC: Model for Privacy-Preserving Outsourced Data Over Cloud

Organizations, companies, even individuals can benefit the amazing advantages of the cloud only when they find that their data is securely processed and stored in the cloud. This makes it necessary to build and develop techniques and models to effectively maintain data privacy before outsourcing it to the cloud. Most of the current techniques seek to avoid data disclosures that lead to privacy leakage. Unfortunately, these techniques still suffer from some types of attacks, adding to the issue of data utility that decreases due to execute the privacy preservation operations. This paper presents a model that avoids two types of data disclosures to prevent privacy leakage. The proposed model is based on combining one of the encryption techniques, which is partial homomorphic encryption, with the anonymization technique, which is k-anonymity. The results demonstrated the effectiveness of the proposed model in improving the level of privacy-preserving at the same time reducing the percentage of the lost data compared to a similar modern model.
On the Quality of the TREC-COVID IR Test Collections

Shared text collections continue to be vital infrastructure for IR research. The COVID-19 pandemic offered an opportunity to create a test collection that captured the rapidly changing information space during a pandemic, and the TREC-COVID effort was created to build such a collection using the TREC framework. This paper examines the quality of the resulting TREC-COVID test collections, and in doing so, offers a critique of the state-of-the-art in building reusable IR test collections. The largest of the collections--called 'TREC-COVID Complete'--is found to be on par with previous TREC ad~hoc collections with existing quality tests uncovering no apparent problems. Yet the lack of any way to definitively demonstrate the collection's quality and its violation of previously used quality heuristics suggest much work remains to be done to understand the factors affecting collection quality.
Cloud-Fog based HealthCare Framework to Identify and Prevent Dengue Fever Outbreak

Increases of medical equipment, monitoring and tracking also contributed to immense amounts of personal details. These tangible effects promote the usage of fog as a middle layer among data collection and cloud subsystem to recognize the occurrence of Dengue fever (DF) and to also generate real-time alerts in time to users. The primary goal of this intelligent device is to identify and overcome the diseases that are caused because of mosquitoes. For this, we use wearable sensors along with required hardware resources. The sensors then collect the required data and sent to fog gateway which can be available to the healthcare centers and users. We will have unified Bilateral-Branch Network (BBN) representation and classifier learning, where each branch will provide its own separate feature. It is easier to define the current global patterns first and subsequently pay close attention to the less general information. Also, using Global Positioning System (GPS) which is a helpful feature as it will reduce people’s vulnerability by helping them re-direct pathways and gives warnings to people who are not infected.
Near-Optimal Topology-adaptive Parameter Synchronization in Distributed DNN Training

Distributed machine learning with multiple concurrent workers has been widely adopted to train large deep neural networks (DNNs). Parameter synchronization is a key component in each iteration of distributed training, where workers exchange locally computed gradients through an AllReduce operation or parameter servers, for global parameter updates. Parameter synchronization often constitutes a significant portion of the training time; minimizing the communication time contributes substantially to DNN training speed-up. Standard ring-based AllReduce or PS architecture work efficiently mostly with homogeneous inter-worker connectivity. However, available bandwidth among workers in real-world clusters is often heterogeneous, due to different hardware configurations, switching topologies, and contention with concurrent jobs. This work investigates the best parameter synchronization topology and schedule among workers for most expedited communication in distributed DNN training. We show that the optimal parameter synchronization topology should be comprised of trees with different workers as roots, each for aggregating or broadcasting a partition of gradients/parameters. We identify near-optimal forest packing to maximally utilize available bandwidth and overlap aggregation and broadcast stages to minimize communication time. We provide theoretical analysis of the performance bound, and show that our scheme outperforms state-of-the-art parameter synchronization schemes by up to 18.3 times with extensive evaluation under various settings.
Augmented Reality in Medical Education: AR Bones

Augmented Reality is a technique that enhances reality with computer-generated imagery, three-dimensional objects, or other information that doesn't physically exist in the real world. It is a growing technology that offers promising potentials in terms of learning and training. This paper focuses on the development process of a mobile-based application utilizing Mobile Augmented Reality. Since there are vast amounts of information on the human skeleton to comprehend, anyone would find it challenging to retain all the details. This application aims to aid students as a supplement in human anatomy studies. Thus, this project is an approach to enhance their current learning processes with the help of Mobile Augmented Reality.
Blockchain‐Based Reliable Fog‐Cloud Service Solution for IIoT

Industrial Internet of things (IIoT) deploys a large number of smart devices to obtain industrial data, which will be transmitted to cloud for analysis to improve industrial productivity. The management of large-scale devices is complicated, and it's also a challenge to choose a high-quality cloud service for data analysis as the number of service with similar functions increases. To address these issues, we propose a reliable fog-cloud service solution with blockchain-based fog-cloud architecture. In fog layer, we build a management blockchain between fog servers and design a management method for industrial devices; In cloud layer, we construct a service blockchain between cloud service providers to form an open “service market”. Quality of service and reputation based matching algorithm and reputation-based consensus algorithm are designed. The simulation results show correctness and efficiency of algorithms, and validate effectiveness of our proposed solution.
Java Federated Learning Framework Architecture

Data is fuel for machine learning approaches, but in several domains, data are sensitive and demand more security and even no data transferring at all. Examples are medicine, military, or private business data. Federated learning is an approach for distributed machine learning with only local data usage restrictions. The main goal is to design and implement a framework of Federated Learning for Java (FL4J) with decentralized learning which works with only local data per node. The framework should accept not only neural networks as model representation but also should be able to compute custom learning algorithm concurrently. This work describes the general principles of federated learning and suggests the framework for such approach implementation.
Dialect Diversity in Text Summarization on Twitter

Discussions on Twitter involve participation from different communities with different dialects and it is often necessary to summarize a large number of posts into a representative sample to provide a synopsis. Yet, any such representative sample should sufficiently portray the underlying dialect diversity to present the voices of different participating communities representing the dialects. Extractive summarization algorithms perform the task of constructing subsets that succinctly capture the topic of any given set of posts. However, we observe that there is dialect bias in the summaries generated by common summarization approaches, i.e., they often return summaries that under-represent certain dialects.
Duality for $$\kappa $$-additive complete atomic modal algebras

In this paper, we give a duality theorem between the category of $\kappa$-additive complete atomic modal algebras and the category of $\kappa$-downward directed multi-relational Kripke frames, for any cardinal number $\kappa$. Multi-relational Kripke frames are not Kripke frames for multi-modal logic, but frames for monomodal logics in which the modal operator $\Diamond$ does not distribute over (possibly infinite) disjunction, in general. We first define homomorphisms of multi-relational Kripke frames, and then show the equivalence between the category of $\kappa$-downward directed multi-relational Kripke frames and the category $\kappa$-complete neighborhood frames, from which the duality theorem follows. We also present another direct proof of this duality based on the technique given by Minari.