Reviewerly/exHarmony huggingface dataset:
Quantum Information Splitting Scheme of Arbitrary Three-Qubit State by Using a Four-Qubit Cluster State and a Bell State

With the rapid development of Internet technology, the confidentiality of information content and network security are facing severe challenges, and quantum teleportation ensures the absolute security of information transmission based on its basic characteristics. In this paper, we propose a scheme for quantum information splitting of arbitrary three-qubit state by using a four-qubit cluster state and a Bell state. The scheme only needs to perform two Bell state measurement operations and two single-qubit measurement operations, and Bob can reconstruct the transmitted arbitrary three-qubit state by means of appropriate unitary operations. Compared with other information splitting schemes of arbitrary three-qubit state, our scheme is characterized by higher transmission efficiency. Then we use channel authentication method to ensure the communication security of our scheme under different attack scenarios. Furthermore, we take comparisons with the other quantum information splitting schemes in five aspects of quantum information bits transmitted, necessary operations, consumption of quantum resource, consumption of classical resource and intrinsic efficiency. It is concluded that our scheme has significant advantages of higher intrinsic efficiency and high security.
Data Augmentation for Pathology Prioritisation: An Improved LSTM-Based Approach

Public hospitals receive large volumes of pathology results everyday. It is therefore challenging for doctors to comprehensively analyse all this data. Pathology data prioritisation would seem to provide at least a partial solution. It has been suggested that deep learning techniques can be used to construct pathology data prioritisation models. However, due to the resource required to obtain sufficient prioritisation training and test data, the usage of deep learning, which requires large labelled training data sets, was found not to be viable. The idea presented in this paper is to use a small seed set of labelled data and then to augment this data. The motivation here was that data augmentation had been previously employed successfully to address data scarcity problems. Four data augmentation methods are considered in this paper and used to train deep learning pathology data prioritisation models. Evaluation was conducted using Urea and Electrolytes pathology data. The results show a best recall and precision of 0.73 and 0.71 respectively.
Maximizing Research Impact: A Panel Discussion On Method Selection for Non-Research Stakeholders

One rarely discussed consideration in method selection is how non-research stakeholders may view the outcomes in light of the selected method. In an applied context, these stakeholders may be the primary decision makers. Thus, it is important to maximize the impact research has on these stakeholders. HF/E researchers should understand their stakeholder’s background and preconceptions, as these affect how the stakeholder consumes research; They may view certain methods as more or less impactful, regardless of validity. This is a sometimes overlooked consideration for HF/E researchers when designing their studies. This panel, drawn from a diverse background, will discuss their experience in accommodating and/or integrating stakeholder perspectives into their research design. Audience members will be given the opportunity to ask panelists questions during the latter portion of the panel.
A Leadership Transfer Algorithm for the Raft

The Raft algorithm is widely used as a consensus algorithm for a private blockchain. However, the followers in the Raft may fail to continuously receive heartbeats and become faulty nodes because of unreliable transmission. Faulty nodes seriously affect consensus efficiency, even causing the network split. The leader cannot receive enough replies during the network split, so the cluster cannot reach a consensus for transactions. To improve the efficiency and stability of the Raft, first, we use the LWLR (Local Weighted Linear Regression) algorithm to predict the time that a follower becomes a faulty node due to the heartbeat loss rate, thereby predicting the node states. Then, we update the network split probability model and propose a leadership transfer algorithm to prevent the network split by replacing the leader. And a leader selection algorithm is also designed to select the next leader based on a reputation model that evaluates the followers’ stability. Finally, we demonstrate that the predicted node failure time is accurate by analyzing the error. And we compare the TPS (Transactions Per Second) of the original Raft and the TPS of the Raft with the leadership transfer algorithm. The experiment results show that: 1) the predicted node failure time is relatively accurate, which is about 85.7%. 2) The leadership transfer algorithm can considerably improve the TPS by approximately 500 times compared to the original Raft when the number of faulty nodes is half of the network size.
Understanding Travel Behaviors and Developing a User-Centered Design of the Residential Mobility Using a Persona-Based Approach

This paper describes a persona-based investigation of travel behaviors of residential people. This research aims to develop mobility services based on real travel behaviors and the needs of the community residents. We interviewed the two targets: the municipality and the company that implements new mobility services in society, and the residents that use the new mobility services in the region. First, the interviews were conducted to identify the persona describing the users intended by the municipality and the company (“Intended Persona”). Second, the persona representing residents was developed based on the interviews of the residents (“Actual Persona”), and it was used to understand actual movements in the target region. Finally, we investigated the differences between the “Intended Persona” and the “Actual Persona” that will identify the users’ expectations and requirements for new mobility services. The results suggest several gaps between the “Intended Persona” and “Actual Persona”, including shopping needs in stores outside the residential area, lifestyle of living only within the town, and resident relationships.
Infodemia and its influence on human behavior during quarantine

The article is devoted to the study of a newly formed during the pandemic term, infodemia, which significantly affects the behavior and life of people. Much effort is needed to fight the virus, but it is very costly to overcome false and unverified information. Misinformation, rumors and all sorts of fake information often lead to destructive increases of information flows to the public consciousness. In fact, destructive information is shared by word of mouth in the media more often than truthful and constructive one. Based on the increase in negative messages, there are negative changes in the emotional and behavioral states of each member of society. Infodemia, as a harmful epidemic of rumors, requires detailed study and improvement of ways to fight it. Identifying ways of perception of unverified information and the correct attitude to it will make it possible to make the right choice in an epidemic situation. Acquaintance with such methods influences the choice of the organization of own life, development of critical thinking and education of the correct attitude and estimation of media information. Mastering the techniques and methods of verifying the veracity of information on the Internet will allow a person to control and cultivate their own media immunity, reduce anxiety and promote the mobilization of behavioral patterns. Some step-by-step actions that help increase stress resistance and the ability to block and neutralize infodemia are described and presented in the article. The informational influence on psychological feeling of the person in the period of quarantine restrictions is considered. The skills of perceiving and dividing information, self-organizing and critical thinking are presented. Infodemia, misinformation, fakes and panic spread much faster than the virus itself. Conspiracy theories flow into the information pool like waterfalls, and then become overgrown with numerous variations and interpretations. Forced observance of quarantine measures, means of psychological, social, medical hygiene causes the emergence of different in content, depth and duration transformations in the behavior of the individual. Techniques and methods of finding constructive behavioral patterns are described. During quarantine and in isolation, people around the world began to work, communicate and spend leisure time online. In all age groups, both adults and children, there was a distribution of normal lifetime in the direction of increasing consumption of social networks and media. Moreover, it is because of this impact on human consciousness that the new term infodemia has emerged, as the growth of unreliable information during pandemic.
Investigation of pressure-injection consolidation of watered soils with clay-cement mortar

The article discusses the consolidation of watered soils with a viscoplastic solution and establishes the efficiency of pressure-injection consolidation of dispersed soils with an alumina-cement solution. A program for physical modeling has been developed. Modeling makes it possible to determine the properties of the soil and conduct research on the deformation of an artificial foundation in compliance with the basic principles of the similarity of processes in the soil mass. The design of an experimental stand for research of changes in filtration and deformation properties of soils has been developed and substantiated. The process of carrying out the experiment and the principle of operation of the installation are described. The necessary and sufficient parameters of compaction of decompacted soils in the bases of foundations have been experimentally established. The results obtained can be used in the design of backfilling of decompacted soils during the reconstruction of the foundations of buildings in mining towns and villages.
VR-based Exploration of Participant Movement in Experimental Psychology

Psychology is the science of behaviour, but the impact of an experimental manipulation is typically only evaluated on aggregated measures of movement. Virtual Reality (VR) can provide an environment that fosters insight, interaction and a more detailed view on movement data captured during VR-based psychological behaviour experiments through different interaction paradigms and good spatial understanding. In this work, we present a case study on the exploration of such data, with the goal of examining user behaviour as a result of different stimuli the participants are exposed to. We adapt an existing immersive analysis system to give insight into captured movement and physiological data, and facilitate the separation of movement behaviour into sections of interest through fitting visualisation metaphors. We evaluate the approach on data captured in an independent psychological evaluation, and report on exemplary observations. Finally, we give an outlook on what is necessary for a more comprehensive analysis system in the future.
A Novel Algorithm of Machine Learning: Fractional Gradient Boosting Decision Tree

The gradient boosting decision tree is a commonly used and effective ensemble machine learning method. In this paper, a factional gradient boosting decision tree scheme is first proposed with several loss functions discussed. This scheme implies that different algorithms can be established when adopting different fractional order gradient methods. It is then shown that with satisfactory convergence of fractional gradient descent algorithms, the proposed algorithms can train models with required accuracy in less number of iterations. Finally, several examples are provided to demonstrate the accuracy and efficiency of the algorithms.
Substitution Attacks Against Sigma Protocols

Inspired by the Snowden revelations, Bellare, Paterson, and Rogaway proposed the notion of Algorithm Substitution Attack (ASA) where the attacker could subvert cryptographic algorithms to leak secret information stealthily. Since their work, there have been several ASAs proposed for various cryptographic schemes such as encryption schemes and digital signatures. In this work, we investigate the first study of ASA against $$\varSigma $$ protocols which are widely considered as an important and useful cryptographic tool. Concretely, we formally define the ASA models for subverting $$\varSigma $$ protocols and propose two concrete attacks that are mainly inspired by the attacks by Bellare et al. (CRYPTO’14) and Chen et al. (ASIACRYPT’20). In our proposed attacks, a subverted prover could leak the secret witness to the outside world in undetectable way by generating biased commitment, and a subverted verifier could break the knowledge soundness by choosing a biased challenge. Several concrete $$\varSigma $$ protocols are also provided to demonstrate the feasibility of our proposed attack. Our work shows that ASAs have powerful impacts on $$\varSigma $$ protocols, and thus it is highly desirable for the research community to design new $$\varSigma $$ protocols that could resist ASAs.
Using Deep Learning for Apartment Complex Closed-Circuit Television System

The technology industry of Taiwan had been developed in the Hsinchu Science Park. Therefore, many residents live nearby Science Park. The residential area is the most populated in Taiwan, which induces apartment complex security issues. Although Taiwan is full of technology, there are not enough object detection systems with screen capturing on the market. There are only alarm systems for home security or normal security monitoring for apartment complex security. Thus, a closed-circuit television system for the apartment complex is developed in this study to support multiple network cameras for remote spaces through the network. That detects specific events and new objects.
Standalone Robot Control System Using Hierarchical Deep Reinforcement Learning

Many robot control systems that require complex processing rely on external computing resources such as ROS. We believe that the usability of such a configuration is limited because the communication delay affects the control. Therefore, this paper standalone robot control system using hierarchical deep reinforcement learning.
A Deep Learning Approach to Adaptive Query Processing for LSH

Approximate Nearest Neighbor(ANN) search has been attracting more attention in recent years. Locality-Sensitive Hashing (LSH) is one of the most promising methods to ANN problem because of its theoretical guarantees and nice empirical performance. It is important to select the appropriate number of hash functions m and collision filtering threshold l to get ANN of a query object. The appropriate parameters can reduce IO cost and improve query accuracy. However, the state-of-the-art LSH method, QALSH employs fixed with a given dataset D and an approximation ratio c. As for various query and different k for KNN problem, the does not change, which would lead to extra IO and decrease accuracy. Deep learning techniques have prominent capability to capture underlying data distributions including outliers. In this paper, we propose a novel method based on deep learning techniques, which can predict for different queries with given parameters including c, k and so on. Firstly, Variational auto-encoder(VAE) is used to transform the original query to a dense representation. Then the given parameters are also embeded to predict . In order to reflect the data distribution of the given dataset, we design a two-stage training strategy. Our experimental result shows the effectiveness and efficiency of our method.
On the Use of LSTM-RNN for Detecting Audio Spoofing Attacks

This paper proposes a long short-term memory recurrent neural network (LSTM-RNN) for detecting audio spoofing attacks. Many algorithms, including deep learning technology, have made a lot of achievements in this field. However, the automatic speaker verification (ASV) system is still vulnerable to a spoofing attack. Therefore, in this paper, we analyze the architecture of LSTM-RNN and apply the LSTM-RNN method. Considering the application of LSTM-RNN in speech recognition, we believe that LSTM-RNN can also play a prominent role in this application. To verify our proposed approach, we take the ASVspoof 2015 dataset for the test. We use two kinds of features in the experiment: Mel-scale Frequency Cepstral Coefficients (MFCC) and Log-FBank. Among them, the Log-FBank feature system can achieve better performance. The experimental results show that the ASV system can be used in practice with the LSTM method.
An Interactive Manta Ray Foraging Optimization Algorithm with Hierarchical Population Structure

The manta ray foraging optimization (MRFO) algorithm is a meta-heuristic method derived by imitating the behavior inspired by manta rays foraging. However, due to the lack of communication among individuals in the population, it suffers from the local optimum trapping problem. In this study, we innovatively propose a hierarchical population structure for it by adding an information interaction layer to the original MRFO population, namely an interactive manta rays foraging optimization (IMRFO) algorithm. In it, the hierarchical population structure successfully realizes the balance between local exploitation and global exploration. The superiority of IMRFO is confirmed in terms of solution quality, convergence speed, population diversity, and search trajectory by experimental findings based on thirty IEEE CEC2017 benchmark functions in comparison with other state-of-the-art meta-heuristic methods.
Sparse Non-local CRF

CRF is a classical computer vision model which is also useful for deep learning. There are two common CRF types: sparse and dense. Sparse CRF connects only the nearby pixels, while dense CRF has global connectivity. Therefore dense CRF is a more general model, but it is much harder to optimize compared to sparse CRF. In fact, only a certain form of dense CRF is optimized in practice, and even then approximately. We propose a new sparse non-local CRF: it has a sparse number of connections, but it has both local and non-local ones. Like sparse CRF, the total number of connections is small, and our model is easy to optimize exactly. Like dense CRF, our model is more general than sparse CRF due to non-local connections. We show that our sparse non-local CRF can model properties similar to that of the popular Gaussian edge dense CRF. Besides efficiency, another advantage is that our edge weights are less restricted compared to Gaussian edge dense CRF. We design models that take advantage of this flexibility. We also discuss connection of our model to other CRF models. Finally, to prove the usefulness of our model, we evaluate it on the classical application of segmentation from a bounding box and for deep learning based salient object segmentation. We improve state of the art for both applications.
A Locality-Sensitive Hashing-based Automatic Group Identification Approach in Group Recommendation

Group recommendations often include two processes, dividing users into groups and aggregating group members’ preferences for recommendation. Because of the increasing number of users and the fact that it is more cost-effective to recommend to homogeneous groups than to heterogeneous groups, the group recommendation prefers to use the automatic identification group method to divide users into groups. However, with the continuous increase of the number of items and users, the time cost required for the process of dividing users into groups also increases sharply. Therefore, in order to effectively deal with massive high-dimensional data, this paper proposes an LSH-based automatic identification group approach called GR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LSH</inf> . Extensive experiments on the movielens 100k dataset prove that GR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LSH</inf> can greatly reduce the time cost of the process while ensuring the accuracy.
Design of an Offical Document Multi Machine Retrieval System based on NLP

Aiming at the problems of insufficient file name retrieval information, large internal retrieval overhead, slow speed and low sharing rate of documents in the existing stand-alone document retrieval software, this paper uses NLP (natural language processing) technology to provide a new system design method for multi-machine retrieval and accurate query of office documents. This method is realized by uploading documents, document text analysis and processing, search engine construction, similar document recommendation, result preview and verification, retrieval result output and so on. This paper will elaborate from five main aspects: the overall system flow, database design, text analysis and processing module design, text retrieval module design and client design.
A Survey of Blockchain-Based Stablecoin: Cryptocurrencies and Central Bank Digital Currencies

With the development of blockchain technology and regulation, blockchain-based stablecoins have explosive growth recently. Our study provides an overview of the applications and regulations of blockchain-based stablecoins, which not only provides a clear view of current development but also has important referential value for guiding regulatory strategies. We begin with a new categorization perspective based on the legal position, the stability mechanism, target user, and decentralization. Then, we investigate the technical application of real-world projects and analysis it comparatively. Further, we explore the different regulations technologies and policies of blockchain-based stablecoins. Finally, several unresolved difficulties and prospective future are discussed.
Meta Proximal Policy Optimization for Cooperative Multi-Agent Continuous Control

In this paper we propose Multi-Agent Proxy Proximal Policy Optimization (MA3PO), a novel multi-agent deep reinforcement learning algorithm that tackles the challenge of cooperative continuous multi-agent control. Our method is driven by the observation that most existing multi-agent reinforcement learning algorithms mainly focus on discrete state/action spaces and are thus computationally infeasible when extended to environments with continuous state/action spaces. To address the issue of computational complexity and to better model intra-agent collaboration, we make use of the recently successful Proximal Policy Optimization algorithm that effectively explores of continuous action spaces, and incorporate the notion of intrinsic motivation via meta-gradient methods so as to stimulate the behavior of individual agents in cooperative multi-agent settings. Towards these ends, we design proxy rewards to quantify the effect of individual agent-level intrinsic motivation onto the team-level reward, and apply meta-gradient methods to leverage such an addition so that our algorithm can learn the team-level cumulative reward effectively. Experiments on various multi-agent reinforcement learning benchmark environments with continuous action spaces demonstrate that our algorithm is not only comparable with the existing state-of-the-art benchmarks, but also significantly reduces training time complexity.