Reviewerly/exHarmony huggingface dataset:
Quantum Bitcoin Mining

This paper studies the effect of quantum computers on Bitcoin mining. The shift in computational paradigm towards quantum computation allows the entire search space of the golden nonce to be queried at once by exploiting quantum superpositions and entanglement. Using Grover's algorithm, a solution can be extracted in time O(2256/t), where t is the target value for the nonce. This is better using a square root over the classical search algorithm that requires O(2256/t) tries. If sufficiently large quantum computers are available for the public, mining activity in the classical sense becomes obsolete, as quantum computers always win. Without considering quantum noise, the size of the quantum computer needs to be ≈104 qubits.
How Modeling Methods for Fuzzy Cognitive Mapping Can Benefit From Psychology Research

Fuzzy Cognitive Maps (FCMs) are aggregate-level simulation models that represent concepts as nodes, capturing relationships via weighted edges, and apply an inference mechanism to update the nodes&#x0027; values until a desired effect is achieved. FCMs are increasingly combined with other techniques. Agent-Based Models (ABMs) use FCMs to represent the &#x2018;mind&#x2019; of each agent, which governs (and is influenced by) interactions with other agents or the environment. A question continues to elude simulationists: what should be the building blocks for such simulations? FCMs can now be optimized using machine learning and quantitative data, which means that an agent&#x0027;s mind can be automatically modified to closely align with an evidence base. However, there are multiple ways in which an FCM can be transformed: which transformations correctly capture how individuals change their mind? In this paper, we explore these questions using psychology research, thus leveraging knowledge on human behaviors to inform social simulations.
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation

Estimation of a dynamical system’s latent state subject to sensor noise and model inaccuracies remains a critical yet difficult problem in robotics. While Kalman filters provide the optimal solution in the least squared sense for linear and Gaussian noise problems, the general nonlinear and non-Gaussian noise case is significantly more complicated, typically relying on sampling strategies that are limited to low-dimensional state spaces. In this letter, we devise a general inference procedure for filtering of nonlinear, non-Gaussian dynamical systems that exploits the differentiability of both the update and prediction models to scale to higher dimensional spaces. Our method, Stein particle filter, can be seen as a deterministic flow of particles, embedded in a reproducing kernel Hilbert space, from an initial state to the desirable posterior. The particles evolve jointly to conform to a posterior approximation while interacting with each other through a repulsive force. We evaluate the method in simulation and in complex localization tasks while comparing it to sequential Monte Carlo solutions.
Problem-Based Learning of Drug Use and Abuse during COVID-19 Contingency

Introduction: Nursing students' experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others.Problem-based learning (PBL) is a pedagogic strategy to strengthen significant learning; then the objective was to establish PBL influence in nursing students' experiences on drug use and abuse during COVID-19 contingency.Methods: Qualitative, phenomenological and descriptive paradigm, 12 female and male nursing students aged 20 -24 years old from the 5 th and 6 th semesters participated.Information collection was through semi-structured interview and a deep one in four cases.A guide of questions about: How the pandemic impacted your life?How did you face it?And what did you learn during this process?Those questions were used.Qualitative data analysis was based on De Souza Minayo, and signed informed consent was obtained from participants.Results: Students' experiences allowed four categories to emerge, with six sub-categories.Category I. Students' experiences on drug use and abuse facing the sanitary contingency; Category II.Students' skills development to identify a problem and design of appropriate solutions; Category III.Developing skills to favor interpersonal relationships; Category IV.Influence of PBL in nursing students' experiences on drug use and abuse during the COVID-19 contingency.Conclusion: PBL favored analysis and thoughts in nursing students' experiences on drug use and abuse during the COVID-19 contingency, they worked collaboratively, developed resilience to daily life situations, and implemented stress coping strategies with their significant learning, which diminished their risk behavior.
Entity Summarization via Exploiting Description Complementarity and Salience

Entity summarization is a novel and efficient way to understand real-world facts and solve the increasing information overload problem in large-scale knowledge graphs (KG). Existing studies mainly rely on ranking independent entity descriptions as a list under a certain scoring standard such as importance. However, they often ignore the relatedness and even semantic overlap between individual descriptions. This may seriously interfere with the contribution judgment of descriptions for entity summarization. Actually, the entity summary is a whole to comprehensively integrate the main aspects of entity descriptions, which could be naturally treated as a set. Unfortunately, the exploration of these set characteristics for entity summarization is still an open issue with great challenges. To that end, we draw inspiration from a set completion perspective and propose an entity summarization method with complementarity and salience (ESCS) to deeply exploit description complementarity and salience in order to form a summary set for the target entity. Specifically, we first generate entity description representations with textual features in the description embedding module. For the purpose of learning complementary relationships within the entire summary set, we devise a bi-directional long short-term memory structure to capture global complementarity for each summary in the summary complementarity learning module. Meanwhile, in order to estimate the salience of individual descriptions, we calculate similarities between semantic embeddings of the target entity and its property-value pairs in the description salience learning module. Next, with a joint learning stage, we can optimize ESCS from a set completion perspective. Finally, a summary generation strategy is designed to infer the entire summary set step-by-step for the target entity. Extensive experiments on a public benchmark have clearly demonstrated the effectiveness of ESCS and revealed the potential of set completion in entity summarization task.
YOLOX-Mobile: A Target Detection Algorithm More Suitable for Mobile Devices

Abstract With the continuous development of network, the increasing popularity of embedded technology, and the continuous improvement of mobile terminal computing ability, how to reduce the size of the model without affecting the accuracy as much as possible, and how to deploy the algorithm in embedded devices has become the current research hotspot. In this paper, we propose a new target detection algorithm called YOLOX-Mobile. Firstly, in order to further reduce the power consumption of embedded devices while maintaining the performance, we replaced the SiLU activation function in the original YOLOX with Mish activation function. Because SiLU activation functions occupy a certain amount of computing and storage resources, and the cost of calculating the Sigmoid type functions on mobile devices is much higher. Therefore, we use Mish functions that are smoother, non-monotonic, unbounded upper and lower bounds are used as activation functions to better meet the low power requirements of embedded devices. Secondly, we used Focal Loss as the Loss function of obj_output to achieve the balance of positive and negative samples, as well as hard-to-classify and easy-to-classify samples. Thirdly, we introduce the Involution operator and use it as the convolution kernel of 3×3. We validated the proposed algorithm on public dataset VOC2012 against the current mainstream YOLOX, YOLOX-M, and YOLOX-S algorithms. The mAP of our proposed algorithm is 78.22% and the detection speed is 55.26 FPS. Compared with the original YOlOX, YOLOX-M and YOLOX-S algorithms, the average FPS is improved by 1.99% and 4.13 FPS, and the average Params Size is reduced by 28.80%. Experimental results show that our proposed algorithm improves the accuracy and speed of detection on top of greatly reducing the network parameters and computation, making it a more suitable target detection algorithm for mobile devices.
Automated Detection of Clipping in Broadband Earthquake Records

Abstract Because the amount of available ground-motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground-motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near-real-time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.
Personalized driving assistance algorithms: Case study of federated learning based forward collision warning

Current designs of advanced driving assistance systems (ADAS) mainly developed uniform collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead to low drivers' trust in. To address this issue, developing personalized driving assistance algorithms is a promising approach. However, current personalization systems were mainly implemented through manually adjusting warning trigger thresholds, which would be less feasible for overall drivers as certain domain expertise is required to set personal thresholds accurately. Other personalization techniques exploited individual drivers' data to build personalized models. Such approach could learn personal behavior but requires impractical large-scale individual data collections. To fill up the gaps, self-adaptive algorithms for personalized forward collision warning (FCW) based on federated learning were proposed in this study. A baseline model was developed by long short-term memory (LSTM) for FCW. Federated learning framework was then introduced to collect knowledge from multiple drivers with privacy preserving. Specifically, a general cloud server model was trained by collecting updated parameters from individual vehicle server models rather than collecting raw data. Besides, a driver-specific batch normalization (BN) layer was added into each vehicle server model to address the heterogeneity of driving behaviors. Experiments show empirically that the proposed federated-based personalized models with the BN layer showed to have the best performance. The average modeling accuracy has reached 84.88% and the performance is comparable to conventional total data collection training approach, where the additional BN layer could increase the accuracy by 3.48%. Finally, applications of the proposed framework and its further investigations have been discussed.
A Fine-Grained Access Control Scheme for Electronic Health Records Based on Roles and Attributes

The electronic medical cloud system has shown its potential to improve the quality of medical care and personal life. At present, there are mainly two forms of access control to electronic medical cloud systems: role-based access control (RBAC) and attribute-based access control (ABAC). But RBAC cannot achieve fine-grained access control, and ABAC cannot achieve the role of RBAC to manage resource functions. This paper proposes a patient-centric access control model that combines RBAC and ABAC in response to this problem. We use the Linear Secret Sharing Scheme (LSSS) access control structure to implement attribute-based access control, and the Casbin access control framework to implement role-based access control. The patient first uses the ciphertext strategy attribute-based encryption algorithm (CP-ABE) on the client to encrypt the electronic health record (EHR), then the patient stores the encrypted EHR data in the cloud. When a data user wants to access patient EHR data, the cloud will determine whether the user role or user attribute meets the access request. After the request is passed, the user can obtain the ciphertext and the plaintext after two decryption steps. Finally, we conduct an extensive safety analysis and performance evaluation, which confirmed the effectiveness and efficiency of our program.
Detection of Insulators on Power Transmission Line Based on an Improved Faster Region-Convolutional Neural Network

Detecting insulators on a power transmission line is of great importance for the safe operation of power systems. Aiming at the problem of the missed detection and misjudgment of the original feature extraction network VGG16 of a faster region-convolutional neural network (R-CNN) in the face of insulators of different sizes, in order to improve the accuracy of insulators’ detection on power transmission lines, an improved faster R-CNN algorithm is proposed. The improved algorithm replaces the original backbone feature extraction network VGG16 in faster R-CNN with the Resnet50 network with deeper layers and a more complex structure, adding an efficient channel attention module based on the channel attention mechanism. Experimental results show that the feature extraction performance has been effectively improved through the improvement of the backbone feature extraction network. The network model is trained on a training set consisting of 6174 insulator pictures, and is tested on a testing set consisting of 686 pictures. Compared with the traditional faster R-CNN, the mean average precision of the improved faster R-CNN increases to 89.37%, with an improvement of 1.63%.
Nonlocal operation enhanced entanglement detection and classification

Based on the nature that nonlocal operation will change the fixed quantum statistical speed limited by the local operation, we study the performance of Ising-type nonlocal operation in multipartite entanglement detection and classification. We first present the formula of quantum statistical speed for general quantum state under the two-body nonlocal operation, and then investigate several important and experimentally relevant states, including N-qubit W state, Twin-Fock state, Q state and N-qubit GHZ state. The results show that the optimal nonlocal operation can be used to address some intractable problems encountered under the local operation, such as the classification of N-qubit W state and entanglement detection of Q state at the edges. Meanwhile, some defects are presented and discussed. Interestingly, the N-qubit GHZ state is found to be stable when it is probed by weaker nonlocal operation (γ≤N−1) and it maybe helpful to the experimental research. Our work provides an alternative route to investigate entanglement detection and classification, especially for the novel and complex quantum system.
BCoT: Introduction to Blockchain-Based Internet of Things for Industry 5.0

TheBCoT InternetIndustry 5.0 of Things Internet of Thingsis reforming Industry 5.0 to featured blockchainBlockchain-driven secure connectivity—fundamental characteristics of IoT IoTencounter challenges like decentralization, security vulnerabilities, and deprived interoperability. Blockchain-based centralized technology addresses the challenges of IoT. Blockchain of ThingsBlockchain of Things(BCoTBCoT) shows the convergence of Blockchain and IoT. The developments in multi-virtual sensor SensorIoT, and heterogeneous multi-system information fusion for BCoT are analyzed in detail. The future challenges of industrial applications of BCoT are discussed in detail. BCoT-based Sustainable Cities and Society (SCS) focuses on developing environmentally, economically, and socially sustainable resilient cities. DewDew computing-based BCoT enables the smart city seamless even at unstable Internet connectivity in remote areas.
The Library in the Information Ecosystem of Open Science

The results of an analysis of trends in the development of the information ecosystem of open science based on the study of the global document flow, open access resources, and scientific data repositories, as well as initiatives in the field of open science, including during the COVID-19 pandemic, are presented. The directions of activity of libraries in these conditions are determined.
An Exploratory Study on a Reinforcement Learning Prototype for Multimodal Image Retrieval Using a Conversational Search Interface

In the realm of information, conversational search is a relatively new trend. In this study, we have developed, implemented, and evaluated a multiview conversational image search system to investigate user search behaviour. We have also explored the potential for reinforcement learning to learn from user search behaviour and support the user in the complex information seeking process. A conversational image search system may mimic a natural language discussion with a user via text or speech, and then assist the user in locating the required picture via a dialogue-based search. We modified and improved a dual-view search interface that displays discussions on one side and photos on the other. Based on the states, incentives, and dialogues in the initial run, we developed a reinforcement learning model and a customized search algorithm in the back end that predicts which reply and images would be provided to the user among a restricted set of fixed responses. Usability of the system was validated using methodologies such as Chatbot Usability Questionnaire, System Usability Scale, and User Experience Questionnaire, and the values were tabulated. The result of this usability experiment proved that most of the users found the system to be very usable and helpful for their image search.
A Backdoor Embedding Method for Backdoor Detection in Deep Neural Networks

As the coming of artificial intelligence (AI) era, deep learning models are widely applied on many aspects of our daily lives, such as face recognition, speech recognition, and automatic driving. AI security is also becoming a burning problem. Because the deep learning model is usually regarded as a black box, it is susceptible to backdoor attacks that embed hidden patterns to impact the model prediction results. To promote backdoor detection research, this work proposes a simple backdoor embedding method to produce deep learning models with a backdoor for validating backdoor detection algorithms. Through conceptual embedding techniques, we decouple the backdoor pattern recognition function and the normal classification function in a deep learning model. One advantage is that the backdoor activation mechanism will not directly interfere with the normal function of the original DNN-based model. Another advantage is that the interference of the final prediction result can be more flexible. The backdoor pattern recognition phase and the model prediction interference phase can be developed independently. The aim is that the deep model needs to have an indistinguishable performance in terms of the normal sample classification while it can be triggered by the hidden backdoor patterns at the right time. The analysis and the experiments validate the proposed method that the embedding backdoor model can achieve almost the same prediction performance as the normal model while the backdoor mechanism can be activated precisely.
COSC-2 – drilling the basal décollement and underlying margin of palaeocontinent Baltica in the Paleozoic Caledonide Orogen of Scandinavia

Abstract. The Collisional Orogeny in the Scandinavian Caledonides (COSC) scientific drilling project aims to characterise the structure and orogenic processes involved in a major collisional mountain belt by multidisciplinary geoscientific research. Located in western central Sweden, the project has drilled two fully cored deep boreholes into the bedrock of the deeply eroded Early Paleozoic Caledonide Orogen. COSC-1 (2014) drilled a subduction-related allochthon and the associated thrust zone. COSC-2 (2020, this paper) extends this section deeper through the underlying nappes (Lower Allochthon), the main Caledonian décollement, and the upper kilometre of basement rocks. COSC-2 targets include the characterisation of orogen-scale detachments, the impact of orogenesis on the basement below the detachment, and the Early Paleozoic palaeoenvironment on the outer margin of palaeocontinent Baltica. This is complemented by research on heat flow, groundwater flow, and the characterisation of the microbial community in the present hard rock environment of the relict mountain belt. COSC-2 successfully, and within budget, recovered a continuous drill core to 2276 m depth. The retrieved geological section is partially different from the expected geological section with respect to the depth to the main décollement and the expected rock types. Although the intensity of synsedimentary deformation in the rocks in the upper part of the drill core might impede the analysis of the Early Paleozoic palaeoenvironment, the superb quality of the drill core and the borehole will facilitate research on the remaining targets and beyond. Protocols for sampling in the hard rock environment and subsequent sample preservation were established for geomicrobiological research and rock mechanical testing. For the former, a sparse sample series along the entire drill core was taken, while the target of the latter was the décollement. COSC-2 was surveyed by a comprehensive post-drilling downhole logging campaign and a combined borehole/land seismic survey in autumn 2021. This paper provides an overview of the COSC-2 (International Continental Scientific Drilling Project – ICDP 5054_2_A and 5054_2_B boreholes) operations and preliminary results. It will be complemented by a detailed operational report and data publication.
Reusable Two-Round MPC from LPN

We present a new construction of maliciously-secure, two-round multiparty computation (MPC) in the CRS model, where the first message is reusable an unbounded number of times. The security of the protocol relies on the Learning Parity with Noise (LPN) assumption with inverse polynomial noise rate $$1/n^{1-\epsilon }$$ for small enough constant $$\epsilon $$ , where n is the LPN dimension. Prior works on reusable two-round MPC required assumptions such as DDH or LWE that imply some flavor of homomorphic computation. We obtain our result in two steps: