Reviewerly/exHarmony huggingface dataset:
Gaussian quantum steering in multi-event horizon spacetime

Authors:
raw_author_name: Shu-Min Wu
raw_affiliation_string: Department of Physics, Liaoning Normal University, Dalian, 116029, China
raw_author_name: Jin-Xuan Li
raw_affiliation_string: Department of Physics, Liaoning Normal University, Dalian, 116029, China
raw_author_name: Xiao-Wei Fan
raw_affiliation_string: Department of Physics, Liaoning Normal University, Dalian, 116029, China
raw_author_name: Wen-Mei Li
raw_affiliation_string: Department of Physics, Liaoning Normal University, Dalian, 116029, China
raw_author_name: Xiao-Li Huang
raw_affiliation_string: Department of Physics, Liaoning Normal University, Dalian, 116029, China
raw_author_name: Hao-Sheng Zeng
raw_affiliation_string: Department of Physics, Hunan Normal University, Changsha, 410081, China

Abstract: Abstract We study Gaussian quantum steering in the Schwarzschild–de Sitter (SdS) spacetime that is endowed with both a black hole event horizon (BEH) and a cosmological event horizon (CEH), giving rise to two different Hawking temperatures. It is shown that the Hawking effect of the black hole always reduces the quantum steering, but the Hawking effect of the expanding universe does not always play the same role. For the first time, we find that the Hawking effect can improve quantum steering. We also find that the observer who locates in the BEH has stronger steerability than the observer who locates in CEH. Further, we study the steering asymmetry, and the conditions for two-way, one-way and no-way steering in the SdS spacetime. Finally, we study the Gaussian quantum steering in the scenario of effective equilibrium temperature. We show that quantum steering reduces monotonically with the effective temperature but now increases monotonically with the Hawking temperature of the black hole, which banishes the belief that the Hawking effect can only destroy quantum steering.
Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology

Authors:
raw_author_name: Yu He
raw_affiliation_string: School of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan 410114, China
raw_author_name: Yafei Ma
raw_affiliation_string: School of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan 410114, China
raw_author_name: Ke Huang
raw_affiliation_string: School of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan 410114, China
raw_author_name: Lei Wang
raw_affiliation_string: School of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan 410114, China

Abstract: This paper proposes a novel deep-learning-enabled framework for abnormal data detection and recovery considering spatial-temporal correlation among sensors. The wavelet scattering transform is adopted to replace the first layer of one-dimension convolutional neural network classification model. The multiple typical sensor failure modes of sensor are identified including bias, drifting, gain, precision degradation, and three complete faults (constant; constant + noise; noise). The bidirectional long-short time memory (BLSTM) network is developed to recover abnormal data through positive and negative propagation. The BLSTM cell involving forget, input, and output gate is utilized to adaptively transmit valuable information of long-term data. Different sensor configurations are conducted to select the optimal input sensors of BLSTM model. The proposed models are then validated by three engineering cases. The results show that the failure mode diagnosis and recovery accuracy are over 90 %. Sensor selection has a large impact on the data reconstruction, and selecting sensors with higher correlation can achieve a higher accuracy. In addition, the data recovery accuracy of the proposed BLSTM model surpasses that of traditional long-short time memory models.
Contextual Feature Representation for Image-Based Insider Threat Classification

Authors:
raw_author_name: Bo Wang
raw_affiliation_string: College of Software, Xinjiang University, Ürümqi 830000, China

Abstract: With the rapid development of technology, insider threat incidents frequently occur in organizations. Detecting insider threats is an essential task in network infrastructure security. In this paper, we design an attention module to extract contextual features and augment abnormal features to generate high-quality images representing user behavior. Then, we use pre-trained ResNet and multi-source feature fusion on behavioral, psychological, and role features, intending to identify malicious insiders accurately. The proposed approaches are evaluated using the CMU-CERT Insider Threat Dataset. Experimental results show the effectiveness of methods and outperform other state-of-the-art methods.
Identifying optical microscope images of CVD-grown two-dimensional MoS<sub>2</sub> by convolutional neural networks and transfer learning

Authors:
raw_author_name: Cahit Perkgoz
raw_affiliation_string: Department of Computer Engineering, Eskisehir Technical University, Eskişehir, Turkey

Abstract: Background In Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential solutions, serving as channel materials with steep sub-threshold swings and lower power consumption. However, the production and development of these 2-dimensional materials require some time-consuming tasks. In order to employ them in different fields, including chip technology, it is crucial to ensure that their production meets the required standards of quality and uniformity; in this context, deep learning techniques show significant potential. Methods This research introduces a transfer learning-based deep convolutional neural network (CNN) to classify chemical vapor deposition (CVD) grown molybdenum disulfide (MoS 2 ) flakes based on their uniformity or the occurrence of defects affecting electronic properties. Acquiring and labeling a sufficient number of microscope images for CNN training may not be realistic. To address this challenge, artificial images were generated using Fresnel equations to pre-train the CNN. Subsequently, accuracy was improved through fine-tuning with a limited set of real images. Results The proposed transfer learning-based CNN method significantly improved all measurement metrics with respect to the ordinary CNNs. The initial CNN, trained with limited data and without transfer learning, achieved 68% average accuracy for binary classification. Through transfer learning and artificial images, the same CNN achieved 85% average accuracy, demonstrating an average increase of approximately 17%. While this study specifically focuses on MoS 2 structures, the same methodology can be extended to other 2-dimensional materials by simply incorporating their specific parameters when generating artificial images.
Complex network effects on the robustness of graph convolutional networks

Authors:
raw_author_name: Benjamin A. Miller
raw_author_name: Kevin Chan
raw_author_name: Tina Eliassi-Rad

Abstract: Abstract Vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target node. This vulnerability decreases users' confidence in the learning method and can prevent adoption in high-stakes contexts. Defenses have been proposed, focused on filtering edges before creating the model or aggregating information from neighbors more robustly. This paper considers an alternative: we investigate the ability to exploit network phenomena in the training data selection process to improve classifier robustness. We propose two alternative methods of selecting training data: (1) to select the highest-degree nodes and (2) to select nodes with many connections to the test data. In four real datasets, we show that changing the training set often results in far more perturbations required for a successful attack on the graph structure; often a factor of 2 over the random training baseline. We also run a simulation study in which we demonstrate conditions under which the proposed methods outperform random selection, finding that they improve performance most when homophily is higher, clustering coefficient is higher, node degrees are more homogeneous, and attributes are less informative. In addition, we show that the methods are effective when applied to adaptive attacks, alleviating concerns about generalizability.
Neural network crossover in genetic algorithms using genetic programming

Authors:
raw_author_name: Kyle Pretorius
raw_affiliation_string: Department of Computer Science, University of Pretoria, Pretoria, South Africa
raw_author_name: Nelishia Pillay
raw_affiliation_string: Department of Computer Science, University of Pretoria, Pretoria, South Africa

Abstract: Abstract The use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from such GAs as they are seen as being highly destructive and detrimental to the performance of the GA. Designing crossover operators that can effectively be applied to NNs has been an active area of research with success limited to specific problem domains. The focus of this study is to use genetic programming (GP) to automatically evolve crossover operators that can be applied to NN weights and used in GAs. A novel GP is proposed and used to evolve both reusable and disposable crossover operators to compare their efficiency. Experiments are conducted to compare the performance of GAs using no crossover operator or a commonly used human designed crossover operator to GAs using GP evolved crossover operators. Results from experiments conducted show that using GP to evolve disposable crossover operators leads to highly effectively crossover operators that significantly improve the results obtained from the GA.
“The sleep data looks way better than I feel.” An autoethnographic account and diffractive reading of sleep-tracking

Authors:
raw_author_name: Anna Nolda Nagele
raw_affiliation_string: Cognitive Science Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom
raw_author_name: Julian Hough
raw_affiliation_string: Future Interactive Technologies Lab, Department of Computer Science, School of Mathematics and Computer Science, Swansea University, United Kingdom

Abstract: Sleep-tracking products are promising their users an improvement to their sleep by focusing on behavior change but often neglecting the contextual and individual factors contributing to sleep quality and quantity. Making good sleep for productive scheduling a personal responsibility does not necessarily lead to better sleep and may cause stress and anxiety. In an autoethnographic study, the first author of this paper tracked her sleep for one month using a diary, body maps and an Oura ring and compared her subjectively felt sleep experience with the data produced by the Oura app. A thematic analysis of the data resulted in four themes describing the relationship between the user-researcher and her wearable sleep-tracker: (1) good sleep scores are motivating, (2) experience that matches the data leads to sense-making, (3) contradictory information from the app leads to frustration, and (4) the sleep-tracker competes with other social agents. A diffractive reading of the data and research process, following Karen Barad's methodology, resulted in a discussion of how data passes through the analog and digital apparatus and what contextual factors are left out but still significantly impact sleep quality and quantity. We add to a canon of sleep research recommending a move away from representing sleep in terms of comparison and competition, uncoupling it from neoliberal capitalistic productivity and self-improvement narratives which are often key contributing factors to bad sleep in the first place.
A novel well log data imputation methods with CGAN and swarm intelligence optimization

Authors:
raw_author_name: Jiansheng Liu
raw_affiliation_string: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, 266580, China
raw_author_name: Tianyu Wu
raw_affiliation_string: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, 266580, China
raw_author_name: Fang Shi
raw_affiliation_string: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, 266580, China
raw_author_name: Yuqiang Xu
raw_affiliation_string: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, 266580, China

Abstract: Well log data plays a vital role in decision-making, resource assessment, production optimization, and environmental management of oil and gas development. However, when exploring and developing deep and ultra-deep oil and gas, the performance of logging equipment is greatly challenged by high temperature, high pressure, and high corrosion wellbore conditions. Log data often needs to be completed or corrected. Data imputation technology has become a powerful tool to fill the missing in well log data. A well log data imputation method based on deep learning is proposed. The proposed method combines conditional generative adversarial networks (CGAN) with swarm intelligence optimization algorithms. The seismic layer velocity is used as a constraint to guide CGAN to generate well log data that matches geological features. Establish an objective function based on multiple conditions to evaluate the quality of generated samples. The swarm intelligence optimization algorithm is used to minimize the objective function. The case study shows that the proposed method obtains pseudo samples that meet specific scenarios and perform better than other algorithms. The proposed method provides a new approach for deep learning algorithms in sequence data prediction.
Cellular geometric semantic genetic programming

Authors:
raw_author_name: Lorenzo Bonin
raw_affiliation_string: Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Via Alfonso Valerio 12/1, 34127, Trieste, TS, Italy
raw_author_name: Luigi Rovito
raw_affiliation_string: Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Via Alfonso Valerio 12/1, 34127, Trieste, TS, Italy
raw_author_name: Andrea De Lorenzo
raw_affiliation_string: Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Via Alfonso Valerio 6/1, 34127, Trieste, TS, Italy
raw_author_name: Luca Manzoni
raw_affiliation_string: Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Via Alfonso Valerio 12/1, 34127, Trieste, TS, Italy

Abstract: Abstract Among the different variants of Genetic Programming (GP), Geometric Semantic GP (GSGP) has proved to be both efficient and effective in finding good solutions. The fact that the operators of GSGP operate on the semantics of the individuals in a clear way provides guarantees on the way the search is performed. GSGP is not, however, free from limitations like the premature convergence of the population to a small–and possibly sub-optimal–area of the search space. One reason for this issue could be the fact that good individuals can quickly “spread” in the population suppressing the emergence of competition. To mitigate this problem, we impose a cellular automata (CA) inspired communication topology over GSGP. In CAs a collection of agents (as finite state automata) are positioned in a n -dimensional periodic grid and communicates only locally with the automata in their neighbourhoods. Similarly, we assign a location to each individual on an n -dimensional grid and the entire evolution for an individual will happen locally by considering, for each individual, only the individuals in its neighbourhood. Specifically, we present an algorithm in which, for each generation, a subset of the neighbourhood of each individual is sampled and the selection for the given cell in the grid is performed by extracting the two best individuals of this subset, which are employed as parents for the Geometric Semantic Crossover. We compare this cellular GSGP (cGSGP) approach with standard GSGP on eight regression problems, showing that it can provide better solutions than GSGP. Moreover, by analyzing convergence rates, we show that the improvement is observable regardless of the number of executed generations. As a side effect, we additionally show that combining a small-neighbourhood-based cellular spatial structure with GSGP helps in producing smaller solutions. Finally, we measure the spatial autocorrelation of the population by adopting the Moran’s I coefficient to provide an overview of the diversity, showing that our cellular spatial structure helps in providing better diversity during the early stages of the evolution.
Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction

Authors:
raw_author_name: Li Teng
raw_affiliation_string: Shanghai Urban Operation(Group)Co., Ltd., Shanghai, 200023, China
raw_author_name: Li Li
raw_affiliation_string: Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
raw_author_name: Ansar Yasar
raw_affiliation_string: Transportation Research Institute, Hasselt University, Hasselt, 3500, Belgium
raw_author_name: Stéphane Galland
raw_affiliation_string: UTBM, CIAD UMR 7533, Belfort, 90010, France

Abstract: Abstract With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.
Networking concert halls, musicians, and interactive textiles: Interwoven Sound Spaces

Authors:
raw_author_name: Federico Visi
raw_affiliation_string: Einstein Center for Digital Future, Berlin, Germany; Fakultät Gestaltung, Universität der Künste Berlin, Berlin, Germany; School of Music in Piteå, Luleå University of Technology, Piteå, Sweden
raw_author_name: Berit Greinke
raw_affiliation_string: Einstein Center for Digital Future, Berlin, Germany; Fakultät Gestaltung, Universität der Künste Berlin, Berlin, Germany
raw_author_name: Emma Wood
raw_affiliation_string: Fakultät Gestaltung, Universität der Künste Berlin, Berlin, Germany
raw_author_name: Cat Hope
raw_affiliation_string: Sir Zelman Cowen School of Music and Performance, Monash University, Melbourne, Australia
raw_author_name: Stefan Östersjö
raw_affiliation_string: School of Music in Piteå, Luleå University of Technology, Piteå, Sweden

Abstract: Interwoven Sound Spaces is an interdisciplinary project which brought together telematic music performance, interactive textiles, interaction design, and artistic research. A team of researchers collaborated with two professional contemporary music ensembles based in Berlin, Germany, and Piteå, Sweden, and four composers, with the aim of creating a telematic distributed concert taking place simultaneously in two concert halls and online. Central to the project was the development of interactive textiles capable of sensing the musicians’ movements while playing acoustic instruments, and generating data the composers used in their works. Musicians, instruments, textiles, sounds, halls, and data formed a network of entities and agencies that was reconfigured for each piece, showing how networked music practice enables distinctive musicking techniques. We describe each part of the project and report on a research interview conducted with one of the composers for the purpose of analysing the creative approaches she adopted for composing her piece.
Towards co-design of rehabilitation technologies: a collaborative approach to prioritize usability issues

Authors:
raw_author_name: J. Mitchell
raw_affiliation_string: The Hopkins Centre, Menzies Health Institute Queensland, Griffith University, Australia

Abstract: Introduction Early stakeholder engagement is critical to the successful development and translation of rehabilitation technologies, a pivotal step of which is usability testing with intended end-users. To this end, several methods employ end-user feedback to identify usability and implementation issues. However, the process of prioritizing identified issues seldom leverages the knowledge and expertise of the range of stakeholders who will ultimately affect the demand and supply of a device. This paper describes a novel method to prioritize end-user feedback using transdisciplinary stakeholder consultation and address it in subsequent product development. The proposed approach was demonstrated using a case study relating to the development of a novel technology for neural recovery after spinal cord injury. Method Feedback from five individuals with chronic spinal cord injury was collected during two-hour usability evaluation sessions with a fully functional high-fidelity system prototype. A think-aloud and semi-structured interview protocol was used with each participant to identify usability and acceptability issues relating to the system in a 3-phase approach. Phase 1 involved extracting usability issues from think-aloud and semi-structured interview data. Phase 2 involved rating the usability issues based on their significance, technical feasibility, and implementation priority by relevant internal and external stakeholders. Finally, Phase 3 involved aggregating the usability issues according to design and implementation elements to facilitate solution generation, and these solutions were then raised as action tasks for future design iterations. Results Sixty usability issues representing nine facets of usability were rated. Eighty percent of issues were rated to be of moderate to high significance, 83% were rated as being feasible to address, and 75% were rated as addressable using existing project resources. Fifty percent of the issues were rated to be a high priority for implementation. Evaluation of the grouped issues identified 21 tasks which were mapped to the product roadmap for integration into future design iterations. Discussion This paper presents a method for meaningful transdisciplinary stakeholder engagement in rehabilitation technology development that can extended to other projects. Alongside a worked example, we offer practical considerations for others seeking to co-develop rehabilitation technologies.