Reviewerly/exHarmony huggingface dataset:
Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.
Linear discriminant analysis for multiple functional data analysis

In multivariate data analysis, Fisher linear discriminant analysis is useful to optimally separate two classes of observations by finding a linear combination of p variables. Functional data analysis deals with the analysis of continuous functions and thus can be seen as a generalisation of multivariate analysis where the dimension of the analysis space p strives to infinity. Several authors propose methods to perform discriminant analysis in this infinite dimensional space. Here, the methodology is introduced to perform discriminant analysis, not on single infinite dimensional functions, but to find a linear combination of p infinite dimensional continuous functions, providing a set of continuous canonical functions which are optimally separated in the canonical space.
Priority-based Parameter Propagation for Distributed DNN Training.

Data parallel training is widely used for scaling distributed deep neural network (DNN) training. However, the performance benefits are often limited by the communication-heavy parameter synchronization step. In this paper, we take advantage of the domain specific knowledge of DNN training and overlap parameter synchronization with computation in order to improve the training performance. We make two key observations: (1) the optimal data representation granularity for the communication may differ from that used by the underlying DNN model implementation and (2) different parameters can afford different synchronization delays. Based on these observations, we propose a new synchronization mechanism called Priority-based Parameter Propagation (P3). P3 synchronizes parameters at a finer granularity and schedules data transmission in such a way that the training process incurs minimal communication delay. We show that P3 can improve the training throughput of ResNet-50, Sockeye and VGG-19 by as much as 25%, 38% and 66% respectively on clusters with realistic network bandwidth
Interactive Projection System for Calligraphy Practice

Calligraphy is representative art from East Asia that fascinates people around the world. However, beginners in calligraphy commonly encounter difficulties in understanding calligraphy features, such as the balance of font characters, stroke orders, and thickness, which may cause frustration during calligraphy practice. To solve these issues, we propose an interactive projection system that supports the calligraphy practice of learners. The proposed system includes three stages: image matching, image blending, and font guidance projection. It provides adaptive calligraphy guidance featuring blended font alternatives according to their matching degrees with the current writing state. Therefore, the proposed system can support calligraphy learners of different mastery levels. Our user study findings verify that the proposed system can effectively support calligraphy learners.
Anomaly detection in bitcoin information networks with multi-constrained meta path

As the most popular digital currency, Bitcoin has a high economic value, and its security has been paid more and more attention. Anomaly detection of Bitcoin has become a problem that must be solved. The existing Bitcoin anomaly detection methods only use static network models, and only the simple structural features such as node attributes and in/out-degree are considered to measure the similarities between nodes. Therefore, we propose a series of constrained anomaly detection algorithms for Bitcoin data. In our algorithms, we first construct a temporal Bitcoin network model for Bitcoin data. Then, combining time constraints, attribute constraints and structure constraints, a multi-constrained meta path is proposed on the basis of the meta path to specify the candidate sets, reference sets and similarity measurement strategies and detect local abnormal users and transactions that are of interest to users from static and dynamic angles with lower space-time overhead. Experiments on real-world Bitcoin data show that the constrained algorithms have certain improvements in recall, precision and F2 score when compared to the algorithms that only considers simple structural features such as node attributes and in/out-degree.
Intercomparison of Satellite-Derived Solar Irradiance from the GEO-KOMSAT-2A and HIMAWARI-8/9 Satellites by the Evaluation with Ground Observations

Solar irradiance derived from satellite imagery is useful for solar resource assessment, as well as climate change research without spatial limitation. The University of Arizona Solar Irradiance Based on Satellite–Korea Institute of Energy Research (UASIBS-KIER) model has been updated to version 2.0 in order to employ the satellite imagery produced by the new satellite platform, GK-2A, launched on 5 December 2018. The satellite-derived solar irradiance from UASIBS-KIER model version 2.0 is evaluated against the two ground observations in Korea at instantaneous, hourly, and daily time scales in comparison with the previous version of UASIBS-KIER model that was optimized for the COMS satellite. The root mean square error of the UASIBS-KIER model version 2.0, normalized for clear-sky solar irradiance, ranges from 4.8% to 5.3% at the instantaneous timescale when the sky is clear. For cloudy skies, the relative root mean square error values are 14.5% and 15.9% at the stations located in Korea and Japan, respectively. The model performance was improved when the UASIBS-KIER model version 2.0 was used for the derivation of solar irradiance due to the finer spatial resolution. The daily aggregates from the proposed model are proven to be the most reliable estimates, with 0.5 km resolution, compared with the solar irradiance derived by the other models. Therefore, the solar resource map built by major outputs from the UASIBS-KIER model is appropriate for solar resource assessment.
Stability and Robustness in Misspecified Learning Models

We present an approach to analyze learning outcomes in a broad class of misspeciﬁed environments, spanning both single-agent and social learning. Our main results provide general criteria to determine — without the need to explicitly analyze learning dynamics — when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). The key ingredient underlying these criteria is a novel “prediction accuracy” ordering over subjective models that reﬁnes existing comparisons based on Kullback-Leibler divergence. We show that these criteria can be applied, ﬁrst, to unify and generalize various convergence results in previously studied settings. Second, they enable us to identify and analyze a natural class of environments, including costly information acquisition and sequential social learning, where unlike most settings the literature has focused on so far, long-run beliefs can fail to be robust to the details of the true data generating process or agents’ perception thereof. In particular, even if agents learn the truth when they are correctly speciﬁed, vanishingly small amounts of misspeciﬁcation can lead to extreme failures of learning.
A Key Recovery System Based on Password-protected Secret Sharing in a Permissioned Blockchain

In today’s fourth industrial revolution, various blockchain technologies are being actively researched. A blockchain is a peer-to-peer data-sharing structure lacking central control. If a user wishes to access stored data, she/he must employ a private key to prove ownership of the data and create a transaction. If the private key is lost, blockchain data cannot be accessed. To solve such a problem, public blockchain users can recover the key using a wallet program. However, key recovery in a permissioned blockchain (PBC) has been but little studied. The PBC server is Honest-but-Curious (HBC), and should not be able to learn anything of the user; the server should simply recover and store the key. The server must also be resistant to malicious attacks. Therefore, key recovery in a PBC must satisfy various security requirements. Here, we present a password-protected secret sharing (PPSS) key recovery system, protected by a secure password from a malicious key storage server of a PBC. We describe existing key recovery schemes and our PPSS scheme.
A DNN Architecture for the Detection of Generalized Spatial Modulation Signals

In this letter, we consider the problem of signal detection in generalized spatial modulation (GSM) using deep neural networks (DNN). We propose a novel modularized DNN architecture that uses small sub-DNNs to detect the active antennas and complex modulation symbols, instead of using a single large DNN to jointly detect the active antennas and modulation symbols. The main idea is that using small sub-DNNs instead of a single large DNN reduces the required size of the NN and hence requires learning lesser number of parameters. Under the assumption of i.i.d Gaussian noise, the proposed DNN detector achieves a performance very close to that of the maximum likelihood detector. We also analyze the performance of the proposed detector under two practical conditions: i) correlated noise across receive antennas and ii) noise distribution deviating from the standard Gaussian model. The proposed DNN-based detector learns the deviations from the standard model and achieves superior performance compared to that of the conventional maximum likelihood detector.
Calibration method of a laser beam based on discrete point interpolation for 3D precision measurement

A laser beam used as a visualizing measuring axis is an important technique in 3D shape measurement. A highly accurate calibration method of a laser beam based on discrete point interpolation is proposed in this paper. A flexible control field constructed by a laser tracker, a theodolite and a target plane with 5 high-precision machining holes is presented. The discrete point interpolation model is established by the coordinates of holes measured by a laser tracker and the angles of holes measured by a theodolite. The coordinates of laser spots on the target plane are obtained based on the angles and discrete point interpolation model, and the direction vector of the laser beam is obtained by linear fitting. The optimal measurement pose of a theodolite is analyzed by the simulation results. The experimental results show that the RMSE of linear fitting of laser beams is no more than 14 µm within a 5 m distance, the RMSE of the spatial points is 0.09 mm and the RMSE of the reconstructed distance is 0.09 mm.
<scp>HEART</scp>: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and <scp>meta‐heuristic</scp> algorithms

Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.
Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.
On Counterfactual Explanations under Predictive Multiplicity.

Counterfactual explanations are usually obtained by identifying the smallest change made to an input to change a prediction made by a fixed model (hereafter called sparse methods). Recent work, however, has revitalized an old insight: there often does not exist one superior solution to a prediction problem with respect to commonly used measures of interest (e.g. error rate). In fact, often multiple different classifiers give almost equal solutions. This phenomenon is known as predictive multiplicity (Breiman, 2001; Marx et al., 2019). In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity. Most notably, it depends on a discrepancy notion between two classifiers, which describes how differently they treat negatively predicted individuals. We then compare sparse and data support approaches empirically on real-world data. The results show that data support methods are more robust to multiplicity of different models. At the same time, we show that those methods have provably higher cost of generating counterfactual explanations under one fixed model. In summary, our theoretical and empiricaln results challenge the commonly held view that counterfactual recommendations should be sparse in general.
Learning Resilient Behaviors for Navigation Under Uncertainty

Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertainty-aware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties.
A Three-Stage Variational Image Segmentation Framework Incorporating Intensity Inhomogeneity Information

A Three-Stage Variational Image Segmentation Framework Incorporating Intensity Inhomogeneity Information
The Power of Simulation for Equivalence Checking in Quantum Computing

The rapid rate of progress in the physical realization of quantum computers sparked the development of elaborate design flows for quantum computations on such devices. Each stage of these flows comes with its own representation of the intended functionality. Ensuring that each design step preserves this intended functionality is of utmost importance. However, existing solutions for equivalence checking of quantum computations heavily struggle with the complexity of the underlying problem and, thus, no conclusions on the equivalence may be reached with reasonable efforts in many cases. In this work, we uncover the power of simulation for equivalence checking in quantum computing. We show that, in contrast to classical computing, it is in general not necessary to compare the complete representation of the respective computations. Even small errors frequently affect the entire representation and, thus, can be detected within a couple of simulations. The resulting equivalence checking flow substantially improves upon the state of the art by drastically accelerating the detection of errors or providing a highly probable estimate of the operations' equivalence.
Role of 5G network technology and artificial intelligence for research and reform of english situational teaching in higher vocational colleges

In order to study the role of English situational teaching in higher vocational colleges, based on information technology and artificial intelligence, this research combines with the needs of English teaching to construct a English situation teaching in higher vocational colleges with the support of 5G network technology and artificial intelligence. Moreover, this research builds a data processing model based on the system architecture diagram of cache placement, uses storage space and computing resources to save more backhaul link bandwidth, and adopts the “many to many” algorithm extended by the “one to many” algorithm, and uses the on-demand method to obtain scenario teaching data from the cloud. In addition, this research constructs the intermediate link of data processing, and uses 5G network transmission to solve the problem of data transmission speed. Finally, this study uses a controlled experiment to evaluate the effectiveness of the artificial intelligence teaching model constructed in this study. The research shows that the English situation teaching method based on 5G network technology and artificial intelligence in vocational colleges has a certain effect and can effectively improve the English scores of vocational college students.
Four-dimensional toric code with non-Clifford transversal gates

The design of a four-dimensional toric code is explored with the goal of finding a lattice capable of implementing a logical CCCZ gate transversally. The established lattice is the octaplex tessellation, which is a regular tessellation of four-dimensional Euclidean space whose underlying 4-cell is the octaplex, or hyperdiamond. This differs from the conventional 4D toric code lattice, based on the hypercubic tessellation, which is symmetric with respect to logical X and Z and only allows for the implementation of a transversal Clifford gate. This paper further develops the established connection between topological dimension and transversal gates in the Clifford hierarchy, generalizing the known designs for the implementation of transversal CZ and CCZ in two and three dimensions, respectively.4 MoreReceived 15 October 2020Accepted 9 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013118Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum gatesSurface code quantum computingTopological quantum computingQuantum Information
Reducing Ambiguity in Requirements Elicitation via Gamification

The overall quality and success of software highly depends on the involvement of stakeholders. Requirements elicitation supports RE analyst to gather requirements from the stakeholders based on their needs. There are multiple elicitation techniques present in literature and used by the practitioners. Some of them are questionnaires, interviews, prototyping, and user stories etc. However, these techniques are based on textual representation of requirements. These techniques are quite common among the requirement engineers yet problems of ambiguity, inconsistency, incompleteness still exist mostly due to their textual nature and lack of stakeholder involvement. Lack of clarity about the system increases the ambiguity of what exactly are the system requirements. Since elicitation is carried at an early stage of development the users are not sure of what they want, as requirements tend to evolve with the help of discussions and interactions among various stakeholders and technical team. Furthermore, the conventional elicitation methods are limited when it comes to stakeholders' participation and involvement, thus leaving a space for more ambiguous and incomplete requirements. In this work, Gamification, a game-based context will be used in non-gaming context for user involvement in fun ways. During elicitation, gamification would help to involve and interact with the stakeholders, with an intention to develop their interest in eliciting and finalizing system requirements. The goal of this paper is to reduce ambiguity during requirements elicitation. This would help in reducing the cost and time of development. Furthermore, we will elicit software requirements using gamification by developing a gamification tool with a focus to elicit unambiguous requirements by ensuring users' participation and maintaining interest. The validation of tool would be done using multiple confirmatory case studies from software industry.