Reviewerly/exHarmony huggingface dataset:
Consistency and Coherency Enhanced Story Generation

Story generation is a challenging task, which demands to maintain consistency of the plots and characters throughout the story. Previous works have shown that GPT2, a large-scale language model, has achieved advanced performance on story generation. However, we observe that several serious issues still exist in the stories generated by GPT2, which can be categorized into two folds: consistency and coherency. In terms of consistency, on the one hand, GPT2 cannot guarantee the consistency of the plots explicitly. On the other hand, the generated stories usually contain coreference errors. In terms of coherency, GPT2 does not take account of the discourse relations between sentences of stories directly. To enhance the consistency and coherency of the generated stories, we propose a two-stage generation framework, where the first stage is to organize the story outline which depicts the story plots and events, and the second stage is to expand the outline into a complete story. Therefore, the consistency of the plots can be controlled and guaranteed explicitly. In addition, coreference supervision signals are incorporated to reduce coreference errors and improve coreference consistency. Moreover, we design an auxiliary task of discourse relation modeling to improve the coherency of the generated stories. Experimental results on a story dataset show that our model outperforms baseline approaches in terms of both automatic metrics and human evaluation.
Evaluation of visualization performance of CNN models using driver model

Convolutional Neural Networks (CNNs) have demonstrated impressive performance in complex machine learning tasks such as classification and regression problems. A reliable neural network structure plays a decisive role in CNN studies. Through comparing and analyzing the structure of neural networks, a model structure for better visualization performance has been discovered, and such a method supports the development of deep learning research. These studies are of particular importance in end-to-end systems for autonomous driving to imitate human driving, where the interpretability of the system is limited. Because of the uncertainty of the ground truth, for the determination of human steering in an image, it is difficult to accurately compare the visualization performance of different CNN models or different visualization methods. For practical applications, however, an objective and quantitative measure for assessing visualization performance is necessary. Therefore, a method to evaluate the visualization performance of CNN models using a driver model instead of human drivers is proposed, to generate a data set which can be used to determine the decisional point (ground truth) in the input image. Then, an exclusive method is also put forth, to quantitatively calculate the relationship between the decisional point (ground truth) and the visualization results produced by CNN models. In this paper, five CNN models as an autonomous steering controller are designed based on PilotNet, and the visualization abilities of each CNN models is compared by three evaluation indicators. By comparing the visualization performance of five different CNN models, it is shown that the proposed method can successfully assess the visualization level of the CNN model.
Decision Tree Model for Email Classification

In addition to the undeniable benefits, the development of the Internet has led to many undesirable security effects. Spam emails are one of the most challenging issues faced by the Internet users. Spam refers to all emails of unsolicited content that arrive in a user's email box. Spam can often lead to network congestion and blocking or even damage to the system for receiving and sending electronic messages. Thus, appropriate classification of spam email from legitimate email has become quite important. This paper presents a new approach for feature selection and Iterative Dichotomiser 3 (ID3) algorithm designed to generate the decision tree for email classification. The experimental results indicate that the proposed model achieves very high accuracy.
Combinatorially equivalent hyperplane arrangements

We study the combinatorics of hyperplane arrangements over arbitrary fields. Specifically, we determine in which situation an arrangement and its reduction modulo a prime number have isomorphic lattices via the use of minimal strong σ-Gröbner bases. Moreover, we prove that the Terao's conjecture over finite fields implies the conjecture over the rationals.
Evaluation of the Performance of Tightly Coupled Parallel Solvers and MPI Communications in IaaS From the Public Cloud

IaaS from the public cloud is becoming a new option for organizations in need of HPC capabilities. IaaS offers greater flexibility in hardware choices and operational costs. Furthermore, IaaS enables small organizations to access resources previously reserved for organizations with larger capitals. This article uses the HPCG benchmark to assess the performance of parallel solvers, which are critical in computational engineering, and microbenchmarks to measure collective MPI operations. The benchmarks encompass IaaS from five cloud vendors (AWS, Google Cloud Platform, Azure, Oracle CIoud Infrastructure, and Packet), and two architectures, ARM and x86_64. The benchmarks cover clusters with up to 4500 cores and illustrate the benefits of higher network bandwidth when scaling up clusters. The results for some of the clusters are particularly promising as they exhibit good scalability and compare well to on-premises supercomputers. Additionally, the study includes a preliminary cost estimate based on on-demand prices for IaaS computational power and memory.
Multilabel Ranking with Inconsistent Rankers

While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where only subjective inconsistent rankings from multiple rankers are associated with each instance. Two ranking methods are proposed from the perspective of instances and rankers, respectively. The first method, Instance-oriented Preference Distribution Learning (IPDL), is to learn a latent preference distribution for each instance. IPDL generates a common preference distribution that is most compatible to all the personal rankings, and then learns a mapping from the instances to the preference distributions. The second method, Ranker-oriented Preference Distribution Learning (RPDL), is proposed by leveraging interpersonal inconsistency among rankers, to learn a unified model from personal preference distribution models of all rankers. These two methods are applied to natural scene images dataset and 3D facial expression dataset BU_3DFE. Experimental results show that IPDL and RPDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multilabel ranking algorithms.
Refined Prediction of Mouse and Human Actions Based on A Data-Selective Multiple-Stage Approach of 3D Convolutional Neural Networks

How to refine and improve the prediction power of a deep learning model is still a challenge. In this paper, we propose a data-selective multiple-stage approach to refine the prediction results from a single 3D convolutional neural network (3D CNN) in terms of precision and recall measurements. In the initial stage of the learning process, a 3D CNN is trained with a pre-trained model and the precisions and recalls for each class are calculated. An over-predicted ratio is also calculated as summarizing the true positive and false negative ratios of each predicted class according to the normalized confusion matrix. From the training result, those with both of the precision and recall larger than a preset threshold and the over-predicted ratio ≤ 1 are directly output as the accepted classes at this stage. For the other classes not satisfied the above three criterions, the corresponding training data are selected as the input for the next stage. If the number of selected clusters is less than that at the beginning of this stage, another new 3D CNN is created and trained with the selected data of unsolved classes. After repeating the procedures until no new 2D CNN is needed to be created, all trained models and accepted classes at each stage are saved. In the testing phase, each data is input to the model at the first stage. If the predicted class is belonged to the accepted class at this stage, the result is output directly. Otherwise, the data is input to the next stage. After repeating the process until the predicted class is belonged to the accepted class at some stage, then the testing phase is terminated. In the results, we have used two video datasets of mouse behaviors in cage and human actions in the wild to examine the proposed method and found that our method can significantly improve both of the precisions and recalls of the predictions in comparison with only using a single 3D CNN model.
An Efficient Adaptive Sampling Approach for Mobile Robotic Sensor Networks using Proximal ADMM

Adaptive sampling in a resource-constrained mobile robotic sensor network for monitoring a spatial phenomenon is a fundamental but challenging problem. In applications where a Gaussian Process is employed to model a spatial field and then to predict the field at unobserved locations, the adaptive sampling problem can be formulated as minimizing the negative log determinant of a predicted covariance matrix, which is a non-convex and highly complex function. Consequently, this optimization problem is typically addressed in a grid-based discrete domain, although it is combinatorial NP-hard and only a near-optimal solution can be obtained. To overcome this challenge, we propose using a proximal alternating direction method of multipliers (Px-ADMM) technique to solve the adaptive sampling optimization problem in a continuous domain. Numerical simulations using a real-world dataset demonstrate that the proposed PxADMM-based method outperforms a commonly used grid-based greedy method in the final model accuracy.
AAAA: SSO and MFA Implementation in Multi-Cloud to Mitigate Rising Threats and Concerns Related to User Metadata

In the modern digital era, everyone is partially or fully integrated with cloud computing to access numerous cloud models, services, and applications. Multi-cloud is a blend of a well-known cloud model under a single umbrella to accomplish all the distinct nature and realm requirements under one service level agreement (SLA). In current era of cloud paradigm as the flood of services, applications, and data access rise over the Internet, the lack of confidentiality of the end user’s credentials is rising to an alarming level. Users typically need to authenticate multiple times to get authority and access the desired services or applications. In this research, we have proposed a completely secure scheme to mitigate multiple authentications usually required from a particular user. In the proposed model, a federated trust is created between two different domains: consumer and provider. All traffic coming towards the service provider is further divided into three phases based on the concerned user’s data risks. Single sign-on (SSO) and multifactor authentication (MFA) are deployed to get authentication, authorization, accountability, and availability (AAAA) to ensure the security and confidentiality of the end user’s credentials. The proposed solution exploits the finding that MFA achieves a better AAAA pattern as compared to SSO.
Weight distributions of two classes of linear codes with five or six weights

Linear codes with few weights have been an interesting subject of study for many years, as these codes have applications in secrete sharing, authentication codes, association schemes, and strongly regular graphs. In this paper, two classes of five-weight or six-weight linear codes over Fp are constructed and their weight distributions are determined by means of exponential sums. In one case, there are some almost optimal codes with respect to the Griesmer bound, which are in fact optimal according to the online code tables.
Controllable dynamics of a dissipative two-level system

Abstract We propose a strategy to modulate the decoherence dynamics of a two-level system, which interacts with a dissipative bosonic environment, by introducing an ancillary degree of freedom. It is revealed that the decay rate of the two-level system can be significantly suppressed under suitable steers of the assisted degree of freedom. Our result provides an alternative way to fight against decoherence and realize a controllable quantum dissipative dynamics.
Optical Flow Enhancement and Effect Research in Action Recognition

The accuracy of video-based action recognition depends largely on the extraction and utilization of optical flow, especially in two-stream networks. The original intention of the introduction of optical flow is to use the time information contained in video, however, the subsequent work shows that optical flow is useful for action recognition because it is invariant to appearance. In this article, we study and discuss this point of view, and propose optical flow enhancement algorithms to improve action recognition accuracy. Our enhancement algorithms improve the invariance to appearance of the representation in optical flow without losing time information, and every action recognition network with optical flow can benefit from our algorithms. We conduct a series of experiments to validate the influence of the proposed algorithms with TSN in terms of several datasets and optical flow calculation methods. As a result, we prove that first order differential algorithms are effective, TSN with our enhancement module significantly outperform original network. Based on these experiments, we also verify the importance of invariance to appearance in optical flow, and provide a reference for the follow-up study of improving action recognition accuracy.
Accuracy Comparison between Learning Method and Signal Processing Method Using Iteration for Severely Blur Images

Blurring is one of the most common types of image degradation. When the blurring function (PSF) is unknown and a degraded image is to be recovered, the conventional method requires the estimation of two unknowns, which are the PSF and its ideal image, from a single input image. Thus, the method of performing alternating a PSF estimation and an ideal image estimation processing has been successful. On the other hand, blind image restoration using AI has made remarkable progress in recent years, enabling clearer estimation. In this paper, we compare the conventional iterative method with the AI method, and aim to improve the performance of images containing large blurring, which was not expected in conventional test images.
On the Efficiency of a CFD-Based Full Convolution Neural Network for the Post-Processing of Field Data

The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology. Assuming a problem of aircraft design as the workhorse, a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet (Visual Geometry Group Net) and FCN (Fully Convolutional Network) techniques. As shown by the results, the model displays a strong fitting ability, and there is almost no over-fitting in training. Moreover, the model has good accuracy and convergence. For different input data and different grids, the model basically achieves convergence, showing good performances. It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics (CFD) and related data processing.
Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things

Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.
A Decision Algorithm for Motion Planning of Car-Like Robots in Dynamic Environments

Motion planning in dynamic environments is essential for many applications such as in search and rescue missions and in servicing tasks. In this paper, I present a new approach for motion planning for an autonomous mobile robot which is requested to operate in a dynamic environment. The robot's working environment is cluttered with static obstacles with a priori knowledge of their shapes and positions and with moving obstacles with unknown geometry and motion. In order to ensure a safe motion for the robot I propose a two-stage approach. First, using the Bump-Surface concept I construct a path by considering only the static obstacles of the environment. Then, the robot starts its motion on the given path. When the robot detects a potential danger situation (i.e., collision), a decision algorithm tries to evaluate the risk of collision and simultaneously to find the safest action for the robot. The proposed approach is evaluated in randomly generated simulated scenarios.
A new result on irreducible NFSRs with respect to cascade connection

Nonlinear feedback shift registers (NFSRs) are widely used in stream cipher design as building blocks. The cascade connection of NFSRs, known as an important architecture, has been adopted in Grain family of stream ciphers. In this paper, a new sufficient condition under which an NFSR cannot be decomposed into the cascade connection of two smaller NFSRs is presented, which is easy to be verified from the algebraic normal form (ANF) of the characteristic function. In fact, our results are also applicable to nonsingular Boolean functions, which actually improve a previous research of Rhodes [6] where the characteristic functions of NFSRs cannot be contained.
Management of Online Education Based on Blockchains

With the rapid development of network technology, online education becomes indispensable to the whole education system, which has been paid an increasing attention to by various education institutions, but the management of online learning process, certificates or other data is still far from perfection. To solve these problems, the paper proposes to use the blockchain technology in online education, which is conducive to the management of educational resources, student data, certification of students' achievements and students' employment. By means of public blockchains and private blockchains, the learning users, education institutions, education regulators and the talent markets can make direct communication with each other as different nodes on the blockchain, which can promote the healthy development of online education.
Power and Privacy in Software Ecosystems: A Study on Data Breach Impact on Tech Giants

[Context and motivation] Concerns about data privacy and protection in companies from various fields and sizes are not only a reality, but a requirement at this day and age. The need to comply with governmental laws and other rules became a driving force in handling personal data. [Question/problem] For major IT companies, especially those in charge of a software ecosystem, such concerns grow tenfold: cases of privacy breach can extend over and affect their platforms, software solutions and relationships with partners and users. [Principal results] This research investigates data breach cases in GAFA (Google, Amazon, Facebook, Apple) ecosystems through the perspective of power, which is a lens of analysis of a network of multiple interdependent actors. We create power models to describe the power relationships among ecosystem players during a privacy issue. [Contribution] Our descriptive case study reveals the actors involved in a data breach scandal, the ecosystem elements that grant them privileges or lack thereof, and consequences that reverberate positively or negatively towards them. We contribute towards stakeholder analysis activities by presenting our power relationships framework, which can be integrated into the requirements process as a technique for security and privacy requirements definition.
Modelling Inference in the Comprehension of Cinematic Narratives

The viewer’s processes of inference making in the cinema involve the framing of hypotheses about the world of the narrative that may be overturned by subsequent information and are, therefore, nonmonotonic. The goal of narrative researchers is to understand the nature of those processes and how texts organise the deployment of those processes in order to present a narrative successfully. To do this we need methods capable of describing processes of hypothesis framing and belief revision. In this paper, I describe the application of the Transferable Belief Model to a hypothetical example of narrative comprehension based on an episode of CSI: Crime Scene Investigation as one such method.
Variational Deep Knowledge Tracing for Language Learning

Deep Knowledge Tracing (DKT), which traces a student's knowledge change using deep recurrent neural networks, is widely adopted in student cognitive modeling. Current DKT models only predict a student's performance based on the observed learning history. However, a student's learning processes often contain latent events not directly observable in the learning history, such as partial understanding, making slips, and guessing answers. Current DKT models fail to model this kind of stochasticity in the learning process. To address this issue, we propose Variational Deep Knowledge Tracing (VDKT), a latent variable DKT model that incorporates stochasticity into DKT through latent variables. We show that VDKT outperforms both a sequence-to-sequence DKT baseline and previous SoTA methods on MAE, F1, and AUC by evaluating our approach on two Duolingo language learning datasets. We also draw various interpretable analyses from VDKT and offer insights into students' stochastic behaviors in language learning.