Reviewerly/exHarmony huggingface dataset:
End-to-end task dependent recurrent entity network for goal-oriented dialog learning

In this paper, we introduce the Task Dependent Recurrent Entity Network (TDREN) to solve Dialogue System Technology Challenges 6 (DSTC 6) track 1. Traditionally, there have been methods such as collecting the intent of the user in a conversation directly using rules. We design an end-to-end structure that properly models the restaurant pre-related user preferences that appear in the dialogue and gives appropriate responses. We perform experiments on the TDREN and achieved 97.7% at precision 1. We propose a new artificial neural network structure and recurrent cell for modeling user preference information. Then, we show that task-oriented dialogue modeling experiment results using the structure and the recurrent cell.
Optimal Speed Setting for Cloud Servers With Mixed Applications

The technique of workload dependent dynamic power management can dynamically and flexibly adjust power and speed according to the current workload. It has been well recognized that improving server performance and reducing energy consumption can be achieved by employing the technique of workload dependent dynamic power management. It is an effective way to deal with the power and performance tradeoff for cloud servers. In this study, applications are divided into different classes, which have different characteristics. The server speed is different in processing tasks from different types. Hence, we explore the technique of variable and task type dependent server speed management to optimize the server performance and to minimize the power consumption of a server with mixed applications. This is also a kind of workload-dependent dynamic power and speed management to deal with the power and performance tradeoff. We establish an M/G/1 queueing model for a server with variable and task type dependent speed, so that our investigation can be conducted analytically. We formulate the problems of power constrained performance optimization and performance constrained power minimization as multivariable optimization problems, and solve the problems by efficient numerical algorithms. We provide numerical data to compare the performance of a server with the optimal speed setting to that of a server with a constant speed, and to compare the power of a server with the optimal speed setting to that of a server with a constant speed. It is shown that the reduction in the average response time can be as high as 9.9% and the reduction in the average power consumption can be as high as 8.0%.
Visualization of complex dynamic datasets by means of mathematical optimization

In this paper we propose an optimization model and a solution approach to visualize datasets which are made up of individuals observed along different time periods. These individuals have attached a time-dependent magnitude and a dissimilarity measure, which may vary over time. Difference of convex optimization techniques, namely, the so-called Difference of Convex Algorithm, and nonconvex quadratic binary optimization techniques are used to heuristically solve the optimization model and develop this visualization framework. This way, the so-called Dynamic Visualization Map is obtained, in which the individuals are represented by geometric objects chosen from a catalogue. A Dynamic Visualization Map faithfully represents the dynamic magnitude by means of the areas of the objects, while it trades off three different goodness of fit criteria, namely the correct match of the dissimilarities between the individuals and the distances between the objects representing them, the spreading of such objects in the visual region, and the preservation of the mental map by ensuring smooth transitions along snapshots. Our procedure is successfully tested on dynamic geographic and linguistic datasets.
Proof of efficient, parallelized, universal adiabatic quantum computation

We give a careful proof that a parallelized version of adiabatic quantum computation can efficiently simulate universal gate model quantum computation. The proof specifies an explicit parameter-dependent Hamiltonian $H(\ensuremath{\lambda})$ that is based on ground state quantum computation [Mizel et al., Phys. Rev. A. 63, 040302 (2001)]. We treat both a 1-dimensional configuration in which qubits on a line undergo nearest-neighbor 2-qubit gates and an all-to-all configuration in which every qubit undergoes 2-qubit gates with every other qubit in the system.
Adaptive Filtering and Temporal Alignment Based Fusion Algorithm for Navigation Systems in the Arctic Region

The navigation systems of vehicles face notable challenges in the Arctic region. To overcome the problems of single-sensor solutions, such as inertial navigation systems and global navigation satellite systems, hybrid navigation solutions with multiple complementary sensors are required. This paper proposes a new decentralized architecture to adapt the practical measurement noise of sensors and improve the robustness of the entire system. Previous studies have examined such techniques as Kalman filters that are primarily used to address stationary Gaussian noise. An approach named interacting dual model based adaptive filter (IDM-AF) is proposed in this study to overcome nonstationary noise and certain non-Gaussian factors, and a method that dynamically estimates the noise covariance matrix is derived in this paper. In accordance with practical requirements of fusing asynchronous sensors, we propose a new information fusion scheme based on the so-called degradation indicators of subsystems and a temporal alignment algorithm. Subsystem level and system level simulations and evaluations demonstrate that the proposed architecture and algorithms enhance navigational performance in the Arctic. Compared to a current method, the proposed architecture is shown to reduce the positioning error by 33.3-46.9% for different scenarios.
Nonparametric Activity Recognition System in Smart Homes Based on Heterogeneous Sensor Data

Throughout the course of life, there is time when we live independently in our house without anyone to look after each other. In order to support these people and ensure their safety with limited medical resources and human labors, it is important to constantly monitor one's activity of daily living (ADL). Therefore, we propose an activity recognition (AR) system for people living independently in smart homes to achieve the concept of “aging in place.” The AR model adopted by the proposed system is powerful to recognize meaningful ADL by integrating heterogeneous data from both ambient and on-body sensors. Moreover, this proposed system adopts a nonparametric approach, which requires much fewer efforts from humans. The average AR precision and recall rates of this proposed system are up to 98.7% and 99.0%, which indicates its feasibility of deployment in a real-life home environment for monitoring users' ADL with promising performance and thus helps realize “aging in place.” Note to Practitioners-With the advance of Internet of Things technologies, it is convenient to collect data about ADL in a smart home environment via ambient sensors and on-body sensors. This proposed system integrates data from these two heterogeneous sensors and discovers potential activities automatically without user labeling or parameter setting. By reducing the above-mentioned user efforts, it is more suitable for users and thus greatly helps realize the concept of “aging in place.”
Image Classification in Arabic: Exploring Direct English to Arabic Translations

Image classification is an ongoing research challenge. Most of the available research focuses on image classification for the English language, however there is very little research on image classification for the Arabic language. Expanding image classification to Arabic has several applications. The present study investigated a method for generating Arabic labels for images of objects. The method used in this study involved a direct English to Arabic translation of the labels that are currently available on ImageNet, a database commonly used in image classification research. The purpose of this study was to test the accuracy of this method. In this study, 2,887 labeled images were randomly selected from ImageNet. All of the labels were translated from English to Arabic using Google Translate. The accuracy of the translations was evaluated. Results indicated that that 65.6% of the Arabic labels were accurate. This study makes three important contributions to the image classification literature: (1) it determined the baseline level of accuracy for algorithms that provide Arabic labels for images, (2) it provided 1,895 images that are tagged with accurate Arabic labels, and (3) provided the accuracy of translations of image labels from English to Arabic.
Musicking with an interactive musical system: The effects of task motivation and user interface mode on non-musicians’ creative engagement

Creative engagement with novel musical interfaces can be rewarding for non-musicians. However, designing novel musical interfaces for non-musicians can be challenging because they lack conceptual and technical musical skills. In this paper we explore the effects of task motivation (experiential goal vs utilitarian goal) and user interface mode (whether the content is editable, and whether content can be replayed), on non-musicians’ creative engagement with novel musical interfaces. We show through an empirical study of twenty-four participants that an experiential exploratory goal encourages users’ creative engagement compared to a utilitarian creative goal. We found that being able to replay records is less important when participants have an experiential exploratory goal than when they have a utilitarian creative goal. Results also indicate that allowing people to replay their musical ideas increased some aspects of their creative engagement which was further increased when they were able to edit their creations. We also found that creative engagement increased when the interface supported users in planning ahead. A descriptive model of non-musician’s creative engagement with musical interfaces is proposed including three modes of musicking. An optimal trajectory of creative engagement through these modes is suggested and a description of inferred motivations, output, status and activities during creative processes is discussed. Design implications are proposed for supporting novices’ creative engagement taking into consideration their motivation and skills, and supporting insight and real-time activity.
HyConv: Accelerating Multi-Phase CNN Computation by Fine-Grained Policy Selection

Existing GPU-based approaches cannot yet meet the performance requirement for training very large convolutional neural networks (CNNs), where convolutional layers (Conv-layers) dominate the training time. In this paper, we find that no single convolution policy can always perform the fastest across all the computing phases. Then, we propose an approach called HyConv to accelerating multi-phase CNN computation by fine-grained policy selection. HyConv encapsulates existing convolution policies into a set of modules, and selects the fastest policy (a.k.a., winner policy) via one-round runtime measurement for computing each phase. Furthermore, HyConv uses a winner database to record the current winner policies, avoiding duplicate measurement later for the same parameter configuration. Our experimental results indicate that over all the used real-world CNN networks, HyConv consistently outperforms existing approaches on either a single GPU or four GPUs, with speedups of up to 3.3× and up to 1.6× over cuDNN-MM respectively. Such improvement can be explained by our result that HyConv delivers obviously better performance for most of single Conv-layers. Furthermore, HyConv has the ability to work with any parameter configuration and thus keeps better usability.
Two-factor authentication for trusted third party free dispersed storage

We propose a trusted third party free protocol for secure (in terms of content access, manipulation, and confidentiality) data storage and multi-user collaboration over an infrastructure of untrusted storage servers. It is achieved by the application of data dispersal, encryption as well as two-factor (knowledge and possession) based authentication and access control techniques so that unauthorized parties (attackers) or a small set of colluding servers cannot gain access to the stored data. The protocol design takes into account usability issues as opposed to the closest prior work Esiner and Datta (2016). We explore the security implications of the proposed model with event tree analysis and report on experiment results to demonstrate the practicality of the approach concerning computational overheads. Given that the protocol does not rely on any trusted third party, and most operations including actual collaboration do not require users to be online simultaneously, it is suitable not only for traditional multi-cloud setups but also for edge/fog computing environments.
An Efficient Algorithm to Compute a Quantum Probability Space

Learning algorithms based on probability organize the observed data in subsets corresponding to binary variables. In this paper, we address the problem of estimating one probability space given a set of observed data about n variables or properties. One problem with estimating one single probability space is the exponential number of events. Approximation is one approach to addressing the problem of the exponential order of the number of events. Alternatively to approximation, we change paradigm - from classical, set-based probability spaces based on sets to quantum probability spaces based on vector subspaces. By changing paradigm, we leverage quantum probability and present an efficient algorithm to calculate a Quantum Probability Space (QPS) in only O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) steps.
Data Consistency Approach to Model Validation

In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modeling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data. This is achieved by automatically gauging the models' ability to generate data that is similar to the observed data. Importantly, the criterion follows from the model class itself and is therefore directly applicable to a broad range of inference problems with varying data types, ranging from independent univariate data to high-dimensional time-series. The proposed data consistency criterion is illustrated, evaluated, and compared with several well-established methods using three synthetic and two real data sets.
The creation of the Flemish research discipline list, an important step forward in harmonising research information (systems)

In 2011, Peters et al. [1] wrote a report on the administrative simplification of research reporting in Flanders. Next to the description of several data flows that could be harmonised, validated and merged into CRIS systems, the report also contained important recommendations on the use of common standards and classifications that could lead to more efficient and qualitative research information (systems) and thus more efficient research reporting. As such, in Flanders several classifications are used in research reporting. In the context of reporting on research output and expertise, data is clustered based on research disciplines. Currently, a variety of research discipline classification lists are utilised in Flanders, each depending on the authority to whom a researcher is obliged to report. Because the existence of several research discipline lists for a region as small as Flanders is redundant and inefficient, one of the most important recommendations of the report by Peters et al. [1] was the creation of a single research discipline code list that could be used to tag information inside CRIS systems for reporting, dissemination and visualisation purposes. The uniform definition and management of the semantics of this research discipline code list for all research actors in Flanders could have an immediate impact on the accuracy of reporting and the policy pursued on the basis thereof. In addition, such managed classification systems can be used in dynamic research information systems that drastically reduce the administrative burden of the research population, which automatically entails an important investment in research and innovation.
A self-adaptive cascade ConvNets model based on label relation mining

Uncertainty is a fundamental and unavoidable feature in daily life, which is the same for a single classifier. Thus, combining the predictions of many different classifiers is a very successful way to reduce the uncertainty. In this paper, we present a Correcting Reliability Level (CRL) supervised three-way decision (3WD) cascade model to implement image classification tasks. Our model simulates the human decision process by using 3WD to judge “certainty” or “uncertainty” of the classification result. When judged as “uncertainty”, CRL will supervise the 3WD and learn more information to make the final prediction. In addition, we introduce two Class Grouping methods to mining the relation between labels, which help us to train several expert ConvNets for different types of images. Experimental results show that our model can effectively reduce the classification error rate compared with the base classifier.
Approach for Video Classification with Multi-label on YouTube-8M Dataset

Video traffic is increasing at a considerable rate due to the spread of personal media and advancements in media technology. Accordingly, there is a growing need for techniques to automatically classify moving images. This paper use NetVLAD and NetFV models and the Huber loss function for video classification problem and YouTube-8M dataset to verify the experiment. We tried various attempts according to the dataset and optimize hyperparameters, ultimately obtain a GAP score of 0.8668.
A coordination framework for multi-agent persuasion and adviser systems

Assistive agents have been used to give advices to the users regarding activities in daily lives. Although adviser bots are getting smarter and gaining more popularity these days they are usually developed and deployed independent from each other. When several agents operate together in the same context, their advices may no longer be effective since they may instead overwhelm or confuse the user if not properly arranged. Only little attentions have been paid to coordinating different agents to give different advices to a user within the same environment. However, aligning the advices on-the-fly with the appropriate presentation timing at the right context still remains a great challenge. In this paper, a coordination framework for advice giving and persuasive agents is presented. Apart from preventing overwhelming messages, the adaptation enables cooperation among the agents to make their advices more impactful. In contrast to conventional models that rely on natural language contents or direct multi-modal cues to align the dialogs, the proposed framework is built to be more practical allowing the agents to actively share their observation, goals, and plans to each other. This allows them to adapt the schedules, strategies, and contents of their scheduled advices or reminders at runtime with respect to each other’s objectives. Challenges and issues in multi-agent adviser systems are identified and defined in this paper supported by a survey study about perceived usefulness and user comprehensibility of advices delivered by multiple agents. The coordination among the advice giving agents are investigated and exemplified with a simulation of activity of daily living in the context of aging in place.
Cognitive Consistency Routing Algorithm of Capsule-Network

Artificial Neural Networks (ANNs) are computational models inspired by the central nervous system (especially the brain) of animals and are used to estimate or generate unknown approximation functions that rely on a large number of inputs. The Capsule Neural Network [1] is a novel structure of Convolutional Neural Networks (CNN) which simulates the visual processing system of the human brain. In this paper, we introduce a psychological theory which is called Cognitive Consistency to optimize the routing algorithm of Capsnet to make it more close to the working pattern of the human brain. Our experiments show that progress had been made compared with the baseline.
Dialogue breakdown detection robust to variations in annotators and dialogue systems

Dialogue breakdown is a significant problem in conversational agents. Timely breakdown detection helps the agents quickly recover from mistakes, minimizing the impact on user experience. In this paper, we focus on two problems: variations in determining a response that breakdowns a conversation i.e., subjectivity, and variations in breakdown types due to designs of conversational agents, i.e., variationality. To address the subjectivity, which decreases the agreement rate among annotators, our methods detect a dialogue breakdown by ensembling detectors trained by different sets of annotators that are grouped using a clustering algorithm. To address the variationality, our methods apply two types of detector architectures to capture global and local breakdowns. The long short-term memory detector considers the global context and the convolutional neural networks detector is sensitive to the local characteristics. The ensemble of all detectors makes a final judgment. The results of the Japanese task in the Dialogue Breakdown Detection Challenge 3 (DBDC3) confirm that our approach significantly outperforms the baseline, which uses the conventional conditional random field. Detailed error analysis reveals that our encoders based on a convolutional neural network and a long short-term memory have different characteristics. It also confirms the effects of annotator clustering.