Reviewerly/exHarmony huggingface dataset:
Exploring Design Principles for Enterprise Chatbots: An Analytic Hierarchy Process Study

Chatbots have attracted tremendous interest in recent years and are increasingly employed in form of enterprise chatbots (ECBs) (i.e., chatbots used in the explicit context of enterprise systems). Although ECBs substantially differ in their design requirements from, for example, more common and widely deployed customer service chatbots, only few studies exist that specifically investigate and provide guidance for the design of ECBs. To address this emerging gap, we accumulated existing design knowledge from previous studies and created a list of 26 design features (DFs) which we integrated into 6 design principles (DPs). Subsequently, 36 practitioners from an IT consulting company which are experienced in using ECBs evaluated the importance of the DPs and DFs following the Analytic Hierarchy Process method. Our results provide evidence that DPs and DFs promoting usability and flexibility are ranked more important than DPs and DFs promoting socialness and human likeness. These findings provide valuable insights, as they are partially contrary to some existing studies investigating the importance of social cues of chatbots in other domains. Overall, the identified lists of DPs and DFs and their importance rankings provide guidance for the design of ECBs and can serve as a basis for future research projects.
Data Labeling with Novel Decision Module of Tri-training

In machine learning, supervised learning methods for classifiers have to need sufficient labeled training data. However, it is quite labor-intensive and expensive to manually label a great number of training data. At the moment, there are two types of research related to data labeling: co-training and tri-training. The former primarily uses the voting system based on two algorithms to obtain the labeled results, and the latter is applied to improving co-training. When the two algorithms produce inconsistent results, they will not be able to label the data correctly. Hence, the method of tri-training makes use of the third algorithm to help judge. Our proposed method, Novel Decision Module of Tri-training (NDMTT), uses the output of their architecture to filter the results with threshold conditions, so the validity of the labeling can be improved. When it is improved, one of the classifications can be relatively increased.
A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies

Steering a wheeled mobile robot through a variety of environments is a complex task. To achieve this, many researchers have tried to convert front-facing camera data stream to the corresponding steering angles based on convolutional neural network model (CNN). However, most of existing methods suffer from higher cost of data acquisition and longer training cycles. To address these issues, this paper proposes an innovative end-to-end deep neural network model that fully considers the temporal relationships in the data and incorporates long short-term memory (LSTM) based on the CNN model. In addition, to obtain enough data to train and test the model, we establish a simulation system capable of creating realistic environment with various weather and road conditions and avoiding static and dynamic obstacles for robots. First, we use the system to capture the raw image sequence in different environments as a training set, and then we test the trained model in the system to realize an autonomous mobile robot that can adapt to various environments. The experimental results demonstrate that the proposed model not only can extract effectively and fully the features of road vision information with the highest correlation for navigation, but also can learn the time dependence of motion states and image features contained in a sequence.
Small Embed Cross-validated JPEG Steganalysis in Spatial and Transform Domain Using SVM

Steganalysis recognizes the manifestation of a hidden message in an artefact. In this paper, the analysis is done statistically, by extracting features that show a change during an embedding. Machine learning approach is employed here by using a classifier to identify the stego image and cover image. SVM is used as a classifier in this paper. A comparative study is done by using steganographic schemes from spatial plus transform domain. The two steganographic schemes are LSB matching and F5. Six unlike kernel functions and four diverse samplings are used for classification. The percentage embedding is as low as 10%.
Retrieval processes and audience design

Conversational partners develop shared knowledge. In referential communication tasks, partners collaboratively establish brief labels for hard-to-name images. These image-label mappings are associated in memory with that partner, evidenced by use of those brief labels with the same partner, and longer descriptions with new partners. According to the people-as-contexts view, the conversational partner functions as a contextual cue to support retrieval of conversationally-relevant information. Inspired by findings from the memory literature that context effects can be stronger when retrieval is more explicit, two experiments test the hypothesis that the speaker will be more likely to invoke the partner as a retrieval cue when retrieval processes are more explicit. The results indicated a strong effect of partner that, contrary to these predictions, was not boosted by explicit retrieval processes. The lack of an effect of retrieval processes speaks to the ubiquity with which language use in conversation is tailored to the particular people with whom we converse.
OpenUIDL, A User Interface Description Language for Runtime Omni-Channel User Interfaces

We extend the concept of cross-device user interfaces into the new, more general, concept of omni-channel user interfaces to better reflect the technological variety offered for developing multi-target user interfaces for interactive applications. We present a model-based approach for developing runtime omni-channel user interfaces for multi-target applications, which consists of: (1) OpenUIDL, a user interface description language for describing omni-channel user interfaces with its semantics by a meta-model and its syntax based on JSON, (2) the definition of a step-wise approach for producing runtime interactive applications based onOpenUIDLwith integration into the development life cycle, (3) the development of a cloud-based, OpenUIDL compliant, Interactive Development Environment that supports the application and the enactment of the step-wise approach and its illustration on several multi-target user interfaces.
Improved iSAM Based on Flexible Re-Linearization Threshold and Error Learning Model for AUV in Large Scale Areas

This paper proposed an improved incremental smoothing and mapping (iSAM) which combines flexible re-linearization threshold with error learning model to improve the efficiency and accuracy of navigation for autonomous underwater vehicle (AUV). The flexible threshold of the proposed method, which can avoid periodic re-linearization of iSAM, is controlled by adaptive threshold. Through the use of flexible re-linearization, the proposed method can reduce the running time and carry out re-linearization timely. Simultaneously, for the first time, the proposed method takes advantages of the fast training time of hidden-layer neural networks and Gaussian Process Regression which is more suitable for non-linearity to get error learning model for iSAM. The proposed method can achieve better accuracy with only a rough model and can be easily transplanted to other systems without cumbersome calculations of the precise model. Our algorithm has been demonstrated by a range of simulated and real datasets, especially in practical application it outperforms current available iSAM algorithms in efficiency and accuracy. The RMSE of proposed method increases by 21.7% and efficiency also has been improved by 56% than iSAM2 in practical application for AUV.
Performance Comparison between SVM and K-Means for Vehicle Counting

Abstract Support vector machine (SVM) and K-means have been two well known methods used in classification. Choosing an accurate classifier for good features to differentiate between the foreground and background has a significant effect in increasing the accuracy of the detection. This paper presents and analyzes performance comparison between SVM and K-means classifiers for vehicle counting targeted for intelligence transportation systems (ITS) application. In particular, precision and recall have been used for the comparison between the two methods. Five videos from different weather conditions have been used for the testing purposes. SVM shows a better performance in terms of precision and recall.
A chatbot response generation system

Developing successful chatbots is a non-trivial endeavor. In particular, the creation of high-quality natural language responses for chatbots remains a challenging and time-consuming task that often depends on high-quality training data and deep domain knowledge. As a consequence, it is essential to engage experts in the chatbot response development process which have the required domain knowledge. However, current tool support to engage domain experts in the response generation process is limited and often does not go beyond the exchange of decoupled prototypes and spreadsheets. In this paper, we present a system that enables chatbot developers to efficiently engage domain experts in the chatbot response generation process. More specifically, we introduce the underlying architecture of a system that connects to existing chatbots via an API, provides two improvement mechanisms for domain experts to improve chatbot responses during their chatbot interaction, and helps chatbot developers to review the collected response improvements with a sentiment supported review dashboard. Overall, the design of the system and its improvement mechanisms are useful extensions for chatbot development systems in order to support chatbot developers and domain experts to collaboratively enhance the natural language responses of a chatbot.
A New Risk Assessment Approach for Agile Projects

Agile is a successful project management mindset developed by the software industry which recently has been extrapolated for other industries. This mindset has been promoting the use and development of several tools that aim to minimize risks associated to the project output, i.e., in Agile the user requirements are the major priority and the risk associated to these requirements is strongly mitigated using tools like Scrum or Kanban. However, risk analysis in Agile projects has an implicit nature without well-defined and systematic methods. This feature was inherited from the software industry where this implicit approach works well, but for industries different from those that develop software it creates serious drawbacks. In fact, the risk associated to the project itself, or in other words, the risk associated to the Agile team capability to implement and manage a given project is not explicitly analyzed in Agile methods. Moreover, the Agile risk mitigation approach do not consider this important point of view in risk management. In this sense, important risk factors have been omitted, risk factors such as scope creep, unrealistic expectations, lack of cooperation, or lack of communication are some of these factors that have been overlooked despite their strong impact on Agile projects. This paper proposes a new approach to perform risk assessment and management of Agile projects. The approach comprises a set of qualitative risk tools inline with the Agile way of doing things, i.e., the proposed model does not jeopardize the flexibility found in Agile methods so appreciated by Agile team members, but at the same time, the proposed model introduces a systematic way of risk analysis which absence in Agile projects has been criticize in literature.
Extraction System Web Content Sports New Based On Web Crawler Multi Thread

Abstract Web crawlers are programs that are used by search engines to collect necessary information from the internet automatically according to the rules set by the user. With so much information about sports news on the internet, it takes web crawlers with incredible speed in the process of crawling. There are several previous studies that discussed the process of extracting information in a web document that needs to be considered both in terms of both aspects, including in terms of the structure of the web page and the length of time needed. Therefore, in this research the web crawler application was developed by applying a multi-thread approach. This multi-thread approach to research is used to produce web crawlers that are faster in the process of crawling sports news by involving news sources more than one address at a time. In addition to the multi-thread approach, adjusting the structure of the website pages is also done to ensure the information to be extracted by web crawling. From the results of the multi-thread implementation test on the crawling process, this study has been able to increase speed compared to the single-thread method of 122.95 seconds. But the results of web update detection, have resulted in a speed that decreased by 6.27 seconds in the crawling process with unequal data and the speed on the crawling process has also decreased by 24.76 seconds on server 1 and by 23.92 seconds on server 2.
Deep adaptive feature enrichment

Features play an important role in the performance of machine learning and classification applications. Usually, separability of classes by using raw or original features are so low, and it is necessary to use complex classifiers with high computational costs or use enrichment modules to increase distinctiveness of features. In this paper, a deep feature enrichment method is proposed to increase the distinguishing power of features using an adaptive neural network-based structure. Proposed method adaptively uses linear/non-linear activation functions for coding, and the dimension of the coding space adaptively adjusted to be lower, the same, or higher than the original feature space. Then the best neural network structure (number of layers and neurons per layers) and the optimum weights for the proposed neural structure are optimized using an evolutionary optimization algorithm. Optimized modules can map/code raw input features into an enriched feature space that can increase the separability of the data points among classes. In fact, our obtained enriched features can adapt themselves to the nature of the training data and they can improve the generalization power also the performance of conventional classifiers. Experimental results on popular UCI datasets such as Glass, Liver, Iris, Wine, Breast cancer and seeds show increase of significant correct recognition rates (11.63% for Glass, 4.35% for Liver, 13.34% for Iris, 27.78% for Wine, 0.72% for Breast cancer and 11.9% for seeds) and also improvement of more than 1.5% of verification rate and 2% of Identification rate for the Yale face database.
A Two-Fold Group Lasso Based Lightweight Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification (AMC) is a hot topic in modern wireless communication, which is a classification problem essentially. The deep learning methods have been applied to AMC gradually, for its excellent performance in classification, regression and decision-making tasks. However, the deep learning methods always come with complex network structure, vast training parameters and extra long training time, which seriously affected its application and promotion on power-limited and resource-constrained devices. In this paper, we propose a lightweight end-to-end AMC model named lightweight deep neural network (LDNN) via a novel group-level sparsity inducing norm, which can help network pruning itself automatically to obtain a highly compact network. In order to solve the problem of recognition confusing types, such as QAM16 and QAM 64 are always been confused in AMC task, a improved two-step training lightweight deep neural network (TLDNN) is well designed to improve the recognition accuracy. Experimental results shows the accuracy improvement of the proposed lightweight compact networks via two-fold group lasso regularization and two-step training schemes.
Ar-SLoTE: A Recognizing Textual Entailment Tool for Arabic Question/Answering Systems

Recognizing the relation of entailment between sentences is an important and common part of linguistic communication. The recognizing textual entailment task has been proposed as a solution for this problem. In this paper, we present an Arabic Recognizing Textual Entailment Tool called Ar-SLoTE "Arabic Semantic Logical Textual Entailment Tool". The proposed tool is composed of five modules: pretreatment, linguistic analysis, first-order logic representation, features extraction and entailment decision modules. It extracts the logical representations of the hypothesis/text pairs in order to extract valuable and informative features namely, predicates-arguments overlap, semantic similarity and named entity matching. Ar-SLoTE is destined especially to Arabic factual question/answering systems and the attained result is very encouraging.
Development of OpenFlow Native Capabilities to optimize QoS

The exponential growth of mobile connected devices with advanced multimedia features imposes a requirement to enhance quality of service (QoS) from heterogeneous systems and networks. In order to satisfy mission-critical multimedia QoS requirements new generation mobile networks must present content-optimized mechanisms in order to use valuable network resources efficiently and provide QoS requirements for each application. This research explores a novel solution for quality of service performance for streaming mission-critical video data in OpenFlow SDN networks. A Meter Band Rate Evaluator (MBE) Mechanism is proposed based on a new band rate description language to improve the native QoS capability of OpenFlow and OpenDaylight. Its design and development are presented and the mechanism is verified through a simulated experiment in an SDN testbed. The results revealed a significant percentage increase in QoS performance when the MBE was enabled. These findings provide support and validation for the effectiveness of the MBE to enhance the native capability of OpenFlow and OpenDaylight for efficient QoS provision.
<scp>Parameter‐expanded</scp> data augmentation for analyzing correlated binary data using multivariate probit models

Data augmentation has been commonly utilized to analyze correlated binary data using multivariate probit models in Bayesian analysis. However, the identification issue in the multivariate probit models necessitates a rigorous Metropolis‐Hastings algorithm for sampling a correlation matrix, which may cause slow convergence and inefficiency of Markov chains. It is well‐known that the parameter‐expanded data augmentation, by introducing a working/artificial parameter or parameter vector, makes an identifiable model be non‐identifiable and improves the mixing and convergence of data augmentation components. Therefore, we motivate to develop efficient parameter‐expanded data augmentations to analyze correlated binary data using multivariate probit models. We investigate both the identifiable and non‐identifiable multivariate probit models and develop the corresponding parameter‐expanded data augmentation algorithms. We point out that the approaches, based on one non‐identifiable model, circumvent a Metropolis‐Hastings algorithm for sampling a correlation matrix and improve the convergence and mixing of correlation parameters; the identifiable model may produce the estimated regression parameters with smaller standard errors than the non‐identifiable model does. We illustrate our proposed approaches using simulation studies and through the application to a longitudinal dataset from the Six Cities study.
Generating Face Images With Attributes for Free

With superhuman-level performance of face recognition, we are more concerned about the recognition of fine-grained attributes, such as emotion, age, and gender. However, given that the label space is extremely large and follows a long-tail distribution, it is quite expensive to collect sufficient samples for fine-grained attributes. This results in imbalanced training samples and inferior attribute recognition models. To this end, we propose the use of arbitrary attribute combinations, without human effort, to synthesize face images. In particular, to bridge the semantic gap between high-level attribute label space and low-level face image, we propose a novel neural-network-based approach that maps the target attribute labels to an embedding vector, which can be fed into a pretrained image decoder to synthesize a new face image. Furthermore, to regularize the attribute for image synthesis, we propose to use a perceptual loss to make the new image explicitly faithful to target attributes. Experimental results show that our approach can generate photorealistic face images from attribute labels, and more importantly, by serving as augmented training samples, these images can significantly boost the performance of attribute recognition model. The code is open-sourced at this link.
Implementation of RC4 Cryptography Algorithm for Data File Security

Abstract Communication in the digital age plays an important role, a person can carry out various transactions or exchange data practically and quickly. So as to pose a great risk to information security, ranging from misuse of access or unauthorized authority, modification, change of information, destruction to theft, this is in accordance with the main principles of data and information security ranging from Confidentiality, Integrity, Authentication, and Availability. In research that has been done using RC4 Cryptography Algorithm for Encryption and Decryption in Data Files shows that the RC4 algorithm can run well and is able to secure the authenticity of the data so it is not easily changed by people who are not responsible or people who do not have good access rights in the form text or files in several pdf, doc, Docx, Xls, xlsx or text file formats and directly affect the execution time of encrypted and decrypted files.
Tailoring Data Visualization to Diversely Informed End Users

Visualization is invaluable for communicating complex data, and it is becoming more important when researchers seek insights, or when they need to share their insights with end-users with diverse level of expertise such as policy-makers or the general public. In this paper, we detail how we approach this issue using the HAVEN and ProjecTable systems we developed. These systems share data of projected renewable energy supply based on several possible scenarios that should help Hawaii achieve 100% renewable energy by 2045. However, each system offers different features directed at end-users interested in different levels of detail. We describe here the two systems and their use, both separately and in conjunction.