Reviewerly/exHarmony huggingface dataset:
Understanding the User Experience of Customer Service Chatbots: What Can We Learn from Customer Satisfaction Surveys?

Understanding and improving user experience is key to strengthening uptake and realizing the potential of chatbots for customer service. In this paper, we investigate customer satisfaction surveys as a source of insight into such user experience. A total of 5,687 customer satisfaction reports on users' interactions with a customer service chatbot, and the corresponding chatbot interactions, are analyzed. The findings demonstrate that customer satisfaction reports are closely associated with the degree to which the problems motivating users' chatbot interactions are resolved. Furthermore, the findings show substantial variation in the performance of different chatbot intents in terms of customer satisfaction and problem resolution. This implies that user experience varies substantially depending on the problems motivating users to interact with the chatbot. Finally, we identify key characteristics of the intents associated with particularly high or low customer experience, suggesting paths towards efficient improvement of chatbot user experience. Based on the findings, we point to key implications for theory and practice and suggest directions for future research.
Do data security measures, privacy regulations, and communication standards impact the interoperability of patient health information? A cross-country investigation

The lack of interoperability is one of the biggest obstacles to the complete digitalization of patient health information in electronic medical records (EMR). The high volume of data breaches has put pressure on care providers to adopt data protection measures to remain compliant with legal requirements. Extreme data protection measures can impede information flow, but they also instill confidence in secure information sharing. This study investigates how the adoption of security measures, privacy regulations, and communication standards has impacted patient health information interoperability at technical (TI), semantic (SI), and organizational (OI) levels within the hospitals. The study utilizes a quasi-experimental research design to probe the relationships of interest. Secondary data from a survey of randomly selected 773 hospitals conducted by the European Commission in over 30 countries in Europe is used to understand the relationships. The study counters selection bias and accounts for systematic differences in adopting treatments of interest in the hospitals using the propensity score-based approaches for the observational data. The empirical models that account for selection bias explain more observational data variations than those that did not. Access control measures on workstations are linked to 44 % lesser odds of experiencing TI problems. However, hospitals with regional and organizational level privacy regulations have 85 % and 76 % higher odds of experiencing SI and OI problems, respectively. On the other hand, hospitals with a single hospital-wide EMR are 53 % and 43 % less likely to experience TI and SI problems, respectively, in comparison to those with multiple EMR systems. The study highlights the differential impacts of data protection measures on the hospitals' three key types of interoperability problems (i.e., TI, SI, and OI). Homogenous EMR systems type and substantial investment in technology are critical to supporting health information interoperability within the hospitals. The study findings inform policy considerations for improving specific aspects of health information's interoperability while preserving patient data privacy and security.
Cross-Scale Residual Network: A General Framework for Image Super-Resolution, Denoising, and Deblocking

In general, image restoration involves mapping from low-quality images to their high-quality counterparts. Such optimal mapping is usually nonlinear and learnable by machine learning. Recently, deep convolutional neural networks have proven promising for such learning processing. It is desirable for an image processing network to support well with three vital tasks, namely: 1) super-resolution; 2) denoising; and 3) deblocking. It is commonly recognized that these tasks have strong correlations, which enable us to design a general framework to support all tasks. In particular, the selection of feature scales is known to significantly impact the performance on these tasks. To this end, we propose the cross-scale residual network to exploit scale-related features among the three tasks. The proposed network can extract spatial features across different scales and establish cross-temporal feature reusage, so as to handle different tasks in a general framework. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for multiple image restoration tasks.
An Automatic Generation Approach of the Cyber Threat Intelligence Records Based on Multi-Source Information Fusion

With the progressive deterioration of cyber threats, collecting cyber threat intelligence (CTI) from open-source threat intelligence publishing platforms (OSTIPs) can help information security personnel grasp public opinions with specific pertinence, handle emergency events, and even confront the advanced persistent threats. However, due to the explosive growth of information shared on multi-type OSTIPs, manually collecting the CTI has had low efficiency. Articles published on the OSTIPs are unstructured, leading to an imperative challenge to automatically gather CTI records only through natural language processing (NLP) methods. To remedy these limitations, this paper proposes an automatic approach to generate the CTI records based on multi-type OSTIPs (GCO), combing the NLP method, machine learning method, and cybersecurity threat intelligence knowledge. The experiment results demonstrate that the proposed GCO outperformed some state-of-the-art approaches on article classification and cybersecurity intelligence details (CSIs) extraction, with accuracy, precision, and recall all over 93%; finally, the generated records in the Neo4j-based CTI database can help reveal malicious threat groups.
Potential and effects of personalizing gameful fitness applications using behavior change intentions and Hexad user types

Abstract Personalizing gameful applications is essential to account for interpersonal differences in the perception of gameful design elements. Considering that an increasing number of people lead sedentary lifestyles, using personalized gameful applications to encourage physical activity is a particularly relevant domain. In this article, we investigate behavior change intentions and Hexad user types as factors to personalize gameful fitness applications. We first explored the potential of these two factors by analyzing differences in the perceived persuasiveness of gameful design elements using a storyboards-based online study ( $$N=178$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn>178</mml:mn></mml:mrow></mml:math> ). Our results show several significant effects regarding both factors and thus support the usefulness of them in explaining perceptual differences. Based on these findings, we implemented “Endless Universe,” a personalized gameful application encouraging physical activity on a treadmill. We used the system in a laboratory study ( $$N=20$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn>20</mml:mn></mml:mrow></mml:math> ) to study actual effects of personalization on the users’ performance, enjoyment and affective experiences. While we did not find effects on the immediate performance of users, positive effects on user experience-related measures were found. The results of this study support the relevance of behavior change intentions and Hexad user types for personalizing gameful fitness systems further.
DistB-CVS: A Distributed Secure Blockchain based Online Certificate Verification System from Bangladesh Perspective

During the academic career, students achieve numerous academic credentials. These educational credentials are offered by the student while applying for a job or scholarship. Therefore, the goal of this paper is to propose a theoretical blockchain-based certificate verification system on the cloud that can offer a potential solution for academic certificate issuing and verification where cryptocurrencies are banned. By this regarding in this research, we address the Blockchain (BC) technology for solving these problems. This BC can be capable of providing immutability and publicly verifiable transactions. Moreover, these properties of BC are used to generate the digital academic credential, which is anti-counterfeited can be verified easily in a little time. In addition, the proposed “DistB-CVS” showed that cryptocurrencies banned country could leverage the BC technology. It satisfies all the requirements necessary for a modern academic certificate verification system. Furthermore, it tries to close the holes and difficulties in the existing systems to verify academic certificate authenticity.
A Multi-Task CNN for Maritime Target Detection

In this letter, we construct MaRine ShiP (MRSP-13), a novel dataset containing 37,161 ship target images belonging to 13 classes with bounding box annotation, and among them there are 3051 images labeled with pixel-level annotation. This dataset equips us with the capability to conduct baseline experiments on maritime target classification, detection and segmentation. We propose a cross-layer multi-task CNN model for maritime target detection, which can simultaneously solve ship target detection, classification, and segmentation. Experimental results have demonstrated the efficiency of the MRSP-13 dataset to be used for maritime target analysis. In addition, the results validate the fact that by adopting the strategies of feature sharing, joint learning, and cross-layer connections, the proposed model achieves superior performance with less annotations. We believe that our MRSP-13 dataset and corresponding baseline experiments will lay down the foundation for further research in maritime target processing.
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path

In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can efficiently learn to understand and generalize to different environments using high-dimensional inputs (a 2D map), while following feasible paths that avoid obstacles in obstacle-cluttered environment. To achieve this, we make use of traditional path planning algorithms, supervised learning, and reinforcement learning algorithms in a synergistic way. The key idea is to decouple the navigation problem into planning and control, the former of which is achieved by supervised learning whereas the latter is done by reinforcement learning. Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment- this is then used by an reinforcement learning algorithm to learn to closely follow the path. This allows the trained agent to achieve good generalization while learning faster. We test our proposed method in the recently proposed Safety Gym suite that allows testing of safety-constraints during training of learning agents. We compare our proposed method with existing work and show that our method consistently improves the sample efficiency and generalization capability to novel environments.
Explaining Learning Models in Manufacturing Processes

The use of advanced machine learning (ML) models for manufacturing could potentially reduce the pre-production testing and validation time for new processes. Once we decide that ML is indeed a suitable tool to apply in smart manufacturing processes, the challenge lies in training, validating, and testing an ML model in a pre-production environment so that engineers can be confident that the model building effort can be successfully transitioned to actual production. This paper aims at explaining the in-works of a given in-situ classifier for predicting the quality welds in ultrasonic welded battery tabs. Predicting the quality of new samples cannot attain full certainty due to characteristics of the data the model was trained on (e.g., noisy or wrongly labeled). By developing explainable methods to such connectionist learning models (also known as black boxes), we show why the classifier outputs were predicted, making these predictions better understood and trustworthy.
AR based robot programming using teaching by demonstration techniques

This paper presents an Augmented Reality tool for supporting the operator's interaction with the robot hardware in production systems. We are focusing on the development of an AR application, which allows the user to interact with a robotic arm and move it by demonstration. The application's purpose is to simplify and accelerate the industrial manufacturing process by introducing an easy and intuitive way of interaction with the hardware, without requiring special programming skills or long training time from the worker. The proposed software is developed for the Microsoft's HoloLens Mixed Reality Headset, integrated with ROS and it has been tested in a case study inspired from the automotive industry.
GraSU

Existing FPGA-based graph accelerators, typically designed for static graphs, rarely handle dynamic graphs that often involve substantial graph updates (e.g., edge/node insertion and deletion) over time. In this paper, we aim to fill this gap. The key innovation of this work is to build an FPGA-based dynamic graph accelerator easily from any off-the-shelf static graph accelerator with minimal hardware engineering efforts (rather than from scratch). We observe \em spatial similarity of dynamic graph updates in the sense that most of graph updates get involved with only a small fraction of vertices. We therefore propose an FPGA library, called GraSU, to exploit spatial similarity for fast graph updates. GraSU uses a differential data management, which retains the high-value data (that will be frequently accessed) in the specialized on-chip UltraRAM while the overwhelming majority of low-value ones reside in the off-chip memory. Thus, GraSU can transform most of off-chip communications arising in dynamic graph updates into fast on-chip memory accesses. Our experiences show that GraSU can be easily integrated into existing state-of-the-art static graph accelerators with only 11 lines of code modifications. Our implementation atop AccuGraph using a Xilinx Alveo#8482; \ U250 board outperforms two state-of-the-art CPU-based dynamic graph systems, Stinger and Aspen, by an average of 34.24× and 4.42× in terms of update throughput, improving further overall efficiency by 9.80× and 3.07× on average.
Soft Speakers: Digital Embroidering of DIY Customizable Fabric Actuators

We introduce Soft Speakers, a systematic approach for designing custom fabric actuators that can be used as audio speakers and vibro-haptic actuators. Digitally-embroidered with e-textiles, we implement Soft Speakers as tactile, malleable and aesthetic designs to be part of wearables, soft furnishing and fabric objects. We present a rapid technique for the DIY fabrication of audio feedback into soft interfaces. We also discuss and evaluate 7 factors for their parametric design in additive and constructive methods. To demonstrate the feasibility of our approach and the breadth of new designs that it enables, we developed 5 prototypes: 3 wearables, a piece of furniture and a soft toy. Studying Soft Speakers with maker-users expanded the design space, empowering users and supporting inclusive design. Our study includes insights on user experience of real-world interactive applications for remote communication, e-learning, entertainment, navigation and gaming, enabled by Soft Speakers’ customizable and scalable form factor.
Interlayer and intralayer scale aggregation for scale-invariant crowd counting

Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images. These challenges are typically addressed using multi-column structures in existing methods. However, such an approach does not provide consistent improvement and transferability due to limited ability in capturing multi-scale features, sensitivity to large density shift, and difficulty in training multi-branch models. To overcome these limitations, a Single-column Scalere-invariant Network (ScSiNet) is presented in this paper, which extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT). Furthermore, in order to enlarge the diversity of densities, a randomly integrated loss is presented for training our single-branch method. Extensive experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in counting accuracy and achieves remarkable transferability and scale-invariant property.
A hybrid ensemble learning method for the identification of gang-related arson cases

Arson is one of the most common crimes, and it has the characteristics of low cost and great harm. In addition to causing casualties and property damage, arson can often have huge social impacts and cause psychological panic in the public. Since arson is more harmful when conducted by a gang, how to effectively identify gang crimes in arson cases has become an important issue. In this paper, we propose a hybrid method that combines ensemble learning and intelligent optimization algorithms to solve this problem. First, we develop the recursive feature elimination (RFE)-based feature selection method to remove redundant features. Second, for the data imbalance problem, we determine the optimal processing algorithm from 18 candidate algorithms. Third, after trying a combination of multiple base classifiers, we obtain the optimal base classifier combination. Fourth, when integrating the prediction results of the base classifier, we propose a weighted ensemble strategy. Finally, we use the differential evolution (DE) algorithm to optimize the parameters of the base classifier and the weight of the combination, which further enhances the identification ability of the model. To verify the actual performance of the proposed method, we conducted experiments on the US National Fire Incident Reporting System (NFIRS) database. The results show that the proposed method is significantly superior to other popular machine learning methods, which proves that this method can provide a more reliable decision basis in the detection of arson cases.
Digital forensics supported by machine learning for the detection of online sexual predatory chats

Chat-logs are informative digital footprints available on Social Media Platforms (SMPs). With the rise of cybercrimes targeting children, chat-logs can be used to discover and flag harmful behaviour for the attention of law enforcement units. This can make an important contribution to the safety of minors on SMPs from being exploited by online predators. The problem is that digital forensic investigation is mostly manual. Thus, a daunting task for forensic investigators because of the sheer volume and variety of data. The solution that is proposed in this paper employs a Digital Forensic Process Model that is supported by Machine Learning (ML) methods to facilitate the automatic discovery of harmful conversations in chat-logs. ML has already been successfully applied in the domain of text analysis for the discovery of online sexual predatory chats. However, there is an absence of approaches that show how ML can contribute to a digital forensic investigation. Thus, the contribution of this paper is to indicate how the tasks in a digital forensic investigation process can be organised so to obtain useable ML results when investigating online predators.
OpenWGL: Open-World Graph Learning

In traditional graph learning tasks, such as node classification, learning is carried out in a closed-world setting where the number of classes and their training samples are provided to help train models, and the learning goal is to correctly classify unlabeled nodes into classes already known. In reality, due to limited labeling capability and dynamic evolving of networks, some nodes in the networks may not belong to any existing/seen classes, and therefore cannot be correctly classified by closed-world learning algorithms. In this paper, we propose a new open-world graph learning paradigm, where the learning goal is to not only classify nodes belonging to seen classes into correct groups, but also classify nodes not belonging to existing classes to an unseen class. The essential challenge of the open-world graph learning is that (1) unseen class has no labeled samples, and may exist in an arbitrary form different from existing seen classes; and (2) both graph feature learning and prediction should differentiate whether a node may belong to an existing/seen class or an unseen class. To tackle the challenges, we propose an uncertain node representation learning approach, using constrained variational graph autoencoder networks, where the label loss and class uncertainty loss constraints are used to ensure that the node representation learning are sensitive to unseen class. As a result, node embedding features are denoted by distributions, instead of deterministic feature vectors. By using a sampling process to generate multiple versions of feature vectors, we are able to test the certainty of a node belonging to seen classes, and automatically determine a threshold to reject nodes not belonging to seen classes as unseen class nodes. Experiments on real-world networks demonstrate the algorithm performance, comparing to baselines. Case studies and ablation analysis also show the rationale of our design for open-world graph learning.
Extractive Hotel Review Summarization based on TF/IDF and Adjective-Noun Pairing by Considering Annual Sentiment Trends

Abstract—The number of hotel reviews are huge and growing day by day. Many travelers rely on this review to take down their decision to book a hotel. Gather valid and useful information from a huge amount of reviews are needs that must be met. Hence, a summarize tool for hotel review is built to create a representative summary. Hotel review data growing a trend of sentiment in a range of time due to the condition and improvement at that time, so the analysis of sentiment trend is done to choose the appropriate representative review data. Then, there are two method to summarize the selected review data. First, summary was obtained from extractive method with selecting most related sentence base on its Term Frequency-Inverse Document Frequency (TF-IDF) score. Second, phrase summary style is built by pairing adjective to the nearest noun and considering the polarity. From those method, obtained Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1 recall and Bilingual Evaluation Understudy (BLEU) score respectively {0.2101and 0.7820} for first method and {0.0670 and 0.03672} for the second method. All the reviews are crawled from TripAdvisor website and have been pre-processed by segmenting/tokenization, case folding, and tagging.