Reviewerly/exHarmony huggingface dataset:
Research on Users’ Trust in Customer Service Chatbots Based on Human-Computer Interaction

As Human-Computer Interaction (HCI) moves towards deep collaboration, it is urgent to study users’ trust in chatbots. This study takes customer service chatbots as an example. Firstly, literature review is conducted on the relevant research on users’ trust in chatbots, and the value chain model of customer service chatbots is analyzed. Taking Taobaoxiaomi as the specific research object, we conducted in-depth interviews with 18 users, organized the interview data with value focused thinking method (VFT), constructed the users’ trust model of customer service chatbots, and carried out an empirical test by questionnaire survey. The results show that professionalism, response speed and predictability have positive effects on users’ trust in chatbots, while ease of use and human-likeness have no significant positive effects on users’ trust. Besides, brand trust has a positive impact on users’ trust in chatbots, risk perception negatively affects users’ trust in chatbots, and human support has no significant negative effect on users’ trust. Finally, privacy concerns have a moderating effect on environmental factors (brand trust, risk et al.). This study will deepen the understanding of human-computer trust and provide reference for the industry to improve chatbots and enhance users’ trust.
Blade: A Blockchain-supported Architecture for Decentralized Services

Decentralized services and applications provide a multitude of advantages for their users, such as improved privacy, control, and independence from third parties. Anyhow, decentralization comes at the cost of certain disadvantages, such as increased application complexity or communication overhead. This aggravates the development and deployment of decentralized services and applications. In this paper we present Blade, a software platform that aims to ease the effort of development and deployment of decentralized services by implementing reusable solutions for recurring challenges developers are facing when designing decentralized service architectures. Blade implements such functionality in a Blade server instance, which can be deployed on a lightweight device, such as a NAS, Raspberry Pi, or router at home. This allows users without expert knowledge to run a Blade instance with already existing hardware with little overhead. Blade supports polyglot Blade modules that implement extended functionality, such as interfaces, frontends, and business logic of decentralized applications. Blade modules are installed via a decentralized, Ethereum-based marketplace that can be accessed via the Blade instances. For identity management, Blade relays on smart contracts, which implement an Ethereum-based registry. This allows users to create and manage their identities in a self-sovereign manner without the need of a central entity. This way, Blade builds a decentralized service ecosystem that supports developers of decentralized applications and services.
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution

In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable. However, most deep learning methods only achieve high performance in one aspect due to the perception-distortion trade-off, and works that successfully balance the trade-off rely on fusing results from separately trained models with ad-hoc post-processing. In this paper, we propose a novel super-resolution model with a low-frequency constraint (LFc-SR), which balances the objective and perceptual quality through a single model and yields super-resolved images with high PSNR and perceptual scores. We further introduce an ADMM-based alternating optimization method for the non-trivial learning of the constrained model. Experiments showed that our method, without cumbersome post-processing procedures, achieved the state-of-the-art performance. The code is available at https://github.com/Yuehan717/PDASR .
Trading Digital Assets owned by Omani Entrepreneurs using Non-fungible Tokens (NFT): A Proposed Framework

The smart technologies include artificial intelligent solutions (AI), Internet of Things (IoT) and Blockchain technology. These are the trending technologies which are emerging in different sectors and domains. Many initiatives have been supported in this regard. This research paper is focusing on a solution using one of these trending technologies which is Blockchain Technology. The artists are worried about thefts related to their artwork or their designs. However, at the same time they want to share their work on digital media for recognition. In such cases it is difficult to trace who is sharing or replicating their work without recognizing them or their original creations and ownership. This paper proposes a solution to maintain artists work ownership and copyright through applying non-fungible tokens (NFT) via blockchain technology. It is a unique token generated on the Blockchain. These tokens are not replaceable. These tokens can be generated for any digital drawing, music composition/lyrics, videos, Artworks, Tweets, GIFs, Pets, Domain names etc. The framework of the market platform has been proposed here implements NFT using Blockchain. Blockchain and non-fungible tokens (NFTs), which are cryptographically unique, rare, non-replicable digital assets established through smart contracts, are provably digital collectible assets, according to this study. Our goal is also to provide a taxonomy for NFTs, review NFT platforms, explore technical issues as well as recent progress in addressing them.
A Blockchain-based Electricity Retail Contracts Management System

With the development of the electricity market, the number of retail contracts generated by the electricity purchasers is increased rapidly. The traditional contracts management methods have gradually revealed disadvantages such as contracts loss, delayed update, and data tampering. As a result, the ensuing retail contracts disputes have also become more and more frequently. In terms to handle with such issues, we proposed a blockchain-based electricity retail contracts system, which utilizes immutability and traceability of blockchain. Leveraging the immutability and traceability of blockchain, our system can effectively store retail contracts which alleviates the problem of existing contract disputes. Moreover, we combine Inter-Planetary File System (IPFS) with blockchain to solve the storage problem. In addition, the data security of our system is guaranteed through the reasonable use of some means of authentication, digital signatures and access control.
System Configuration Models: Towards a Specialization Approach

Nowadays, system configuration helps to achieve mass customization and manage a wide variety of systems. System configuration is based on a model that gathers all relevant knowledge for a family of systems. This knowledge model can be difficult to formalize and keep up to date; indeed, the knowledge must be made explicit, and can come from different departments and experts within an organization. In addition, it must reflect the different variants and options of a family of systems. Therefore, we try to answer the following question: how to better formalize and structure knowledge for system configuration to define configuration models and use the benefits of specialization in terms of modeling? Thus, in our proposal, we introduce the elements required to formalize the knowledge and define configuration models. This formalization will be done in a structured way using the association of ontology and Constraint Satisfaction Problem (CSP). We use the abilities of ontology to model knowledge of the different artifacts, their characteristics, and composition, and the abilities of CSP to model the relations between artifacts. In our proposal, we also define artifacts at different levels of abstraction using specialization which allows experts to detail or refine the formalized knowledge. We illustrate our proposals on a simplified but realistic example of a bicycle.
Tyche: A Library for Probabilistic Reasoning and Belief Modelling in Python

This paper presents Tyche, a Python library to facilitate probabilistic reasoning in uncertain worlds through the construction, querying, and learning of belief models. Tyche uses aleatoric description logic (ADL), which provides computational advantages in its evaluation over other description logics. Tyche belief models can be succinctly created by defining classes of individuals, the probabilistic beliefs about them (concepts), and the probabilistic relationships between them (roles). We also introduce a method of observation propagation to facilitate learning from complex ADL observations. A demonstration of Tyche to predict the author of anonymised messages, and to extract author writing tendencies from anonymised messages, is provided. Tyche has the potential to assist in the development of expert systems, knowledge extraction systems, and agents to play games with incomplete and probabilistic information.
Research and practice of natural resources monitoring and linkage updating technology: A case study of Shanghai

With the national proposal and promotion of ecological civilization construction, the demand for natural resources monitoring application services with unified management of natural resources has become increasingly strong. In this paper, we reviewed the current situation of domestic natural resource monitoring and linkage updating technology and proposed a set of natural resource monitoring and linkage updating technology frameworks based on the demand of natural resource management. The feasibility of the framework is verified by being applied to Shanghai, which solves the application demand of high frequency and multi-thematic natural resource elements monitoring in the process of urban management in Shanghai. We integrated the natural and management attributes of natural resources based on the database linkage update, coordinated the monitoring, management, database construction, and sharing applications of natural resources, providing an important reference for natural resource monitoring and data application sharing.
Cement Concrete Strength Quality Evaluation by Dynamic Time Series Features

Cement concrete strength quality is tested by the standard testing methods and the historic test results always documented in the formatted formal reports. They are difficult to gear for quality data analysis from multi-dimensional. Firstly, according to data engineering, the data attributes refer to time and strength quality in original cement concrete test reports are defined, extracted and preprocessed; Secondly, by time sliding windows the historic strength test data are divided into dynamic time periods and the features of mean, variation, median, coefficient of variation, mode, and full distance are constructed during every time period. Finally, the detailed evaluation results of cement concrete strength quality are provided by integrating the multi-features of multi-periods. Experimental results show that the raw data are defined and preprocessed to be less redundancy and reliable by data engineering and the integrated evaluation results become more accuracy and effective by the dynamic time series features.
Motivation and Overview of Normalization in DNNs

Input normalization is extensively used in machine learning models, and is advocated in many textbooks or courses for that the improved performance of the learner can be obtained. Intuitively, normalizing an input removes the difference in magnitude between different features of examples, which benefits learning. This characteristic is important for non-parametric models, for example the K-nearest neighbor (KNN) classifier, in which we need to calculate the distance or similarity between the examples. If certain dimensions of features have large range in magnitude, the distance/similarity between examples will be dominated by these dimensions, which will impair the performance of the learner. Besides, normalizing an input can improve the optimization efficiency for parametric models. There exist theoretical advantages to normalization for linear models, as we will illustrate.
Psychological Aspects of the Formation of Ethical Relationship in Preschool Children

The article is a study of the problem of psychological features of the formation of moral behavior in preschool children on the basis of scientific, theoretical, methodological, socio-political sources, analysis of the current situation in practice. It describes how to define and justify the relationship between the concept of moral behavior in preschool children and the psychological and social moral characteristics of children.
GTAT: Adversarial Training with Generated Triplets

To circumvent the grave problem of present adversarial training methods, i.e. distortion of classification surface, we in this paper propose a generated Triplet-based adversarial training method-GTAT, in which a Generator generates a semi-hard Triplet by design, rather than directly invoking the existing clean examples and adversarial examples. Through this kind of generated semi-hard Triplet constraint, GTAT can reshape the classification boundaries appropriately across various classes, arising from two-facet synergies: i) pull the intra-class examples together with tight distances; and ii) push away the inter-class examples with broad distances. This synergy will simplify and broaden the classification surfaces across different classes. Extensive experiments on the popular MNIST and CIFAR-10 datasets show that our proposed GTAT significantly outperforms other state-of-the-art adversarial training methods. We believe GTAT opens a door for the adversarial training from a new horizon of rationally generating semi-hard Triplet-satisfied adversarial training (retraining) examples, instead of straightly performing retraining on the generated adversarial examples and existing clean examples, or on the generated adversarial examples only.
Uncertainty-Based Selective Clustering for Active Learning

Labeling large amount of data is one of important issues in deep learning due to high labeling cost. One method to address this issue is the use of active learning. Active learning selects from a large unlabeled data pool, a set of data that is more informative to training a model for the task at hand. Many active learning approaches use uncertainty-based methods or diversity-based methods. Both have had good results. However, using uncertainty-based methods, there is a risk that sampled data may be redundant, and the use of redundant data can adversely affect the training of the model. Diversity-based methods risk losing some data important for training the model. In this paper, we propose the uncertainty-based Selective Clustering for Active Learning (SCAL), a method of selectively clustering for data with high uncertainty to sample data from each cluster to reduce redundancy. SCAL is expected to extend the area of the decision boundary represented by the sampled data. SCAL achieves a cutting-edge performance for classification tasks on balanced and unbalanced image datasets as well as semantic segmentation tasks.
Representation and Compression of Skin Cancer Images using Haar Wavelet

The main idea of compression is to decrease the number of bits used for representing an image. Wavelets have various predominant applications such as image representation and compression, which is necessary for the storage of huge data and transmission of the same there by increasing the rate of transmission. This paper presents image representation and image compression using Haar wavelet transform. The main objective of the proposed method is to obtain optimum results for the performance metrics used to evaluate the compression. Various threshold values are applied and better results are achieved with reduced Mean Square Error (MSE), increase in Compression Ratio (CR), Success Rate (SR), better Peak Signal to Noise Ratio (PSNR) & Structural Similarity Index Measure (SSIM). The promising outcome found with respect to compressed image retaining significant image details, attaining good image quality and better compression rate.
Spatiotemporally Enhanced Photometric Loss for Self-Supervised Monocular Depth Estimation

Recovering depth information from a single image is a long-standing challenge, and self-supervised depth estimation methods have gradually attracted attention due to not relying on high-cost ground truth. Constructing an accurate photometric loss based on photometric consistency is crucial for these self-supervised methods to obtain high-quality depth maps. However, the photometric loss in most studies treats all pixels indiscriminately, resulting in poor performance. In this paper, we propose two modules based on the spatial and temporal cues to refine the photometric loss. Delving into the geometric model of photometric consistency, we introduce a depth-aware pixel correspondence module (DPC) inside the monocular depth estimation pipeline. It reduces the uncertainty of photometric errors by applying the homography matrix to the projection of corresponding pixels in far regions instead of the fundamental matrix. Furthermore, we design an omnidirectional auto-masking module (OA) to boost the robustness of our model, which utilizes temporal sequences to generate disturbance poses and hypothetical views to distin-guish dynamic objects with different directions that violate the photometric consistency. Experiments on the KITTI and the Make3d datasets reveal that our framework achieves state-of-the-art performance.
What Are the Most Natural and the Most Frequent Transformations

In data processing, we deal with numerical values of different physical quantities. Computers just treat these values as numbers, but from the physical viewpoint, it is important to understand that the numerical values are not absolute: they change if we change the measuring unit and/or the starting point for measuring the corresponding quantity.
An Ontological Analysis of Digital Technology

In a growing number of industries, digitalization enables new value co-creation, leading to innovative changes in the economy and society. Digitalization arises from the Information Technology domain, blending and encapsulating digital technologies’ transformational effects. Digital technologies are fundamental components for organizations to transform digitally; therefore, what is digital technology? It is a type of technology involving one or more digital objects whose components include one or more bitstrings. The Theory of Digital Objects proposes to capture digital objects’ ontological complexity and understand how their identity and usage are bound up with a “dual nature” constituting physical form and social function. This paper presents an ontological analysis of this theory, grounded on the Unified Foundational Ontology (UFO), using OntoUML to identify the core components to answer what digital technology is. The analysis adds ontological clarity to the Theory of Digital Objects.
Adaptive Model Compression of Ensembles for Evolving Data Streams Forecasting

Ensemble methods combining several models have shown superior predictive performance in data streams forecasting compared to individual models. Besides, they can cope with evolving data streams and concept drift as they allow adaptation. However, ensembles are renowned for their complexity and computational costs which makes them unsuitable in cases where both resources and time are limited such as IoT applications. In this paper, we propose to use model compression in the streaming setting in order to overcome the aforementioned drawbacks. We show that compressing a highly performing dynamic ensemble into an individual model leads to better predictive performance when compared to an individual learner while significantly reducing computational costs. We conduct an extensive experimental study on both real and synthetic time series to measure the impact of compression on both predictive performance and computational cost.
Saving the Nusantara's Ulama Scientific Treasures with Digitization

This study aims to preserve and save the scientific treasures of the scholars of the archipelago in the form of coastal ancient manuscripts at the Jami' Lasem Mosque Library, Rembang. The condition of the ancient manuscripts is now damaged, weathered due to age. If not saved immediately, the contents of the manuscript will be damaged and unreadable. Though the contents are very useful for today's life. The effort that needs to be done is to digitize the manuscript, while the original manuscript can be preserved by means of preservation. Digitization is not only to preserve the contents of the manuscript but also to maintain health and the environment. We managed to digitize four of the 14 manuscripts in the Lasem Mosque library. The potential of ancient manuscripts in Lasem still needs to be traced again considering that Lasem is an old city where an old pesantren was founded in Java.
Attacks on ML Systems: From Security Analysis to Attack Mitigation

The past several years have witnessed rapidly increasing use of machine learning (ML) systems in multiple industry sectors. Since security analysis is one of the most essential parts of the real-world ML system protection practice, there is an urgent need to conduct systematic security analysis of ML systems. However, it is widely recognized that the existing security analysis approaches and techniques, which were developed to analyze enterprise (software) systems and networks, are no longer very suitable for analyzing ML systems. In this paper, we seek to present a vision on how to address two unique ML security analysis challenges through ML-system-specific security analysis. This paper intends to take the initial step to bridge the gap between the existing computer security analysis approaches and an ‘ideal’ ML system security analysis approach.
Design Thinking the Human-AI Experience of Neurotechnology for Knowledge Workers

Neurotechnology promises cognitive enhancement as a way for humanity to extend its information-processing capability without invasive brain surgeries and pharmacological side effects. Notable advancements in this field have achieved high-bandwidth wireless communication interfaces between human brains and computers. Human-centered design proposes that human-technology experiences should focus on human needs. This paper explains how design thinking has been applied as a methodology to design the user experience of an attention-based neurotechnology solution that leverages artificial intelligence (AI) to enhance the flow performance and cognitive well-being of knowledge workers (KWs). Using the d.school design thinking process, we started with a mindset that favored empathy, creative confidence, and ambiguity to discover and define the problems confronting KWs. After diverging with deep empathy and converging on user personas and problem definition, the design thinking process branched into an iterative prototyping cycle that transformed our initial ideas into a human-centered AI-powered neurotechnology. We utilized the functional prototypes for testing assumptions and performing a comprehensive design evaluation. Our final solution incorporated a gamified user interface with visual elements, affordances, and a coherent human-AI experience. Expert software evaluators conducted a series of cognitive walkthroughs and heuristic evaluations by simulating the user personas and performing an end-to-end user scenario with the prototype. The design thinking process generated a neurotechnology service with a human-AI experience that enables KWs to achieve healthy flow performance while enhancing cognitive well-being.