Reviewerly/exHarmony huggingface dataset:
Special Session: Dehumanization of Robotic Assistants and Subsequent Unethical and Abusive Customer Behavior in Frontline Encounters: An Abstract

The rapid technological advancements have revolutionized service encounters (Huang and Rust 2017) and provided an opportunity for companies to improve customer experiences through automatic service interactions (van Doorn et al. 2017). As an increasing number of companies begin to incorporate robotic assistants into their service designs, such as LoweBot adopted by Lowe’s as a shopping assistant (King 2014) and Pepper adopted by Pizza Hut as a waiter (Santos 2018), some of these companies have observed abusive customer behavior directed towards robotic assistants. For example, the co-founder of Starship Technologies, Ahti Heinla, indicated that it is not uncommon to find its food-delivery robots being kicked by people (Hamilton 2018). This type of abusive customer behavior may negatively influence not only organizations through the financial loss associated with the damage of robotic assistants but also other customers through the spread of such behavior. Given the importance of this issue, this research aims to examine why and when customers engage in abusive customer behavior directed towards robotic assistants, a behavior that is both dysfunctional and unethical because it violates the commonly accepted norm of morally appropriate conduct. Drawing upon moral disengagement theory (Bandura 1986), we propose that customers may abuse robotic assistants in service encounters due to psychological freedom derived from a lack of moral self-regulation that would refrain them from unethical and dysfunctional behavior. Specially, we expect that customers are more likely to morally disengage and abuse a robotic assistant if the assistant’s style is non-humanoid as opposed to humanoid. The non-humanoid style allows customers to view the assistant as a machine without human characteristics and to abuse it without experiencing self-condemnation. The negative influence of humanoid style on moral disengagement and abusive behavior is expected to be strengthened when the assistant’s features are hedonic as opposed to utilitarian. Hedonic experiences tend to involve people-focused service delivery, whereas utilitarian experiences tend to involve equipment-focused service delivery (Ng et al. 2005). Thus, we suggest that customers are more likely to attend to a humanoid cue of a robotic assistant when its features are hedonic than when they are utilitarian. The study results provided support for the proposed interactive effect of robotic assistants’ style (humanoid vs. nonhumanoid) and feature (hedonic vs. utilitarian) on customer abusive behavior. This paper offers managerial and theoretical implications regarding how to manage customers’ unethical and abusive behavior toward robotic assistants.
A Framework to Support ICS Cyber Incident Response and Recovery

During the past decade there has been a steady increase in cyber attacks targeting Critical National Infrastructure. In order to better protect against an ever-expanding threat landscape, governments, standards bodies, and a plethora of industry experts have produced relevant guidance for operators in response to incidents. However, in a context where safety, reliability, and availability are key, combined with the industrial nature of operational systems, advice on the right practice remains a challenge. This is further compounded by the volume of available guidance, raising questions on where operators should start, which guidance set should be followed, and how confidence in the adopted approach can be established. In this paper, an analysis of existing guidance with a focus on cyber incident response and recovery is provided. From this, a work in progress framework is posited, to better support operators in the development of response and recovery operations.
Pembangunan Aplikasi E-Commerce Pemasaran Batik pada Toko Batik Rifqi

The use of the internet in the business world serves as a business strategy application, such as: marketing, sales, and customer service. This is evidenced by the many large companies that have used or used the internet for business development, for example, online stores. In doing business, entrepreneurs are faced with challenges as to how they can serve and provide the required information to customers quickly, without being limited by place, distance and time. it happens because of influential factors such as company location, limited operating hours, lack of product information can reduce the number of transactions and can even cause customers to switch to other companies better. The system development method used is SDLC (System Development Life Cycle), namely software requirements analysis, design, code generation, testing, support, or maintenance. MySQL as a database for PHP applications. With this e-commerce website, it is expected to make it easier for buyers to be able to buy batik that is sold in stores easily via the internet. With this website the seller can easily promote a wider range of people
Global deformation model for 3D facial combination

We propose an automatic face and head deformation method that combines 3D faces with an arbitrary head model. With the rapid development of Computer Vision and Deep Learning, 3d scans of human faces are becoming easier to obtain. How to complete the scanned 3D face data and make it a complete full head model, or give the scanned 3D face different 3D hairstyles, has always been an open question. For this reason, we propose a Global Deformation Model (GDM) which is implemented by multiple iterations. By building a Global Deformation Model (GDM), a full-head 3D data with complex hairstyles could be combined with 3D face data. In this way, the scanned face is automatically completed as a full-head model. Experiments show that compared with other deformation algorithms and full-head reconstruction methods, our method has better automation and robustness. It shows good deformation results in complex 3D data. We provide an attractive solution for graphic design, Virtual Reality, 3D printing, and other industries, which can be widely used in consumer scenes.
Trustless Architecture and the V-Form Organisation

Blockchain (distributed ledger technology) is an institutional technology that allows trust to be manufactured instead of being earned. Trust is an important component of business and trade and has previously been subsumed into information costs. It is only now that the importance of trust is being fully appreciated. Arun Sundarajaran has suggested that the creation of new forms of trust has driven the expansion economic activity throughout history. In this chapter we argue that the industrialisation of trust is going, again, to drive a massive expansion in economic activity through the emergence of new organisation forms that will deliver high-powered market incentives deep into what would appear to be hierarchical organisations. We are labelling these (as yet speculative) organisations forms the V-form organisation. In this chapter we discuss the importance of trust, the evolution of trust, and the industrialisation of trust. We argue that current organisational forms have exhausted the levels of trust that have previously sustained them. Blockchain technology offers a new industrialised form of trust that can drive further economic activity.
MetaAMC: meta learning and AutoML for model compression

Model compression reduces the computation costs of an over-parameterized network without performance damage, and channel pruning is among the predominant approaches to compress deep neural networks. In this paper, we propose a novel approach called MetaAMC combining meta learning and Auto ML for automatic channel pruning of very deep neural networks. It leverages meta learning and reinforcement learning to provide the model compression policy. Compared to not only the original AMC and MetaPruning but also the state-of-the-art pruning methods, we have demonstrated superior performances on MobileNet V1/V2 and ResNet-50.
Data-Driven Forecasting for Grid-Connected Solar Power Plants

Author(s): Larson, David Philip | Advisor(s): Coimbra, Carlos F M | Abstract: Forecasting is one of the enabling technologies for the integration of weather-dependent renewable resources (e.g., solar and wind) into the electric grid. Accurate forecasts can reduce operational costs associated with intra-day variability, reduce imbalance charges incurred by plant operators due to inaccurate energy bids, decrease utility costs associated with day-ahead scheduling (thereby reducing overall OaM costs), as well as assist grid operators with balancing energy demand schedules. As the market penetration of solar-based power generation continues to grow, accurate and reliable forecasting techniques become increasingly more important. In this work, two key areas of solar forecasting are advanced. First, we develop intra-day (g1-hour) and day-ahead (g24-hour) forecasting methods to directly predict the generation of operational solar power plants, without the need for intermediate solar irradiance forecasts and resource-to-power modeling. Here we take a data-driven approach, leveraging Machine Learning (ML) techniques and publicly available, spatially resolved meteorological and remote sensing datasets. The proposed methods are analyzed and validated using two grid-connected 1 MW photovoltaic (PV) power plants in California. Second, we develop a method to directly and efficiently estimate cloud optical properties from longwave remote sensing data. The output of solar-based power generation systems is strongly dependent on cloud cover and optical depth, but in most solar forecasting methodologies cloud optical properties are over-simplified due to a lack of real-time, accurate estimates. The proposed estimation method builds upon a two-stream, spectrally resolved infrared radiation model coupled with high-resolution (5-minute, 2 km) spectral satellite imagery. We show that the proposed method can provide real-time, accurate estimates of cloud optical depth (COD) and cloud top height for all-sky (clear or cloudy) conditions during both daytime and nighttime.
Framework for reversible data hiding using cost-effective encoding system for video steganography

Importances of reversible data hiding practices are always higher in contrast to any conventional data hiding schemes owing to its capability to generate distortion free cover media. Review of existing approaches on reversible data hiding approaches shows variable scheme mainly focussing on the embedding mechanism; however, such schemes could be furthermore improved using encoding scheme for optimal embedding performance. Therefore, the proposed manuscript discusses about a cost-effective scheme where a novel encoding scheme has been used with larger block sizes which reduces the dependencies over larger number of blocks. Further a gradient-based image registration technique is applied to ensure higher quality of the reconstructed signal over the decoding end. The study outcome shows that proposed data hiding technique is proven better than existing data hiding scheme with good balance between security and restored signal quality upon extraction of data.
The impact of learning using adaptive intellectual learning systems on the psychological well-being of the learner

In the article, the theoretical approaches as to the description of the phenomenon of psychological well-being are analyzed. Besides, the peculiarities of the adaptable intellectual educational systems (AIES) are explored. Also, the possibility of the positive influences of the usage of adaptable intellectual educational systems on the psychological well-being of the individual who strives for education is displayed. In addition, the questions on the further examination with the usage of the information and communication technologies on such topics as mental health and physiological health of a human being as the components of the productive life are raised as well.
The Specialty of Design (Category of Arts) under the Category-based Enrollment Mode

Inspired by the South China University of Technology (SCUT) in constructing "Double First-Class" disciplines, the characteristics of design-related specialties and the latest talent training targets, this thesis reflects SCUTâ€™s undergraduate training scheme and curriculum system, and probes into problems and countermeasures in the reform of enrollment and training mode for the category of design.
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighbouring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25\% and 94.74\%, respectively. Moreover, the method can realize the real-time image processing at 27.85 FPS, showing promising potential for embedded advanced driving-assistance system applications. Our code is publicly available at this https URL.
Penerapan Metode Bermain Peran dan Latihan Untuk Meningkatkan Minat dan Kemampuan Berbicara Bahasa Inggris Narrative dan Report Siswa

Role Play is one method that can provide opportunities to enhance the active role of students to be physically and emotionally involved in the learning process. This research was conducted at SMP N 6 Kota Metro. Starting from the prasiklus to map priority problems, the first cycle was performed as a role play in several meetings, followed by strengthening exercises in cycle II. The conclusion is that the ability to speak English increases and reaches an average value of 7.29. Improving students' interest in learning English is getting better. As many as 92% of students have the status, and 8% of the students have the Very Interest status. and Very Interests totaling 92% exceeded the planned achievement indicators, the cycle was stopped. The characterization exercise then displays the results of the exercise as students take part in classroom learning that has changed students' perceptions of English lessons, the learning process, and awareness of the importance of mastering English for the
Rapid Biogeography-based Silhouette Optimization for Data Clustering

Heuristic algorithm can be used for the NP-complete data clustering problem. This research aims to develop the rapid biogeography-based silhouette optimization (RBBSO) to search for the optimal data clustering solution. Valid index silhouette with sufficient clusters for the unsupervised data with considerable computation time reduction is used for this exploration. Suitable migration and mutation are also proposed to balance the converged and diversified search for data clustering by using valid index silhouette to adapt new good solutions while keeping good ones. Specifically, the computational time of RBBSO is reduced by migration based on the centroid of habitat (solution) and mutation, considering the silhouette value of each data. Experimental simulation results show that the performance of the proposed RBBSO is competitive in data clustering which uses benchmark problems.
Modelling Conceptualisations: Combining Image Schemas to Model Events

The notion that image schemas are used as conceptual building blocks in language and conceptualisations as a whole has been repeatedly pushed. It was repeatedly demonstrated that the qualification for image-schematic concepts and the identification between the different image schemas are problems for the research field. It was suggested that more complex image schemas could be viewed as combinations of simpler image-schematic structures, or components from different families. This chapter explores this by looking specifically at image schema combinations. After introducing three different types of image-schematic combinations it also aims to demonstrate how these combinations can be considered to construct the conceptualisation of complex image schemas and simple events as in ‘image schema profiles’. This is placed into the framework of formalising image schemas by discussing their usefulness in commonsense reasoning problems as well as an ISLFOL formalisation of the dynamic aspects of CONTAINMENT and the simple image-schematic events BLOCKAGE, CAUSED MOVEMENT and BOUNCING. The chapter includes considerations and discussions on :
CASE BASED REASONING DIAGNOSIS PENYAKIT KULIT PADA KUCING DENGAN METODE SIMILARITAS EUCLIDEAN DISTANCE DAN METODE SIMILARITAS NEAREST NEIGHBOR

Cat health is also very important like human health. Especially in the type of disease that is quite serious. The problem that often arises is that most cat owners who do not know the symptoms and illnesses suffered by cats and do not immediately check with a veterinarian on the grounds that the costs incurred are not cheap. Therefore, to reduce the problem above, the system was built using Case Based Reasoning with Euclidean Distance Similarity method and Nearest Neighbor Similarity method by utilizing the knowledge of old cases to new cases by finding the similarity value of each case so as to produce a maximum level of accuracy. . The data contained in this system there are 3 types of disease data, 10 data symptoms and 3 data solutions obtained through a direct interview process with a veterinarian. Case data (source case) from this research are 50 data and for test data that is 50 data. The system accuracy of this study is 96% with a threshold of 0.1-0.6 for the Euclidean Distance Similarity method and the accuracy of the system with the Nearest Neighbor Similarity method of 96% with a threshold of 0.1-0.4.
Quantitative Analysis of Construction Site Risk and Threshold of the Early Warning System

This paper starts from definition of risk, considering the probability of accidents and the consequences, obtains the mathematical measurement of risk, and calculates the risk value of the construction site. The probability of risk event causing casualties is determined by UWB positioning technology, according to the position measure of the construction worker, the machinery and casualty loss measured by the compensation. The risk threshold and the real-time warning threshold are finally calculated to provide a basis for decision-making.
FEATURES OF EDUCATIONAL ACTIVITIES OF STUDENTS IN TECHNICAL UNIVERSITIES OF RUSSIA

The article considers the pedagogical heritage of the Russian Orthodox Church, which contains an inexhaustible educational potential that is not subject to ideological fluctuations, since it is based on unchanging values.In Orthodoxy and the harsh Russian nature lie the origins of the Russian soul and the keys to its understanding.In Orthodoxy, Russian military commanders and soldiers, writers and artists, scientists, naturalists, doctors, devotees of piety were brought up.At present, the Orthodox Church can offer a holistic, positive educational system that has been tested for ten centuries.The interaction of the state and the Russian Orthodox Church is a unique phenomenon of joint, complementary resources to address critical issues of a moral nature.
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This study explores the effects of an SSI-based program focused on character and values for engineering college students. Theparticipants were...
Ontology-Guided Policy Information Extraction for Healthcare Fraud Detection.

Financial losses in Medicaid, from Fraud, Waste and Abuse (FWA), in the United States are estimated to be in the tens of billions of dollars each year. This results in escalating costs as well as limiting the funding available to worthy recipients of healthcare. The Centers for Medicare & Medicaid Services mandate thorough auditing, in which policy investigators manually research and interpret the policy to validate the integrity of claims submitted by providers for reimbursement, a very time-consuming process. We propose a system that aims to interpret unstructured policy text to semi-automatically audit provider claims. Guided by a domain ontology, our system extracts entities and relations to build benefit rules that can be executed on top of claims to identify improper payments, and often in turn payment policy or claims adjudication system vulnerabilities. We validate the automatic knowledge extraction from policies based on ground truth created by domain experts. Lastly, we discuss how the system can co-reason with human investigators in order to increase thoroughness and consistency in the review of claims and policy, to identify providers that systematically violate policies and to help in prioritising investigations.
Foodie: An Automated Food Ordering System

This paper proposes an automated food selection and ordering system that reduces the time between ordering and serving dishes to customers. This system will provide an experience of convenience to the restaurant employees while they are on duty as well as the consumer who dine-in at the restaurant, thus leading to more customer satisfaction which will help in improving business consecutively. The proposed system is fully secure and uses QR code and UID verification for placing the order for a specific restaurant. Every table in the restaurant will be equipped with electronic menu facility which will be user-friendly, and the customer will be able to select, place and pay through this interactive interface. This processing method of placing order in restaurant increases efficiency and reduces time based on QR code without the need of nearness of waiter at the table by replacing some stages of traditional order service.
Realizing video analytic service in the fog-based infrastructure-less environments

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process.