Reviewerly/exHarmony huggingface dataset:
Interactive Clustering of Cooking Recipe Instructions: Towards the Automatic Detection of Events Involving Kitchen Devices

Cooking recipes are a rich source of semantic information. They contain instructions for food preparation tasks, specifying the actions that should be carried out which typically involve various ingredients and kitchen devices. In an IoT scenario, instructions in cooking recipes can form the basis for automatically controlling kitchen devices without any programming. However, as these instructions are written in natural language, they first need to be transformed or parsed into machine-interpretable commands. As a step towards this, we investigate methods for identifying the various types of actions (events) that kitchen devices are involved in. We cast this problem as a clustering task, whereby recipe instructions involving a given device of interest, are automatically grouped according to the type of event described. Each sentence in every instruction is represented by its embedding vector which is computed using a BERT-based model, specifically one pre-trained using a Roberta architecture. We cluster these sentence embeddings using our newly proposed interactive machine learning (IML)-based framework underpinned by the HDBScan clustering technique. We demonstrate that our IML framework can detect events in sentences with satisfactory accuracy, reaching almost the same level as human performance.
Performance Analysis of Least Significant Bit Based Watermarking Technique Under Various Types of Attacks

Abstract Digital information transferred through internet should be protection from unauthorized and attack resource using one of the information security system method, one of these methods was digital watermarking was used in order to prevent information from various types of attack adversary since it satisfies all security system requirements (authentication, integrity and confident). Digital watermarking is used digital image for several purposes such as authentication, copyright, and many purposes. This paper we embedded (number of bits) MSB of watermark image in the (number of bits) LSB of cover image by proposed embedding algorithm, where the ratio of appearing information of the watermark in the watermarked image depends on a number bits embedded. Such that increasing the number bits increase the information display. The performance of the algorithm for watermarking and quality of reconstructed image have been measured in terms of MSE and PSNR. This paper produced performance analysis of image watermarking technique depend on least significant bit against several attacks (Gaussian, salt and pepper noise, Poisson and Speckle). The result of the attack appears that salt and pepper attack give best result than other types when it applied on the original image and watermarked image. Simulation results show that best results were achieved against Salt and Pepper attack in terms of less MSE and high PSNR. MATLAB language is used for implementing the suggested Algorithm.
Blockchain Reinventing the Healthcare Industry

The fragmented and disorganized nature of data in healthcare poses a variety of challenges to scientific research and medical applications. This mainly stems from the lack of traceability of transactions, complex disconnected networks, and lack of data interoperability. This complexity leads to difficulties in conducting research and clinical trials, and to the problem of counterfeit drugs in the market. This triggers lack of availability and accessibility of data for researchers and medical experts. Blockchain technology offers comprehensive solutions to these problems, and hence it has been of enormous interest in the healthcare sector. Blockchain technology with its innate transparency, traceability, data security, and distributed nature can help to overcome the data related problems in healthcare. This chapter provides an overview of the use of blockchain in the healthcare industry and explores various use-cases and applications. This chapter also discusses case-studies and various challenges faced while adapting blockchain in the healthcare industry.
Strange Post-human Attractors: Algorithmic improvisation as acousmatic <i>poiēsis</i>

Contemporary thought is moving away from the notion that the human is a clear-cut concept. In particular, non-anthropocentric views are proliferating within the interdisciplinary area of critical post-humanism, with emphasis on non-dualistic views on relations between human and technology. This article shows how such a view can inform electroacoustic and computer music practice, and sees improvisation linked with composition as a fruitful avenue in this. Following a philosophical preparation and a discussion of relevant music discourse, two computer music works created by the author are discussed to demonstrate a model of music-making that merges composition and improvisation, based on the concepts of cognitive assemblages and intra-action, following the writings of N. Katherine Hayles and Karen Barad, respectively. The works employ techniques related to artificial intelligence and cybernetics, such as machine learning algorithms, agent-based organisation and feedback systems. It is argued that acousmatic sound is an important aspect of this practice. The research is thus situated not only in the frames of improvisation practice and music technology but also within spatial acousmatic composition and performance.
Method for Robot Capturing Object Thrown out in Natural Wind Environment

Abstract It is difficult for a robot to capture object which is thrown out in the environment with natural wind. Equation of state and measurement of the object in the environment with natural wind is studied. Using the measurement equation and the state equation when there is no natural wind, the average acceleration of the object caused by natural wind can be estimated. Moreover, the variance of the object acceleration caused by natural wind can be estimated using the statistical characteristics and probability distribution of natural wind. Then a method for robot capturing the object based on Kalman filter is proposed. Simulation results show that the trajectory of the object changes obviously when two groups of typical natural wind are used. The object trajectory can be tracked by the robot accurately after it deviates from the object trajectory for about eight sampling periods.
Exploration of the Blended Teaching Mode of Colleges and Universities English on Unipus from the Perspective of “Internet + ”

At present, with the widespread popularization of “Internet + ” technology in the field of higher education, colleges and universities English teaching is facing unprecedented challenges. The effective combination of English teaching as an important part of higher education and “Internet + ” technology has always been a frontier issue in education, the exploration of its teaching methods and teaching models has always been the focus of research. This article mainly uses “Internet + ” as the research medium to discuss the colleges and universities English blended teaching model based on the Unipus smart teaching cloud platform. The article proposes to make full use of the advantages of modern teaching methods and stimulate students’ interest in English learning, make students’ learning from passive to active, and truly cultivate their independent learning ability.
Development of virtual laboratory applications based on virtual reality using the requirements engineering method

Abstract The virtual laboratory application that has been designed in Industrial Engineering Department of Diponegoro University can simulate several work operations on turning machines as well as the introduction of tools to the work process bench. In addition to these aspects, applications that have been designed also do not provide a virtual environment that has complete equipment such as in a production system laboratory. Another drawback is that there is no usage guide in the form of a video tutorial for an initial understanding of users when operating a work operation on a turning machine. In practice, mistakes still occur frequently in working on turning machines. According to students, errors often occur due to a lack of understanding from students themselves even though virtual reality tools have been provided in the training of turning machine operations. Based on that problem this research tries to develop virtual laboratory using the Requirement Engineering method and HTC VIVE Pro Full Kit hardware that can correct the shortcomings of the previous development. The output of this research is a virtual reality-based virtual laboratory application that has been tested and evaluated by considering several aspects such as interaction, visualization, information, reusability, performance, and reliability.
Mobile and Wearable Interactions in the Retail Domain

The increasing popularity of mobile and wearable devices and the growing trend towards shopping as a leisure activity offer new opportunities for interaction in the retail domain. This workshop brings together researchers and practitioners to jointly explore the field and to find out how mobile interactions need to be designed to be attractive to customers and retailers alike. In addition to the discussion of topics, the workshop seeks to establish a basis for a continuous exchange between those who are active in the cross-section of research between mobile interactions and retail.
Pedagogic Potential of Master’s Programme in the Development of Research Competence of Tertiary Students

Master’s programme creates potential for solutions to issues of laying the groundwork for self-development of research competence of tertiary students. Master’s programme contributes to stimulation of research and scientific-methodological activities of students as well as individualization of Master’s programmes on the basis of baccalaureate.
Design and Implementation of Visual Detection System for Driving Environment

With the growth of car ownership, traffic safety problems become more and more serious. Traditional passive safety measures can only reduce losses, but fail to effectively avoid occurrence of traffic accidents in driving. Aiming at deficiencies in traditional target detection methods, this paper uses a deep learning based target detection method on an established driving environment dataset, so as to realize recognition and positioning of road moving targets under complicated road conditions. In addition, this paper analyzes and verifies the feasibility of the method for detection and classification of different targets in the driving environment, and compares the detection speed and accuracy of different target detection algorithms. Based on the analysis of experimental results, a detection method based on OHEM (Online Hard Example Mining) Algorithm and combined with Faster R-CNN is proposed. It has been verified in the experiment that, the improved algorithm of OHEM + Faster R-CNN proposed in this paper prevails over YOLOv3 in detection efficiency of small targets. Its recognition accuracy for larger targets remains over 90%, and mAP reaches up to 0.906.
Effect of material-technological properties on the reconstruction of utility parameters of bells

An important utility parameter of bells, including church bells, is their sound – that depends on geometry of the body, changes in section thickness and material from which they are made. Knowing the geometry of casting and chemical composition of the alloy, it seems that the current state of art allows recreating not only solid, but also acoustic properties of the casting. Hence, the purpose of this work is to make a casting using reverse engineering tools, compare acoustic properties of the newly formed bell with the prototype, and test material for a bell alloy. For the host and casted bell the note and octave are identical, the sound differs only in the number of cents away from the exact note. Compared with the numerical model, the acoustic properties of newly formed bell were found with slight deviations. The reasons for registered differences between the components of the original sound and the copy are seen as differences during melting, pouring and solidification processes of the alloy. Material tests carried out on samples taken from the new bell also showed microporosity of the casting and change in microhardness of the α and eutectoid phase (α + δ) along height of the bell cross-section. The chemical composition of bronze also varied along with the change of measuring points at the rib height and the fraction of eutectoid in the alloy structure. Material tests showed a significant influence of cast porosity on its acoustic properties.
Visualizing Searcher Gaze Patterns

Information retrieval researchers often use eye-tracking to gain insights into searchers' decision making processes. In this paper, we present a visualizing method for summarizing the gaze patterns of multiple searchers on search engine result pages (SERPs). Unlike traditional eye-tracking heat maps, this method includes timing information as part of the visualization, providing additional clarity about searcher fixations as time passes. We demonstrate the visualization technique using eye-tracking data collected as part of a previously published search engine user study and show its value in communicating different patterns of searchers' gaze behavior under different user types and query types. We include a code sample in R to facilitate adoption of the method.
The Use of Graph Databases for Artificial Neural Networks

Storing and using trained artificial neural network (ANN) models face technical difficulties. These models are usually stored as files and cannot be run directly. An artificial neural network can be structurally expressed as a graph. Therefore, it would be much more useful to store ANN models in a database and use the graph database as this database system. In this study, training and testing stages of ANN models are provided with software that will allow multiple researchers to conduct joint research on ANN models. The developed software platform is aimed to increase the representation power of the currently used methods by transferring the models developed in the popular ANN frameworks used today. With the study conducted, even someone who has started learning artificial neural network models from scratch will see the process and can visually develop their own model. When models are stored in the graph database, it will be easier to making versions and observing how the model grows. In addition, data to be input and output to the model can be stored in this database, also. In order to feed ANN models with input data and produce outputs, the graph database's own query language was used. This eliminates the dependency on another software library.
Blockchain anonymous trading system based on multi-hop payment

In recent years, with the emergence of bitcoin, blockchain technology has attracted more and more attentions from academia and industry. The advantages of blockchain are decentralized trust, not tampered, traceable and consensus features. However, transaction processing speed is 7 transactions per second in the bitcoin network. The transaction processing performance is not suitable to many applications in e-commerce field. Hence, the scalability of transaction consensus on blockchain has become a challenge issue. There are existing works to solve the issue, such as the lightning network, which exploits cross-chain consensus to improve the scalability of the bitcoin transaction. However, the anonymity between seller and buyer is still a privacy challenge for the fast blockchain payments based on the lightning. In this paper, we propose a blockchain anonymous trading framework based on multi-hop payment to solve the scalability problem of the blockchain transactions and improve the efficiency of transactions among nodes. Firstly, we use Three-session encryption to build multiple encryption transaction channels between a pair of nodes. Secondly, Dijkstra is used to find a minimal anonymous service fee path. Finally, the experimental results show that lightning network with encryption transaction channels can improve the use efficiency, reduce the extra cost of transaction and guarantee the privacy information of users.
Building Bridges and Not Walls

This virtual half-day workshop will explore human-machine communication (HMC) and communication studies/science theories as used in HRI studies. Submitted papers can include a 1) discussion of theories from the disciplines of media and communication that can guide HRI studies and vice versa, 2) analysis of related constructs (variables) that intersect both fields but with different nomenclatures (e.g., credibility vs. trust, presence vs. im- mediacy), or 3) exploration of quantitative and qualitative study designs from media and communication and their application to HRI. For more information and submission information, go to https://www.combotlabs.org/hmchri2021.html
Yeni Bir Sezgisel Optimizasyon: Arama ve Kurtarma Algoritmasi ve Fonksiyon Optimizasyon Problemlerinin Çözümü

Heuristic techniques are usually inspired by nature, based on swarm intelligence optimization methods. There are plenty of them and researchers developing new ones by inspiring nature or observing the living being. Search and rescue algorithm(SaRA) is a very new heuristic optimization and inspired by the humankind deportment. In this study SaRA is proposed and applied this new technique to function optimization test problems to find the global minimums. SaRA’s performance was experimented with 21 benchmark minimization problems and competitive results were obtained when compared to Dynamic Random Search Technique and Random Selection Walk Technique.
Cloud Services Discovery Assistant for Business Process Development

Business Process (BP) development can be defined as the process of constructing a workflow application by composing a set of services performing BP’s activities. In this respect, cloud services prove indispensable to build business applications with higher performance, lower operating cost, and faster time-to-market. The crucial challenge facing several companies in cloud-based BP development is to effectively address the business activities and cloud services matching issue. Formerly, we present this issue as a discovery challenge of suitable cloud services performing abstract BP’s activities. Business activities are generally named and described with non-standard format based on natural language and subjective terminology. In parallel, the great variety and the exponential proliferation of cloud services over the Web introduce several functionally similar offers with heterogeneous descriptions. Therefore, efficient and accurate cloud service discovery performing business activities requires a high level of expertise and a steep documentation curve. To address this challenge, firstly, we offer a publicly availaible cloud data-set, named ULID (Unified cLoud servIces Data-set), where services offered by different cloud providers are collected, unified and classified based on their functional features. Secondly, we introduce the concept of cloud-aware BP by proposing a Domain-Specific Language (DSL) named “BP4Cloud” to enrich BP modeling and specify cloud services requirements. Based on ULID data set and “BP4Cloud” language we propose an Activity-Services Matching algorithm that automates the discovery of cloud services performing BP’s activities. As a part of the evaluation, we set up by clarifying the specification of BP4Cloud elements through a proof of concept implementation applied on a real BP. Then, we proceed by evaluating the precision and recall of our Activity-Service Matching algorithm.
Seismic Hazard Maps With The Effect Of Local Geology For Washington, DC

We have developed improved seismic hazard maps with the effect of local geology for the Washington, DC area. The input ground motion prediction equations, source model, and logic tree for the analysis is taken from the 2014 U.S. Geological Survey national seismic hazard model (Petersen et al., 2014). We have added an improved local geology model based on the overburden thickness map of Froelich (1976). As in our preliminary effort (Cramer et al., 2016) we use three shear-wave velocity profiles for Piedmont, Fall Line, and Coastal Plain regions (Olgun et al, 2015). We developed reference profiles from the three Olgun et al. profiles that extend to hard rock for site amplification relative to the rock conditions for the ground motion prediction equations. Our seismic hazard maps include both probabilistic (2% in 50 years) and scenario (M6.0 at Mineral, VA) maps. The local geology in the Washington DC area strongly amplifies higher frequency ground motions (peak ground acceleration, 0.2 s spectral acceleration) in keeping with the three site-specific profiles of Olgun et al. (2015) and the observations of Pratt et al. (2017). The soil response is driven by the 10 to 20 m thick low shear-wave velocity (200–300 m/s) top layers of the reference profiles. These low velocity layers are composed of residual soil and/or alluvium. The thicker Cretaceous Potomac Formation sediments, up to 600 m thick in the SE corner of the study area, have an effect on seismic hazard at 1.0 s and longer periods. The greatest effect on 1.0 s spectral acceleration seismic hazard is from the ~200 m thick sediments near the SE edge of Washington DC. Our maps have a resolution of 0.005 degree (500 m) and have some of the sub-km scale detailed geology variation in the Washington, DC area. These maps can serve as a guide to improving the understanding of seismic hazard and risk in the area and stimulate further work on a more detailed local geology model with higher resolution.
Off-the-Grid: Fast and Effective Hyperparameter Search for Kernel Clustering

Kernel functions are a powerful tool to enhance the k-means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dramatic impact on the result. In supervised settings, these can be tuned via cross-validation, but for clustering this is not straightforward and heuristics are usually employed. In this paper we study the impact of kernel parameters on kernel k-means. In particular, we derive a lower bound, tight up to constant factors, below which the parameter of the RBF kernel will render kernel k-means meaningless. We argue that grid search can be ineffective for hyperparameter search in this context and propose an alternative algorithm for this purpose. In addition, we offer an efficient implementation based on fast approximate exponentiation with provable quality guarantees. Our experimental results demonstrate the ability of our method to efficiently reveal a rich and useful set of hyperparameter values.
The Shape of the (15,3)-Arc of PG(2,7)

Marcugini et al. proved, by computer-based proof, the unicity of the maximum (k,3)-arc in PG(2,7). In this paper, we show how the (15,3)-Arc in PG(2,7) may be described using only geometrical properties. The description we provide, believing it is novel, relies on the union of a conic and a complete external quadrangle.
Wavelet-Based Laplace Transformation for Initial- and Boundary-Value Problems

IfLaplace transform the function $$f(t)$$ is defined on $$t \ge 0$$ , and the integral $$\int_{0}^{ + \infty } {f(t)e^{ - st} } dt$$ (s is a complex parameter) converges in a domain of s, such an integral is referred to as the Laplace transformLaplace transform of $$f(t)$$ , denoted by.