Reviewerly/exHarmony huggingface dataset:
Bazaar-Contract: A Smart Contract for Binding Multi-Round Bilateral Negotiations on Cloud Markets

Amazon's EC2 On-Demand marketspace is the dominant platform for trading Cloud services such as virtual machines. On such platforms consumers and providers do not negotiate with each other. Instead, consumers purchase predefined virtual machines at fixed-prices and so this approach is also termed take-it-or-leave-it approach. In the last years more dynamic platforms emerged such as Amazon's spot marketspace which was relaunched at the end of 2017 - here consumers can bid for virtual machines. The recent efforts of Amazon and other Cloud providers such as VirtuStream show that dynamic trading mechanisms are a promising approach for realizing future Cloud markets. Hence, multi-round bilateral negotiations which are executed autonomously have gained popularity in the scientific community. A key challenge towards the adoption of autonomous multi-round bilateral negotiations is to ensure the integrity and transparency so that the generated agreements are legally biding. In the paper at hand we present an approach which uses a smart contract - called Bazaar-Contract - to ensure integrity and transparency during multi-round bilateral negotiations. Thereby, consumers and providers exchange offers by calling methods of the Bazaar-Contract. Moreover, Cloud referees can use these Bazaar-Contracts in order to manage penalties resulting from poor service quality. In order to prove the technical feasibility of our approach we implemented the Bazaar-Contract using Ethereum and the Inter-Planetary File System. We evaluate the economical feasibility of our approach by considering the gas costs.
A Weighted Threshold Visual Cryptography

Over the past years, a visual secret sharing scheme gave each share the same ability to reconstruct the secret image. That means each participant had the same significance to reveal the secret. However, it is not realistic in real world for some applications. In this paper, the weighted $$ (k,n) $$ -threshold visual cryptography scheme is proposed, in which the secret image is encoded into some shares with different weights and the sum of weights for all shares has to be equal to $$ n $$ , according to the share-holders of different priority levels. In the decoding phase, when the shares with different weights are superimposed and the sum of weights of all stacking shares is equal to or larger than $$ k $$ , the secret is then visually recognizable. Otherwise, no information about secret image is revealed. The proposed scheme is the first weighted $$ (k,n) $$ -threshold visual cryptography method with no pixel expansion. It is suitable for applications in the real life.
Data Mining Algorithms for a Feature-Based Customer Review Process Model with Engineering Informatics Approach

The data generated from online communication acts as potential gold mines for discovering knowledge for end users. Large amount of data is also generated in the form of web documents, emails, blogs, and feedback, etc. Text analytics and opinion mining are used to extract human thoughts and perceptions from unstructured texts. This paper proposes a method that focuses on analysing different classification and clustering algorithms aimed at extracting and consolidating opinions of customers from social media sites like Facebook, Twitter and through surveys, at multiple levels of granularity to monitor and measure customer satisfaction. This is an automated approach, in which algorithms aid in the process of knowledge assimilation identification and the analytics. Domain experts ratify the knowledge base and provide training data sets for the system to intuitively gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive, negative or neutral. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be congregated at any desired level of specificity i.e. feature level or product level, user level or service level, etc. It has been found that J48 classification algorithm and simple k-means clustering algorithm are most suitable for restaurant customer reviews.
Analysis of Differences in the Manner to Move Objects in a Real and Virtual Space

One crucial property of virtual reality (VR) is “self-projection”. This means that an avatar representing a user in a virtual space is the user itself with a higher level of reality. It can be referred to as a “sense of embodiment (SoE)”. Using head-mounted display (HMD), a three-dimensional (3D) virtual space that is generated by a computer with visual sense can be recognized. Besides, if the user can touch objects in the virtual space and feel the haptic sense on one’s hands using haptic devices, the SoE will undoubtedly increase. However, since the workspace of the user’s hand in using the haptic device has limitations, the task performed in the virtual space differs from the task performed in the real space. Therefore, in this paper, we evaluate the degree of agreement between the performance of a task in a virtual space and real space through experiments consisting of the same task. As the haptic device for virtual space we used SPIDAR-GCC, which is a type of parallel wire haptic device. In the real space, we asked seven research participants to move a tennis ball and a cola-can placed on a desk to a prescribed position. With regard to the experiments in the virtual space, we developed two 3D spaces where a tennis ball or a cola-can are placed on a desk. Then, using HMD and SPIDAR-GCC, we asked the participants to move these objects to a prescribed position. We recorded these tasks in the form of videos and analyzed them. The result of the analyses revealed that there are significant differences in the manner in which these objects were moved.
Deceptive Actions and Robot Collision Avoidance

The problem of collision avoidance is extensively studied in contemporary robotics. Efficiency and effectiveness of multirobot task cooperation and safety of human-robot interactions depend critically on the quality of the solution of the problem of collision avoidance. Classical path planning and obstacle avoiding approaches can not guarantee a sufficiently high quality of the solution of the problem of multirobot and human collision avoidance. So, to solve the problem with a sufficiently high quality, some special algorithms and approaches are needed. In this paper, we consider the notion of deception in context of the problem. We consider the possibility of generating and using deception to manipulate another robot. In particular, we consider two robots that solve their own tasks. In addition, we consider two robots one of which solves its own task and the second robot simulates the behavior of a pedestrian. We study the possibility of using fraud to improve the robot’s own performance and to improve safety of collision avoidance.
Fast Telemetry and Communication Scheme Based on Doppler Diversity Reception under Large Dynamic Doppler for Hypersonic Vehicles

Large dynamic Doppler caused by high-speed flight of hypersonic vehicles brings a great challenge to acquisition, tracking and correct demodulation of telemetry and communication signals. Different from receiving schemes of existing telemetry and communications, a novel communication method based on Doppler diversity reception for hypersonic vehicles was proposed in this study. Firstly, the proposed method offers a coarse acquisition of Doppler frequency offset and its rate-of-change by fast-coarse acquisition method, and then decreases the large Doppler frequency offset into a certain range through compensation. Finally, signals with residual Doppler frequency offset are processed directly by Doppler diversity reception method without a complicated tracing method. Different from the traditional receiving scheme with acquisition and tracking module, the proposed scheme lowers requirements on acquisition accuracy and neglects the accurate tracking module, thus shortening telemetry and communication time significantly for hypersonic vehicles. Simulation results demonstrate that the proposed method can achieve satisfactory performance when the residual Doppler is no higher than 1 kHz with normalized Doppler frequency offset and when the normalized artificial frequency shift is 0.01 and 0.4 respectively.
Deep learning advancements: closing the gap

This article explains how recent development in the field of Artificial Intelligence (AI) makes gap between human and machine smaller than ever before, by explaining and comparing traditional approach user in development of AI systems with new approach that has been used by AI system AlphaZero, developed by DeepMind. Traditionally AI systems have been tested in chess and the same has been done to demonstrate the power of AlphaZero. But, instead of playing against human, it played against the best (at the time) chess program Stockfish. While chess programs (before AlphaZero), were using powerful hardware and embedded built-in formal knowledge about the game, AlphaZero is using completely new approach, running on standard hardware and using deep learning. It learned about the game by playing a large number of games with itself, learning in the process. Article will also explain what is so revolutionary in AlphaZero approach to AI and how this new approach can be used in different areas of processing visual information, bio-medicine, autonomous driving, robotics and AI generated images/videos of humans.
Utilizing Apple’s ARKit 2.0 for Augmented Reality Application Development

When it comes to practical augmented reality applications, mobile platform tools are the most deserving. Thanks to the nature of mobile devices and their everyday usage, the ideal basis for this kind of content has inadvertently formed itself. Consequently, within the iOS development environment, Apple's Xcode program enables application development using the ARKit library which delivers a host of benefits. Amongst the plethora of advantages, this paper focuses on utilizing features such as the ability to measure distances between two points in space, horizontal and vertical plane detection, the ability to detect three-dimensional objects and utilize them as triggers, and the consolidated implementation of ARKit and MapKit libraries in conjunction with the Google Places API intended for displaying superimposed computer-generated content on iOS 11 and later iterations of Apple's mobile operating system.
Dealer: An Efficient Pricing Strategy for Deep-Learning-as-a-Service

Deep learning combining with cloud computing is a surging technology recently which is a new paradigm called DLAS (deep learning as a service). To supply good services, resource utilization and user performance must be considered and satisfied. In this paper, we formulate a competitive market between a provider and users in cloud computing. Then, we schedule multi-types computational virtualized resources including CPU, GPU and TPU to maximize the revenue of users and the provider. To find the optimal solution, we propose two efficient decision and pricing strategies called Dealer strategies for users and the provider, respectively. Finally, We evaluate our method compared with Elastic pricing strategy and Amazon EC2, and Dealer strategies can bring better revenue than others.
Interaction of Low Cost Mobile Virtual Reality Environments – Using Metaphor in an Astronomy Laboratory

The virtual reality VR has become a tool to simulate events and situations that would be inaccessible for people such as travel in space and visit different planets, it is for this reason that the VR has become a new alternative for the teaching of courses such as astronomy. In this research a low cost VR environment has been created for the teaching of astronomy where metaphors are used to teach complex concepts, working with professors and experts in the area of virtual reality to create an intuitive environment for new users to VR technologies, tests were conducted with two educational institutions of 150 students where a comparison of four virtual reality environments was made.
Auxiliary Tools for Enhanced Depth Perception in Vascular Structures

This chapter discusses the concept of Auxiliary Tools in depth perception. Four recent techniques are considered, that apply the concept in the domain of liver vasculature visualization. While an improvement is evident, the evaluations and conducted studies are found to be biased and not general enough to provide a convincing assessment. The chapter provides background information about human visual perception and a brief history on vascular visualization. Then four state-of-the-art methods are discussed. Finally, a comparative discussion points out objectives for future follow-up work.
Visualizations of the training process of neural networks

In this paper we present an approach to the visualizations of the neural networks training process. The main goal of such visualizations is to better understand the training process of neural networks, and to help with the creation and optimization of new neural networks. We implemented a tool which can be used to create a large number of visualizations by using different combinations of created transformations (for example: diff, plot2, angle-path, vec-dist-dev). To be able to visualize the learning process, we had to record all the weights from every n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> iteration. For experiments we used three neural networks: a neural network for digit recognition (DR), a neural network for word2vec representation of words (WV), and a neural network which learned the simple logical gate XOR, (XR). For the purpose of further comparisons, we also constructed one example of a damaged network (DR', WV', XR') for each of the three original networks (DR, WV, XR). We constructed over 30 visualizations and then chose 6 visualizations which could be easily interpreted and present them in this paper. The results indicate that there may exist some regularities in the visualizations of original networks and that damaged network visualizations differ from the original networks.
Design of Android Warehouse Management Software based on Web Service

In the light of existing problems of computer-side warehouse management system, this paper puts forward developing an Android software and introduces smart phones to warehousing management. System adopts C/S three-layer architecture including the database layer, the service layer and the user layer. The service layer uses Web Service technology to provide data exchange service for Android software and the database. As the supplement of computer-side warehouse management system, the android software has most functions of computer-side warehouse management system. It ensures workers conduct warehousing operations anywhere in the warehouse, increases the mobility of warehousing management and improves service level and efficiency of enterprise.
Testing for Unobserved Heterogeneity via <i>k-means</i> Clustering

Clustering methods such as k-means have found widespread use in a variety of applications. This article proposes a split-sample testing procedure to determine whether a null hypothesis of a single cluster, indicating homogeneity of the data, can be rejected in favor of multiple clusters. The test is simple to implement, valid under mild conditions (including nonnormality, and heterogeneity of the data in aspects beyond those in the clustering analysis), and applicable in a range of contexts (including clustering when the time series dimension is small, or clustering on parameters other than the mean). We verify that the test has good size control in finite samples, and we illustrate the test in applications to clustering vehicle manufacturers and U.S. mutual funds.
Improved free form evolution for angry birds structures

This paper presents an original approach based on evolutionary algorithms for building structures that are stable under gravity for the physics-based puzzle game Angry Birds, with the ultimate objective of creating Angry Birds levels with the minimum number of constraints. We have created a custom open source evolutionary computation library that implement an evolutionary algorithm whose main challenges have been to design a fitness function that, first, avoids when possible the time-consuming actual execution of the game, and, then, takes into account the different ways in which a structure is not sound and eliminates them. In order to test the method six experiments have been carried out, obtaining a variety of stable structures, which is the first path for the generation of levels that are aesthetically pleasing as well as playable.
Region-wise Modeling of Facial Skin Age using Deep CNNs

We propose a deep learning approach for predicting the apparent age of a person's skin. Our method works by first normalizing a frontal image of a face and cropping rectangular-shaped skin patches that are each normalized and fed into separately trained region-specific CNNs. Each regional CNN model is fine tuned using a novel data augmentation technique that artificially reduces the apparent age of the skin through a series of smoothing operations that act as a proxy for subjects with younger looking skin. The deep features extracted from each of these regions are then used to train a separate set of regression models that predict the skin age. We evaluate our method using two strategies: the first looks at how well the predicted regional skin age clusters around the true biological age of the subject, for which we achieve a 1-off accuracy of approximately 83%. In the second strategy, we validate that our models predict apparent skin age based on a user study that asked over 15 judges to compare image pairs of subjects with the same chronological age, but with different skin age predictions. For this second study, we achieve an average 66% accuracy based on consensus rating across all human raters, and as high as 76% for some age groups.
Early Stage Squamous Cell Lung Cancer Detection

Smoking and consuming alcohol leads to dangerous disease Squamous cell Lung Cancer (SqCLC). It is widespread all over the world today. The mortality rate of this cancer is on the higher side as it is diagnosed in stage III or IV. Small nodules are formed in the lungs in the starting stage, and gradually spreads in and around lung regions by a process of metastasis. Only few symptoms are seen in early stages of Cancer. Diagnosing Lung Cancer in the early stage is essential. The paper attempts to diagnose Lung Cancer in early stages by processing the Chest Computed Tomography (CT) image and segment the small lung nodules. The method uses the median filter to filter the noise and Watershed transform in combination with Morphology-based region of interest segmentation to fragment the nodules. Various metrics of the nodules in the image are calculated and the stage of Lung Cancer is determined.
Participatory Communication Approach for RD: Evidence from Two Grassroots CR Stations in Rural India

The traditional approach of communication for rural development (RD) was greatly influenced by the dominant paradigm of development. The retort against this paradigm gave birth to the participatory approach of communication wherein the common people in rural areas were considered as the ‘subjects’ of development in conjunction with their active involvement. It is the era when alternative communication medium like community radio (CR) was accepted as a tool of participatory RD in developing counters like India. Based on case studies of India’s pioneer CRs (Sangam Radio and Radio Bundelkhand), using media ethnography tools, a qualitative enquiry was carried out to explore its role in the process of RD by inclusion of voices of rural subalterns in their own development.
Evaluation Method of Teachers’ Teaching Ability Based on BP Neural Network

The evaluation of teachers’ teaching ability is an important part of educational activities, and a reasonable evaluation method plays an important role in improving teachers’ ability. At present, most of the evaluation methods used by schools and educational institutions are manually formulated some evaluation indicators. These methods are usually influenced by the personal preferences and the implementation is time-consuming. According to these problems, this paper proposes a method to evaluate teachers’ teaching ability based on BP neural network. Through constructing the templates, we extract teachers’ information and establish the knowledge base. Then a BP neural network is used to teaching ability evaluation. Finally, the experimental results prove the proposed method is effective.
CTRAS: a tool for aggregating and summarizing crowdsourced test reports

In this paper, we present CTRAS, a tool for automatically aggregating and summarizing duplicate crowdsourced test reports on the fly. CTRAS can automatically detect duplicates based on both textual information and the screenshots, and further aggregates and summarizes the duplicate test reports. CTRAS provides end users with a comprehensive and comprehensible understanding of all duplicates by identifying the main topics across the group of aggregated test reports and highlighting supplementary topics that are mentioned in subgroups of test reports. Also, it provides the classic tool of issue tracking systems, such as the project-report dashboard and keyword searching, and automates their classic functionalities, such as bug triaging and best fixer recommendation, to assist end users in managing and diagnosing test reports. Video: https://youtu.be/PNP10gKIPFs
Implementation of Flexible Displays for Smart Textiles Using Processes of Printed Electronics

Utilisation of light emitting diodes (LEDs) and printed electroluminescent elements for manufacturing of flexible displays to be integrated in textile items was analysed. The comparative investigation focused on the necessary manufacturing processes, on the architecture of driver electronics, on achievable display brightness, on lifetime expectations and reliability aspects of the systems. Printed electroluminescent display demonstrators were manufactured and integrated in jackets for the currently running field test. Description of the produced systems as well as the results of the comparative analysis are presented.
DBSCAN and CLARA Clustering Algorithms and their usage for the Soil Data Clustering

In this paper two clustering algorithms DBSCAN and CLARA were applied over the pedological database of Montenegro. Both algorithms clusterize data based on their density distribution. DBSCAN enables discovering clusters of arbitary shapes, without domain knowledge. On the other hand, CLARA forms clusters of approximatly equal size and shape for databases with uniformly spaced data. The used databases is composed of chemical and mechanical-physical parameters of soil samples. There are no clear transitions between different types of soil and large differences in values of their parameters at the boundary points of the clusters. Thus, CLARA is proved to be better for clustering pedologic data, which is confirmed by means of simulations. The results obtained by the CLARA are comparable with the results obtained by the analysis of soil in Montenegro by the expert.