Reviewerly/exHarmony huggingface dataset:
Blockchain E-Voting Done Right: Privacy and Transparency with Public Blockchain

Some forms of voting have been here ever since. Mostly used form all over the world are paper ballots. Electronic voting schemes are being popular only in the last decade and they are still unsolved. E-voting schemes bring problems mainly regarding security, credibility, transparency, reliability, and functionality. Estonia is the pioneer in this field and may be considered the state of the art. But there are only a few solutions using blockchain. Blockchain can deliver an answer to all of the mentioned problems and furthermore bring some advantages such as immutability and decentralization. The main problems of technologies utilizing blockchain for e-voting are their focus on only one field or lack of testing and comparison. In this paper, we present a blockchain-based e-voting platform, which can be used for any kind of voting. It is fully utilized by blockchain and all processes can be handled within it. After the start of the voting, the platform behaves as fully independent and decentralized without possibilities to affect the voting process. The data are fully transparent, but the identity of voters is secured by homomorphic encryption. We have tested and compared our solution in three different blockchains. The results show, that both public and private blockchains can be used with only a little difference in the speed. The key novelty of our solution is a fully decentralized management of e-voting platform through blockchain, transparency of the whole process and at the same time security and privacy of the voters thanks to homomorphic encryption.
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation

Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature. The current I2I translation approaches require training images from the two domains that are either all paired (supervised) or all unpaired (unsupervised). In practice, obtaining paired training data in sufficient quantities is often very costly and cumbersome. Therefore solutions that employ unpaired data, while less accurate, are largely preferred. In this paper, we aim to bridge the gap between supervised and unsupervised I2I translation, with application to semantic image segmentation. We build upon pix2pix and CycleGAN, state-of-the-art seminal I2I translation techniques. We propose a method to select (very few) paired training samples and achieve significant improvements in both supervised and unsupervised I2I translation settings over random selection. Further, we boost the performance by incorporating both (selected) paired and unpaired samples in the training process. Our experiments show that an extremely weak supervised I2I translation solution using only one paired training sample can achieve a quantitative performance much better than the unsupervised CycleGAN model, and comparable to that of the supervised pix2pix model trained on thousands of pairs.
Multi-Domain Effects and the Internet of Battlefield Things

This paper reviews the definitions and characteristics of military effects, the Internet of Battlefield Things (IoBT), and their impact on decision processes in a Multi-Domain Operating environment (MDO). The aspects of contemporary military decision-processes are illustrated and an MDO Effect Loop decision process is introduced. We examine the concept of IoBT effects and their implications in MDO. These implications suggest that when considering the concept of MDO, as a doctrine, the technological advances of IoBTs empower enhancements in decision frameworks and increase the viability of novel operational approaches and options for military effects.
Engineering Slam as a Project of Popularizing Sciences and Engineering Competencies

This paper deals with the Engineering Slam Project that was conceived and is being implemented at the engineering university in order to awake students’ interest in engineering professions and in new trends in science, engineering, and applied areas. Engineering Slam represents sessions with experts, in the course of which informal communication takes place, as well as direct dialogs, discussions, and answers to questions. The speakers present their developments and their businesses. This is a kind of a “battle” among the speakers, resulting in assessing all presentations and identifying the best ones. Engineering Slam allows demonstrating to young people all the facets of scientific and engineering activities in the context of the digitalization of economy and industry 4.0, push the boundaries of research and project-related thought in engineering areas, and involve school, college and university students into the process of technology entrepreneurship and in commercializing the results obtained.
A Dynamic Simulation Environment for Container-based Cloud Data Centers using ContainerCloudSim

With the addition of the new type of cloud service known as Container as a Service (CaaS), the need for allocation and scheduling policies as well as modeling and simulation containerization environment for testing has increased. CloudSim simulation tool, which is a framework for modeling and simulation of cloud computing infrastructures and services, has provided an extension called ContainerCloudSim as a testing environment for containers. However, there are still gaps in the simulation that need to be addressed in order to provide accurate modeling for the virtualized container environment. In this paper, we focus on filling these gaps by adding three main features to the CloudSim tool. The first feature is providing datacenters with the capability of supporting both virtualization techniques: virtual machines and containers. The second feature is having a dynamic simulation and application cloudlets that are scheduled dynamically through the simulation time and the third feature is supporting the simulation of applications with dependent tasks. A test case is provided to show the contribution of our proposal, which provides a more realistic testing environment and increases the number of allocation and scheduling algorithms that could be tested upon CloudSim.
Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata. Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications. In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph.
Spectral representation learning for one-step spectral rotation clustering

Conventional spectral clustering is generally divided into two main steps, 1) constructing a reliable spectral representation by similarity matrix or Laplacian matrix; 2) performing K-means clustering on the spectral representation. In this paper, we propose a novel spectral clustering algorithm to improve clustering performance by separately improving these two steps in a same learning framework. Specifically, we first utilize two dimensionality reduction methods to learn the robust low-dimensional spectral representation, and a hypergraph structure to make the spectral representation keep the high-order relation of original data. Furthermore, a spectral rotation clustering method is embedded into the spectral representation learning model to conduct one-step clustering, which effectively reduces the deviation of clustering and obtains a reliable clustering performance. Besides, an effective optimization algorithm is further proposed to solve the objective problem to have a fast convergence. Experimental analysis on real data sets showed that our proposed clustering method outperformed the classical and state-of-the-art spectral clustering methods in terms of four frequently-used clustering metrics.
3D-CariGAN: An End-to-End Solution to 3D Caricature Generation From Normal Face Photos

Caricature is a type of artistic style of human faces that attracts considerable attention in the entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model that generates high-quality 3D caricatures directly from a normal 2D face photo. The most challenging issue for our system is that the source domain of face photos (characterized by normal 2D faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and textures). To address this challenge, we: (1) build a large dataset of 5,343 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space; (2) reconstruct a normal full 3D head from the input face photo and use its PCA representation in the 3D caricature shape space to establish correspondences between the input photo and 3D caricature shape; and (3) propose a novel character loss and a novel caricature loss based on previous psychological studies on caricatures. Experiments including a novel two-level user study show that our system can generate high-quality 3D caricatures directly from normal face photos.
A Survey on Hyperspectral Imaging for Mineral Exploration using Machine Learning Algorithms

Economic growth of the country mainly depends on the mineral and energy sources. In recent years, there is an increased pressure to reduce the environmental and social impact through the mineral exploration. The data from remote sensing satellite play a vital role and is capable of detecting minerals resources. Hyperspectral remote sensing is an effective tool for mineral exploration applications, as it has been providing significant development over the past three decades. In this paper, an up-to-date and focused review of mineral mapping techniques using the Hyperspectral Imaging (Hyperion data) is provided. A key factor on the success of hyperspectral imaging is the spatial, spectral and temporal resolution and the capability of covering large surfaces of earth using the modern sensor technologies. This review mainly focuses on the fundamentals of hyperspectral imaging and Analytical Imaging & Geophysics (AIG) techniques for mineral mapping. The procedures to be followed in the ENVI software, which is used to analyse and process the Hyperion data, determine the exact locations of different minerals using AIG classification techniques are provided. This review can be a useful baseline for future research in mineral exploration using hyperspectral image analysis.
Unsupervised Context-Sensitive Bangla Spelling Correction with Character N-gram

We propose an unsupervised context-aware spelling error detection and correction method for traditional Bangla written script. The context-aware capability is introduced by generating a representation of information from the context words and using the concept of cosine similarity of find the best candidate for the misspelled words. We used fast-text embedding concept of using character n-gram embedding. The character n-gram embedding is capable of generating embedding for unknown words, which is a capability missing in any conventional methods. We performed our experiments on Bangla language collected from popular newspapers, blogs, Bangla Wikipedia and Banglapedia. Our proposed method outperforms conventional methods.
Automated Prediction of Item Difficulty in Reading Comprehension Using Long Short-Term Memory

Standardized tests are an important tool in education. During the test preparation process, the difficulty of each test item needs to be defined, which previously relied on expert validation or pretest for the most part, requiring a considerable amount of labor and cost. These problems can be overcome by using machines to predict the difficulty of the test items. In this study, long short-term memory (LSTM) will be used to predict the test item difficulty in reading comprehension. Experimental results show that the proposed method has a good prediction for agreement rate.
Towards Image-Based Dark Vendor Profiling

Anonymity networks, such as Tor, facilitate the hosting of hidden online marketplaces where dark vendors are able to anonymously trade paraphernalia such as drugs, weapons, and hacking services. Effective dark marketplace analysis and dark vendor profiling techniques support dark web investigations and help to identify and locate these perpetrators. Existing automated techniques are text-based, leaving non-textual artifacts, such as images, out of consideration. Though image data can further improve investigative analysis, there are two primary challenges associated with dark web image analysis: (a) ethical concerns over the presence of child exploitation imagery in illegal markets, and (b) the computational overhead needed to download, analyze, and store image content. In this research, we investigate and address the aforementioned challenges to enable dark marketplace image analysis. Namely, we examine image metadata and explore several image hashing techniques to represent image content, allowing us to collect image-based intelligence and identify reused images among dark marketplaces while preventing exposure to illegal content and decreasing computational overhead. Our study reveals that approximately 75% of dark marketplace listings include image data, indicating the importance of considering image content for investigative analysis. Additionally, 2% of considered images were found to contain metadata and approximately 50% of image hashes were repeated among marketplace listings, suggesting the presence of easily obtainable incriminating evidence and frequency of image reuse among dark vendors. Finally, through an image hash analysis, we demonstrate the effectiveness of using image hashing to identify similar images between dark marketplaces.
Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods.
An Interface for Agent Supported Conversational Search

Recent years have seen a growth in interest in the development of conversational search systems intended to support users in their information seeking activities using incremental iterative dialogue-based search. This work has primarily focused on communication of information exclusively via spoken dialogue. While this is sufficient for simple question type queries, it is an inefficient means of engagement for more complex or exploratory queries. We introduce a prototype multi-view search interface which combines a conversational search assistant with an extended standard graphical search interface.
EnLoB: Energy and Load Balancing-Driven Container Placement Strategy for Data Centers

Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing DCs are often underutilized. To be precise, almost 30% of the installed DCs in the United States are comatose. In addition to this, the existing DC architecture leads to extensive energy utilization, which severely hampers the environment and places a severe risk on the power sector. Thus, it is highly essential to reduce DC's energy utilization through efficient resource consolidation approaches. In this work, we investigate the joint impact of resource consolidation and load balancing on cutting down the energy utilization indices of the cloud DCs. In this vein, we formulate a multi-objective optimization problem (MOOP) for container placement across heterogeneous infrastructure, primarily with the intent to minimize the overall energy consumption and balance the load amongst the operating hosts. However, due to the hardness of the underlying problem and its infeasibility to furnish optimal solutions in polynomial time, we designed an online solution based on the incremental exploration of the solution space to map containers on the available array of hosts such that the objectives mentioned above can be attained. Finally, we evaluated the performance of the proposed algorithm in contrast to an existing algorithm on real-time workload traces obtained from PlanetLab. The obtained results confirm the superior performance of the proposed algorithm relative state-of-the-art.
A novel weighted TPR-TNR measure to assess performance of the classifiers

Assessing performance of different classifiers and selecting the best one is one of the most important tasks in classification problem. The assessment of classifiers becomes more complex when dataset is imbalanced because most of the frequently used performance metrics can be misleading. Many real world classification problems such as fraud detection, churn prediction, medical diagnosis, and cyber-security suffer from the problem of imbalanced datasets. Therefore, in all such classification tasks it is very important to select the best classifier very carefully. In this study we propose new weighted TPR-TNR measure to assess performance of the classifiers. The proposed measure takes into consideration imbalance ratio of the dataset and assign different weights to the TPR and TNR to assess classifiers performance. We have used five different datasets to assess performance of twelve different classifiers using weighted TPR-TNR measure and compared it with the existing measures. The experimental results show that the weighted TPR-TNR measure is more suitable to assess performance of the classifiers when dataset is imbalanced.
Embodied Self-Aware Computing Systems

Embodied self-aware computing systems are embedded in a physical environment with a rich set of sensors and actuators to interact both with their environment and with their own embodiment. Through this interaction, they learn about their situation, their own state, and their performance. Although they are application specific like traditional embedded systems (ESs), they are significantly more flexible, robust, and autonomous; they can adapt to a wide range of environmental variation and can cope with deterioration and shortcomings of their own performance. As such, embodied self-aware computing systems are an evolution of traditional embedded and cyber-physical systems into the direction of more autonomy, robustness, and flexibility. When traditional ESs operate in a changing world by demanding unchanging and fully characterized computing resources, embodied self-aware computing systems adapt to a changing world and changing computing resources. This article surveys the methods and methodologies used for embodied self-aware computing systems structured along with the faculties of: 1) sensory observation and abstraction; 2) self-aware assessment; and 3) hierarchical goals and control. The discussion is exemplified by application cases in the areas of systems-on-chip, control systems, health monitoring, and condition monitoring in industrial production systems.
Remote Cooling Sensation Presentation Controlling Mist in Midair

In this study, we propose a method to display a cooling sensation using ultrasound-driven cold air flow cooled by mist vaporization. So far, noncontact thermal display using ultrasound-driven cold air flow has been reported, and the system uses dry ice as cold air source. Since this system limits the range of practical application, we use mist instead of dry ice to extend the application. In fact, we demonstrated transporting cold air to a localized spot on user's skin and it was shown that the cooling effect was higher in case using mist. In this paper, we construct a system to enhance the cooling effect of mist vaporization and measured the temperature change of the user's skin using thermography camera. We confirm that this system performance is higher than previous method.
Improving Requirement Prioritization and Traceability using Artificial Intelligence Technique for Global Software Development

Global software development (GSD) organizations encouraged to improve software quality of products. The GSD used highly skilled and advance technology at one place through the internet. The main challenges face in GSD with benefits are communication, coordination and control. These issues create ambiguities and incompleteness in requirements prioritization and traceability process due to lots of stakeholders and human efforts involved. Therefore, we proposed a framework to improve requirements prioritization and traceability process using artificial intelligent technique. The framework was evaluated using experiment and compared with existing solutions. Results described that proposed framework significantly improved requirements prioritization and traceability with less human interaction to reduce GSD challenges.