Reviewerly/exHarmony huggingface dataset:
Increasing the Learning Efficiency in Decision-Making Field using the Workshop Activity in Moodle

The decision-making field is one of the most critical fields in education for entrepreneurs. Students must adopt different concepts, methods, and tools that improve both - their analytic and intuitive decision making. One of the most used decision-making methods is the analytic hierarchy process (AHP). Students must know how to recognize that a specific problem requires AHP analysis and to apply it correctly. This study presents the implementation of AHP education using the Workshop activity supported by the learning management system Moodle. In the first part of this activity, the students have to make a paper of AHP analysis of one real strategic or tactical problem related to any organization they choose: they have to structure the problem using the structuring methods, collect pairwise comparisons from decision-makers, calculate the group priorities and make the sensitivity analysis. In the second part of the activity, the students do peer assessment - they evaluate other students' projects using the rubrics and qualitative assessment. The analysis reveals better results in understanding the AHP when compared to traditional ways of learning (direct assessment of papers by teachers without peer assessment). The different ways of learning are evaluated by comparing the points achieved for the AHP papers, points achieved for the AHP test, and using a short survey - all of them are implemented in Moodle as well.
Decentralized Marketplace Using Blockchain, Cryptocurrency, and Swarm Technology

Over 1.8 billion people purchased goods online in 2018, and as a result, 2.8 trillion dollars were spent. Companies like Amazon, eBay, and PayPal thrive on being the middleman between sellers and buyers of online goods. Our project uses Blockchain Technology to decentralize the online marketplace and remove the middleman as well as the fees associated with it. To do this, we use smart contracts in the Ethereum Blockchain while maintaining a decentralized database using Swarm for the Webhosting. Finally, in order to phase out current online market systems, the same technology can be used to have a shared inventory, or ledger, between many marketplaces allowing manufacturers, or sellers, to announce their product freely among multiple existing Web site marketplaces concurrently.
A Human-like Strategy For Pervasive Service Composition

Insecurity is a common problem that all cities face, and different solutions have been developed throughout history to prevent or eradicate this problem. Using existing technology or developing new one is a good strategy; currently there has been technological breakthroughs, both in software and hardware, that allows us to use of Artificial Intelligence (AI) algorithms. Cognitive processes are studied in order to build solutions of problems in a way similar to what humans do. In this work, we propose the interaction among cognitive processes and info-communication devices to enable an artificial cognitive system to compose pervasive services useful to solve complex problems of public safety. Specifically, to deter predatory crimes when the predator decides to perform the crime and is within the victim's vicinity.
Introduction to Neuro Fuzzy Hybrid Model

Today, the use of intelligent techniques in medicine is becoming more common, and just to mention a few: neural networks, fuzzy logic and evolutionary computation Yang et al. in Eng Optim 46: 1222–1237, 2014 []; Yu and Li in Appl Soft Comput 30: 614–627 (2015) []; Meng et al. in J Exp Theor Artif Intell 28: 673–687 (2016) []; Gopinathannair and Olshansky in F1000Prime Rep 7: 60 (2015) []; Wilson in Tex Heart Inst J 32: 616 (2005) []; Lai et al. in Texas Heart Inst J 44(5): 350–351 (2017) []. The main idea of the book is to describe a fuzzy neural hybrid model that can make a diagnosis, which can be fast and accurate and, therefore, it is necessary to have a 24-h patient monitoring database.
Leukemic cell detection model based on deep learning

Abstract This project examines the feasibility of building a deep learning model that can accurately distinguish leukemic cells from normal cells, as well as the viability of analyzing the black box model so as to explain it in human terms. An accurate and explainable model can be deployed in areas previously inaccessible to the limited existing methods of detecting leukemia in its early stages. It is acknowledged that this is a very difficult process, as cells from individuals vary and cancerous cells are highly morphologically similar to normal cells. There are two major steps to this project. First, we built the cell classification model by supervised training on a convolutional neural network with provided labeled training data. Many parameters had to be adjusted to yield better results when tested on separate testing data. Second, we created a program designed to help explain the neural network, and the model is inserted into the program and tested on multiple examples. We examined the results, which highlighted regions the algorithm deems important in making its decision, and attempted to explain the algorithm as much as possible. Though far from perfect, the results are intriguing. Our algorithm had an accuracy of 68% on the testing data set. This is clearly suboptimal; however, this is also significantly higher than random results, demonstrating that our algorithm learnt useful processes and that this research direction is worth exploring further. The main difficulty is the aforementioned similarity between leukemic and normal cells; thus, the training process must be complex and fine-tuned to yield good results. After analyzing the results from the explanation program, we determined that our algorithm focuses on the contour of cells in its decision-making process, especially unsmooth or protruded sections. To conclude, our algorithm is far from the level required for practical applications; however, we demonstrated that such algorithms are possible and can be explained.
AFDM : an Analytical Framework of Dialogue Manager

A computer system which provides the possibility of human- like conversation with the system for users, is called dialogue system. A dialogue system consists of different components. Dialogue manager is the core component of every dialogue system composed of Dialog State Tracker and Policy Learning. A dialogue management system is able to direct a conversation between agents, including both human and computer. A variety of models have been developed for dialogue manager. This paper provides an Analytical Framework for Dialogue Managers (AFDM) which overview wide range of techniques applied for developing dialogue manager component. AFDM framework works in three parts including: 1) a classification of current approaches applied to dialogue management task, 2) introducing the evaluation measures and 3) analytical evaluations. Also, we discuss the main qualitative properties of each approach. This framework is designed in order to pave the way for future research with the aim of a) improving current models, b) selecting a method based on its properties, or c) comparing future strategies.
Selecting goals in oversubscription planning using relaxed plans

Planning deals with the task of finding an ordered set of actions that achieves some goals from an initial state. In many real-world applications it is unfeasible to find a plan achieving all goals due to limitations in the available resources. A common case consists of having a bound on a given cost measure that is less than the optimal cost needed to achieve all goals. Oversubscription planning (OSP) is the field of Automated Planning dealing with such kinds of problems. Usually, OSP generates plans that achieve only a subset of the goals set. In this paper we present a new technique to a priori select goals in no-hard-goals satisficing OSP by searching in the space of subsets of goals. A key property of the proposed approach is that it is planner-independent once the goals have been selected; it creates a new non-OSP problem that can be solved using off-the-shelf planners. Extensive experimental results show that the proposed approach outperforms state-of-the-art OSP techniques in several domains of the International Planning Competition.
SAI: Sports Analysis and Informatics

The video analysis of basketball games is important because there is a growing interest in the professional and high school community to constantly improve players’ skills. As the cameras are now ubiquitous, there is enough visual data of the game, thus fostering research in the analysis of the games’ data. Automated algorithms that characterize different aspects of the game will be extremely useful for coaches as well as players. This paper presents a computer vision technique to analyze the placement of players in a 3-point shot, thus allowing for post-game analysis by coaches/players to quantify necessary information for improvement. Our focus is on the study of offensive players’ placement during a 3-point shot made by a particular player. In total, we analyzed 4 high school players and 1 NBA player in multiple games across a recent full season. Our results are encouraging, suggesting the use of our system for real-time monitoring of games by coaches/players and also for post-game analysis.
Research on Unmanned Motion Planning Algorithm for Urban Roads Based on Multiple Condition Screening

With the rapid development of driverless driving technology, higher requirements have been placed on the motion planning algorithms of unmanned vehicles. This paper proposes an unmanned motion planning algorithm for urban roads based on multiple condition screening. This algorithm screens a part of the trajectories through the initial condition screening, then performs cost function calculation and ranking on the remaining trajectories, and add them to the set of alternative trajectories. The purpose is to improve the calculation efficiency of the subsequent cost function. Considering that the cost function and the proportional coefficient of the traditional algorithm for obstacle avoidance require a lot of manual experiment adjustment and selection and may miss the optimal solution. This paper cancels the use of the obstacle avoidance cost function and performs obstacle collision detection and screening in the alternative trajectories set. The purpose is to obtain the optimal trajectory in the set without collision under the same sampling density. Simulation results show that the algorithm can achieve the task of driverless vehicle motion planning on urban roads. Compared with the traditional algorithm, the algorithm is more efficient, avoids manual adjustment of parameters, and is closer to the optimal solution with higher reliability.
Towards Development of Comprehensive Framework for Evaluation of Potential Consequences of Cyber-Attacks

Evaluation and analysis of different architectures of information systems attract attention of both, researchers and practitioners, especially when security requirements are considered and analyzed. On the other side, there is no systematic framework which enables evaluation of potential consequences and impacts of cyber-attacks and malicious activities on the system. Much more attention is put on creating preventive and proactive measures, thus keeping systems capable to respond when attach occurs. In this paper we present innovative comprehensive framework for evaluation of systems from the aspect of potential consequences and impacts, built upon well-known approaches and principles in different fields of software engineering, requirement eliciting and cyber security.
Twitter Data Curation Primer

This work was created as part of the “Specialized Data Curation” Workshop #3 held at Washington University in St. Louis, MO on November 5-6, 2019.
Occlusion Detection for Video Inpainting Coherence Problem

The amazing interest for all inclusive appropriation and utilization of picture and video content our different system has forced the computerized purchaser hardware industry to dispatch a practically endless assortment of electronic gadgets equipped for gaining, preparing, altering and putting away these appealing kinds of substance. A PC vision procedure is proposed to evacuate an undesirable item in a video arrangement for computerized handling. As of late different enhancements have been utilized in video creation. We propose a novel system of video inpainting which applies to both static and free moving camera recordings. The technique can be utilized for object evacuation, mistake covering and foundation reproduction. The proposed approach will decrease the calculation unpredictability and furthermore give outwardly satisfying outcomes.
Well-being by Formula? Exploring the Role of Somaesthetics in Making Self-tracking More Sustainable

Self-tracking, a phenomenon which dataify individuals’ daily activities and experiences, presents both opportunities and challenges to understand which factors and in what ways may influence individuals’ health status or well-being. Technology can enhance the era of personal happiness and increase social well-being. We hope to provide a design principle and operational design method to promote the well-being consideration of self-tracking products and technologies. In order to propose such principles and methods, it is necessary to first analyze the key factors of happiness in using self-tracking products. This article proposes “The Self-tracking Sustainability Formula”, which includes three elements as information, well-being drives, and material. Such a formula could possibly conceptually explain the components of a self-tracking product with sustainable potential. On this basis, this paper further explores how self-tracking products can positively influence issues such as environmental protection, personnel management, and friendly social relations from the dimensions of “information” and “well-being drives” to form more sustainable. Production and consumption ecology. This paper argues that, in order to enhance such a process of sustainable well-being, we as user researchers and product designers must design better ways of visualizing and interacting with “datafied well-being”. From the citations of dashboards, we draw upon both the principles of Somaesthetics to propose an “avatar” as the central interface component. An avatar, or a humanized animal image, is proposed to provide a more intuitive understanding of one’s physical, emotional, and social well-being, looking forward to helping self-tracking products become more sustainable by considering sustainable design principles and forms in the design process and motivating users to be more willing to accept sustainability Self-tracking products.
Information Exchange in Public-Culture Service Domain Bason on CIEM

This paper proposes a novel framework for information exchange in public-culture service domain, which leverages: (i) a widely available translation language (i.e., eXtensible Markup Language (XML)), and (ii) a consistent implementation methodology framework provided for within the China Information Exchange Model (CIEM). The evaluation about public-culture effectiveness measurement mission, demonstrates the efficiency of the proposed framework.
Lexical-semantic configuration of ordinary relational identities in multicultural groups of university students

The terms used to designate ordinary relational identities seem easy to learn and translate. However, these interpersonal identities reflect complex mental constructs that are very sensitive to the variables that interact in human communication, particularly cultural variation. This paper describes a pilot project developed at the University of Valencia aimed at exploring the meaning differences in these identities within multicultural university groups, in order to help students improve their social and academic communication skills. The results reveal significant differences in their pragmatic meanings. Therefore, linguistic, intercultural and cognitive adjustment processes become necessary for their adequate understanding and translation.
GENs: generative encoding networks

Mapping data from and/or onto a known family of distributions has become an important topic in machine learning and data analysis. Deep generative models (e.g., generative adversarial networks) have been used effectively to match known and unknown distributions. Nonetheless, when the form of the target distribution is known, analytical methods are advantageous in providing robust results with provable properties. In this paper, we propose and analyze the use of nonparametric density methods to estimate the Jensen-Shannon divergence for matching unknown data distributions to known target distributions, such Gaussian or mixtures of Gaussians, in latent spaces. This analytical method has several advantages: better behavior when training sample quantity is low, provable convergence properties, and relatively few parameters, which can be derived analytically. Using the proposed method, we enforce the latent representation of an autoencoder to match a target distribution in a learning framework that we call a generative encoding network. Here, we present the numerical methods for bandwidth estimation; derive the expected distribution of the data in the latent space; show the advantages over the adversarial counterpart; study the properties of the latent space such as entropy, sample generation, interpolation; and demonstrate the application of the method in the real world.
Identifying unpredictable test examples with worst-case guarantees

Often times, whether it be for adversarial or natural reasons, the distributions of test and training data differ. We give an algorithm that, given sets of training and test examples, identifies regions of test examples that cannot be predicted with low error. These regions are classified as ? or equivalently omitted from classification. Assuming only that labels are consistent with a family of classifiers of low VC dimension, the algorithm is shown to make few misclassification errors and few errors of omission in both adversarial and covariate-shift settings. Previous models of learning with different training and test distributions required assumptions connecting the two.
Personalizing 3D Free-Hand Input for Intuitive Smartphone Augmented Reality Interactions

My research goal is to build practical and intuitive 3D free-hand interactions for augmented reality (AR) on smartphones and to explore relevant behavioral-data-driven interaction techniques. In service of that goal, I have developed two preliminary AR systems on the smartphone: a Portal-ble system powered by visual, auditory, and haptic feedback and a set of grabbing accommodations that allows the user's hand to interact with AR contents directly. I have also developed a Portalware system that incorporates a smartphone-wearable interaction scheme to expand visual feedback beyond the smartphone's display and have leveraged this system to improve 3D mid-air sketching. My next steps are to use behavioral data such as device motion, hand motion and user postures to create dynamic and personalized interaction systems that facilitate intuitive AR interaction. Ultimately, these individually-tailored systems unlock new interaction possibilities for the general public and expand the usage scenarios for smartphone AR applications.
Sentiment Analysis of Bank BNI User Comments Using the Support Vector Machine Method

The rapid development in the world of information and communication technology has made social media users increase. By looking at various kinds of social media, it is always filled with a variety of service users such as the use of mobile-based banking applications. In Indonesia, almost all banking services use banking facilities such as BNI. By looking at the phenomena that occur in these problems, a study was conducted on comments related to BNI mobile application-based services that are used to improve and update the quality of BNI services to customers so that they can compete with other banks. Thus the researcher aims at classifying the existing BNI Mobile Banking Application user comments on the Google Play service into positive and negative comment sentiment by applying the Support Vector Machine Media method which aims to improve and renew the BNI Mobile Banking Application service system to provide service satisfaction to users BNI. In research conducted using k-fold cross-validation testing obtained SVM kernel linear accuracy values of 78,19% for 60% data training and 40% data testing, meanwhile for 80% data training and 20% data testing get accuracy 76,94% and SVM kernel linear using K-Fold Cross Validation the highest value of 78,45% at 10 fold Cross-Validation. This algorithm has a lightweight computation as evidenced by a dataset of 580 data which only takes 2.5 seconds. K-Fold Cross Validation is proven to be able to optimize a test that was previously worth 78,19% with K-Fold Cross Validation rising to 78,45%
Minimum Time Search in Unmanned Aerial Vehicles using Ant Colony Optimisation based Realistic Scenarios

Unmanned aerial vehicles (UAV), or drones, are aircrafts without a human pilot on board. UAVs find a target in minimum time using Minimum Time Search (MTS) methods. Different optimisation paradigms, such as cross-entropy optimisation (CEO) and ant-colony optimisation (ACO) can be used for MTS. In this work, a set of simulation scenarios has been designed to test the ACO solution to the MTS problem. Simulations performed for each scenario take into account a heuristic function and its effect on the probability of detection of target and estimated time for detection. The results obtained for various scenarios based on external and internal factors in UAV trajectory planning (size of search grid, target distribution, etc.) are compared to categorise the best set of such factors across four input domains. Results show a huge variance in the role played by the heuristic function and choice of feature thresholds for each scenario.
Search for Wino-Bino Production Using the Emulated Recursive Jigsaw Reconstruction Technique with Run 2 Data

The previous chapter, Chap. 8 , introduces the Emulated Recursive Jigsaw Reconstruction (eRJR) technique, developed to emulate the RJR technique using simplified, laboratory frame variables. This technique, defined and validated in Sect. 8.3 , reproduces the three-lepton excesses in the low-mass region and ISR regions in the laboratory frame using $$36.1~\text{ fb}^{-1}$$ of pp collision data collected between 2015 and 2016 by the ATLAS detector at the LHC.
Performance analysis of Gaussian entangled quantum illumination target detection

Quantum illumination makes use of the strong correlation between entangled photons, making it possible to detect targets that break through physical limits. The Gaussian entangled state is a continuous variable entangled state with a high number of Hilbert spatial modes, which is currently a viable alternative to quantum lighting. In order to explore the target detection ability of Gaussian entangled state, this paper starts from the Gaussian entanglement model of Lloyd, establishes the target detection error probability boundary model under Gaussian entangled quantum illumination, and explores the optimal detection condition of Gaussian entangled state based on the detection signal-to-noise ratio and the number of measurements. The results show that when the Gaussian entangled state is optimal for the coherent state, the number of measurements is inversely proportional to the signal-to-noise ratio. Under the same error probability, the difference between the detection distances of the two states is determined by the atmospheric attenuation coefficient. The smaller the attenuation coefficient is, the smaller the attenuation coefficient is, the difference is greater.