Reviewerly/exHarmony huggingface dataset:
Human Learning in Data Science

As machine learning becomes a more and more important area in Data Science, bringing with it a rise of abstractness and complexity, the desire for explainability rises, too. With our work we aim to gain explainability focussing on correlation clustering and try to pursue the original goals of different Data Science tasks,: Extracting knowledge from data. As well-known tools like Fold-It or GeoTime show, gamification is a very mighty approach, but not only to solve tasks which prove more difficult for machines than for humans. We could also gain knowledge from how players proceed trying to solve those difficult tasks. That is why we developed Straighten it up!, a game in which users try to find the best linear correlations in high dimensional datasets. Finding arbitrarily oriented subspaces in high dimensional data is an exponentially complex task due to the number of potential subspaces in regards to the number of dimensions. Nevertheless, linearly correlated points are as a simple pattern easy to track by the human eye. Straighten it up! gives users an overview over two-dimensional projections of a self-chosen dataset. Users decide which subspace they want to examine first, and can draw in arbitrarily many lines fitting the data. An offset inside of which points are assigned to the corresponding line can easily be chosen for every line independently, and users can switch between different projections at any time. We developed a scoring system not only as incentive, but first of all for further examination, based on the density of each cluster, its minimum spanning tree, size of offset, and coverage. By tracking every step of a user we are able to detect common mechanisms and examine differences to state-of-the-art correlation and subspace clustering algorithms, resulting in more comprehensibility.
An Automatic System for Learning and Dialogue Based on Assertions

When someone says a statement about a particular subject, we memorize the assertion and, implicitly, we can construct all the possible questions that have as a right answer to the assertion just heard. This means that, in this specific case, our learning process based on assertions subsists. When we read a book, we do nothing but learn through a succession of assertions. In this article, we present a system for automatically constructing a conversational agent, which uses only assertions to build the dialog engine. The whole architecture is based on the “Robot Operating System” (ROS), and the experiments were conducted using a humanoid robot.
Towards a Rich and Dynamic Human Digital Memory in Egocentric Dataset

Memories have always been a considerable importance of a person’s life and experiences. A digital human memory as a field of study focuses on encapsulating this phenomenon, in digital form, during the thread of a lifetime. By spreading hardware everywhere, massive amount of data is being generated together by people and the surrounding environment. With all this demountable information available, successfully exploring, researching and collating, together, to form a human digital memory, is a new challenge and requires novel and efficient algorithmic solutions. The main goal of this work is going to propose a new method to automatically create rich and dynamic human digital memory in egocentric dataset from the life logging images of a person. For this purpose, we will propose a technique using Convolutional Neural Network (CNN) model. For validation, we will apply the proposed method on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons.
Artificial Intelligence to Detect Timing Covert Channels

The peculiarities of the batch data transmission networks make it possible to use covert channels, which survive under standard protective measures, to perform data leaks. However, storage covert channels can be annihilated by means of limiting the flow capacity, or by use of encryption. The measures against storage covert channels cannot be implemented against timing covert channels (TCCs), otherwise their usage has to be conditioned by certain factors. For instance, while packet encryption an intruder still possesses the ability to covertly transfer the data. At the same time, normalization of inter-packet delays (IPDs) influences the flow capacity in a greater degree than sending fixed-length packets does. Detection can be called an alternative countermeasure. At the present time, detection methods based on artificial intelligence have been widespreadly used, however the possibility to implement these methods under conditions of a covert channel parametrization has not been investigated. In the current work, we study the possibility to implement artificial intelligence for detecting TCCs under conditions of varying covert channel characteristics: flow capacity and encoding scheme. The detection method is based on machine learning algorithms that solve the problem of binary classification.
Visualization in Reproducible Science

The increasing prevalence of Open Science has brought reproducibility to the center of discussion of the scientific community as a requirement for ensuring the transparency and correctness of a research workflow. The current publishing landscape is evolving, as shown by the emergence of notebook technologies powering a new generation of interactive Web Journals. These use state-of-the-art interactive graphical visualizations and on-demand data processing to research papers, allowing readers to trace every step of the process, from raw data to the finalized visualization. Since there are many Research Notebook technologies and interactive graphical visualization solutions to choose from, we present a summary comparative overview of Web Journals and the Notebook engines that power the interactive, data driven visualizations inside their publications. Given our focus on visualization, our metrics are the support for the most advanced, popular and widely adopted data visualization frameworks. We conclude that Jupyter Notebook is currently the best alternative for the average user, given its popularity and support, combined with broad support for powerful and high-level interactive visualization grammars.
HCI Design Principles and Visual Analytics for Media Analytics Platform

Media industries and advertisers are increasingly turning to big data analytics to better understand audience media consumption patterns, as evidenced by Canada’s Globe and Mail’s applications Sophi and TasteGraph [1, 2]. Data analytics and interface design provide complementary perspectives for large datasets. HCI design principles have been applied to Business Intelligence (BI) platforms, including techniques which filter and summarize large data sets, and are equally relevant to media informatics platforms for advertisers, buyers, sellers, and planners [1, 7–9]. This analysis becomes more challenging when managing highly scalable and multi-dimensional audience survey data [3–6]. According to Dewdney and Ride visualization tools are essential for effective decision making in the communications industry as these ease cognitive load and decision-making [15]. Kirk proves that a visualization system becomes a successful tool when it builds on the user’s extant domain knowledge, providing enhanced insights [13]. The research aims to leverage visualization design principles as defined by Tulp and Meirelles [6, 7] and in order to improve the UI/UX and visual analytic capabilities of a leading media analytics platform providing planners, advertisers, and media buyers with an interface to better understand their audience. We have analyzed and assessed the different application report parameters that explore television and radio survey datasets from a leading analytics firm. We propose design prototypes which are comprised of enhanced symbolic icons [9] through badges and glyphs, consistent colours [10], and layouts which maintain a visual hierarchy and filtration techniques [10, 11, 14] in order to minimize information clutter and cognitive overload. We propose a variety of interface designs that address user needs using HCI, heuristic design principles and novel visualization techniques [6, 7, 12, 15]. Next steps include validating our design prototypes through rigorous user testing and building high fidelity prototypes.
Automatic Image Enhancement from a Mobile Synthetic Vision System

The work discusses basic image contrasting algorithms and noise compensation methods, an algorithm for estimating image quality based on an integral quality indicator, as well as approaches for estimating noise values in images. The results of contrasting algorithms work (with a numerical estimation) and the most prominent image filtering methods are presented. A description is given for an automatic image enhancement algorithm from a mobile synthetic vision system based on a choice of contrasting algorithms using an integral quality indicator, and a space-time filter using a pyramidal version of Lucas-Kanade optical flow algorithm is proposed.
Automated design of random dynamic graph models

Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. Examples include modeling the evolution of relationships and communities in a social network or tracking the activity of users within an enterprise computer network. In the case of static graph representations, random graph models are often useful for analyzing and predicting the characteristics of a given network. Even though random dynamic graph models are a trending research topic, the field is still relatively unexplored. The selection of available models is limited and manually developing a model for a new application can be difficult and time-consuming. This work leverages hyper-heuristic techniques to automate the design of novel random dynamic graph models. A genetic programming approach is used to evolve custom heuristics that emulate the behavior of a variety of target models with high accuracy. Results are presented that illustrate the potential for the automated design of custom random dynamic graph models.
Employing Cross-genre Unstructured Texts to Extract Entities in Adapting Sister Domains

This paper presents a method to employ unstructured texts of different domains for extracting entities from the closely related domains. To investigate the domain closeness, we trained our model in one domain and test in rest of the domains. The second challenge lies in the fact that if we retrain our model using a pair of closely related domains, we achieve better results while testing on texts from mixed genre in contrast to those individual genres. Working on the idea of domain adaptation, we carried out experiments with single-domain and mixed-domain training data and observed the precision, recall, and f-score of our entity extraction system. The performance of our system was optimal in a semi-supervised dataset with closely related or sister domains.
OPDisp - Open Architecture for Extensible Public Displays

Currently public displays have many functionalities, but still present several challenges with respect to content producing and user interaction. Most are non-interactive, only used for static information dissemination, while others rely on tactile displays adding interactivity, but limiting to single-user interactions. Displays that resort to mobile devices to promote engagement and enhance user experience usually require the installation of specific applications. This work proposes an open architecture for the production and deployment of interactive presentations: content on the mobile device complements the information presented on the display. We developed a prototype and conducted a real world experiment to evaluate the usability of the system, achieving good preliminary results.
EPT: EDNS Privacy Tunnel for DNS

DNS privacy concerns are growing. Recursive resolvers such as ISP DNS and Google Public DNS are serving massive clients, which could fingerprint individual users and analysis the domain interest of users easily. In order to mitigate user privacy leaks on recursive resolvers, in this paper we propose an EDNS privacy tunnel (EPT) extension for DNS. EPT can hide the query domain name from recursive resolvers through public key encryption, avoid big data analysis on individual users, defense against censorship and lying recursive resolvers.
A system for managing classes with a large number of participants

This paper describes a system that enables complete managing and tracking of project-based classes with a large number of students and teachers. The system provides a complete platform solution from matching up students with mentors, tracking student activities and automatic presentation group creation. This paper also presents a novel approach for matching students and mentors. Matching is made using single- and two-sided algorithms with preference list. Once matched, students with different mentors are grouped by an advanced method so that each new group consists of a similar number of students with a high, average and low academic score.
Experience based decisional DNA to support smart product design

This paper presents the idea of Smart Virtual Product Development (SVPD) system to support product design. The foundations of the system are based upon smart knowledge management techniques called Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). It enhances the industrial pr oduct development process by using the previous experiential knowledge gathered from the formal decisional activities. This experiential knowledge is collected from the group of similar products having some common functions and features. The developed system comprises of three modules: design knowledge management (DKM), manufacturing capability analysis and process planning (MCAPP), and product inspection planning (PIP). The working of design knowledge management module is presented in this study and is validated by using an industrial case study, which suggests that it is capable of capturing and reusing the required design knowledge for material selection process. The developed system has the capability to facilitate decision making and mistake proofing during early stages of product design. It can be beneficial for small and medium enterprises (SMEs) involved in product development.
Delegated Preparation of Quantum Error Correction Code for Blind Quantum Computation

The universal blind quantum computation protocol allows a client to delegate quantum computation to a remote server, and keep information private. Since the qubit errors are inevitable in any physical implementation, quantum error correction codes are needed for fault-tolerant blind quantum computation. In this paper, a quantum error correction code preparation protocol is proposed based on remote blind qubit state preparation (RBSP). The code is encoded on the brickwork state for fault-tolerant blind quantum computation. The protocol only requires client emitting weak coherent pulses, which frees client from dependence on quantum memory and quantum computing.
Fear of Living

Currently, immersive environments have shown considerable promise in being used in mental health treatment. The holodeck is a space where the physical and virtual become indistinguishable and could be seen as the ultimate extension of these systems. This holonovel looks at some of the possibilities that holodeck technology could provide in dealing with mental health and psychological issues in the future. With this potential comes ethical and other challenges which the holonovel also considers.
Using genetic programming for combining an ensemble of local and global outlier algorithms to detect new attacks

Modern intrusion detection systems must be able to discover new types of attacks in real-time. To this aim, automatic or semi-automatic techniques can be used; outlier detection algorithms are particularly apt to this task, as they can work in an unsupervised way. However, due to the different nature and behavior of the attacks, the performance of different outlier detection algorithms varies largely. In this ongoing work, we describe an approach aimed at understanding whether an ensemble of outlier algorithms can be used to detect effectively new types of attacks in intrusion detection systems. In particular, Genetic Programming (GP) is adopted to build the combining function of an ensemble of local and global outlier detection algorithms, which are used to detect different types of attack. Preliminary experiments, conducted on the well-known NSL-KDD dataset, are encouraging and confirm that, depending on the type of attacks, it would be better to use only local or only global detection algorithms and that the GP-based ensemble improves the performance in comparison with commonly used combining functions.
Combining artificial neural networks and evolution to solve multiobjective knapsack problems

The multiobjective knapsack problem (MOKP) is a combinatorial problem that arises in various applications, including resource allocation, computer science and finance. Evolutionary multiobjective optimization algorithms (EMOAs) can be effective in solving MOKPs. Though, they often face difficulties due to the loss of solution diversity and poor scalability. To address those issues, our study [2] proposes to generate candidate solutions by artificial neural networks. This is intended to provide intelligence to the search. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional search operators base on a binary crossover. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.
Theory: Foundations of Quality in Natural and Synthesized Speech

Speech is one of the most important modes to communicate and interact in human–human interaction (HHI). It contains semantic and pragmatic meaning, often in an underspecified and indirect way, by referencing to situational and world knowledge.
Hair Recognition Based on Multi-task Convolution Network

Aiming at the problem that the prior art is difficult to utilize the mutual relationship between labels to achieve efficient classification of hair style multi-labels, a hair styling classification method based on multi-task convolutional neural network is proposed., a multi-task joint learning model was constructed to try to realize the simultaneous recognition of hair shape and color. First, all the labels are jointly learned through the shared network layer, and the correlation between labels is automatically mined and utilized from the perspective of feature extraction. Then complete specific categories of learning tasks at different sub-network layers, thereby eliminating ambiguity in multi-label classifications. Finally, multiple classifiers are trained to achieve parallel prediction of all labels. The research shows that the proposed method can extract multiple features of the hair at the same time and directly classify and identify it. Compared with the single task network, the precision has obvious advantages.
Social Sentiment Analysis Using Classifiers and Ensemble Learning

Abstract Instant message applications has become the trend of present social network for connecting people nowadays. With such massive information transmitted between end users, it may provide valuable hidden knowledge for data analysing and help us to obtain some insights of how the messages can influence current social trends. During the analyses, the author uses several different classifiers to conduct sentiment collection and use Ensemble Learning to aggregate the results. As a result, the author maps data about users’ sentiment graph in database of given category.
Evaluation of Immersive Interfaces for Tactical Decision Support

The benefits and limitations of immersive technologies in military decision-making are not well understood. Here, we describe the framework of an experiment which seeks to empirically determine the effects of immersive and non-immersive technology on decision-making. In this experiment, users are shown tactical spatial information about a building layout and told they must decide which of three pre-determined breach points is optimal for maximizing mission success and minimizing risk to the ground team. To ensure observable effects are related to immersion and not simply perception of depth, we deploy a between-subjects design with three viewing conditions: data shown in 2D on a desktop display, data shown in 3D on desktop display, and data shown in 3D in a head-mounted display (HMD). Dependent variables include decision accuracy, time to task completion, decision confidence, and score on the System Usability Scale. In the VR version of the experiment, full telemetry is captured to track when and for how long users interacted with specific information in the scenario environment. Pilot results suggest that tracking these metrics will allow for intricate comparison of decision-making behaviors between display types.
Research of Mobile Recommender Service Based on User Preferences and Social Tags

In current mobile recommendation service field, it is a hot spot that how to solve the complicated mobile information overload problem of the mobile environment. In the Web3.0 era, social tags are sufficient to describe users' interest preferences and implicit features, and become an effective alternative to important factors such as user preferences, contextual information, and context-aware attributes. Therefore, a tag recommendation algorithm based on user preferences and social tags (UPST-TB) with time weights is proposed. Firstly, normalized the mobile information of multidimensional data onto social tags data, making it a single data problem; Secondly, depth-weighted analysis of user-interest tag-resource triplet by using TF-IDF, weighted centrality, and heat conduction diffusion pre-processing method. Finally, recommendation by using the mobile recommendation service model under the smart network. The experimental results show that the performance of the proposed algorithm has significant improvement.
Human-Food Interaction Framework: Understanding Student-Athletes’ Extreme Food Needs

The food and kitchen technology industry is quickly growing and changing as user lifestyle preferences shift. This shift is arguably occurring most rapidly in Silicon Valley. There has been tremendous growth in every aspect of the food process, from food delivery services to cooking robots and automation to the ingredients themselves. However, are such food and kitchen technologies addressing the future needs of users? To understand the future needs of Silicon Valley users, we decided to look at extreme users: student athletes. By interviewing six athletes at Stanford University with extreme food needs, we gained insights on the broader future of food. To analyze the needs, we developed a preliminary Human-Food Interaction (HFI) Framework, which allowed us to understand the overall user journey and the specific user needs in each step of this journey (ex. delivery, storage). After analyzing needs, we categorized Silicon Valley food tech services into the different steps in HFI framework. As a result, we found that there is a significant gap between extreme user needs, which are indicative of future needs, and the services currently available in the market.