Reviewerly/exHarmony huggingface dataset:
Link Prediction in Signed Networks

Signed networks represent the real world relationships, which are both positive or negative. Recent research works focus on either discriminative or generative based models for signed network embedding. In this paper, we propose a generative adversarial network (GAN) model for signed network which unifies generative and discriminative models to generate the node embedding. Our experimental evaluations on several datasets, like Slashdot, Epinions, Reddit, Bitcoin and Wiki-RFA indicates that the proposed approach ensures better macro F1-score than the existing state-of-the-art approaches in link prediction and handling of sparsity of signed networks.
On Connectedness of Discretized Sets

Constructing a discretization of a given set is a major problem in various theoretical and applied disciplines. An offset discretization of a set X is obtained by taking the integer points inside a closed neighborhood of X of a certain radius. In this note we determine a minimum threshold for the offset radius, beyond which the discretization of an arbitrary (possibly disconnected) set is always connected. The results hold for a broad class of disconnected subsets of $$\mathbb {R}^n$$ , and generalize several previous results.
Study on Assessing User Experience of Augmented Reality Applications

With the development of the augmented reality technology and the popularisation of smartphones, the application of augmented reality based on mobile devices demonstrates an optimistic development prospect. The current development of mobile augmented reality is mainly technology-oriented, mostly emphasises on technology advancement as a basis of measure while placing insufficient emphasis upon user experience. User-centric design is increasingly important in the design of mobile applications. As it is crucial to quantify and evaluate user experiences of AR application to gain insight in pivotal areas for future development, which this research proposes that the application of the Delphi-AHP method is capable of identifying those areas via five first-level indicators and 20 second-level indicators. This method is tested and verified with six model display applications, which discovered the most important first-level indicators to affect user experience is a system’s functionality and its display.
Intelligent Automated Text Processing System - An NLP Based Approach

The various organizations or information technology industries usually adopt multiple systems having their own database and associated logs. Every data can be easily exported into excel for reliable and easy access. The scenario of data storage can be seen as at most 1,048,576 rows and 16,384 columns in an excel worksheet and facilitate the huge storage of data/information. However, it is indeed a difficult task to extract the information which is most useful from such data, especially for spreadsheet neophytes. There exists various techniques to address the issue, where in the Natural Language Processing techniques provides a feasible solution and intelligent automation with which the above mentioned task can be made easier in a significant way. This paper has proposed, designed, and implemented a system that comprises of natural language techniques for retrieval of spreadsheet information and the technologies behind storing and retrieving information from the database are outlined. The results justifies the system implementation.
Usability Evaluation of Short Dwell-Time Activated Eye Typing Techniques

Gaze-based interfaces introduce dwell time-based selection method to avoid the Midas Touch problem - it is a fixed amount of time the users must fixate their gaze upon an object before it is selected. In gaze-based text typing, spending such time on each character key composition effectively decreases the overall eye typing rate. Researchers proposed several interaction mechanisms to minimize or diminish the dwelling in the desktop environment, however, they lack in understanding the usability of such mechanisms for regular users, specifically in a constrained eye tracking setup. We conducted a within-subject usability evaluation of four such representative short dwell-time activated eye typing techniques. The results of the first-time usability study, longitudinal study and subjective evaluation conducted with 15 participants confirm the superiority of controlled eye movement-based advanced eye typing method (Adv-EyeK) compare to the other techniques.
A Mechanism to Generate Voice for Speech Impaired Through Hand Gesture

Speech is a major form of communication among human beings. An impairment to the speech generation components such as the brain, larynx and vocal tract diminishes its generation. The speech impairment forces the person to communicate through gestures and expressions which may not be understood well to other individuals. Here, we propose an artificial speaking system for the people with speaking difficulties. It is based on capturing the information from gestures through flex sensors and accelerometers. For every gesture, there is a specific meaning which is used for day to day communications. The message is kept in a voice playback and recorder integrated circuit. For every action, the flex sensors give signals to the microcontroller. The artificial voice is generated by pre-loaded speech through loudspeakers.
Teaching to Find Design Opportunities for Behavior Change Through Causal Layered Analysis

Change is exponential. Products and services are developed faster, hold a shorter shelf-life disrupted by new offerings, and exist within global challenges such as climate change and sustainability. We live in the Anthropocene Era with human-induced Climate Change and the sixth mass extinction. Design challenges for product/service creation are shifting from a paradigm of customer-centric focus linear system to a new paradigm that includes a broader planetary context with a circular economy. The Human-Centered Design Thinking process popularized by IDEO/Stanford d.school identifies human-centered design opportunities for products and services through five steps: empathize, define, ideate, prototype, and test. Unfortunately, a mere customer-centric view of product or service is insufficient for planetary-scale challenges requiring a broader focus that includes all life forms and the planet’s health. Thus, moving forward, design for the 21st century requires broader perspectives; design educators are challenged to overcome the limitations of Human-Centered Design Thinking. The point of this paper is that Human-Centered Design Thinking needs to be used in conjunction with new design methods that take into account the larger planetary context over time. In this paper, I discuss how futures thinking methods can augment design thinking methods to overcome limiting human-centered worldviews, epistemologies, and ontologies. In particular, how Causal Layered Analysis (CLA) can augment designers’ creative responses to behavior change challenges such as rapid-decarbonization through four layers (e.g., litany, systems, worldview, myths/metaphors). We provide a case study on CLA from design courses taught to both undergraduate and graduate design students.
T-norms driven loss functions for machine learning

Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data. These approaches can potentially learn competitive solutions with a significant reduction of the amount of supervised data. A large class of neural-symbolic approaches is based on First-Order Logic to represent prior knowledge, relaxed to a differentiable form using fuzzy logic. This paper shows that the loss function expressing these neural-symbolic learning tasks can be unambiguously determined given the selection of a t-norm generator. When restricted to supervised learning, the presented theoretical apparatus provides a clean justification to the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by a neural-symbolic method. Therefore, the methodology allows the development of a novel class of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature.
Graph Cuts Based Tomography Enhanced by Shape Orientation

The topic of this paper includes graph cuts based tomography reconstruction methods in binary and multi-gray level cases. A energy-minimization based reconstruction method for binary tomography is introduced. This approach combines the graph cuts and a gradient based method, and applies a shape orientation as an a priori information. The new method is capable for reconstructions in cases of limited projection view availability. Results of experimental evaluation of the considered graph cuts type reconstruction methods for both binary and multi level tomography are presented .
Visual Saliency: How Text Influences

Posters are widely used a powerful tool for communication. They are very informative but are normally viewed for only 3 s, which calls for efficient and effective information delivery. It is thus important to know where people would look for posters. Saliency models could be of great help where expensive and time-consuming eye-tracking experiment isn't an option. However, current datasets for saliency model training mainly deal with natural scenes, which makes research on saliency models for posters difficult. To address this problem, we collected 1700 high-quality posters as well as their eye-tracking data where each image is viewed by 15 participants. This could be the groundwork for future research in the field of saliency prediction for posters. It is noticeable that posters are rich in texts (e.g. title, slogan, description paragraph). The various types of texts serve respective functions, making some relatively more important than others. Nevertheless, the difference is largely neglected in current studies where researchers put same emphasis on all text regions, and the problem is especially crucial when it comes to saliency model for posters. Our further analysis of the eye-tracking results with focus on text offers some insights into the issue.
Cognitive modeling of the development of complex managed systems

Abstract The purpose of this research is to form a set of methodological provisions related to the use of cognitive modeling in the management of complex systems in conditions of high dynamism of studied processes. The rapid development of information and analytical tools provides new opportunities for applying cognitive modeling to a wider range of systems. The methodological basis of the study is the system methodology and its direct implementation is based on the system sequence “mission – goal – functions - structure – behavior – result”. Cognitive modeling in the management of complex systems takes into account their hierarchical structure, which entails the hierarchy of the corresponding models, which is directly reflected in the generated models or considered when building local models for systems of a certain hierarchy level. Complex systems also determine the complexity of the models that represent them. Therefore, the paper proposes stratification of the modeling process and its results on five levels: the representation model, the understanding model, the interaction model, the management model and the behavior model. The dynamics of simulated systems and their external environment assumes an iterative (cyclic) nature of cognitive modeling. The paper presents a structural cognitive model of managing the functioning and development of complex systems as its result.
Discriminative sparsity preserving projection via global constraint for unconstrained face recognition

The unconstrained face images collected in the real environments include many complicated and changeable interference factors, and sparsity preserving projections cannot well characterise the low-dimensional intrinsic structure embedded in the high-dimensional unconstrained face images, which is important for subsequent recognition task. To deal with this problem, in this study the authors propose a new dimensionality reduction method named as discriminative sparsity preserving projection via global constraint. It seeks an optimal sub-space in which the samples in intra-classes are as compact as possible, while the samples in inter-classes are as separable as possible by adopting the compactness constraint terms of reconstruction coefficients and the penalty terms of global distribution. Extensive experiments are carried out on Faces in Labeled the Wild database and PubFig database which are two representative unconstrained face sets, and the corresponding experimental results illustrate the effectiveness of the proposed method.
An Analysis of the Current Policies for Social Media Use in Saudi Higher Education

This paper presents a research study that investigated and analyzed the status of social media policies at Saudi Arabian higher education institutions (HEIs). This study is the first attempt to review social media policies in 68 Saudi universities and colleges. Social media policies published on the institutions websites were collected. This study focuses on the readability, accessibility, and content coverage of these policies. In addition, the study identified potential gaps concerning learning and teaching with social media policies, as well as how well these policies meet student and faculty needs. A summary of the current status of social media policies at Saudi HEIs and recommendations for improving the current status were presented as the result of this study.
Synthesis of Incremental Linear Algebra Programs

This article targets the Incremental View Maintenance (IVM) of sophisticated analytics (such as statistical models, machine learning programs, and graph algorithms) expressed as linear algebra programs. We present LAGO, a unified framework for linear algebra that automatically synthesizes efficient incremental trigger programs, thereby freeing the user from error-prone manual derivations, performance tuning, and low-level implementation details. The key technique underlying our framework is abstract interpretation, which is used to infer various properties of analytical programs. These properties give the reasoning power required for the automatic synthesis of efficient incremental triggers. We evaluate the effectiveness of our framework on a wide range of applications from regression models to graph computations.
Reversible Computations in Logic Programming

In this work, we say that a computation is reversible if one can find a procedure to undo the steps of a standard (or forward) computation in a deterministic way. While logic programs are often invertible (e.g., one can use the same predicate for adding and for subtracting natural numbers), computations are not reversible in the above sense. In this paper, we present a so-called Landauer embedding for SLD resolution, the operational principle of logic programs, so that it becomes reversible. A proof-of-concept implementation of a reversible debugger for Prolog that follows the ideas in this paper has been developed and is publicly available.
Evaluation of Leap Motion Controller Usability in Development of Hand Gesture Recognition for Hemiplegia Patients

A hand gesture recognition system is developed for hemiplegia patients to undergo rehabilitation which can encourage patients’ motor function. The Leap Motion controller has been studied to detect human hand motion for development of hand gesture controlled robotic arms. It was shown that the Leap Motion sensor is useful to obtain the coordinate position and orientation of each human finger, palm and wrist movements. A set of test program has been designed using healthy hand to investigate the accuracy and reliability of the sensor. The test results show the effectiveness of the device in the recognition of the human hand gestures with a high accuracy rate of 100% for opening and closing of hand, 97.61% for whole hand tapping and 99.6% for right movement while 98.71% for left movement of whole hand lateral rotation.
Specializing Word Embeddings (for Parsing) by Information Bottleneck (Extended Abstract)

Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser. We compress each word embedding to either a discrete tag or a continuous vector. In the discrete version, our automatically compressed tags form an alternative tag set: we show experimentally that our tags capture most of the information in traditional POS tag annotations, but our tag sequences can be parsed more accurately at the same level of tag granularity. In the continuous version, we show experimentally that moderately compressing the word embeddings by our method yields a more accurate parser in 8 of 9 languages, unlike simple dimensionality reduction.
Making Object Detection Available to Everyone—A Hardware Prototype for Semi-automatic Synthetic Data Generation

The capabilities of object detection are well known, but many projects don't use them, despite potential benefit. Even though the use of object detection algorithms is facilitated through frameworks and publications, a big issue is the creation of the necessary training data. To tackle this issue, this work shows the design and evaluation of a prototype, which allows users to create synthetic datasets for object detection in images. The prototype is evaluated using YOLOv3 as the underlying detector and shows that the generated datasets are equally good in quality as manually created data. This encourages a wide adoption of object detection algorithms in different areas, since image creation and labeling is often the most time consuming step.
Semi-Supervised Graph Neural Network with Probabilistic Modeling to Mitigate Uncertainty

Probabilistic Graph Neural Network (PGNN) introduced here proposes a machine learning application that combines deep learning on graphs with probabilistic modeling, to account for uncertainty in both inputs and outputs. Graph neural network models (GNNs) have been extensively used in modeling graph-structured data. However this class of models learn point estimates conditioned on arbitrary weights initializations, which can lead to poor convergence and sub-optimal generalization. By enhancing deep learning with Bayesian modeling, PGNN aims to strengthen the prediction accuracy, and provide more stable predictions. In order to achieve this goal, PGNN learns a distribution over network weights and encodings, thus solving the epistemic and aleatoric uncertainty inherited in network parameters and model predictions. Through Markov Chain Monte Carlo Random Walk Metropolis sampling of weights and likelihood distributions, PGNN essentially generates an ensemble of models in one iteration, computing an estimate of credible intervals over the predictions. PGNN is applied to the task of semi-supervised node classification and tested on 3 benchmark datasets, yielding improved accuracy, and more stable predictions when compared with standard GNNs and other Bayesian graph models.
The Performance Evaluation of SSVEP-BCI Actuated Wheelchair with Parameter Setting of Time-Window Length and Stimulation Layout

Due to the advantages of prominent brain responses, less training duration, and robustness to artifacts, steady-state visual evoked potential (SSVEP) based braincomputer interface (BCI) has been recently applied to intelligent wheelchair navigation. Recent researches mainly focused on the implementation of a specific recognition method to analyze the wheelchair navigation performance under fixed stimulation target number. The navigation performance evaluation under different recognition methods and different BCI target numbers is rarely compared. In this paper, the navigation performance comparison between different numbers of stimulation targets and different recognition methods was carried out. The multichannel integrated GT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">circ</inf> and canonical correlation analysis (CCA) recognition methods were used to compare the wheelchair navigation performance under different time-window (TW) lengths. And the 3-target and 4-target arrangement layouts were adopted to compare the navigation performance between the “No feedback” and the “Feedback” conditions. Experimental results showed that the GT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">circ</inf> recognition method with TW length of 2 s would promote the navigation efficiency and different stimulation target number could be tailored for different wheelchair application purposes, which would bring tutorial instructions to the applicable navigation of the BCI-actuated wheelchair.
Equality Checking for General Type Theories in Andromeda 2

We designed a user-extensible judgemental equality checking algorithm for general type theories that supports computation rules and extensionality rules. The user needs only provide the equality rules they wish to use, after which the algorithm devises an appropriate notion of normal form. The algorithm is a generalization of type-directed equality checking for Martin-Löf type theory, and we implemented it in the Andromeda 2 prover.
Towards Practical Use of Bedside Sensing/Voice-Calling System for Preventing Falls

The authors have been studying a bedside sensing/voice-calling system for older adults to prevent falls. The basic tasks of the system are as follows: i) monitoring the behavior of an older adult on a bed, ii) voice-calling the older adult by using a bedside robot agent when detecting any activity that leads to leaving the bed, iii) letting the older adult to postpone leaving the bed with the voice-call, and iv) launching nurse calls to allow nurses/caregivers to attend to the older adult, before the older adult leaves the bed.
Hypertext as a Tool for Exploring Personal Data on Social Media

Social networks such as Facebook are required to provide users with their personal data. However, these dumps do not provide users insight in overarching themes in their online behavior. In this poster, we discuss the development of Mother, a spatial hypertext system for visual data exploration. First insights include that the less obvious connections are more interesting and relevant to the user than very close semantic or temporal connections.