Reviewerly/exHarmony huggingface dataset:
How People Judge the Usability of a Desktop Graphic User Interface at Different Time Points: Is there Evidence for Memory Decay, Recall Bias or Temporal Bias?

Abstract The System Usability Scale (SuS) survey is a widely respected tool for measuring usability. Generally, a SuS score is administered directly after a usability test to assess the usability and user experience of digital products. However, some researchers have used SuS as a survey as part of longitudinal ‘in the wild’ trials where SuS is often completed some period after the trial. The aim of this research was to determine if a participant’s memory of a product’s usability would change if a SuS survey was administered at different times after a test. Hence, we sought to understand if recalling the usability of a digital technology was affected by temporal bias or memory decay. This paper includes results and findings from two studies, study 1 involved evaluating a web application and study 2 involved evaluating a virtual learning environment. Collectively the two studies had 212 participants (n = 212). The findings conclude that there is no significant change of the user’s recollection of the usability of digital product as evidenced by an analysis of users who completed multiple SuS surveys over a short term period of 3 weeks or over an extended period of time of 6 months. RESEARCH HIGHLIGHTS The system usability scale is used to test for memory decay and temporal bias in judging the user experience of technologies at different time points. 212 participants took part in two studies ranging from 3 weeks to 6 months. There is no evidence that there is a temporal bias or memory decay when users complete a SuS survey at the two different time points of 3 weeks and 6 months.
Interactive Mapping Specification with Exemplar Tuples

While schema mapping specification is a cumbersome task for data curation specialists, it becomes unfeasible for non-expert users, who are unacquainted with the semantics and languages of the involved transformations. In this article, we present an interactive framework for schema mapping specification suited for non-expert users. The underlying key intuition is to leverage a few exemplar tuples to infer the underlying mappings and iterate the inference process via simple user interactions under the form of Boolean queries on the validity of the initial exemplar tuples. The approaches available so far are mainly assuming pairs of complete universal data examples, which can be solely provided by data curation experts, or are limited to poorly expressive mappings. We present a quasi-lattice-based exploration of the space of all possible mappings that satisfy arbitrary user exemplar tuples. Along the exploration, we challenge the user to retain the mappings that fit the user’s requirements at best and to dynamically prune the exploration space, thus reducing the number of user interactions. We prove that after the refinement process, the obtained mappings are correct and complete. We present an extensive experimental analysis devoted to measure the feasibility of our interactive mapping strategies and the inherent quality of the obtained mappings.
Scaling Multinomial Logistic Regression via Hybrid Parallelism

We study the problem of scaling Multinomial Logistic Regression (MLR) to datasets with very large number of data points in the presence of large number of classes. At a scale where neither data nor the parameters are able to fit on a single machine, we argue that simultaneous data and model parallelism (Hybrid Parallelism) is inevitable. The key challenge in achieving such a form of parallelism in MLR is the log-partition function which needs to be computed across all K classes per data point, thus making model parallelism non-trivial. To overcome this problem, we propose a reformulation of the original objective that exploits double-separability, an attractive property that naturally leads to hybrid parallelism. Our algorithm (DS-MLR) is asynchronous and completely de-centralized, requiring minimal communication across workers while keeping both data and parameter workloads partitioned. Unlike standard data parallel approaches, DS-MLR avoids bulk-synchronization by maintaining local normalization terms on each worker and accumulating them incrementally using a token-ring topology. We demonstrate the versatility of DS-MLR under various scenarios in data and model parallelism, through an empirical study consisting of real-world datasets. In particular, to demonstrate scaling via hybrid parallelism, we created a new benchmark dataset (Reddit-Full) by pre-processing 1.7 billion reddit user comments spanning the period 2007-2015. We used DS-MLR to solve an extreme multi-class classification problem of classifying 211 million data points into their corresponding subreddits. Reddit-Full is a massive data set with data occupying 228 GB and 44 billion parameters occupying 358 GB. To the best of our knowledge, no other existing methods can handle MLR in this setting.
Autoencoders for Educational Assessment

In educational assessment research, a common goal is to determine students’ knowledge about some construct. This knowledge is latent and can be represented by continuous variables which influence the individual’s performance on a test. Item response theory (IRT) models structure this relation, defining specific functions between the knowledge of the individual, and the probability of answering an item correctly. Previous research implies that neural networks can emulate these models, and, with a modification in its architecture, overcome some of the limitations concerned to “big data” analysis. In this work, we compare two different types of neural networks for this application: autoencoders (AE) and variational autoencoders (VAE). Not only can these neural networks be used as similar predictive models, but they can recover and interpret parameters in the same way as in the IRT approaches.
Magical Realism and Augmented Reality

We describe the development and implementation of a 7-month-long project which used a series of creative workshops designed in collaboration with a cultural institution and conducted with children to draw influences from magical realist literature into the development AR applications. The project culminated in the release of an AR app for Android and iOS platforms, Magical Reality. After describing the design and implementation of the research we discuss its findings as they support the two facets of our contribution to DIS: First, we assess our attempts to apply inspiration, derived from workshopping ideas from magical realist literature with children to the design of AR experiences, making recommendations for future design practice seeking to include comparable influences. Second, we consider the degree to which our workshops were successful in combining specialist knowledges from across the different departments of a cultural organization to answer sectoral challenges and describe both advantages and challenges for future collaborative work.
The Basic Processes of Creating a “Megascience” Project

This paper is devoted to the study of the basic processes for the implementation of “megascience” projects and to ensure the possibility of their effective implementation using management information systems. All stages of the “megascience” project are inherent in all sorts of risks that may arise from the lack of accounting for resources and the limitations of the project. The work showed that the “megascience” project is a large and complex system, each of the stages of the project life cycle is an independent project and it can be considered as a business project, the result of which can be a commercially viable installation that has no analogues in the World, or new knowledge and technology. There is also showed the need to create an appropriate integral information management system of the “megascience” project. An approach to the creation of such a system was also proposed, and 12 sets of tasks of information systems were considered, requiring solutions for its creation.
Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier

Neuroimaging investigation is an essential parameter to detect infarct lesion in stroke patients. Precise detection of brain lesions is an important task related to impaired behavior. In this paper, we aimed to develop an automatic method to segment and classify infarct lesion in diffusion-weighted imaging (DWI) of brain MRI. The method includes hybrid fuzzy [Formula: see text]-means (HFCM) clustering in which the structure of [Formula: see text]-means clustering is modified with rough sets and fuzzy sets to improve the segmentation performance with self-adjusted intensity thresholds. Quantitative evaluation was carried out on 128 MRI slices of brain image collected from ischemic stroke patients at the Department of Radiology, IMS and SUM Hospital, Bhubaneswar. The informative statistical features have been extracted using gray-level co-occurrence matrix (GLCM) and used to classify the types of stroke infarct according to the Oxfordshire Community Stroke Project (OCSP) classification. The parameters such as accuracy, Dice similarity index (DSI) and Jaccard index (JI) were utilized to evaluate the effectiveness of the proposed method in detecting the stroke lesions. The segmentation method achieved the average accuracy, DSI and JI of 96.8%, 95.8% and 92.2%, respectively, in support vector machine (SVM) classifier. The obtained results are higher in terms of random forest (RF) classification. With a high Dice coefficient of 0.958 and other evaluated parameters, the proposed method outperforms earlier published results.
Near Earth Asteroid Scout CubeSat Science Data Retrieval Optimization Using Onboard Data Analysis

Small spacecraft are continually evolving in capability and mission complexity. As spacecraft size decreases, physical limitations present new challenges for mission designers. These include limited instrument aperture, low communications bandwidth, and reduced attitude control. Software techniques can address these limitations to retain the capabilities of larger spacecraft, in a small form factor. These techniques move the first order science analysis, which is traditionally completed on the ground, onboard the spacecraft. This can minimize the amount of data volume required for first order decision making. We present a collection of techniques designed for mitigating limited pointing stability for target acquisition, onboard image calibration and decision making, in a low bandwidth environment. These technologies will fly on the Near Earth Asteroid Scout (NEA Scout) interplanetary CubeSat mission in 2019 [1]. To support verification and validation, the flight software implementations of these algorithms have been run on New Horizons, Rosetta and terrestrially acquired data sets. These experiments validate the capability to detect the target, through noise and target location uncertainty.
Four-Direction Search Scheme of Path Planning for Mobile Agents

Summary This paper presents a neural network-based four-direction search scheme of path planning for mobile agents, given a known environmental map with stationary obstacles. Firstly, the map collision energy is modeled for all the obstacles based on neural network. Secondly, for the shorted path-search purpose, the path energy is considered. Thirdly, to decrease the path-search time, a variable step-length is designed with respect to collision energy of the previous iteration path. Simulation results demonstrate that the variable step-length is effective and can decrease the iteration time substantially. Lastly, experimental results show that the mobile agent tracks the generated path well. Both the simulation and experiment results substantiate the feasibility and realizability of the presented scheme.
Let’s put a smile on that scale: Findings from three web survey experiments

Graphical symbols such as smileys and other emoticons are prevalent in everyday life. Paralleling their increasing use in private text messaging and even in business communication, smileys and other emoticons also have been used more frequently in surveys. So far, only a few studies have tested the effects of smiley faces as rating scale labels on the response process in web surveys. This study compared smiley face scales with verbally labeled rating scales in three web survey experiments. We found no convincing evidence that using smiley face scales altered response behavior, with the exception that these scales increased response times, which indicates a higher response burden. Based on our findings, we would advise against using smiley face scales when the scales have not been sufficiently tested, and convincing reasons exist for using them.
Tools and Techniques for Improving Cyber Situational Awareness of Targeted Phishing Attacks

Phishing attacks continue to be one of the most common attack vectors used online today to deceive users, such that attackers can obtain unauthorised access or steal sensitive information. Phishing campaigns often vary in their level of sophistication, from mass distribution of generic content, such as delivery notifications, online purchase orders, and claims of winning the lottery, through to bespoke and highly-personalised messages that convincingly impersonate genuine communications (e.g., spearphishing attacks). There is a distinct trade-off here between the scale of an attack versus the effort required to curate content that is likely to convince an individual to carry out an action (typically, clicking a malicious hyperlink). In this short paper, we conduct a preliminary study on a recent realworld incident that strikes a balance between attacking at scale and personalised content. We adopt different visualisation tools and techniques for better assessing the scale and impact of the attack, that can be used both by security professionals to analyse the security incident, but could also be used to inform employees as a form of security awareness and training. We pitched the approach to IT professionals working in information security, who believe this may provide improved awareness of how targeted phishing campaigns can impact an organisation, and could contribute towards a pro-active step of how analysts will examine and mitigate the impact of future attacks across the organisation.
Toward a Language of Blended Causality for Transforming Behavioral Data into Reflective User Experiences

With increasingly pervasive integration of technologies in everyday life, more data are generated around individuals' behaviors. Some of these data are accessible to individuals for reflection yet mostly presented in numbers or graphs, or represented by common metaphorical items, like virtual badges. Grounded in embodied cognition theories including conceptual metaphor and blending, and insights from social psychology, the idea of "blended causality" argues that behavioral data should be represented in virtual terms through blending behavioral consequences with users' existing knowledge of comparable causality. This paper emphasizes elaboration of blended causality into multiple imaginative narratives for reflective user experiences and reports the application of the extended guidelines as a creative design support tool in a series of workshops for designing representations of behavioral data. The concepts developed from the workshops vary in topics and blends, showing the effectiveness of the tool and informing a language of blended causality. Designers can use it to delineate representational mappings in terms of embodied experiences for examination and communication with team members like engineers.
InvisiPage

State-of-art secure processors like Intel SGX remain susceptible to leaking page-level address trace of an application via the page fault channel in which a malicious OS induces spurious page faults and deduces application's secrets from it. Prior works which fix this vulnerability do not provision for OS demand paging to be oblivious. In this work, we present InvisiPage which obfuscates page fault channel while simultaneously making OS demand paging oblivious. To do so, InvisiPage first carefully distributes page management actions between the application and the OS. Second, InvisiPage secures application's page management interactions with the OS using a novel construct which is derived from Oblivious RAM (ORAM) but is customized for page management. Finally, we lower overheads of our approach by reducing page management interactions with the OS via a novel memory partition. For a suite of cloud applications which process sensitive data we show that page fault channel can be tackled while enabling oblivious demand paging at low overheads.
Automatic Deictic Gestures for Animated Pedagogical Agents

We present a system that automatically generates deictic gestures for animated pedagogical agents (APAs). The system takes audio and text as input, which define what the APA has to say, and generates animated gestures based on a set of rules. The automatically generated gestures point to the exact locations of elements on a whiteboard nearby the APA, which are calculated by searching for keywords mentioned in the speech. We conducted a study with 100 subjects, in which we compared lecture videos containing gestures automatically-scripted by the system to videos of the same lecture containing manually-scripted gestures. The study results show that the manually-scripted and automatically-scripted lectures had comparable number of gestures, and that the gestures were timed equally well.
Proxemics Play

Proxemics Play has been suggested as a concept for designing socially engaging play experiences. We expand on this concept by investigating the role of fixed and semi-fixed architectural features in configuring meaningful socio-spatial relations of collocated play. We draw on ideas of perceptual, control and deixis proxemics to design and evaluate three cross-device games, illustrating novel directions for proxemics play. The games also illustrate new ways for mobile devices to support physical activity as well as face-to-face social interaction between children.
Optimizing SVM Classifier Through Approximate and High Level Synthesis Techniques

Leveraging the inherent error resilience of a large number of application domains, approximate computing is established as an efficient design alternative to improve their performance. Support Vector Machine (SVM) classifier is a widely adopted machine learning algorithm, that exhibits high error resilience and requires real-time execution. In this paper, we propose a highly optimized approximate SVM FPGA accelerator, utilizing arrhythmia detection in ECG signals as a case study. The proposed methodology applies two algorithmic approximation techniques, i.e., precision scaling and loop perforation, implemented in a coordinated manner in High-Level Synthesis (HLS). As a second level of performance enhancement, an exploration of the in-build optimization techniques of the HLS tool, with respect to the applied approximation, is also performed. Experimental evaluation shows that the proposed approximate SVM classifier attains a 15× speedup, while maintaining an accuracy of 96.7%.
Research on Software Defect Classification

With the increase of people's demand, the complexity of software design is getting higher and higher, and the possibility of software defects is increasing. In general, testers improve the quality of software by fixing defects, so the analysis of defects will significantly improve the efficiency of repair. Researchers have done a lot of experiments in this area and have achieved a lot of results. However, as the number of defects increases, manual analysis can no longer meet the needs of defect analysis. In order to help testers quickly locate and resolve defects, automatic defect classification method was proposed. At present, some methods for automatic classification of defects have been studied, but there is no summary of manual classification and automatic classification. Based on this, this paper first introduces the related concepts of defects and defect management, defect analysis and defect classification from the perspective of defect classification. Then the importance of software defect classification is explained. Finally, the research on software defects in recent years is summarized, and the existing methods of defect classification are compared. It is found that effective defect analysis will greatly improve the efficiency of software testing and reduce the cost of software maintenance.
Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data

Leveraging information from multiple scales is crucial to understanding complex diseases such as cancer where this could have a significant impact in improving diagnoses, patient management and treatment decisions. Recent advances in Convolutional Neural Networks (CNNs) have enabled major breakthroughs in biomedical image analysis, in particular for histopathology and radiology images. Our main contribution is a methodology to combine independent CNN models built for these two types of images in order to improve diagnostic accuracy. We train separate CNN models and combine them using a Dropout-Enabled meta-classifier. Our framework achieved second place in the MICCAI 2018 Computational Precision Medicine Challenge.
Reversible Circuits Synthesis from Functional Decision Diagrams by using Node Dependency Matrices

Decision diagrams are a data structure suitable for reversible circuit synthesis. Functional decision diagrams (FDDs) are particularly convenient in synthesis with Toffoli gates, since the functional expressions for decomposition rules used in them are similar to the functional expressions of Toffoli gates. The main drawback of reversible circuit synthesis based on decision diagrams is the usually large number of ancilla lines. This paper presents two methods for the reduction of the number of ancilla lines in reversible circuits derived from FDDs by selecting the order of implementation of nodes. In the first method, nodes are implemented by levels, starting from the bottom level to the top. The method uses appropriately defined level dependency matrices for choosing the optimal order of implementation of nodes at the same level. In this way, the optimization is performed level by level. The second method uses a diagram dependency matrix expressing mutual dependencies among all the nodes in the diagram. This method is computationally more demanding than the first method, but the reductions of both the number of lines and the Quantum cost of the circuits are larger.