Reviewerly/exHarmony huggingface dataset:
Information-Driven Path Planning

The era of robotics-based environmental monitoring has given rise to many interesting areas of research. A key challenge is that robotic platforms and their operations are typically constrained in ways that limit their energy, time, or travel distance, which in turn limits the number of measurements that can be collected. Therefore, paths need to be planned to maximize the information gathered about an unknown environment while satisfying the given budget constraint, which is known as the informative planning problem. This review discusses the literature dedicated to information-driven path planning, introducing the key algorithmic building blocks as well as the outstanding challenges. Machine learning approaches have been introduced to solve the information-driven path planning problem, improving both efficiency and robustness. This review started with the fundamental building blocks of informative planning for environment modeling and monitoring, followed by integration with machine learning, emphasizing how machine learning can be used to improve the robustness and efficiency of informative path planning in robotics.
Scalable Statistical Root Cause Analysis on App Telemetry

Despite engineering workflows that aim to prevent buggy code from being deployed, bugs still make their way into the Facebook app. When symptoms of these bugs, such as user submitted reports and automatically captured crashes, are reported, finding their root causes is an important step in resolving them. However, at Facebook's scale of billions of users, a single bug can manifest as several different symptoms according to the various user and execution environments in which the software is deployed. Root cause analysis (RCA) therefore requires tedious manual investigation and domain expertise to extract out common patterns that are observed in groups of reports and use them for debugging. In this paper, we propose Minesweeper, a technique for RCA that moves towards automatically identifying the root cause of bugs from their symptoms. The method is based on two key aspects: (i) a scalable algorithm to efficiently mine patterns from telemetric information that is collected along with the reports, and (ii) statistical notions of precision and recall of patterns that help point towards root causes. We evaluate Minesweeper on its scalability and effectiveness in finding root causes from symptoms on real world bug and crash reports from Facebook's apps. Our evaluation demonstrates that Minesweeper can perform RCA for tens of thousands of reports in less than 3 minutes, and is more than 85% accurate in identifying the root cause of regressions.
Visual Semantic-Based Representation Learning Using Deep CNNs for Scene Recognition

In this work, we address the task of scene recognition from image data. A scene is a spatially correlated arrangement of various visual semantic contents also known as concepts, e.g., “chair,” “car,” “sky,” etc. Representation learning using visual semantic content can be regarded as one of the most trivial ideas as it mimics the human behavior of perceiving visual information. Semantic multinomial (SMN) representation is one such representation that captures semantic information using posterior probabilities of concepts. The core part of obtaining SMN representation is the building of concept models. Therefore, it is necessary to have ground-truth (true) concept labels for every concept present in an image. Moreover, manual labeling of concepts is practically not feasible due to the large number of images in the dataset. To address this issue, we propose an approach for generating pseudo-concepts in the absence of true concept labels. We utilize the pre-trained deep CNN-based architectures where activation maps (filter responses) from convolutional layers are considered as initial cues to the pseudo-concepts. The non-significant activation maps are removed using the proposed filter-specific threshold-based approach that leads to the removal of non-prominent concepts from data. Further, we propose a grouping mechanism to group the same pseudo-concepts using subspace modeling of filter responses to achieve a non-redundant representation. Experimental studies show that generated SMN representation using pseudo-concepts achieves comparable results for scene recognition tasks on standard datasets like MIT-67 and SUN-397 even in the absence of true concept labels.
Competency profile on the use of ICT resources by Spanish music teachers: descriptive and inferential analyses with logistic regression to detect significant predictors

The purposes of this study were twofold: (1) determine the level of digital competence that Spanish teachers have in the use of technological devices, Web 2.0 general tools and Web 2.0 music tools, according to the proficiency profiles established by the European Framework DigCompEdu; and (2) identify the significant predictors which affect Spanish teachers who have a high level of competence. The study uses an ex-post facto design with 115 Spanish secondary school music teachers. The results showed that teachers had a profile between Novice and Expert regarding the use of educational technology. In addition, age, attitude towards ICT and motivation in teaching practice are predictive variables that help predict high competence in the use of Web 2.0 general resources and technological devices in the classroom, while having studied at a conservatory and working as a teacher in a conservatory significantly influence the use of Web 2.0 music tools.
Information Efficiency in the Cryptocurrency Market:The Efficient-Market Hypothesis

This study tested the efficient-market hypothesis (EMH) to examine information efficacy in the cryptocurrency market. We conducted three random walk tests to verify the weak-form EMH and used the event study method to test the semi-strong-form EMH. The analysis results demonstrated that 54 (6.04%) of the total of 893 cryptocurrency units satisfied the weak-form EMH, and 24 (2.695%) met the semi-strong market hypothesis. Furthermore, we found that, among the cryptocurrency exchanges that were established before November 2017, large size exchanges were more likely to satisfy the weak- and semi-strong-form EMHs.
Designing Civic Technology with Trust

As the role technology plays in relationships between people and their governments grows, developing a better understanding of how trust can inform designing civic technology with trust is urgent work for human computer-interaction researchers. This paper reports our efforts to design with trust through a two-year design-ethnography with the City of Atlanta Mayor's Office of Immigrant Affairs. We developed a sociotechnical system—Code Enforcer—to help this office guide immigrant residents through successfully engaging the city's code enforcement process. To inform the design process, we adapted our framework of trust-as-distance. While the framework was instrumental for integrating issues of trust throughout our design process, it also introduced tensions between how and by whom trust was enacted and interpreted. By reflecting on these tensions, we tease out the political and moral elements of designing with trust vital for HCI to navigate moving forward.
Integrated remote sensing and geophysical techniques for shallow base metal deposits (Zn, Pb, Cu) below the gossan zone at Kalabar, Western Aravalli Belt, India

Occurrences of small base metal deposits are reported along the western margin of Aravalli Belt in India. Surface manifestation of one such deposit is found to occur as gossan zone within the host rock of amphibolite in Kalabar area of Rajasthan State. Assessment of this occurrence through drilling technology is a time-consuming exercise, more expensive and requires huge manpower. In the present study, an attempt has been made to rediscover the mineral deposit in Kalabar area using remote sensing and High-resolution Electrical Resistivity Tomography (HERT) techniques. Spectral data analysis with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor shows the characteristic mineral constituents of the gossan zone. 2D electrical resistivity tomograms helped in identifying a narrow vertical zone with low resistivity but high chargeability value up to a depth of 127.9 m. Geospatial analysis of the resistivity data provides the size of the sub-surface deposits. Several exploratory drillings confirm the existence of such deposit up to a depth of 180 m due to presence of highly conductive metallic ore body (Zn, Pb, Cu). Thus, a unique approach is adopted in mineral exploration technology through integrated analysis of surface to sub-surface information. HERT technique is found to be a cost-effective geophysical survey for identifying the geometry and type of shallow base metal deposits.
Generating Empathetic Responses by Injecting Anticipated Emotion

Showing empathy and reacting to users’ feeling are important social skills for current dialogue generation systems. In previous research, empathetic responses are generated by 1) only modeling the emotion of dialogue history or 2) indirectly leveraging the predicted emotion label of responses. In this paper, we propose a novel empathetic response generation method that incorporates the anticipated emotion into response generation by minimizing the divergence between distribution of responses’ anticipated emotion and ground-truth emotion. The anticipated emotion is predicted by an auxiliary emotion predictor whose input is the previous utterances. Additionally, we treat the generation as deliberation process and design a two-round training method to refine the response iteratively. Experimental results show that the proposed model outperforms the previous state-of-the-art for emphatic dialogue generation task.
Measuring Awareness of Social Engineering in the Educational Sector in the Kingdom of Saudi Arabia

Social engineering is one of the most inventive methods of gaining unauthorized access to information systems and obtaining sensitive information. This type of cybersecurity threat requires minimal technical knowledge because it relies on the organization’s human element. Social engineers use various techniques, such as phishing, to manipulate users into either granting them access to various systems or disclosing their private data and information. Social engineering attacks can cost organizations more than 100,000 USD per instance. Therefore, it is necessary for organizations to increase their users’ awareness of social engineering attacks to mitigate the problem. The aim of this study is to provide a measurement of social engineering awareness in the Saudi educational sector. To achieve the aim of this study, a questionnaire was developed and evaluated. A total of 465 respondents completed the survey and answered questions related to measuring their knowledge of social engineering. The results show that 34% of participants (158 participants) had previous knowledge of social engineering approaches. The results also indicate that there are significant differences between participants with prior knowledge of social engineering and those with no such knowledge in terms of their security practices and skills. The implication of this study is that training is an essential factor in increasing the awareness of social engineering attacks in the Saudi educational sector.
A Generalized Tool for Accurate and Efficient Image Registration of UAV Multi-lens Multispectral Cameras by N-SURF Matching

The original multispectral (MS) images obtained from multi-lens multispectral cameras (MSCs) have significant misregistration errors, which require image registration for precise spectral measurement.However, due to the nonlinearity intensity differences among MS images, performing image matching is difficult to find sufficient correct matches (CMs) for image registration, and results in a complex coarse-to-fine solution.Based on the modification of speed-up robust feature (SURF), we proposed a normalized SURF (N-SURF) that can significantly increase the amount of CMs among different pairs of MS images and make one-step image registration possible.In this study, we first introduce N-SURF and adopt different MS datasets acquired from three representative MSCs (MCA-12, Altum, and Sequoia) to evaluate its matching ability.Meanwhile, we utilized three image transformation models-affine transform (AT), projective transform (PT), and an extended projective transform (EPT) to correct the misregistration errors of MSCs and evaluate their co-registration correctness.The results show that N-SURF can obtain 6-20 times more CMs than SURF and can successfully match all pairs of MS images, while SURF failed in the cases of significant spectral differences.Moreover, visual comparison, accuracy assessment, and residual analysis show that EPT can more accurately correct the viewpoint and lens distortion differences of MSCs than AT and PT, and it can obtain co-registration accuracy of 0.2-0.4pixels.Subsequently, using the successful N-SURF matching and EPT model, we developed an automatic MS image registration tool that is suitable for various multilens MSCs.
Toward Understanding the Effects of Virtual Character Appearance on Avoidance Movement Behavior

This virtual reality study was conducted to assess the impact of the appearance of virtual characters on the avoidance movement behavior of participants. Five experimental conditions were examined. Under each condition, one of the five different virtual characters (classified as mannequin, human, cartoon, robot, and zombie) was studied. Each participant had to experience only one condition and was asked to perform the collision avoidance tasks two times. During the walking task, the motion of participants was recorded. After finishing the collision avoidance segment of the study, a questionnaire that examined different concepts (emotional reactivity, emotional contagion, attentional allocation, behavioral independence, perceived skill, presence, immersion, virtual character realism, and virtual character unpleasantness) was distributed to the participants. Based on the collected measurements (avoidance movement behavior and self-reported ratings), we tried to understand the effects of the appearance of a virtual character on the avoidance movement behavior, and its possible correlation to subjective ratings. The results obtained from this study indicated that the appearance of the virtual characters did affect the avoidance movement behavior and also some of the examined concepts. Additionally, participant avoidance movement behavior correlates with some subjective ratings.
Synthesis of Forms: Integrating Practical and Reflective Qualities in Design

Synthesis, or the integration of hitherto separated elements, is a prominent concept in theories of design processes. Synthesis often happens when there is a need to make a decision, though it is often the result of a combination of different alternatives, instead of deciding in favor of one and eliminating another. In many design studies, synthesis has been investigated in the contexts of everyday design—bicycle frames, sewing machines, commercial architecture. We were interested in how it might apply in contexts of reflective design, whose pragmatics often depend on different interrelationships between users and technological products. In this paper, we argue that designing everyday use objects for reflection requires a synthesis of two apparently opposite forms: conventionally practical forms, since they are everyday use objects, and evocative forms, since they make users think. We provide two examples of everyday objects for reflection that we believe synthesize both conventionally practical and evocative forms, analyzing the design processes that led to these forms, and discussing how these reflective designs embody different forms of synthesis.
Skyscraper visualization of multiple time-dependent scalar fields on surfaces

We present a system to visualize multiple time-dependent scalar fields defined on 3D surfaces as they arise from numerical simulations. Domain experts want to understand correlations between multiple scalar fields to verify underlying processes. However, existing visualization techniques require high exploration effort to perceive temporal changes and relations of multiple fields on 3D surfaces. This makes analysis time-consuming and carries a high risk of overlooking regions with specific correlations. Therefore, we introduce a novel concept to facilitate exploration of multiple time-dependent scalar fields simultaneously. The basic idea is to use a novel glyph-based depiction, called skyline visualization, which is inspired by the skyscraper metaphor from information visualization. The skyscrapers are represented by 2D and 3D bar charts, where their floors show the temporal behavior of scalar fields of the corresponding surface region. The user can interact with the skyline visualization to see at which surface areas the scalar fields take on striking values or whether correlations exist between them. Furthermore, the user can immediately see in which regions scalar fields change strongly over time or show strongly inverted behavior. We evaluated our system with several CFD engineers, radiologists and visualization experts. Compared to existing systems, they were able to find regions of interest quickly and with high accuracy.
A pilot study of an automated personal identification process: Applying machine learning to panoramic radiographs

This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification.Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process.Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients.Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.
The Impact of Avatar Appearance, Perspective and Context on Gait Variability and User Experience in Virtual Reality

Gait supervision plays an important role in the diagnosis, analysis and rehabilitation of motor impairments and neurodegenerative disorders. For example, in Parkinson's disease, gait assessment is used for progression observation and medication guidance. Previous work has presented the potential of virtual reality (VR) supported gait applications. While virtual environments and user representation strategies are used for gait applications, the influence of appearance and context cues on gait performance is not extensively researched. In this paper, we analyzed the influence of avatar appearance, environment awareness, and camera perspective on gait parameters relevant for clinical application. Four different avatar appearances, varying in abstraction, two environmental settings, as well as an egocentric and exocentric camera perspective were compared in three walking tasks on a treadmill. Our results show that variability, as an indicator for gait stability, is significantly impacted by VR exposure in comparison to a real world (in vivo) baseline. Further, our results revealed that walking tasks influence gait behavior significantly different in VR compared to in vivo. Overall, these findings suggest that particular care has to be taken when assessing gait characteristics acquired from subjects immersed in VR and that equivalence of results with in vivo may not be blindly assumed.
Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding

In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information. Previous studies on Chinese SLU do not consider the word information, failing to detect word boundaries that are beneficial for intent detection and slot filling. To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU, which consists of (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection and (2) character-level word adapter, which is applied at each character for selectively controlling weights on word information as well as character information. Experimental results on two Chinese SLU datasets show that our model can capture useful word information and achieve state-of-the-art performance.
The Challenges of Large‐Scale, Web‐Based Language Datasets: Word Length and Predictability Revisited

Abstract Language research has come to rely heavily on large‐scale, web‐based datasets. These datasets can present significant methodological challenges, requiring researchers to make a number of decisions about how they are collected, represented, and analyzed. These decisions often concern long‐standing challenges in corpus‐based language research, including determining what counts as a word, deciding which words should be analyzed, and matching sets of words across languages. We illustrate these challenges by revisiting “Word lengths are optimized for efficient communication” (Piantadosi, Tily, &amp; Gibson, 2011), which found that word lengths in 11 languages are more strongly correlated with their average predictability (or average information content) than their frequency. Using what we argue to be best practices for large‐scale corpus analyses, we find significantly attenuated support for this result and demonstrate that a stronger relationship obtains between word frequency and length for a majority of the languages in the sample. We consider the implications of the results for language research more broadly and provide several recommendations to researchers regarding best practices.
Multi-camera Sports Players 3D Localization with Identification Reasoning

Multi-camera sports players 3D localization is always a challenging task due to heavy occlusions in crowded sports scenes. Traditional methods can only provide players' locations without identifying information. Existing methods of localization may cause ambiguous detection and unsatisfactory precision and recall, especially when heavy occlusions occur. To solve this problem, we propose a generic localization method by providing distinguishable results that have the probabilities of locations being occupied by players with unique ID labels. We design the algorithms with a multi-dimensional Bayesian model to create a Probabilistic and Identified Occupancy Map (PIOM). By using this model, we jointly apply deep learning-based object segmentation and identification to obtain sports players' probable positions and their likely identification labels. This approach not only provides players 3D locations but also gives their ID information that is distinguishable from others. Experimental results demonstrate that our method outperforms the previous localization approaches with reliable and distinguishable outcomes.