Reviewerly/exHarmony huggingface dataset:
Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.
Machine Learning Techniques for Estimating Amount of Coolant Required in Shipping of Temperature Sensitive Products

This paper implements and compares machine learning algorithms to predict the amount of coolant required during transportation of temperature sensitive products. The machine learning models use trip duration, product threshold temperature and ambient temperature as the independent variables to predict the weight of gel packs need to keep the temperature of the product below its threshold temperature value. The weight of the gel packs can be translated to number of gel packs required. Regression using Neural Networks, Support Vector Regression, Gradient Boosted Regression and Elastic Net Regression are compared. The Neural Networks based model performs the best in terms of its mean absolute error value and r-squared values. A Neural Network model is then deployed on as webservice to score allowing for client application to make rest calls to estimate gel pack weights
PiHearts: Resonating Experiences of Self and Others Enabled by a Tangible Somaesthetic Design

A human's heart beating can be sensed by sensors and displayed for others to see, hear, feel, and potentially "resonate'' with. Previous work in studying interaction designs with physiological data, such as a heart's pulse rate, have argued that feeding it back to the users may, for example support users' mindfulness and self-awareness during various everyday activities and ultimately support their health and wellbeing. Inspired by Somaesthetics as a discipline, we designed and explored multimodal displays, which enable experiencing heart beats as natural stimuli from oneself and others in social proximity. In this paper, we report on the design process of our design PiHearts and present qualitative results of a field study with 30 pairs of participants. Participants were asked to use PiHearts during watching short movies together and report their perceived experience in three different display conditions while watching movies. We found, for example that participants reported significant effects in experiencing sensory immersion when they received their own heart beats as stimuli compared to the condition without any heart beat display, and that feeling their partner's heart beats resulted in significant effects on social experience. We refer to resonance theory to motivate and discuss the results, highlighting the potential of how digitalization of heart beats as rhythmic natural stimuli may provide resonance in a modern society facing social acceleration.
Tradeoff-Focused Contrastive Explanation for MDP Planning

End-users' trust in automated agents is important as automated decision-making and planning is increasingly used in many aspects of people's lives. In real-world applications of planning, multiple optimization objectives are often involved. Thus, planning agents' decisions can involve complex tradeoffs among competing objectives. It can be difficult for the end-users to understand why an agent decides on a particular planning solution on the basis of its objective values. As a result, the users may not know whether the agent is making the right decisions, and may lack trust in it. In this work, we contribute an approach, based on contrastive explanation, that enables a multi-objective MDP planning agent to explain its decisions in a way that communicates its tradeoff rationale in terms of the domain-level concepts. We conduct a human subjects experiment to evaluate the effectiveness of our explanation approach in a mobile robot navigation domain. The results show that our approach significantly improves the users' understanding, and confidence in their understanding, of the tradeoff rationale of the planning agent.
Engagement in Learning in the Massive Open Online Course: Implications for Epistemic Practices and Development of Transformative Digital Agency with Pre- and In-Service Teachers in Norway

This study examines how participants engage in learning in the Pedagogical Information and Communication Technology (ICTPED) Massive Open Online Course (MOOC) aimed to enhance Norwegian pre- and in-service teachers’ professional digital competence. The study also provides an insight into how participants’ engagement in learning in the ICTPED MOOC may have enhanced their transformative digital agency. Analyses of participants’ engagement in learning draw on P.Y. Galperin’s pedagogical theory. The data comprised 310 participants’ responses to the questionnaire administered to all pre- and in-service teachers engaged in the ICTPED MOOC in 2016—2019. Mixed methods were applied for data analyses by providing quantitative and qualitative evidence about the participants’ engagement in the course. Findings reveal that the majority of participants engaged in learning by reading the textual information embedded in the course. In doing so, they followed the sequential progression informed by the course design. Other participants engaged in learning by watching the videos and listening to the audio files embedded in the course as well as by attempting the assignments and other activities. By following these approaches, the participants created their individual learning trajectories and therefore may have enhanced their digital agency and affected the epistemic practices in the course.
A Core Ontology for Economic Exchanges

In recent years, there has been an increasing interest in the development of well-founded conceptual models for Service Management, Accounting Information Systems and Financial Reporting. Economic exchanges are a central notion in these areas and they occupy a prominent position in frameworks such as the Resource-Event Action (REA) ISO Standard, service core ontologies (e.g., UFO-S) as well as financial standards (e.g. OMG’s Financial Industry Business Ontology - FIBO). We present a core ontology for economic exchanges inspired by a recent view on this phenomenon. According to this view, economic exchanges are based on an agreement on the actions that the agents are committed to perform. This view enables a unified treatment of economic exchanges, regardless the object of the transaction. We ground our core ontology on the Unified Foundational Ontology (UFO), discussing its formal and conceptual aspects, instantiating it as a reusable OntoUML model, and confronting it with the REA standard and the UFO-S service ontology.
Getting smart: towards critical digital literacy pedagogies

Many researchers agree that awareness of agency and how it is enacted through different participants holds a key role in developing digital literacy. Recognition of the ways in which digital tools can appropriate and shape humans’ semiotic work and even act out roles in communication is part of critical digital literacy. In digital environments, humans interact with the content (not just consume it) through interfaces that may include touchscreen, voice commands, eye movement and hand gesture, often accompanied by non-human agents (e.g. bots). The need to make transparent the hidden design intentions of these non-human liaisons is becoming more and more essential, particularly for formal educational scenarios where digital tools are commonly used.This study explores how different roles of human and non-human agents in an online environment can be made salient and how teachers can promote learner awareness of hidden agents at the layer of digital interfaces. Drawing on the notion of “exchange structure” from conversational analysis and functional linguistics, audio recordings and re-constructed screenshots are analyzed. Results highlight the actions and intentionality of human and non-human agents that form multimodal turn-taking sequences. Suggestions for developing critical digital literacy pedagogies (CDLP), including making hidden agents more salient, are proposed.
Philosophy of education in a new key: A ‘Covid Collective’ of the Philosophy of Education Society of Great Britain (PESGB)

This article is a collective writing experiment undertaken by philosophers of education affiliated with the PESGB (Philosophy of Education Society of Great Britain). When asked to reflect on questions concerning the Philosophy of Education in a New Key in May 2020, it was unsurprising that the effects of the coronavirus pandemic on society and on education were foremost in our minds. We wanted to consider important philosophical and educational questions raised by the pandemic, while acknowledging that, first and foremost, it is a human tragedy. With nearly a million deaths reported worldwide to date, and with everyone effected in one way or another by Covid-19, there is a degree of discomfort, and a responsibility to be sensitive, in reflecting and writing about it academically. Members of this 'Covid Collective' come from various countries, with perspectives from Great Britain and Ireland well represented, and we see academic practice as a globally connected enterprise, especially since the digital revolution in academic publishing. The concerns raised in this article relate to but move beyond Covid-19, reflecting the impact of neoliberalism [and other political developments] on geopolitics with educational concerns as central to our focus.
Controllable Neural Prosody Synthesis

Speech synthesis has recently seen significant improvements in fidelity, driven by the advent of neural vocoders and neural prosody generators. However, these systems lack intuitive user controls over prosody, making them unable to rectify prosody errors (e.g., misplaced emphases and contextually inappropriate emotions) or generate prosodies with diverse speaker excitement levels and emotions. We address these limitations with a user-controllable, context-aware neural prosody generator. Given a real or synthesized speech recording, our model allows a user to input prosody constraints for certain time frames and generates the remaining time frames from input text and contextual prosody. We also propose a pitch-shifting neural vocoder to modify input speech to match the synthesized prosody. Through objective and subjective evaluations we show that we can successfully incorporate user control into our prosody generation model without sacrificing the overall naturalness of the synthesized speech.
Integrated-Photonic Characterization of Single-Photon Detectors for Use in Neuromorphic Synapses

We show several techniques for using integrated-photonic waveguide structures to simultaneously characterize multiple waveguide-integrated superconducting-nanowire detectors with a single fiber input. The first set of structures allows direct comparison of detector performance of waveguide-integrated detectors with various widths and lengths. The second type of demonstrated integrated-photonic structure allows us to achieve detection with a high dynamic range. This device allows a small number of detectors to count photons across many orders of magnitude in count rate. However, we find a stray light floor of -30 dB limits the dynamic range to three orders of magnitude. To assess the utility of the detectors for use in synapses in spiking neural systems, we measured the response with average incident photon numbers ranging from less than $10^{-3}$ to greater than $10$. The detector response is identical across this entire range, indicating that synaptic responses based on these detectors will be independent of the number of incident photons in a communication pulse. Such a binary response is ideal for communication in neural systems. We further demonstrate that the response has a linear dependence of output current pulse height on bias current with up to a factor of 1.7 tunability in pulse height. Throughout the work, we compare room-temperature measurements to cryogenic measurements. The agreement indicates room-temperature measurements can be used to determine important properties of the detectors.
Quality Assessment of Free-Viewpoint Videos by Quantifying the Elastic Changes of Multi-Scale Motion Trajectories

Virtual viewpoints synthesis is an essential process for many immersive applications including Free-viewpoint TV (FTV). A widely used technique for viewpoints synthesis is Depth-Image-Based-Rendering (DIBR) technique. However, such technique may introduce challenging non-uniform spatial-temporal structure-related distortions. Most of the existing state-of-the-art quality metrics fail to handle these distortions, especially the temporal structure inconsistencies observed during the switch of different viewpoints. To tackle this problem, an elastic metric and multi-scale trajectory based video quality metric (EM-VQM) is proposed in this paper. Dense motion trajectory is first used as a proxy for selecting temporal sensitive regions, where local geometric distortions might significantly diminish the perceived quality. Afterwards, the amount of temporal structure inconsistencies and unsmooth viewpoints transitions are quantified by calculating 1) the amount of motion trajectory deformations with elastic metric and, 2) the spatial-temporal structural dissimilarity. According to the comprehensive experimental results on two FTV video datasets, the proposed metric outperforms the state-of-the-art metrics designed for free-viewpoint videos significantly and achieves a gain of 12.86% and 16.75% in terms of median Pearson linear correlation coefficient values on the two datasets compared to the best one, respectively.
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Many important problems in science and engineering, such as drug design, involve optimizing an expensive black-box objective function over a complex, high-dimensional, and structured input space. Although machine learning techniques have shown promise in solving such problems, existing approaches substantially lack sample efficiency. We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model. In contrast to previous approaches, we actively steer the generative model to maintain a latent manifold that is highly useful for efficiently optimizing the objective. We achieve this by periodically retraining the generative model on the data points queried along the optimization trajectory, as well as weighting those data points according to their objective function value. This weighted retraining can be easily implemented on top of existing methods, and is empirically shown to significantly improve their efficiency and performance on synthetic and real-world optimization problems.
Detecting Trending Terms in Cybersecurity Forum Discussions

We present a lightweight method for identifying currently trending terms in relation to a known prior of terms, using a weighted logodds ratio with an informative prior.We apply this method to a dataset of posts from an English-language underground hacking forum, spanning over ten years of activity, with posts containing misspellings, orthographic variation, acronyms, and slang.Our statistical approach supports analysis of linguistic change and discussion topics over time, without a requirement to train a topic model for each time interval for analysis.We evaluate the approach by comparing the results to TF-IDF using the discounted cumulative gain metric with human annotations, finding our method outperforms TF-IDF on information retrieval.
Writing Strategies for Science Communication: Data and Computational Analysis

Communicating complex scientific ideas without misleading or overwhelming the public is challenging.While science communication guides exist, they rarely offer empirical evidence for how their strategies are used in practice.Writing strategies that can be automatically recognized could greatly support science communication efforts by enabling tools to detect and suggest strategies for writers.We compile a set of writing strategies drawn from a wide range of prescriptive sources and develop an annotation scheme allowing humans to recognize them.We collect a corpus of 128K science writing documents in English and annotate a subset of this corpus. 1We use the annotations to train transformer-based classifiers and measure the strategies' use in the larger corpus.We find that the use of strategies, such as storytelling and emphasizing the most important findings, varies significantly across publications with different reader audiences.
Quantitative Foresighting as a Means of Improving Anticipatory Scientific Capacity and Strategic Planning

<h2>Summary</h2> In a rapidly changing world, scientists and research institutions need to plan for the infrastructure, skills, and policy engagement that will help society navigate social-ecological challenges. Foresighting draws on approaches used in strategic and long-range (>10 years) planning and participatory futures studies. Here, we describe a new quantitative approach to develop and rank 14 foresight scenarios across a range of general and marine-relevant science domains. Indicators for each foresight were used to assess the time-specific probability of each scenario being realized. Assessments by scientists in 2 consecutive years showed foresight scoring consistency and revealed surprises. Despite high variation among scientists in scoring the year that each indicator would occur, there was overall consistency across the foresights between years. We show that foresighting can be quantitative and that individual performance and changes in likelihood can be evaluated. This approach can motivate and guide strategic planning and investment decisions by scientific institutions in response to different anticipated futures and build skills in futures thinking.
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision

Evaluating the trustworthiness of a model's prediction is essential for differentiating between 'right for the right reasons' and 'right for the wrong reasons'.Identifying textual spans that determine the target label, known as faithful rationales, usually relies on pipeline approaches or reinforcement learning.However, such methods either require supervision and thus costly annotation of the rationales or employ non-differentiable models.We propose a differentiable training-framework to create models which output faithful rationales on a sentence level, by solely applying supervision on the target task.To achieve this, our model solves the task based on each rationale individually and learns to assign high scores to those which solved the task best.Our evaluation on three different datasets shows competitive results compared to a standard BERT blackbox while exceeding a pipeline counterpart's performance in two cases.We further exploit the transparent decision-making process of these models to prefer selecting the correct rationales by applying direct supervision, thereby boosting the performance on the rationale-level. 1
Representation Learning for Type-Driven Composition

This paper is about learning word representations using grammatical type information. We use the syntactic types of Combinatory Categorial Grammar to develop multilinear representations, i.e. maps with n arguments, for words with different functional types. The multilinear maps of words compose with each other to form sentence representations. We extend the skipgram algorithm from vectors to multi- linear maps to learn these representations and instantiate it on unary and binary maps for transitive verbs. These are evaluated on verb and sentence similarity and disambiguation tasks and a subset of the SICK relatedness dataset. Our model performs better than previous type- driven models and is competitive with state of the art representation learning methods such as BERT and neural sentence encoders.
Classification of Categorical Data Based on the Chi-Square Dissimilarity and t-SNE

The recurrent use of databases with categorical variables in different applications demands new alternatives to identify relevant patterns. Classification is an interesting approach for the recognition of this type of data. However, there are a few amount of methods for this purpose in the literature. Also, those techniques are specifically focused only on kernels, having accuracy problems and high computational cost. For this reason, we propose an identification approach for categorical variables using conventional classifiers (LDC-QDC-KNN-SVM) and different mapping techniques to increase the separability of classes. Specifically, we map the initial features (categorical attributes) to another space, using the Chi-square (C-S) as a measure of dissimilarity. Then, we employ the (t-SNE) for reducing dimensionality of data to two or three features, allowing a significant reduction of computational times in learning methods. We evaluate the performance of proposed approach in terms of accuracy for several experimental configurations and public categorical datasets downloaded from the UCI repository, and we compare with relevant state of the art methods. Results show that C-S mapping and t-SNE considerably diminish the computational times in recognitions tasks, while the accuracy is preserved. Also, when we apply only the C-S mapping to the datasets, the separability of classes is enhanced, thus, the performance of learning algorithms is clearly increased.
Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory

As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.