Reviewerly/exHarmony huggingface dataset:
Designing Meaningful Interactions for Social Innovation: An Application of Design Thinking

This paper aims to explore how design thinking as a methodology can facili-tate interaction design for social innovation. Design thinking is an interdisci-plinary approach to developing human-centered products, services, and expe-riences. Following the approaches of design thinking and research through design, we conducted an interaction design workshop with the topics of UN global goals. A series of works were developed through a design workshop in consideration of three social innovation topics. The findings show that de-sign thinking is an applicable methodology in interaction design practice. We propose that interaction designers and researchers should develop their own design thinking mode and establish their place in the design research. Limita-tions and future work are also presented.
Residual Statics Estimation with Quantum Annealing

Summary Quantum computing could be a potential game-changer in industry sectors relying on the efficient solutions of large-scale global optimization problems. Exploration geoscience, is full of optimization problems and hence is a good candidate for application of quantum computing. It was recently suggested that quantum annealing, a form of adiabatic quantum computer, is a much better suited quantum computing platform for optimization problems than gate-based quantum computing. In this work, we show how the residual statics estimation problem can be solved on the quantum annealer and present our first results obtained on a quantum computer.
Software Analytics of Open Source Business Software

This paper applies software analytics to open source code.Open-source software gives both individuals and businesses the flexibility to work with different parts of available code to modify it or incorporate it into their own project.The open source software market is growing.Major companies such as AWS, Facebook, Google, IBM, Microsoft, Netflix, SAP, Cisco, Intel, and Tesla have joined the open source software community.In this study, a sample of 40 open source applications was selected.Traditional McCabe software metrics including cyclomatic and essential complexities were examined.An analytical comparison of this set of metrics and derived metrics for high risk software was utilized as a basis for addressing risk management in the adoption and integration decisions of open source software.From this comparison, refinements were added, and contemporary concepts of design and data metrics derived from cyclomatic complexity were integrated into a classification scheme for software quality.It was found that 84% of the sample open source applications were classified as moderate low risk or low risk indicating that open source software exhibits low risk characteristics.The 40 open source applications were the base data for the model resulting in a technique which is applicable to any open source code regardless of functionality, language, or size.
Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations

Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information.In this context, tensor decomposition has successfully modeled interactions between entities and relations.Their effectiveness in static knowledge graph completion motivates us to introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER.Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features, which is model-agnostic and offers a more generalized representation of time.We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing.The experiments show that our proposed methods perform on par or better than the state-of-the-art semantic matching models on two benchmarks.
Developing future foreign language teachers professional competence by creating a favorable educational environment

A favorable educational environment in the context of a higher educational institution contributes to the efficient training of future specialists and the development of their professional competence.It is a set of specially created circumstances, tools, and activities in the educational process, which enhance students` motivation and contribute to their academic achievements.The paper analyzes and substantiates the efficiency of a favorable educational environment in the educational process and its influence on developing the professional competence of future specialists, namely future foreign language teachers as in modern society, an individual needs to be able to acquire new means and methods of teaching and learning form various internal and external sources.The scientific research conducted in this paper allowed us to identify the following prerequisites for the formation of a favorable educational environment: organization of a motivating educational environment; organization of students' reflective activity on the process and the results of their individual and group work as; the use of interactive teaching methods that stimulate the development of independence, critical thinking, teamwork skills; the use of ICT for increasing students' engagement in the educational process.Creating a favorable educational environment is a key element in student-centered learning providing the teachers and lecturers with the opportunity to vary the work, diversify the materials and tools for teaching and learning and make the monitoring process easier and more efficient.In the paper, it is proved that for future teachers, especially foreign language teachers, developing professional competence during the study is vital as they will have an opportunity to gain the necessary experience to start their future career being already equipped with the skills and methods, strategies and tools for developing their students' communicative competence.
DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem

This paper describes our contribution to the Answer Localization track of the MedVidQA 2022 Shared Task.We propose two answer localization approaches that use only textual information extracted from the video.In particular, our approaches exploit the text extracted from the video's transcripts along with the text displayed in the video's frames to create a set of features.Having created a set of features that represents a video's textual information, we employ four different models to measure the similarity between a video's segment and a corresponding question.Then, we employ two different methods to obtain the start and end times of the identified answer.One of them is based on a random forest regressor, whereas the other one uses an unsupervised peak detection model to detect the answer's start time.Our findings suggest that for this task, leveraging only textrelated features (transmitted either verbally or visually) and using a small amount of training data, lead to significant improvements over the benchmark Video Span Localization model that is based on deep neural networks.
Complex Scenario Design for Investigating Cognitive Process for Problem-Solving Collaboration

Complex problem solving (CPS) has been a field that uses computer-simulated scenarios and has been applied in problem-solving-related studies. However, the problem scenario has not been thoroughly discussed as an essential factor in determining the reliability of the studies. Consequently, there are no systematic principles for scenario design in CPS studies. This study was performed to establish fundamental standards for complexity analysis in the CPS scenario design. We created a high-fidelity problem scenario to investigate the cognitive processes in CPS discussions. The reliability of the system and scenario was validated by five industrial experts. The findings of this study can be applied to future experiment designs, meta-analysis methods, and study replications.
A Custom State LSTM Cell for Text Classification Tasks

Text classification is the task of assigning a class to a document. Machine Learning enables the automation of Text Classification Tasks, amongst others. Recent advances in the Machine Learning field, such as the introduction of Recurrent Neural Networks, Long Short Term Memory and Gated Recurrent Units, have greatly improved classification results. These type of networks include internal memory states that demonstrate dynamic temporal behaviour. In the LSTM cell, this temporal behaviour is supported by two distinct states: current and hidden. We introduce a modification layer within the LSTM cell, where we are able to perform extra state alterations for one or both states. We experiment with 17 single state alterations, 12 for the current state and 5 for the hidden state. We evaluate these alterations in seven datasets that deal with hate speech detection, document classification, human to robot interaction and sentiment analysis. Our results demonstrate an average F1 improvement of 0.5% for the top performing current state alteration and 0.3% for the top performing hidden state alteration.
Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval

This paper considers the phase retrieval (PR) problem, which aims to reconstruct a signal from phaseless measurements such as magnitude or power spectrograms. PR is generally handled as a minimization problem involving a quadratic loss. Recent works have considered alternative discrepancy measures, such as the Bregman divergences, but it is still challenging to tailor the optimal loss for a given setting. In this paper we propose a novel strategy to automatically learn the optimal metric for PR. We unfold a recently introduced ADMM algorithm into a neural network, and we emphasize that the information about the loss used to formulate the PR problem is conveyed by the proximity operator involved in the ADMM updates. Therefore, we replace this proximity operator with trainable activation functions: learning these in a supervised setting is then equivalent to learning an optimal metric for PR. Experiments conducted with speech signals show that our approach outperforms the baseline ADMM, using a light and interpretable neural architecture.
Review on Photomicrography based Full Blood Count (FBC) Testing and Recent Advancements

With advancements in related sub-fields, research on photomicrography in life science is emerging and this is a review on its application towards human full blood count testing which is a primary test in medical practices. For a prolonged period of time, analysis of blood samples is the basis for bio medical observations of living creatures. Cell size, shape, constituents, count, ratios are few of the features identified using DIP based analysis and these features provide an overview of the state of human body which is important in identifying present medical conditions and indicating possible future complications. In addition, functionality of the immune system is observed using results of blood tests. In FBC tests, identification of different blood cell types and counting the number of cells of each type is required to obtain results. Literature discuss various techniques and methods and this article presents an insightful review on human blood cell morphology, photomicrography, digital image processing of photomicrographs, feature extraction and classification, and recent advances. Integration of emerging technologies such as microfluidics, micro-electromechanical systems, and artificial intelligence based image processing algorithms and classifiers with cell sensing have enabled exploration of novel research directions in blood testing applications.
The Application of Augmented Reality Technology in Museum Display Design

In view of the current museum the way a single, low efficiency, in the continuous development of augmented reality technology and the popularization of the intelligent terminal, this paper discusses the development of the modern museum, as well as the advantages of augmented reality at the museum display design and application, in order to show museum in the spread of culture, improve efficiency made some innovative design Through the design of exhibition mode, the problem of exhibition in the operation process of traditional museums can be solved, and the positive role of museums in the city can be enhanced.
Autonomous fire-fighting with heterogeneous team of unmanned aerial vehicles

This paper addresses autonomous fire-fighting missions with a heterogeneous team of Unmanned Aerial Vehicles (UAVs). We use UAVs with different capabilities to extinguish cooper- atively fires on the ground and on facades of high-rise buildings. The former is done by deploying fireproof blankets, whereas a water-jet system is used to extinguish fires on building facades. First, we propose two novel mechanisms on board UAVs for fire extinction: a system to deploy a blanket on the ground automatically, and a device to shoot water at facade fires. Then, we describe our software architecture for cooperative multi-robot missions in outdoor and unstructured environments, where robustness and reliability play a key role. The system was particularly developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), where we achieved the best performance in the fire-fighting Challenge. However, the conceptual design of our extinguishing devices and our software architecture are more general and could be applied to other search and rescue situations. We present not only our results in the final stage of MBZIRC but also our simulations and field experiments throughout the months previous to the competition, during which we assessed our system and tuned its performance.
Team JSK at MBZIRC 2020: Interception of fast flying target using multilinked aerial robot

The JSK (Jouhou System Kougaku) team from The University of Tokyo was selected as one of the 32 finalist teams out of 134 applicants to participate in the second edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC) held in 2020. One of the challenges (Challenge 1) required an aerial robot to detect, follow and catch a yellow ball hanging underneath a fast-moving drone. In this challenge, we developed an articulated aerial robot that could intercept the target. This study presents reliable vision-based detection, target-trajectory estimation, fast interception-planning, and -control. Furthermore, we discuss outdoor experiments for evaluating our hardware and software systems, as well as the competition results in Abu Dhabi. With our developed system, we achieved third place in Challenge 1, as one of only four teams in the entire competition to successfully intercept the moving target.
On the Smith normal form of least common multiple of matrices from some class of matrices

For nonsingular matrices of arbitrary order over a commutative principal ideal domain under some restrictions on their canonical diagonal forms, the Smith normal form of their least common right multiple is constructed. Cite as: A. M. Romaniv, “On the Smith normal form of least common multiple of matrices from some class of matrices,” Mat. Met. Fiz.-Mekh. Polya , 64 , No. 2, 47–51 (2021), https://doi.org/10.15407/mmpmf2021.64.2.47-51
Proximity Enhanced Graph Neural Networks with Channel Contrast

We consider graph representation learning in an unsupervised manner. Graph neural networks use neighborhood aggregation as a core component that results in feature smoothing among nodes in proximity. While successful in various prediction tasks, such a paradigm falls short of capturing nodes' similarities over a long distance, which proves to be important for high-quality learning. To tackle this problem, we strengthen the graph with three types of additional graph views, in which each node is directly linked to a set of nodes with the highest similarity in terms of node features, neighborhood features or local structures. Not restricted by connectivity in the original graph, the generated views provide new and complementary perspectives from which to look at the relationship between nodes. Inspired by the recent success of contrastive learning approaches, we propose a self-supervised method that aims to learn node representations by maximizing the agreement between representations across generated views and the original graph, without the requirement of any label information. We also propose a channel-level contrast approach that greatly reduces computation cost. Extensive experiments on six assortative graphs and three disassortative graphs demonstrate the effectiveness of our approach.
Iterative Few-shot Semantic Segmentation from Image Label Text

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and mutually refine the mask predictions of support and query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method not only outperforms the state-of-the-art weakly supervised approaches by a significant margin, but also achieves comparable or better results to recent supervised methods. Moreover, our method owns an excellent generalization ability for the images in the wild and uncommon classes. Code will be available at https://github.com/Whileherham/IMR-HSNet.
Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations

We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to existing multi-agent imitation learning techniques, BTIL explicitly models and infers the time-varying mental states of team members, thereby enabling learning of decentralized team policies from demonstrations of suboptimal teamwork. Further, to allow for sample- and label-efficient policy learning from small datasets, BTIL employs a Bayesian perspective and is capable of learning from semi-supervised demonstrations. We demonstrate and benchmark the performance of BTIL on synthetic multi-agent tasks as well as a novel dataset of human-agent teamwork. Our experiments show that BTIL can successfully learn team policies from demonstrations despite the influence of team members' (time-varying and potentially misaligned) mental states on their behavior.
Learning Higher-Order Logic Programs From Failures

Learning complex programs through inductive logic programming (ILP) remains a formidable challenge. Existing higher-order enabled ILP systems show improved accuracy and learning performance, though remain hampered by the limitations of the underlying learning mechanism. Experimental results show that our extension of the versatile Learning From Failures paradigm by higher-order definitions significantly improves learning performance without the burdensome human guidance required by existing systems. Our theoretical framework captures a class of higher-order definitions preserving soundness of existing subsumption-based pruning methods.
Chinese medical dialogue information extraction via contrastive multi-utterance inference

Abstract Medical Dialogue Information Extraction (MDIE) is a promising task for modern medical care systems, which greatly facilitates the development of many real-world applications such as electronic medical record generation, automatic disease diagnosis, etc. Recent methods have firstly achieved considerable performance in Chinese MDIE but still suffer from some inherent limitations, such as poor exploitation of the inter-dependencies in multiple utterances, weak discrimination of the hard samples. In this paper, we propose a contrastive multi-utterance inference (CMUI) method to address these issues. Specifically, we first use a type-aware encoder to provide an efficient encode mechanism toward different categories. Subsequently, we introduce a selective attention mechanism to explicitly capture the dependencies among utterances, which thus constructs a multi-utterance inference. Finally, a supervised contrastive learning approach is integrated into our framework to improve the recognition ability for the hard samples. Extensive experiments show that our model achieves state-of-the-art performance on a public benchmark Chinese-based dataset and delivers significant performance gain on MDIE as compared with baselines. Specifically, we outperform the state-of-the-art results in F1-score by 2.27%, 0.55% in Recall and 3.61% in Precision (The codes that support the findings of this study are openly available in CMUI at https://github.com/jc4357/CMUI.).
Diversity of ghost notes in tubas, euphoniums and saxhorns

The ghost note is a natural note which can be played exclusively on bass brass instruments with a predominantly-expanding bore profile such as tubas, euphoniums or saxhorns. It stands between the pedal note-the lowest natural note playable, or first regime-and the instrument's second regime. However, if the interval between the pedal note and the second regime remains close to an octave regardless of the instrument, the interval between the pedal note and the ghost note vary from a minor third to a perfect fourth. References about this note are very scarce, and it is not commonly known among tuba players.This study shows that an elementary brass model describing the player coupled to the instrument is capable of bringing both the ghost and the pedal note to light. Here, we adopt a dynamical systems point of view and perform a bifurcation analysis using a software of numerical continuation. The numerical results provided in terms of frequency intervals between pedal note and ghost note are compared with frequency intervals experimentally inferred from recordings of seven different types of tuba, each of them being played by two professional tuba players.
Machine Learning and IoT-Based Automatic Health Monitoring System

The Internet of things (IoT) has made healthcare applications more accessible to the rest of the globe. On a wide scale, IoT has been employed to interconnect therapeutic aids in order to provide world-class healthcare services. The novel sensing devices can be worn to continuously measure and monitor the participants’ vital parameters. Remotely monitored parameters can be transferred to medical servers via the Internet of things, which can then be analyzed by clinicians. Furthermore, machine learning algorithms can make real-time decisions on the abnormal character of health data in order to predict disease early. This study presents a machine learning and Internet of things (IoT)-based health monitoring system to let people measure health metrics quickly. Physicians would also benefit from being able to monitor their patients remotely for more personalized care. In the event of an emergency, physicians can respond quickly. In this study, the Espressif modules 8266 are used to link health parameter sensors, which are implanted to measure data and broadcast it to a server. With the real-time data from the sensors, three statistical models were trained to detect anomalous health conditions in the patients: K-nearest neighbors (KNNs), logistic regression, and support vector machine (SVM). Due to abnormal health markers, these models uncover patterns during training and forecast disease in the subject.