Reviewerly/exHarmony huggingface dataset:
Reliable autonomous navigation of mobile robot in unconstrained environment

Path optimization is one of the crucial problems in unmanned ground vehicles. It is associated with various concerns such as smooth trajectory, collision free planning, less time/space complexity, etc. Rapid Exploring Random Tree (RRT) provides an effective path planning however, it introduces sharp turns leading to increased travel time. Further, the mobile robot needs to align its heading angle ($\theta$) with the trajectory angle ($\beta$) else it may collide with the obstacles. Hence in this work, Improved RRT Theta algorithm is proposed that is an amalgamation of improved RRT with modified B Spline curve technique. The heading angle is aligned with the trajectory angle for collision free navigation of mobile robot and a modified B Spline method adds the new control points in the trajectory to reduce the number of sharp turns. The proposed technique is tested for different simulation based environmental conditions. The results reveal that the proposed technique proves to be an efficient path planning technique with collision free smooth movement, reduced time lag and path length as compared to the conventional RRT technique. The proposed technique may be quite helpful in autonomous navigation, trajectory tracking, path planning etc.
Learning to rank query expansion terms for COVID-19 scholarly search

With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a relevant text to a topic of interest. In this paper, we propose a Contextual Query Expansion framework based on the clinical Domain knowledge (CQED) for formalizing an effective search over PubMed to retrieve relevant COVID-19 scholarly articles to a given information need. For the sake of training and evaluation, we use the widely adopted TREC-COVID benchmark. Given a query, the proposed framework utilizes a contextual and a domain-specific neural language model to generate a set of candidate query expansion terms that enrich the original query. Moreover, the framework includes a multi-head attention mechanism that is trained alongside a learning-to-rank model for re-ranking the list of generated expansion candidate terms. The original query and the top-ranked expansion terms are posed to the PubMed search engine for retrieving relevant scholarly articles to an information need. The framework, CQED, can have four different variations, depending upon the learning path adopted for training and re-ranking the candidate expansion terms. The model drastically improves the search performance, when compared to the original query. The performance improvement in comparison to the original query, in terms of RECALL@1000 is 190.85% and in terms of NDCG@1000 is 343.55%. Additionally, the model outperforms all existing state-of-the-art baselines. In terms of P@10, the model that has been optimized based on Precision outperforms all baselines (0.7987). On the other hand, in terms of NDCG@10 (0.7986), MAP (0.3450) and bpref (0.4900), the CQED model that has been optimized based on an average of all retrieval measures outperforms all the baselines. The proposed model successfully expands queries posed to PubMed, and improves search performance, as compared to all existing baselines. A success/failure analysis shows that the model improved the search performance of each of the evaluated queries. Moreover, an ablation study depicted that if ranking of generated candidate terms is not conducted, the overall performance decreases. For future work, we would like to explore the application of the presented query expansion framework in conducting technology-assisted Systematic Literature Reviews (SLR).
Assessing Spatial User Experience for Design Guidelines: A Comparative Study of Outpatient Waiting Rooms With Conventional Versus Modern Features

Objectives: Identify waiting room design features that are most appreciated by outpatients and their companions in conventional and modern waiting rooms. Evaluate if end users evaluate the environment differently from experts and in what aspects. Provide evidence-based design guidelines that orient designers and healthcare managers. Background: Built environments are relevant in patients’ evaluation of overall healthcare service. For outpatients, waiting frequently consumes the largest amount of time; thus, waiting room interior design has great potential to enhance their experience. Methods: This study compares perceptions of two types of waiting rooms—conventional and modern—based on the spatial user experience (SUE) model. In the first stage of the study, we compared user evaluations of conventional waiting rooms ( n = 137) and modern waiting rooms ( n = 426) with respect to the eight SUE model dimensions using multigroup structural equation modeling. In the second stage, an expert ergonomist and two professional interior designers assessed both types of waiting rooms. Results: Results showed that modern waiting rooms were perceived to be significantly better in all SUE dimensions. We also found experts’ evaluations were overall consistent with users’ perceptions. Discrepancies were only found in temperature perception, signage evaluation, and spatial appreciation. Conclusions: Participants valued modern style waiting room features such as good quality signage, use of armchairs and sofas, a controlled environment, and decoration. We suggest involving end users in the design process to respond to their needs and promote a positive experience. Finally, we provide easy-to-adopt design guidelines to improve patients’ waiting room experience.
Geometric Adaptations of PDE-G-CNNs

Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. The recently introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalizes G-CNNs while simultaneously reducing network complexity and increasing performance. In PDE-G-CNNs the usual building blocks of neural networks are replaced with solvers for evolution PDEs, these PDEs being convection, diffusion, dilation, and erosion. We investigate three geometric adaptations of PDE-G-CNNs:
Analysis and Comparison of Different Frontend Frameworks

A frontend framework is pre-written code that provides the architecture for a project, along with certain features to help with the development process. With the availability of many frameworks, each with its own features and advantages, it is important to understand the difference between the various frameworks and make the right choice of framework for a particular project. This paper elaborates on the features of three popular frameworks: React, Angular and Vue, draws a comparison among them, and provides guidelines on how to make the choice of a suitable framework for a project.
A Conception for Intermediality through Text and Music in the Education of Future Kindergarten and Primary-school Teachers

The article examines the methodological foundations of courses included in the educational process of students from the bachelor’s and master’s programs in pre-school and primary-school pedagogy at the Faculty of Education of Trakia University – Stara Zagora, Bulgaria. The potential of the proposed learning content, which is oriented towards the communication between literary and musical works through the intermediality, is analyzed by modeling a conceptual construct. The specifics of the derived approaches and options for their interaction in the environment of the kindergarten and the primary school (from integration to synergy) are presented. Emphasis is placed on the mastering of competence by future teachers for the implementation of an educational process with the application of strengthened connections between the cognitive fields of the Bulgarian language, literature and music in order to acquire the key cultural competence.
An efficient framework for quantum video and video editing

This paper proposes an Efficient Framework for Quantum Video (EFQV) in quantum computer. EFQV adds time index information into different frames on BRQI (a Quantum Image Representation based on Bitplanes, BRQI) images, and each bitplanes quantum image adds a time index. Complement of Colors (COC) operator, Reverse of Bitplanes (ROB) operator, and Shift of Frames (SOF) operator are designed for EFQV and its operators are tested on the IBM quantum computation framework qiskit. Experimental results show the effectiveness of EFQV and its operations on quantum computing environment. Compared with other frameworks, EFQV has advantages in time complexity and quantum cost.
Sistem Pengenalan Ucapan Bahasa Daerah Menggunakan Metode Mel Frequency Cepstral Coefficient (MFCC) dan Adaptive Neuro-Fuzzy Inference System (ANFIS)

Bugis language is one of the traditional languages in South Sulawesi which has various dialects. Different ways of speaking and tone of voice that cause voice of each person also vary. The design of the Bugis language speech recognition system is one way to digitize and indicate the Bugis regional language. This study uses Mel Frequency Cepstral Coefficient (MFCC) method as a feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) as an algorithm for classification and recognition. This research was conducted by collecting various voice data from various age groups and regions. Voice data is collected in wav audio format and then extracted into numerical results to use as database on system. Test was carried out using various database models and the amount of voice data. The test results show that databases with single data produce a higher level of accuracy with a percentage of truth above 50%. While the test results with a database of 37 speakers with a total data of 740 have a low level of accuracy and are below 50%. Testing in outdoors has a lower accuracy around 10%. Error value in the data training process in the range of 0.5 - 1.5.
Object Affordance-Based Implicit Interaction for Wheelchair-Mounted Robotic Arm Using a Laser Pointer

With the growth of the world's population, limited healthcare resources cannot provide adequate nursing services for all people in need. The wheelchair-mounted robotic arm (WMRA) with interactive technology could help to improve users' self-care ability and relieve nursing stress. However, the users struggle to control the WMRA due to complex operations. To use the WMRA with less burden, this paper proposes an object affordance-based implicit interaction technology using a laser pointer. Firstly, a laser semantic identification algorithm combined with the YOLOv4 and the support vector machine (SVM) is designed to identify laser semantics. Then, an implicit action intention reasoning algorithm, based on the concept of object affordance, is explored to infer users' intentions and learn their preferences. For the purpose of performing the actions about task intention in the scene, the dynamic movement primitives (DMP) and the finite state mechanism (FSM) are respectively used to generalize the trajectories of actions and reorder the sequence of actions in the template library. In the end, we verified the feasibility of the proposed technology on a WMRA platform. Compared with the previous method, the proposed technology can output the desired intention faster and significantly reduce the user's limb involvement time (about 85%) in operating the WMRA under the same task.
New Constructions of Equality Test Scheme Without Random Oracles

The proliferation of big data has brought exponential amount of increase in data that is being remotely stored around the globe. Thus, making it imperative to secure the remote data through some encryption mechanism to ensure privacy preservation. However, it often becomes difficult to perform operations over the encrypted data. In order to solve this problem, the equality test function based public key encryption (PKEwET) is proposed. PKEwET approach basically allows secure comparison over encrypted data without revealing the underlying data. This work aims to improve Water’s scheme while introducing a new functionality. More precisly, equality test is being introduced to Water’s scheme so that the encrypted data may be compared without decryption process. To achieve this, an authorization mechanism is being included in which the authorized party uses the trapdoor to test the ciphertext. The scheme is designed under standard model. The security of the proposed scheme is proved with two types of adversaries under the standard model. Finally, the superiority of the proposed scheme in terms of performance is also discussed.
A role for breast ultrasound Artificial Intelligence decision support in the evaluation of small invasive lobular carcinomas

To evaluate the diagnostic performance of an Artificial Intelligence (AI) decision support (DS) system in the ultrasound (US) assessment of invasive lobular carcinoma (ILC) of the breast, a cancer that can demonstrate variable appearance and present insidiously.Retrospective review was performed of 75 patients with 83 ILC diagnosed by core biopsy or surgery between November 2017 and November 2019. ILC characteristics (size, shape, echogenicity) were recorded. AI DS output (lesion characteristics, likelihood of malignancy) was compared to radiologist assessment.The AI DS system interpreted 100% of ILCs as suspicious or probably malignant (100% sensitivity, and 0% false negative rate). 99% (82/83) of detected ILCs were initially recommended for biopsy by the interpreting breast radiologist, and 100% (83/83) were recommended for biopsy after one additional ILC was identified on same-day repeat diagnostic ultrasound. For lesions in which the AI DS output was probably malignant, but assigned a BI-RADS 4 assessment by the radiologist, the median lesion size was 1 cm, compared with a median lesion size of 1.4 cm for those given a BI-RADS 5 assessment (p = 0.006). These results suggest that AI may offer more useful DS in smaller sub-centimeter lesions in which shape, margin status, or vascularity is more difficult to discern. Only 20% of patients with ILC were assigned a BI-RADS 5 assessment by the radiologist.The AI DS accurately characterized 100% of detected ILC lesions as suspicious or probably malignant. AI DS may be helpful in increasing radiologist confidence when assessing ILC on ultrasound.
Hybrid High-Dimensional Quantum Key Distribution for a Composable Quantum Network

High-dimensional quantum key distribution (HD-QKD) possesses a higher noise-tolerance ability and secure key rate. In the work presented here, by developing a mapping method for multiple degrees of freedom (DOFs), we propose and implement a HD-QKD system with a quantum bit error rate as low as 0.57%. Our HD-QKD system can stably achieve high secure key rates and is composable, allowing one to build HD quantum networks compatible with multiple DOFs. The results indicate that the proposed scheme could bridge HD and two-dimensional terminals encoded in different DOFs and could support large-scale deployment of quantum networks with high performance. Our work significantly enhances the performance of HD-QKD and strengthens the practical applications of large-scale quantum networks in a hybrid way.
Bit Catastrophes for the Burrows-Wheeler Transform

A bit catastrophe, loosely defined, is when a change in just one character of a string causes a significant change in the size of the compressed string. We study this phenomenon for the Burrows-Wheeler Transform (BWT), a string transform at the heart of several of the most popular compressors and aligners today. The parameter determining the size of the compressed data is the number of equal-letter runs of the BWT, commonly denoted r. We exhibit infinite families of strings in which insertion, deletion, resp. substitution of one character increases r from constant to $$\varTheta (\log n)$$ , where n is the length of the string. These strings can be interpreted both as examples for an increase by a multiplicative or an additive $$\varTheta (\log n)$$ -factor. As regards multiplicative factor, they attain the upper bound given by Akagi, Funakoshi, and Inenaga [Inf & Comput. 2023] of $$\mathcal{O}(\log n \log r)$$ , since here $$r=\mathcal{O}(1)$$ . We then give examples of strings in which insertion, deletion, resp. substitution of a character increases r by a $$\varTheta (\sqrt{n})$$ additive factor. These strings significantly improve the best known lower bound for an additive factor of $$\varOmega (\log n)$$ [Giuliani et al., SOFSEM 2021].
Probabilistic Lexicase Selection

Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexicase selection has been proven to be an NP-hard problem, which discourages deeper theoretical understanding and practical improvements to the algorithm. In this work, we introduce probabilistic lexicase selection (plexicase selection), a novel parent selection algorithm that efficiently approximates the probability distribution of lexicase selection. Our method not only demonstrates superior problem-solving capabilities as a semantic-aware selection method, but also benefits from having a probabilistic representation of the selection process for enhanced efficiency and flexibility. Experiments are conducted in two prevalent domains in genetic programming: program synthesis and symbolic regression, using standard benchmarks including PSB and SRBench. The empirical results show that plexicase selection achieves state-of-the-art problem-solving performance that is competitive to the lexicase selection, and significantly outperforms lexicase selection in computation efficiency.
Duality for Nonlinear Filtering II: Optimal Control

This paper is concerned with the development and use of duality theory for a nonlinear filtering model with white noise observations. The main contribution of this paper is to introduce a stochastic optimal control problem as a dual to the nonlinear filtering problem. The mathematical statement of the dual relationship between the two problems is given in the form of a duality principle. The constraint for the optimal control problem is the backward stochastic differential equation (BSDE) introduced in the companion paper. The optimal control solution is obtained from an application of the maximum principle, and subsequently used to derive the equation of the nonlinear filter. The proposed duality is shown to be an exact extension of the classical Kalman-Bucy duality, and different from other types of optimal control and variational formulations given in literature.
Control prediction based on cumulative gaze dwell time while browsing contents

The utilization of gaze behavior for control has been studied as one of the hands-free control methods. With the recent spread of Head Mounted Display devices, it has become a vital issue to establish hands-free control methods. Previously proposed approaches using the eye gaze behavior have several problems to be solved for natural manipulation during content browsing. Therefore, in this study, considering the case where the gaze point leaves the decision area for a short period, we propose a method that enables accurate control prediction based on gaze behavior during content browsing by counting the cumulative gaze dwell time. Through experimentation, we confirmed that the proposed method would be effective for the accurate prediction of user intention. The limitation of our method and further issues to be addressed were also clarified.
Few-Shot Radar Jamming Recognition Network via Time-Frequency Self-Attention and Global Knowledge Distillation

Radar jamming recognition aims to accurately recognize the type of jamming to provide guidance for radar countermeasures. Although previous deep learning-based methods have made promising performance, they mainly rely on convolutional neural network (CNN) based on local processing, which ignore the global information in the time-frequency domain data of the jamming signal and also require much inference time to obtain the final recognition results. In this paper, a novel few-shot jamming recognition network via time-frequency self-attention and global knowledge distillation (JR-TFSAD) is proposed by jointly considering the global information in the time-frequency spectrum of the jamming signal and the real-time performance of the recognition network. A time-frequency self-attention model (TFSA) is proposed to extract the global deep features of radar jamming signals by learning the correlation between two arbitrary points in the time-frequency spectrum of the jamming signal, thus improving the recognition accuracy. Moreover, to effectively reduce the inference time of the method while preserving the recognition accuracy, a global knowledge distillation model (GKD) is further constructed to perform jamming recognition by distilling the global knowledge from the TFSA model. The experimental results on the simulated and measured mixed dataset verify that the proposed method has higher recognition accuracy and shorter inference time compared to the state-of-the-art methods.
Geochemical Background in Heavy Metals on Basaltic Soils from the Lisbon Volcanic Complex

Anthropic activities have increasingly influenced soil quality due to growing urban development. This study aims to establish a methodology for determining reference levels for heavy metal concentrations in soils by using X-ray fluorescence, an analysis technique that relies on simple and relatively fast sample preparation. To test the proposed methodology, a study was carried out on soils and outcrops originating from the basalts of the Lisbon Volcanic Complex in Odivelas. To verify the concentrations of heavy metals obtained via X-ray fluorescence, soil samples were also tested by atomic emission spectrometry. The results compare favourably between the pairs of V, Cr, and Pb measurements, showing a concordance between the methods. The obtained concentrations and their cumulative frequency curves were used to determine the upper limit for the background concentration in V, Cr, Ba, Ni, Zn, Cu, and Pb. The 95th percentile was chosen as the upper limit of background for the studied soils. These soils show a large variability in their composition that prevents a clear definition of the boundary between which of them correspond to natural concentrations, and which are most likely caused by anthropogenic influences. Some of the values obtained are higher than those found in guidelines for use at contaminated sites and deserve further investigation to confirm contamination or not.
Sentiment Identification on Tweets to Forecast Cryptocurrency’s Volatility

Cryptocurrencies have had a huge presence on social media since their creation.In current days, the constant increase of the mass of data produced by this environment has attracted several researchers to try to identify patterns with the potential to allow identification of the volatility in the crypto market before it happens.This approach involves the concept of the wisdom of the crowds, a popular theory in the economy field that in the current days may have the perfect tools to prove itself true.This scenario creates an opportunity to unite two new technologies, social media, and cryptocurrencies to the newest Natural Language Processing (NLP) tools, and produces a study in a rich and unexplored field.Executing a detailed sentiment analysis, this study intents to analyze the forecast of the volatility of cryptocurrencies through the detection and evaluation of several categories of sentiments on messages on twitter when it is associated with a specific cryptocurrency.To achieve this, an NLP model was trained with the GoEmotions dataset to identify and categorize emotions, and results were used to calculate the forecast of the cryptocurrency.