Reviewerly/exHarmony huggingface dataset:
UCORM: Indexing Uncorrelated Metric Spaces for Concise Content-Based Retrieval of Medical Images

The large amount of medical exams generated by hospitals has a great potential to boost the support for physicians on decision making tasks. This requires efficient and reliable computational systems to retrieve relevant information in real-time. Existing Content-Based Image Retrieval (CBIR) systems rely on Metric Access Methods (MAMs) to speed-up the retrieval task. In this context, images are represented by Feature Extraction Methods (FEMs), according to information such as color or texture. However, MAMs usually index images based on a single FEM. Whenever physicians want to search for similar images using multiple FEMs simultaneously, they need to perform separated queries. In this work, we propose UCORM, an access method capable of indexing images using multiple FEMs by overlapping different metric spaces. UCORM selects the best FEMs to generate a concise yet accurate indexing space. It relies on an interesting use of Pearson correlation, that we named PCMS, to compute the correlation between different FEMs. PCMS allows UCORM to improve the retrieval task by minimizing the overlapping between metric spaces, resulting on fewer intermediary images when performing a query. Experimental analysis shows that UCORM prunes well the data distribution regions with low correlation between FEMs. Also, two medical application scenarios support our claim that UCORM is well-fitted for clinical environments.
Active Learning with Spatial Distribution based Semi-Supervised Extreme Learning Machine for Multiclass Classification

Unlabeled samples are often readily available in our daily lives. However, valuable information contained in a large number of unlabeled samples tends to be ignored by general supervised learning models. To make full use of unlabeled samples, we propose a novel framework that combines active learning with semi-supervised learning. On one hand, we expect to label as few samples as possible while achieving guaranteed classification performance, hence it's of vital importance to design a specific active learning strategy to select only the most valuable batch of samples for expert labeling. On the other hand, the introduction of distribution information in unlabeled sample pool will bring great benefits to the model. Both labeled samples and unlabeled samples can be used for training semi-supervised classification model. In this paper, uncertainty-based active learning and manifold-based semi-supervised learning are integrated into our framework. Extreme learning machine (ELM) is adopted as our base classifier. Moreover, a novel uncertainty criterion, called Bell-Function-based uncertainty, is proposed for active learning selection for the first time. Empirical results on six public benchmark datasets show that our algorithm produces better performance in comparison with other approaches.
A functional model of jazz improvisation

We present a model of jazz improvisation where short-term decision making by each performer is modeled as a function from contexts to music. Contexts can be shared, such as an agreed-upon chord progression, or they can also be private---a current state for each musician. We formalize this model in Haskell to generate potentially infinitely long jazz improvisations, and we have also used the same model in Python to support real-time human-computer interaction through jazz.
The cosmological constant effect on the quantum entanglement

In Schwarzschild-de Sitter space-time, the effect of a gravitational field near a massive body black hole on the spin entanglement has been studied for two-qubits in the case of triplet and singlet states of a two particles system in a circular geodesic motion. It is found that the effect of the cosmological constant on the robustness of the Wooters concurrence is more important in the triplet state than in the singlet one even in the extended region around the black hole
Haptic Feedback with a Reservoir Computing-Based Recurrent Neural Network for Multiple Terrain Classification of a Walking Robot

Terrain classification is an important feature for walking robots because it allows the robots to stably move and operate on the terrain. Different terrain classification techniques have been developed. The techniques include the use of different exteroceptive and proprioceptive sensors with different classification methods. Whereas these techniques have been widely used to classify flat, hard, and rough terrains, their application to soft terrains has not been fully addressed. Achieving soft-terrain classification will expand the operational range of walking robots. Thus, in this study, we propose a new technique to classify various terrains including soft ones. The technique exploits haptic feedback (expressed only through ground contact force measurement of a legged robot) and neurodynamics with the temporal memory of a reservoir computing-based recurrent neural network. We used six different terrains to evaluate the performance of the proposed technique. The terrains include sand (loose ground), foams with different softness levels (soft ground), and floor (hard ground). The experimental results show that we can successfully classify all terrains with an accuracy of above 70%. Furthermore, owing to the temporal memory of the network, if the haptic feedback is transiently missing, the network will be still be able to classify the terrain considerably well.
Modeling Drone Crossing Movement with Fitts’ Law

Drones have begun to find extensive use in commercial, scientific, recreational, agricultural, and military applications in recent times. Drone maneuvers involve several pointing and crossing operations. In this regard, previous studies have shown that drone pointing operations can be modeled by the two-part model. In this study, we conduct a crossing operation experiment to control a drone to fly through a frame with a target width. Subsequently, we verify the applicability of Fitts’ law and the two-part model to drone crossing operations. Fitts’ law and the two-part model are both found to be suitably valid for crossing operations ( $$R^2 >0.940$$ ). Upon comparing the AIC values of the two models, we find that Fitts’ law, which has fewer parameters, is a better model for the crossing operation. Our results indicate that the drone operation time in crossing operations can be suitably predicted. In addition, based on models, we can compare drones and evaluate interfaces in drone crossing operations.
Development of Phase Congruency to Estimate the Direction of Maximum Information (tDMI) in Images with Straight Line Segments

This paper aims to better understand a new property of Phase Congruency (PC) based image processing technique. It is illustrated that by optimizing the PC orientation, the direction that contains maximum information can be detected in images with straight line segments. We formulate the Direction of Maximum Information (tDMI) and develop the PC orientation optimization paradigm to compute it in such images. We also develop the Holmholtz Principle and Hough Transform based Line Segment Detection (HP-LSD and HT-LSD) methods to compute tDMI. We apply these methods to identify tDMI in 32 test images corrupted with gaussian and speckle noises with different variances. All methods show fairly good robustness to the noise variance. However, the results show that the PC orientation optimization method detects tDMI with estimation errors significantly lower than those of HP-LSD and HT-LSD methods. Furthermore, PC estimates tDMI directly, whereas, HT-LSD and HP-LSD require to measure lines' widths and lengths. The worst estimation error is obtained by using HT-LSD method, with an average error 102.81% for images with straight line segments of the same thickness. The average error of HP-LSD method over all 32 test images and noise conditions is 47.58%. The average estimation error of PC method over all scenarios is 15.80%, which shows more than 30% and 70% improvement compared to HP-LSD and HT-LSD methods, respectively. The results also show that it is better to consider both maximum and minimum momentums into the PC orientation optimization.
Product Recommendation in Case-based Reasoning

Recommender systems aim to deliver users with intelligent methods for navigation and identification of complex information spaces, especially in the e-commerce realm. However, these systems need to overcome certain limitations that adversely impact their performances, such as overspecialization of recommendations and cold-start problem. To address these concerns, we propose a case-based recommendation approach, which is a form of content-based recommendation, in this paper. This approach is well-suited to many product recommendation domains owing to its clear organization of users' needs and preferences. Additionally, it employs a feature weighting technique to improve the performance of the recommender by enhancing its accuracy and precision. We herein present the results for different case structures, different numbers of similar cases retrieved, and various feature-weighting approaches, which indicate that better results are obtained with the proposed recommender when compared to the KNN retrieval algorithm.
Bridging the Gap - On a Humanoid Robotics Rookie League

The 2050 robot-human soccer game is among the most prominent goals of RoboCup. All RoboCup leagues contribute to this goal, for example the Simulation Leagues with research on strategic game play, the Standard Platform League with stable walking and vision algorithms and the Humanoid League with mechatronics of bipedal robots. However, especially in the Humanoid League, the swift improvement in performance of the robots makes it significantly harder for newcomers to enter into this field of research. With robots increasing in size, with new challenges in mechatronics for bipedal robots and software increasing in complexity, the gap for new teams is widening on the course to the 2050 game. There have been many approaches to easy entry, such as the introduction of a two-league system in the Standard Platform (SPL) and Small Size League (SSL) or an ‘educational challenge’ at regional @home events. While the SPL and SSL approaches require fully developed hard- and software in order to compete, as the @home challenge, we propose an entry-level league with a reduced set of requirements to bridge the gap between the Junior level and advanced Humanoid League. We believe that the Humanoid League can only reach the 2050 goal if new researchers and universities can be attracted on a regular basis. Attracting new researchers requires an easy entry path for new teams, suitable for undergraduate students and universities with a limited budget. The ‘Humanoid Rookie (Sub-) League’ (HRL) will give new researchers and teams the time to gather experience and funds that are necessary to successfully participate in and contribute to the Humanoid League’s development towards the 2050 game. This paper intends to spark discussion about the current state and the roadmap of the Humanoid League within the RoboCup community.
Kaleidographic

Abstract Kaleidographic is a dynamic and interactive data visualization tool that allows users to observe and explore relations between any number of variables. The tool is useful for displaying the complex ways in which textual elements interact across a range of texts. Thus far, the tool has been used to display the results of corpus studies as well as corpus-assisted multimodal discourse analyses that investigate text-image relations. To facilitate broader applications of the tool, it is now publicly available online for use without charge. This paper explains the background and motivation for Kaleidographic and presents two case studies demonstrating its utility. Limitations of the tool are discussed and its potential uses in corpus linguistics research and beyond are introduced.
Understanding the Acceptance of Teaching Method Supported by Enterprise WeChat in Blended Learning Environment

Blended learning in the world has been drawing more attention. And lots of teaching methods in the blended learning environment have also emerged. This study attempts to examine the learners' continuous intention of the teaching method supported by Enterprise WeChat among college students who have taken "The Introduction to Educational Technology" class. In this study, five hypotheses were answered by using the linear regression. Results shows that perceived usefulness and learner satisfaction are shown to be good predators to behavior intention. Also, perceived usefulness and confirmation are significantly associated with learner satisfaction. Confirmation is significantly positive with perceived usefulness. Main findings of this study were discussed, which could provide new ideas for future education.
Event-Driven Strategies in Crypto Assets

Due to structural, regulatory and security-related advantages, crypto assets attract an unparalleled attention all over the world and lead to a heated discussion, whether crypto assets can be considered as a viable option for alternative investments. However, aside from regulatory uncertainty, a lack of profound academic studies and quantitative time series analysis seems to be a vast entry barrier for institutional investors, leading to the fact that the majority share of crypto assets are still being held by young, risk-tolerant retail investors, with high affinity for blockchain technology and innovation. This paper applies important findings of asset pricing theory on the rising academic field of crypto assets and deduces practical implications for retail and institutional investors. Our study includes descriptive statistics and correlation analysis of crypto and capital markets, as well as an event and price impact analysis. Our empirical findings document no evidence for an increase of market efficiency of crypto assets as proxied by bitcoin and reveal significant inefficiencies, leaving valid arguments for either passive or active investment strategies. In addition, our event study findings show significant evidence for insider trading which should call market regulators for actions to fight tax regulations and ensure a fair market environment.
Compositional Learning of Human Activities With a Self-Organizing Neural Architecture

An important step for assistive systems and robot companions operating in human environments is to learn the compositionality of human activities, i.e, recognize both activities and their comprising actions. Most existing approaches address action and activity recognition as separate tasks, i.e., actions need to be inferred before the activity labels, and are thus highly sensitive to the correct temporal segmentation of the activity sequences. In this paper, we present a novel learning approach that jointly learns human activities on two levels of semantic and temporal complexity:~1)~transitive actions such as reaching and opening, e.g. a cereal box, and 2) high-level activities such as having breakfast. Our model consists of a hierarchy of GWR networks which process and learn inherent spatiotemporal dependencies of multiple visual cues extracted from the human body skeletal representation and the interaction with objects. The neural architecture learns and semantically segments input RGB-D sequences of high-level activities into their composing actions, without supervision. We investigate the performance of our architecture with a set of experiments on a publicly available benchmark dataset. The experimental results show that our approach outperforms the state of the art with respect to the classification of the high-level activities. Additionally, we introduce a novel top-down modulation mechanism to the architecture which uses the actions and activity labels as constraints during the learning phase. In our experiments, we show how this mechanism can be used to control the network's neural growth without decreasing the overall performance.
Authorization of Searchable CP-ABE Scheme with Attribute Revocation in Cloud Computing

Most searchable attribute-based encryption schemes only support the search for single-keyword without attribute revocation, the data user cannot quickly detect the validity of the ciphertext returned by the cloud service provider. Therefore, this paper proposes an authorization of searchable CP-ABE scheme with attribute revocation and applies the scheme to the cloud computing environment. The data user to send the authorization information to the authorization server for authorization, assists the data user to effectively detect the ciphertext information returned by the cloud service provider while supporting the revocation of the user attribute in a fine-grained access control structure without updating the key during revocation stage. In the random oracle model based on the calculation of Diffie-Hellman problem, it is proved that the scheme can satisfy the indistinguishability of ciphertext and search trapdoor. Finally, the performance analysis shows that the scheme has higher computational efficiency.
Local Metric Learning Based on Anchor Points for Multimedia Analysis

Distance metric learning has been shown to be an effective and efficient method which can lead to significant improvements in classification, clustering, and retrieval. Conventional metric learning methods which apply a global metric to capture the input features and their correlations are unsuitable for heterogeneous data, whose features vary according to different clusters. Thus, a number of local metric learning methods have been proposed to overcome this limitation. Unfortunately, most of them suffer from over-fitting or need the data set to be under a strong assumption, such as Gaussian distribution. To avoid over-fitting caused by learning local metric matrices separately in the previous researches, we propose a novel local metric learning method in which we learn a metric matrix for each pair of instances as linear combinations of basis metric matrices defined on different clusters of the input space without any assumption for data set. We get the parameters of the linear combinations by using the relationship between direct distance and anchor~distance between a pair of data points. Additionally, we design a novel expectation-maximization based optimization method to learn the linear combinations and basis metric matrices. Experimental results show that our method outperforms state-of-the-art methods on various data sets.
An Inferential Approach to Mining Surprising Patterns in Hypergraphs

A novel pattern mining algorithm and a novel formal definition of surprisingness are introduced, both framed in the context of formal reasoning. Hypergraphs are used to represent the data in which patterns are mined, the patterns themselves, and the control rules for the pattern miner. The implementation of these tools in the OpenCog framework, as part of a broader multi-algorithm approach to AGI, is described.
Performative Approaches in Designing Costumes: Ergonomics in Immersion and Storytelling

The case studies explore experimental practices and approaches to design based on costumes that lead to the characters’ construction and of the plot itself and thus contribute to the immersion of performers and spectators into corresponding fictions. Semiotics and somatics gather in a visual vocabulary based on textiles, colors and garment shapes that tell stories, taking spectators to plausible universes or situations contextualized scenically. Participatory design is considered, with costumes as working tools for performers and directors. User-centered design processes that include the moving body as the mill of a costume’s definition promote a staging under construction that turns the plot into a metaphor of the costumes’ facets. This brings new symbolic and theoretical frameworks for both performing and designing. Thus, ergonomics is central in the creative process, implying original designs for these staging practices with no preliminary grounds and that rely on costumes as triggers for acting.
Comparative Study on School-Based Environmental Education in Japan and Korea

The purpose of this study is to compare some details of environmental education (EE) implemented in schools and identify the differences between such programs in Japan and Korea. In particular, the study examines the difference brought about by the presence or absence of an independent subject. This is because previous studies reveal that Korean schools have an independent subject named Environment, however, its adoption rate is low. The study examines the following aspects to clarify the status of EE in Japanese and Korean schools: first, classes related to the environment that are offered in the two countries; second, specific details of the national curriculum for EE as an independent subject taught in Korea; and, third, the diffusion system surrounding schools in Japan and Korea. Consequently, it is suggested that having an independent subject makes it clear that even if its adoption rate is low, it contributes to clarifying the identity of EE (concept, learning, etc.).
A Hybrid Deep Reinforcement Learning Algorithm for Intelligent Manipulation

Conventional collaborative robots can solve complex problems through programming approaches. But the current tasks are different and nonrepetitive, many problems cannot be solved by conventional programming methods. Deep reinforcement learning provides a framework for solving robotic control tasks using machine learning techniques. However, the existing model-free deep reinforcement learning algorithms lack unified framework for comparing sample efficiency with final performance. In this paper, a hybrid deep reinforcement learning framework and its application in robot control are proposed based on the existing model-free deep reinforcement learning algorithms. In the acting process, the distributed actors acting with the environment are used to acquire the data, while prior actors are used to solve the cold boot problem of the algorithm. In the learning process, prioritized experience replay and multi-step learning are designed for the improvement on the final performance. Simulations are represented to show the practicality and potential of the proposed algorithm. Results show that the hybrid deep reinforcement learning algorithm in this paper has a significant improvement on the final performance and sample efficiency while it can ensure the stability and convergence.
Developing a Speckle Pattern to Evaluate Adhesive Joints Using Digital Image Correlation

Digital Image Correlation (DIC) is a known Non-Destructive Evaluation (NDE) method used in various industries. DIC requires a randomly produced pattern, known as speckle pattern, to track and generate strain maps. The speckle patterns are fundamental for DIC applications. DIC uses images of these speckle patterns before and after a load has been applied to generate strain maps by comparing the changes between speckle pattern images. In this study, a speckle pattern is sought to implement within adhesive joints. The speckle pattern must be trackable using ultrasonic imaging; therefore, it is chosen from materials with high acoustic impedance metals. By tracking the speckle pattern and using DIC, defects within joints will be revealed. This allows non-destructive analysis to be conducted on adhesive joints to detect defects which may go undetected using conventional ultrasonic techniques.
Use of Genetic Algorithm for Optimal Codes Search

This paper shows the practical use of genetic algorithm (GA) in the search of novel multi-phase signal-code sequences that can be used in radar systems as well as communications. A few examples of such sequences and their characteristics compared to well-known signals and codes are presented.