Reviewerly/exHarmony huggingface dataset:
Collaborative Learning with Disentangled Features for Zero-shot Domain Adaptation

Typical domain adaptation techniques aim to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain in the same label space. However, it is often hard to get even the unlabeled target domain data of a task of interest. In such a case, we can capture the domain shift between the source domain and target domain from an unseen task and transfer it to the task of interest, which is known as zero-shot domain adaptation (ZSDA). Most of existing state-of-the-art methods for ZSDA attempted to generate target domain data. However, training such generative models causes significant computational overhead and is hardly optimized. In this paper, we propose a novel ZSDA method that learns a task-agnostic domain shift by collaborative training of domain-invariant semantic features and task-invariant domain features via adversarial learning. Meanwhile, the spatial attention map is learned from disentangled feature representations to selectively emphasize the domain-specific salient parts of the domain-invariant features. Experimental results show that our ZSDA method achieves state-of-the-art performance on several benchmarks.
Learning Deep and Wide Contextual Representations Using BERT for Statistical Parametric Speech Synthesis

In this paper, we propose a method of learning deep and wide contextual representations for statistical parametric speech synthesis (SPSS) using BERT, a pre-trained language representation model. Traditional acoustic models in SPSS utilize phoneme sequences and prosody labels as input, and can not make full use of the deep linguistic representations of current and surrounding sentences. Therefore, this paper designs two context encoders, i.e., a sentence-window context encoder and a paragraph-level context encoder, to integrate the contextual representations extracted from multiple sentences by BERT into Tacotron2 via an extra attention module. The parameters of BERT are pre-trained and then fine-tuned together with other components in the model. Experimental results on the Blizzard Challenge 2019 dataset show that both context encoders can reduce the errors of acoustic feature prediction and improve the subjective performance of synthetic speech comparing with the baseline Tacotron2 model.
An instance-dependent simulation framework for learning with label noise

We propose a simulation framework for generating instance-dependent noisy labels via a pseudo-labeling paradigm. We show that the distribution of the synthetic noisy labels generated with our framework is closer to human labels compared to independent and class-conditional random flipping. Equipped with controllable label noise, we study the negative impact of noisy labels across a few practical settings to understand when label noise is more problematic. We also benchmark several existing algorithms for learning with noisy labels and compare their behavior on our synthetic datasets and on the datasets with independent random label noise. Additionally, with the availability of annotator information from our simulation framework, we propose a new technique, Label Quality Model (LQM), that leverages annotator features to predict and correct against noisy labels. We show that by adding LQM as a label correction step before applying existing noisy label techniques, we can further improve the models’ performance. The synthetic datasets that we generated in this work are released at https://github.com/deepmind/deepmind-research/tree/master/noisy_label .
Automatically Extracting OWL Versions of FOL Ontologies

While OWL and RDF are by far the most popular logic-based languages for Semantic Web Ontologies, some well-designed ontologies are only available in languages with a much richer expressivity, such as first-order logic (FOL) or the ISO standard Common Logic. This inhibits reuse of these ontologies by the wider Semantic Web Community. While converting OWL ontologies to FOL is straightforward, the reverse problem of finding the closest OWL approximation of an FOL ontology is undecidable. However, for most practical purposes, a "good enough" OWL approximation need not be perfect to enable wider reuse by the Semantic Web Community.
Adiabatic Quantum Computing

Adiabatic quantum computing (AQC) is an approach to quantum computing based on the adiabatic theorem. The adiabatic theorem states that if a Hamiltonian, \( \widehat{H} \), changes slowly in time, then the state remains in the ground state. By starting with some simple Hamiltonian and evolving it to some \( \widehat{H} \) representing the problem to be solved, the lowest energy configuration becomes the solution to the problem.
Advancing English Language Learners' Speaking Skills Using VoiceThread in Mobile Learning for Russian Tertiary Context

This research examines the effectiveness of using the VoiceThread (VT) application (both web and mobile-based) for the development of oral English skills of Russian university students. The study involved 44 full-time bachelor students in non-linguistic departments. The results were evaluated by on-line questionnaire using SurveyMonkey. The survey indicated that the majority of students agreed that mobile VoiceThread developed their oral language skills (M = 4.75) and they enjoyed using it for language learning (M = 4.13). The novelty of the research lies in a more integral assessment of the entire set of oral linguistic skills as a result of their development using a specific mobile learning platform widely used throughout the world. The results can be used in practice to implement mobile learning platforms for teaching oral language skills to students with basic knowledge of a foreign language.
Exploiting Two-Level Information Entropy across Social Networks for User Identification

As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -measure ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>F</mi> <mn>1</mn> </math> ), and area under curve (AUC).
HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.
Metaheuristics and Software Engineering: Past, Present, and Future

This work aims at giving an updated vision on the successful combination between Metaheuristics and Software Engineering (SE). Mostly during the 90s, varied groups of researchers dealing with search, optimization, and learning (SOL) met SE researchers, all of them looking for a quantified manner of modeling and solving problems in the software field. This paper will discuss on the construction, assessment, and exploitation tasks that help in making software programs a scientific object, subject to automatic study and control. We also want to show with several case studies how the quantification of software features and the automatic search for bugs can improve the software quality process, which eases compliance to ISO/IEEE standards. In short, we want to build intelligent automatic tools that will upgrade the quality of software products and services. Since we approach this new field as a cross-fertilization between two research domains, we then need to talk not only on metaheuristics for SE (well known by now), but also on SE for metaheuristics (not so well known nowadays). In summary, we will discuss here with three time horizons in mind: the old times [before the term search-based SE (SBSE) was used for this], the recent years on SBSE, and the many avenues for future research/development. A new body of knowledge in SOL and SE exists internationally, which is resulting in a new class of researchers able of building intelligent techniques for the benefit of software, that is, of modern societies.
A Method for Constructing Geographical Knowledge Graph from Multisource Data

Global problems all occur at a particular location on or near the Earth’s surface. Sitting at the junction of artificial intelligence (AI) and big data, knowledge graphs (KGs) organize, interlink, and create semantic knowledge, thus attracting much attention worldwide. Although the existing KGs are constructed from internet encyclopedias and contain abundant knowledge, they lack exact coordinates and geographical relationships. In light of this, a geographical knowledge graph (GeoKG) construction method based on multisource data is proposed, consisting of a modeling schema layer and a filling data layer. This method has two advantages: (1) the knowledge can be extracted from geographic datasets; (2) the knowledge on multisource data can be represented and integrated. Firstly, the schema layer is designed to represent geographical knowledge. Then, the methods of extraction and integration from multisource data are designed to fill the data layer, and a storage method is developed to associate semantics with geospatial knowledge. Finally, the GeoKG is verified through linkage rate, semantic relationship rate, and application cases. The experiments indicate that the method could automatically extract and integrate knowledge from multisource data. Additionally, our GeoKG has a higher success rate of linking web pages with geographic datasets, and its exact coordinates have increased to 100%. This paper could bridge the distance between a Geographic Information System and a KG, thus facilitating more geospatial applications.
Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN

This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pre-trained model. Our model was tested and its performance was evaluated and compared to the existing models. It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%. The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’ engagement levels.
Ingredient Substitute Recommendation Based on Collaborative Filtering and Recipe Context for Automatic Allergy-Safe Recipe Generation

Recipe sharing websites have become even more popular in the past few decades, and such repositories are able to keep hundreds of thousands of cooking recipes at the same time. Many recipe websites are developed with the participation of their community of users, which are allowed to upload new recipes and to provide evaluations and comments on the available recipes. However, in such repositories, the amount of recipes that are safe for users with special needs, such as food restrictions or allergies, is much smaller than ordinary food recipes, what may restrict the access and usability provided by such websites to that public. In this work, we propose a new recipe recommendation and generation system, based on a data-driven approach for single ingredient substitution, in such a way that recipes containing forbidden ingredients, according to a category of user food restrictions, are adapted by replacing such ingredients by safe ingredients. The proposed ingredient substitute recommendation system is based on a filtering process that takes into consideration the original recipe context, the relationship among sets of ingredients and the user preferences, towards the generation of recipes that are safe, and at the same time contemplate both user needs and tastes. The proposed system is evaluated by means of a qualitative analysis, showing promising results.
Tackling Gender-Based Cyber Violence against Women and Girls in Malawi Amidst the COVID-19 Pandemic

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Automatic Curriculum Design for Object Transportation Based on Deep Reinforcement Learning

This paper presents an automatic curriculum learning (ACL) method for object transportation based on deep reinforcement learning (DRL). Previous studies on object transportation using DRL have a sparse reward problem that an agent receives a rare reward for only the transportation completion of an object. Generally, curriculum learning (CL) has been used to solve the sparse reward problem. However, the conventional CL methods should be manually designed by users, which is difficult and tedious work. Moreover, there were no standard CL methods for object transportation. Therefore, we propose an ACL method for object transportation in which human intervention is unnecessary at the training step. A robot automatically designs curricula itself and iteratively trains according to the curricula. First, we define the difficult level of object transportation using a map, which is determined by the predicted travelling distance of an object and the existence of obstacles and walls. In the beginning, a robot learns the object transportation at an easy level (i.e., travelling distance is short and there are less obstacles around), then learns a difficult task (i.e., the long travelling distance of an object is required and there are many obstacles around). Second, training time also affects the performance of object transportation, and thus, we suggest an adaptive determining method of the number of training episodes. The number of episodes for training is adaptively determined based on the current success rate of object transportation. We verified the proposed method in simulation environments, and the success rate of the proposed method was 14% higher than no-curriculum. Also, the proposed method showed 63% (maximum) and 14% (minimum) higher success rates compared with the manual curriculum methods. Additionally, we conducted real experiments to verify the gap between simulation and practical results.
Longest Common Rollercoasters

For an integer \(k\ge 3\), a k-rollercoaster is a numeric string such that any maximal strictly non-increasing or non-decreasing substring has length at least k. We consider the problem of computing the longest common k-rollercoaster between two integer strings S and T, i.e., the longest k-rollercoaster that is a subsequence common to both S and T. We give two algorithms that solve this problem; The first runs in O(nmk) time and space, where n, m are respectively the lengths of S and T. The second runs in \(O(rk\log ^3 m \log \log m)\) time and O(rk) space, where \(r=O(mn)\) is the number of pairs (i, j) of matching points such that \(S[i]=T[j]\), assuming that \(m\le n\) and that S, T only consist of characters which occur in both strings. The second algorithm is faster than the first one when r is sub-linear in \(nm / \log ^3\,m \log \log m\).
Application of Image Recognition Method Based on Diffusion Equation in Film and Television Production

On the basis of studying the basic theory of anisotropic diffusion equation, this paper focuses on the application of anisotropic diffusion equation in image recognition film production. In order to further improve the application performance of P-M (Perona-Malik) anisotropic diffusion model, an improved P-M anisotropic diffusion model is proposed in this paper, and its application in image ultrasonic image noise reduction is discussed. The experimental results show that the model can effectively suppress the speckle noise and preserve the edge features of the image. Based on the image recognition technology, an image frame testing system is designed and implemented. The method of image recognition diffusion equation is used to extract and recognize the multilayer feature points of the test object according to the design of artificial neural network. To a certain extent, it improves the accuracy of image recognition and the audience rating of film and television. Use visual features of the film and television play in similarity calculation for simple movement scene segmentation problem, at the same time, the camera to obtain information, use the lens frame vision measuring the change of motion of the camera, and use weighted diffusion equation and the visual similarity of lens similarity calculation and motion information, by considering the camera motion of image recognition, effectively solve the sports scene of oversegmentation problem such as fighting and chasing.
Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments

Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can be incomplete, skewed, contradictory, and redundant impacting the reasoning. Basing decisions on these data can lead to inconsistent, irrational, and unreasonable cyber-physical systems' movements, adversely impacting the system's reliability and integrity. This paper presents the assessment of robust reasoning for autonomous cyber-physical systems in dynamic environments. In this work, robust reasoning is considered as 1) the capability of drawing conclusions with available data by applying classical and non-classical reasoning strategies and algorithms and 2) act and react robustly and safely in dynamic environments by employing robustness analysis to provide options on possible actions and evaluate alternative decisions. The result of the research shows that different common existing strategies, algorithms and analyses can be provided together with a comparison of their applicabilities, benefits, and drawbacks in the context of cyber-physical systems operating in dynamically changing environments. The conclusion is that robust reasoning in cyber-physical systems can handle dynamic environments. Moreover, combining these strategies and algorithms with robustness analysis can support achieving robust behavior in autonomous cyber-physical systems while operating in dynamically changing environments.
Mobile Robot Dynamic Path Planning Based on Improved A* Algorithm

In order to solve the problems of large storage, excessive redundant nodes and inflection points of paths, and unsmooth track in the traditional A*algorithm. This paper presents a fusion algorithm of mobile robot path planning based on the improved A* and dynamic window approach. Firstly, selectively expand the neighborhood according to the connection between the node to be expanded and the target node, and reduce the expanded nodes in openlist and closelist. Secondly, according to the key node extraction strategy, eliminate the redundant path nodes and unnecessary inflection points generated by A* tracking. Finally, the dynamic window approach is integrated to carry out real-time dynamic path planning. The Matlab simulation experiment results show that the improved fusion algorithm shortens the path length, and the smoothness and security of the path are also optimized.
Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines

Due to the recent COVID-19 pandemic that has swept the globe, more people are working from home. People use synchronous applications to communicate remotely because they are not able to meet face-to-face. However, few research studies on the issues surrounding the virtual conference application, particularly those that include collaborative activities, have been conducted. The usability study recruited 16 participants (in four groups of four) to communicate synchronously while performing collaborative activities, such as drawing together on a shared screen. According to the findings of the usability study, users do not often use the collaborative tools provided by the current virtual conference application. This is due to low exposure and unfamiliarity with the use of collaborative tools. The findings also show that users frequently do not turn on the web camera due to several reasons, including privacy, connectivity issues, the environment, and background distraction. Turning on the web camera can also cause anxiety due to shyness in front of the camera. However, some participants prefer to turn on the web camera so that they can see each other’s reactions when performing collaborative activities. The article provides several guidelines to assist in the design of virtual conference applications, including a simple familiar intuitive interface to encourage the use of collaborative tools and also introduces the use of virtual avatars as a way to represent oneself during online meetings to allow affective sharing while respecting the privacy of its users.