Reviewerly/exHarmony huggingface dataset:
Reading Dysfluency in Indian Classrooms: An Insight

Reading text in English is an important skill for students of higher education in India, as their understanding of their core subjects in specific and the world of information in general is based upon this skill. The ability to read English fluently, increases their job prospects also, as real time job environments use English as the official medium of communication and require that students read English with ease. Technical students who engage primarily with numerical data, diagrams and other non-textual content in their core subjects, have an especially hard time coping with tasks which involve reading textual English. There are several problems that lead to lack of fluency and thus lack of understanding of the content. This paper will study reading “ dysfluency “ problems technical students face, when reading English like word recognition difficulties, inability to read in sense groups, problems to do with accuracy, automaticity and expression. This paper will attempt to offer strategies to overcome this problem like Loud reading, Echo reading and Choral reading and some unconventional reading practices that can help solve this very common but important skill gap.
Adversarial Dense Contrastive Learning for Semi-Supervised Semantic Segmentation

Semi-supervised dense prediction tasks, such as semantic segmentation, can be greatly improved through the use of contrastive learning. However, this approach presents two key challenges: selecting informative negative samples from a highly redundant pool and implementing effective data augmentation. To address these challenges, we present an adversarial contrastive learning method specifically for semi-supervised semantic segmentation. Direct learning of adversarial negatives is adopted to retain discriminative information from the past, leading to higher learning efficiency. Our approach also leverages an advanced data augmentation strategy called AdverseMix, which combines information from under-performing classes to generate more diverse and challenging samples. Additionally, we use auxiliary labels and classifiers to prevent over-adversarial negatives from affecting the learning process. Our experiments on the Pascal VOC and Cityscapes datasets demonstrate that our method outperforms the state-of-the-art by a significant margin, even when using a small fraction of labeled data.
InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Many prior works focus on designing various transfer modules to transfer the style statistics to the content image. Although effective, ignoring the clear disentanglement of the content features and the style features from the first beginning, they have difficulty in balancing between content preservation and style transferring. To tackle this problem, we propose a novel information disentanglement method, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InfoStyler</i> , to capture the minimal sufficient information for both content and style representations from the pre-trained encoding network. InfoStyler formulates the disentanglement representation learning as an information compression problem by eliminating style statistics from the content image and removing the content structure from the style image. Besides, to further facilitate disentanglement learning, a cross-domain Information Bottleneck (IB) learning strategy is proposed by reconstructing the content and style domains. Extensive experiments demonstrate that our InfoStyler can synthesize high-quality stylized images while balancing content structure preservation and style pattern richness.
Classification of Tweets Related to Natural Disasters Using Machine Learning Algorithms

Identifying and classifying text extracted from social networks, following the traditional method, is very complex. In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work aims to identify, classify and analyze tweets related to real natural disasters through tweets with the hashtag #NaturalDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geolocated tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed to gain an initial understanding of the data. Fourth, the training and testing process of the BNB, MNB, L, KNN, DT, and RF models was initiated, using tools and libraries for this type of task. The results of the trained models demonstrated optimal performance: BNB, MNB, and LR models achieved a performance rate of 87% accuracy; and KNN, DT, and RF models achieved performances of 82%, 75%, and 86%, respectively. However, the BNB, MNB, and LR models performed better with respect to performance on their respective metrics, such as processing time, test accuracy, precision, and F1 score. Demonstrating, for this context and with the trained dataset that they are the best in terms of text classifiers.
Modeling experts, knowledge providers and expertise in Materials Modeling: MAEO as an application ontology of EMMO’s ecosystem

This work presents the MarketPlace Agent and Expert Ontology (MAEO), an ontology for modeling experts, expertise, and more broadly, knowledge providers and knowledge seekers for the subject areas of Materials Modeling. MAEO had its inception within the “MarketPlace” European project, whose purpose is to bring about a single entry point for gathering scientific and industrial stakeholders in Materials Modeling. As such, this project aimed to build an online platform where experts and knowledge providers can be searched, found and brought into contact with users, or knowledge seekers, and with one another. MAEO was developed in order to fulfill the requirements of this online platform and thus support it, but is also part of a wider ecosystem of Materials Modeling-related ontologies, at whose core lies the Elementary Multiperspective Material Ontology (EMMO). MAEO is thus an EMMO-compliant application ontology, and has been loosely aligned with a number of existing ontologies, including Friend-Of-A-Friend (FOAF) and five recently-developed EMMO-based domain ontologies for the classification of materials, models, manufacturing processes, characterization methods and software products related to Materials Modeling. Here, a detailed description of the axiomatization of MAEO and its interconnected ontologies is provided, along with results coming from its deployment and experimentation in a StarDog triplestore. Availability. The axiomatization of the ontology is stored in a GitHub repository available at: https://github.com/emmo-repo/MAEO-Ontology, and is published at the following URL: http://emmo.info/emmo/application/maeo/experts.
Comprint: Image Forgery Detection and Localization Using Compression Fingerprints

Manipulation tools that realistically edit images are widely available, making it easy for anyone to create and spread misinformation. In an attempt to fight fake news, forgery detection and localization methods were designed. However, existing methods struggle to accurately reveal manipulations found in images on the internet, i.e., in the wild. That is because the type of forgery is typically unknown, in addition to the tampering traces being damaged by recompression. This paper presents Comprint, a novel forgery detection and localization method based on the compression fingerprint or comprint. It is trained on pristine data only, providing generalization to detect different types of manipulation. Additionally, we propose a fusion of Comprint with the state-of-the-art Noiseprint, which utilizes a complementary camera model fingerprint. We carry out an extensive experimental analysis and demonstrate that Comprint has a high level of accuracy on five evaluation datasets that represent a wide range of manipulation types, mimicking in-the-wild circumstances. Most notably, the proposed fusion significantly outperforms state-of-the-art reference methods. As such, Comprint and the fusion Comprint+Noiseprint represent a promising forensics tool to analyze in-the-wild tampered images.
Retracted: An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence

[This retracts the article DOI: 10.1155/2022/1078056.].
Self-Supervised Signed Graph Attention Network for Social Recommendation

In recent times, social recommendation has become a popular technique in recommender systems due to its ability to enhance the accuracy of recommendations by leveraging the social relationships among users. Despite its widespread use, the prevalent social recommendation methods are often marred by sparsity and noise issues that negatively impact their practicality. Additionally, these methods fail to consider complex user interactions, which could potentially provide additional information. To address these limitations, this paper proposes a novel self-supervised signed graph attention network (SSAN) that incorporates user attitudes in the construction of higher-order user relations. This approach integrates signed networks in the formation of reasonable higher-order local neighborhood relations and aggregates user interests in different social relations through graph convolution and balance theory. Furthermore, two self-supervised signals, derived from social theory, are designed and incorporated into the recommendation framework to better exploit the rich structural and semantic information in social relationship graphs. Empirical results on three publicly available datasets demonstrate that SSAN outperforms existing state-of-the-art social recommendation methods.
Knowledge Graph Embedding Method Based on Entity Metric Learning

A knowledge graph is a knowledge base of graph structure that stores massive facts and their relations in the form of triplets. Embedding entities and relations in knowledge graph into low-dimensional continuous vector space is of great significance for downstream tasks, whereas most existing methods give an end-to-end fixed representation. Based on deep metric learning, we give different embeddings for the same entity in different relations and positions, thus dynamically representing entities in knowledge graph and effectively modeling complex relations. In addition, a path-based negative sampling method is proposed to widen the distance between similar entities to better deal with the aggregation problem of embedding entities in the representation space under one-to-many relation. Experiments show that the MRR and [email protected] of this method on the linked prediction dataset FB15k-237 reach 0.360 and 0.267 respectively, which are higher than the mainstream distance models and semantic matching models. Finally, the MRR-Dimension curves demonstrates that our model can perform well in both low and high embedding dimensions and has the potential to be applied to large-scale knowledge graph.
Generating Synthetic Data Using a Knowledge-based Framework for Autonomous Productions

Instead of taking images with a camera, synthetic data is generated in a computer simulation. One advantage of this is that training data can be generated on-demand, e.g., to automatically retrain robots when a task changes. While this makes synthetic data a promising approach for autonomous productions, realizing such autonomous setups is difficult with current systems for generating synthetic data, which usually require a programmer for every dataset to be generated.To overcome this problem, we present a novel framework for generating synthetic data. This framework restructures the generation process into asynchronous phases to increase the level of autonomy in two ways. First, by letting programmers write parameterized scripts, many different datasets can be autonomously generated. Secondly, by introducing a user interface, domain experts are enabled to influence the generation process on their own without a programmer. Furthermore, by being built as a new layer on top of existing systems for generating synthetic data, our framework shows a new way to maximize compatibility with other research on synthetic data generation.To test our framework, we have developed a fully functional prototype based on it. Successfully using this prototype for an example experiment, we conclude that our ideas work. Future research can use our prototype for more elaborate experiments on autonomous productions and to further assess its usability.
BSDA in Visual Recognition: Balanced Semantic Data Augmentation for Long-Tailed Data

Deep convolutional neural networks have shown remarkable success in many visual recognition tasks, but they struggle with long-tailed datasets commonly encountered in natural world datasets. In such datasets, the greatest amount of data belongs to majority classes (head classes) with significantly less data for the minority (tail) classes. The class imbalance greatly reduces the performance of traditional networks. Existing methods use re-sampling and re-weighting strategies as the primary methods to deal with these long-tailed datasets. We address the problem using a data augmentation approach inspired by a recently proposed implicit semantic data augmentation (ISDA) algorithm. In this paper, we propose an approach using dual branches with balanced semantic data augmentation (BSDA) to perform effective semantic data augmentation for long-tail recognition by learning good representations. The deep feature branch learns good representations of inputs, and the classifier branch searches for meaningful semantic transformations, with different sampling strategies in each branch. Both branches share the same weights, with focus shifting from one to the other during training. We also propose an augment coefficient to apply different augmentation intensities for different categories. We test our methods on the CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 datasets. The results demonstrate the effectiveness of our methods.
Event-Triggered Online Learning Fuzzy-Neural Robust Control for Furnace Temperature in Municipal Solid Waste Incineration Process

Affected by high temperature and serious pollution, some key parameters in incinerator are difficult to obtain directly, and the detectable data also contain outliers and noise, which makes it extremely difficult to mine the relationship between variables and derive control rules. To solve these problems, an event-triggered online learning fuzzy-neural robust controller is proposed and applied to the furnace temperature control of municipal solid waste incineration (MSWI) process. First, the outliers are eliminated by the box-plot method, and the data is denoised with a Gaussian filter. Second, the controlled object model based on T-S fuzzy neural network is constructed by data-driven method. Third, an online learning fuzzy neural controller is designed to be imposed on the established controlled object model, which adaptively adjusts the network structure by calculating the information transmission strength of the rule neurons. Meanwhile, an event-triggered mechanism is introduced to reduce actuator wear and save energy while maintaining control effectiveness. Then, the convergence of the controller is deduced by Lyapunov’s second law. Finally, the validity of the proposed method is verified by process data from a real MSWI plant in Beijing, China. The results show that the method proposed in this paper can build the furnace temperature model from imperfect data, and the controller can grow and prune neurons autonomously, which improves the control accuracy and efficiency of the furnace temperature under external disturbances. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Municipal solid waste (MSW) has complex and variable components, and its incineration process relies on manual operations. However, manual operations have hysteresis and subjectivity, which will increase the workload of workers and also cause problems such as unstable incineration. Furnace temperature is the main controlled variable of MSWI process, which is closely related to MSW burnout rate and pollutant emission concentration. Therefore, an event-triggered online leaning fuzzy-neural robust controller is designed in this paper, which can keep the furnace temperature stable by automatically adjusting the primary air volume and drying grate speed under external disturbances. In addition, the controller can adjust its structure adaptively according to different operating conditions, and it is equipped with an event-triggered mechanism to meet the control requirements while reducing the frequency of controller updates, which improves equipment life and reduces energy consumption. Finally, the controller is validated by the process data of a real MSWI plant.
Localization Uncertainty Estimation for Autonomous Underwater Vehicle Navigation

High-precision positioning capability is a crucial technology for achieving accurate navigation in autonomous underwater vehicles (AUVs). However, due to severe electromagnetic wave attenuation underwater and the unavailability of the global positioning system (GPS), inertial-navigation-based dead reckoning is considered the primary method for underwater positioning. Unfortunately, errors accumulated during the navigation process lead to unbounded drift, and filtering-based methods have been used to mitigate the errors, but with limited success. In this paper, we propose a precise underwater dead-reckoning mathematical model that recursively calculates the ground truth and corresponding errors based on an AUV’s motion model, and we derive empirical formulas. Compared to related methods, this approach not only models the cumulative errors of relative noise measurements, but also provides recursive expressions with corresponding statistical moments. The experimental results demonstrate that this formula significantly reduces positioning errors in underwater navigation tasks.
Multimodal Interaction for Cobot Using MQTT

For greater efficiency, human–machine and human–robot interactions must be designed with the idea of multimodality in mind. To allow the use of several interaction modalities, such as the use of voice, touch, gaze tracking, on several different devices (computer, smartphone, tablets, etc.) and to integrate possible connected objects, it is necessary to have an effective and secure means of communication between the different parts of the system. This is even more important with the use of a collaborative robot (cobot) sharing the same space and very close to the human during their tasks. This study present research work in the field of multimodal interaction for a cobot using the MQTT protocol, in virtual (Webots) and real worlds (ESP microcontrollers, Arduino, IOT2040). We show how MQTT can be used efficiently, with a common publish/subscribe mechanism for several entities of the system, in order to interact with connected objects (like LEDs and conveyor belts), robotic arms (like the Ned Niryo), or mobile robots. We compare the use of MQTT with that of the Firebase Realtime Database used in several of our previous research works. We show how a “pick–wait–choose–and place” task can be carried out jointly by a cobot and a human, and what this implies in terms of communication and ergonomic rules, via health or industrial concerns (people with disabilities, and teleoperation).
Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System

Recently proposed systems aim at achieving privacy using locality-sensitive hashing. We show how these approaches fail by presenting attacks against two such systems: Google's FLoC proposal for privacy-preserving targeted advertising and the MinHash Hierarchy, a system for processing mobile users' traffic behavior in a privacy-preserving way. Our attacks refute the pre-image resistance, anonymity, and privacy guarantees claimed for these systems. In the case of FLoC, we show how to deanonymize users using Sybil attacks and to reconstruct 10% or more of the browsing history for 30% of its users using Generative Adversarial Networks. We achieve this only analyzing the hashes used by FLoC. For MinHash, we precisely identify the movement of a subset of individuals and, on average, we can limit users' movement to just 10% of the possible geographic area, again using just the hashes. In addition, we refute their differential privacy claims.
Trifecta: Faster High-Throughput Three-Party Computation over WAN Using Multi-Fan-In Logic Gates

Multi-party computation (MPC) has been a very active area of research, and recent industrial deployments exist. Practical MPC is currently limited to low-latency, high-throughput network setups, i.e., local-area networks (LAN). However, many use cases require the participation of different entities located in different data centers, i.e., communication over wide-area networks (WAN). Although, constant-round MPC exists, it has very high communication cost. In this paper we investigate the reduction of the round complexity of secret-shared based multi-party computation. We propose a new three-party computation protocol that allows to compute multi-fan-in gates in one round without any precomputation. Our protocol outperforms related work, including constant-round protocols, over WANs. For example, we improve throughput of AES-128 over WAN by a factor of more than 2.2x compared to related work.
Eyelid’s Intrinsic Motion-Aware Feature Learning for Real-Time Eyeblink Detection in the Wild

Real-time eyeblink detection in the wild is a recently emerged challenging task that suffers from dramatic variations in face attribute, pose, illumination, camera view and distance, etc. One key issue is to well characterize eyelid’s intrinsic motion (i.e., approaching and departure between upper and lower eyelid) robustly, under unconstrained conditions. Towards this, a novel eyelid’s intrinsic motion-aware feature learning approach is proposed. Our proposition lies in 3 folds. First, the feature extractor is led to focus on informative eye region adaptively via introducing visual attention in a coarse-to-fine way, to guarantee robustness and fine-grained descriptive ability jointly. Then, 2 constraints are proposed to make feature learning be aware of eyelid’s intrinsic motion. Particularly, one concerns the fact that the inter-frame feature divergence within eyeblink processes should be greater than non-eyeblink ones to better reveal eyelid’s intrinsic motion. The other constraint minimizes the feature divergence of non-eyeblink samples, to suppress motion clues due to head or camera movement, illumination change, etc. Meanwhile, concerning the high ambiguity between eyeblink and non-eyeblink samples, soft sample labels are acquired via self-knowledge distillation to conduct feature learning with finer supervision than the hard ones. The experiments verify that, our proposition is significantly superior to the state-of-the-art ones (i.e., advantage on F1-score over 7%) and with real-time running efficiency. It is also of strong generalization capacity towards constrained conditions. The source code will be released upon acceptance.