Reviewerly/exHarmony huggingface dataset:
EQGTL: An Ensemble Model for Relevant Question Generation using Transfer Learning

The manner in which a question is phrased determines the quality and type of response that is obtainable. An answer is likely to change when a question is rephrased. Thus, the need for well-formed questions indeed facilitates quality communication and information exchange in natural language. Moreso, Automated Question Generation Models (AQGM) greatly reduce human errors and any grammatical deficiencies that may arise in the formulation of relevant questions. Existing AQGM methods employ muddled structures that necessitate complex computational assets and increase the ambiguity of the task. In this paper we proposed an automated Ensemble Question Generation Model using the Transfer Learning (EQGTL) framework which utilizes a transfer learning approach to tune Bert Disambiguation Bert-WSD) Model and Text-to-Text Transfer Transformer (T5) model. For our use case, we relied on the robustness of the pre-trained T5 model and finetuned it for generating questions and correct answers alongside multiple incorrect answers described as distractors. When tested with an unseen set of free-form text, our model performs admirably well, generating relevant questions and answers that are grammatically correct and contextually relevant. Tutees can use questions generated by our model to assess their level of comprehension, and instructors as well can use these questions to quickly build key ideas at any time. Our proposed model serves as a valuable solution that can be applied to multiple domains and subject areas.
Semantic-Based Cognitive Service Discovery in Multi-Cloud Environments

While service discovery is an area of active research in which many approaches were proposed, particularly for Web services, this issue still needs to be mature for Cloud computing. Little attention is given to the problem of discovering Cloud services on the Internet, and existing solutions have significant limitations, such as time consumption and poor performance. This paper proposes a semantic-based approach for Cloud cognitive service discovery in Multi-Cloud environments to accurately answer user functional requirements. A set of experiments is carried out to validate the method.
Review of Underwater Image Enhancement Methods

Due to the effect of light attenuation and scattering, the underwater image will produce color distortion and image blur. Underwater image enhancement algorithm is an important technology in the development and utilization of Marine resources, and it has achieved good results in underwater image color restoration and sharpness improvement. On the basis of introducing the research background, significance and hot issues of underwater image enhancement, the classical algorithms and the latest progress of underwater image color restoration and sharpness improvement are summarized, underwater image quality evaluation system and dataset is sorted out. Finally, combined with the existing problems and technical difficulties of underwater image enhancement algorithm, the future research focus of underwater image enhancement algorithm is prospected.
Software Defect Prediction: A Comparative Analysis of Machine Learning Techniques

The early prediction of defective modules in developing software can help the development team to utilize the available resources efficiently to deliver high quality software product in limited time. The classification technique, which works by extracting hidden defective patterns among software properties, is a good way to determine errors. In this research, twelve commonly used NASA datasets are utilised to forecast software flaws using a variety of machine learning classification methods. Nave Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), AdaBoost Algorithm(ADB), Gradient boosting(GB), Logistic Regression(LR), XG-Boost Algorithm Multi - layer perceptron (MLP), K Nearest Neighbor (KNN) are some of the classification techniques. Performance of used classification techniques is evaluated by using various measures such as: Precision, Recall, F-Measure, Accuracy and Overall average Performance measure. Results shows that the Mean Gradient boosting as well as Logistic Regression provide greater performance for Defect Prediction among other Machine Learning Classifiers. Furthermore, the experimental results reveal that Gradient Boost(GB) outperforms all nine machine learning classifiers in terms of overall average performance.
Analyzing the impact and application of Augmented Reality in Education: The case of students with special educational needs

This study presents a systematic review of the literature which analyzes the impact and application of Augmented Reality (AR) technology in the tutoring of students with special educational needs. In recent years, Information and Communication Technology (ICT) promotes learning in a pluralistic and multisensory environment that favors students with special educational needs. Augmented Reality is an emerging technology which has been widely used in the field of education, but only few studies are related to special education. This paper investigates the research in AR educational systems addressed to students with special educational needs. In total, 26 studies between 2014 and 2022 were selected and analyzed. Specifically, this systematic review examines the advantages and limitations of AR use in Special Education, the AR platforms and tools used in learning scenarios and the different types of students with special educational needs.
Quantum Key Distribution: Basic Protocols and Threats

The last decade, the evolution in quantum computing has been enormous and real and reliable quantum computers are being developed quickly. One of the consequences of the upcoming quantum era is to make key distribution protocols insecure, as most of them are based on discrete algorithm problems. On the other hand, quantum computing provides a powerful and prominent tool for the safe transmission of information and cryptographic schemes and many of them have already been proposed. In this work, we discuss about quantum cryptography and we present certain quantum key distribution protocols. We also discuss about 'potential attacks that can be performed against the quantum cryptographic schemes which are based on quantum system's imperfections and loopholes.
Free-Form Multi-Modal Multimedia Retrieval (4MR)

Due to the ever increasing amount of multimedia data, efficient means for multimedia management and retrieval are required. Especially with the rise of deep-learning-based analytics methods, the semantic gap has shrunk considerably, but a human in the loop is still considered mandatory. One of the driving factors of video search is that humans tend to refine their queries after reviewing the results. Hence, the entire process is highly interactive. A natural approach to interactive video search is using textual descriptions of the content of the expected result, enabled by deep learning-based joint visual text co-embedding. In this paper, we present the Multi-Modal Multimedia Retrieval (4MR) system, a novel system inspired by vitrivr, that empowers users with almost entirely free-form query formulation methods. The top-ranked teams of the last few iterations of the Video Browser Showdown have shown that CLIP provides an ideal feature extraction method. Therefore, while 4MR is capable of image and text retrieval as well, for VBS video retrieval is based primarily based on CLIP.
Federated Learning with GAN-Based Data Synthesis for Non-IID Clients

Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this chapter, we propose a novel framework, named Synthetic Data Aided Federated Learning (SDA-FL), to resolve this non-IID challenge by sharing synthetic data. Specifically, each client pretrains a local generative adversarial network (GAN) to generate differentially private synthetic data, which are uploaded to the parameter server (PS) to construct a global shared synthetic dataset. To generate confident pseudo labels for the synthetic dataset, we also propose an iterative pseudo labeling mechanism performed by the PS. The assistance of the synthetic dataset with confident pseudo labels significantly alleviates the data heterogeneity among clients, which improves the consistency among local updates and benefits the global aggregation. Extensive experiments evidence that the proposed framework outperforms the baseline methods by a large margin in several benchmark datasets under both the supervised and semi-supervised settings.
Fusion-Based Low-Light Image Enhancement

Recently, deep learning-based methods have made remarkable progress in low-light image enhancement. In addition to poor contrast, the images captured under insufficient light suffer from severe noise and saturation distortion. Most existing unsupervised learning-based methods adopt the two-stage processing method to enhance contrast and denoise sequentially. However, the noise will be amplified in the contrast enhancement process, thus increasing the difficulty of denoising. Besides, the saturation distortion caused by insufficient illumination is not considered well in existing unsupervised low-light enhancement methods. To address the above problems, we propose a novel parallel framework, which includes a saturation adaptive adjustment branch, brightness adjustment branch, noise suppression branch, and fusion module for adjusting saturation, correcting brightness, denoise, and multi-branch fusion, respectively. Specifically, the saturation is corrected via global adjustment, the contrast is enhanced through curve mapping estimation, and we use BM3D to preliminary denoise. Further, the enhanced branches are fed to the fusion module for a trainable guided filter, which is optimized in an unsupervised training manner. Experiments on the LOL, MIT-Adobe 5k, and SICE datasets demonstrate that our method achieves better quantitation and qualification results than the state-of-the-art algorithms.
Towards Captioning an Image Collection from a Combined Scene Graph Representation Approach

Most content summarization models from the field of natural language processing summarize the textual contents of a collection of documents or paragraphs. In contrast, summarizing the visual contents of a collection of images has not been researched to this extent. In this paper, we present a framework for summarizing the visual contents of an image collection. The key idea is to collect the scene graphs for all images in the image collection, create a combined representation, and then generate a visually summarizing caption using a scene-graph captioning model. Note that this aims to summarize common contents across all images in a single caption rather than describing each image individually. After aggregating all the scene graphs of an image collection into a single scene graph, we normalize it by using an additional concept generalization component. This component selects the common concept in each sub-graph with ConceptNet based on word embedding techniques. Lastly, we refine the captioning results by replacing a specific noun phrase with a common concept from the concept generalization component to improve the captioning results. We construct a dataset for this task based on the MS-COCO dataset using techniques from image classification and image-caption retrieval. An evaluation of the proposed method on this dataset shows promising performance.
Information Set Decoding for Lee-Metric Codes Using Restricted Balls

The Lee metric syndrome decoding problem is an NP-hard problem and several generic decoders have been proposed. The observation that such decoders come with a larger cost than their Hamming metric counterparts make the Lee metric a promising alternative for classical code-based cryptography. Unlike in the Hamming metric, an error vector that is chosen uniformly at random of a given Lee weight is expected to have only few entries with large Lee weight. Using this expected distribution of entries, we are able to drastically decrease the cost of generic decoders in the Lee metric, by reducing the original problem to a smaller instance, whose solution lives in restricted balls.
V-FIRST 2.0: Video Event Retrieval with Flexible Textual-Visual Intermediary for VBS 2023

In this paper, we present a new version of our interactive video retrieval system V-FIRST. Besides the existing features of querying by textual descriptions and visual examples, we propose the usage of an image generator that can generate images from a text prompt as a means to bridge the domain gap. We also include a novel referring expression segmentation module to highlight the objects in an image. This is the first step towards providing adequate explainability to retrieval results, ensuring that the system can be trusted and used in domain-specific and critical scenarios. Searching by a sequence of events is also a new addition, as it proves to be pivotal in finding events from memory. Furthermore, we improved our Optical Character Recognition capability, especially in the case of scene text. Finally, the inclusion of relevant feedback allows the user to explicitly refine the search space. All combined, our system has greatly improved user interaction, leveraging more explicit information and providing more tools for the user to work with.
A Study of a Cross-modal Interactive Search Tool Using CLIP and Temporal Fusion

Recently, the CLIP model demonstrated impressive performance in text-image search and zero classification tasks. Hence, CLIP was used as a primary model in many cross-modal search tools at evaluation campaigns. In this paper, we show a study performed with the model integrated to a successful video search tool at the respected Video Browser Showdown competition. The tool allows more complex querying actions on top of the primary model. Specifically, temporal querying and Bayesian like relevance feedback were tested as well as their natural combination – temporal relevance feedback. In a thorough analysis of the tool's performance, we show current limits of cross-modal searching with CLIP and also the impact of more advanced query formulation strategies. We conclude that current cross-modal search models enable users to solve some types of tasks trivially with a single query, however, for more challenging tasks it is necessary to rely also on interactive search strategies.
SRes-NeRF: Improved Neural Radiance Fields for Realism and Accuracy of Specular Reflections

The Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene using a multilayer perceptron (MLP) combined with classic volume rendering and uses positional encoding techniques to increase image resolution. Although it can effectively represent the appearance of a scene, they often fail to accurately capture and reproduce the specular details of surfaces and require a lengthy training time ranging from hours to days for a single scene. We address this limitation by introducing a representation consisting of a density voxel grid and an enhanced MLP for a complex view-dependent appearance and model acceleration. Modeling with explicit and discretized volume representations is not new, but we propose Swish Residual MLP (SResMLP). Compared with the standard MLP+ReLU network, the introduction of layer scale module allows the shallow information of the network to be transmitted to the deep layer more accurately, maintaining the consistency of features. Introduce affine layers to stabilize training, accelerate convergence and use the Swish activation function instead of ReLU. Finally, an evaluation of four inward-facing benchmarks shows that our method surpasses NeRF's quality, it only takes about 18 min to train from scratch for a new scene and accuracy capture the specular details of the scene surface. Excellent performance even without positional encoding.
A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly Detection

Unmanned Aerial Vehicles and the increasing variety of their applications are raising in popularity. The growing number of UAVs, emphasizes the significance of drones’ reliability and robustness. Thus, there is a need for an efficient self-observing sensing mechanism to detect real-time anomalies in drone behavior. Previous works suggested prediction models from control theory, yet, they are complex by nature and hard to implement, while Deep Learning solutions are of great utility. In this paper, we propose a real-time framework to detect anomalies in drones by analyzing the sound emitted from them. For this purpose, we construct a hybrid Deep Learning based Transformer and a Convolutional Neural Network inspired by the well-known VGG architecture. Our approach is examined over a dataset that is collected from a single microphone set located on a micro drone in real-time. Our approach achieves an F1-score of 88.4% in detecting anomalies and outperforms the VGG-16 architecture. Moreover, the framework presented in this paper reduces the number of parameters of the well-known VGG-16 from 138M, into a shrunk version with 3.6M parameters only. Additionally, our real-time approach, results in a smaller number of parameters in the neural network, and yet yields high accuracy in anomaly detection in drones with an average inference time of 0.2 seconds per second. Moreover, with an earphone that weighs less than 100 grams on top of the UAV, our method is shown to be beneficial, even in extreme conditions such as a micro-size dataset that is composed of three hours of flight recordings. The presented self-observing method can be implemented by simply adding a microphone to drones and transmitting the captured audio for analysis to the remote control or performing it onboard the drone using a dedicated microcontroller.
Aerial Image Dehazing Network Compression: Towards Micro-UAV’S Real-Time Online Inferencing

Dehazing Generative Adversarial Networks (Dehazing GANs) have been widely used in the field of haze-free images. However, due to the huge computational costs and memory usage of dehazing GANs, they are difficult to be applied on the edge devices with limited resources. Although there are many efforts to solve these issues, these compressed dehazing GANs can not meet the real-time requirements on edge devices. In this way, we propose an efficient multi-granularity distillation scheme for aerial image dehazing network compression (AIDNC). We are the first one to introduce online distillation to compress dehazing GANs, where the student model is compressed by lightweight residual structure and channel pruning. In AIDNC, the student generator removes the discriminator and is optimized by the guidance information provided by gradually promoted teacher generator. Experimental results on two dehazing datasets show that AIDNC achieves $$17.43\times $$ MACs and $$218.41\times $$ parameters compression with few loss for image quality. It is proved that AIDNC is an efficient framework for the aerial image real-time dehazing on edge devices, like micro-UAVs.
Supervised Machine Learning for Breast Cancer Risk Factors Analysis and Survival Prediction

The choice of the most effective treatment may eventually be influenced by the breast cancer survival prediction. For the purpose of predicting the chances of a patient surviving, a variety of techniques were employed, such as statistical, machine learning, and deep learning models. In the current study, 1904 patient records from the METABRIC dataset were utilized to predict a 5-year breast cancer survival using a machine learning approach. In this study, we compare the outcomes of seven classification model to evaluate how well they perform using the following metrics: recall, AUC, confusion matrix, accuracy, precision, false positive rate, and true positive rate. The findings demonstrate that the classifiers for Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RD), Extremely Randomized Trees (ET), K-Nearest Neighbor (KNN), and Adaptive Boosting (AdaBoost) can accurately predict the survival rate of the tested samples, which is 75,4%, 74,7%, 71,5%, 75,5%, 70,3%, and 78%.
Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition

Currently, federated learning (FL) can enable users to collaboratively train a global model while protecting the privacy of user data, which has been applied to human activity recognition (HAR) tasks. However, in real HAR scenarios, deploying an FL system needs to consider multiple aspects, including system accuracy, fairness, robustness, and scalability. Most existing FL frameworks aim to solve specific problems while ignoring other properties. In this paper, we propose FedCHAR, a personalized FL framework with a hierarchical clustering method for robust and fair HAR, which not only improves the accuracy and the fairness of model performance by exploiting the intrinsically similar relationship between users but also enhances the robustness of the system by identifying malicious nodes through clustering in attack scenarios. In addition, to enhance the scalability of FedCHAR, we also propose FedCHAR-DC, a scalable and adaptive FL framework which is featured by dynamic clustering and adapting to the addition of new users or the evolution of datasets for realistic FL-based HAR scenarios. We conduct extensive experiments to evaluate the performance of FedCHAR on seven datasets of different sizes. The results demonstrate that FedCHAR could obtain better performance on different datasets than the other five state-of-the-art methods in terms of accuracy, robustness, and fairness. We further validate that FedCHAR-DC exhibits satisfactory scalability on three large-scale datasets regardless of the number of participants.
EvIs-Kitchen: Egocentric Human Activities Recognition with Video and Inertial Sensor Data

Egocentric Human Activity Recognition (ego-HAR) has received attention in fields where human intentions in a video must be estimated. The performance of existing methods, however, are limited due to insufficient information about the subject's motion in egocentric videos. We consider that a dataset of egocentric videos along with two inertial sensors attached to both wrists of the subject to obtain more information about the subject's motion will be useful to study the problem in depth. Therefore, this paper provides a publicly available dataset, EvIs-Kitchen, which contains well-synchronized egocentric videos and two-hand inertial sensor data, as well as interaction-highlighted annotations. We also present a baseline multimodal activity recognition method with two-stream architecture and score fusion to validate that such multimodal learning on egocentric videos and intertial sensor data is more effective to tackle the problem. Experiments show that our multimodal method outperforms other single-modal methods on EvIs-Kitchen.
No $((n,K,d&lt; 127))$ Code Can Violate the Quantum Hamming Bound

It is well-known that pure quantum error correcting codes (QECCs) are constrained by a quantum version of the Hamming bound. Whether impure codes also obey such a bound, however, remains a long-standing question with practical implications for the efficacy of QECCs. We employ a combination of previously derived bounds on QECCs to demonstrate that a subset of all codes must obey the quantum Hamming bound. Specifically, we combine an analytical bound due to Rains with a numerical bound due to Li and Xing to show that no ((n,k,d < 127)) code can violate the quantum Hamming bound.