Reviewerly/exHarmony huggingface dataset:
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges

In recent years, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges.
Using Twitter to Detect and Tag Important Events in Sports Media

In this paper we examine the effectiveness of using a filtered stream of tweets from Twitter to automatically identify events of interest within the video of live sports transmissions. We show that using just the volume of tweets generated at any moment of a game actually provides a very accurate means of event detection, as well as an automatic method for tagging events with representative words from the tweet stream. We compare this method with an alternative approach that uses complex audio-visual content analysis of the video, showing that it provides near-equivalent accuracy for major event detection at a fraction of the computational cost. Using community tweets and discussion also provides a sense of what the audience themselves found to be the talking points of a video.
Virtual Reality in Tourism: Is it ‘Real’ Enough?

Virtual Reality Technology is increasingly becoming popular in the tourism sector.So far, the most researched application is the marketing of destinations.In contrast, the technology has also been mentioned as a means to limit or reduce the number of tourists at a specific sight or destination.In this respect vr is considered as a substitute for the actual trip.This paper addresses this issue by looking at the possibility to apply vr-technology to transfer the real-life experience into the digital world.In a qualitative research framework, visitor behaviour and experience are investigated when encountering vr sights in order to better understand items driving technology adoption.Structured content analysis is applied for data analysis where coding follows an adjusted Unified Theory of Acceptance and Use of Technology model.For interpretation purposes a pure qualitative framework was chosen.We find that enjoyment is an important driver for vr technology acceptance, whereas facilitating conditions and outcome expectations seem to be obstacles for it.Perceived usefulness is evaluated controversially.While the technology is not acknowledged as a substitute for a regular holiday trip, especially for travellers who take pleasure in active holidays or appreciate social interaction, it was recognised as an alternative for special occasions such as brief getaways from everyday life or short city trips.Overall, when appropriately implemented the technology might not only be useful to decrease visitor concentration in touristic hotspots or to decrease negative aspects associated with frequent travel but could further be applied to sites where visitors do not engage physically because sites are too distant, expensive, inhospitable, unsafe or fragile.
Better Dense Trajectories by Motion in Videos

Currently, the most widely used point trajectories generation methods estimate the trajectories from the dense optical flow, by using a consistency check strategy to detect the occluded regions. However, these methods will miss some important trajectories, thus resulting in breaking smooth areas without any structure especially around the motion boundaries (MBs). We suggest exploring MBs in video to generate more accurate dense point trajectories. Estimating MBs from the video improves the point trajectory accuracy of the discontinuity or occluded areas. Then, we obtain trajectories by tracking the initial feature points through all frames. The experimental results demonstrate that our method outperforms the state-of-the-art methods on the challenging benchmark.
Foundations of differentially oblivious algorithms

It is well-known that a program's memory access pattern can leak information about its input. To thwart such leakage, most existing works adopt the technique of oblivious RAM (ORAM) simulation. Such an obliviousness notion has stimulated much debate. Although ORAM techniques have significantly improved over the past few years, the concrete overheads are arguably still undesirable for real-world systems --- part of this overhead is in fact inherent due to a well-known logarithmic ORAM lower bound by Goldreich and Ostrovsky. To make matters worse, when the program's runtime or output length depend on secret inputs, it may be necessary to perform worst-case padding to achieve full obliviousness and thus incur possibly super-linear overheads.Inspired by the elegant notion of differential privacy, we initiate the study of a new notion of access pattern privacy, which we call δ)-differential We separate the notion of (ϵ, δ)-differential obliviousness from classical obliviousness by considering several fundamental algorithmic abstractions including sorting small-length keys, merging two sorted lists, and range query data structures (akin to binary search trees). We show that by adopting differential obliviousness with reasonable choices of ϵ and δ, not only can one circumvent several impossibilities pertaining to full obliviousness, one can also, in several cases, obtain meaningful privacy with little overhead relative to the non-private baselines (i.e., having privacy almost for free). On the other hand, we show that for very demanding choices of ϵ and δ, the same lower bounds for oblivious algorithms would be preserved for (ϵ, δ)-differential obliviousness.
Efficient k-nearest neighbors search in graph space

The k-nearest neighbors classifier has been widely used to classify graphs in pattern recognition. An unknown graph is classified by comparing it to all the graphs in the training set and then assigning it the class to which the majority of the nearest neighbors belong. When the size of the database is large, the search of k-nearest neighbors can be very time consuming. On this basis, researchers proposed optimization techniques to speed up the search for the nearest neighbors. However, to the best of our knowledge, all the existing works compared the unknown graph to each train graph separately and thus none of them considered finding the k nearest graphs from a query as a single problem. In this paper, we define a new problem called multi graph edit distance to which k-nearest neighbor belongs. As a first algorithm to solve this problem, we take advantage of a recent exact branch-and-bound graph edit distance approach in order to speed up the classification stage. We extend this algorithm by considering all the search spaces needed for the dissimilarity computation between the unknown and the training graphs as a single search space. Results showed that this approach drastically outperformed the original approach under limited time constraints. Moreover, the proposed approach outperformed fast graph edit distance algorithms in terms of average execution time especially when the number of graphs is tremendous.
Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders

Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems. Conventional machine learning approaches normally depend on selected features within a human annotated training set. However, it is quite laborious to obtain a well labeled data set and its generation can easily be influenced by accumulative errors caused by human factors. To overcome this, we will present our processing pipeline for query matching realized through neural networks within the task of medical procedure classification. The pipeline is built upon convolutional neural networks (CNN) and autoencoder with logistic regression. On the task of relevance determination between query and category text, the autoencoder based method has achieved a micro F1 score of 70.29%, while the convolutional based method has reached a micro F1 score of 60.86% with high efficiency. These two algorithms are compared in experiments with different configurations and baselines (SVM, logistic regression) with respect to their suitability for the task of automatic encoding. Advantages and limitations are discussed.
Kinematics-based incremental visual servo for robotic capture of non-cooperative target

This paper presents the concept and experimental results of a kinematics-based incremental visual servo control approach for robotic manipulators with an eye-in-hand configuration to capture non-cooperative targets autonomously. The vision system is adopted to estimate the real time position and motion of the target by an integrated algorithm of photogrammetry and the adaptive extended Kalman filter. The unknown intercept point of trajectories of the target and the end-effector is dynamically predicted and updated based on the target estimates and is served as the desired position of the end-effector. An incremental control law is developed for the robotic manipulator to avoid multiple solutions of the robotic inverse kinematics. The end-effector is then controlled by the proposed control scheme to approach the dynamically estimated interception point directly. The proposed approach is validated experimentally by custom built robotic manipulator. To demonstrate the effectiveness of the proposed approach, uncertainties, such as, joint flexibility of the robotic manipulator, backlash of actuators, nonlinear target motion, camera mounting bias, etc., have not been considered in the control law. The experimental results show that the predicted minimum tracking time is reduced asymptotically as the end-effector approaches the target, which demonstrate the proposed control scheme is effective and reliable. The advantages of the proposed control approach are: it does not require a robotic dynamic model that most of the existing robotic control based on; it avoids the multiple solution problem in the inverse kinematics; it is insensitive to system uncertainties; and it is much easier for engineering implementation.
Volume holographic optical encryption and decryption in photorefractive LiNbO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e911" altimg="si1.gif"><mml:msub><mml:mrow /><mml:mrow><mml:mi mathvariant="bold">3</mml:mi></mml:mrow></mml:msub></mml:math>:Fe crystal

Abstract We present general volume holographic encryption and decryption system by means of two wave mixing process that can apply to variety of diffraction domains, including the conventional Fourier hologram domain (using 4f-optical processor), the Fresnel hologram domain, and fractional Fourier hologram domain. A rigorous theory for volume holographic encryption and decryption is developed and presented under the framework of Collins diffraction integral formalism. The ability for volume holographic encryption and decryption are successfully demonstrated in a photorefractive LiNbO3:Fe crystal. We also demonstrate a robustness test of our holographic decryption system with respect to misalignment of the phase key in both longitudinal and transversal directions away from the original position and blind decryption without knowledge of the correct key.
Mining Dynamics of Research Topics Based on the Combined LDA and WordNet

A large volume of research documents are available online for us to access and analysis. It is very important to detect and mine the dynamics of the research topics from these large corpora. In this paper, we propose an improved method by introducing WordNet to LDA. This approach is to find latent topics of large corpora, and then we propose many methods to analyze the dynamics of those topics. We apply the methodology to two large document collections: 1940 papers from NIPS 00-13 (1987–2000) and 2074 papers from NIPS 14-23 (2001–2010). Six experiments are conducted on the two corpora and the experimental results show that our method is better than LDA in finding research topics and is feasible in discovering the dynamics of research topics.
Multi-modal multi-view Bayesian semantic embedding for community question answering

Semantic embedding has demonstrated its value in latent representation learning of data, and can be effectively adopted for many applications. However, it is difficult to propose a joint learning framework for semantic embedding in Community Question Answer (CQA), because CQA data have multi-view and sparse properties. In this paper, we propose a generic Multi-modal Multi-view Semantic Embedding (MMSE) framework via a Bayesian model for question answering. Compared with existing semantic learning methods, the proposed model mainly has two advantages: (1) To deal with the multi-view property, we utilize the Gaussian topic model to learn semantic embedding from both local view and global view. (2) To deal with the sparse property of question answer pairs in CQA, social structure information is incorporated to enhance the quality of general text content semantic embedding from other answers by using the shared topic distribution to model the relationship between these two modalities (user relationship and text content). We evaluate our model for question answering and expert finding task, and the experimental results on two real-world datasets show the effectiveness of our MMSE model for semantic embedding learning.
Improving the Performance of Sentiment Classification on Imbalanced Datasets With Transfer Learning

In recent years, many sentiments classification models, such as deep learning models and traditional machine learning models, claim that they can achieve state-of-the-art performance in sentiment analysis problems.Admittedly, this is based on the premise that the training samples are class balanced.However, in the real world, the training data sets we can get are often imbalanced, which will cause the trained classifier to tend to predict the test samples into a majority, making the recall of minority very low.In order to minimize the influence of the imbalanced data class on the model performance, a transfer learning method based on a convolution neural network is proposed in this paper.First, we use a CNN-based model for pre-training in the class-balanced source domain data set, before transferring the model to the target domain for fine-tuning to improve the recall of minority class; furthermore, we propose a transfer learning-based under-sampling technique, which can under-sample the majority class in the target domain.In the data set after under-sampling, we again fine-tune the pre-trained model, so that the recall and precision of the minority class have been greatly improved.The experiments on real-world data sets show that our proposed under-sampling method has obvious advantages compared with others.
Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing theabnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affectedarea. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiatethem. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads toearly diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzyclustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First ordermoments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzyclustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is appliedto choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken fromthe Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separatedthe masses from mammogram images and proved to have good results in terms of cluster validity indices indicatingthe clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposedmethod proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists indiagnosing breast cancer at an early stage.
Enhancing Reading Skills for Saudi Secondary School Students through Mobile Assisted Language Learning (MALL): An Experimental Study

This study addresses the issue of integrating mobile-assisted language learning (MALL) systems into L2 reading instruction in the Saudi secondary schools in order to improve the reading comprehension skills of struggling EFL students. The focus is to find out whether students&amp;rsquo; language performance is accelerated by using MALL together with teacher instruction versus conventional instruction alone. In order to assess the effectiveness of MALL systems and activities in improving reading comprehension skills in EFL contexts, an experimental study was carried out where 120 participants of grade ten students in four public secondary school of Riyadh District in Saudi Arabia were randomly divided into two groups: experiment and control. Reading skills of the participants&amp;rsquo; were measured by pre-test and post-test by a panel of three national experts. The comparison between the experimental group and the control group pinpoint that MALL materials and systems improve reading comprehension skill among EFL students. The findings indicate clearly that there was a significant difference between MALL users and nonusers in favour of the experimental group (p &amp;lt; .05). It can be then generalized that MALL systems and applications in general provide a motivating learning environment for teaching reading which has its positive implications on improving the reading skills of students.
Sentiment Analysis for Airlines Services Based on Twitter Dataset

Opinion mining has been ordinarily connected with the examination of a content string to decide if a corpus is of a negative or positive sentiment. As of late, opinion mining has been stretched out to address issues, for example, recognizing objective from subjective suggestions and deciding the sources and points of various suppositions communicated in text informational collections, for example, tweets, message board, web blogs, movie reviews, and news. Companies can use sentiment extremity and opinion point acknowledgment to pick up a more profound comprehension and the general extent of estimations. These experiences can progress focused insight, enhance client benefit, accomplish better brand picture, and upgrade competitiveness. In the aircraft service industry, it is hard to gather information about clients' input by polls, yet Twitter gives a sound information source to them to do client opinion examination. This paper presents positive, negative sentiment, and their correlation about customer tweets. BIRCH clustering and Association rule mining have been used in this chapter to get inside the dataset and retrieve hidden knowledge.
Design considerations for an optical link supporting intersatellite quantum key distribution

Quantum key distribution (QKD) is a method for establishing secure cryptographic keys between two parties who share an optical, “quantum” channel and an authenticated classical channel. To share such keys across the globe, space-based links are required and in the near term these will take the form of trusted node, key management satellites. We consider such channels between two nanosatellite spacecraft for polarization entanglement-based QKD, and the optical channel is described in detail. Quantum channels between satellites are useful for balancing keys within constellations of trusted node QKD satellites and, in the future, may have applications in long-distance qubit exchange between quantum computers and in fundamental physics experiments. The nanosatellite mission proposed uses an optical link with 80-mm diameter optical terminals. If such a link could be maintained with 10-μrad pointing accuracy, then this would allow QKD to be performed for satellite separations up to around 400 km. A potential pointing and tracking system is also described although currently this design would likely limit the satellite separation to 100 to 150 km.
Experiences of internet users regarding cyberhate

Purpose The purpose of this paper is to explore internet users’ experiences of cyberhate in the aspects of ethnicity, religion, sexual preference and political perspective. Design/methodology/approach Researchers employed an exploratory survey method to examine internet users’ experiences of cyberhate. The participants of the study were determined by purposive sampling methods to attain maximum variety among internet users with high-level digital literacy skills. The data were collected from 355 internet users affiliated with two universities in Turkey and the USA using a personal data form and a survey (Cyberhate Experience Survey) of which reliability and validity indexes were ensured. Findings The results indicated that participants have observed and experienced cyberhate at different levels targeting their ethnic, religious, gender-based and political identities. The findings also pointed out that gender, income and socio-political identities are significant variables on exposure to cyberhate regardless of cultural and educational boundaries. The majority of survey respondents reported that they had encountered cyberhate mostly on social media platforms. Social implications The findings of the study imply that to address the hate speech problem comprehensively, the author must enlighten people, change their way of thinking and broaden their perspectives by using measures such as intercultural dialogue, critical thinking, media literacy, education on tolerance and diversity. Originality/value This research was intended to contribute into the need to elaborate on various aspects of cyberhate, which is shared by academics, thinkers, journalists and educators. It may also serve to clarify how frequently internet users encounter hateful content and harassment online, which can have social consequences and influence young people’s trust to other people.