Reviewerly/exHarmony huggingface dataset:
A novel multi-agent simulation based particle swarm optimization algorithm

Recently, there has been considerable research on combining multi-agent simulation and particle swarm optimization in practice. However, most existing studies are limited to specific engineering fields or problems without summarizing a general and universal combination framework. Moreover, particle swarm optimization can be less effective in complex problems due to its weakness in balancing exploration and exploitation. Yet, it is not common to combine multi-agent simulation with improved versions of the algorithm. Therefore, this paper proposes an improved particle swarm optimization algorithm, introducing a multi-level structure and a competition mechanism to enhance exploration while balancing exploitation. The performance of the algorithm is tested by a set of comparison experiments. The results have verified its capability of converging to high-quality solutions at a fast rate while holding the swarm diversity. Further, a problem-independent simulation-optimization approach is proposed, which integrates the improved algorithm into multi-agent systems, aiming to simulate realistic scenarios dynamically and solve related optimization problems simultaneously. The approach is implemented in a response planning system to find optimal arrangements for response operations after the Sanchi oil spill accident. Results of the case study suggest that compared with the commonly-used shortest distance selection method, the proposed approach significantly shortens the overall response time, improves response efficiency, and mitigates environmental pollution.
JOURNALISM TEACHING PRACTICE DURING THE COVID-19 PANDEMIC: QUALITATIVE ANALYSIS OF TEACHERS’ ATTITUDES

The transition to full online teaching format has posed a number of challenges of journalism higher education. The goal of this research is to show Georgian journalism teachers’ attitudes towards online learning during the covid-19 pandemic. To reach this goal and explore this issue a social constructivism framework is used. The main research questions: How effective is this process? What are the strengths and weakness of full online learning? What opportunities of the development are arisen? This study is based on the qualitative approach and a semi-structured interviews method is used for this research. The interviews (n=17) were conducted among journalism professors, different Georgian media schools. This study was carried out from 20 June till 20 October 2021. According to the results of this study, there are a lot of challenges facing during the full remote classes but nevertheless there are a number of development opportunities. At the end of the research the author suggests certain recommendations for media schools.
Supporting Sonic Interaction in Creative, Shared Virtual Environments

Abstract This chapter examines user experience design for collaborative music making in shared virtual environments (SVEs). Whilst SVEs have been extensively researched for many application domains including education, entertainment, work and training, there is limited research on the creative aspects. This results in many unanswered design questions such as how to design the user experience without being detrimental to the creative output, and how to design spatial configurations to support both individual creativity and collaboration. Here, we explore multi-modal approaches to supporting creativity in collaborative music making in SVEs. We outline an SVE, LeMo, which allows two people to create music collaboratively. We then present two studies; the first explores how free-form visual 3D annotations instead of spoken communication can support collaborative composition processes and human–human interaction. Five classes of use of annotation were identified in the study, three of which are particularly relevant to the future design of sonic interactions in virtual environments. The second study used a modified version of LeMo to test the support for a creative collaboration of two different spatial audio settings, which according to the results, changed participants’ behaviour and affected their collaboration. Finally, design implications for the auditory design of SVEs focusing on supporting creative collaboration are given.
Hierarchical Prototypes Polynomial Softmax Loss Function for Visual Classification

A well-designed loss function can effectively improve the characterization ability of network features without increasing the amount of calculation in the model inference stage, and has become the focus of attention in recent research. Given that the existing lightweight network adds a loss to the last layer, which severely attenuates the gradient during the backpropagation process, we propose a hierarchical polynomial kernel prototype loss function in this study. In this function, the addition of a polynomial kernel loss function to multiple stages of the deep neural network effectively enhances the efficiency of gradient return, and only adds multi-layer prototype loss functions in the training stage without increasing the calculation of the inference stage. In addition, the good non-linear expression ability of the polynomial kernel improves the characteristic expression performance of the network. Verification on multiple public datasets shows that the lightweight network trained with the proposed hierarchical polynomial kernel loss function has a higher accuracy than other loss functions.
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Opinion Mining of Movie Reviews Using Hybrid Deep Learning Technique

Due to Internet, vast amount of data is generated day by day; from those data to find useful insights, there is need to identify and extract the subjective information. Today’s trends show that people are buying any products or watching any movie on Web sites, and they write the feedbacks related to that product or movie, which will be helpful to business in terms of profit. For that, the need is analysis of written reviews which will be done by sentiment analysis. It is a method which is used to gauge opinions of individuals or groups of persons related to their products or movies. This method will extract the meaningful insights from the written reviews in the form of positive, negative, or neutral. Analysis of sentiment is also known as opinion mining. In this paper, hybrid deep learning model (CNN + LSTM) is applied on IMDB movie review dataset and performs a comparison with CNN model.
Application of Virtual Reality Technology and Unsupervised Video Object Segmentation Algorithm in 3D Model Modeling

3D modeling is the most basic technology to realize VR (virtual reality). VOS (video object segmentation) is a pixel-level task, which aims to segment the moving objects in each frame of the video. Combining theory with practice, this paper studies the process of 3D virtual scene construction, and on this basis, researches the optimization methods of 3D modeling. In this paper, an unsupervised VOS algorithm is proposed, which initializes the target by combining the moving edge of the target image and the appearance edge of the target and assists the modeling of the VR 3D model, which has reference significance for the future construction of large-scale VR scenes. The results show that the segmentation accuracy of this algorithm can reach more than 94%, which is about 9% higher than that of the FASTSEG method. 3D modeling technology is the foundation of 3D virtual scene; so, it is of practical significance to study the application of 3D modeling technology. At the same time, it is of positive significance to use the unsupervised VOS algorithm to assist the VR 3D model modeling.
A Robotics-Based Machine Learning Approach for Fall Detection of People

For a person when carrying out household chores or even when walking on the streets, there is a risk of falling. This risk increases throughout the years due to the natural aging process. In this work, a bibliographic review was performed to find related papers who discussed different techniques for fall classification. The aim of this study was to develop two ML models: an SVM and a k-NN model, to classify the fall. An accelerometer, gyroscope, and magnetometer located on the waists of 15 volunteers are the application sensors. The extracted features were the mean, standard deviation, and range for each sensor. The best accuracy obtained was 93.89%, a sensitivity of 85.10%, and a specificity of 96.99%. All results were obtained by simulations, by using the test set separated in the first stage of the implementation. So, a shortcoming is the fact that the ML models were not tested with a hardware implementation. In future works, the models can be embedded into a microcontroller and classify data in real time.
A Dynamic Adjust‐Head Siamese network for object tracking

Abstract Siamese network based trackers formulate tracking as a similarity matching problem between a target template and a search region. Virtually all popular Siamese trackers use cross‐correlation to measure the similarity between the deep feature of template and search image. However, the emphasis for feature extraction in different parts of the image are the same. Besides, the global matching between the template and search region also seriously neglects the part‐level information and the deformation of targets during tracking. In this study, to tackle the above issues, a simple but effective Dynamic Adjust‐Head (SiamDAH) model is proposed to extract features from different parts of an object. In addition, an improved pixelwise cross‐correlation model (PWCC) is designed to enhance the naive cross‐correlation operation to produce multiple similarity maps associated with different parts of the target. Experiments on serval challenging benchmarks including OTB‐100, GOT‐10k, LaSOT, and TrackingNet demonstrate that the proposed SiamDAH outperforms many state‐of‐the‐art trackers and achieves leading performance.
OCCLUSION SCREENING USING 3D CITY MODELS AS A REFERENCE DATABASE FOR MOBILE AR-APPLICATIONS

Abstract. Creating an immersive Augmented Reality (AR) experience requires aligning the digital content with the real environment so that the digital content appears and interacts in a similar way to a real object. For that, the virtual object must persist in its position across sessions and be occluded completely or partly, depending on whether a real-world object is in the line of sight. This research makes use of the cutting-edge technology available in the field of AR/MR, the Microsoft HoloLens 2.0, where the in-device Time of Flight (ToF) camera is used to scan the environment to create a Spatial Map. The CityGML LOD1 model enriches the Spatial Map and, in addition, is used as an occlusion mask via a custom rendering pipeline. Spatial Anchors can be tracked using the Simultaneous Localization And Mapping (SLAM) technique, used in conjunction with World Locking Tools to anchor the whole scene to the real-world coordinates. The developed occlusion effect is used in urban planning scenarios to introduce a new area design featuring a car-free environment. The proposed method is evaluated based on performance indicators, including frames per second (FPS) since it is highly correlated to user comfort. The findings of the users’ study demonstrate that the occlusion effect achieves its purpose since most of the participants reported enhancements in the depth perception and overall experience by enabling the occlusion screening.
Hyper-parallel nonlocal Toffoli gates assisted by quantum dots inside a double-side optical microcavity

<p indent="0mm">Implementing three-qubit Toffoli gates nonlocally plays an important role in quantum communication networks and distributed quantum computation. Most of previous protocols for implementing quantum Toffolis gate consider only implementing Toffoli gates with one degree of freedom. In this paper, we present a scheme for parallel implementation of nonlocal Toffoli gates in polarization and spatial-mode degrees of freedom assisted by quantum dots inside a double-side optical microcavity. Alice and Bob let photons traverse the double-side optical microcavity sequentially and perform single-particle measurements on electron spin in quantum dots and the entangled photons. Charlie performs single-particle measurements and applies the corresponding unitary operation in polarization and spatial-mode degrees of freedom according to Alice and Bob’s measurement results. Alice and Bob can implement hyper-parallel nonlocal Toffoli gates by performing unitary operations according to Charlie’s measurement result. This protocol has the advantage of having high channel capacity by the hyperentangled state as the quantum channel.
A Comparative Analysis of the Use of ‘Thereof’ in an English Non-translated Text and the English Machine- and Human-translated Versions of the Hungarian Criminal Code

Owing to the recent rise of neural language translation, a paradigm shift has been witnessed regarding the role of translators and reviewers. As neural machine translation is increasingly more capable of modelling how natural languages work, the traditional tasks of translators are being gradually replaced by new challenges. More emphasis is placed on pre- and post-editing (revision) skills and competences, presumably enabling the production of higher quality and near human-made translations.&#x0D; In my paper, I attempt to demonstrate through the qualitative and quantitative comparison of machine-translated legal texts (acts) to human-translated ones the relevant challenges and dynamic contrasts arising in the process of translating. Through the qualitative and quantitative analysis of the original Hungarian (source language) Criminal Code and its English (target language) machine and human translations, I aim to highlight the peculiar challenges emerging in the process of translation. I also aim to demonstrate what patterns can be observed in translations produced by human and non-human translators.
Effective and scalable black-box fuzzing approach for modern web applications

Web applications’ security is critical because we share sensitive data through them frequently, which attracts attackers who exploit their vulnerabilities. Detecting and exploiting such vulnerabilities automatically is challenging because of the applications’ increasing complexity and strong dependence upon dynamic features such as JavaScript. In this paper, we propose an approach that addresses the difficulties presented in web applications by using dynamic analysis techniques in a black-box fashion to explore applications’ space. It also performs a client-side validation analysis to increase the coverage and therefore, identify more vulnerabilities. We implemented our approach with a tool and evaluated its effectiveness using real-world web applications. Our system discovered 207 unique URLs, submitted 102 web forms successfully, and exploited 32 vulnerabilities automatically. A detailed comparison of state-of-the-art black-box fuzzing approaches showed that our system exceeds them in coverage, the number of vulnerabilities detected, and performance.
Connecting Spatially Separated Laboratory Environments by Combining Virtual and Augmented Reality Technology

This paper proposes a technical solution to connect an existing Virtual Reality laboratory to a real-world laboratory by combining Augmented Reality glasses (Microsoft HoloLens 2) and a Virtual Reality Headset (HTC Vive Pro) in a single networking application. Furthermore, it investigates educational opportunities and challenges of combining both a virtual and a real lab for higher engineering education.
Human activity recognition based on hybrid learning algorithm for wearable sensor data

Human Activity Recognition (HAR), based on sensor devices and the Internet of Things (IoT), attracted many researchers since it has diversified applications in health sectors, smart environments, and entertainment. HAR has emerged as one of the important health monitoring applications and it necessitates the constant usage of smartphones, smartwatches, and wearable devices to capture patients' daily activities. To predict multiple human activities, deep learning (DL)-based methods have been successfully applied to time-series data that are generated by smartphones and wearable sensors. Although DL-based approaches were deployed in activity recognition, they still have encountered a few issues when working with time-series data. Those issues could be managed with the proposed methodology. This work proposed a couple of Hybrid Learning Algorithms (HLA) to build comprehensive classification methods for HAR using wearable sensor data. The aim of this work is to make use of the Convolution Memory Fusion Algorithm(CMFA) and Convolution Gated Fusion Algorithm(CGFA) that model learns both local features and long-term and gated-term dependencies in sequential data. Feature extraction has been enhanced with the deployment of various filter sizes. They are used to capture different local temporal dependencies, and thus the enhancement is implemented. This Amalgam Learning Model has been deployed on the WISDM dataset, and the proposed models have achieved 97.76%, 94.98% for smartwatch and smartphone of CMFA, 96.91%, 84.35% for smartwatch and smartphone of CGFA. Experimental results show that these models demonstrated greater accuracy than other existing deep neural network frameworks.
Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation

Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poisoning attack from the user side to inject carefully-crafted bogus data into the LDP protocols in order to maximally skew the final estimate by the data curator. In this work, we further advance this knowledge by proposing a new fine-grained attack, which allows the attacker to fine-tune and simultaneously manipulate mean and variance estimations that are popular analytical tasks for many real-world applications. To accomplish this goal, the attack leverages the characteristics of LDP to inject fake data into the output domain of the local LDP instance. We call our attack the output poisoning attack (OPA). We observe a security-privacy consistency where a small privacy loss enhances the security of LDP, which contradicts the known security-privacy trade-off from prior work. We further study the consistency and reveal a more holistic view of the threat landscape of data poisoning attacks on LDP. We comprehensively evaluate our attack against a baseline attack that intuitively provides false input to LDP. The experimental results show that OPA outperforms the baseline on three real-world datasets. We also propose a novel defense method that can recover the result accuracy from polluted data collection and offer insight into the secure LDP design.
Evaluating Unsupervised Denoising Requires Unsupervised Metrics

Unsupervised denoising is a crucial challenge in real-world imaging applications. Unsupervised deep-learning methods have demonstrated impressive performance on benchmarks based on synthetic noise. However, no metrics are available to evaluate these methods in an unsupervised fashion. This is highly problematic for the many practical applications where ground-truth clean images are not available. In this work, we propose two novel metrics: the unsupervised mean squared error (MSE) and the unsupervised peak signal-to-noise ratio (PSNR), which are computed using only noisy data. We provide a theoretical analysis of these metrics, showing that they are asymptotically consistent estimators of the supervised MSE and PSNR. Controlled numerical experiments with synthetic noise confirm that they provide accurate approximations in practice. We validate our approach on real-world data from two imaging modalities: videos in raw format and transmission electron microscopy. Our results demonstrate that the proposed metrics enable unsupervised evaluation of denoising methods based exclusively on noisy data.
Shapley Head Pruning: Identifying and Removing Interference in Multilingual Transformers

Multilingual transformer-based models demonstrate remarkable zero and few-shot transfer across languages by learning and reusing language-agnostic features. However, as a fixed-size model acquires more languages, its performance across all languages degrades, a phenomenon termed interference. Often attributed to limited model capacity, interference is commonly addressed by adding additional parameters despite evidence that transformer-based models are overparameterized. In this work, we show that it is possible to reduce interference by instead identifying and pruning language-specific parameters. First, we use Shapley Values, a credit allocation metric from coalitional game theory, to identify attention heads that introduce interference. Then, we show that removing identified attention heads from a fixed model improves performance for a target language on both sentence classification and structural prediction, seeing gains as large as 24.7\%. Finally, we provide insights on language-agnostic and language-specific attention heads using attention visualization.
A nearly optimal randomized algorithm for explorable heap selection

Explorable heap selection is the problem of selecting the $n$th smallest value in a binary heap. The key values can only be accessed by traversing through the underlying infinite binary tree, and the complexity of the algorithm is measured by the total distance traveled in the tree (each edge has unit cost). This problem was originally proposed as a model to study search strategies for the branch-and-bound algorithm with storage restrictions by Karp, Saks and Widgerson (FOCS '86), who gave deterministic and randomized $n\cdot \exp(O(\sqrt{\log{n}}))$ time algorithms using $O(\log(n)^{2.5})$ and $O(\sqrt{\log n})$ space respectively. We present a new randomized algorithm with running time $O(n\log(n)^3)$ using $O(\log n)$ space, substantially improving the previous best randomized running time at the expense of slightly increased space usage. We also show an $\Omega(\log(n)n/\log(\log(n)))$ for any algorithm that solves the problem in the same amount of space, indicating that our algorithm is nearly optimal.
The Effect of Augmented Reality Technology on the Performance of University Students

In this study, which was created with distance education and augmented reality technology, it was aimed to determine the effect on the performance of university students. When the research is examined, it is seen that the quantitative research method is used. The study was carried out in the fall semester of 2021–2022. A total of 422 volunteer university students who continue their achievements in the study participated in the research. In the research, augmented reality, virtual technology and distance education were taught to university students for 5 weeks via online application training. The data collection tool, which was developed by the researchers and organised by experts in the field, was used in the study, simply called ‘Augmented Reality’. The data collection tool was used to collect data of the people who participated in the research and created the research by solving it with the help of virtual programmes. In the analysis of the collected and compiled data, frequency analysis and t-test were used by using the SPSS programme, which is preferred in many studies, and the results were added to the research in the presence of tables. According to the results obtained from the research, it was concluded that university students’ views on distance education and augmented reality were high and there was a difference.&#x0D; Keywords— Distance Education, Augmented Reality, University Students