Reviewerly/exHarmony huggingface dataset:
Visual Interaction Networks and Leadership in Walking Crowds

Collective motion in human crowds is a self-organized behavior that emerges from visual interactions between pedestrians. While the macroscopic patterns are generated by local interactions among neighbors, some individuals play the role of “leaders” who strongly influence the crowd. Can “leadership” be attributed to personal qualities, or does it emerge from spatial relations within a crowd? We describe a method to reconstruct the structure and dynamics of interaction networks in human crowds and quantify individual leadership. We used Time-Dependent Delayed Correlation (TDDC) and network theory to analyze motion-capture data on human “swarms” (N=10, 16, 20). Participants were instructed to walk about the tracking area while staying together as a group, yielding 15 min of continuously tracked data. TDDC allows us to infer the leadership relation for all pairs of pedestrians, based on the sign of the time delay. Interaction networks were reconstructed at 1s intervals, with the degree of influence represented by the %time in the lead. Network links were pruned using (i) influence, (ii) reaction time, (iii) field of view, or (iv) visual occlusion criteria. We introduce a new measure of the relative influence of every pedestrian, called net leadership. By representing interaction networks spatially, we disentangle the role of individual influence and spatial position on leadership emergence. Network structure reveals that, whereas individual differences in net leadership are small, leadership strongly depends on spatial position: there is a leadership gradient from the front to the back of the crowd. Moreover, network dynamics reveal that the individuals occupying leadership positions change over periods of seconds. The results demonstrate that visual interaction networks can be reliably reconstructed and used to analyze influence and leadership in a crowd. The method enables predictions of crowd behavior in experiments using covert or explicit leaders to steer or split a real crowd.
Multi-Modal Deep Learning Models for Alzheimer's Disease Prediction Using MRI and EHR

Alzheimer's Disease (AD) is an irreversible and progressive neurodegenerative disorder with three stages: cognitively normal (CN), mild cognitive impairment (MCI), and clinical dementia. Progression and stage prediction of dementia plays an important role in prognosis and treatment. In this work, we developed a multi-modal AD progress prediction model that integrates magnetic resonance imaging (MRI) and electronic health record (EHR) to classify patients into three stages: CN, MCI, and AD. We trained deep auto-encoder to extract features from EHR data, and ResNet and 3D U-Net for MRI imaging data. We developed an entropy-based weighted sum classification method to integrate the classification results from each individual modality to generate final prediction. We experimented on Alzheimer's Disease Neuroimaging Initiative (ADNI) data to demonstrate that the multi-modality integration model outperforms single modality models in accuracy, precision, recall, and F1 scores. In addition, our model achieves competitive performance in comparison with other state-of-the-art multi-modality integration methods on AD progression prediction.
Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network

Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings.
Development of a Visual Perception System on a Dual-Arm Mobile Robot for Human-Robot Interaction

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.
MENINGKATKAN MOTIVASI BELAJAR SISWA MELALUI POHON LITERASI PADA SISWA KELAS 3 DI SDN 2 TELAGA BIRU

MENINGKATKAN MOTIVASI BELAJAR SISWA MELALUI POHON LITERASI PADA SISWA KELAS 3 DI SDN 2 TELAGA BIRU&#x0D; &#x0D; &#x0D; Fadhilah Hajarwati Abasa, Amna Limonub, Helmi Y Supuc, Ni Ketut Sri Ariyantid, Abdul Rahmate, Isnantof&#x0D; aFakultas Ilmu Pendidikan, fadhilahabas01@gmail.com, Universitas Negeri Gorontalo&#x0D; bFakultas Ilmu Pendidikan, amnalimonu2002@gmail.com2,Helmisupu4@gmail.com, Universitas Negeri Gorontalo&#x0D; cFakultas Ilmu Pendidikan, witnansihrahmatyatanaiyo@gmail.com, Universitas Negeri Gorontalo&#x0D; dFakultas Ilmu Pendidikan, niketutsriariyanti@gmail.com, Universitas Negeri Gorontalo&#x0D; eFakultas Ilmu Pendidikan, abdulrahmat@ung.ac.id, Universitas Negeri Gorontalo&#x0D; fFakultas Ilmu Pendidikan, isnanto@ung.ac.id, Universitas Negeri Gorontalo&#x0D; &#x0D; &#x0D; &#x0D; ABSTRACT&#x0D; &#x0D; The literacy tree is one of the media used to increase student motivation, especially in grade 3 elementary school. In the literacy tree, there are several motivational words for student learning so that they can be read by 3rd grade students. This literacy tree not only increases students' learning motivation but also increases students' interest in reading and students' creativity. Reading the motivational words that we made together with Grade 3 students will motivate students to learn, their interest in reading will increase and their creativity will increase through making literacy trees. The main purpose of this literacy tree is to increase student learning motivation in grade 3 students at SDN 2 Telaga Biru.
Developing a framework for open and FAIR data management practices for next generation risk‐ and benefit assessment of fish and seafood

Risk and risk-benefit assessments of food are complex exercises, in which access to and use of several disconnected individual stand-alone databases is required to obtain hazard and exposure information. Data obtained from such databases ideally should be in line with the FAIR principles, i.e. the data must be Findable, Accessible, Interoperable and Reusable. However, often cases are encountered when one or more of these principles are not followed. In this project, we set out to assess if existing commonly used databases in risk assessment are in line with the FAIR principles. We also investigated how access, interoperability and reusability of data could be improved. We used the OpenFoodTox and the Seafood database as examples and showed how commonly used freely available open-source tools and repositories can be implemented in the data extraction process of risk assessments to increase data reusability and crosstalk across different databases.
A Systematic Literature Review of the Use of Metaverse Platform in Educational Contexts

Objectives The purpose of this study was to review the empirical research on the use of metaverse platform in educational contexts through systematic literature review. Among the empirical studies where metaverse platform was used in educational contexts, detailed research contexts, results of the studies, and limitations of the studies were analyzed, and implications for using metaverse platform for educational purpose were examined. Methods A systematic review of literature was conducted where empirical studies in the past 10 years that were published in KCI indexed journals were examined using a list of selection criteria. A total of 14 empirical research studies were selected and analyzed. In this study, 3 researchers developed the research questions and determined the selection criteria for the review. The search and selection process was from March 4, 2022 to April 1, 2022, and the selected studies were systematically analyzed and the results were derived. Results The research context, platforms and their functions used, main constructs examined in the studies were examined, and research findings as well as limitations from the studies were summarized. The result showed that university setting was the most studied context and Gathertown was the most popular metaverse platform. As for the functions used, metaverse was used as communication/collaboration tool, emotional supporting tool, and knowledge formation tool most frequently. The most studied constructs include learning presence, satisfaction, immersiveness, 4C competencies, interaction, affective state, media efficacy, understanding, and intent to use metaverse. Conclusions The use of metaverse in educational contexts can enhance students’ self-directed leanring and can bring positive influence on collaboration as well. However, there has been little research conducted examining the effect of metaverse use on learning performance in instructional contexts. More research is needed in the area where metaverse environments are designed based on specific learning objective in specific subject matters to examine the effec of metaverse use. In addition, instructors need to fully understand the functions and characteristics of metaverse so that the learning environments can be designed in a way that learners can feel learning presence. More research is needed to advance the knowledge of design and development of guidelines for incorporating metaverse in educational contexts.
Hybrid No-Reference Quality Assessment for Surveillance Images

Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics.
Auto-regressive extractive summarization with replacement

Abstract Auto-regressive extractive summarization approaches determine sentence extraction probability conditioning on previous decisions by maintaining a partial summary representation. Despite its popularity, the framework has two main drawbacks: 1) the partial summary representation is irresolutely denoted by a weighted summation of all the processed sentences without any filtering, resulting in a noisy representation and degrading the effectiveness of extracting subsequent sentences; 2) earlier sentences are biased towards a higher extraction probability due to the sequential nature of sequence tagging. To address these two problems, we propose the Auto-regressive Extractive Summarization with Replacement (AES-Rep), a novel auto-regressive extractive summarization model. In particular, the AES-Rep model consists of two main modules: the extraction decision module that determines whether a sentence should be extracted, and the replacement locater module that enables extracted deficient sentences to be replaced with latter sentences by comparing their expressiveness with respect to the main idea of the document. These modules update the partial summary with explicit actions using elaborated multidimensional guidance. We conduct extensive experiments on the benchmark CNN and DailyMail datasets. Experimental results show that AES-Rep can achieve better performance compared with various strong baselines in terms of multiple ROUGE metrics.
The protection of AI-generated pictures (photograph and painting) under copyright law

Artificial intelligence (AI), and especially deep learning technologies, are increasingly used by AI artists in various artistic fields. Among its many possible uses in the arts (for example in the creation of music, poems, screenplays, and many others), AI can generate new pictures. This contribution analyses the criterion of originality under copyright law for AI-generated pictures, from the input to the output stage.
Channel estimation of 5G OFDM system based on ConvLSTM network

In view of the requirement of high speed and low delay in 5G system, traditional channel estimation algorithms are difficult to meet the requirements. This paper regards the channel estimation problem in communication systems as an image processing problem in deep learning, and proposes a channel estimation network based on ConvLSTM network. Convolutional neural network is used in channel estimation, and LSTM structure is introduced to capture the correlation of the channel. The parameters are set to generate the channel data information set of the physical downlink shared channel (PDSCH) based on the 5G new radio (NR) standard, which is used to evaluate the performance of the proposed and existing algorithms. Experimental simulations show that the proposed algorithm has obvious performance improvement and strong robustness compared with least squares algorithm, practical channel estimation and T-CNN network based on image processing.
Validation of a novel software‐based platform to extract oculometric measures

Abstract Purpose: Eye‐tracking systems are a valuable tool in the assessment of disorders by detecting eye movement abnormalities. However, current systems require lab settings, extraction of oculometric measures entails dedicated long sessions, and operation requires qualified personnel. Recently, a novel software‐based platform was developed, using a webcam, enabling the extraction of oculometric measures in a short test with seamless operation. The aim of this study was to validate this platform as compared with a validated eye‐tracker. Methods: 82 participants (51 females, age range 21–88) were evaluated with a software‐based platform (NeuraLight, Israel) in a 15‐minute eye‐tracking test. Participants were presented with different stimuli for various tasks. During the test, data from a validated eye‐tracker (Tobii Pro, Sweden) were captured. Correlations between oculometric measures captured by both methods were calculated using Pearson correlation. Observable information on demographic data was measured using Fisher information matrix. Results: Eye and gaze positions captured by the NeuraLight platform were highly correlated with eye‐tracker ( r = 0.9–0.99, p &lt; 0.01). Saccade and anti‐saccade latency, pro‐saccade and anti‐saccade error rates, position and velocity gain for all tasks were all highly correlated ( r &gt; 0.9, p &lt; 0.01). Relative root mean square error of all measures in both methods was &lt;0.1. Saccadic latency and fixation time were found to be in relation with participants' age. Conclusions: NeuraLight platform was significantly correlated with a validated eye‐tracker, providing high accuracy for eye and gaze positions and oculometric measures. Relation between oculometric measures and age was established. The platform can be implemented for assessment of eye movements in practice. Future studies are needed to use the platform in various conditions.
Performance of Arithmetic Optimization Algorithm for ELM Tuning Applied to IoT security

In this paper, recently proposed arithmetic optimization algorithm for adopted for extreme learning machine tuning with the goal of improving security internet of things systems. One of the greatest challenges with extreme learning machine is finding initial values of weights and biases and determining satisfying number of neurons in the hidden layer for every particular task. To tackle this challenge, which is NP-hard by nature, the arithmetic optimization algorithm was employed. The proposed method was evaluated against ToN IoT Windows 10 dataset for internet of things security for binary classification, and compared to other extreme learning structures evolved by other state-of-the-art metaheuristics under the same experimental conditions. According to experimental findings, arithmetic optimization algorithm is a promising method for tuning extreme learning machine for this particular challenge.
Research on Smooth Edge Feature Recognition Method for Aerial Image Segmentation

With the continuous development of aerial photography technology, its imaging quality is higher and higher, and the post-processing technology requirements for aerial images are getting higher and higher. Aerial image target recognition technology has been a hot research content in recent years. This technology relies on computer vision and image processing algorithm. But aerial images have certain particularities, including long shooting distances, complex image backgrounds, and variable target angles. The above factors can easily lead to indistinguishability between the target boundary and the background information of the aerial images. In order to solve that problem, a smooth edge feature information recognition method for aerial images is proposed. The energy fitting term related to the gray value inside and outside the curve is introduced, with that the method can get rid of the dependence of the detection operator as the stopping function of the curve evolution. In order to prevent the algorithm from falling into a local optimal solution in the iterative process, the Dirac function with a non-zero value in the domain is adopted. With synthetic and natural images, the effectiveness and accuracy of the method is verified. The robustness of the algorithm will be verified in the future researches by the acquired aerial image data set.
A Fraud Prevention and Secure Cognitive SIM Card Registration Model

Background: Most subscriber identification module (SIM) which usually finds their way to mobile phone users are primarily unregistered or pre-registered.Criminals buy these SIM cards, which have fake personal information, activate them and then use them as a channel of attacking vulnerable mobile phone users.Objective: to investigate the existing standards of the registration process, the weakness and how fraudsters leverage the shortcomings of the existing registration to attack unsuspecting subscribers.Methods: The study also proposed an automated theoretical model as an augmented model to ensure the SIM registration process and implementation become secure.Results: In our investigation, we identified that there had been a rise in fraudulent activities in Ghana, and the criminals have adapted to the new trend of committing a crime using mobile phones.The research presented a proposed conceptual model and algorithm for the new SIM registration.The study further conducted a comparative analysis of the principal component adopted to measure the robustness of the registration platform.The criminals mostly use social engineering tactics to trick their victims into disclosing sensitive information or sending money for services yet to be rendered.MNOs request an ID card before registering and activating SIMs, yet criminals can outwit the registration processes and get SIM cards registered through unapproved channels.Conclusion: We found out that the robustness of our model shall prevent SIM pre-registration and unapproved SIM activation due to verification mechanisms in the proposed model.A cognitive learning https://www.indjst.org/
Complementary relations between l <sub> p </sub> norm coherence and mixedness of quantum states

Abstract As an important resource, quantum coherence is one of the fundamental features of the quantum world. It is essential to understand the relationship between the resource and the mixedness of an open quantum system. We present complementary relations between the l p <?CDATA $(1\leqslant{p}\lt2)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>⩽</mml:mo> <mml:mi>p</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> norm of coherence and the normalized linear entropy mixedness. We then study the geometry of the complementary relations for Bell-diagonal states, as well as the evolutions of Werner and isotropic states under bit flip, phase flip, bit-phase flip, depolarizing, generalized amplitude damping and amplitude damping channels. Our results include some existing conclusions as special cases.
Combining Algorithmic Information Dynamics Concepts and Machine Learning for Electroencephalography Analysis: What Can We Get?

Electroencephalography (EEG) as an example of electrophysiological monitoring methods has a rather long history of successful application for the diagnosis and treatment of diseases, and this success would not have been possible without effective methods of mathematical, and more recently, computer analysis. Most of these methods are based on statistics. Among the methods of EEG analysis, there is a group of methods that use different versions of Shannon’s entropy estimation as a “main component” and that do not differ significantly from traditional statistical approaches. Despite the external similarity, another approach is to use the Kolmogorov–Chaitin definition of complexity and the concepts of algorithmic information dynamics. The algorithmic dynamics toolbox includes techniques (e.g., block decomposition method) that appear to be applicable to EEG analysis. The current paper is an attempt to use the block decomposition method along with the recent addition to the management of EEG data provided by machine learning, with the ultimate goal of making this data more useful to researchers and medical practitioners.