Reviewerly/exHarmony huggingface dataset:
Web-based simulation and motion planning for human-robot and multi-robot applications

Authors:
raw_author_name: Tudor B. Ionescu
raw_affiliation_string: Human-Machine Interaction, TU Wien, Vienna, Austria

Abstract: This paper introduces the human-robot and multi-robot programming and simulation capabilities of a web-based, open-source robot programming and simulation tool called Assembly. The tool supports scenarios with up to three robots and provides support for simulating human arms working closely with a robot. To facilitate the simulation of scenarios involving human-robot and robot-robot collaboration, the paper proposes an improved version of the neural network-based Adaptive Simplex Motion Planner, which is capable of performing motion planning at a rate of over 3500 Hz. The improved toolpath planner uses an obstacle avoidance approach that leverages ray casting and shadow volume teachniques. The new planner is evaluated on the basis of two human-robot and multi-robot simulation scenarios and the results are compared to those of the original algorithm.
Survey Paper on Hospital Management System using Blockchain

Authors:
raw_author_name: IJSREM Journal

Abstract: Electronic health records are health information of patients that are saved digitally in a network. The information of the patient are stored in the blockchain and these details are stored in the block chain as a blocks of data. The data is encrypted by the algorithm known as AES which is used to encrypt all the data of the patients. A Blockchain network is used in the healthcare system to exchange patient data improve the performance, security, and transparency of sharing medical data in the health care system. The three main feature of blockchain technology – Security, Decentralization Transparency make any application secure and not accessible by unauthorized parties. Key Words: Health records, Blockchain, Encryption of data, Decryption of data, Security.
Active Learning for Discrete Latent Variable Models

Authors:
raw_author_name: Aditi Jha
raw_affiliation_string: Princeton Neuroscience Institute and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, U.S.A. aditijha@princeton.edu
raw_author_name: Jonathan W. Pillow
raw_affiliation_string: Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A. pillow@princeton.edu

Abstract: Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.
Aspect-based sentiment analysis with metaphorical information

Authors:
raw_author_name: Long Yu
raw_affiliation_string: Network and Information Center, Xinjiang University, Xinjiang, China
raw_author_name: Shengwei Tian
raw_affiliation_string: School of Software, Xinjiang University, Xinjiang, China
raw_author_name: Jun Long
raw_affiliation_string: School of Information Science and Engineering, Central South University, Changsha, China
raw_author_name: Tiejun Zhou
raw_affiliation_string: Xinjiang Internet Information Center, Xinjiang, China
raw_author_name: Bo Wang
raw_affiliation_string: School of Software, Xinjiang University, Xinjiang, China

Abstract: Aspect-Based Sentiment Analysis (ABSA) has been the focus of increasing study in recent years. Previous research has demonstrated that incorporating syntactic information, such as dependency trees, can enhance ABSA performance. Despite the widespread use of metaphors in daily life to express emotions more vividly, few studies have integrated this literary device into ABSA. In this paper, we propose a novel ABSA model that utilizes Metaphor Identification Procedure (MIP) to encode both the sentence and aspect word as a single unit, thereby overcoming these limitations. Our experimental results demonstrate that our model achieves competitive performance in ABSA.
Integrating learning factors and Bayesian network for interpretable knowledge tracing

Authors:
raw_author_name: Xiu-Li Diao
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Qing-Tian Zeng
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Hua Duan
raw_affiliation_string: College of Mathematics and Systems, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Zheng-guo Song
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Hua Zhao
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

Abstract: Knowledge tracing aims to model learners’ knowledge mastery based on their historical interaction records and predict their future performance. Due to its great potential in enabling personalized learning in intelligent tutoring systems, it has received extensive attention. However, most deep learning-based knowledge tracing methods have significant predictive performance. It is difficult to extract meaningful interpretations from the thousands of parameters in neural networks. The interpretability of knowledge tracing refers to the ability of learners to easily understand the predicted results.To address this problem, based on learning factors that influence the learner’s exercise performance, this paper proposes a novel knowledge tracing model which is named Integrating Learning factors and Bayesian network for interpretable Knowledge Tracing (LBKT). Firstly, meaningful learning factors, including knowledge mastery, learning ability, and exercise difficulty, are calculated from learners’ historical interaction records using deep learning and statistical methods. Then, Bayesian network is constructed to capture the causal relation between the three learning factors and exercise response. Finally, the Bayesian network is generated through structure and parameter learning to obtain interpretable prediction of future exercise performance. The proposed model named LBKT is evaluated on three public real-world educational datasets. The experiment results demonstrate that our approach achieves better predictive performance compared to baseline knowledge tracing methods, while also exhibiting significant superiority in model interpretability.
Suppression of chaos in a partially driven recurrent neural network

Authors:
raw_author_name: Toshio Aoyagi
raw_affiliation_string: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan

Abstract: The dynamics of recurrent neural networks (RNNs), and particularly their response to inputs, play a critical role in information processing. In many applications of RNNs, only a specific subset of the neurons generally receive inputs. However, it remains to be theoretically clarified how the restriction of the input to a specific subset of neurons affects the network dynamics. Considering RNNs with such restricted input, we investigate how the proportion, $p$, of the neurons receiving inputs (the ``input neurons'') and the strength of the input signals affect the dynamics by analytically deriving the conditional maximum Lyapunov exponent. Our results show that for sufficiently large $p$, the maximum Lyapunov exponent decreases monotonically as a function of the input strength, indicating the suppression of chaos, but if $p$ is smaller than a critical threshold, ${p}_{c}$, even significantly amplified inputs cannot suppress spontaneous chaotic dynamics. Furthermore, although the value of ${p}_{c}$ is seemingly dependent on several model parameters, such as the sparseness and strength of recurrent connections, it is proved to be intrinsically determined solely by the strength of chaos in spontaneous activity of the RNN. This is to say, despite changes in these model parameters, it is possible to represent the value of ${p}_{c}$ as a common invariant function by appropriately scaling these parameters to yield the same strength of spontaneous chaos. Our study suggests that if $p$ is above ${p}_{c}$, we can bring the neural network to the edge of chaos, thereby maximizing its information processing capacity, by amplifying inputs.
Optimizing feature selection and parameter tuning for breast cancer detection using hybrid GAHBA-DNN framework

Authors:
raw_author_name: J. Raja Sekar
raw_affiliation_string: Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India

Abstract: Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. It fuses the benefits of HBA with parallel processing and efficient feedback with GA’s excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods.
Probing fractal spatiotemporal inhomogeneity in a quantum walk

Authors:
raw_author_name: Marcelo A. Pires
raw_affiliation_string: Universidade Federal de Alagoas, 57480-000, Delmiro Gouveia-AL, Brazil
raw_author_name: Caio B. Naves
raw_affiliation_string: Department of Physics, Stockholm University, AlbaNova University Center, 106 91 Stockholm, Sweden; Instituto de Física de São Carlos, Universidade de São Paulo, CP 369, 13560-970, São Carlos-SP, Brazil
raw_author_name: Diogo O. Soares-Pinto
raw_affiliation_string: Instituto de Física de São Carlos, Universidade de São Paulo, CP 369, 13560-970, São Carlos-SP, Brazil
raw_author_name: Sílvio M. Duarte Queirós
raw_affiliation_string: Centro Brasileiro de Pesquisas Físicas, Rua Dr Xavier Sigaud 150, 22290-180, Rio de Janeiro-RJ, Brazil; National Institute of Science and Technology for Complex Systems, 22290-190, Rio de Janeiro-RJ, Brazil

Abstract: We investigate the transport and entanglement properties exhibited by quantum walks with coin operators concatenated in a space-time fractal structure. Inspired by recent developments in photonics, we choose the paradigmatic Sierpinski gasket. The 0-1 pattern of the fractal is mapped into an alternation of the generalized Hadamard-Fourier operators. This two-state coin-operator approach overcomes the intricacies caused by the utilization of high-dimensional coin operators required in prior studies of discrete-time quantum walks on fractals. In fulfilling the blank space on the analysis of the impact of inhomogeneity in quantum walk properties, specifically, fractal deterministic inhomogeneity, our results show a robust effect of entanglement enhancement as well as an interesting road to superdiffusive spreading with a tunable scaling exponent attaining robust superdiffusion, subballistic though. Explicitly, with this fractal approach it is possible to obtain an increase in quantum entanglement with reduced impact on standard quantum walk theoretical spreading. Alongside those features, we analyze further properties such as the degree of interference and visibility. The present model corresponds to an application of fractals in an experimentally viable setting, namely, the building block for the construction of photonic patterned structures.
Maximal information leakage from quantum encoding of classical data

Authors:
raw_author_name: Farhad Farokhi
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia

Abstract: An alternative measure of information leakage for quantum encoding of classical data is defined. An adversary can access a single copy of the state of a quantum system that encodes some classical data and is interested in correctly guessing a general randomized or deterministic function of the data (e.g., a specific feature or attribute of the data in quantum machine learning) that is unknown to the security analyst. The resulting measure of information leakage, referred to as maximal quantum leakage, is the multiplicative increase of the probability of correctly guessing any function of the classical data upon observing measurements of the quantum state. Maximal quantum leakage is shown to satisfy the postprocessing inequality (i.e., applying a quantum channel reduces information leakage) and independence property (i.e., leakage is zero if the quantum state is independent of the classical data), which are fundamental properties required for privacy and security analysis. It also bounds accessible information. Effects of global and local depolarizing noise models on the maximal quantum leakage are established.
COMPARISON OF THE SPEECH TEXTS OF INDONESIAN PRESIDENT JOKO WIDODO AND PRESIDENT SUSILO BAMBANG YUDHOYONO: STUDY USING A CORPUS LINGUISTIC APPROACH

Authors:
raw_author_name: None Ermanto
raw_author_name: Havid Ardi
raw_author_name: Novia Juita
raw_author_name: Norliza Jamaluddin

Abstract: Purpose: This research has several objectives. First, determine lexical density and compare the lexical density. Second, to determine the key lexical density and compare the key lexical density. Third, to test the independence of the relationship between lexical variations and the text of President Joko Widodo's and President Susilo Bambang Yudhoyono's speeches. Theoretical Reference: The theoretical basis used in this research is the lexical analysis approach in linguistics. The application of lexical perspective analysis is expected to be able to review the communication used by each individual. The theoretical lexical discussion will also use a statistical independence analysis approach. The application of a statistical independence analysis approach is used to review a person's individual language abilities. Method: This research uses a qualitative and quantitative corpus linguistics approach. The corpus linguistic application used in this research is the KORTARA application (Korpus Nusantara). The research data is a corpus of 9 texts of President Joko Widodo's speeches and a corpus of 9 texts of President Susilo Bambang Yudhoyono which are official speeches every 16 August before the DPR of the Republic of Indonesia. Results and Conclusion: The results of this research reveal that the text corpus of President Joko Widodo's speech is richer and more varied than the text corpus of President Susilo Bambang Yudhoyono's speech in lexical use. This research also revealed that there is a relationship between lexical variation and the type of text of the President of the Republic of Indonesia's speech with a confidence level of 95%. The difference in lexical variation and frequency between the text corpus of President Joko Widodo's speech and the text corpus of Susilo Bambang Yudhoyono's speech is statistically significant at p &lt; 0.05. Implication of Research: The implication of this research is the realization of the KORTARA corpus linguistic approach (Korpus Nusantara) which can facilitate research for small and large scale data. This research also reveals that the application of a statistical approach provides maximum results in the analysis of large-scale linguistic phenomena. Originality/value: The current study makes a valuable empirical contribution by combining statistical analysis using corpus and qualititative analysis to give comprehensive conclusion. This study is the answer toward the question about the reliability and validity of linguistic studies.
Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Authors:
raw_author_name: Zeeshan Ahmad

Abstract: &lt;p&gt;Human Action Recognition (HAR) is a progressive research area in the field of computer vision and machine learning. Earlier methods on HAR were based on single sensor modality, either vision-based sensor or wearable inertial sensor. Both of these modalities have some limitations that prevent widespread adoption of HAR; e.g. visual sensors typically require elaborate hardware setup and are limited to a small operating area, where inertial sensors are prone to drift. The solution is to fuse the information from different modalities. In this dissertation, we present novel multimodal sensor fusion frameworks that overcome the limitations of single sensor modality. In these frameworks, we convert all data streams to images through innovative signal to image conversion schemes and feed them to Convolutional Neural Networks (CNN), thus enabling extraction of higher-level features that CNNs are proven to be capable of especially from images. Moreover, we propose fast and robust multilevel fusion schemes by extracting features from multiple layers of the CNNs and employing statistical methods such as Canonical Correlation Analysis and gated fusion, instead of the more popular single stage fusion. We applied these fusion frameworks for HAR using depth and inertial sensors. At the input of each fusion framework, we transform depth and inertial sensor data into images called Sequential Front view Images (SFI) and Signal Images (SI). The SFI and SI images are then fused through our proposed multilevel frameworks for more accurate HAR while maintaining computational speed. We evaluate the proposed frameworks on three public multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTDMHAD Kinect V2 and achieved accuracies of 99.3%, 99.85% and 99.8% respectively.&lt;/p&gt; &lt;p&gt;While the proposed frameworks were developed with HAR as a target application area, they can be applied to other fusion problems as well. We show the generalizability of frameworks by applying them to a different domain, where ECG (1D time series) data is converted to multimodal images and fed through our fusion frameworks for arrythmia classification and stress assessment. Preliminary results in these applications are encouraging, further strengthening the significance of the proposed frameworks.&lt;/p&gt;
The impact of digital media on the education of different digital generations: contributions and challenges for accelerating information and promoting the learning of connected individuals

Authors:
raw_author_name: Francisco Benilson Soares Da Silva

Abstract: The present work is the result of a bibliographic research, based on the discussion about the effectiveness of the use of digital media in the classroom, promoting comparative relations with the current reality and exposing some primordial media resources to enhance the school teaching process of the Digital Generation. The purpose was the analog realization of how learning was transmitted to the students of the past, establishing a reflection of how it is given to the transmission of knowledge in contemporary times, evidencing that the education of the XXI century, has advanced significantly with the advent and influences related to the use of the New Digital Technologies based on the challenge of educating the generation born in the Digital Age. The methodological procedure covered the writings and analysis of scientific articles through a critical reflection on the generation of Screenagers and Education.The bibliographic investigation provided the expansion of knowledge on the subject, supported by authors who address the teaching-learning process mediated by the New Digital Technologies, demonstrating the possibilities and limits of using digital technologies in school as a pedagogical resource in the generation of the Digital Age, contributing to a quality and transformative teaching..
Recoverability of quantum channels via hypothesis testing

Authors:
raw_author_name: Anna Jenčová
raw_affiliation_string: Mathematical Institute, Slovak Academy of Sciences, Štefánikova 49, 814 73, Bratislava, Slovakia

Abstract: Abstract A quantum channel is sufficient with respect to a set of input states if it can be reversed on this set. In the approximate version, the input states can be recovered within an error bounded by the decrease of the relative entropy under the channel. Using a new integral representation of the relative entropy in Frenkel (Integral formula for quantum relative entropy implies data processing inequality, Quantum 7 , 1102 (2023)), we present an easy proof of a characterization of sufficient quantum channels and recoverability by preservation of optimal success probabilities in hypothesis testing problems, equivalently, by preservation of $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -distance.