Reviewerly/exHarmony huggingface dataset:
Long Horizon Episodic Decision Making for Cognitively Inspired Robots

Authors:
raw_author_name: Shweta Singh
raw_author_name: Vedant Ghatnekar
raw_author_name: Sudaman Katti

Abstract: The Human decision-making process works by recollecting past sequences of observations and using them to decide the best possible action in the present. These past sequences of observations are stored in a derived form which only includes important information the brain thinks might be useful in the future, while forgetting the rest. we propose an architecture that tries to mimic the human brain and improve the memory efficiency of transformers by using a modified Transformer XL architecture which uses Automatic Chunking which only attends to the relevant chunks in the transformer block. On top of this, we use Forget Span which is technique to remove memories that do not contribute to learning. We also theorize the technique of Similarity based forgetting to remove repetitive memories. We test our model in various tasks that test the abilities required to perform well in a human-robot collaboration scenario.
A multi-task fusion model based on a residual–multi-layer perceptron network for mammographic breast cancer screening

Authors:
raw_author_name: Yutong Zhong
raw_affiliation_string: School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, PR China.
raw_author_name: Yan Piao
raw_affiliation_string: School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, PR China. Electronic address: piaoyan@cust.edu.cn.
raw_author_name: Baolin Tan
raw_affiliation_string: Technology Co. LTD, Shenzhen 518000, PR China.
raw_author_name: Jingxin Liu
raw_affiliation_string: Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun 130033, PR China.

Abstract: Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram that are relevant to the target task. This can lead to the neglect of other important morphological features of the lesion as well as other auxiliary information from the internal breast tissue. To obtain more comprehensive and objective diagnostic results, in this study, we developed a multi-task fusion model that combines multiple specific tasks for CAD of mammograms.We first trained a set of separate, task-specific models, including a density classification model, a mass segmentation model, and a lesion benignity-malignancy classification model, and then developed a multi-task fusion model that incorporates all of the mammographic features from these different tasks to yield comprehensive and refined prediction results for breast cancer diagnosis.The experimental results showed that our proposed multi-task fusion model outperformed other related state-of-the-art models in both breast cancer screening tasks in the publicly available datasets CBIS-DDSM and INbreast, achieving a competitive screening performance with area-under-the-curve scores of 0.92 and 0.95, respectively.Our model not only allows an overall assessment of lesion types in mammography but also provides intermediate results related to radiological features and potential cancer risk factors, indicating its potential to offer comprehensive workflow support to radiologists.
Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran

Authors:
raw_author_name: Pooria Kianoush
raw_affiliation_string: Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran 1584743311, Iran; National Iranian Oil Company, Exploration Directorate (NIOC-EXP), Tehran 1994814695, Iran
raw_author_name: Adel Shirazy
raw_affiliation_string: Department of Mining Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 1591634311, Iran; Geophysical Institute, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
raw_author_name: Aref Shirazi
raw_affiliation_string: Department of Mining Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 1591634311, Iran
raw_author_name: Amin Beiranvand Pour
raw_affiliation_string: Institute of Oceanography and Environment (INOS), Higher Institution Center of Excellence (HICoE) in Marine Science, University Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia

Abstract: The crucial parameters influencing drilling operations, reservoir production behavior, and well completion are lithology and reservoir rock. This study identified optimal reservoir rocks and facies in 280 core samples from a drilled well in the Asmari reservoir of the Mansouri field in SW Iran to determine the number of hydraulic flow units. Reservoir samples were prepared, and their porosity and permeability were determined by measuring devices. The flow zone index (FZI) was calculated for each sample using MATLAB software; then, a histogram analysis was performed on the logarithmic data of the FZI, and the number of hydraulic flow units was determined based on the obtained normal distributions. Electrical facies were determined based on artificial neural network (ANN) and multi-resolution graph-based clustering (MRGC) approaches. Five electrical facies with dissimilar reservoir conditions and lithological compositions were ultimately specified. Based on described lithofacies, shale and sandstone in zones three and five demonstrated elevated reservoir quality. This study aimed to determine the Asmari reservoir’s porous medium’s flowing fluid according to the C-mean fuzzy logic method. Furthermore, the third and fourth flow units in the Asmari Formation have the best flow units with high reservoir quality and permeability due to determining the siliceous–clastic facies of the rock units and log data. Outcomes could be corresponded to the flow unit determination in further nearby wellbores without cores.
Photonic neuromorphic architecture for tens-of-task lifelong learning

Authors:
raw_author_name: Yuan Cheng
raw_affiliation_string: Sigma Laboratory, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
raw_author_name: Jianing Zhang
raw_affiliation_string: Sigma Laboratory, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
raw_author_name: Tiankuang Zhou
raw_affiliation_string: Sigma Laboratory, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
raw_author_name: Yuyan Wang
raw_affiliation_string: Beijing National Research Center for Information Science and Technology (BNRist), Beijing, 100084, China
raw_author_name: Zhihao Xu
raw_affiliation_string: Sigma Laboratory, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
raw_author_name: Xiaoyun Yuan
raw_affiliation_string: Sigma Laboratory, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
raw_author_name: Lu Fang
raw_affiliation_string: Sigma Laboratory, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

Abstract: Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.
Tutorials and mobile learning in higher education: Enhancing and accessibility

Authors:
raw_author_name: Lakhoua Mohamed Najah
raw_affiliation_string: National Engineering School of Carthage, University of Carthage, Tunis, Tunisia

Abstract: This comprehensive paper delves into two vital facets of contemporary higher education. The first segment investigates the transformative force of mobile learning (m-learning), elucidating its far-reaching impact on the educational landscape. Offering unprecedented flexibility and accessibility, m-learning redefines the dynamics between students and educators. The exploration navigates through potential benefits, challenges, and broader implications, providing a nuanced understanding of the evolving landscape of higher education in the digital age. The second segment focuses on the impact of active teaching in a first-year class at the National Engineering School of Carthage (ENICarthage) in Tunisia. Active teaching, recognized for engaging students in the learning process, is scrutinized for its effectiveness in enhancing theoretical understanding and graded assignment performance. This research introduces a dynamic dimension by incorporating mobile learning and tutorials as integral components of active teaching strategies. With its technological leverage, mobile learning aims to enhance student engagement, while advanced tutorials feature simulation functionality for an immersive learning experience. Through statistical analysis, the paper contributes to a nuanced comprehension of the positive influence of active teaching on academic achievements, offering practical insights into the application of active teaching methods and their potential benefits for enhancing student learning outcomes. This synthesis thus presents a holistic view of the dynamic interplay between mobile learning and active teaching in shaping the contemporary higher education landscape.
Resilience of navigation strategy and efficiency to the impact of acute stress

Authors:
raw_author_name: Tom Bullock
raw_affiliation_string: Department of Psychological &amp; Brain Sciences, University of California, Santa Barbara, CA, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, USA
raw_author_name: Tyler Santander
raw_affiliation_string: Department of Psychological &amp; Brain Sciences, University of California, Santa Barbara, CA, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, USA
raw_author_name: Scott T. Grafton
raw_affiliation_string: Department of Psychological &amp; Brain Sciences, University of California, Santa Barbara, CA, USA; Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, CA, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, USA
raw_author_name: Barry Giesbrecht
raw_affiliation_string: Department of Psychological &amp; Brain Sciences, University of California, Santa Barbara, CA, USA; Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, CA, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, USA
raw_author_name: Mary Hegarty
raw_affiliation_string: Department of Psychological &amp; Brain Sciences, University of California, Santa Barbara, CA, USA; Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, CA, USA

Abstract: Increased cortisol may differentially impact navigation strategies. Three within-subjects experiments investigated how the Trier Social Stress Test (TSST), the Cold Pressor Test (CPT), and a prolonged bout of physical exercise (PE), all known to increase cortisol, affect navigation strategy and efficiency. Participants learned an environment, were stressed, and then navigated. Cortisol was higher in all stress sessions relative to a no-stress control session. TSST and CPT did not affect strategy or efficiency, while PE increased shortcut use. Results suggest navigation strategies are resilient to cortisol-based stress however questions remain as to how strategy shifts occur.
Solving the Lipkin model using quantum computers with two qubits only with a hybrid quantum-classical technique based on the generator coordinate method

Authors:
raw_author_name: Yann Beaujeault-Taudière
raw_affiliation_string: Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France and Laboratoire Leprince-Ringuet (LLR), École Polytechnique, CNRS/IN2P3, F-91128 Palaiseau, France
raw_author_name: Denis Lacroix
raw_affiliation_string: Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France

Abstract: The possibility of using the generator coordinate method (GCM) using hybrid quantum-classical algorithms with reduced quantum resources is discussed. The task of preparing the basis states and calculating the various kernels involved in the GCM is assigned to the quantum computer, while the remaining tasks, such as finding the eigenvalues of a many-body problem, are delegated to classical computers for post-processing the generated kernels. This strategy reduces the quantum resources required to treat a quantum many-body problem. We apply the method to the Lipkin model. Using the permutation symmetry of the Hamiltonian, we show that, ultimately, only two qubits is enough to solve the problem regardless of the particle number. The classical computing post-processing leading to the full energy spectrum can be made using standard generalized eigenvalues techniques by diagonalizing the so-called Hill-Wheeler equation. As an alternative to this technique, we also explored how the quantum state deflation method can be adapted to the GCM problem. In this method, variational principles are iteratively designed to access the different excited states with increasing energies. The methodology proposed here is successfully applied to the Lipkin model with a minimal size of two qubits for the quantum register. The performances of the two classical post-processing approaches with respect to the statistical noise induced by the finite number of measurements and quantum devices noise are analyzed. Very satisfactory results for the full energy spectra are obtained once noise corrections techniques are employed.
ITC-net-audio-5: an audio streaming dataset for application identification in network traffic classification

Authors:
raw_author_name: Mehdi Teimouri
raw_affiliation_string: Information Theory and Coding (ITC) Laboratory, University of Tehran, Tehran, Iran

Abstract: Abstract Objectives An essential aspect of network traffic classification is application identification. This involves capturing and analyzing the traffic patterns of applications. There are a few publicly available datasets that specifically capture streaming data from network-based applications. Therefore, our objective is to generate an up-to-date dataset with a focus on audio streaming data. This dataset can be a valuable resource for identifying audio streaming applications in the field of network traffic classification. Data description The dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 files in PCAP format captured by Wireshark and PCAPdroid tools during voice calls and online music playback. The concurrent utilization of these tools facilitates the avoidance of capturing background traffic.
Data Sovereignty in Information Systems

Authors:
raw_author_name: Malte Hellmeier
raw_affiliation_string: Fraunhofer ISST, Speicherstr. 6, 44147, Dortmund, Germany
raw_author_name: Boris Otto
raw_affiliation_string: Chair for Industrial Information Management, TU Dortmund, Joseph-von-Fraunhofer-Str. 2-4, 44227, Dortmund, Germany

Abstract: Abstract Data has become a strategic asset for societal prosperity and economic competitiveness. There has long been an academic consensus that the value of data unfolds during its use. Consequently, many stakeholders have called for expanding the use and reuse of data, including the public and open variety, as well as that from private data providers. However, citizens and organizations want self-determination over their data use, that is, data sovereignty. This fundamentals paper applies a literature review to conceptualize the term in Information Systems (IS) research by summarizing current findings and definitions to add further structure to the field. It contributes to the current research streams by introducing a core conceptual model consisting of seven interacting core aspects, involving trust between data providers and consumers for data assets, supported by data infrastructure and contractual agreements on all data lifecycle stages. We evaluate and discuss this conceptual model through recent field examples and provide an overview of future research opportunities.
Big data challenges and opportunities in Internet of Vehicles: a systematic review

Authors:
raw_author_name: Atefeh Hemmati
raw_author_name: Mani Zarei
raw_author_name: Amir Masoud Rahmani

Abstract: Purpose Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research. Design/methodology/approach This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis. Findings This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues. Research limitations/implications This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted. Originality/value This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.
People follow emotion display rules when choosing emoticons on social media

Authors:
raw_author_name: Anthony Cherbonnier
raw_author_name: Nicolas Michinov

Abstract: This study examined whether people use emoticons on Facebook in the same way they display emotions via facial expressions in face-to-face interaction. In an online experimental study (N = 124), participants completed the “Emoticon Display Rules on Facebook” (EDRF) scale adapted from the Display Rule Assessment Inventory (DRAI). People tend to use more positive emoticons in public settings, and more negative ones in private settings. They also use more and more intense emoticons with close friends and acquaintances than strangers. This study provides further support for the positivity bias on Facebook and shows that emoticons are simply used to express emotions in the same ways as in face-to-face interactions.
Multi-object tracking with adaptive measurement noise and information fusion

Authors:
raw_author_name: Xi Huang
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, No.100 Waihuanxi Road, Guangzhou Higher Education Megacenter, Guangzhou 510006, Guangdong, China
raw_author_name: Yinwei Zhan
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, No.100 Waihuanxi Road, Guangzhou Higher Education Megacenter, Guangzhou 510006, Guangdong, China

Abstract: Multi-object tracking (MOT) is a challenging task in computer vision that aims to estimate the trajectories of multiple objects in a video sequence. Observation-Centric SORT (OCSORT) is a pure motion-based MOT algorithm that uses the Kalman filter as the motion model and three observation-centric techniques: Re-Update, Momentum and Recovery, to enhance the data association. However, OCSORT is limited by camera motion error, constant measurement noise and lack of appearance information. In this paper, we propose three methods to address these limitations and improve the performance of OCSORT. First, we use Enhanced Correlation Coefficient Maximization (ECC) to compensate for the camera motion between adjacent frames. Second, we adjust the measurement noise scale for the Kalman filter according to the detection confidence. Third, we introduce a deep visual feature model to extract appearance information and propose a method to effectively use both motion and appearance information. The proposed method first filters out the inappropriate appearance information based on motion information and then combines the filtered appearance information with the motion information by minimization. We evaluate our algorithm on three MOT benchmarks: MOT17, MOT20 and DanceTrack. The results show that our algorithm achieves state-of-the-art performance on all datasets, especially on DanceTrack, where the objects have highly nonlinear motion and frequent occlusion. Compared to OCSORT, our algorithm improves Higher Order Tracking Accuracy (HOTA) by 1.1%, 0.8%, and 3.5%, and ID F1 Score (IDF1) by 1.7%, 1.9%, and 4.3% on MOT17, MOT20 and DanceTrack, respectively.