Reviewerly/exHarmony huggingface dataset:
A study on the Regional regeneration and youth cultural activities in the island fishing village of Gukhwado

Authors:
raw_author_name: Gun-Woong Lee

Abstract: Korea is facing a declining population and an aging population, which is leading to a rise in sprawl. Various policies and measures are being explored at the national level to solve this problem, but they are not having much effect. The biggest problem with regional decline is the disappearance of young people and the aging population, which is caused by the influx of young people into large cities such as the Seoul metropolitan area and the natural marginalization of rural areas. This problem is more severe in island fishing villages than in general rural areas. The Gyeonggi-do Fishermen’s and Fisherwomen’s Support Center conducted capacity-building projects for fishermen and cultural activities for youth centered on Pungdo Island in 2020 and Gukhwado in 2021, and achieved significant results. In particular, the center conducted unconventional youth cultural activities by analyzing problems from the perspective of young people and exploring alternatives for promoting Gukhwado and revitalizing tourism. The youth, mainly university students, analyzed the strengths and weaknesses of Gukhwado and came up with revitalization plans based on them. For example, they pointed out problems in marginalized areas, such as signage problems, marine garbage, and problems receiving electronic boards. In order to resolve these inconveniences and promote the hardware-oriented development of the Fishing Village New Deal 300, the government proposed a plan to implement the ‘Friendly Chrysanthemum Seed’ program, install digital tourist information signs, campaigns and educational activities, Smart Island Ecotourism, establish smart city services, install public wireless internet (Wi-Fi), and provide convenience for tourists. It is of great significance that it solved the problem of Gukhwado through youth cultural activities and presented a new solution to solve the disappearance of island fishing villages. The government is carrying out the Fishing Village New Deal 300, and while such hardware-centered development is important, it is also necessary to transform the island fishing village into a livable island through cultural activities and establish it as a prosperous island. Gukhwado, which was presented as an example, is also a representative area where young people are disappearing, and by pointing out the problem and deriving solutions from the perspective of young people, we are diagnosing the fundamental problem of “why” young people are turning away from island fishing villages. In this respect, Gukhwado can be seen as a novel youth cultural activity success story.
Super Neurons

Authors:
raw_author_name: Serkan Kiranyaz
raw_affiliation_string: Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
raw_author_name: Junaid Malik
raw_affiliation_string: Department of Computing Sciences, Tampere University, Tampere, Finland
raw_author_name: Mehmet Yamac
raw_affiliation_string: Huawei Technologies Oy, Helsinki, Finland
raw_author_name: Esin Guldogan
raw_affiliation_string: Microsoft, Espoo, Finland
raw_author_name: Turker Ince
raw_affiliation_string: Electrical &amp; Electronics Engineering Department, Izmir University of Economics, &#x0130;zmir, Turkey
raw_author_name: Moncef Gabbouj
raw_affiliation_string: Department of Computing Sciences, Tampere University, Tampere, Finland

Abstract: Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localized</i> (fixed) kernel operations. This severely limits the receptive field and information flow between layers and thus brings the necessity for deep and complex models. It is highly desired to improve the receptive field size without increasing the kernel dimensions. This requires a significant upgrade over the generative neurons to achieve the “non-localized kernel operations” for each connection between consecutive layers. In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection. The kernel localization process varies among the two super-neuron models. The first model assumes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">randomly localized</i> kernels within a range and the second one learns (optimizes) the kernel locations during training. An extensive set of comparative evaluations against conventional and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deformable</i> convolutional, along with the generative neurons demonstrates that super neurons can empower Self-ONNs to achieve a superior learning and generalization capability with a minimal computational complexity burden. PyTorch implementation of Self-ONNs with super-neurons is now publically shared.
Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies

Authors:
raw_author_name: Zhao-Min Chen
raw_affiliation_string: Zhejiang Key Laboratory of Intelligent Informatics for Safety & Emergency, Wenzhou University, Wenzhou, 325035, China
raw_author_name: Yifan Liao
raw_affiliation_string: The College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
raw_author_name: Wenyao Yu
raw_affiliation_string: Department of Mathematics, University of California, San Diego, California, 92093, USA
raw_author_name: Guodao Zhang
raw_affiliation_string: Institute of Intelligent Media Computing, Hangzhou Dianzi University, Hangzhou, 310018, China; The Key Laboratory of Computer Vision and Systems (Ministry of Education), Tianjin University of Technology, Tianjin, 300384, China
raw_author_name: Yisu Ge
raw_affiliation_string: Zhejiang Key Laboratory of Intelligent Informatics for Safety & Emergency, Wenzhou University, Wenzhou, 325035, China
raw_author_name: Tan Ke
raw_affiliation_string: Educational Technology Center, The PLA General Hospital, Beijing, 100853, China

Abstract: Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.
From Clustering to Cluster Explanations via Neural Networks

Authors:
raw_author_name: Jacob Kauffmann
raw_affiliation_string: Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
raw_author_name: Lukas Ruff
raw_affiliation_string: Aignostics, Berlin, Germany
raw_author_name: Gregoire Montavon
raw_affiliation_string: Berlin Institute for the Foundations of Learning and Data, Berlin, Germany; Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
raw_author_name: Wojciech Samek
raw_affiliation_string: Berlin Institute for the Foundations of Learning and Data, Berlin, Germany; Berlin Institute of Technology, Berlin, Germany; Fraunhofer Heinrich Hertz Institute, Berlin, Germany
raw_author_name: Klaus-Robert Muller
raw_affiliation_string: Berlin Institute for the Foundations of Learning and Data, Berlin, Germany; Berlin Institute of Technology, Berlin, Germany; Korea University, Seoul, South Korea; Max Planck Institut f&#x00FC; cken, Germany; r Informatik, Saarbr&#x00FC

Abstract: A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems, however, the label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters. While powerful methods exist for extracting the cluster structure in data, they typically do not answer the question why a certain data point has been assigned to a given cluster. We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks&#x2014;or &#x201C;neuralized.&#x201D; Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations.
Transformer-based visual object tracking via fine–coarse concatenated attention and cross concatenated MLP

Authors:
raw_author_name: Long Gao
raw_affiliation_string: The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
raw_author_name: Pan Liu
raw_affiliation_string: The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
raw_author_name: Yan Jiang
raw_affiliation_string: The Department of Electronic and Electrical Engineering, the University of Sheffield, Sheffield, S10 2TN, UK
raw_author_name: Yunsong Li
raw_affiliation_string: The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
raw_author_name: Jifeng Ning
raw_affiliation_string: The College of Information Engineering, Northwest A&F University, Yangling, 712100, China

Abstract: Transformer-based trackers have demonstrated promising performance in visual object tracking tasks. Nevertheless, two drawbacks limited the potential performance improvement of transformer-based trackers. Firstly, the static receptive field of the tokens within one attention layer of the original self-attention learning neglects the multi-scale nature in the object tracking task. Secondly, the learning procedure of the multi-layer perception (MLP) in the feed forward network (FFN) is lack of local interaction information among samples. To address the above issues, a new self-attention learning method, fine–coarse concatenated attention (FCA), is proposed to learn self-attention with fine and coarse granularity information. Moreover, the cross-concatenation MLP (CC-MLP) is developed to capture local interaction information across samples. Based on the two proposed modules, a novel encoder and decoder are constructed, and augmented in an all-attention tracking algorithm, FCAT. Comprehensive experiments on popular tracking datasets, OTB2015, LaSOT, GOT-10K and TrackingNet, reveal the effectiveness of FCA and CC-MLP, and FCAT achieves the state-of-art on the datasets.
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

Authors:
raw_author_name: Wenhui Huang
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Jurong West, Singapore
raw_author_name: Yanxin Zhou
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Jurong West, Singapore
raw_author_name: Xiangkun He
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Jurong West, Singapore
raw_author_name: Chen Lv
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Jurong West, Singapore

Abstract: Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-the-art (SOTA) baselines. The demonstration video (https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code (https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are also provided.
High-order relational generative adversarial network for video super-resolution

Authors:
raw_author_name: Rui Chen
raw_affiliation_string: Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, School of Microelectronics, Tianjin University, Tianjin 300072, China
raw_author_name: Yang Mu
raw_affiliation_string: Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, School of Microelectronics, Tianjin University, Tianjin 300072, China
raw_author_name: Yan Zhang
raw_affiliation_string: Beijing Institute of Remote Sensing Information, Beijing 100192, China

Abstract: Video super-resolution can reconstruct a sequence of high-resolution frames with temporally consistent contents from their corresponding low-resolution sequences. The key challenge for this task is how to effectively utilize both inter-frame temporal relations and intra-frame spatial relations. The existing methods for super-resolving the videos commonly estimate optical flows to align the features of multiple frames based on temporal correlations. However, motion estimation is often error-prone and hence largely hinders the recovery of plausible details. Moreover, high-order contextual dependencies in the feature space are rarely exploited for further enhancing the spatio-temporal information fusion. To this end, we propose a novel generative adversarial network to super-resolve low-resolution videos, which makes full use of patch embeddings and is effective in exploring high-order spatio-temporal relations of the feature patches. Specifically, a motion-aware relation module is designed to handle the alignment between neighboring frames and reference ones. Depending on a patch-matching strategy for adaptive selection of multiple most similar patches, the cross-scale graph is constructed to reliably aggregate these patches using a feature pyramid. Based on the structure of multi-scale graph, a context-aware relation module is developed to capture high-order dependencies among resulting warped patches for better leveraging long-range complementary contexts. To further enhance reconstruction ability, the temporal position information of video sequences is also encoded into this module. Dual discriminators with cycle consistent constraints are adopted to provide more informative feedback to the generator while maintaining the global coherence. Extensive experiments have demonstrated the effectiveness of the proposed method in terms of quantitative and qualitative evaluation metrics.
Benchmarking Class Incremental Learning in Deep Learning Traffic Classification

Authors:
raw_author_name: Giampaolo Bovenzi
raw_affiliation_string: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy
raw_author_name: Alfredo Nascita
raw_affiliation_string: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy
raw_author_name: Lixuan Yang
raw_affiliation_string: Data Communication Network Algorithm and Measurement Technology Laboratory, Paris Research Center, Huawei Technologies France, Boulogne-Billancourt, France
raw_author_name: Alessandro Finamore
raw_affiliation_string: Data Communication Network Algorithm and Measurement Technology Laboratory, Paris Research Center, Huawei Technologies France, Boulogne-Billancourt, France
raw_author_name: Giuseppe Aceto
raw_affiliation_string: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy
raw_author_name: Domenico Ciuonzo
raw_affiliation_string: Data Communication Network Algorithm and Measurement Technology Laboratory, Paris Research Center, Huawei Technologies France, Boulogne-Billancourt, France
raw_author_name: Antonio Pescapé
raw_affiliation_string: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy
raw_author_name: Dario Rossi
raw_affiliation_string: Data Communication Network Algorithm and Measurement Technology Laboratory, Paris Research Center, Huawei Technologies France, Boulogne-Billancourt, France

Abstract: Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are characterized by a computationally-intensive training procedure that badly matches the fast-paced release of new (mobile) applications, resulting in significantly limited efficiency of model updates. To address this shortcoming, in this work we systematically explore Class Incremental Learning (CIL) techniques, aimed at adding new apps/services to pre-existing DL-based traffic classifiers without a full retraining, hence speeding up the model’s updates cycle. We investigate a large corpus of state-of-the-art CIL approaches for the DL-based TC task, and delve into their working principles to highlight relevant insight, aiming to understand if there is a case for CIL in TC. We evaluate and discuss their performance varying the number of incremental learning episodes, and the number of new apps added for each episode. Our evaluation is based on the publicly available MIRAGE19 dataset comprising traffic of 40 popular Android applications, fostering reproducibility. Despite our analysis reveals their infancy, CIL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.
Topic Particles, Agreement, and Movement in an Arabic Dialect

Authors:
raw_author_name: A Holmberg
raw_affiliation_string: School of English Literature, Language, and Linguistics, Newcastle University, anders.holmberg@newcastle.ac.uk

Abstract: The dialect of North Hail, a variety of Najdi Arabic, has a set of sentence-initial particles marking topics of various kinds that correspond to those argued by Frascarelli and Hinterhölzl (2007) to be characteristic of Italian and German: Shift-Topic, Contrastive Topic, and Familiar Topic. In their work, as in much other work in the cartographic tradition, a hierarchy of abstract topic heads is postulated in the C-domain, which host the topical phrases as specifiers. In North Hail Arabic, the topic heads are overt, realized as particles. Some of the topic heads mark topics by attraction to the C-domain; other particles mark topics by ϕ-feature agreement. The particles in the C-domain agree in ϕ-features with a TP-internal DP, subject or object. Analyzed in terms of Agree (Chomsky 2001, 2008), arguments and adverbials are assigned particular topic values either by agreement or by movement. The particles thus provide evidence that topicality can be a syntactic feature, inherent in lexical items (the particles) and assigned to constituents by operations familiar from standard syntactic relations like subject agreement and case. The theory articulated observes the Inclusiveness Condition, known to be a problem for the cartographic theory of topic and focus.
A Comparison of Uncertainty Quantification of Deep Learning models for Time Series

Authors:
raw_author_name: Young-In Yoon

Abstract: With the advancement of artificial intelligence, machine learning, and deep learning, their applications in various industries, particularly finance, have increased. However, interpreting predictions from deep learning models poses challenges, especially in finance where result interpretation is important. This study aims to determine the uncertainty of stable deep learning models, despite changes in the model like dropout, to establish standards for reliable models and identify those detecting anomal data through model uncertainty. In the experiment, the traditional statistical model ARIMA and deep learning models mainly used for time series analysis, CNN, LSTM, MLP, and CNN-LSTM. Uncertainty was measured from a Bayesian perspective using MC Dropout. The experimental results confirmed that deep learning models performed better than statistical models for various patterns of time series data. It was observed that, even if the performance was not the best, LSTM based models exhibited low uncertainty, indicating stability. Consequently, this study highlights the importance of considering uncertainty along with accuracy in model selection. Moreover, it was confirmed that models with higher uncertainty are suitable for anomaly detection, making CNN based models particularly fitting for this purpose.