Reviewerly/exHarmony huggingface dataset:
Behavioral Mereology: A Modal Logic for Passing Constraints

Mereology is the study of parts and the relationships that hold between them. We introduce a behavioral approach to mereology, in which systems and their parts are known only by the types of behavior they can exhibit. Our discussion is formally topos-theoretic, and agnostic to the topos, providing maximal generality; however, by using only its internal logic we can hide the details and readers may assume a completely elementary set-theoretic discussion. We consider the relationship between various parts of a whole in terms of how behavioral constraints are passed between them, and give an inter-modal logic that generalizes the usual alethic modalities in the setting of symmetric accessibility.
Penerapan Metode A*Star Pada Pencarian Rute Tercepat Menuju Destinasi Wisata Cagar Budaya Menes Pandeglang

Banten is a province that has historical cultural heritage, one of which is located in Pandeglang Regency, Menes District. The cultural heritage that is owned is in the form of a relic of the Dutch era building that is still exist today. This triggers many tourists to visit this place. The problem is that tourists have difficulty finding the fastest route to these tourist attractions. The purpose of this research is to find the fastest route to get to the cultural heritage tourist attractions. This study uses the A * Star method, which is believed to be very accurate in solving these problems because it has a heuristic calculation that is able to calculate existing nodes. The data used in this study are data from google maps, after the image is obtained, the data will be converted into a grid which will then become the benchmark for calculations. Based on the results of this research, it can be concluded that the use of the A * Star method is used to find the closest route to cultural heritage tourist attractions in Menes District, Pandeglang Regency. The fastest route is through Serang City - Maja intersection - Cipacung intersection - Mengger intersection - Batubantar junction - Alaswangi village - Menes with a distance of 29,400 km. Keywords : A*Star method, fastest route, travel places, cultural heritage
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition

In this work we tackle the challenging problem of anime character recognition. Anime, referring to animation produced within Japan and work derived or inspired from it. For this purpose we present DAF:re (DanbooruAnimeFaces:revamped), a large-scale, crowd-sourced, long-tailed dataset with almost 500 K images spread across more than 3000 classes. Additionally, we conduct experiments on DAF:re and similar datasets using a variety of classification models, including CNN based ResNets and self-attention based Vision Transformer (ViT). Our results give new insights into the generalization and transfer learning properties of ViT models on substantially different domain datasets from those used for the upstream pre-training, including the influence of batch and image size in their training. Additionally, we share our dataset, source-code, pre-trained checkpoints and results, as Animesion, the first end-to-end framework for large-scale anime character recognition: https://github.com/arkel23/animesion
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders

In this paper, we demonstrate how deep autoencoders can be generalized to the case of inpainting and denoising, even when no clean training data is available. In particular, we show how neural networks can be trained to perform all of these tasks simultaneously. While, deep autoencoders implemented by way of neural networks have demonstrated potential for denoising and anomaly detection, standard autoencoders have the drawback that they require access to clean data for training. However, recent work in Robust Deep Autoencoders (RDAEs) shows how autoencoders can be trained to eliminate outliers and noise in a dataset without access to any clean training data. Inspired by this work, we extend RDAEs to the case where data are not only noisy and have outliers, but also only partially observed. Moreover, the dataset we train the neural network on has the properties that all entries have noise, some entries are corrupted by large mistakes, and many entries are not even known. Given such an algorithm, many standard tasks, such as denoising, image inpainting, and unobserved entry imputation can all be accomplished simultaneously within the same framework. Herein we demonstrate these techniques on standard machine learning tasks, such as image inpainting and denoising for the MNIST and CIFAR10 datasets. However, these approaches are not only applicable to image processing problems, but also have wide ranging impacts on datasets arising from real-world problems, such as manufacturing and network processing, where noisy, partially observed data naturally arise.
Automatic First Break Picking Method Based on Constrained Markov Decision Processes(CMDPs)

Summary Picking the first-breaks is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate the first-breaks picking as constrained Markov decision processes (CMDPs) in feature space. By designing reasonable actions and transition probability matrices constrained by image segmentation operators, the improved method introduces the lateral continuity of the seismic data and reduces the difficulty of selecting the initial state to ensure the picking of first breaks is more accurate and automatic. Testing this approach on the field seismic data reveals its properties and shows it can accurately do the automatic seismic events picking, especially the adjacent complex waveforms.
Tennis posture classification and recognition based on an improved KNN

Motion recognition is widely used in somatosensory games, rehabilitation training and robot motion learning. Tennis training can identify and classify the captured actions, and improve the performance of computer-aided tennis teaching timely and accurately. Traditional image based or video based human posture capture recognition are easily affected by complex background environments, different lighting conditions and other factors in practical applications. In this paper, Microsoft Kinect is used as the sensor device to capture the data information of the tennis player. Firstly, Kinect's depth sensing technology is used to obtain human skeleton data. Secondly, in order to improve the efficiency of classification, this paper reduces the dimension of data by extracting the feature value of human skeletons. Thirdly, a kind of KNN algorithm which defines the dimension weight is proposed to implement the movement classification, compared with the 94% accuracy of the algorithm, the accuracy of the KNN algorithm is 92.4%, the accuracy of the decision tree algorithm is 92.81%, and the accuracy of the CNN algorithm The accuracy is 89.97%. The evaluation method of tennis action is defined to provide guidance for users. By comparing the difference between the user’s postures and the standard postures in the joint positions and angles which is prone to get error, this method can correct the user's postures and build up the function of movements guidance. From the teaching effect of tennis aficionados and general tennis players, this method is more practical and targeted than the traditional tennis graphics and video teaching.
Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

Cancer of the breast is one of the world's most prevalent causes of death for women. Early and efficient identification is important for can care choices and reducing mortality. Mammography is the most effective early breast cancer detection process. Radiologists cannot however make a detailed and reliable assessment of mammograms due to fatigue or poor image quality. The main aim of this work is to establish a new approach to help radiologists identify anomalies and improve diagnostic precision. The proposed method has been applied through the implementation of preprocessing then segmentation of the images to get the region of interest that was used to find a texture features that were calculated based on first Order (statistical features), Gray-Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns LBP (LBP). In the features selection phase mutual information (MI) algorithm is applied to choose from the extracted features collection suitable features. Finally, Multilayer Perceptron has been applied in two stages to classify the mammography images first to normal or abnormal, and secondly, classification of abnormal images into benign or malignant images. This method was implemented and gave an accuracy of 92.91 % for the first level and 93.15% for the second level classification.
Visualizing Very Large Image Data Sets At Interactive Rates

This paper presents a system for real-time visualization of very large image data sets using ondemand loading and dynamic view prediction. We use a robust image representation scheme for efficient adaptive rendering and introduce a fast perspective view generation module to extend the applicability of the system to panoramic images. We demonstrate the effectiveness of the system by applying it both to imagery that does not require perspective correction and to very large panoramic data sets requiring perspective view generation. Furthermore, we extend and generalize the system to enable a wide range of applications. In a broad set of applications, the system permits smooth, real-time interactive navigation of very large panoramic and non-panoramic image data sets on average personal computers without the use of specialized hardware.
The Use Of Instagram As Mobile Learning To Improve Students Vocabulary

In the recent last decade, social media has influenced teenagers in the world for being conscious with smartphone`s benefits to getting information, entertainment, and edutainment. Instagram is one of favorite social media for teenagers. In this study the researcher intends to discuss the use of Instagram to improve the vocabulary of the first semester students of the Department English Education at Universitas Muhammadiyah Surakarta. This is descriptive qualitative research using the theory of hermeneutic phenomenology approach. Data collection methods in this study is interview, while the method of data analysis is content interpretative data analysis. The subject of the research are four students of English Education Department in the first semester. Data validity is member checking. The results of this study are (1) Instagram is a social media platform as a source of new vocabulary, through content written by other users using English raises curiosity of the words meaning (2) Instagram allows students to study in anywhere and at anytime, flexibility make learning be comfortable without coercion, and (3) Instagram has accessible connection and has affordable prices, students can access Instagram installed on the smartphone easily and free.
Theory of Mind for Deep Reinforcement Learning in Hanabi

The partially observable card game Hanabi has recently been proposed as a new AI challenge problem due to its dependence on implicit communication conventions and apparent necessity of theory of mind reasoning for efficient play. In this work, we propose a mechanism for imbuing Reinforcement Learning agents with a theory of mind to discover efficient cooperative strategies in Hanabi. The primary contributions of this work are threefold: First, a formal definition of a computationally tractable mechanism for computing hand probabilities in Hanabi. Second, an extension to conventional Deep Reinforcement Learning that introduces reasoning over finitely nested theory of mind belief hierarchies. Finally, an intrinsic reward mechanism enabled by theory of mind that incentivizes agents to share strategically relevant private knowledge with their teammates. We demonstrate the utility of our algorithm against Rainbow, a state-of-the-art Reinforcement Learning agent.
Currency Recognition and Calculation System using Machine Learning Techniques

Different currencies are being processed in money exchange shops and banks around the globe on a daily basis, where money exchange and transfer takes place. Identifying different currency is a difficult task and can lead to financial loss. There are approximately 180 currencies being used around the world, and each of them differ in color, size and texture. Thus, to correctly identify different currencies, a currency recognition systems needs to be designed. In this paper, we propose the design of an AlexNet based currency recognition system to recognize different international currency notes. We use 10-fold Cross Validation to obtain the cross-validation results of the AlexNet model. The features for the Alex model is extracted from the images back and front of each currency note. We also explore and implement deep learning models to compare the performance of the AlexNet model.
Hubungan Antara Minat Baca Terhadap Kemampuan Membaca Pemahaman Siswa Kelas V SD Gugus I Kecamatan Ulaweng Kabupaten Bone

The problem studied in this study was the relationship between reading interest and reading comprehension ability of fifth-grade students at SD Gugus 1, Ulaweng District, Bone Regency. This study is a correlation study that aims to determine the description of reading interest, the description of reading comprehension ability, and the relationship between reading interest and reading comprehension ability in fifth-grade students at SD Gugus I, Ulaweng District, Kab. Bone. A research approach is a quantitative approach. This type of research is a correlation study. The research data were obtained through questionnaires and tests. The population in the study were all fifth-grade students of SD Gugus I, Ulaweng District, Bone Regency, in the 2019/2020 academic year totaling 111 students. The sample in the study amounted to 58 students. The data analysis technique is a descriptive statistical analysis and correlation analysis. The results showed that the reading interest of the fifth-grade students of SD Gugus I, Ulaweng District, Bone Regency was in the high category, and the reading comprehension ability of the fifth-grade students of SD Gugus I Elementary School, Ulaweng District, Bone Regency was in the medium category and there was a significant relationship between reading interest and the students' reading comprehension ability class V.
Reflecting on Social Media Behavior by Structuring and Exploring Posts and Comments

Abstract Social networks use several user interaction techniques for enabling and soliciting user responses, such as posts, likes and comments. Some of these triggers may lead to posts or comments that a user may regret at a later stage. In this article, we investigate how users may be supported in reflecting upon their past activities, making use of an exploratory spatial hypertext tool. We discuss how we transform raw Facebook data dumps into a graph-based structure and reflect upon design decisions. First results provide insights in users motivations for using such a tool and confirm that the approach helps them in discovering past activities that they perceive as outdated or even embarrassing.
PEMILIHAN BIBIT KELINCI NEW ZEALAND WHITE (NZW) TERBAIK DENGAN MENGGUNAKAN METODE VIKOR

The New Zealand White (NZW) rabbit is a rabbit originating from America that has now spread to Indonesia. NZW rabbits have the advantage that they have large meat weight, small bones and a harvest period of about 3.5 months. However, the quality of the rabbits produced is very influential in the initial selection of seeds. Therefore, a decision support system is needed to select so that the resulting rabbits are as expected. One of the selection methods used in selecting the best rabbit seeds is the Visekriterijumsko Kompromisno Rangiranje (VIKOR) method. VIKOR is a decision-making method that works by looking at the closest solution / alternative as an approach to the ideal solution in ranking. The purpose of this study is to recommend the selection of the best NZW rabbit seeds using the VIKOR method. The results showed that the VIKOR method was able to select the best NZW Rabbit seeds from a number of existing data.
A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System

User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two groups: the siamese models that build static user and item representations from their reviews respectively, and the interaction-based models that encode user and item dynamically according to the similarity or relationships of their reviews. Although the interaction-based models have more model capacity and fit human purchasing behavior better, several problematic model designs and assumptions of the existing interaction-based models lead to its suboptimal performance compared to existing siamese models. In this paper, we identify three problems of the existing interaction-based recommendation models and propose a couple of solutions as well as a new interaction-based model to incorporate review data for rating prediction. Our model implements a relevance matching model with regularized training losses to discover user relevant information from long item reviews, and it also adapts a zero attention strategy to dynamically balance the item-dependent and item-independent information extracted from user reviews. Empirical experiments and case studies on Amazon Product Benchmark datasets show that our model can extract effective and interpretable user/item representations from their reviews and outperforms multiple types of state-of-the-art review-based recommendation models.
Forensik Mobile pada Layanan Media Sosial LinkedIn

The research explores mobile forensic on LinkedIn social media. Forensic mobile finds digital evidence of job hoax cases in LinkedIn, investigation using the NIST (National Institute of Standard and Technology) method. Data collection techniques using Andriller tools in investigations. Data examination using tools Root Browser, Autopsy in the forensic process. data analysis using tools MOBILedit in the forensic process. The investigation found digital evidence of log activity, a status update on LinkedIn. Other results found in the investigation are 17 WiFi password, 117 download history, 263 phone calls, 1 file deleted, 1 file hidden, and 1 file raised, the research has reached the expected target.
How to Leverage Advanced TensorFlow and Cloud Computing for Efficient Deep Learning on Large Seismic Datasets

Summary Several seismic applications benefit from using all available receivers and a long time-window, allowing greater representation of signal and noise. Neural networks have the ability to utilise spatio-temporal data and extract high level patterns thanks to their non-linear function compositions. However, the training of such networks is memory intensive, often resulting in the downsizing of data introducing constraints on the number of traces and/or the length of the recording. Through the example of developing a deep learning model for passive seismic event detection on a large array of ∼3500 sensors, we describe an end-to-end workflow from synthetic labelled data creation to distributed model training to model deployment. We demonstrate how to overcome the memory challenges of large input data by utilizing TensorFlow’s data generators for on-the-fly generation and loading of large seismic recordings during the training procedure. Furthermore, we illustrate how training time can be drastically reduced by distributing training across multiple machines with GPU capability. Kubernetes and cloud resources are leveraged for ease of orchestration of compute resources and scaling up horizontally. Finally, we highlight that whilst training is computationally expensive, the trained model can be deployed on a standard, non-GPU machine for real-time detection of passive seismic events.
THE PHENOMENON “BETWEEN”: PARA-SCIENTIFIC KNOWLEDGE AS A TRANSITIONAL TYPE OF KNOWLEDGE

The article considers the phenomenon of the transitional type of knowledge arising at the junction of the main types of cognition. It is shown that forms of knowledge naturally arise in the cognitive space, in which, on the one hand, the level of compliance with the standards of the basic type of cognition for them is reduced and, on the other hand, signs of a different type of cognition are shown. This is an intermediate, hybrid type of knowledge, belonging to which the concepts form a kind of transitional zones between all fields of knowledge. The composition and features of this transitional type of knowledge are considered on the example of para-scientific knowledge.
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects

The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.
Introduction to the Symposium: How Will Artificial Intelligence Affect International Law?

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Collaborative Teacher-Student Learning via Multiple Knowledge Transfer

Knowledge distillation (KD), as an efficient and effective model compression technique, has been receiving considerable attention in deep learning. The key to its success is to transfer knowledge from a large teacher network to a small student one. However, most of the existing knowledge distillation methods consider only one type of knowledge learned from either instance features or instance relations via a specific distillation strategy in teacher-student learning. There are few works that explore the idea of transferring different types of knowledge with different distillation strategies in a unified framework. Moreover, the frequently used offline distillation suffers from a limited learning capacity due to the fixed teacher-student architecture. In this paper we propose a collaborative teacher-student learning via multiple knowledge transfer (CTSL-MKT) that prompts both self-learning and collaborative learning. It allows multiple students learn knowledge from both individual instances and instance relations in a collaborative way. While learning from themselves with self-distillation, they can also guide each other via online distillation. The experiments and ablation studies on four image datasets demonstrate that the proposed CTSL-MKT significantly outperforms the state-of-the-art KD methods.