Reviewerly/exHarmony huggingface dataset:
Interactive Cartographic Storytelling with Complex Spatio-Temporal Structures and Social Connections

In this paper, we describe the design of an interactive cartographic storytelling platform for the 1906 Atlanta Race Massacre, a horrific incident that had a profound impact on the civil and human rights movement in the United States. This four-day event happened at various locations in downtown Atlanta and involved many people. Although multiple books and articles have been written about the 1906 Atlanta Race Massacre, they described the past events using conventional storytelling methods. We want to tell this story from a cartographic perspective because the locations are essential to this story. We also want to connect the past with the present because most people walking on the same streets today do not know the history and significance of the locations. Furthermore, most people are unaware that some major institutions are intricately connected to the people involved in the 1906 events. Telling the story this way requires us to handle a complex spatio-temporal structure and an extensive social network, which is unusual in traditional cartographic storytelling. In this paper, we discuss our design decisions and rationals. We believe our discussion will benefit other interactive story designers who deal with similar complex stories.
Diagram Question Answering with Joint Training and Bottom-Up and Top-Down Attention

Diagram question answering is a challenging multi-modal machine learning task that focuses on answering questions according to given diagrams on specific fields. Compared to natural imaged, these diagrams have more abstract expressions and complex logical relations, which makes diagram question answering more difficult. In this paper, we propose a new approach for diagram question answering task. We add bottom-up and top-down attention to identify regions of interest to questions and use a same model to jointly train multiple choice questions and true false questions. Our approach on test dataset of official CCKS2022 textbook diagram question answering session achieves the accuracy of 58.09%.
A New 3D Face Model for Vietnamese Based on Basel Face Model

In recent years, many 3D face reconstruction from image methods have been introduced and most of them have shown incredible results. However, methods such as photogrammetry require images from multiple views and can be very time-consuming. Deep learning based methods, on the other hand, are faster and more efficient but heavily rely on the base face models and training datasets. Meanwhile, most base face models lack Asian facial features, and high-quality Vietnamese facial image databases are still not available yet. In this paper, we propose an approach that increases the accuracy of Vietnamese 3D faces generated from a single image by creating a new mean face shape and training a convolution neural network with our dataset. This method is compact and can improve the quality of 3D face reconstruction using facial image data with specific geographical and race characteristics.
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning

Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning models incrementally from scratch. In this paper, we explore an alternative framework to incremental learning where we continually fine-tune the model from a pre-trained representation. Our method takes advantage of linearization technique of a pre-trained neural network for simple and effective continual learning. We show that this allows us to design a linear model where quadratic parameter regularization method is placed as the optimal continual learning policy, and at the same time enjoying the high performance of neural networks. We also show that the proposed algorithm enables parameter regularization methods to be applied to class-incremental problems. Additionally, we provide a theoretical reason why the existing parameter-space regularization algorithms such as EWC underperform on neural networks trained with cross-entropy loss. We show that the proposed method can prevent forgetting while achieving high continual fine-tuning performance on image classification tasks. To show that our method can be applied to general continual learning settings, we evaluate our method in data-incremental, task-incremental, and class-incremental learning problems.
A Shared Cloud-based Architecture for Geochemical Data Systems Applied to Terrestrial and Planetary Materials: The Astromaterials Data System and EarthChem

EarthChem and the Astromaterials Data System provide comprehensive data services to research communities that acquire and use laboratory analytical data of terrestrial and planetary materials, respectively, including data publication, data access, data data synthesis, and data preservation.Both systems are developed and operated by the Geoinformatics Research Group (GRG) at the Lamont-Doherty Earth Observatory of Columbia University with the goal to operate a reliable, resilient, scalable, and sustainable data infrastructure in the most efficient and effective manner.Over the past few years, the GRG has created a shared software platform for the two systems that includes service-oriented architecture, public cloud-based infrastructure, and Continuous Integration and Continuous Delivery (CI/CD) pipelines with GitHub Actions (Figure 1 Service-Oriented Architecture of the Astromat/Earthchem Data System).Each component of the architecture is intended for specific purposes, including data storage; interfaces for users to search, access, explore, visualize, analyze, and contribute data; software tools for data curators to compile, track, validate, ingest, manage, and annotate data; and machine-actionable interfaces that connect the databases to internal and external software tools. [1] The infrastructure leverages the services provided by AWS, such as S3, Cloudfront, Fargate, etc. to implement the service-oriented architecture illustrated in Fig. 1.Also included is a collection of AWS CloudFormation templates for automating AWS resource management.A series of CI/CD pipelines built with GitHub Actions are used to automate the processes of building, testing, delivering or deploying the applications to AWS infrastructure,resulting in more efficient use of developers' time and effort and greatly improving the reliability of the applications.The shared platform provides the necessary robust reliability and scalability for the data systems, and greatly reduces the need for system maintenance.At the same time, it makes it easier to extend and modify the system to meet the needs of different scientific communities.
Evaluation of Genetic Algorithm Optimization by Comparative Analysis of Error Values

In this paper to test the performance of GA a comparative study has been carried out between PI and PD Controller. The two different objective functions have been chosen that is integral absolute error and integral total absolute error on different generations the controller has been run as a result the minimum value of error has been found in controller in which the objective function was ITAE and a detailed evaluation of Genetic Algorithm has been carried out through out the paper. Optimization and search issues may be solved using genetic algorithms, which are seen as a search process in computers. Global search heuristics are another name for them. Many of these methods are derived from concepts found in evolutionary biology such as mutation, selection, and cross-breeding. For programmes, these algorithms provide a way to automatically enhance their settings. The simulations are tallied to ensure that GA delivers the system promising outcomes.
A Design of Fuzzy Rule-Based Models With Data Privacy

One of the currently visible requirements of data analysis concerns data privacy. The quest for privacy triggers design challenges in the construction of machine learning models. In this study, we propose a novel and systematic design methodology of fuzzy design rule-based models completed in the following privacy-driven environment. Multidimensional data are available for model design in two different formats: those attributes that are privacy sensitive are provided at the higher nonnumeric level of abstraction represented in the form of information granules, while the remaining ones are available directly. It is shown that the granular manifestation of data becomes beneficial in the formation of the conditions of the rules, whereas numeric attributes are available for the construction of the conclusions of the rules. The generic development scheme is discussed. Some generalization is introduced to cope with a number of data islands associated with the corresponding data owners. It is shown that the formation of the data structure calls for a mechanism of collaborative clustering. A way of handling information granules at different levels of information granularity is discussed. Some illustrative examples are presented.
DAZeTD: Deep Analysis of Zones in Torn Documents

In a crime scene, document fragments with similar contents might lead to significant evidence. A criminalist when encounters such a scene with an enormous amount of torn document pieces, automated analysis becomes imperative in procuring potential evidence in a fast and reliable way. To analyze document fragments with similar contents, a processing module to segment the homogeneous zones based on the content type is a prerequisite. This paper proposes a deep learning-based module DAZeTD that can detect textual (printed/handwritten) and non-textual ragged zones. For classifying the content of the zones, we adopt the scheme of vision transformer; and to draw the zone boundaries, we employ outer isothetic cover. We created a dataset of 881 torn documents on which we performed rigorous experiments. We obtained an overall 87.71% mAP@0.5, which is quite promising.
Development of rescue robot using hydraulic actuator

The disasters have occurred frequently in Japan. When conducting rescue activities at a disaster site, the risk of a secondary disaster can be reduced by navigating a rescue robot before the rescue team members and checking the situation at the disaster site. When performing rescue activities, the rescue robot applied to the installed manipulator to open/close valves and remove rubbles. At the disaster site, there are places where water is overflowing due to the rupture of pipes or damage to the tank. Waterproof is required to work in that area. Therefore, in this research, we designed and developed a manipulator with improved waterproof to be mounted on a rescue robot with a hydraulic actuator.
Deepfake as the basis for digitally collaging “impossible faces”

Subject of study. In this study, we present a novel method for the synthesis of video images using deepfake face swap, a technology that enables the creation of authentic video clips with false or swapped faces without evident traces of manipulation. The creation process of video images of “impossible faces,” such as a chimeric face, whose left and right sides belong to different people, or Thatcherized faces, wherein the eyes and mouth regions are rotated by 180°, is described stepwise using the DeepFaceLab software as an example. Aim of study. This study aimed to present and validate deepfake technology as a method of digitally collaging “impossible face” images. Method. Examples of method implementation were demonstrated using experimental results related to the perception patterns of moving “impossible faces” and their differences. Main results. The phenomena of perception previously recorded under static conditions were reproduced for dynamic models; accordingly, new interpretations can be inferred. Original faces were evaluated positively under static and dynamic conditions, independent of image inversion. Images of “impossible faces” under all conditions were perceived as unattractive, disharmonious, bizarre, and artificial. Expositions with audio speech increased the adequacy of evaluations under conditions of straight orientation. Practical significance. Digital collaging methods can significantly expand the capabilities of researchers in the field of interpersonal perception. Moreover, information technologies can expedite the creation of stimuli models for “impossible face” images, which are necessary for the further investigation of the human psyche in the course of communication.
Action Planning Including Holding Pattern Selection for Organizing Office Chairs by a Dual-Arm Mobile Robot

This paper describes action planning for a dual-arm mobile manipulator aiming to move a chair to an instructed position. The initial posture of the mobile manipulator for task execution is determined by the evaluation based on the size of free space. This process includes the selection whether to use both arms or one of them. Next, QP optimization is used to calculate the suitable joint angles of the mobile manipulator to approach the target posture while satisfying the constraint conditions. In the proof simulation, the feasibility of the proposed method was examined by performing tidying tasks in several different conference room layouts.
Block Structured Graph Priors in Gaussian Graphical Models

Colombi, AlessandroGaussian graphical models are a powerful statistical tool to describe the concept of conditional independence between variables through a map between a graph and the family of multivariate normal models. The structure of the graph is unknown and has to be learned from the data. Inference is carried out in a Bayesian framework: thus, the structure of the precision matrix is constrained by the graph through a $${\text {G-Wishart}}$$ prior distribution. In this work we first introduce a prior distribution to impose a block structure in the adjacency matrix of the graph. Then we develop a Double Reversible Jump Monte Carlo Markov chain that avoids any $${\text {G-Wishart}}$$ normalizing constant calculation when comparing graphical models. The novelty of this procedure is that it looks for block structured graphs, hence proposing moves that add or remove not just a single link but an entire group of them.
Block-based Fragile Image Watermarking based on SVD-LSB and Chaos System

This research proposes a fragile image watermarking method with a combination of spatial and transformations based on singular value decomposition (SVD) and a least significant bit (LSB). Before the watermark is embedded, the cover image is processed in two stages of scrambling, the first stage is carried out on pixels, and the second stage is carried out on a 3×3 block basis. The watermark is embedded with an XOR operation between the watermark and the authentication bit. The authentication bits are generated from the beta chaotic map, the singular matrix image block and the image block group. Based on the test results, this method can detect the tamper well with a true positive rate (TPR) of more than 93 and a false positive rate (FPR) of less than six. The proposed method is also quite robust because the extracted watermark still gets a correlation coefficient value of more than 0.7 after various attacks being tested for various attacks such as adding noise, cropping and tampering.
Applying the Generalized Compass Operator for Image Dominant Orientation Estimation

Evaluation of the orientation of objects on frames from digital cameras is one of the problems of intelligent image analysis and processing. In this paper, we focus on the two-stage algorithm for image dominant orientation using first rough estimation of orientation angle through principal component analysis of gradient covariance matrix and second more accurate orientation estimation using the generalized compass operator. We compared two types masks of compass operator – Prewitt-like and extended Prewitt-like – for orientation estimation task. We demonstrated that for extended Prewitt-like mask with a size of 5?5 pixels using the generalized compass operator provides an estimation error of the dominant direction no more than 1° compared with methods, based on principal component analysis technique.
Characterizing Functions Mappable over GADTs

It is well-known that GADTs do not admit standard map functions of the kind supported by ADTs and nested types. In addition, standard map functions are insufficient to distribute their data-changing argument functions over all of the structure present in elements of deep GADTs, even just deep ADTs or nested types. This paper develops an algorithm that characterizes those functions on a (deep) GADT’s type arguments that are mappable over its elements. The algorithm takes as input a term t whose type is an instance of a (deep) GADT $$\textsf{G}$$ , and returns a set of constraints a function must satisfy to be mappable over t. This algorithm, and thus this paper, can in some sense be read as defining what it means for a function to be mappable over t: f is mappable over an element t of $$\textsf{G}$$ precisely when it satisfies the constraints returned when our algorithm is run on t and $$\mathsf G$$ . This is significant: to our knowledge, there is no existing definition or other characterization of the intuitive notion of mappability for functions over GADTs.
Content-Based Medical Image Retrieval Using a Class Similarity-Aware Cross-Entropy Loss

This paper addresses the problem of content-based image retrieval (CBIR) in a database of medical images using a two-step strategy. The first step consists in building a classification model using a state-of-the-art convolutional neural network for a preliminary screening of the query images. The classification model is trained using a weighted cross-entropy cost function that accounts for the similarity between classes. The second step of our CBIR method consists in searching for similar images in the database given the predicted class from the previous step. A histogram of oriented gradients (HOG) feature descriptor is used to reduce all images to lower dimensional feature vectors, and the similarity between a query image and the images in the database is evaluated by computing the Euclidean distance between the HOG feature vectors. The proposed method achieved an error score of 123.02 on the IRMA dataset, which represents an improvement of 7.12% over the state-of-the-art result.
The Price of Verifiability: Lower Bounds for Verifiable Random Functions

Verifiable random functions (VRFs) are a useful extension of pseudorandom functions for which it is possible to generate a proof that a certain image is indeed the correct function value (relative to a public verification key). Due to their strong soundness requirements on such proofs, VRFs are notoriously hard to construct, and existing constructions suffer either from complex proofs (for function images), or rely on complex and non-standard assumptions. In this work, we attempt to explain this phenomenon. We first propose a framework that captures a large class of pairing-based VRFs. We proceed to show that in our framework, it is not possible to obtain short proofs and a reduction to a simple assumption simultaneously. Since the class of “consecutively verifiable” VRFs we consider contains in particular the VRF of Lysyanskaya and that of Dodis-Yampolskiy, our results explain the large proof size, resp. the complex assumption of these VRFs.
Automation of dataset labeling in Machine Learning for Japanese language analysis

Annotation work is necessary for natural language processing based on machine learning for robots. For the purpose to realize the automation of this task, we studied some automatic labeling methods for product review evaluation, and proposed two methods using the analysis of adjectives and learning of human ratings. The proposed two methods were evaluated by experiments and their results were compared. Based on these results, we also discussed the usefulness of the proposed methods when applying to other datasets.
Geochronology, petrogenesis, and tectonic implications of Early Paleozoic intermediate-basic complex of East Ujimqin Banner area

内蒙古东部二连浩特-东乌旗一线出露的早古生代岩浆活动的成因及动力学背景对于揭示恢复区域的构造演化历史具有重要意义。本文选取东乌旗地区早古生代中-基性杂岩进行了锆石U-Pb年代学、岩石地球化学和Lu-Hf同位素分析。定年结果显示,变辉石闪长岩成岩年龄为465&#177;5Ma,片麻状角闪辉长岩为456&#177;3Ma,表明该杂岩形成时限为中-晚奥陶世。地球化学组成上,东乌旗早古生代中-基性杂岩相对富集轻稀土元素而亏损重稀土元素,Eu负异常相对明显,相对富集Rb、Sr等大离子亲石元素(LILs),亏损Nb、Ta、Zr等高场强元素(HFSE),兼具MORB和岛弧玄武岩的特征。随着时间的推移,岛弧玄武岩的印记正逐渐减弱,而MORB成分不断增加。岩浆来源于被俯冲流体为主、熔体为辅交代的地幔部分熔融。杂岩中的变辉石闪长岩<i>ε</i><sub>Hf</sub>(<i>t</i>)=8.27~12.03,片麻状角闪辉长岩<i>ε</i><sub>Hf</sub>(<i>t</i>)值为-2.12~1.10,总体呈现出早期亏损成分先出熔,后期富集成分再出熔的特征。结合区域岩石地层资料,综合分析表明东乌旗中-基性杂岩形成于弧后盆地初始裂解-不成熟弧后盆地阶段,兴安地块西部南缘的苏尼特左旗-锡林浩特-大石寨早古生代岩浆岩带和北侧的二连浩特-东乌旗早古生代岩浆岩带共同构成了古亚洲洋北缘早古生代弧盆系,其演化特征可以与地块东部进行类比。
Analysis of the Anytime MAPF Solvers Based on the Combination of Conflict-Based Search (CBS) and Focal Search (FS)

Conflict-Based Search (CBS) is a widely used algorithm for solving multi-agent pathfinding (MAPF) problems optimally. The core idea of CBS is to run hierarchical search, when, on the high level the tree of solutions candidates is explored, and on the low-level an individual planning for a specific agent (subject to certain constraints) is carried out. To trade-off optimality for running time different variants of bounded sub-optimal CBS were designed, which alter both high- and low-level search routines of CBS. Moreover, anytime variant of CBS does exist that applies Focal Search (FS) to the high-level of CBS – Anytime BCBS. However, no comprehensive analysis of how well this algorithm performs compared to the naive one, when we simply re-invoke CBS with the decreased sub-optimality bound, was present. This work aims at filling this gap. Moreover, we present and evaluate another anytime version of CBS that uses FS on both levels of CBS. Empirically, we show that its behavior is principally different from the one demonstrated by Anytime BCBS. Finally, we compare both algorithms head-to-head and show that using Focal Search on both levels of CBS can be beneficial in a wide range of setups.
Analysis of the Online Home Consumption Database Based on Data Mining

In current times where there are smart devices for households, and that apart from having different functions that are helpful in daily household chores, such as being able to maintain a full pantry, and to generate errand lists, in the market these devices have a high cost for this reason is that it is proposed to create a low cost smart device, in this document an analysis is made using data mining, with tensor flow, of the purchases generated by the users, derived from the current situation by the pandemic of the COVID-19, generated an increase in online shopping, this analysis is intended to be a support for online errand shopping to visualize classification and prediction in the comparisons of users.