Reviewerly/exHarmony huggingface dataset:
Scalable Graph Isomorphism: Combining Pairwise Color Refinement and Backtracking via Compressed Candidate Space

Graph isomorphism is a core problem in graph analysis of various application domains. Given two graphs, the graph isomorphism problem is to determine whether there exists an isomorphism between them. As real-world graphs are getting bigger and bigger, applications demand practically fast algorithms that can run on large-scale graphs. However, existing approaches such as graph canonization and subgraph isomorphism show limited performances on large-scale graphs either in time or space. In this paper, we propose a new approach to graph isomorphism, which is the framework of pairwise color refinement and efficient backtracking. The main features of our approach are: (1) pairwise color refinement and binary cell mapping (2) compressed CS (candidate space), and (3) partial failing set, which together lead to a much faster and scalable algorithm for graph isomorphism. Extensive experiments with real-world datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of running time.
Emerson’s abolitionist perfectionism

This article aims to rewrite Emerson’s moral perfectionism – his anti-foundationalist pursuit of an always more perfect state of self and society – onto his moral and intellectual participation in the abolitionist movement. I argue that Cavell artificially separated Emerson’s moral perfectionism from his extensive, decades-long abolitionism. The source of Cavell’s oversight is his participation in the long-standing norm of dichotomizing Emerson’s work into the theoretical ‘essays’ and the ‘anti-slavery writings’ or the philosophical and the polemical. Recent scholars of Emerson have questioned and even dismissed this dichotomy, however, while recentring Emerson’s politics in his oeuvre as a whole. They find much to praise, and also plenty to criticize, in Emerson’s abolitionist writings. I follow and extend that scholarly trend here and introduce what I call Emerson’s abolitionist perfectionism as an expansion of Cavell’s influential work on moral perfectionism.
Value Driven Design for Playful Technology Enhanced Installations in Public Settings

Art and installations in public settings are often created on commission from a municipality or similar public sector. Within the public sector there are many values that the community strives to enhance. Values related to democracy, inclusion, and aesthetics are but a few. When designing for public environments, the design process might be affected by the need to strive for those values in addition to, or even rather than, the more common considerations focused on user experience or commercial aspects. In this pictorial we present how identified core values influenced a design process aimed at designing innovative IoT-enhanced playground installations in a public setting. Inspired by annotated portfolios, we explicate how these core values influenced the final design.
A Model Design of Blockchain-Based Data Storage for E-Government Application

E-government is a gradual system engineering, which needs to integrate new information technology. In e-government application scenarios, a large number of data (i.e. text, audio, video, graphics and animation) are managed by platforms maintained by data providers, which usually adopt centralized architecture design. However, this centralized architecture may lead to a single point of failure and data ownership disputes. It is difficult to ensure the integrity of the data and track the trusted traceability of data usage. In order to solve these problems, this paper proposes a cost-effective blockchain data storage framework for e-government data management. Generally speaking, in the field of e-government data management, the application trend of blockchain can be summarized into two aspects: one is to lay the foundation for the development of public services and government management through the establishment of identity authentication system based on blockchain; the other is to make use of the characteristics of blockchain, which can not be tampered with and has full historical records, so that the information resources of different institutions can be connected, which can achieve the goal Now the definition of data ownership and the traceability of data access.
A Robust Watermarking Algorithm for Video Game Artwork Based on Pose Estimation Neural Network

Video game artworks serve a crucial role in video game development, the associated copyright protection remain largely unsolved. We propose a robust watermarking for protecting the copyright of video game artwork. Specifically, the watermarking algorithm uses pose estimation neural network to select pose joint points, and construct feature areas with the pose joint points as the center. Compared with existing local feature watermarking methods, the pose joint points are selected by the pose estimation neural network are much more evenly distributed, stable and orderly. Next non-maximum suppression (NMS) is used to select non-overlapping feature areas. Then discrete Wavelet transformation (DWT) performs on each feature area, and then dividing each sub-band into blocks. Finally, each block is decomposed by QR decomposition and select R matrix to embed watermarks. In the extraction procedure, the video game artwork is corrected using affine transformation before extraction. Experimental results indicate that the proposed algorithm outperforms the state-of-the-art methods in terms of imperceptiblity, robustness and computational cost. The algorithm is robust to common image attacks and geometric attacks, can effectively resist special attacks including text addition, tampering weapon, and cutting body parts of video game artwork.
ConCaT: Construction of Category Trees from Search Queries in E-Commerce

Category trees play a central role in e-commerce platforms, enabling browsing-style information access. Building category trees that reflect users' dynamic information needs is a challenging task, mostly carried out by in-house taxonomists. This manual construction often leads to trees that are lacking or outdated since it is hard to keep track of market trends, seasonal changes, holidays, and special events.To support a browsing experience that better matches the user information needs, and to considerably reduce the manual work performed by taxonomists, we propose CONCAT - a system that leverages the demand-based nature of the query paradigm to automatically build a category tree that is maximally similar to the result sets for search queries. We demonstrate the effectiveness of CONCAT on real-world data, taken from a large e-commerce platform, by interacting with the ICDE'21 participants who act both as the consumers and the taxonomists.
Corpus Construction and Entity Recognition for the Field of Industrial Robot Fault Diagnosis

The fault logs record the fault information generated during the operation process of industrial robots. It contains a large amount of fault knowledge and solution information. It is necessary to extract this information and build the fault diagnosis knowledge graph of industrial robots, which can support remote fault diagnosis of industrial robots without human help. At present, the research of fault diagnosis knowledge graph is still relatively scarce. In this paper, we propose a method of named entity recognition for extracting the knowledge of industrial robot fault diagnosis. The contribution of our paper is to establish the fault field dataset Fault-Data, propose the ontology concept of the fault diagnosis field, and obtain a good field recognition effect through the verification of the entity recognition model of fault diagnosis. Experimental results show that the F value of named entity recognition reaches 91.99%, which provides a certain reference significance for subsequent knowledge extraction and knowledge graph construction.
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection

The human face has a high potential for biometric identification due to its many individual traits. At the same time, such identification is vulnerable to biometric copies. These presentation attacks pose a great challenge in unsupervised authentication settings. As a countermeasure, we propose a method that automatically analyzes the plausibility of facial behavior based on a sequence of 3D face scans. A compact feature representation measures facial behavior using the temporal curvature change. Finally, we train our method only on genuine faces in an anomaly detection scenario. Our method can detect presentation attacks using elastic 3D masks, bent photographs with eye holes, and monitor replay-attacks. For evaluation, we recorded a challenging database containing such cases using a high-quality 3D sensor. It features 109 4D face scans including eleven different types of presentation attacks. We achieve error rates of 11% and 6% for APCER and BPCER, respectively.
Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes

Hospital processes are getting more and more complex, starting from the creation of therapy plans over intra-hospital transportation up to the coordination of patients and staff members. In this paper, multi-agent simulations will be used to optimize the coordination of different kinds of individuals (like patients and doctors) in a hospital process. But instead of providing results in form of optimized schedules, here, behavioral rules for the different individuals will be learned from the simulations, that can be exploited by the individuals to optimize the overall process. As a proof-of-concept, the approach will be demonstrated in different variants of a hospital optimization scenario, also showing its robustness to several changes in the scenario.
CL-Gym: Full-Featured PyTorch Library for Continual Learning

Continual learning (CL) has become one of the most active research venues within the artificial intelligence community in recent years. Given the significant amount of attention paid to continual learning, the need for a library that facilitates both research and development in this field is more visible than ever. However, CL algorithms’ codes are currently scattered over isolated repositories written with different frameworks, making it difficult for researchers and practitioners to work with various CL algorithms and benchmarks using the same interface. In this paper, we introduce CL-Gym, a full-featured continual learning library that overcomes this challenge and accelerates the research and development. In addition to the necessary infrastructure for running end-to-end continual learning experiments, CL-Gym includes benchmarks for various CL scenarios and several state-of-the-art CL algorithms. In this paper, we present the architecture, design philosophies, and technical details behind CL-Gym <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Practical Security of RSA Against NTC-Architecture Quantum Computing Attacks

Abstract Quantum algorithms can greatly speed up computation in solving some classical problems, while the computational power of quantum computers should also be restricted by laws of physics. Due to quantum time-energy uncertainty relation, there is a lower limit of the evolution time for a given quantum operation, and therefore the time complexity must be considered when the number of serial quantum operations is particularly large. When the key length is about at the level of KB (encryption and decryption can be completed in a few minutes by using standard programs), it will take at least 50-100 years for NTC (Neighbor-only, Two-qubit gate, Concurrent) architecture ion-trap quantum computers to execute Shor’s algorithm. For NTC architecture superconducting quantum computers with a code distance 27 for error-correcting, when the key length increased to 16 KB, the cracking time will also increase to 100 years that far exceeds the coherence time. This shows the robustness of the updated RSA against practical quantum computing attacks.
Polygonal Point Set Tracking

In this paper, we propose a novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target contour, and our goal is to track corresponding points on the target contour. Those outputs enable us to apply various visual effects such as motion tracking, part deformation, and texture mapping. To this end, we propose a new method to track the corresponding points between frames by the global-local alignment with delicately designed losses and regularization terms. We also introduce a novel learning strategy using synthetic and VOS datasets that makes it possible to tackle the problem without developing the point correspondence dataset. Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods. In addition, we present visual-effects applications of our method on part distortion and text mapping.
A Study on Application of the Outline-making Learning to College Writing Education

This study was carried out in order to verify the effectiveness of writing education by applying the Outline-making Learning, OML model, a systematic structural design method developed to improve writing skills in college students, to writing education by genre. The study involved 30 college freshmen in the humanities and social sciences, engineering, and arts and physical education department of K university in 2020, In order to analyze the effectiveness of OML application, the evaluation categories were divided into five categories, grammar, content, expression, format, and context, targeting three genres: expository writing, persuasive writing, and reports before and after OML education. The results showed significant effects that could suggest the applicability of OML to three genre writing in general, and showed the greatest effect in the persuasive writing. The application of OML also helped college students improve their creative problem-solving skills in the writing process. Therefore, OML is expected to be available for various genres of writing in college students" writing education.
Boolean Functions with a Few Walsh Transform Values

Boolean functions have been extensively studied in coding theory, cryptography, sequence design and graph theory. By adding two products of three linear functions to some known bent functions, in this paper, we construct a class of bent functions and obtain their dual functions. In the meantime, a class of semi-bent functions and some classes of five-valued Walsh spectra are given.
CE-PeopleSeg: Real-time people segmentation with 10% CPU usage for video conference

Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual back-ground, an essential video conference function to protect users’ privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://github.com/geekJZY/EfficientPeopleSeg.git.
A Comparative Analysis of Supervised Word Sense Disambiguation in Information Retrieval

As the amount of information increases every day, there is a need of information retrieval for finding useful information from large amount of information. This paper presents and evaluates supervised word sense disambiguation (WSD) algorithms in information retrieval (IR). Word ambiguity problem is a key issue in the information retrieval systems. Supervised WSD is considered as the effective method than other methods in information retrieval. The effective usage of supervised WSD in IR is the main objectives of this research paper. The paper defines the role of supervised WSD in IR and discusses the best-known supervised algorithms in detail. We use Weikato environment for knowledge analysis (WEKA) tool to assess four supervised WSD algorithms Naïve Bayes, support vector machine (SMO), decision tree (J48), and KNN (IBK) through a series of experiments, and the result concludes that the algorithms performance is based on the features of the datasets.
Biological Named Entity Recognition and Role Labeling via Deep Multi-task Learning

Bioscience is an experimental science. The qualitative and quantitative findings of the biological experiments are often exclusively available in the form of figures in published papers. In this paper, we introduce the SourceData model, which captures a key aspect of the biological experimental design by categorizing biological entity involved in the experiment into one of the six roles. Our work aims at determining whether a given entity is subjected to a perturbation or is the object of a measurement (entity role labeling) through automatic natural language algorithms. We use state-of-the-art transformer models (e.g., Bert and its variants) as a strong baseline, find that after jointly trained with biological named entity recognition task by deep multi-task learning (MTL), the F1 score gets improved by 2% compared to previous single-task architecture. Also, for named entity recognition task, the MTL method achieves comparable performance in five public datasets. Further analysis reveals the importance of fusing entity information at the input layer of entity role labeling task and incorporating global context.
On Distinguishing Sequences of Several Classes of Reversible Finite State Machines

The existence of distinguishing sequence (DS) is an important property for finite state machines (FSMs). Such an input sequence can distinguish all the states of the FSM. An FSM possessing a DS can be therefore easily analyzed in a case of unexpected failure and the state when the machine terminated can be identified. In this paper, we investigate length and existence of DSs of several classes of reversible finite state machines (RFSM). Reversible FSMs can be directly constructed on a quantum computer and therefore understanding their properties is important for the efficient usage and implementation of these devices in quantum technologies. In particular, we consider output balanced machines, where for every input the output assignment is balanced. We also consider output unbalanced machines that have the same output assignment for each input. Finally, we investigate a hierarchy of six classes of such machines. All machines we study in this paper possess a DS exists, unlike some RFSMs that might not have DS. In addition the length of DS of all of these machines is shorter than the general length of DS from [1].
R tutorial: libraries and loading data into R

In order for us to work with data, we need to load this data into R. While R has many functions that allows us to incorporate data from a variety of sources in numerous formats, this chapter focuses on loading csv files into R or taking advantage of the pre-loaded datasets and functions stored in libraries. Libraries particularly represent a rich source of datasets, as they often cover a wide variety of fields. Beyond the data and libraries presented in this book, the online R community has a plethora of resources available to students of all levels.
Real-Time Multi-obstacle Detection and Tracking Using a Vision Sensor for Autonomous Vehicle

In this paper, an effective approach for real-time multi-obstacle detection and tracking in the navigation module is discussed.Francis, Sobers Anavatti, Sreenatha G. Garratt, Matthew Abbass, Hussein A. To calculate a feasible path for an autonomous ground vehicle (AGV) from the start position to goal position, efficient Dstar lite global planner is added and adhered to ROS nav $$\_$$ core package. Later, the clustering of points based on distance from a laser scanner data is carried out to perform multi-obstacle detection and followed by tracking. Then, the clusters are categorised as static and dynamic obstacles from their location, orientation, speed and size of an individual cluster. Using this approach, dynamic obstacles’ paths are estimated from their respective past positions. To predict the dynamic obstacle for the next five-time steps, linear extrapolation and line fitting are employed. The estimated obstacles’ path data are published as a PointCloud ROS message, then it is subscribed by the costmap node of the ROS navigation package. The costmap automatically updates the obstacle map layer and rebuilds the 2D occupancy grid map with new information about obstacles. Then, the path planner replans the path using updated costmap to avoid obstacles in the dynamic environment. Finally, real-time experiments are conducted to validate the efficacy of this intelligent system.
Computer Image Processing System Based on Feature Extraction Technology

Abstract This article studies the methods of computer image acquisition, processing and recognition. The study found that the images collected from the computer image processing system often have blurring phenomenon. For this reason, the paper proposes a wavelet transform feature extraction algorithm based on colour images, which can separate the image and the background without greying the colour image. Through experimental research, it is found that this method can efficiently and quickly solve the problem of automatic image registration.
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing

Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-of-the-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, network prediction alone might not suffice, and assignment of a confidence value for the prediction can prove crucial. In this work, we propose a generic method for introducing stochasticity in the network predictions, and utilize this for smoothing decision boundaries and rejecting low confidence predictions, thereby boosting the robustness on accepted samples. The proposed Feature Level Stochastic Smoothing based classification also results in a boost in robustness without rejection over existing adversarial training methods. Finally, we combine the proposed method with adversarial detection methods, to achieve the benefits of both approaches.
Learning Visual Servo Policies via Planner Cloning

Learning control policies for visual servoing in novel environments is an important problem. However, standard model-free policy learning methods are slow to learn. This paper explores planner cloning: using behavior cloning to learn policies that mimic the behavior of a full-state motion planner in simulation. We propose Penalized Q Cloning (PQC), a new behavior cloning algorithm. We show that it outperforms several baselines and ablations on some challenging problems involving visual servoing in novel environments while avoiding obstacles. Finally, we demonstrate that these policies can be transferred effectively onto a real robotic platform, achieving approximately an 87% success rate both in simulation and on a real robot.