Reviewerly/exHarmony huggingface dataset:
Towards Non-Technical Designer Control over PCG Systems: Investigating an Example-Based Mechanism for Controlling Graph Grammars

Increasingly, PCG systems are developed to help game designers create content for their games. However, game designers have limited control over the content current PCG systems generate. We investigate an interaction mechanism non-technical users can use to control generative grammars without the need for understanding the grammar’s rules. To demonstrate this control mechanism, we present a system, built using a probabilistic graph grammar, that allows designers to specify their desired generative space by defining a region on an expressive range plot. We ran a user study with game design students to assess its viability. Our findings suggest that designers have an easier time controlling the grammar using this mechanism over manually interacting with grammars rules.
Autoscaling Datacenter Servers in Multi tenant Video on Demand Cloud Application

Growing complexity of cloud based interconnected systems have led to an increased complexity in datacenter resource management. Handling datacenter resources and providing uninterrupted service in a typical multitenant cloud application is a challenge. In this paper we present Autoscaling of Datacenter Servers (ADS) for multi-tenant Video on Demand (VoD) cloud application. In the proposed solution, video chunks are distributed in the servers considering various factors such as chunk-hit count, server capacity. Moreover, the underutilized servers are powered-off. We use Quality of Service (QoS) parameters startup latency and stalling events to perform comparative evaluation under three different scenarios based on the percentage of Mobile User Access (MUA). The proposed Autoscaling of Datacenter Servers (ADS) is compared with two existing multi-tenant architectures. Multi-tenant, secure, Load Disseminated SaaS architecture (MLD) and Towards Modeling a Variable Architecture for Multi-Tenant SaaS-Applications (MVA).
Multidimension Joint Networks for Action Recognition

Motion types, spatial and temporal features are two crucial elements of information for video action recognition. 3D CNNs boast good recognition performance but are computationally expensive and less competitive on temporal feature extraction. 2D CNNs are computationally cheap and more adaptive to temporal features but can not achieve promising performance. In this work, we design a multidimension joint (MJ) module which can be embedded into 2D CNNs and strike the balance between computational cost and performance. To this end, we propose a spatio-temporal features and motion changes extraction module which includes two paths: Spatio-temporal joint (STJ) path and Temporal motion enhance (TME) path. The STJ path utilizes 3D CNNs to describe spatio-temporal representation. The TME path calculates feature-oriented temporal differences, which is used to enhance motion-sensitive channels.
Analysis of Neutrality of AutoML Search Spaces with Local Optima Networks

AutoML tackles the problem of automatically configuring machine learning pipelines to specific data analysis problems. Optimization methods are used to explore this space of hyperparameters but little is known about this search space. Understanding the characteristics that make a space difficult to search can help developing better search methods besides better understaing the parameter and their interactions. This work uses a technique, named Local Optima Network (LON), which builds a graph from the fitness landscape of the problem. The fitness landscape is a way to represent the quality of all possible solutions in the search space. In particular, we use derivatives of the original LON, Monotonic LON (MLON) and Compressed MLON (CMLON). These variants have the advantage of dealing with the neutrality present in search spaces, i.e., regions of the space with solutions of the same quality. This paper analyzes the use of MLON and CMLON built from the fitness landscape of AutoML problems to better understand the search space of AutoML problems. The results indicate the presence of neutrality in many datasets and that it may have links to the fitness variance in the search space.
Research on remote guidance of hardware operation and maintenance of computer room based on AR

At present, AR remote guidance used in computer room operation and maintenance inspection usually uses circle annotation and arrow as annotation guidance. The guidance of operation mode is not clear enough, and primary operation and maintenance personnel are still difficult to understand. Therefore, a remote guidance method of computer room hardware operation and maintenance based on 3D dynamic editing AR is proposed. The 3D model of computer room hardware is used as the scene basis, the experts edit, annotate the model remotely. Finally, the dynamic operation guidance is sent to the AR display device of the field operator to form the AR dynamic operation guidance of the virtual and real fusion. The experimental results using this method show that the 3D remote annotation can effectively realize the AR remote guidance. Compared with the screenshot operation guidance, the guidance efficiency and accuracy are higher, and the field operators receive better information.
SpeakerPool: A remote speech data collection platform

The collection of speech production data for academic use has traditionally been carried out by recording participants in-person. While certain types of research require traditional data collection methods, many factors, such as distance and world events, make remote collection a valuable alternative for other types of research. Despite the existence of capable technologies and the advantages of remote collection methods, to the authors' knowledge there is no freely accessible, reusable, and research-oriented platform for the remote collection of speech production data. We aim to address this with SpeakerPool: a web application that, in addition to providing an easy-to-use recording interface for participants, has built-in functionalities for the automation of many tasks. Users are able to access SpeakerPool regardless of platform using modern web browsers on both mobile and non-mobile devices. This greatly reduces compatibility limitations and bypasses the need to download software. In the present study, we discuss the general advantages and disadvantages of remote speech data collection and investigate the usefulness of SpeakerPool by collecting and analyzing a pilot data set of Malagasy speech.
Towards Robust Face Recognition with Comprehensive Search

Data cleaning, architecture, and loss function design are important factors contributing to high-performance face recognition. Previously, the research community tries to improve the performance of each single aspect but failed to present a unified solution on the joint search of the optimal designs for all three aspects. In this paper, we for the first time identify that these aspects are tightly coupled to each other. Optimizing the design of each aspect actually greatly limits the performance and biases the algorithmic design. Specifically, we find that the optimal model architecture or loss function is closely coupled with the data cleaning. To eliminate the bias of single-aspect research and provide an overall understanding of the face recognition model design, we first carefully design the search space for each aspect, then a comprehensive search method is introduced to jointly search optimal data cleaning, architecture, and loss function design. In our framework, we make the proposed comprehensive search as flexible as possible, by using an innovative reinforcement learning based approach. Extensive experiments on million-level face recognition benchmarks demonstrate the effectiveness of our newly-designed search space for each aspect and the comprehensive search. We outperform expert algorithms developed for each single research track by large margins. More importantly, we analyze the difference between our searched optimal design and the independent design of the single factors. We point out that strong models tend to optimize with more difficult training datasets and loss functions. Our empirical study can provide guidance in future research towards more robust face recognition systems.
Risk evaluation of tailings dam based on game theory and matter-extension theory

The safety and stability of tailings dam are directly related to the safety of life and property of downstream residents and the ecological and environmental safety of downstream rivers and farmland. During the operation stage of the tailings pond, safety assessment shall be carried out from seepage damage of the tailings dam, instability of the dam foundation, structural damage of the dam body, flooded roof, and safety management, etc. Aiming at a tailings pond in the running stage of Guangxi Province in China, by establishing the evaluation index system and its grading standard of the tailings dam-break, the subjectivity and objectivity of the evaluation index are taken into consideration by the combination of matter-element extension theory and game theory. Meanwhile, in order to solve the problem of the defect of the correlation function when the eigenvalue exceeds the controlled field and of the incommensurability for different units, a linear dimensionless method is adopted. The results show that the grade of tailings dam-break is I, and the eigenvalue of grade variable is 1.3, that is, the tailings pond is a “normal tailing reservoir”, but there is a risk of evolution to the “sickness tailing reservoir”. Thus, corresponding measures must be taken to prevent. In addition, the result of the two methods is consistent by comparing with the unascertained measure method, which not only validates the accuracy of the evaluation method, but also embodies the unique advantage of possessing the eigenvalue of grade variable to alert its evolution direction.
Exploring the Effectiveness of Appearance Descriptor in DeepSORT

Tracking-by-detection approaches have demonstrated their strength in addressing Multiple Object Tracking (MOT) problems. DeepSORT, one of the classical tracking-by-detection MOT methods, relies on a deep appearance descriptor to extract global appearance features of identities. Although the appearance descriptor acts as a key component of such tracking-by-detection methods, which is responsible for modeling appearance information, the relationship between it and tracking performance remains unclear, especially whether further improvements to it will be reflected in the tracking performance. To explore that, extensive experiments are conducted on the appearance descriptor by applying various traditional optimization methods. Furthermore, we propose an Evolutionary Neural Architecture Search (ENAS) strategy for the appearance descriptor named Genetic-SORT to assist exploration. The experimental results demonstrate that tracking performance fails to follow the improvements applied on the appearance descriptor and even shows a negative correlation, which is contrary to our intuition.
Design and Implementation of System of the Web Vulnerability Detection Based on Crawler and Natural Language Processing

Application code analysis and static rules are the most common methods for Web vulnerability detection, but this process will generate a large amount of contaminated data and network pressure, the false positive rate is high. This study implements a detection system on the basis of the crawler and NLP. The crawler visits page in imitation of a human, we collect the HTTP request and response as dataset, classify the dataset according to parameter characteristic and whether the samples get to interact with a database, then we convert text word vector, reduce the dimension and serialized them, through train dataset by NLP algorithm, finally we obtain a model that can accurately predict Web vulnerabilities. Experimental results show that this method can detect Web vulnerabilities efficiently, greatly reduce invalid attack test parameters, and reduce network pressure.
Recent Trends in Human Re-identification Techniques – A Comparative Study

Human re-identification is an essential one in recent days for automated video crime avoidance. Human re-identification is collecting impetus owing to its purpose in security and crime inquiry. Handcrafted features namely, size, color and texture histograms with small-scale assessment were used for re-identification in early times. But nowadays many deep learning-based methods have emerged to provide excellent performance in a very wide range of applications together with image annotation, face recognition and speech recognition. Deep learning features are heavily dependent on large-scale labeling of samples. This paper concentrates on analyzing different existing human re-identification using handcrafted features, deep features and combined features. The main objective of this study is to provide a detailed insight to the reader with the different aspects of re-identification methodologies and future direction of researches. The experimental evaluations on recent re-identification methods have been presented for a better understanding of the performance of existing human re-identification methods on different benchmark datasets namely VIPeR, Market1501, and CUHK03.
Hybrid Adaptive Enhanced Network for Single Image Super Resolution

Proper utilization of image features is crucial for single image super-resolution tasks. Existing models rely on the large capacity of the network to deeply mine the high-level semantic information of images and ignore the rational use of multi-scale image information and intermediate information in the network. To solve these problems, we propose a hybrid adaptive enhance network (HAEN), which consists of composite adaptive enhancement module (CAEM) and gradual adaptive fusion (GAF) structure, to model the interdependencies among layers, scales. Concretely, the proposed CAEM adaptively emphasizes multi-scale features by considering correlations among scales. Furthermore, we constructed a feature GAF structure, which saturates and effectively utilizes the intermediate features through adaptive parameter learning to gradually fuse output features of hybrid enhance residual groups (HERG) from front to back. Extensive experiments show that HAEN has achieved a significant improvement over the state-of-the-art SISR methods on benchmark datasets.
Local Performance Trade-Off in Heterogeneous Federated Learning with Dynamic Client Grouping

As an emerging machine learning paradigm, federated learning typically employs client selection to reduce computational and communication overheads of the learning process in mobile edge environment. Although client selection can coordinate large-scale clients for efficient training, the inherent characteristic of the scheme, i.e., the partial client participation can lead to a performance bias of the global model among clients. Besides, the system heterogeneity and data heterogeneity in mobile edge environment can exacerbate the negative impact of such bias on federated learning. To improve unbalanced local performances on different clients of the global model caused by client selection, this paper proposes a Federated Learning Clients Dynamic Grouping Method based on Local Computational Efficiency (FedGLCE). Considering the differences of computational power between clients and combining the idea of grouping clients, FedGLCE separates the framework of federated learning into several client groups, each with cohesive local computing efficiency, to balance the client participation in local training while guaranteeing that clients are not discarded. Specifically, FedGLCE calculates the local computational efficiency of clients based on the number of local data and updated local training time. Then, to complete client grouping, a polynomial distribution set is constructed and mapped to different groups, and one client is randomly selected from each group to participate in the local training. Compared with state-of-the-art client grouping approaches, FedGLCE effectively balances the participation of clients and improves the performance bias of the global model among clients under evaluations on MNIST-Fed and CIFAR-10-Fed heterogeneous datasets in federated learning.
Domain Generalization via Maximum Intra-class Average Diameter Minimization

Domain generalization aims to train a model which can generalize to unknown domains after training on several source domains. When the training domain and testing domain are under similar distributions, the well-trained model can achieve similar performance on two domains. But when generalize to unseen domains, the performance of the model will drop significantly, because of the existence of the domain-shift. To solve domain generalization problems, it is important to extract rich and general features from data. To improve the generalize ability, recently, lots of contrastive learning based feature alignment approaches had been proposed. Instance-level contrastive learning approaches force the model to learn domain-invariant features by pushing the positive pairs closer in the representation space. But they ignored the effect of class-level feature distribution in the representation space. In this study, the authors propose a simple method Maximum Intra-class Average Diameter minimization (MIADM) to regularize the class-level feature distribution. The authors evaluate our method on three benchmark datasets including PACS, VLCS, and OfficeHome. The extensive experimental results demonstrate that our method achieves competitive performance compared with SOTA methods.
Improving Classroom Object Detection with Bootstrapped Semi-Supervised Learning

With the development of artificial intelligence, smarter classroom education becomes more and more popular. In classroom intelligence, object detection plays a vital role . However, due to the lack of large classroom dataset, it is hard to obtain excellent classroom domain-specific detection model. To address the problem, we propose a novel bootstrap semi-supervised algorithm. The algorithm uses general labeled detection datasets, such as VOC, and large unlabeled classroom datasets for training, and improves model performance through bootstrap learning. We conduct experiments on the common YOLOv5-S model. The results demonstrate the effectiveness of our work, and facilitate the study of classroom teaching evaluation.
What is a Reasonable Way to Make Predictions?

Predictions are usually based on what is called laws of nature: many times, we observe the same relation between the states at different moments of time, and we conclude that the same relation will occur in the future. The more times the relation repeats, the more confident we are that the same phenomenon will be repeated again. This is how Newton’s laws and other laws came into being. This is what is called inductive reasoning. However, there are other reasonable approaches. For example, assume that a person speeds and is not caught. This may be repeated two times, three times—but here, the more times this phenomenon is repeated, the more confident we become that next time, he/she will be caught. Let us call this anti-inductive reasoning. So which of the two approaches shall we use? This is an example of a question that we study in this paper.
Application of Improved BP Neural Network in Blog Post Heat Prediction

Microblog has become a public platform for individuals, organizations and business groups to express their ideas. Blog posts of microblog are the carriers of many interests. This method used Simplified Chinese Language Exploration and Word Measurement (SC-LIWC) software to extract the emotional vocabulary of a large number of blog posts, and extracted six main emotional vocabulary and three heat-related factors to form sample data, and used to train the improved BP neural network. Finally, through the comparative analysis of test samples, predicted the trend of new samples. The results show that the method is reasonable in predicting the forwarding rate, response rate and comment rate of blog posts, which provides a good reference for heat prediction of blog post.
Sparse Relation Graph for Group Activity Recognition

Modeling relations between actors is critical for understanding group activities of dynamic scenes. Existing Group Activity Recognition (GAR) methods usually build strong connection in each actor pair. However, not all the connetions are necessary because not all actors are visible or related to each other. Based on this observation, we provide a Sparse Relation Graph (SRG) for GAR, in which the key relations are focused to mine more discriminative features. Then a graph convolutional network is designed for automatically learning the key relations. Extensive experiments on two popular group activity datasets, the Volleyball dataset and the Collective Activity dataset, demonstrate the effectiveness of our method. Especially in the Volleyball dataset, SRG can get better performance with less but delicate information.
A new method of constructing medical Chinese readability corpus based on big data

With the development of Internet technology, a large amount of information appears in the world every day. The growth rate of information is absolutely a terrifying thing - we call it information explosion. However, due to the particularity of language teaching, students are often at a loss when facing texts of different levels, and it is difficult to choose articles suitable for their reading level. As the pillar of medical professional teaching, studying the teaching content and teaching difficulty of medical professional textbooks has become an important topic to improve medical professional teaching, to help international students enter the medical profession as soon as possible, and to carry out medical practice smoothly. However, there is a lack of scientific automatic construction method of medical Chinese (MC) readable corpus. We established Medical Chinese Textbook Corpus (MCTC), and it is divided into six categories with the help of repeated binary clustering (RBC). Hanyu Shuiping Kaoshi (HSK) vocabulary and Medical Chinese Proficiency Test (MCT) vocabulary are used to measure the vocabulary proportion of each grade in the text. The experiment shows that this method is effective.