Reviewerly/exHarmony huggingface dataset:
Learning a unified tracking‐and‐detection framework with distractor‐aware constraint for visual object tracking

Most of the correlation filter (CF)-based trackers utilise the circulant structure of the training samples to learn discriminative filters to identify the tracked target, which has shown excellent performance in terms of both tracking accuracy and speed. However, CF-based trackers possess two potential drawbacks: the search regions are limited to the small local neighbourhood for the high-speed tracking purpose; thus, they usually have very few context information and tend to drift from a target in extreme attributes, e.g. background clutter, large-scale variation, and fast motion. Another is that once the tracking target is lost under large displacement motion, it cannot be re-identified in subsequent frames. In this study, the authors propose a unified tracking-and-detection framework with distractor-aware (UTDF-DA), which involves both context learning and target re-identification with a target-aware detector to solve the drawbacks mentioned above. They first incorporate the distractor constraint as context knowledge into a continuous correlation filter for distractor-aware filter learning. Then a single-shot multibox detector-based target-aware detector is trained by domain-specific meta-training approach for deep detection features and hard-negative samples generation. Moreover, they propose the spatial-scale consistency verification method for the target re-identification task. Compared with existing state-of-the-art trackers, UTDF-DA (the authors’) tracker can achieve improved tracking performance in terms of both accuracy and robustness; they demonstrate its effectiveness and efficiency with comprehensive experiments on OTB-2015, VOT-2016, and VOT-2017 benchmarks.
Interpretable and Comparative Textual Dataset Exploration Using Near-Identity Mention Relations

Dataset exploration is a set of techniques crucial in many research and data science projects. For textual datasets, commonly used techniques include topic modeling, document summarization, and methods related to dimension reduction. Despite their robustness, these techniques suffer from at least one of the following drawbacks: document summarization does not explicitly set documents in relation, the others yield summaries or topics that often are difficult to interpret and yield poor results for topics that consist of context-dependent terms. We propose a method for dataset exploration that employs cross-document near-identity resolution of mentions of semantic concepts, such as persons, other named entity types, events, actions. The method not only sets documents in relation and thus allows for comparative dataset exploration, but also yields well interpretable document representations. Additionally, due to the underlying approach for cross-document resolution of concept mentions, the method is able to set documents in relation as to their near-identity terms, e.g., synonyms that are not universally valid but only in the given dataset.
An investigation of AI in games: educational intelligent games vs non-educational games

Attracting and motivating the player is considered as one of the major reasons of the success of games. In fact, games are the form of art which is characterized by attractiveness. Artificial Intelligence (AI) is used in games to have an interesting and interactive experience of the player. However, the objectives of increasing the attractiveness with AI techniques differ between games for pleasure and games for educational contexts. While non-educational intelligent games focus mainly on the entertainment side, intelligent educational games dive into deep issues. Various AI techniques are used in games such as pathfinding algorithms, decision trees, intelligent narrative technologies and intelligent agents. In one hand, this paper presents a literature review about noneducational intelligent games and intelligent educational games. The paper classifies the presented intelligent games according to the AI techniques used in each game. In the other hand, a discussion is conducted to observe the limitations and the future directions concerning the use of AI in games.
Look-ahead natural evolutionary strategies

Natural1 evolution strategy (NES) algorithms have seen a variety of successful applications recently, but still need to improve algorithmic convergence. To address this issue, we propose the use of a look-ahead scheme predictively to estimate the natural acceleration in the algorithm. This estimation is to assist convergence towards the global optimum faster. The resultant algorithm is termed a "Look-Ahead Natural Evolutionary Strategy" (LANES) algorithm. It is tested against a combination of multiple benchmarks using a number of well-known benchmark problems. Compared with the original NES, the LANES increases the optimizer overheads, but offers a steady improvement in optimality, accuracy, and convergence.
Reply to letter to the editor “Low‐rank seismic denoising with optimal rank selection for Hankel matrices” by S. Trickett

There are no data to be allowed to open to the public.
Investigation of Regional Gap in the Yellow River Basin and Analysis of High-quality Development Strategies

In order to further study the high-quality development of the Yellow River Basin, based on the GDP and resident population data of 93 cities (or states, leagues) along the Yellow River Basin from 2000 to 2017, according to the analysis of the absolute differences in the Yellow River Basin, using the Theil index method, this paper measured the economic gaps in the three regions and empirically examined the impact of regional coordinated development strategies on the regional economic development gaps in different periods in the Yellow River Basin. The research shows that: (1) The per capita GDP gap among the provinces and regions in the Yellow River Basin has been increasing year by year. (2) From a spatial perspective, the overall gap in the Yellow River Basin is slowly declining; the development gaps in the three major zones have a greater impact on the overall basin, and after 2009, it has converged to a steady state. (3) Further research found that during the implementation of different regional coordinated development strategies, it has a significant impact on the regional economic gap in the Yellow River Basin. The implementation of the high-quality development strategy of the Yellow River Basin needs to take full consideration of the province’s own conditions, endowments, and competitive advantages, and take sustainability as its main feature to promote ecological protection and high-quality development of the Yellow River Basin by classification.
Scaling Optimizations for Large-Scale Distributed Data with Lightweight Coresets

Lightweight coresets are compact representations of data sets such that clustering methods present competitive results in relation to the complete data set. They are constructed by sampling important points from the complete set. We propose a fast method to approximate the sampling of lightweight coresets from very large data sets which are distributed among multiple machines. We show that the proposed method is much faster and scalable, reaching results 48 times faster than the original lightweight coresets, while holding similar properties.
An evolutionary approach for constructing multi-stage classifiers

Multi-stage classification is a supervised learning approach that distributes a set of features, each with an associated importance and cost of generation, across a series of classifiers (stages). Inputs are processed in a pipeline fashion through the stages, each of which utilizes only a subset of the complete feature set, until a confident classification decision can be made or until all features and stages have been exhausted. This design benefits from processing inputs in parallel and ensures that labels are assigned using only the necessary features, but the number and composition of stages used by the model can have significant impact on overall performance. Unfortunately, identifying these critical design aspects becomes more difficult as the number of features and possible stages increases, often making brute-force search or human intuition impractical.
SECURITY ISSUES AND ENVIRONMENTS IN CLOUD COMPUTING

This paper will explore the service models and background and also presents the existing research issues implicating in cloud computing such as security, privacy, reliability, and so on. Cloud Computing, Architecture and Characteristics of Cloud Computing and also different Services and Deployment models provided in Cloud Computing. This paper provides a better understanding of the cloud computing and identifies important research issues in this rapidly growing area of computer science.
Overview of Container Cloud Task Scheduling

With the rapid development of cloud computing, virtualization technology represented by container technology is gradually replacing the original virtual machine technology. Introducing the container cloud and its main research direction. Starting from task scheduling, combs and summarizes the current research status of the container cloud task scheduling algorithm, and introduces the current four mainstream container scheduling platforms. According to the independence of scheduling task, it is divided into independent task scheduling and non independent workflow task scheduling. According to the different task scheduling objectives, it is divided into single objective optimization task scheduling algorithm and multi-objective task scheduling algorithm. After a detailed analysis of the advantages and disadvantages of several representative algorithms, the existing improved methods are summarized.
Virtual Self Defense Trainer - Analyzing and Scoring User Pose

Self-defence is an important skill for self safety especially for women in times of danger. However, self-defence training is not accessible to many. This paper presents a framework that estimates human pose using a webcam and openpose and analyzes the pose in order to give a score and performance based review. This framework aims at teaching self defense to users. The idea is to capture the pose of the user via an RGB camera and estimate pose using openpose. Then features are extracted and the poses aligned in the temporal domain. The result of the overall pipeline is a score and natural language feedback for each feature extracted. The results show that our system performs quite well on the video dataset prepared.
Traveler Behavior Cognitive Reasoning Mechanism

In this work, we use the kosko’s fuzzy cognitive maps to represent the reasoning mechanism in complex dynamic systems. The proposed approach focuses on two points: the first one is to improve the learning process by providing a connection between Kosko’s FCMs and reinforcement learning paradigm, and the second one is to diversify the states of FCM concepts by using an IF-THEN rules base based on the Mamdani-type fuzzy model. An important result is the creation of the transition maps between system states for helpful knowledge representation. After transition maps are validated, they are aggregated and merged as a unique map. This work is simulated under Matlab with Fuzzy Inference System Platform.
Estimating Importance From Web Reviews Through Textual Description and Metrics Extraction

The evolution of e-commerce has contributed to the increase of the information available, making the task of analyzing the reviews manually almost impossible. Due to the amount of information, the creation of automatic methods of knowledge extraction and data mining has become necessary. Currently, to facilitate the analysis of reviews, some websites use filters such as votes by the utility or by stars. However, the use of these filters is not a good practice because they may exclude reviews that have recently been submitted to the voting process. One possible solution is to filter the reviews based on their textual descriptions, author information, and other measures. This chapter has a propose of approaches to estimate the importance of reviews about products and services using fuzzy systems and artificial neural networks. The results were encouraging, obtaining better results when detecting the most important reviews, achieving approximately 82% when f-measure is analyzed.
A Case Study on Distributed Consensus Problem on Cloud-Based Systems

Fault tolerance is a vital element in cloud computing to achieve high performance. In cloud computing fault tolerance particularly cluster management, network discovery and consistent system master node replication, consensus and coordination play a major role. This paper provides a comprehensive overview of the topic of fault tolerance, i.e., the problem of consensus in cloud computing; highlighting the important concepts along with the explanation of Byzantine Agreement problem and consensus problems in multi-agent systems. There are multiple algorithms/protocols like RAFT and PAXOS available to approach this problem. We present generalized consensus implementation by solving consensus for dual failure nodes. We also describe Apache Zookeeper as our coordination service to obtain consensus in a distributed system.
Depending on HTTP/2 for Privacy? Good Luck!

The new multi-threaded server operation feature in HTTP/2 results in multiplexed object transmission. This obfuscates the sizes of the encrypted objects, based on which passive network eavesdroppers inferred sensitive information. Therefore, recent works speculate that HTTP/2 can have an unanticipated positive effect on communication privacy in addition to the privacy provided by TLS/SSL. Orthogonal to these works, we show that it is possible for an on-path passive eavesdropper to completely break the privacy offered by the schemes that leverage HTTP/2 multiplexing. Our adversary works based on the following intuition: restricting only one HTTP/2 object to be in the server queue at any point of time will eliminate multiplexing of that object and any privacy benefit thereof. Our adversary achieves this by altering network parameters such as jitter, bandwidth and packet drop rate to ensure that no new client request reaches the server while it is serving a previously requested object. Our adversary was able to break the privacy of a real-world HTTP/2 website 90% of the time. To the best of our knowledge, this is the first privacy attack on HTTP/2.
Semantic Space Autoencoder for Cross-Modal Data Retrieval

The primary aim of cross-modal retrieval is to enable the user to retrieve data across different modalities in a flexible manner. Through this paper, we tackle the problem of retrieving data across different modalities, where the input is given in one form, and relevant data of another type is retrieved as the output, as per the requirement of the user. Most of the techniques or approaches that have been used so far have not considered the feature and semantic information preservation. As a result of this negligence, they are not able to obtain effective results. Here, we have proposed a two-stage learning method that does the projection of low dimensional embeddings to multimodal data that preserve both feature and semantic information, which enabled us to get satisfactory results. In this paper, we have proposed an autoencoder for cross-model retrieval that can process both visual as well as textual data based on their semantic similarity.
Online Data Literacy Services during the COVID-19 Epidemic Prevention: Taking Peking University Library as an Example

Data literacy is a critical part of effective researching and a growing demand for data literacy services among college students and faculty is currently observed, especially in the COVID-19 health emergency which has been remarkably severe since December 2019. With the network technology, PKUL innovated different modes of data literacy services, such as recommending data resources, providing digital lectures in collaboration with departments or database suppliers, and giving online services of data visualization processing via conference applications, or APPs. The teachers and students embraced these innovations, which widened the scope of online library data quality services.
Analysis of Postgraduate Admission Scores Based on K-means Algorithm

Postgraduate admission score are important reference indicator for the supervisor to understand the learning ability and learning style of students, and formulate postgraduate education programs. With the expansion of the university enrollment scale, the increase in the number of students and in the complexity of postgraduate admission scores, the traditional analysis method cannot meet the current needs for the analysis of postgraduate admission scores. In this paper, the K-means clustering algorithm was used to analyze and classify the postgraduate admission scores, so as to discover the characteristics of student score distribution, find out the relationship among the scores, and find the direction suitable for students' development by understanding their learning status in various subjects, thereby achieving personalized postgraduate education and training. The result provides reference for the formulation of postgraduate education programs and the choice of research directions for postgraduates. First, the applicability of several major clustering algorithms applied to postgraduate admission scores was analyzed; second, the K-means clustering algorithm was introduced; finally, data analysis and preprocessing were performed on postgraduate admission scores, and through the experiment, the practicability of K-means clustering algorithm in the analysis of postgraduate entrance scores was illustrated.
Publindex: A Web Service to Automatically Evaluate Research Publications According to Customized Criteria

We introduce Publindex, a system that retrieves, classifies, and returns research publications of a given researcher according to the criteria and in the format predefined by the user.
An ontological Rule-Based Approach for Software Product Lines Evolution

A software product line usually constitutes a long-term investment and, therefore, has to undergo continuous evolution to correct, improve, or extend assets or products. Software Product Line (SPL) evolution needs to gather product line knowledge to be able to successfully conduct evolution solutions. Despite various attempts, applying SPL evolution proposals remains limited and no promising approach has been proposed to evolve product lines under a common knowledge management framework. Ontologies emerge as one of the most appropriate knowledge management tools for supporting knowledge representation, processing, storage and retrieval. Given great importance to knowledge for product line evolution, and the potential benefits of managing SPL knowledge, we propose an evolution-oriented knowledge management approach. This approach provides a continuous evolution of SPLs by means of an ontological rule-based knowledge management framework. The framework delivers formal semantics and evolution rules to help evolving SPLs by using a core ontology. This ontology, kernel of the framework, represents a common conceptualization of SPLs knowledge. It encompasses three sub-ontologies to consider knowledge of SPLs requirements, architecture and traceability. In this paper, we present our approach, the associated framework and the sub-ontology associated to SPLs requirements knowledge. This sub-ontology manages knowledge associated to Feature Models, as a key requirement model of SPLs. Based on the Electric Parking Brake system, the paper proposes some improvements and evolution rules of the associated Feature Model considering the proposed framework.
A Comparative Study of the Relationship between the Subjective Difficulty, Objective Difficulty of Search Tasks and Search Behaviors

This study uses comparative research to compare the effects of the subjective difficulty and objective difficulty of search tasks on users' search behaviors. Data regarding users' opinions about task difficulty was obtained via a post-search questionnaire using a 5-Likert scale. When measuring subjective difficulty, tasks with ratings above 3 were considered difficult. When measuring objective difficulty, tasks with ratings higher than the average difficulty score were considered difficult. The study's findings indicate that it is better to develop task difficulty prediction models that are based on subjective difficulty because these models are more stable. Models based on objective difficulty could not match the performance of models based upon subjective difficulty. The findings shed light on issues related to experiment design that will be valuable for future research.
A New Model to Compute The Information Content of Concepts Based on Cilin

Information Content (IC) calculation plays an important role, because measuring similarities between words is a basic element of computational linguistics and artificial intelligence applications. This paper presents a novel IC value calculation model. Different from previous studies, the novel model considers the context characteristics of concepts in knowledge ontology, such as the depth of concepts, the number of sibling nodes or the number of leaf nodes of concepts, and introduces the dynamic parameter K to dynamically adjust the influence of depth factor on IC value. The experimental results based on PKU-500 dataset show that the correlation coefficient between human judgment and the calculation results of the new model is about 0.50, and compared with the existing IC calculation model, the accuracy of the similarity can be improved by 2% to 10%.
Some Effective Techniques for Recognizing a Person Across Aging

To identify a person across aging is a very challenging and interesting task. It has gained a lot of attention from the researchers as it has a wide range of real-life applications like finding missing children, renewal of passport, renewal of a driving license, finding criminals, etc. Many researchers have also proposed their own methodologies; still there is a gap to fill in. Hence, the authors have proposed some techniques for the betterment of the system performance. The first one is with GLBP as a novel feature, second with Convolutional Neural Network (CNN) and the last one is a modification of the previous method with biased face patches as inputs. CNN is found to be the perfect solution for face recognition problem over aging as there is no need for any complicated preprocessing and feature extraction steps. FGNET and MORPH II datasets are used for testing the performance of the system. All these techniques outperform available state-of-the-art methods in the Rank-1 recognition rate.
Predict Next User Location to Improve Accuracy of Mobile Advertising

The main feature in the location based on advertising is sending the latest ads from POIs nearest the user. To accomplish the task, the device should transmit the current location at interval time to LBA server, LBA server will provide the latest ads nearest location received from the user. With short interval this procedure will run out the power of a device quickly and high internet bandwidth. To solve this long interval can be used. But another problem arises with fast moving users. User received POIs at a location that has been leaved. We proposed predict next user's location for long interval push strategy. The results of our experiments show received POIs more accurate and with long interval will help reduce waste of energy and internet bandwidth in mobile advertising.
THE EMPOWERMENT OF THE STUDENT USING COLLABORATIVE PROJECTS FOR ENGINEERING BASED ON OPEN SOURCE HARDWARE AND COMMERCIAL DEVELOPMENT ENVIRONMENTS

Information about the paper titled "THE EMPOWERMENT OF THE STUDENT USING COLLABORATIVE PROJECTS FOR ENGINEERING BASED ON OPEN SOURCE HARDWARE AND COMMERCIAL DEVELOPMENT ENVIRONMENTS" at IATED Digital Library