Reviewerly/exHarmony huggingface dataset:
A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

Exploiting single models for a better ensemble one is popular for a classification or clustering task. For educational data clustering, our work proposes a kernel-induced weighted object-cluster association ensemble method, named kWOCA, to discover the inherent structure of the data and return the student clusters of higher quality. kWOCA is an advanced version that can automatically determine the number of desired clusters based on Bayesian Information Criterion. It also conducts consensus clustering in the feature space for non-linearly separated clusters where the discrimination between the objects is captured from the base clusterings. Besides, it encodes the differences between the clusters in each base clustering and those between the base clusterings in the ensemble when making a synthesis of the base clusterings. A kernel-induced weighted object-cluster association matrix is defined to store such rich information. Using this matrix, kWOCA outperforms the existing clustering ensemble methods. Experimental results on the real educational data sets and the benchmark Iris data set show the better effectiveness of kWOCA with higher Normalized Mutual Information values. As a result, groups of the most similar students based on their study performance can be discovered better. These resulting student clusters can be then further analyzed for academic affairs.
An Improved Tracking Algorithm Based on MDNet

Abstract The MDNet algorithm works well on tracking problems of the video sequence, but the speed is very slow. We have made some improvements to accelerate the process of feature extraction. It enhances the expressive ability of the feature map by removing the max pooling layer and using the method of expanding convolution to increase the receptive field of each point on the feature map. In addition, MDNet is building on CNN, and there are problems that similar targets have a large interference to the results. To address this problem, We use RNN to capture the long-term dependence of the target before and after the target data in the sequence data, and introduce the RNN to model the structure information of the target object, and then fuse the RNN feature and CNN feature of the tracked target object. In addition, another new loss term is introduced to make the targets in different domains away from each other in the shared feature space, thereby improving the algorithm’s ability to discriminate similar interferers. Compared with MDNet, our improved algorithm is much faster and the accuracy is improved.
Using Query Reformulation to Compare Learning Behaviors in Web Search Engines

Web search engines have gained importance as tools capable of connecting informal and self-learning with formal learning by aiding individuals in retrieving relevant information through the formulation and modification of their queries. Understand the differences between query states and their transitions becomes increasingly important, as doing so makes the optimization of search engines' results according to educational uses and needs possible. This paper introduces the ESKiP Taxonomy of Query States, a classification framework validated in an experiment involving two different query log datasets. It enables the comparison between the behaviors of users in search for knowledge (learners) and users performing transactional or factual searches in Web search engines.
Firepower Distribution Method in Wargame System Based on Machine Learning and Wavelet Analysis

It is the key to the success or failure of military operations to formulate practical and feasible operational plans quickly and in a timely manner. Aiming at the development and analysis of the auxiliary action plan of the computer chess system, the machine learning and wavelet analysis are used to analyze the research of the wargsme system from the planning and drafting process, which provides a reference for the fire distribution of the wargame system.
Supervised Two-Stage Transfer Learning on Imbalanced Dataset for Sport Classification

Sport classification is a crucial step for content analysis in a sport stream monitoring system. Training a reliable sport classifier can be a challenging task when the data is limited in amount and highly imbalanced. In this paper, we introduce a supervised two-stage transfer learning (Two-Stage-TL) method to solve the data shortage problem. It can progressively transfer features from a source domain to the target domain using a properly selected bridge domain. For the class imbalance issue, we compare several existing methods and demonstrate that the log-smoothing class weight is the most applicable way for this specific problem. Extensive experiments are conducted using ResNet50, VGG16, and Inception-ResNet-v2. The results show that Two-Stage-TL outperforms classical One-Stage-TL and achieves the best performance using log-smoothing class weight. The in-depth analysis is useful for researchers and developers in solving similar problems.
Enabling Service Queue Isolation in Multi-Tenant Data Centers

To isolate service queues in data centers, recent solutions assume Explicit Congestion Notification (ECN)-enabled end-hosts. However, this assumption is not valid in multi-tenant environments where tenants own the network stack. This letter presents BarberQ, a multi-queue management scheme that isolates service queues without dependency on transport protocols. At the heart of BarberQ lies dynamic buffer sharing through packet eviction. Our benchmark results demonstrate that BarberQ not only achieves minimum guarantees and work conservation, but also provides low latency.
Data Integration of Legacy ERP System Based on Ontology Learning from SQL Scripts

To tackle the problem of low-efficiency integration of heterogeneous data from various legacy ERP systems, a data integration approach based on ontology learning are presented. Considering the unavailability of database interface and diversity of DBMS and naming conventions of legacy information systems, a data integration framework for legacy ERP systems based on ontology learning from structured query language (SQL) scripts (RDB) are proposed. The key steps and technicality of the proposed framework and the process of ontology-based semantic integration are depicted.
Smart Car Based on Open MV Vision System

Abstract Modern society is developing rapidly, social informationization, and the degree of science and technology has become higher and higher. The development of intelligence has gradually entered people’s vision, and will eventually become a part of human life in the future. In order to facilitate people’s use, a target tracking method based on Open MV vision system is proposed. The machine vision Open MV module is used to collect image information, and through image information processing, the information is transmitted to the central control chip STM32 core control board. And the various modules that control the car work together. The method can resist the interference of illumination changes, achieve path optimization to a certain extent, and achieve real-time processing under limited computing resources. At the same time, the design is simple and the cost is low. The experimental results have been obtained through experimental tests, and the follow-up of the trolley has been successfully realized. Later, it can be used as a reference for the design of the driverless car and can also be used for the design of the smart car toy.
Sextus Empiricus' Fourth Conditional and Containment Logic

In his Outlines of Pyrrhonism 2.110–113, Sextus Empiricus presents four different accounts of the conditional, presumably all from the Hellenistic period, in increasing logical strength. While the interpretation and provenance of the first three accounts is relatively secure, the fourth account has perplexed and frustrated interpreters for decades or longer. Most interpreters have ultimately taken a dismissive attitude towards the fourth account and discounted it as being of both little historical and logical interest. We argue that this attitude is unwarranted and demonstrate that the conditional expressed in the fourth account can profitably be understood as a precursor of analytic entailment, familiar from containment logics such as those developed by Parry and Angell. Exploiting recent work by Fine, we present a formal truthmaker semantics for this conditional and show how it sheds light on a number of longstanding issues in the interpretation of this passage.
Gesture Recognition by Leap Motion Controller and LSTM Networks for CAD-oriented Interfaces

This paper presents a gesture recognition approach for CAD interfaces where the Leap Motion Controller is used for its high precision in modelling user hands. A simple, compact and effective hand representation is proposed to encode trajectory and pose across time. Recognition is based on Recurrent Neural Networks, particularly suited for processing data sequences. An effective data augmentation technique is also described to increase the size of the training set. Experiments conducted on a novel dataset of gesture performed by 30 volunteers show the effectiveness of the proposed technique; the dataset will be made available to the community for future studies.
Ontology-Based Semantic Search for National Database of Natural Resources and Environment

Semantic search helps the user queries to be understandable for electric agents searching. In this way, ontology plays the main role to define the semantic and the relations between user queries. The national database of natural resources and environment is a very large database system. Therefore, building a search engine software for the system with high accuracy and fast speed is very important for sharing information in the field of natural resources and environment. However, the existing search engine software in the national database needs to be improved to better meet user’s needs. We proposed the architecture of ontology-based semantic search for the national database of natural resources and environment. Based on the proposed architecture, we have built semantic search software (VnNRESS) to demonstrate better results than the existing search software (NRESearch).
Autocropping: A Closer Look at Benchmark Datasets

Image cropping is a common image editing task that aims to improve the composition as well as the aesthetics of an image by extracting well-composed sub-regions of the original image. For choosing the “best” autocropping method it is therefore important to consider on which datasets this method is validated and possibly trained. In this work we conduct a detailed analysis of the main datasets in the state of the art in terms of statistics, diversity and coverage of the selected sub-regions, namely the ground-truth candidate views. An analysis of how much semantics of ground-truth candidate views is preserved with respect to original images and a comparison among dummy autocropping solutions and state of the art methods is also presented and discussed. Results show that each dataset models the cropping problem differently, and in some cases very high performance can be reached by using a dummy autocropping strategy.
Preservation of Attribute Couplet Attack by Node Addition in Social Networks

Social Network is one of the most desired platform where an immense amount of data are available from many different social platforms. Publishing data without hiding sensitive data or diplomatic data about individuals is a crucial problem which cannot guarantees the privacy. Therefore published data needs to remove identifying particulars of the individuals (anonymized) before the data is released. Anonymizing data is more challenging and a popular privacy preserving model for data publishing in social networks. However even after anonymizing the data sets, attackers try to find new methods to derive private information of individuals with some background knowledge and identify them. One of such method is attribute couplet attack where the attacker has some background information about the data and derive the identity using a pair of node attributes. In the existing approach, the k-couplet anonymity achieves the privacy under the attribute couplet attack by using edge modification approach. This will change the distance properties between nodes and might also introduce undesirable and misleading fake relations. In this paper, we design an algorithm named Couplet Anonymization by using node addition approach. Adding new nodes and connecting them to some of the nodes in the original network can avoid this attribute couplet attack and gives a better chance to preserve the network properties. This node addition helps to reduce the misleading fake relations and also preserves the utility of the social networks.
Impact of diffuse solar radiation on HAM simulation accuracy

Advance in the numerical simulation models brings higher need for more accuracy outdoor boundary conditions - climate data sets. With influence of the urban heat islands, measured climate on the meadows, far from pawed surfaces creates uncertainty in the simulation. Another problem is the modelling of solar radiation impact on the surface temperatures. Solar radiation heats up the surface of the structure and increase the speed of vapor transport. To obtain correct results, global and diffuse solar radiation is needed in the HAM software. In this paper, influence of the diffuse part of solar radiation is analyzed. As it would be shown, correct modeling of the solar radiation, both global and diffuse is very important to reach good precision. This analysis is done in software WUFI Pro and compared with the experimental measurement of the wooden wall fragments.
Considering Social Presence in the Designing of Ubiquitous Learning Environments

Ubiquitous learning is an emerging research area in which learning occurs at the right time, in the right place, with the help of various technologies. Since ubiquitous learning helps to improve learning, motivation, and creativity, effective ubiquitous learning environments should be designed. Theories, models, and strategies should be considered to design these environments. One of these models is the community of inquiry approach, which has three elements: social presence, teaching presence, and cognitive presence. In this study, social presence is selected as a focus. In this context, the purpose of the study is to present techniques for establishing social presence in ubiquitous learning environments. In line with this purpose, first, ubiquitous learning and social presence are explained. Then, techniques to establish social presence in ubiquitous learning environments are expressed. Following on, a sample ubiquitous learning environment (a ubiquitous history museum) is designed and presented, considering social presence as a specific element.
Study on Behavior Prediction Using Multi-object Recognition and Map Information in Road Environment

Autonomous vehicle technology offers the possibility of significant benefits to social welfare such as reducing crashes, congestion, fuel consumption, and so on. Among them, collision prediction and avoidance system are especially important topics in real road scenes. Here we introduce the novel behavior prediction system based on multi-object recognition and map information. In multi-object recognition, it is necessary to recognize the object on the road with high accuracy and speed. Therefore, we perform a method to apply Variable Window Approach to 2DHOG feature extraction method. In predicting behavior, the distance relationship between the host vehicle and the obstacle is important. We will combine the digital map and the map obtained from Visual SLAM to clarify the relationship between these distances. Finally, we verify the usefulness of this system by multi-object recognition experiment and behavior prediction experiment.
Measurement Error Correction by Nonparametric Bayesian Networks: Application and Evaluation

In this paper a procedure for measurement error correction based on nonparametric Bayesian networks is proposed. The performance of the proposed method is evaluated using a validation sample collected by Banca d’Italia and a major Italian bank group to investigate the measurement error mechanism in the main financial variables amounts observed in the Banca d’Italia survey on Household Income and Wealth. Specifically, in this paper attention is focused on the bond amounts. By means of Uninet’s programmatic engine working directly from R, data can be corrected unit by unit by sampling from the nonparametric Bayesian network. Thanks to the validation sample, the distances between the true and the imputed values are computed and the procedure is evaluated.
Fuzzy Logic Based Optimization Algorithm

Through the history, humans have been succeeded by solving multiple problems during their day to day life. They use simple rules of thumb from their past experiences to solve several difficulties. Under such circumstances, many researchers have tried to emulate the human reasoning based on mathematical approaches. Based on simple if-then rules, fuzzy logic is one of the disciplines in artificial intelligence which emulates the human reasoning in terms of linguistic variables. In fuzzy logic, linguistic variables represent natural language variables which humans commonly used to specify semantic rules from several processes. On the other hand, metaheuristics have been proposed as alternative search mechanisms to find optimal solutions for complex optimization problems where classical mathematical methodologies present some limitations by working under multimodal surfaces. This chapter presents a novel metaheuristic algorithms called Fuzzy Logic Optimization Algorithm (FLOA). The proposed algorithm models the search strategy which an expert human in optimization could follow to solve optimization problems based on simple if-then rules. The FLOA, uses a Takagi-Sugeno inference model, where the output is a weighted sum of four fuzzy rules; Attraction, repulsion, perturbation and randomness. The performance of the proposed method is compared against the performance results of several state-of-art metaheuristics, evaluating several test functions. The numerical results are statistical validated using a non-parametric framework to eliminate the random effect.
Arcon

Contemporary end-to-end data pipelines need to combine many diverse workloads such as machine learning, relational operations, stream dataflows, tensor transformations, and graphs. For each of these workload types, there exists several frontends (e.g., SQL, Beam, Keras) based on different programming languages as well as different runtimes (e.g., Spark, Flink, Tensorflow) that optimize for a particular frontend and possibly a hardware architecture (e.g., GPUs). The resulting pipelines suffer in terms of complexity and performance due to excessive type conversions, materialization of intermediate results, and lack of cross-framework optimizations. Arcon aims to provide a unified approach to declare and execute tasks across frontend-boundaries as well as enabling their seamless integration with event-driven services at scale. In this demonstration, we present Arcon and through a series of use-case scenarios demonstrate that its execution model is powerful enough to cover existing as well as upcoming real-time computations for analytics and application-specific needs.
Obstacle contour extraction method based on improved Grabcut algorithm

Abstract Obstacle distance measurement has an important role in the design of mobile robots, which allows the robot to timely complete the movement steering to avoid damage caused by crushing. The accurate segmentation and edge detection of the target obstacle in the image is the premise of completing the measurement. In this paper, based on the classification and location of specific obstacles, a method of obstacle segmentation based on improved Grabcut algorithm is proposed, and the edge contour is extracted by Canny operator. The method classifies the image’s background pixels using a Laplacian Mixture Model and modifies the covariance matrix to change the probability density function of that model, thereby reducing the misclassification probability of the pixels. In addition, the method crops the input image in advance to reduce the running time of the algorithm. This paper also introduces a way to initialize the image’s Trimap, considering the visual angle of the sweeper. The above four improvements enable the method to accurately segment obstacle targets and extract contour curves. The accuracy of the method and the good real-time performance on the embedded platform are verified by comparison experiments.
Performance evaluation of image transmission using diversity selection combining technique

Abstract This paper discussed a simulation of SPIHT image compression transmission on wireless channels by using RS code and diversity selection combining technique in wavelet domain to get better reconstruction image by minimizing errors that caused by fading and noise. RS Code and Diversity Selection Combining Method were used to combat errors during image transmission on wireless channels. When the bit stream data arrived at the receiver, the coding was used to recover the errors in the bitstream. Diversity was used to obtain multiple data streams corresponding to the transmitted image at the receiver. These individual image data streams were combined to form a composite image with the better quality. The results show both methods demonstrated quality improvement of the received image.
Research on Convolutional Neural Network for Object Classification in Outdoor Video Surveillance System

Nowadays indoor and outdoor video surveillance systems are very widespread. Earlier, in the days of the first surveillance systems, the processing power allowed only monitoring and recording the surveillance footage, but now it becomes possible to use various methods of video analysis; in this article, we investigate the convolutional neural networks application in the objects classification. In our previous work we developed the outdoor video surveillance system for detecting objects using fixed and PTZ cameras. System provides detection of moving objects with low computational cost and high accuracy. This paper summarizes the results of work on the existing outdoor video surveillance system for detecting objects and our new convolutional neural network object classifier based on the Keras and TensorFlow packages. Reliable determination of the object type allows the system to make decisions on processing the object information. The considered classifiers allow performing both simple classification (a person/not a person) and more complex one (error/person/car/animal) with insignificantly lower reliability. Object tracking in consecutive video frames can remarkably reduce the number of classification operations, because there is no need in performing them for each frame in case the object class has been identified with enough reliability. In addition, the integration of the developed networks into the existing video surveillance system is briefly described.