Reviewerly/exHarmony huggingface dataset:
Heuristic and learning method for obstacle avoidance with mobile robot

In this paper, a fuzzy controller obstacle avoidance of the mobile robot Pioneer II is proposed. The fuzzy inference system FIS of this controller is performed by two methods: heuristic and reinforcement learning. the manual tuning of the fuzzy control system can be long and difficult. In contrast, reinforcement learning has proven theoretically and practically its ability to automatically optimize some parameters of the FIS. For that, the Fuzzy Actor-Critic Learning algorithm allows the determination of the parameters of the conclusions among of an available set fixed by the operator. The proposed algorithm allows the automatic determination of the parameters of the conclusions of the fuzzy rules. The simulations show that the two controllers (heuristic, RL controller) are able to avoid the different shapes of obstacles contained in known environments, and they show exceptionally good robustness when changing the environment (shape of obstacles, location of obstacles in the environment.
A Research Map to Leverage Augmented Reality in K12 Science Education

This chapter presents a research map of designing an AR-integrated science trail for an inquiry-based curriculum for K12 science learning. Despite the potential advantages of using AR in education and its suitability for supporting science learning through simulation activities, AR's concrete uses are relatively not as well understood as that of other technologies. Prior studies have shown the value of inquiry-based learning complemented by computer technology in a scenario-based learning environment. To appropriate the potential of AR in education, this chapter aims at summarizing pedagogical affordances of AR in science education and exemplifying an AR-integrated science trail design. A comprehensive research map of designing an AR-enhanced science trail by integrating inquiry-based learning is elucidated in this chapter to provide insights for the design of AR-integrated subject learning and out-of-classroom learning.
Apprenticeship Bootstrapping Reinforcement Learning for Sky Shepherding of a Ground Swarm in Gazebo

The coordination of unmanned air–ground vehicles has been an active area due to the significant advantages of this coordination wherein unmanned air vehicles (UAVs) have a wide field of view, enabling them to effectively guide a swarm of unmanned ground vehicles (UGVs). Due to significant recent advances in artificial intelligence (AI), autonomous agents are being used to design more robust coordination of air–ground systems, reducing the intervention load of human operators and increasing the autonomy of unmanned air–ground systems. A guidance and control shepherding system design allows for single learning agent to influence and manage a larger swarm of rule-based entities. In this chapter, we present a learning algorithm for a sky shepherd-guiding rule-based AI-driven UGVs. The apprenticeship bootstrapping learning algorithm is introduced and is applied to the aerial shepherding task.
A Novel Quantum Sequential Signature Protocol With Y-SNOP States

With the development of quantum technology, an arising researching point is to solve some new cipher problems with quantum technology to ensure their security.As we know, the sequential signature means to sign a contract or a document by many signers one by one.It has been widely used to realize layered authentication.In this paper, we propose a quantum sequential signature protocol with a set of strongly nonlocal orthogonal product (Y-SNOP) states.It is proved that the present protocol is secure, i.e., anyone cannot deny or forge a valid signature even some of them conspire.Compared with the existing quantum multi signature with some nonlocal orthogonal product (NOP) states, the present protocol seems more efficient and easier to be realized in Noisy Intermediate-Scale Quantum (NISQ) device as no entangled resources are required.
On learning context-aware rules to link RDF datasets

Abstract Integrating RDF datasets has become a relevant problem for both researchers and practitioners. In the literature, there are many genetic proposals that learn rules that allow to link the resources that refer to the same real-world entities, which is paramount to integrating the datasets. Unfortunately, they are context-unaware because they focus on the resources and their attributes but forget about their neighbours. This implies that they fall short in cases in which different resources have similar attributes but refer to different real-world entities or cases in which they have dissimilar attributes but refer to the same real-world entities. In this article, we present a proposal that learns context-aware rules that take into account both the attributes of the resources and their neighbours. We have conducted an extensive experimentation that proves that it outperforms the most advanced genetic proposal. Our conclusions were checked using statistically sound methods.
A Comparative Analysis of a Mouse and Touchpad Based on Throughput and Locations for a Laptop Computer

In the present era, the use of laptop computer becomes an inseparable part of human life. This throws a challenge to the designers of the computer that the device may be designed keeping in mind the human capability. The human–computer interaction is generally done through the pointing devices such as a mouse, touchpad, joystick, etc. Hence, they should be designed to improve the performance by way of increasing the speed and accuracy during a certain task. In the present research, an analysis was carried out to study the two input pointing devices, namely touchpad and mouse on the basis of throughput and location of the laptop computer. For this, an interface based on ISO 9241, [2000] was designed to study the performance of the subjects. Eighteen subjects were chosen to perform this task to test the accuracy and speed of these two pointing devices. The experiment was performed with five different locations of the laptop. Results are found in terms of throughput using modified Fitts law. From the analyses, it was found that throughput for mouse ranges from 2.86 to 3.27 bits/s, and for touchpad it ranges from 1.89 to 2.16 bits/s. it was also evident from the results that the mouse gave better results at the farther position while the touchpad was proved to be better at closer distances.
Privacy Preserving Proxy for Machine Learning as a Service

In this paper, we propose a new framework called Privacy Preserving Proxy (PPP) to protect the online models. PPP is designed like a gateway to work on behalf of the users when requesting service, potentially masking the true origin of the online model from client. Instead of connecting directly to the machine learning-as-a-service(MLaaS) the clients submit the request to the PPP, which evaluates the request and executes some functions to maximize the safety of the models. PPP can potentially host a wide range of functions or triggers to control the complexity of the request or provide additional benefits such as privacy and security. We propose two different methods including Random Model Assignment and Intelligent Rounding Policy which can function inside PPP to protect the privacy of online models. To test the PPP we test TL-GAN based face reconstruction attack as a sophisticated approach which can reveal inefficiency of rounding policy as countermeasure. Our experiments show that, PPP can potentially nullify such complicated attacks in the future.
College Students’ Comprehensive Quality Evaluation System Based on Association Rules

In the current era of rapid development of information society, most of the work is developed and progressed rapidly with the help of computer systems. In the process of vigorously promoting education informatization, many colleges and universities have started information systems such as educational administration and student management and accumulated a large amount of complete data, but managers' use of data is limited to query and result analysis. With the development of Chinese contemporary university construction and school teaching management, the workload of students' data information management and maintenance will also increase. Using association rules to comprehensively analyze the basic situation of college graduates can quickly, accurately and efficiently excavate the factors affecting a person's comprehensive quality from a large amount of information, which has important guiding significance for families and schools. In order to reflect the comprehensive quality of students more effectively, this paper discusses the basic structure and design idea of the comprehensive quality evaluation system for college students based on association rules.
Image Dehazing in Disproportionate Haze Distributions

Haze removal techniques employed to increase the visibility level of an image play an important role in many vision-based systems. Several traditional dark channel prior-based methods have been proposed to remove haze formation and thereby enhance the robustness of these systems. However, when the captured images contain disproportionate haze distributions, these methods usually fail to attain effective restoration in the restored image. Specifically, disproportionate haze distribution in an image means that the background region possesses heavy haze density and the foreground region possesses little haze density. This phenomenon usually occurs in a hazy image with a deep depth of field. In response, a novel hybrid transmission map-based haze removal method that specifically targets this situation is proposed in this work to achieve clear visibility restoration and effective information maintenance. Experimental results via both qualitative and quantitative evaluations demonstrate that the proposed method is capable of performing with higher efficacy when compared with other state-of-the-art methods, in respect to both background regions and foreground regions of restored test images captured in real-world environments.
How to move from one ALIS to another: The experience of data transport

The problem of transfer from one ALIS (automated library information system) to another is decomposed. The models of ALIS implementation depending of the library’s computerization status are defined; these are fresh implementa-tion; implementation with further data transfer and simultaneous use of the data-base by several ALIS. Various methods of database transport are discussed, i.e. backup recovery; server image porting; tabular data transport through SQL-calls; export-import of bibliographic data and accounts in MARC format. The experience of ALIS databases transport into MegaPro integrated library system on Dubna University Library’s server is examined. The questionnaire-survey for the affiliate University libraries and recommendations for software, backup copying and pro-cedures for data conversion period are presented. Recommendations are also given for choosing the moment of data conversion; the factors for transport model selection and that of in-conversion information losses are specified.
Possibilistic Classifier Combination for Person Re-identification

Possibility theory is particularly efficient in combining multiple information sources providing incomplete, imprecise, and conflictive knowledge. In this work, we focus on the improvement of the accuracy rate of a person re-identification system by combining multiple Deep learning classifiers based on global and local representations. In addition to the original image, we explicitly leverages background subtracted image, middle and down body parts to alleviate the pose and background variations. The proposed combination approach takes place in the framework of possibility theory, since it enables us to deal with imprecision and uncertainty factor which can be presented in the predictions of poor classifiers. This combination method can take advantage of the complementary information given by each classifier, even the weak ones. Experimental results on Market1501 publicly available dataset confirm that the proposed combination method is interesting as it can easily be generalized to different deep learning re-identification architectures and it improves the results with respect to individual classifiers.
Poster: Digital and Non-digital Reading: Differences for Future Engineers of IT Sector

The paper is devoted to the study on how the reading format (on a computer screen and on paper) affects the effectiveness of tasks solved by students – future engineers of the IT sector. A research was held to study the impact of the reading format on composing concept maps following reading texts on paper or on computer screen. The results showed advantages in completing assignments when reading on paper.
Embodiment through digital intangibility: Infrastructures of musicking

The paper examines the sonic experiences of the listener in digital environments by using a Bluetooth speaker as an example. It discusses how the everyday use of a speaker highlights human beings’ material and multi-sensory situatedness in digital environments. Based on the analytical approaches concerning embodiment, movement, and infrastructures, the paper aims to develop further the idea of musicking in everyday life contexts. It suggests that in addition to the social importance of music, the material approach to musicking reveals new political and ethical questions, especially those concerning the power of code and planetary sustainability.
When BERT Meets Bilbo: A Learning Curve Analysis of Pretrained Language Model on Disease Classification

Natural language processing tasks in the health domain often deal with limited amount of labeled data. Pre-trained language models show us a promising way to compensate for the lake of training data, such as Bidirectional Encoder Representations from Transformers (BERT). However, previous downstream tasks often used training data at such a large scale that is unlikely to obtain in health domain. In this work, We conducted a learning curve analysis on a disease classification task to study the behavior of BERT and baseline models can still benefit downstream tasks when training data are relatively small in the context of health NLP.
Self-supervised Hierarchical Graph Neural Network for Graph Representation

Graph neural networks (GNNs) gain significant interest in the domain of network representation learning. To obtain a graph level vector representation from individual node embeddings, hierarchical pooling algorithms are proposed in the recent literature which adhere the hierarchical structure of an input graph. A major limitation for most of the existing supervised GNNs is their dependency on large number of graph labels (often 80%-90%) to train the parameters of the neural architecture. But obtaining labels of a large number of graphs is expensive for real world applications. So in this work, we propose an unsupervised hierarchical neural network, referred as GraPHmax, for obtaining graph level representation. We propose the concept of periphery representation and show its effectiveness to obtain discriminative features of an input graph. Further, inspired by the concepts from self-supervised learning, we propose to maximize periphery and hierarchical information in the context of hierarchical GNN. Thorough experimentation on both synthetic and real-world graph datasets shows that GraPHmax is not only able to outperform unsupervised graph embedding techniques, it often achieves state-of-the-art performance even with respect to a set of popular supervised GNN algorithms.
The Decision Support System For The Acceptance of Pencak Silat Athletes in Pra-PON and PUSLATDA Team Selection Using Technique For Other Preference By Similarity To Ideal Solution Method

Pencak Silat is one of the traditional martial arts that originated from Indonesia. The parent of Pencak Silat organization in Indonesia is INDONESIAN PENCAK SILAT ASSOCIATION (IPSI). Pencak Silat is one of the most reliable sports branches at the International-scale Olympics. Proven at the ASEAN GAMES 2018 Olympics and then the Pencak Silat Sports Branch is one of the sports branch of the most gold medal with a total of 14 medals. With the increasingly tight competition of Pencak Silat Sports, the trainer desperately needs athletes who fit certain criteria in order to be reliable in a tournament. In this study aims to assist the trainer in selecting athletes that correspond to the criteria. Therefore, a decision support system is required to assist the trainer in selecting qualified athletes. In this research, using the Technique for Other Preference by Similarity To Ideal Solution method is used to advise athletes who are eligible to be contested according to the calculation result. Result system of decision support system with Topsis method of this research has been tested by Pencak Silat Trainer and produce the results of the selection that is expected by the trainer.&#x0D; Keywords: Pencak Silat, Decision Support Systems, Technique For Other Preference by Similarity To Ideal Solution, Web
GEDet: Adversarially Learned Few-shot Detection of Erroneous Nodes in Graphs

Detecting nodes with erroneous information in graphs is important yet challenging, due to the lack of examples and the diversified s cenarios o f e rrors. W e i ntroduce GEDet, a few-shot learning based framework to detect erroneous nodes in graphs. GEDet consists of two novel components, each addresses a unique challenge. (1) To cope with the lack of examples, we introduce a graph augmentation module to enrich training labels. The module not only generates additional synthetic training labels by simulating different erroneous scenarios, but also exploits non-local relations to enrich neighborhood information. (2) To further improve the accuracy, we introduce an adversarially learned module that can better detect erroneous nodes by distinguishing nodes with synthetic and real labels encoded by graph autoencoders. Unlike conventional error detection models, GEDet yields effective classifiers that are optimized for a few yet diversified examples in the presence of multiple error scenarios. We show that using only a small number of examples, GEDet significantly improves the competing methods such as constraint-based detection and anomaly detection, with a gain of 35% on recall, and 30% on precision.
Speaker Identification Based On Ivector And Xvector

Abstract As an important branch of AI (Artificial Intelligence), speaker recogniti-on technology has developed rapidly in recent years. At present, speaker recognition technologies based on traditional methods and deep learning methods are very mature and have achieved good results. As a key technology in human-machine voice intera-ction, speaker recognition has changed human life in many ways. However, there is almost no research on speaker recognition in Tibetan. This paper uses Gaussian mix-ture model and deep neural network TDNN as the theoretical basis. On the Tibetan language corpus, the speaker identification on the ivector of GMM-UBM and the speaker identification on the xvector of TDNN are performed respectively, and the experimental results are compared. The experimental system is divided into prepro-cessing, feature extraction, model training and decision.
Road Information Detection Method Based on Deep Learning

Abstract Autonomous driving technology has developed rapidly in recent years, and computer vision has shown significant role in automated vehicles. Researchers use the combination of computer vision and deep learning to improve the speed and accuracy of road element detection technology. This article focuses on the semantic segmentation and road detection elements, especially in lane line segmentation and signal light detection. It summarizes the visual inspection technology and a series of visual perception algorithms based on deep learning. Although semantic segmentation and target detection technologies in general scenarios are already very mature, they are not ideal when applied to autonomous driving environments, especially where segmentation and detection are performed simultaneously. Therefore, this paper builds a fusion network of lane line segmentation and signal light detection suitable for autonomous driving scenarios.
The representation of gappy sentences in four-valued semantics

Abstract Three-valued logics are standardly used to formalize gappy languages, i.e., interpreted languages in which sentences can be true, false or neither. A three-valued logic that assigns the same truth value to all gappy sentences is, in our view, insufficient to capture important semantic differences between them. In this paper we will argue that there are two different kinds of pathologies that should be treated separately and we defend the usefulness of a four-valued logic to represent adequately these two types of gappy sentences. Our purpose is to begin the formal exploration of the four-valued logics that could be used to represent the phenomena in question and to show that these phenomena are present in natural language, at least according to some semantic theories of natural language.
Towards architectural view-driven modernization

Architecture-Driven Modernization claims that MDA (Model-Driven Architecture) concepts should be used for modernization projects in the process. One of the largest challenges when contemplating ADM (Architecture-Driven Modernization) is the modernization of developed system in order to ensure interoperability between tools for maintenance, evolution, assessment and allow the success of modernization projects that preserving architecture quality. ADM supports reengineering by providing standard meta-models, such as Knowledge Discovery Metamodel (KDM). Modernizing a large legacy or existing system is therefore a complex and expensive process that requires a thorough understanding of the system design architecture and its components. However, legacy systems have also experienced several modifications that lead them to deviate from their original designed architecture and lose its properties of quality. To facilitate the analysis of the system, the projection process proposed as part of our approach is based on facet cutting which allows determining the views having anomalies in order to facilitate quality control. The proposed approach in this paper is of great importance in the context of the modernization process, since it is committed to improving the KDM framework as methods used to assist in the detection of architecture violation.
Animal Behavior Prediction with Long Short-Term Memory

A foundational step in the study of any animal is the establishment of an accurate behavioral model. Building a model that is capable of defining and predicting an animal's behavior is critical to advancing ethological theory and research, however many animal models fail to be sufficiently thorough or often do not exist at all. Great pools of data are available for improving these models through recorded video of animals posted on video hosting sites throughout the internet, however these sources are largely left unused due to their sheer quantity being too much for researchers to manually observe and annotate. This paper proposes a method for efficiently converting video of animals at any length into models capable of making accurate behavioral prediction. This predictive model is developed through a data processing pipeline merging an ensemble meta-algorithm for behavior classification with a long short-term memory network for temporal pattern recognition and prediction. The application of this pipeline produced results with a higher degree of predictive accuracy compared to more traditional autoregressive techniques. These findings suggest the method has significant potential as a tool for efficiently developing new models and findings in the study of animal behavior. The method's performance in prediction also suggests that it may have further application in building models for the prediction of events or failures in inorganic, semi-stochastic processes such as video monitored mechanical systems and equipment.