Reviewerly/exHarmony huggingface dataset:
Manipulator Meta-Imitation Learning Algorithm with Memory Weight Integration

Abstract Versatility is one of the key characteristics of general agent. In order to enable the manipulator to quickly and effectively acquire the ability to perform multiple tasks in an unknown environment, a large capacity model is essential. In this paper, the memory weight integration term adapted to meta-learning algorithm is proposed. By adjusting the plasticity of neurons, the manipulator can learn to learn more effectively in the process of learning multi-task and improve the forgetting problem of multi-task learning. Then, this paper combines the memory weight integration with meta-imitation learning, so that the manipulator can acquire new skills from a single demonstration task. Finally, a 7-DoF manipulator in PusherEnv experiment is used to explore the influence of different integration coefficients on the algorithm. The results show that the memory weight integration can effectively improve the success rate of tasks.
DEW 2019: Data Exploration in the Web 3.0 Age

Now in its third edition, the Data Exploration in the Web 3.0 Age (DEW) track of the IEEE International WETICE Conference continues to bring together researchers and practitioners from both the Academia and Industry working in the areas related to data exploration, in a very broad sense. Papers accepted for presentation at DEW 2019 are representatives of emerging topics in the fields of data and text mining, machine learning, semantic web and Internet of things.
Analysis of Utilization in the Message Card Production by Use of Fusion Character of Handwriting and Typeface

Many Japanese people hope to receive handwritten messages from others even though they have resistance to handwrite messages for them. In previous studies, we have revealed that fusion character with handwriting and typeface could reduce the resistance toward handwriting. However, the system used in the previous studies did not allow users to change the type of typeface and the fusion rate, so it was not clear how our fusion method can be utilized. In this paper, we implemented a prototype system to create message cards with a fusion method which enables users to control the fusion rate and to select the typeface. Then, we conducted experiments on whether writers change the way to use the system according to their relationship with the reader or the effect of the message card’s design. The results of the experiments revealed that the writer changed the way to use the system depending on card designs and their relationship with the reader.
Specific Emitter Identification Based on Feature Selection

For the high dimension of fingerprint feature set in the process of specific emitter identification (SEI), feature selection method is utilized to reduce the feature dimension and improve individual recognition rate. This paper adopted the filter feature selection in four ways: MIFS, mRMR, CMIM, and JMIM fingerprint feature set of high-dimensional feature selection and combined with PCA dimensionality reduction algorithm to minimize the feature dimension. The simulation results show that feature selection is feasible in individual recognition of the radiation source and can be effectively combined with dimension reduction algorithm.
Named Entity Recognition in Local Intent Web Search Queries

Semantic understanding of web queries is a challenging problem as web queries are short, noisy and usually do not observe the grammar of a written language. In this paper, we specifically study the user web search queries with local intent on Bing. Local intent queries deal with searching for local businesses and services in a location. Hence, local query parsing translates into the classical problem of Named Entity Recognition (NER) in NLP. State-of-the-art NER systems rely heavily on hand-crafted features and domain-specific knowledge to effectively learn from the small, supervised training corpora that is available. In this paper, we use deep learnt neural model that relies solely on features extracted from word embeddings learnt in an unsupervised way, using search logs. We propose a novel technique for generating domain specific embeddings and show that they significantly improve the performance of existing models for the NER task. Our model outperforms the existing CRF based parser currently used in production.
Construction and Application of Data Standard in Big Data Environment

School informatization Construction has gone through 10 years, Multiple systems operate independently. Meanwhile, the role of unstructured data resources about security monitoring, smart card system, online course resources has become more and more important in the construction of intelligent campus. According to the present condition of the school, this paper give a data model for the information construction of colleges and universities. Solving the System Fusion put to use Hadoop distributed system Architecture between structured data and unstructured data, Providing basis for data analysis and decision-making. Big data will become the evolutive direction of Intelligent Campus in the next few years, It will promote the construction of school informatization about Construction and implementation.
Passive Adaptive Robot Obstacle Analysis and Optimization

Abstract In order to improve the obstacle performance of robots in complex terrain environment, a crawler passive adaptive robot is proposed, which is mainly composed of car body module and track module. The link mechanism of the track module has a degree of freedom, and the carbon fiber material has the characteristics of light weight and high rigidity. The obstacle course of the crawler module is introduced, and the mathematical model is established. The obstacle obstacle ability is analyzed. On this basis, the optimization parameters and the objective function are proposed. The rod length parameters of the linkage mechanism are optimized by matlab. The analysis and simulation motion analysis of the optimized linkage mechanism verify the rationality of the obstacle performance and structural parameter design of the robot.
The implicit network inferred from users’ residences and workplaces enhancing collaborative recommendation on smartphones

Personalized recommendation based on side information extracted from social networks has achieved promising performance in numerous applications. However, such side information is generally derived from users’ explicit interactions, such as Twitter connections or trust lists, which is not always available in most scenarios. Alternately, obtaining such side information from users’ implicit networks is easy. Thus, in this paper, we consider the similarity network for users’ semantic locations, assuming that users with neighboring semantic locations have similar consumption habits. To demonstrate this, we evaluate our study in a practical scenario: the smartphone recommendation based on operator records. A novel recommendation paradigm is designed, which includes three key steps: discovery of semantic locations, hierarchical construction of the neighbor network, and items’ popularity-based recommendation based on interpersonal similarities. The empirical results illustrate that our method outperforms the state-of-the-art methods (13% coverage improvement than models without introducing networks and about 5% higher than model with a call-log based network).
Application of Wavelet Analysis in the Vibration Signal of the High Dam Drainage

Abstract Wavelet analysis is a localized time-frequency analysis method,it uses the combination of time domain and frequency domain to show the characteristics of a signal. In this paper uses the wavelet analysis to decompose the vibration signal in different frequency band. And we can get the energy of the signal in different frequency band and the vibration characteristics of the slab by decomposing different frequencies and the different scales of the signal analysis at the different frequency band. According to the signal noise sigma, doing the wavelet threshold noise reduction uses the general threshold, the threshold set by Birge-Massart strategy, the threshold set by penalty strategy and the strategy of the threshold to estimate sample signal, then gets the best scheme.
Optimizing Terms Reduction Process for Bilingual Clustering of Malay-English Corpora

The document clustering process groups the unstructured text documents into a predefined set of clusters in order to provide more information to the users. There are many studies conducted in clustering monolingual documents. With the enrichment of current technologies, the study of bilingual clustering would not be a problem. However clustering bilingual document is still facing the same problem faced by a monolingual document clustering which is the “curse of dimensionality”. Hence, this encourages the study of term reduction technique in clustering bilingual documents. The objective in this study is to study the effects of reducing terms considered in clustering bilingual corpus in parallel for English and Malay documents. In this study, a genetic algorithm (GA) is used in order to reduce the number of feature selected. A single-point crossover with a crossover rate of 0.8 is used. Not only that, this study also assesses the effects of applying different mutation rate (e.g., 0.1 and 0.01) in selecting the number of features used in clustering bilingual documents. The result shows that the implementation of GA does improve the clustering mapping compared to the initial clustering mapping. Not only that, this study also discovers that GA with a mutation rate of 0.01 produces the best parallel clustering mapping results compared to GA with a mutation rate of 0.1.
Practical Application of a Tongue-Operated Joystick Device with Force Feedback Mechanism

The human tongue has superior movement and tactile sensations.For individuals with severe disabilities, a tongue operated interface device can be used to operate life-support equipment, such as powered wheelchairs and robotic manipulators.A joystick-type device can directly translate various tongue motions to external equipment behavior.In addition, the user can interactively communicate with the equipment by tactile feedback.This helps the user to control the equipment safely and skillfully.Considering these factors, in a previous study [1], we developed a novel tongue-operated joystick device with reaction force feedback mechanism.We described the design process including the analysis of human tongue movement and tactile sensations and showed fundamental performances of reaction force feedback with the prototype device.In this study, we discuss the shape of the operational part that is used by the tongue.Two types of operational tools are prepared and their operability and perception of reaction force feedback are compared.Furthermore, we confirm the effectiveness of reaction force feedback to operate the joystick device safely and skillful controlling a mobile robot in an unknown environment.
Hybrid of Swarm Intelligent Algorithms in Medical Applications

In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both the dermatology and hepatitis datasets. The results obtained could allow researchers to quickly identify the most suitable algorithm for application in a particular medical dataset.
Nine Years of Courses on Software Product Lines at Universidad de los Andes, Colombia

Software Product Lines has been taught in Universidad de los Andes, Colombia, since 2011. The content, activities and evaluation in these courses have changed during this period of time. For instance, while topics such as the processes to engineer product lines, feature models to specify domain variability, and design patterns to implement the variability are common to all these courses, other topics such as the product line maturity levels, some techniques to implement variability and recent automation practices for testing, continuous integration and delivery have varied with the time. In addition, topics and activities, such as the course project that has been present in all the courses, had also been modified. This paper (1) describes the evolution of our courses on Software Product Lines, presenting commonalities and variabilities in their topics, activities and evaluation techniques and (2) discusses some lessons learned during its recent design as a Blended Learning course.
Efficient Semantic-Based Vehicle Retrieval in Long-term Car Park Videos

The proliferation of video data has resulted in huge potential in Big Data technology and applications for video surveillance, security, multimedia tools and analytics. However, large-scale video data over a long period of time necessitates an efficient representation such that the task of retrieving video shots is rapid and accurate. This paper proposes an efficient and comprehensive framework for semantic based vehicle retrieval from long-term car park videos. Colour and motion semantics are respectively retrieved using intuitive colour term and sketch-based trajectory querying. The contribution of this work is twofold. First, we present a strategy for extracting the dominant colour for similarity matching against the Munroe ground truth tuples. Secondly, our proposed framework introduces a unique sketch-based method of retrieving vehicle motions, which relies on user-drawn trajectories. Using the spatio-temporal atom representation for extraction from videos, our approach obtained reasonably good precision scores at very fast retrieval speeds based on one-month long of daytime car park videos.
A Gamma-Levy Hybrid MetaHeuristic for HyperParameter Tuning of Deep Q Network

In this study we propose a novel metaheuristic algorithm; namely “Gamma-Levy Hybrid Metaheuristic with Conditional Evolution (GLHM-CE)”. The proposed algorithm is evaluated over 28 Blackbox Problems of CEC-2013, Special Session on Real-Parameter Optimization and compared with modern metaheuristicBanerjee, Abhijit and evolutionary algorithms like SHADE, Co-DE, and JADE. The statistical results show that GLHM-CE successfully circumvents local minimas on high dimensional blackbox functions and has a fast convergence. GLHM-CE is then used to optimize the hyperparameters of a static Deep Q Neural Network evaluated on OpenAI GymGhosh, Dipendranath Cartpole problem. The results evaluated over a total episodalDas, Suvrojit run of 5000 shows a better stability of the DQN when the hyperparameters are optimized by GLHM-CE.
Automated Evaluation of Outlier Performers in Programming Sessions Using Case-Based Reasoning Techniques

Manual evaluation of software program is always a daunting task as it is subjective, time-consuming and requires significant amount of effort. It may also involve human bias. Automated analysis and evaluation can address the above problems adequately. In our paper, we have proposed an automated performance evaluation framework for programming assignment tasks, maintained within a Case-based corpus. The corpus should mandatorily consist of different programming techniques utilized to solve the problem set in an assignment, each one saved as a case instance. Candidate programs are maintained in a separate corpus and then statistically measured to detect outlier performance with respect to the degree of novelty of the approach, quality based on time complexity and amount of plagiarism detected. All new approaches would automatically update the existing base.
Design and Implementation of Intelligent Voice Service for New Generation of Power Grid Dispatch and Control System

Aiming at the new requirement of natural language human-computer interaction in the new generation of power grid dispatch and control system, this paper designs and implements intelligent voice interaction service based on the voice engine of companies such as iFLYTEK and SinoVoice, combining the technology of Microservice, Redis database, Word2Vec, etc. By shielding the interface differences of commercial voice engines and providing a simple and consistent service-based interface, it is conducive to the convenient development of voice-related applications in the power grid dispatch and control system. The platform can flexibly change and expand the underlying commercial voice engine according to the actual needs, manage the context in the multi-round dialogue process. It can also store a large amount of voice information to provide support for later data mining and user behavior analysis.
Research on Virtual and Identity Identity, Correlation and Analysis Technology of Internet-related Economic Crimes Based on Big Data

Abstract In recent years, social activities on the Internet have been frequent, and people have carried out network social activities by registering different IDs on various platforms, which has formed a new identity - Internet virtual identity. A large number of virtual identity exchanges form a social network relationship diagram, but the network virtual identity does not require real-name authentication, which increases the security maintenance and supervision of the network. Therefore, the investigation of network-related economic crimes under big data faces new challenges. In response to this situation, a combination of virtual and real identity, association and analysis techniques based on big data is proposed. Firstly, through the integration and processing of heterogeneous data, the data resources integration under the condition of decentralized data source and multi-database data source is realized. Secondly, the verification and analysis platform of Internet information is built by Solr which is an open source framework. Finally, using full-text search technology to achieve target search and virtual identity association analysis under big data.
Robust adaptive divided difference filter based on forgetting factors and its applications

Summary This paper proposes robust and adaptive divided difference filters (RADDFs) based on forgetting factors which are robust to dynamic systems with biases or uncertainties. The RADDFs are founded on the principle of covariance matching. The robustness of RADDFs is reflected in that it amplifies the innovation covariance to compensate the effect of dynamic biases or uncertainties. The forgetting factor is adjusted adaptively. Then, the scalar forgetting factor is further extended to multiple forgetting factors. The proposed RADDFs are illustrated by Mars entry navigation system with atmospheric density uncertainty, lift over drag ratio uncertainty, and ballistic coefficient uncertainty. To validate the filter performance by multiple forgetting factors, a typical tight coupling nonlinear system with abrupt biases is used.
Design and Application of Centralized Instrument Transformer Measuring System in Series Compensation System

This paper analyzes the requirements of the high-voltage platform measuring system of the series compensation system. Through the analysis of the platform of series compensation system, this paper study’s the noise source and route of TA measuring system on the platform and puts forward a centralized TA measuring system. The system uses signal processing and photoelectric conversion module based on low-power CPLD and reliable redundant power supply. The module features in centralized TA measuring system are also introduced. The system was successfully applied in the Canadian series compensation project.
Vulnerability detection techniques and tools and their relationship to agile methods and software quality and service models

Information systems need to be safe to achieve their goals. Thus, during development one needs to detect possible vulnerabilities present in the software. This paper aims to describe the use of vulnerability detection techniques and tools (VDTT) in complex environments and its relationship to software quality and service models in teams that use agile methods. To do so, a survey was carried out listing 18 techniques and tools that used three well-known processes of secure software development. The survey was applied to 76 members of agile software development teams who have already deployed, are in the process of deploying, or are going to deploy vulnerability detection techniques and tools in the projects. From the data collected, we were able describe the use of VDTT in complex development environments and associations between the adoption of software quality and service models and the use of VDTTs. For this purpose, Fischer's exact test was used to analyze and interpret the associations found.
A Hybrid Pupil and Iris Tracking Using RGB-IR Camera

In eye tracking methods, near-infrared image or color image has been used separately. In this research, we propose a hybrid eye tracking method using an RGB-IR camera, which can capture a color image and a near-infrared image simultaneously, to compensate for the weakness of each technique. Pupil detection can be worked better than iris detection because the eyelid often occludes iris. On the other hand, iris area can be used for model fitting because iris size is constant. Therefore, the hybrid eye tracking method works as a glint-free model-based approach in our proposed method. The model fitting can be automated as initialization, and the relationship between the 3D eyeball model and eye camera is solved. Additionally, 3D eyeball pose can be estimated without using first Purkinje image, and the pupil size can be measured quantitatively. We evaluated the accuracy of the estimated point-of-gaze and the pupil size, and we confirmed the feasibility of the proposed method through experiments.
Human-Interaction Weakly-Supervised Deep Networks for Semantic Segmentation

Weakly-supervised semantic segmentation models help relieve the burden on data labeling. However, there is a huge performance gap between weakly-supervised and fully-supervised models since weakly-supervised models have trouble in recognizing co-occurring objects and non-discriminative object regions, especially when weak supervision of image-level label is considered. In this work, we propose a unified framework to bring in human interaction to assist with weakly-supervised model at inference stage. The proposed method, Human-Interaction WeaklySupervised Deep Networks (HIWSDN), utilizes humandrawn squiggles and seed map from classification network to dynamically update the trained weakly-supervised model. HIWSDN achieves superior segmentation performance, which is a 5.6% improvement over the original weakly-supervised model.