Reviewerly/exHarmony huggingface dataset:
Cooperativeness Measure Based on the Hypervolume Indicator and Matching Method for Concurrent Negotiations

Multi-agent systems involve concurrent negotiation problems wherein many agents negotiate concurrently; thus, such problems have gained research attention as one of the most important challenges. Considering bilateral negotiation with all opponents is unrealistic when the negotiation problems involve many agents. In this situation, agents attempt to negotiate with cooperative opponents to improve the negotiation result. However, discovering cooperative opponents is difficult because there is no reliable measure to evaluate cooperativeness. In this paper, we propose a cooperativeness measure based on the hypervolume indicator (CHI) to evaluate the cooperativeness among agents. CHI is the hypervolume of the negotiation space, which is Pareto dominated by the set of all the bids. Furthermore, we propose a matching method based on the cooperativeness measure for concurrent negotiations. The proposed method finds the matching by maximizing the sum of cooperativeness of each pair. In the experiments, we evaluate CHI and our matching method by analyzing the negotiation results of three state-of-the-art agents in ANAC 2016. The experimental results demonstrate the following: (1) there is an extremely strong correlation between CHI and social welfare and (2) total social welfare of a matching by our method is quite close to the theoretical optimal.
Study on Breast Tumor Detection Based on Lasso-BP Neural Network

In recent years, breast cancer, as one of the most threatening tumors for women's health in China, affects women's health with a growth rate of 2% every year. The Lasso algorithm was used to screen the characteristics of breast cancer data, and then the BP neural network was used to classify the 9 breast cancer data determination factors in the UCI dataset and the remaining 8 determination factors after screening. The experimental results showed that: In the detection of breast cancer based on BP neural network, the remaining 8 breast cancer data features are used to classify benign and malignant tumors, and the classification accuracy rate is higher than that of the original 9 breast cancer data features.
Machine Learning Based Approach to Process Characterization for Smart Devices in 3D Industrial Manufacturing

A key differentiator between the additive manufacturing and the traditional injection molding is the precision-manufacturing. An error-free print job significantly guarantees good part quality with minimum wastage of the material and energy. In practice, however, achieving error-free production is quite challenging and this emphasizes the need to learn what behavior of the machine leads to an erroneous job. Knowing the health of the printer or the print job helps quantify print job performance, as well as build system alerts to take reactive actions. Intending to run the model dynamically while the machine is printing, time-series based deep learning models like LSTM are most suitable. This paper presents a machine learning based anomaly detection approach to discover patterns in sensors' measurements in the streaming mode in MultiJet Fusion 3d printer developed at HP Inc. A hybrid architecture of LSTM and Auto-encoder has been proposed to learn the printer behavior generate an alarm in the event of an anomaly. The results of both LSTM and LSTM-Autoencoder models have also been discussed by taking real-life examples of 3D printing jobs.
Non-blind steganalysis

The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in non-blind scenarios. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis.
Real-Time Motion Control of a Spark Robot Using the Robot Operating System and MATLAB

With the rapid development of science and technology, robots begin to penetrate into people’s daily life from the industrial field or other special fields, and the development of robotics technology is becoming simpler. As an open source meta-operation system for robots, ROS has been recognized by many developers because of its outstanding advantages, which has become a standardized platform for robotic development. MATLAB has powerful data processing ability and graphical programming, by which a large number of mature algorithms can be used. Combining MATLAB with ROS, it can greatly reduce the difficulty of robot development. This paper presents a convenient and fast method for robot motion control. It takes a Spark robot as the development object to realize and study the performance of tracking a moving object. Spark is a two-wheel differential-controlled robot with multiple infrared obstacle avoidance sensors, which can be equipped with depth cameras, laser sensors, infrared imaging sensors, sound sensors and other instruments, being suitable for online condition inspection and monitoring. Experimental evaluation show that the proposed method allows the realization of different motions, verifying that this application development method can meet the requirement for not only the performances but being convenient and fast that can be achieved with little programming skills.
Study for Robustness Evaluation of High-Speed Railway Capacity Utilization: A Method Based on Bayesian Network

To cope with the noise present in capacity utilization, this paper proposes a robustness evaluating framework for optimizing capacity utilization for high speed railway system. Firstly, we compare and analyze the similarities and differences between the capacity utilization and the corresponding robustness characteristics of high-speed railway and those in the regular railway system. Secondly, we analyze the statistical data of train delay events on the high-speed railway. Then, the Bayesian network with the delay events of each train as the basic event is constructed. Under the condition of increasing random disturbance, the simulation is performed by GeNIe software, and the probability of delay occurrence of each train is obtained. The robustness evaluation indexes for capacity utilization is constructed, of which includes the random disturbance amplitude and the delay probability of each train. Finally, an example is analyzed by taking the Beijing-Shanghai high-speed railway as an example to verify the feasibility of the method.
World librarians: A socio-technical system providing library search services to offline schools and libraries in Malawi

World Librarians is a socio-technical system that strives to solve the information access problem many remote offline schools and libraries have in lesser developed contexts. In this Case Report, we describe the system we have developed over the course of three years, where we first establish solar-powered computer labs in remote schools and libraries in Malawi, and then provide them digital information that they want, rather than what we in the Global North think they need. After providing background on these issues, we describe the socio-technical underpinnings and workflow in the World Librarians program. This involves the establishment of “Requester nodes” in the offline schools and libraries, the management of these deployments using a cloud-based WL app, and the operations of the WL “Searcher node” at the University of Massachusetts Amherst, as well as a novel micro-payment system that enables the transfer of large digital datasets through the use of teacher or librarian cell phones and data plans. We close the report with findings from a preliminary survey suggesting that the WL program is making a positive impact on the schools and libraries served. At its core, WL represents a global librarian support system working to remove the barriers to educational information for all global citizens, with central attention and awareness to Global North/South information power dynamics.
A Key based Distributed Approach for Data Integrity and Consistency in JSON and XML(Hierarchical Data Exchange Formats)

The payload delivery is a challenging task as data may be corrupted anywhere by anyone in this present era. The existing standards of symmetric and asymmetric key algorithms provide confidentiality and authentication; meantime data integrity & consistency are the other two important parameters. The lightweight data like JavaScript Object Notation (JSON) and ExtensibleMarkup Language (XML) are introduced to make data access easier and faster. These objects follow the traditional method of generating digests using traditional approaches such as MD5 and Hash algorithms, but they need better treatment since they are different by nature. Message digests can be generated in a distributed fashion to achieve the highest level accuracy. This paper suggests a distributed approach for data integrity and consistency in JSON and XML and discusses the advantages of the same.
&lt;b&gt;Earthquake Countermeasure Technology for Small to Huge Earthquakes&lt;/b&gt;

In the field of railway earthquake countermeasures, earthquake resistant design, aseismic reinforcement, etc., are implemented before earthquakes. On the other hand, measures such as earthquake early warning, operational restrictions, are implemented after earthquakes. Pre-and post-earthquake measures are generally combined to strike a proper balance. Though the targets of these countermeasures are primarily large earthquakes, there is a pressing need for new countermeasures for small earthquakes, such as rapid resumption of operations after small earthquake. This paper describes recent advances in the development of countermeasures for small earthquakes that occur more frequently as well as those for large and huge earthquakes for which some issues are still pending.
Identity based encryption with equality test

In this research work, we propose a notion of identity-based encryption scheme with equality Test (IBE-ET). This scheme allows authorized users to test the equivalence between two messages whereby the messages are encrypted using different previously known identities. Most existing IBE-ET researches only allows checking whether two ciphertexts encrypted under same identities contain the same message. IBE-ET has many interesting applications in constructing searchable encryption and partitioning encrypted data. However, the original IBE-ET schemes lack an authorization mechanism for a user to control the comparison of its ciphertexts with others. In this research, we propose the authorization mechanism for IBE-ET. We give the definitions of the policies and propose an IBE-ET scheme supporting these two types of authorization at the same time. Our scheme supports authorization such that receiver authorizes the tester to be able to perform equality test on all of receivers’ ciphertexts. The receiver gives the tester authorization corresponding to a specific ciphertext then the tester only acquires the equality test on that particular ciphertext. The tester without decryption can verify the equality between two ciphertexts after he or she receives two authorizations.
A Job Interview Dialogue System That Asks Follow-up Questions: Implementation and Evaluation with an Autonomous Android

A spoken dialogue system that plays the role of an interviewer for job interviews is presented. In this work, ourgoal is to implement an automated job interview system where candidates can use it as practice before the real interview.Conventional job interview systems ask only pre-defined questions, which make the dialogue monotonous andfar from human-human interviews. We propose follow-up question generation based on the assessment of candidateresponses and keyword extraction. This model was integrated into the dialogue system of the autonomous androidERICA to conduct subject experiments. The proposed job interview system was compared with the baseline systemthat did not generate any follow-up questions and selected among pre-defined questions. The experimental resultsshow that the proposed system is significantly better in subjective evaluations regarding impressions of job interviewpractice, the quality of questions, and the presence of the interviewer.
Research on Total Radiation Dose Detector Based on Fiber Induced Loss

With the wide application of radiation in the fields of space orbit, medical treatment, food, etc., the online monitoring of the total radiation dose becomes particularly important. A method for designing a total radiation dose sensor in a high-dose environment using ordinary quartz fiber has been proposed. The energy deposition attenuation model of the cylindrical probe was established, and the energy deposition results after penetration of the copper attenuation layer with a thickness of 0.1mm-1mm were simulated using geant4, which showed a high consistency with the attenuation model. Experiments have verified that the attenuation curve caused by the induced loss of the fiber exhibits a saturation phenomenon when the attenuation thickness is 0.5mm and works in the linear section at 1mm and 1.5mm. It also shows that the model can be used to make a high-dose radiation sensing system with ordinary quartz fiber and the design provides a theoretical basis.
RUSDataBoost-IM: Improving Classification Performance in Imbalanced Data

Real-world datasets often have skewed distribution; that is, one class may have much larger instances as compared to other classes. Rarely occurred events are very hard to judge due to their scarcity and casualty; however, the misinterpretation of rare events can lead to high costs. The traditional classifiers usually incline toward the standard class, and rare class is missed due to class imbalance. We propose RUSDataBoost-IM, a novel ensemble approach based on DataBoost-IM, which merges random undersampling (RUS) with the Boosting method. Our approach is as simple as DataBoost-IM but improves the precision of the base classifiers by using the random undersampling approach. Experiments on 29 highly imbalanced datasets show the superiority of our proposal over other ensemble methods. The experimental outcomes prove that our hybrid ensemble approach is proven to be a promising alternative on different benchmarks and can be applied to any imbalanced classification scenario.
A corpus augmentation approach for improving the performance of dialogue systems in the Greek language

One of the main challenges in dialogue systems is their efficiency when trained with small corpora. Although this problem has been solved for dialogue systems in the English language, as there are many corpora available in the literature, there are inherent limitations in other languages. In this paper, the authors study the problem of training end-to-end dialogue systems with a small dataset by proposing a corpus augmentation approach using a morphological lexicon to find the synonyms of the words in the initial corpus and enhance it with more question and answer pairs. The proposed approach was tested under different architectures, including transformers and recurrent neural networks (RNN). The obtained results showed that training the dialogue systems with the augmented corpus can improve their performance achieving an average increase of about 32% for the RNN and 43% for the transformer with respect to the BLEU-2 score. Finally, hypothesis testing has been applied to investigate on the validity of the results.
Exploring Visual Attention and Machine Learning in 3D Visualization of Medical Temporal Data

Temporal data visualization supports planning and decision-making processes as it helps understanding patterns and relationships among time-based data. In the Healthcare area, the anamnesis procedure offers to physicians a large volume of valuable information, which is usually analyzed considering temporal aspects. Contributing to overcome the limited use of three-dimensional (3D) space, in this article we present a VR approach named 3D Block ARL to support interactive visualization of medical temporal data where the interface design is based on VA concepts. Additionally, we use a rule-based learning method to associate users' preferences to graphical elements aiming to personalize the proposed 3D visualization interface. Our results indicate that VA can be a valuable resource to improve the design of Information Visualization interface tools in the context of temporal medical data as well as to personalize the visualizations according to the preferences of users.
A Novel Approach for Breast Cancer Data Classification Using Deep Forest Network

This work presents a deep forest network-based breast cancer classification system. Post application of principal component analysis (PCA) on the dataset reduced the dimensionality of data from nine dimensions to four dimensions to keep most uncorrelated features. Subsequent application of tenfold cross-validated deep forest-based classifier resulted in improved prediction accuracy. The proposed model is compared with contemporary neural network systems. The proposed model outperforms the contemporary neural network model with substantial prediction accuracy of 99.11%. The major contribution of this work is development and application of deep forest model for breast cancer classification.
Rate Forecast about Aircraft Grounded due to Maintenance with Modified Neural Network

It was important for the ground crew to forecast the rate of the aircraft grounded due to maintenance. Using the neural network to forecast the index, it depended on the network structure, the algorithm, the training samples quantity and representation ability. The paper ameliorated the network configuration and arithmetic, It constructed the modified network to forecast the value and used the ameliorated method to enlarge its ability during the forecast process. The results showed the method could solve the generalization capability and the sample problems, the forecast results was meaningful.
Multi-System Sensors' Route Forecast to Aerial Target Based on Data Mining

When multi-system is submitted to target interference, the only way to realize target initial positioning is to begin from the target associated angle by multi-system sensors. Applies the improved triangulation method and method of MVEKF tracking algorithm based on multi-platform to raise capacity of system cooperative localization, perform accuracy forecast. Introduce with the method of association rules analysis of data mining to meet the space-time requirement in problem of target route forecast; Adopt the methods of setting augmented matrix, building vector subspace and vector inner product in target dynamics database of the detective system, extract frequent item sets in dynamic database of the detection system, quickly find the database tuple dynamic additions and deletions and the solution update of frequent item sets while the Minsup changes, making the system collaboration dynamic library update quickly. Integrate the associated angle analysis of the system observed target, it is able to realize real-time target route forecast, so that effectively reduce the probability of target penetration.
Generating Quality IF-THEN Rules for Diabetes using Linguistic Summarization

Linguistic summarization is an approach of extraction of knowledge or linguistic patterns from datasets. Since summaries are brief, they promote quick analysis of data. Numerous researchers have utilized this approach to produce summaries which are easy to comprehend. Furthermore, linguistic summarization has been used to generate IF-THEN rules in the literature which not only convey data easily but is utilized in making decisions. In this paper we follow this approach to generate IF-THEN rules for diabetes on a dataset. This constructed dataset consists of responses from individuals for five parameters, crucial in the diagnosis of diabetes. Consequently, the quality of the rules produced using linguistic summarization is checked by the four quality measures namely: degree of truth, coverage, reliability and outliers. Among these, the degree of reliability is useful to find rules that represent dataset completely, and the outliers are used to find rules that deviate from the original result. Our experiment reveals results that are promising when compared to the PIMA dataset.
Video Captioning for Proactive Video Management Using Deep Machine Learning

Inspired by the new advancements in machine learning and search engine technology, it is possible to caption videos and index them for efficient managing in applications like YouTube, Dailymotion, Netflix, etc. This system can generate a set of tags which when put together will give a sensible translation of the video in text format. The key challenge is the semantic gap between identification of objects by the human brain and the computer AI. In this proposed model, we propose to transcribe the video clips based on the contents and construct a pipeline through the convolutional neural network (CNN) which will efficiently and accurately process the video and search for the contents. This would require processing of the video clip and indexing it in a database. The same indexing will enable the use of this technology in content-based image retrieval (CBIR) tasks. The user can search for videos based on the context, and the time spent is seeking through the video to search for the scene is solved. We use deep learning model to search through video content and show that it performs efficiently.
Efficient and reproducible research in quantum open systems using Maxima with a Fortran interface

maxqosim is a software package that facilitates the analysis and simulation of quantum open systems, providing a framework where theoretical calculations, efficient numerical calculations and exposition can be coordinated and automated. maxqosim consists of Maxima and Fortran routines, revolving around the symbolic manipulation of equations relevant to quantum open systems theory and their efficient numerical simulation. maxqosim package aims to be a tool for reproducible, extendable and efficient research in quantum open systems.
Detection of Violation of MVVM Design Pattern in Objective-C Programs

In object-oriented software development, several design patterns are used to design software to solve different problems. The Model-View-ViewModel or MVVM pattern is a design pattern that can be used to design structure of software which comprises three components. The Model component holds application data. The View component displays visual elements and controls on the screen. The ViewModel component is the intermediary between the Model and View and transforms model information for display on a view. The advantages of this pattern are that there is a clear separation between domain logic in the Model and the display on the View, and tests and maintenance of different components are facilitated. However, there are several developers in a software development team, and not all developers use the MVVM pattern. This makes the code not compliant with the pattern and affects code maintenance. To help developers to revise the code to comply with the MVVM pattern, this paper has studied the MVVM design pattern, summarized principles of the pattern, and proposed the criteria to detect pattern violation in Objective-C code. An automated tool has been developed to detect MVVM violation and report violation locations in the code so that developers can revise the code to follow the pattern. An experiment shows that the tool performs satisfactorily in pattern violation detection, compared with manual detection, with the precision of 0.81, recall of 0.77, and F1 of 0.79.