Reviewerly/exHarmony huggingface dataset:
Cross-domain text classification algorithm based on instance-transfer learning

Cross-domain text classification has broad application prospects in the field of data mining. Since transfer learning can help target domain data to achieve the sharing and transfer of semantic information with the help of existing knowledge domains, transfer learning are generally used to achieve cross-domain text processing. Based on this, we propose a cross-domain text classification algorithm -MTrA. The algorithm is based on TrAdaBoost, taking into account the distribution differences between the source domain and the target domain. It uses the Maximum Mean Discrepancy(MMD) as the initial weight parameter of the two domain. MTrA adds a weight backfill factor that considers the accuracy of the source domain classification and balances the weight update method of the source domain data. Through the verification in the dataset 20 Newsgroups, Compared with the traditional TrAdaBoost algorithm, it improves the classification accuracy by 9.4% on average. it proves the effectiveness and advantages of the algorithm.
AnonyTrust: An Anonymous Trust Authentication System for Pervasive Social Networking

Pervasive social networking (PSN) is facilitating and enriching people’s life at any time and in any place. One of the most important issues in PSN is its security and privacy, since users hope their privacy not to be disclosed in social activities. Trust relationship plays a crucial role in the PSN system, and can be utilized to support trustworthy PSN system with anonymous authentication. Thus, this paper proposes an anonymous trust authentication (AnonyTrust) scheme that can simultaneously authenticate identities and trust levels of users with privacy preservation. It also can achieve conditional traceability with a trusted server (TS), and can switch online and offline states with multiple authorized access points (APs). The security analysis and performance evaluation show the scheme is secure and efficient regarding security, anonymity, computational complexity, and communication cost. To verify the feasibility of the proposed scheme, a lightweight secret chat application called AnonyChat is developed in practice. The results show AnonyChat performs well and efficiently in Android devices.
Nuclei Segmentation Approach for Digestive Neuroendocrine Tumors Analysis Using optimized Color Space Conversion

Microscopic examination plays a significant role in the decision making for a reliable diagnosis of digestive neuroendocrine tumors (NETs), an immunohistochemical (IHC) analysis should be conducted by pathologists in order to identify cell morphology, tissue structure, and various histological disorders. The visual and manual assessment task, performed by experts, is tedious, time-consuming, and prone to inter-observer variability. Hence, there is an urgent need for developing an automatic nuclei segmentation approach which can provide an accurate number of cancerous histological tissues and overcome the issue of overlapping cells. In the proposed study, a morphological method for microscopic image segmentation is presented, this approach is mainly based on the choice of the appropriate color space, which highlights stained cells nuclei caused by stain variability and insufficient lighting conditions. Stromal cells, that differ from tumor cells in their particular form and small size, should be removed using shape criterion. Then marker-controlled watershed technique is applied in order to reduce the over-segmentation and to detach the connected cells in the resulting images. The proposed method is compared to ground truth segmentation, the results gave a Dice score of 0.959.
Interactive Applied Graph Chatbot with Semantic Recognition

Companies and small medium businesses (UMKM) need to interact with customers to increase their customer retention rate. One of the ways is to use chatbot. Aside from being cost-effective, this method is also very effective and very easy for companies to use. To make an easy and effective chatbot requires a combination of two scientific fields, artificial intelligence and software engineering. This study has the following features. 1) Affective sentiment analysis, this feature is inspired by Russel's Circumplex Model. Adjective words will be mapped in a matrix with values based on the Russell Circumplex Model. This model will pay attention on the adjective words, polarity, and affection degree of a sentence. 2) Conjunction sentiment analysis, consider of free way of interaction nowdays, a sentiment analysis system need to determine the sentiment value in multilevel sentences. This multilevel sentence has one or more conjunctions. This conjunction sentiment system will break sentences based on conjunction. The fractional sentence will be processed by affective sentiment analysis. The system then considers the nature of the conjunction to determine the sentiment of the whole sentence. 3) Graph Chatbot, this feature is used to make it easy for companies to modify and generate their bots. This bot will interact with customers like humans, based on a graph chat map that has been created. Chatbot graphs were developed using javascript library Vue js to make it easier to manipulate the visual graph. The system produces satisfying accuracy. Graph chatbot can handle procedural conversations very well. The flow of conversation in accordance with the graph that has been defined. Graph chatbot has 100% accuracy and successfully responds to all user conversations. The sentiment method has 63% accuracy.
Synthesis of Registered Multimodal Medical Images with Lesions

The collection and annotation of medical images data have always been a challenge in many data-driven medical image processing tasks, especially for registered multimodal medical images data. This can be effectively alleviated by utilizing the image synthesis technology. However, directly-synthesized medical images generated by current methods usually have unreasonable structures or contours and uncontrollable lesions. In this paper, we proposed a new method to synthesize registered multimodal medical images from a random normal distribution matrix based on the Generative Adversarial Networks. Besides, the corresponding lesions can be generated efficiently based on the selected lesion labels. We performed validation experiments on multiple public datasets to verify the effectiveness of synthetic lesions and the availability of synthetic data. The results show that our synthetic data can be used as pre-trained data or enhanced data in medical image intelligent processing tasks to greatly improve the generalization ability of the model.
Two Improved Topic Word Detection Algorithms

Topic word extraction is the task of identifying single or multi-word expressions that represent the main topics of a document. In this paper, two improved algorithms for extracting and discovering topic words are proposed in the Rapid Topic word Detection (RTD) Algorithm and CategoryTextRank (CTextRank) Algorithm, which can effectively obtain information by extracting and filtering the topic words in the text. The algorithms overcome the shortcomings of traditional topic words discovering algorithms that require deep linguistic knowledge, domain or language specific annotated corpora. The two algorithms we proposed can process both short and long text. The biggest advantage of the algorithms is that they are unsupervised machine learning algorithms. They need not be trained to process text directly to get topic words. The Accuracy rate, recall rate and F-measure index have been greatly improved when using the two algorithms which show that the results obtained compare favorably with previously published results on datasets Inspec and SemEval. The first algorithm Rapid Topicword Detection improves the metrics compared to PositionRank and TextRank, the second algorithm CategoryTextRank improves the metrics compared to TextRank, SingleRank and TF-IDF.
Capstone Project Implementation Using Infrastructure as a Service: The Learning Experience

This research paper discusses the learning experiences during the implementation of a postgraduate network security capstone project using cloud services as the technical infrastructure. Using mixed methods, interpretations are drawn from students' voices in a medium-sized Australian university, about the convoluted challenges and issues associated with the learning redesign of an advanced unit consistent with technology trends and industry demands. Despite finding the IaaS platform challenging and difficult to use, students perceived the learning experience to be highly rewarding and current in the development of their technical skills and job readiness.
Sonotris: Testing the Influence of Musical Tempo on Tetris Players Performance

In this demo paper, we present Sonotris, a Tetris game with a dynamic soundtrack specifically designed to experiment with the influence of musical tempo on player performance. The different stages of the game use music whose tempo is either perfectly synchronous with the game, or accelerates progressively to prepare the speed steps, or is slightly out of sync, or in total opposition to the tension of the game. Our first tests on a group of 41 players showed a significant influence of tempo on the performance of novice players in Tetris and even more for those with a neutral evaluation of the music.
Uncertainty Estimates in Deep Generative Models Using Gaussian Processes

We propose a new framework to estimate the uncertainty of deep generative models. In real-world applications, uncertainty allows us to evaluate the reliability of the outcome of machine learning systems. Gaussian processes are widely known as a method in machine learning which provides estimates of uncertainty. Moreover, Gaussian processes have been shown to be equivalent to deep neural networks with infinitely wide layers. This equivalence suggests that Gaussian process regression can be used to perform Bayesian prediction with deep neural networks. However, existing Bayesian treatments of neural networks via Gaussian processes have only been applied so far to supervised learning; we are not aware of any work using neural networks and Gaussian processes for unsupervised learning. We extend the Bayesian Gaussian process latent variable model, an unsupervised learning method using Gaussian processes, and propose a Bayesian deep generative model by approximating the expectations of complex kernels. With a series of experiments, we validate that our method provides estimates of uncertainty from the relevance between variance and the output quality.
Recognizing Multiplayer Behaviors Using Synthetic Training Data

Accurate recognition of group behaviors is essential to the design of engaging networked multiplayer games. However, contemporary data-driven machine learning solutions are difficult to apply during the game development process, given that no authentic gameplay data is yet available for use as training data. In this paper, we investigate the use of synthetic training data, i.e., gameplay data that is generated by AI-controlled agent teams programmed to perform each of the behaviors to be recognized in groups of human players. The particular task we focus on is to recognize group movement formations in player-controlled avatars in a realistic virtual world. We choose five typical military team movement patterns for the formation recognition task and train machine learning models using procedurally generated unit trajectories as training data. The experiments were conducted using ResNet and EfficientNet, which are two popular convolutional neural network architectures for image classifications. The synthetic data is augmented by creating variations in image rotation, unit spacing, team size, and positional perturbations to bridge the gap between synthetic and human gameplay data. We demonstrate that high-accuracy behavior recognition can be achieved using deep neural networks by applying the aforementioned data augmentation methods to simulated gameplay data.
An algorithm and a flexible Fortran code for the computation of solar energy reception on a curved surface

The increasing applications of solar energy have led to the development of various solar collectors with complex geometries. Conventional methods cannot directly calculate the solar radiation on such surfaces. This research is aimed to develop a method and a computer code (Fortran) to determine solar energy reception on curved surfaces. The presented method can be flexibly applied to any surface function and the solar radiation model. The method is based on dividing a curved surface into a large number of small flat elements and computing the solar radiation on each of these elements, which will be finally summed to attain the total radiation. The results of the presented method were compared with an analytical method for a cylindrical surface, which showed excellent accuracy (relative error of 0.0042%). The results were also evaluated by ANSYS-Fluent commercial software with two different geometries (a hemisphere and paraboloid). The relative difference between the Fortran code and ANSYS-Fluent results was 0.85 and 4.43% for the hemisphere and paraboloid, respectively. Moreover, the results were compared with an experimental test on a semi-spherical solar collector, which indicated the relative error of 0.13%. The developed Fortran code for the algorithm can be easily converted to other programming languages.
The Logical Architecture Essential for the Creation of a Comprehensive Patient Healthcare Profile on Blockchain

This document proposes a logical architecture and implementation approach for a patient centric healthcare information system (referred to as the healthcare consolidator) that supports the sharing of patient data amongst healthcare professionals. Within the context of the logical architecture, we identify where blockchain technology could be applied specifically, data quality and, patient privacy and control over access permissions to their healthcare data. We then go on to discuss how the healthcare consolidator can be used to improve a patients healthcare by presenting their healthcare data in order of relevancy to their current condition to speed diagnosis, treatment and highlight any inconsistencies in their data.
Location and analysis of partial discharge in GIS based on on-line monitoring and improved signal waveform feature method

Online monitoring technology is the real-time monitoring and fault pre-diagnosis of effective GIS technology, the time delay method is a major means of fixing the GIS partial discharge, which is based on the waveform feature points of time delay calculation method has been widely applied in the practical engineering, this paper to improve the signal waveform characteristic point, puts forward the feature points is replaced by a rising edge delay calculation method, through together based on uhf partial discharge phenomena found in the on-line monitoring system, the signal waveform feature points and rising along the method has carried on the comparative study. The research shows that compared with the signal waveform characteristic point method, the rising edge method not only improves the positioning accuracy, but also inherits the advantages of simple operation and low cost of signal waveform characteristic point method, which has certain engineering application value.
Software reuse and mass personalization

This tutorial explores the impact of the socio-economic trends of customization and mass personalization on software reuse through software product line development.
The Generation Method of Power Flow Simulation Samples Based on Power Flow Cut Adjustment

The operation of large-scale power grid dispatching is complex, which requires deep learning to study its characteristics, while the assessment of the potential risk of power grid dispatching operation requires a large number of samples. At present, the problem is the lack of samples. In this paper, a new generation method of power grid simulation samples based on power flow cut adjustment is proposed to solve the problem of insufficient power grid simulation samples. Using this method, a large number of samples can be generated quickly, which provides more effective samples for the application of data-driven method in power system research.
A Hardware-Software Codesign Case Study: The SHA3-512 algorithm Implementation on the LEON3 Processor

With the ever-increasing role that software is playing in embedded systems, software performance is one of the embedded system implementation goals. In this paper we consider the software cryptographic hash-functions implementation on hardware platforms. In fact, Hash functions are used in several information-security applications like message authentication codes, digital signatures and other forms of authentication. As a case study, we consider The SHA3-512 algorithm implementation on the LEON3 soft core Processor. The SHA3-512 is programmed and optimized using the C language in order to be implemented on LEON3 using the ML507 Virtex-5 Xilinx FPGA board.
Spectrum Activity Surveillance: Modeling and Analysis From Perspectives of Surveillance Coverage and Culprit Detection

Spectrum activity surveillance (SAS) is essential to dynamic spectrum access (DSA)-enabled systems with a two-fold impact: it is a primitive mechanism to collect usage data for spectrum efficiency improvement; it is also a prime widget to collect misuse forensics of unauthorized or malicious users. While realizing SAS for DSA-enabled systems appears to be intuitive and trivial, it is, however, a challenging yet open problem. On one hand, a large-scale SAS function is costly to implement in practice; on the other hand, it is not clear how to characterize the efficacy and performance of monitor deployment strategies. To address such challenges, we introduce a three-factor space, composed of <i>spectrum</i>, <i>time</i>, and <i>geographic region</i>, over which the SAS problem is formulated by a two-step solution: 3D-tessellation for sweep (monitoring) <i>coverage</i> and graph walk for detecting <i>spectrum culprits</i>, that is, devices responsible for unauthorized spectrum occupancy. In particular, our system model transforms SAS from a globally collective activity to localized actions, and strategy objectives from qualitative attributes to quantitative measures. With this model, we design low-cost deterministic strategies for dedicated monitors, which outperform strategies found by genetic algorithms, and performance-guaranteed random strategies for crowd-source monitors, which can detect adversarial spectrum culprits in bounded time.
Internet of Things and Banking Frauds-Friends or Foes? A Study of Indian Public and Private Sector Banks

Ever since the introduction of banking business in this complex structure of human living, banks are believed to be one of the institutions which are recommended for trustworthiness and treasure repositories for the general public. These are the institutions which were and are still trusted for their veracity by the civilians of every nation. However, with the passage of time the increase in the technology results in the virtue of banking organization which seems to be under a big question mark and the main reason of this integrity loss of banking organizations in the present era is an epidemic known as fraud. Moreover, bank frauds are one of the crucial areas to be resolved from the angle of internet of things which includes many of the technological loopholes. Therefore, the present study aims to provide an appropriate solution to the problem of fraud in the present scenario of increasing trends of Internet of things in the banking industry especially in Indian context. The study discussed about the present fraud preventive measures used by the public and private sector banks in India and has also provided an elaborated model for fraud risk management which gives a comprehensive structure to be utilized in regard to the fraud prevention in banks.
Research on Multi-robot Task Allocation Algorithm Based on HADTQL

This paper proposes forward a Heuristically Accelerated Dynamic Team Q-learning (HADTQL) algorithm for solving multi-robot collaborative task allocation problem based on multi-agent reinforcement learning. It aims at making multiple robots collaborate to avoid all obstacles and accomplish all tasks while optimizing the path they took relatively. Firstly, the author constructs an appropriate state action space according to the specific information about the environment. Secondly, the whole learning process is divided into two stages by using dynamic exploration coefficient, which ensures the diversity of the early learning and the stability of the later learning. Thirdly, in order to help robots with reasonable action selection, the improved reward function is adopted to provide real-time rewards by utilizing the experience generated by the reinforcement learning of multiple agents. Finally, the heuristic function is introduced to guide the multi-agent reinforcement learning for the next action selection. The simulation experiment shows that the proposed algorithm can find an optimal task execution sequence and complete all tasks collaboratively with a relatively optimal path under the premise of avoiding obstacles in the environment automatically. Compared with the Team Q-learning (TQL) algorithm, this algorithm can allocate tasks reasonably with the high effectiveness and practicability.
A Method of Improving the Efficiency of Verification and Validation for the Railway Signaling System

This paper describes how to make full use of the testing assets reusability to improve the efficiency and quality of the project's requirements validation, design verification and system validation. The core technology of the scheme is accurate text recognition, the reuse of requirement tracking matrix is taken as an example to introduce the implementation scheme and application effect.
Localization of Wheeled Soccer Robots Using Particle Filter Algorithm

Localization is a method used to determine the position of an object. In mobile robots, especially wheeled soccer robots, determining the location of robots against landmarks is needed. It can determine the movements and strategies of soccer robots in a game. There are several methods can be used for localization, namely the odometry method and the extended Kalman filter. But the methods are strongly influenced by the results of reading sensors and tire slips on the robots. In this article, we proposed a particle filter algorithm for localization of wheeled soccer robots. This particle filter method is suitable for non-linear, non-Gaussian systems, and can be used in a real-time environment. We applied the particle filter algorithm to localize the position of the wheeled soccer robot. The particle filter algorithm generally has three basic stages, prediction stage, weight update and resampling. As initialization process, the particles are spread randomly or around the initial position of the robot. The prediction stage is done during the robot movement using a motion model applied to each particle. After that, the weight of each particle is updated based on the likelihood of particle data to the robot data. The resampling step is performed to update the weight of each particle. Particles with the smallest weight value will disappear by themselves and particles with large weight will retain. The position presumption is obtained through the estimation process that is by averaging the position of all particles. The experiment is done using simulation on the field with 450 × 300 pixels or 900 × 600 cm which each pixel represent 2 cm on the real soccer for competition. The particles used in the experiment are 100, 200, 400, 800, and 1600 particles. The experiment results show the satisfied result with number of particles error is 100 particles and the error is between 0.363 cm and 23.055 cm.