Reviewerly/exHarmony huggingface dataset:
Semisupervised Game Player Categorization From Very Big Behavior Log Data

Extracting the specific category of the players, such as the malignant Bot, from the huge log data of the massive multiplayer online role playing games, denoted as MMORPGs, is an important basic task in game security and personal recommendation. In this article, we propose a parallel semisupervised framework to categorize specific game players with a few label-known target samples, which are denoted as bait players. Our approach first presents a feature representation model based on the players’ level granularity, which can acquire aligned feature representations in the lower dimensional space from the players’ original action sequences. Then, we propose a semisupervised clustering method, extended from the bisecting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means model, to extract the specified players with the help of those bait players. Due to massive amounts of game log data, the computation complexity is an extreme challenge to implement our feature representation and semisupervised extraction approaches. We also propose a hierarchical parallelism framework, which allows the data to be computed horizontally and vertically simultaneously and enables varied parallel combinations for the steps of our semisupervised categorization approach. The comparable experiments on real-world MMORPGs’ log data, containing more than 465 Gbytes and million players, are carried out to demonstrate the effectiveness and efficiency of our proposed approach compared with the state-of-the-art methods.
A Novel AR Maintenance Training System and Its Effect Prediction

Augmented Reality (AR) applications in aviation industry have been significant increased in recent years, especially in aviation maintenance training. The visualization and interaction capabilities of AR can impact on maintenance, but the effect of AR on training process has not been clarified. The purpose of this paper is to evaluate the AR composite materials maintenance training process with eye movement data for future studies and applications. ARCM-Tutor is a prototype that integrates AR with lay-up process in bonded repair of composite structures. The laminate simulation training was conducted for a group of 16 students of safety discipline of continued airworthiness course at the Civil Aviation Flight University of China. The training was carried out based on the two different way and eye-tracking was used to record gaze data generated in this process. Then, the eye movement behavior such as first fixation time, average fixation time, saccadic velocity and pupil diameter in the process were analyzed. The correlation between eye movement and learning performance was established. The eye movement indexes including average fixation time, pupil diameter and saccade velocity, which could characterize the training effect were extracted. The fuzzy c-means clustering method was used to classify the eye movement feature vector attach to training performance. The experimental results show that this method can infer the training performance effectively.
Mobile image processing system based on the Raspberry Pi 4 platform

The article presents an image processing system based on the Raspberry Pi (RPi) platform. At the beginning of the article, the basic assumptions and purpose of the system are discussed. The following section presents the structure and operation of the system. The window application managing the system and allowing to perform contextual and spectral transformations on images as well as the measurement of parameters such as image processing time and mean square error (MSE) was discussed. The transformations performed were based both on ready formulas contained in the OpenCV library and the author's implementations, including the function implementing the Fast Fourier Transform algorithm radix-2. Examples of transformations were presented along with their usefulness. In the end, the development potential of the created system is presented and its application in specific solutions is proposed.
A Property Management System Using Image Recognition by YOLO

Property management is one of important operation in companies. In this research, we propose a property management system using markers, YOLO (image recognition), and OCR. The performance of the system is evaluated on a PC without GPU by processing time.
About Content of the Book

Dynamic objects, that is such, which pass through various states, are being described by more or less formal methods, devised for a class of tasks they belong to. The search for convenient and appropriate description, suggests sometimes making conversion between descriptive languages. That is, modelling or simulating one by means of another. Especially, when one of them enjoys a rich formal base or application tool.
The needs and availability analysis of irrigation water of Way Wayah Irrigation Area

Abstract Optimization of agricultural land is an effort to mprove and increase land support capacity, in such a way become more productive farmland. The Local Government of Pringsewu Regency planned to develop irrigation network by utilizing Way Wayah river. This study aims to design irrigation area in Way Wayah and to analyse water availability of Way Wayah river and water need of the planned irrigation area. Irrigation network design includes analysis of determining the location of the weir, so that it can be estimated the area of designed paddy field, the catchment area as well as the availability of water resources. Analysis includes spatial analysis using Geographical Information System, hydrologic analysis for water availability and Penman method for irrigation water need. The results show that (1) the planned weir will be located in Giri Tunggal with catchment area of 7,238.9 ha, the area of total paddy rice fields (existing and planned paddy rice fields) is 960.46 ha; (2) The need for water are 6196.88 m 3 ha -1 season -1 and 6556.96 m 3 ha -1 season -1 for planting seasons 1 and 2 respectively. Water availability is sufficient during preparation and planting periods
Scalable Machine Learning Algorithms to Design Massive MIMO Systems

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but its scaling properties for this purpose have not been widely studied. Machine learning algorithms are typically evaluated to learn SISO communications and low modulation orders, whereas current wireless standards use MIMO and high-order modulation schemes to increase capacity. The memory requirements of current Machine learning algorithms for wireless communications increase exponentially with the number of antennas and thus they cannot be used for advanced physical layers and massive MIMO. In this paper, we study the requirements of end-to-end Machine learning models for large-scale MIMO systems, determine the bottlenecks of the architecture, and design different solutions that vastly reduce overhead and allow training higher MIMO and modulation orders. We show that by training the autoencoder in a bit-wise manner, the memory requirements are reduced by several orders of magnitude, which is a critical step for Machine learning-based physical layer design in practical scenarios. Additionally, our design also improves performance over the classical autoencoder for MIMO.
Unsupervised Image Region of Interest Detection using Discrete Cosine Transform

Spatial domain analysis of images has been recognised as an important task in image retrieval and computer vision, however, its application in the determination of image Region of Interests often require the inclusion of supervised training and similarity computations, both of which can be difficult to implement during the modelling of the images in a large number of images. however, unsupervised image Region of Interest determination via Frequency domain analysis can be implemented without training or similarity computation therefore it is more suitable for the modelling of images in a large set. This paper demonstrates image frequency domain analysis via Discrete Cosine Transform and presents its application in the unsupervised determination of Region of Interests for the elimination of the spatial incoherency often associated with the Bag-of-Visual Word Modelling.
Comparative Analysis of Determined Generators for Random Bits, Defined in the Nist Special Publication 800-90A

The main document, which has been elaborated and introduced by the national institute of standards and technologies of the USA (National Institute of Standards and Technology—NIST), and which defines the recommendations concerning the building of pseudorandom sequences generators with the using of various methods and a respective mathematical apparatus, is the national standard NIST Special Publication 800-90A “Recommendation for Random Number Generation Using Deterministic Random Bit Generators”. Let us examine the main statements of this document, which determine various approaches to the PRS-forming.
A Survey on the Recognition of College Students' Learning Motivation by Different Teaching Methods

Teaching method is the embodiment of the behavior of both teachers and students in teaching activities. Different teaching methods are suitable for different teaching objectives and course contents. The reform and application of teaching methods are the needs of the continuous development of education. The junior college students were the subjects and survey of network questionnaire and field interview were used to evaluate the recognition of students on 11 teaching method in the present study. The results showed that college students' recognition of traditional teaching methods, such as case discussion, teaching and enterprise practice, which were top 3 teaching methods. But the recognition of homework/exercises, talk teaching and systematic training for the whole week came to the bottom. However all the 11 teaching methods did not have significant differences. Different teaching methods all have their merits. The key was that teachers should analyze the learning situation and choose the appropriate teaching methods according to the teaching content and teaching objectives of the course. However, colleges are keen on concentrated practical training, and the teaching mode of offering full-week practical courses, which should arouse great attention. It is necessary to plan and improve students' motivation and interest in full-week practical learning from the perspectives of top-level professional design, careful planning of courses, professional implementation process and effectiveness of result assessment.
Pedestrian passage and height detection system based on deep learning

Object detection is an important research direction in the field of machine vision, which is widely used in industry, security and intelligent transportation. In the intelligent traffic gate detection, most of the visual detection is concentrated on face detection, which can not detect the behavior of people who follow or walk side by side and their height. In order to solve this problem, we proposed a visual detection algorithm based on Yolo V3 deep learning network model, and trained the network model for pedestrian detection; The tracking algorithm based on Yolo V3 sort adopts two-line detection, which can not only detect the following, side-by-side and other abnormal behaviors, but also effectively solve the problem of single line detection; At the same time, the target pedestrian is segmented by using Yolo V3 grabcut, and the segmented image is filtered and binarized. After traversing the pixels, the height detection is realized based on the optical axis aggregation model. The experimental results show that the improved network model realizes the detection of ticket evasion behavior such as tailing, side by side and height detection, improves the accuracy of pedestrian detection, and can meet the needs of intelligent transportation field.
Research on hot news information discovery and development trend prediction based on decision tree

Traditional methods have the problems of low detection accuracy and high recall rate when searching hot news information. Therefore, this paper designs a hot news information discovery and development trend prediction method based on decision tree. On the basis of calculating the weight of user attention parameters, the interference information of user attention parameters is eliminated by decision tree algorithm, and then the data set of user attention parameters is established. Based on this, the feature of news text is extracted as topic vector, and the media attention of news information is calculated. Combined with user attention and media attention, the hot index of news information is calculated. The news with a high hot index is regarded as the discovered hot information, and the news with a fast hot index growth rate is regarded as the predicted hot information. Experimental results show that this method improves the accuracy of news information detection and reduces the recall rate of news information detection.
Ethical questions in name authority control

This edited collection is the first ever volume of papers dedicated to the ethical aspects of name authority control in library catalogues and similar databases. Like other publications from Librar...
Environment Understanding Framework for Intelligent Mobile Systems

Environment perception is a key requirement for intelligent mobile system. Vision based methods that support this task have advanced impressively in the past years, mainly due to the advent of deep learning techniques and dedicated hardware. Still, the state of the art techniques for simultaneously solving the problem of object detection as well as that of semantic segmentation are computationally expensive and, thus, not straightforward to use by intelligent mobile systems. In this paper, we introduce a novel framework for environment understanding based on semantic instance segmentation and pre- and post-processing techniques to speedup the instance detection and classification, while keeping the accuracy at state of the art levels. The designed method employs an initial fast segmentation. This procedure splits the image into several regions based on U-disparity maps. Afterwards, each region is fed through a semantic instance segmentation network. Further, to avoid multiple predictions due to segmentation and/or prediction, a filtering heuristic is used to post-process the resulting instances. Finally, we evaluate the proposed framework using available benchmarks, highlighting the real-time operation capability and the solution’s accuracy.
Professional objectives of higher education: Imbalances and adaptation

This article analyzes the systemic issues of personnel education in higher education, reveals their causes and consequences. Qualitative and quantitative characteristics of university graduates are one of the conditions for the state economic development. The goals and objectives of higher education should meet the needs of the state, industries, regions, and business. The lack of strong signals from consumers of educational services creates an imbalance between the higher education system and the market requirements and results in the conservatism of higher education. In this context, the purpose of this article is to develop a conceptual model for assessing the contribution of higher education to the national economy as one of the drivers of its transformation. The need to optimize the professional structure of education, with reference to the potential and prospects of the national economy, is emphasized. Research methods involve analysis and synthesis, induction and deduction, system and logical methods. The study result is a model that takes into account a number of indicators of employer satisfaction in university graduates, as well as indicators of regional and industrial security of specific professionals with higher education. An effective motivation for employers to take a more active part in the organization of higher education is the system of educational loans. The state’s management of the credit sphere creates opportunities for consumers of educational services to direct financial flows primarily to education in priority specialties. The effectiveness of this tool requires ensuring its accessibility to employers and differentiating interest rates.
HHL Analysis and Simulation Verification Based on Origin Quantum Platform

Abstract Solving large-scale linear equations is of great significance in many engineering fields, such as weather forecasting and bioengineering. The classical computer solves the linear equations, no matter adopting the elimination method or Kramer’s rule, the time required for solving is in a polynomial relationship with the scale of the equation system. With the advent of the era of big data, the integration of transistors is getting higher and higher. When the size of transistors is close to the order of electron diameter, quantum tunneling will occur, and Moore’s Law will not be followed. Therefore, the traditional computing model will not be able to meet the demand. In this paper, through an in-depth study of the classic HHL algorithm, a small-scale quantum circuit model is proposed to solve a 2×2 linear equations, and the circuit diagram and programming are used to simulate and verify on the Origin Quantum Platform. The fidelity under different parameter values reaches more than 90%. For the case where the matrix to be solved is a sparse matrix, the quantum algorithm has an exponential speed improvement over the best known classical algorithm.
Simplified ciphertext-policy attribute-based encryption scheme with attribute level collusion resistance for cloud storage

Abstract The core objective of our paper is that to overcome the challenges of secure data sharing problem in cloud where users can access data if they acquire a certain set of attributes or credentials. At present, there is only one way for implementing such procedures is by employing a reliable server to mediate access control, store the data. The service providers have whole control over cloud applications, client’s data and hardware. However, if the data stored in the cloud server is conceded, then the data confidentiality will be compromised. With simplified CP-ABE data is confidential even though storage server is un trusted. Hence privacy of cloud data is enhanced by proposing a methodology that uses a privacy policy, encrypts the data based upon various group of attributes related to the user. The decryption of the data can be done only when his/her attributes match with the privacy policy. Simplified CP-ABE makes it easier to secure, broadcast and control the access of sensitive information, specifically across the cloud’s server and decrease collision resistance.
MAC Based Security Integration using Face Recognition in Cloud Environment

Abstract The major challenges, which come across face recognition system, are to find the age and gender in 2D/3D image of the person specifically in cloud environment. This paperis centered on face detection with MAC (Media Access Control) and biometric technology. Face scanning along with machine’s MAC address and biometric technologies has been shown to improve security controls. Face recognition can be used to search and label users and their assigned machines for sensitive purposes. Following that, it was stored in a specific database with their unique ID. In addition, the verification process has begun by comparing the models in the database. Face scanning along with speech and biometric technologies is used to improve security controls. Face recognition system may also be set up in high security machines to improve protection by allowing only registered individuals or others users. Related strategies for determining the age and gender and 2D/3D image from a specific picture are explored, as well as several modern methods for preserving protection. In this paper, the full model is explored independently with security implemented in cloud environment. The proposed model of the paper provides the integrated security features using MAC address of machine and face recognition of the machine user.
A study on VLSI implementation of image enhancement techniques

Abstract In real‐time, the images captured are not always appreciable for visual perception. The quality of the image relies on the illumination condition. Contrast enhancement is required to improve the visual quality of the image. In this article, the image enhancement techniques that are adaptable for real‐time images are studied. Further, an investigation is done on implementing the image processing algorithms in very large‐scale integration (VLSI) hardware. As the image data are comparatively larger, the computational complexity and the memory requirement are more. From the study, the need for efficient computation of such image processing algorithms is realized. The efficient computation of image processing algorithms on hardware platforms, in particular on field programmable gate array (FPGA), is reviewed. As an experiment, the gamma‐transformation technique used for image enhancement is realized. Initially, the prototype is modeled using MATLAB, and then it is implemented using an FPGA.
Automatic Recognition and Correction of Volleyball Players’ Release Angle Based on Feature Statistics

The constant reform of the competition rules has promoted the innovation of volleyball techniques and tactics. In order to improve the training efficiency and competitive level of volleyball players, this study designed a volleyball player shooting angle automatic recognition and correction method based on the process of feature statistics. Firstly, the basic structure of the information acquisition system is analyzed, and the acquisition process is determined. Then, grayscale and binarization operations are carried out for color-moving images to separate their foreground and background, and a median filtering algorithm is used to remove the image noise. Then, the image pyramid of different sizes is generated by the filter. Based on setting the datum direction, the feature of volleyball shooting is extracted by using the line formula. On this basis, we construct a support vector machine (SVM) classifier to statistically classify the features, use the histogram additive kernel support vector machine method to obtain the lens angle recognition results, and correct the lens angle through feature point matching. Simulation experiments show that this method can effectively remove image noise and make the image signal-to-noise ratio higher, and it can effectively identify whether volleyball players’ release Angle is correct, to achieve the purpose of timely correction.
BDI-Dojo: developing robust BDI agents in evolving adversarial environments

The Belief-Desire-Intention (BDI) architecture is a widely-used model for developing multi-agent systems. BDI agents pursue their goals over time using a collection of plan recipes that are programmed by the developers. Thus, traditional BDI agents are limited in dealing with dynamic environments where uncertainties are not known beforehand, such as those introduced by adversarial forces. In this paper, we present the BDI-Dojo framework for developing robust BDI agents by training them using reinforcement learning against similarly learning-equipped adversarial agents. This adversarial training approach empowers BDI agents to become more resilient in uncertain, dynamic environments.