Reviewerly/exHarmony huggingface dataset:
Classifying Mental Workload Levels Using Semi-Supervised Learning Technique

Real-time monitoring and analysis of human operator’s mental workload (MWL) is crucial for development of adaptive/intelligent human-machine cooperative systems in various safety/mission-critical application fields. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, it is usually difficult to acquire sufficient labeled data to train the ML model. This paper proposes semi-supervised extreme learning machines (SS-ELM) for MWL pattern classification using solely a small number of labeled data. The experimental data analysis results are presented to show the effectiveness of the proposed SS-ELM paradigm to effectively exploit a large number of unlabeled data for the real-world 3- or 4-class MWL classification problem.
Sentence Similarity Computation Based on Deep Learning

Computing the semantic similarity of two sentences is a task central to automated understanding of natural languages. In data mining Technology, sentence similarity computation is a key problem. As we know a sentence can be presented by many kinds of style, if we want to describe what a sentence means we should dip into the semantic level and consider about the dependency structure. In this paper, we propose a novel method for calculate the sentence similarity score between two sentences based on deep learning. The sentence similarity score is computed using semantic knowledge notably the synonymy relationships and syntactico-semantic knowledge especially the semantic class and thematic role. Furthermore, we show that the use of recurrent and recursive neural networks can provide a 16% to 70% improvement over baseline models.The experimentation is carried out on data from the SemEval-2015 Task 2 and is revealed significant, which outperforms the Support Vector Model (SVM) models in terms of Recall and Precision measure.
Study on Ecosystem Structure and Function of Longji Terrace

Longji Terrace, one of the three ancient terraces in China, is a comprehensive tourist scenic spot with agricultural terraces as the main body and integrating natural landscape, cultural landscape and ethnic customs. In this paper, taking Longji Terrace in Guilin as the research object, using geographic information technology to study its ecosystem structure from the aspects of space, components, etc., to explore the material and energy flow patterns of the ecosystem, and to analyze the service functions of the ecosystem, so as to provide basic information for the healthy and sustainable development of the local ecological environment.
Underwater Image Processing to Detect Polymetallic Nodule Using Template Matching

Underwater image processing is extensively different from visual image processing on land, dueto scattering of light, absorption and refraction. It is very difficult to get clear and recognizable underwater images, because of back-scattering, noise, blurriness, turbidity, lower brightness& contrast, etc. This paper deals with exploring techniques for detection of Polymetallic Nodules abundance lying on the sea bed. In order to recognize the polymetallic nodules on the sea bed and to quantify, we propose three stages of processing on optical image captured by AUV by applying simple and effective techniques such as histogram, histogram equalization and CLAHE through template matching approach with the creation of multi-scale templates from available underwater optical images with different lightings. Through this approach, the matching objects are detected with less error and retrieved more appropriate results and quantified the number of nodules available in the given area. As a testing process, hundreds of multi-scale templates are created from different source images in different lightings and tested with source and non-source images which furnished better results by setting the suitable threshold value. The processing was done using existing hardware and no extra electronics devices and sensors were used.
Perception of Deep Multi Tower Head Network on Person Attribute Recognition

Recent approaches in person attribute recognition has achieved outstanding performance but some of the existing major datasets like PETA, Rapv2 has similar images in both train and test set. To address above problems, we prepared a new dataset named HA-TSAI and a new model DeepMTH Net for person attribute recognition. We test the performance of Deep Multi Tower Head network (proposed model) on HA-TSAI which exceeds performance of attention based architectures and other architectures in overall mean accuracy. Deep MTH uses separate pipe lines for each category so that its able to store features of a specific category in a specific pipeline and perform better than other architectures. We also test an attention based model with various backbones and other architectures like ALM, VCC on HA-TSAI, PETA, RAPv2 and PA100k and we release the performance details, with experimental results we prove that Resnet back bone based models tend to perform better than the state-of-art backbone models. Experimental results shows the advantages of our model than the state-of-the-art. The code is made publicly available at https://github.com/ajithvallabai/Deep_MTH_Net.
A Detailed Survey on Machine Intelligence Based Frameworks for Software Defect Prediction

Software defect prediction has an important role to play in improving the quality of programming and helps to reduce the time and cost of programming testing. AI focuses on the advancement of computer programs that can be instructed to develop and change at a time when new information is presented. The capacity of a machine to improve its exposure depends on past results. Machine learning improves the productivity of human learning, finds new things or structures that are obscured to people, and discovers important data in the archive. For this reason, distinctive machine learning procedures are used to remove unnecessary, incorrect information from the data set. Software defect prediction is seen as an exceptionally significant capability when a product project is arranged and a much larger effort is expected to address this intricate issue using product measurement and deformity dataset. Metrics are the link between the mathematical value and are subsequently applied to the product for anticipation of deformity. The essential objective of this study paper is to comprehend the existing strategies for foreseeing programming deformity.
Research on Optimization Method of College Student Management Based on Comprehensive Quantitative Analysis of Employment Quality—Take Heilongjiang Bayi Agricultural University for Example

Colleges and universities are the base and cradle of talent cultivation, the employment quality of college students is the key index to measure the educational achievements of colleges and universities, and student management is an important work throughout the college career, for it has important influence to the student employment quality. In the new situation of changing market structure and talent demand, the scale of colleges and universities is expanding, the enrollment rate of students is increasing, and the employment quality and management methods of college students are facing new challenges. This paper takes Heilongjiang Bayi Agricultural University as an example, based on the comprehensive quantitative analysis of the employment quality of college students to explore the optimization methods and strategies of college student management under the new situation.
PHILOSOPHICAL AND PEDAGOGICAL FUNDAMENTALS OF THE CREATION OF THE MUSEUM OF SCIENCE OF THE JUNIOR ACADEMY OF SCIENCES OF UKRAINE

The article presents the philosophical and pedagogical principles of creating a science museum for young students in the context of the development of scientific education. The global and conceptual transformations in public life over the last 20 years, which contribute to the change of approaches to the development of scientific education both in the world and in the country, are analyzed. The experience of high-tech countries of the world on the education of a new generation of researchers and a role in this process of interactive scientific spaces, possibilities of use of the potential of museums for the realization of educational and scientific programs is revealed. The peculiarities of fundamentally new approaches to the organization of museum spaces for young researchers, which form a research search approach, stimulate the creativity of gifted students in contrast to the reproductive acquisition of knowledge, are considered. The importance of new approaches and methods of museum pedagogy in modern conditions, in accordance with changes in education, psychology of modern students, the requirements of the new Ukrainian school to the level of knowledge of students and competencies of teachers. It is established that in the modern conditions of high-tech society, interactive scientific spaces should become centers of popularization of science, creative coworking spaces of communication for teachers-researchers and playgrounds-experiments for children. It is noted that the philosophical and pedagogical principles for the creation of the Museum of Science of the Academy of Sciences are increasingly relevant in the context of the development of scientific education, education of innovative, creative personality and should be based on general philosophical and cultural values.
Internet of Things from a regulatory point of view

This paper examines the Internet of Things (IoT) as a critical area of interest to regulatory authorities. Although the IoT technologies are driven by market forces, without facilitation from the regulatory authority IoT rollout would be challenging. IoT is a major trend with enormous possibilities, potential advantages, and side effects. Since regulatory authorities have a key role in customer protection, fostering innovation and growth, the outdated or nonexistent regulatory framework for the IoT could be one of the barriers for the IoT long term growth and avoiding the side effects could be difficult to achieve.
Robust Portfolio Selection Using Sparse Estimation of Comoment Tensors

Large investment universes are usually fatal to portfolio strategies optimizing higher moments because of computational and estimation issues resulting from the number of parameters involved. In this paper, we introduce a parsimonious method to estimate higher moments that consists of projecting asset returns onto a small set of maximally independent factors found via independent component analysis (ICA). In contrast to principal component analysis (PCA), we show that ICA resolves the curse of dimensionality affecting the comoment tensors of asset returns. The method is easy to implement, computationally efficient, and makes portfolio strategies optimizing higher moments appealing in large investment universes. Considering the value-at-risk as a risk measure, an investment universe of up to 500 stocks and adjusting for transaction costs, we show that our ICA approach leads to superior out-of-sample risk-adjusted performance compared with PCA, equally weighted, and minimum-variance portfolios.
The Effect of Agency on Cognitive Load in Dyads Learning Physics with a Serious Computer Game

This paper articulates an innovative systems dynamics model of learning based on a predictive cognitive architecture by interrelating six modules: knowledge, affect, cognition, performance, external agents, and context. To test aspects of this model, this paper focuses on cognitive load theory predicting that a manipulation of the learning task can affect at least one of the three types of load (intrinsic, germane and extraneous). More precisely, agency is hypothesized to affect either the intrinsic or extraneous load. Therefore, the goal of this paper is to explore the effect of agency on cognitive load. Thirty-six dyads (1 player and 1 watcher) played a serious game for learning physics for 120 min while dual-EEG was recorded for all participants. Results of time series analysis show that agency (being a player or a watcher) as no effect on the overall cognitive load when the comparison is made either by group (all players versus all watchers), or within a single dyad. Moreover, nor did agency affect instantaneous cognitive load for a vast majority of dyads. Indeed, only four dyads exhibited one or two significant cross-correlations. However, those exceptional cases cannot be generalized. Finer-grained analyses are proposed in the discussion to better explore the role of agency on cognitive load in further research.
Privacy-Preserving Computing Framework for Encrypted Data Under Multiple Keys

With the popularity of cloud computing, data owners encrypt their data with different keys before uploading data to the cloud due to privacy concerns. Homomorphic encryption makes it possible to calculate on encrypted data but is usually restricted to single key. Based on an additively homomorphic encryption supporting one multiplication, we propose a general computing framework to execute common arithmetic operations on encrypted data under multiple keys such as addition, multiplication, exponentiation. Our scheme rely on two non-colluding servers and is proven to be secure against semi-honest attackers. The experimental evaluations demonstrate the practicality of our computing framework in terms of lower computational complexity and communication overhead .
Implementation of AES and ECDSA for Encrypted Message in Instant Messaging Application

Instant messaging was one of the communication technologies that were widely used by the public. The amount of mobile instant messaging users could open up crime opportunities, including message sabotation. The use of cryptography was one of the right methods to maintain data or information security while a communication occurred. Message security was done by combining a symmetric encryption algorithm with a digital signature algorithm. Combination of AES and ECDSA is done as a method of securing messages. AES is used for message encryption, ECDSA is used as an identifier for the sender of the message. Confidentiality and data integrity is guaranteed through encrypting messages in the database and with carrying out digital signatures.
Design of Intelligent Detection and Alarm System for Accident Anti-collision Based on ARM Technology

With the increase of social transportation, traffic accidents are not uncommon, and various disputes often arise in accident responsibility. Law enforcement agencies can only understand the situation at the scene of the accident through the two parties who caused the accident, and the two sides of the accident often have their own opinions. This article designs an anti-collision intelligent detection and alarm system. When cars collide, the collision video recorded during driving can be used as direct evidence, saving a lot of time for both sides of the accident and law enforcement agencies, and improving the efficiency of law enforcement. This article first introduces the research significance of the ARM-based accident collision detection and alarm system, and then deeply analyzes the key technologies used in the system, including the main control A10, embedded system Melis, bus I2C and acceleration sensor BMA250. Then analyze the entire hardware module of the system. After that, it analyzes in detail the initialization and control of the acceleration sensor BMA250 in the software, and the alarm processing process when an accident occurs. Finally, we summarized the design and research of the entire system, analyzed the system's shortcomings, and looked forward to the work that needs to be improved in the future.
Generative Dialogue System Using Neural Network

A conversation between human beings and computers is considered one of the most long standing problems in computer science; involving interdisciplinary techniques from the field of information retrieval, machine learning, natural language processing and artificial intelligence. It is an Interactive Entity that automatically makes conversations to exchange seamless information between people with little knowledge of the computer. The challenges lie in how we respond to people and to maintain a continuous conversation. This research implements a generative model-based method for speech production. This research is for the development of a dialogue system that produces speeches using the recurrent neural network and its associated memory unit.
Cloud Computing Transformation: Considering Adoption of Mixed Security

Companies built IT Center and operated an on-premise system because IT is a kind of asset. But the pace of change in management is accelerating and the pressure on cost efficiency in the IT sector is increasing. Cloud computing is considered to secure system reliability and reduce management costs. IT resources can be efficiently utilized and paid for used. Cloud computing for each service unit was considered as a way to meet an efficient utilization of IT resources without compromising existing system reliability when providing new services. Cloud system enables real-time orchestration, and has excellent security. As a hybrid method that implemented cloud computing while maintaining the existing infrastructure, it was possible to operate an uninterrupted system as a result of system construction Therefore, companies were deploying and migrating their intranet services to cloud servers. However, it is also true that cloud migration makes system complexity increase, and system reliability is compromised by the increase of management points especially securities. It is necessary to learn about practice-oriented cloud migration and how to maintain mixed security policies when maintaining on-premises systems is effective.
Container Memory Live Migration in Wide Area Network

Container memory live migration is an iterative process of memory pages transmission, which is challenging in Wide Area Network (WAN) because the migration often takes too long to finish or even fails due to limited bandwidth. To facilitate container memory live migration in WAN, a key point is to reduce the size of data transmission. In light of this, we leverage popularity values to express the refresh frequency of container memory pages, and propose a new container memory live migration method based on dirty page popularity perception. Our method calculates memory dirty page popularity values and uses these values to perform adaptive memory transmission during migration. We illustrate the proposed method with a detailed discussion on the key algorithm and data structure, and we also implement a prototype system for evaluation. Experimental results reveal that our method improves the container memory live migration efficiency by reducing the total transmitted data with a negligible performance overhead.
AI Caravan Moves On. Does It Need Copyright Incentives?

The Article aims to ascertain whether there is a genuine demand for granting copyright incentives with respect to AI-results.At first, the Article analyses whether AI-assisted and AI-generated objects are subject to copyright protection and, drawing onthe anthropocentric nature of copyright, answers in the negative. This conclusion does not apply when AI remains merely a tool in thehands of a human author.Then the Article evaluates arguments for and against the change of copyright law to provide AI with copyright incentives. Giventhe current level of AI, the Article finds granting copyright incentives and, consequently, protection to AI-assisted and AI-generatedobjects as premature and unjustified, at least now.The Article shifts the discussion on incentives from copyright to sui generis right. Such regime is expected to encourage deve -lopment of the AI industry as well as disincentivize unfair practices.
A Low-cost System for High-frequency Solar Imagery and Power Data Acquisition

Abstract With advances in solar energy research and increasingly accurate forecast techniques, intermittency no longer stands as a barrier to the adoption of solar energy. Coupling reliable data and knowledge on the inherent variability of the solar resource with advanced learning and forecast models, renewable energy can take an even bigger role in today’s energy paradigm. The objective of this work is to develop and test a low-cost data acquisition system able to provide relevant data for solar energy forecast models. The results yielded from the performed tests indicate high correlation between image derived attributes and power measurements 20 s ahead.
Blind method for noise estimation using frequency domain Natural Scene features

Noise is a commonly encountered distortion which generally affects the high frequency regions in images. It can lead to masking effect when the level of noise is high. To distinguish between natural and noise-afflicted images and to quantify the level of degradation, several statistical features have proved to be noteworthy. In this work, a method is proposed utilizing divisive normalization based on Natural Scenes Statistics (NSS) model which closely relates to human visual perception. It extracts features from spatial as well as frequency domain. Extracted features are used to drive the Machine Learning (ML) model Gaussian Process Regression (GPR) for mapping of the scores. Several methods have been proposed till date which are compared on three databases LIVE, CSIQ and TID2013. The trained model gives the highest correlation to Human Visual System for assessment of noise in natural images.
Dense 3D organ modeling from a laparoscopic video

Laparoscopic surgery provides for patients such advantages as a small incision range and quick postoperative recovery. Unfortunately, surgeons struggle to grasp 3D spatial relationships in the abdominal cavity. Methods have been proposed to present the 3D information of the abdominal cavity using AR or VR. Although 3D geometrical information is crucial to perform such methods, it is difficult to reconstruct dense 3D organ shapes using a feature-point-based 3D reconstruction method such as structure from motion (SfM) due to the appearance characteristics of organs (e.g., texture-less and glossy). Our research solves this problem by estimating depth information from laparoscopic images using deep learning. We constructed a training dataset from both RGB and depth images with an RGB-D camera, implemented a depth image generator by applying a generative adversarial network (GAN), and generated a depth image from a single-shot RGB image. By calibration with a laparoscopic camera and an RGB-D camera, the laparoscopic image was transformed to an RGB image. We generated depth images by inputting the transformed laparoscopic images into a GAN generator. The scale parameter of the depth image with real-world dimensions was calculated by comparing the depth value and the 3D information estimated by SfM. Consequently, the density of the organ model increased by back-projecting the depth image to the 3D space.
Secure Image Parameters Comparison for DES, AES and Rubik’s Cube Encryption Algorithms

This chapter compares the statistical analysis tests of the Data Encryption Standard, Advanced Encryption Standard and Rubik’s cube encryption algorithm. Cryptography means keeping the information secret, hidden or concealed—the efficient transmission of image and security based on cryptography. Statistical analysis tests are used to quantify the quality of encryption techniques. The quality of the encrypted images is measured by using metrics, namely, Histograms, Number of Pixel Changing Rate, Unified, Entropy, Correlation Coefficient Visual inspection and Speed Test. In the image encryption algorithm, the concept of entropy analysis was introduced by O. Edward. For Rubik’s cube, the value of entropy is very close to the theoretical value of 8-bits in the case all the three images. The ciphertext images obtained using the Rubik’s Cube encryption algorithm has values of the correlation coefficient are very close to zero in all directions, which means that the adjacent pixels are very weakly correlated.