Reviewerly/exHarmony huggingface dataset:
Medical Image Segmentation Based on Combination Method

Abstract This paper studies the data segmentation of medical images based on compressed sensing. We do not use image data as interpolation data, but as constraints to construct surface patches with quadratic polynomial approximation accuracy, study the properties and correlation of pixels, and take these edge points as constraints, and map the discrete image data into continuous mathematical functions, so that the continuous function or point data not only has the shape recommended by the image data, but also has a higher segmentation accuracy, and then realize the fine segmentation of images, which provides a new research idea to solve the difficult problems of spatial data representation, and has good theoretical significance and application value.
A Preliminary Research on Standardization Service Mode for Small-and-Medium-Sized Enterprises

With weak awareness of standardization work, small-and-medium-sized enterprises (SMEs) are currently less initiative in acquiring standardization services. What SMEs need is the guidance regarding standardization-related services. It is necessary to offer SMEs attractive service products to draw their attention to standardization-related capacities and guide them to accept standardization. This research is aimed to build service bases for SMEs and identify the supply-demand relation of standardization-related services and their forms, so it is of great significance in three dimensions. First, enterprises will no longer accept services passively or without clear objectives, with capacities in standardization stimulated, explored and developed. Second, it helps to further standardize and improve the standardization-related service sector's service capability and enhance the influence of standardization service institutions in business circles. Third, for governments, the new mode may further accelerate the development of the standardization service sector.
Peer Assessment Based on the User Preference Matrix

Peer assessment not only provides a solution to inefficient teacher-student interaction between the students and teachers at universities, but can effectively improve the students' learning efficiency. However, students are seldom engaged in peer assessment tasks and the scores they get from peer assessment differ from those given by the teachers. An automatic ranking method for English essays based on the user preference matrix and the stochastic gradient descent method was proposed in this study. Experiments showed that this new method could improve the performance of the stochastic gradient descent method when the credibility of the student's evaluation was considered. Teachers can obtain the rankings of students' essays by analyzing a few peer-assessment scores obtained by this method and optimize their teaching strategies accordingly.
Cybersecurity policy in Nigeria

Since the late 1990s, Colombia has been growing its economy, has been an active protagonist of the 2015 Paris summit on climate change, has been able to sign an historic peace agreement with the FARC-EP (Revolutionary Armed Forces of Colombia-Popular Army) in 2016, and has been successful in its commitment to join the OECD. On cybersecurity issues, the Colombian landscape is dominated by: the country’s long-term struggle to achieve internal stability and sustainable economic growth; its positioning on the international stage; and its relationship with International Organizations. Three characteristics that Mohammed Ayoob or Carlos Escude would surely understand as typical of “third world” states. From the second half of 2000 onward, Colombia has been actively looking for international partners to fight against cyberthreats. In this context, the Colombian predicament could be described as such: To increase the general efficiency of the Colombian economy and reassure foreign investors, the state decided to modernize its administration privileging the use of the e-administration.
Wordnet – a Basic Resource for Natural Language Processing: The Case of plWordNet

This paper presents a wide scope of wordnet applications on the example of applications of plWordNet – a wordnet of Polish. Wordnets are large lexical-semantic databases functioning as primary resources for language technology. They are machine-readable dictionaries. Thus, they are indispensible for tasks such as basic flow of text processing, text mining, word sense disambiguation, information extraction and retrieval. On a larger scale, wordnets are used in research, education and business. In this paper a few examples of specific plWordNet applications are described in detail.
Continuous particle swarm optimization

An alternative continuous particle swarm optimization model will be presented in order to improve the standard particle swarm optimization. The main idea is to extend the discrete formulation in the continuous time space describing the bird flying motion evolution as a continuous time function, solution of a Cauchy problem defined by a second order ordinary differential equation associated with random initial conditions. A convergence analysis of the algorithm will be presented as well as a comparison with the discrete algorithm in terms of performance and computational time.
An Environmental Characterization Method for Monocular Vision Obstacle of Rotor Aircraft

Abstract Combining the structure of coding and anti-coding in the field of deep learning, we explore a method to represent the complex environment of four rotor aircraft. First, we use this structure to explore the process of dimensionality reduction and reconstruction of high dimensional image data. Second, combining this structure, we use the concept of transposition convolution to propose a network structure for depth distance estimation using monocular vision images. Finally, we get the direct physical feature information used to describe complex environment.
Multiscale and Nonlinearility Convolutional Regression for Locating the Eye’s Pupil Center

Pupil detection has been an active research topic in recent years due to its usability in many areas including interaction design and medical diagnosis. Many existing methods are accurate in pupil localization; however, many of them were designed and tested under specialized lighting conditions. Furthermore, there are challenges to cope with images whose color of the iris and the pupil are both dark. In this work, we proposed deep regression models to locate the eye's pupil center in target groups whose iris and pupil are both dark in color and images were acquired in a typical lighting condition. The proposed neural network model concurrently utilizes features from multiple convolutional layers for final regression. In other words, features from different layers which contain different degrees of nonlinearities are concatenated so that a neural network model can explicitly employ these features together for predicting the location of the eye's pupil. Ablation analysis indicated that these model characteristics were essential for robustness. All experimented models were trained and validated against 2,500 eye images from the MPIIGaze dataset. Additional 2,515 test images were obtained from the MMU2 and BioID datasets to evaluate robustness of the method. Our experiments showed that robust and accurate localization of the pupil center could be achieved over datasets whose image were acquired under non-specialized lighting conditions. Our best model had average pixel error of 1.11 pixels, and had error within two pixels for 95.5 percent of test samples.
Computing Band Structures of Randomly-Generated 2D Photonic Crystals with Extreme Learning Machines

We designed Extreme Learning Machines (ELM) for calculating band structures (BST) of randomly-generated bi-dimensional (2D) photonic crystals and demonstrated fast-training ELM models able to perform fast and accurate BST computations.
Improved Logistic-Sine Chaotic Map based S-box and Bit-Plane Scrambling Encryption Schemes Performance on Compressed ECG Data

Telehealth technology, especially Telehealth Electrocardiograph (ECG), is currently developing. Along with the development of Telehealth ECG, data transmission security on the system becomes very important due to the high level of sensitivity and confidentiality of health data. ECG data encryption approach based on digital image encryption schemes is chosen because it produces a high degree of difference with initial data and low computational complexity levels. This is based on the assumption that parts of ECG data signals have a high degree of correlation. We proposed two digital image encryption schemes, such as the bit-plane scrambling and Double Chaotic S-box, on ECG data. The Double Chaotic S-box encryption scheme proves to be superior to the bit-plane scrambling encryption scheme in the aspect of execution time and keyspace. Having a pretty large keyspace of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">268</sup> and compared to the baseline encryption method, namely SIT, which needs 343.93s to encrypt, the Double Chaotic S-box method needs the only 1.45s to encrypt. This result shows that the proposed method enormous potential as a fast ECG encryption scheme with a high-security level.
Application of Fish-swarm and Genetic Algorithm in Optimization of Chaotic Image Encryntion

The security of digital images has attracted much attention. In order to reduce the correlation of image, increase the key space and improve security, a new image encrytion algorithm based on fish-swarm and genetic algorithm and chaotic function is proposed in this paper. The algorithm applies fish-swarm and genetic algorithm to chaos scrambling images for optimization. Artificial fish-swarm algorithm begins to converge quickly, then has slow convergence. It is insensitive to initial value and can avoid local extremum. It is robust for genetic algorithm. But genetic algorithm is easy to fall into local extremum and sensitive to initial value. This paper proposes to combine artificial fish-swarm algorithm with genetic algorithm and optimize chaotic image encryption algorithm. Firstly, the chaotic function is used to generate multiple encrypted images as the initial fish swarm of the fish swarm algorithm, then the selection, crossover and mutation of genetic algorithm are integrated into the improved artificial fish-swarm algorithm to optimize the chaotic encrypted image iteratively to obtain the best encrypted image. The simulation results and security analysis show that the algorithm has the advantages of low correlation, higher security and stronger ability to resist statistical analysis attacks and differential attacks comparing with other image encrytion algorithms.
Predicting Entity Relationships Using Hidden Markov Random Fields. An Application to Conversion of Greeklish Text

We present results of a new optimal selection algorithm, applied to the transliteration of greeklish text. Our method uses existing greek text samples to “learn” greek word correlations by applying a Hidden Markov Random Field model to the underlying word relationships. The model “learns” the semantic correlations between word pairs in the training samples. When presented with trial greeklish text, the model uses this knowledge to disambiguate between all the possible transliterations of each greeklish word, by selecting the transliterations that have the highest learned correlation. Trials demonstrate that a model trained in text focused in a single topic can achieve up to 100% accuracy in sample sentence transliteration.
Keluthi Roomba - A Robotic Hand for Cleaning Sewage

The manual scavengers are highly exposed to poisonous gases such as methane, hydrogen disulphide during the process of sewage cleaning. As a result they are subjected to health related issues such as bronchitis, skin cancer, respiratory problems the worst being loss of life. To automate the process of manual scavenging, this project proposes a Robotic hand which is controlled by a Mobile application. This system automatically clears the blockages in the underground water flow. This system is user-friendly and can be used for cleaning the sewage along roads and the underground sewage.
Data Augmentation for improving Hate Speech Detection on Social Networks

The recent growth of social networks led to an increase in the dissemination of hate speech online and, as a consequence, studies about hate speech detection have emerged. Part of them are based on supervised learning techniques, which require large labeled databases, however this is still a scarce resource. Most of the existing databases on hate speech consist of texts in English. Although there are some databases in Portuguese, they are rather small with few examples, a factor which can limit both the performance of simple deep neural networks and the usage of more complex architectures. In order to work around this setback, this paper analyses the usage of Data Augmentation in texts to improve the training performance of recursive neural networks (LSTM) and convolutional neural networks (CNN) applied to the hate speech detection task. Data Augmentation is a regularization process used on deep neural networks to avoid overfitting. These techniques are common in the field of computer vision, however, due to the complexity of natural languages, this process is not as frequently used in tasks involving texts. In this paper, we experimented on a public hate speech database in Portuguese using Data Augmentation techniques such as text generation from the original database to enhance the results. The experiments show an improvement in the results, demonstrating that the techniques are promising.
Cross-Media Retrieval Based on Canonical Correlation Analysisand Decision Tree

Abstract The retrieval of approximate data from massive multimedia data is the key of computer science research. With the explosive growth of data scale, data retrieval needs to face the overlapping massive multimodal data called “dimension disaster”, and the traditional retrieval methods appeared to be inefficient. The cross-media retrieval with canonical correlation analysis (CCA) is a kind of way to map different media features to the largest correlation isomorphism subspaceand then compare the similarity between cross-media data in this subspace. However CCA is a linear model and it is difficult to map the lower data to the higher semantic. This paper proposed a cross-media retrieval method based on CCA and Decision Tree to solve the problem. CCA was uesd to depict the correlation between image and text feature, Decision Tree was uesd toapproach to feedback and repeatedly adjust the correlation. Experiments on the Wikipedia text-image datasetsverified that the Tree-CM model can mine the complex correlation between cross-media data and has better performance than other state-of-the-art models.
Going Big and Deep: Using Convolutional Neural Network to Leverage Training Data from Multiple Domains for Cross-Domain Sentiment Classification on Product Reviews

Training a classifier for sentiment polarity detection in product reviews when labeled data is not available for a particular domain poses a challenge, which can be addressed through cross-domain sentiment analysis. We experimented with Convolutional Neural Network (CNN) to learn sentiment polarity (positive or negative) from labeled data available in many different source domains and test its performance on a target domain that it is not trained on. Extensive experiments were conducted on 14 different domains using Amazon product reviews. Our preliminary findings show that cross-domain CNN models trained with multiple source domains achieved accuracy of above 80% across all the domains and outperform the in-domain models trained using limited labeled data from the same domain. In fact, the cross-domain CNN models demonstrated better performance when a larger number of source domains are used for training. Therefore, going deep and big is a promising direction to explore for cross-domain sentiment classification.
Research on Rayleigh Scattering and Brillouin Scattering of Bidirectional Wavelength Division Multiplexing System

Abstract In order to study the influence of Rayleigh and Brillouin scattering on the performance of bidirectional Wavelength Division Multiplexing (WDM) system. On the VPI simulation platform, a two-way WDM simulation system was built. Rayleigh scattering and Brillouin scattering were generated by setting the parameters of the optical fiber module and system parameters such as Brillouin Scattering, Rayleigh scattering, Rayleigh Distortion, and Rayleigh Distortions. Then, the frequency spectrum of the received signal and the bit error rate were observed and analysed. Experimental results show that Rayleigh and Brillouin scattering will cause the performance of the two-way WDM system to degrade. By separating the back propagation channel, the impact of these two effects on the performance of the two-way WDM system can be reduced.
Enhancement of Query Execution Time in SPARQL Query Processing

During couples of decades, the popularity of Semantic Web is increased in both academic and industry. RDF is a data model standardized by W3C for the Semantic Web and a query language used for retrieving the RDF data is SPARQL query. Most of the real-world applications has widely used RDF as a standardized data format, so that efficient data storage and query processing techniques for the RDF data need to be considered. In this paper, an indexing structure and query processing technique are developed to enhance the execution time of three different types of SPARQL queries such as conjunctive, optional, and alternative queries. As the RDF data is formed in graph structure, the indices are constructed by extracting the subgraphs around each vertex based on the edges nature of the vertex. Although the size of query is large especially in conjunctive query, our proposed graph-based index and query processing method can help to save the query execution time. Experimental results show that our proposed method works well for all three different types of SPARQL queries and outpaces the competition at processing the conjunctive queries.
A ciascuno la propria parola. «Educare linguisticamente all’originalità» in Giuseppe Lombardo Radice

Is it possible to re-read the contribution of Giuseppe Lombardo Radice in an intercultural key? Like other important figures, the famous pedagogist was immortalized with a book—or rather a classic—that in this case explains and clarifies important topics related to education and schooling. Drawing inspiration from the enlightened intuition presented in Lezioni di Didattica e ricordi di esperienza magistrale (1913), the article explores the theme of language education as a means to foster originality, the pedagogical principles of which still provide important insights into the contemporary debate on issues related to interculturality and inclusion in today's heterogeneous and multilingual school.
Local patch vectors for rotation-invariant and noise-tolerant texture description

A new texture descriptor is introduced in this paper. In comparison with the traditional LBP-based methods, the proposed method incorporates more global information in Local Patch Distance Vectors (LPDV) and Local Patch Angle Vectors (LPAV) which are the results of distance measurement between vectorized patches. We show that in traditional LBP-based descriptors, with a few levels of quantization, different texture patterns could have exactly the same codes, while in the proposed method, different patterns would have different vectors with continuous entry values. Following LBP-based methods, we perform circular shifting on the LPDVs for rotation invariance, but we show that the circular shifts in LPDV-LPAV do not produce similar codes as in LBP-based methods. For evaluating the proposed method in texture description, two well-known texture datasets are used for classification. Our experiments show that in the presence of noise, the proposed method outperforms the traditional LBP-based methods and in noise-free condition the performance of the proposed LPDV-LPAV is very close to the results of previous works.
Eigenface based Facial Expression Recognition

In this paper, the facial expression recognition system is described based on Eigenface. Facial expression recognition has become increasingly common in several applications in the fields of video conferences, surveillance, financial services, health treatment, and access control system. The performance of recognition depends mainly on several challenges. Lighting variations are also the most challenging in the recognition process. In this system, illumination invariant techniques applied for decreasing the lighting affected faces as a preprocessing stage. Eigen faces are used as feature extraction step. K-NN and Multi-SVMs classifier are used to identify the facial expression and to test the performance of the proposed system. The standard dataset of JAFFE is used in the experiments and 84.29% accuracy is achieved for facial expression recognition. The accuracy of the framework is contrasted with the state of the art methods tested on the JAFFE database.
IoT-based Architecture for Automatic Detection of Fall Incident using Accelerometer Data

Fall is an unintentional incident that could happened in our daily life. For the elderly, fatal fall incident might increase the risk of death. There is a need to quickly do the first aid after fall incident occur. IoT based architecture made it possible to monitor fall incident remotely. The monitoring device records the activity and object movement using tri-axial accelerometer sensor attached to user’s waist. The system implemented simple thresholding technique based on total acceleration recorded over time. Various scenarios were performed in order to test the system including normal daily activities and fall incident. Using sensitivity and specificity measurement to evaluate the system, the proposed system achieved the value of 98% and 96% respectively.
Image detail extraction via dark region approximation

Abstract In this paper, we propose a simple but effective approximation—dark region approximation (DRA) to extract details from gray-scale images. The DRA is based on an assumption: there is little illumination in the dark regions of visible images. The Retinex model using the DRA is developed to improve the performance of preserving details from the dark regions in gray-scale images during reflectance estimation. Then, Gaussian field criterion is introduced to construct the objective function which could be solved by quasi-Newton method, in order to estimate the reflectance via the DRA-based Retinex model. The reflectance is considered as the final result of image detail extraction. Experiments on a variety of gray-scale images demonstrate the power of the DRA and the superiority of our method.