Reviewerly/exHarmony huggingface dataset:
Readiness of teachers for designing a humanistically oriented educational environment of a university

In the current article, the assumptions are proposed based on the analysis of theoretical sources and on the perspective of considering the semantic field of a humanistically oriented educational environment as a complex of elements that reveal the willingness and humanistic position of subjects. The research hypothesis lies in the assumption that such components of the “Self-concept” as creativity, intentionality, authenticity, and the desire for self-actualization contribute to the formation of teachers’ readiness for designing a humanistically oriented educational environment. The purpose of the study is to study the components of teachers’ readiness for constructing a humanistically oriented educational environment. The results of the experimental study testified to the expansion of semantic boundaries and awareness of the subjective significance of higher values, as well as to the willingness of teachers to design a humanistically oriented educational environment.
Finding All Minimal Maximum Subsequences in Parallel

A maximum contiguous subsequence of a real-valued sequence is a contiguous subsequence with the maximum cumulative sum. A minimal maximum contiguous subsequence is a minimal contiguous subsequence among all maximum ones of the sequence. We have previously designed and implemented a domain-decomposed parallel algorithm on cluster systems with Message Passing Interface that finds all successive minimal maximum subsequences of a random sample sequence from a normal distribution with negative mean. The parallel cluster algorithm employs the theory of random walk to derive an approximate probabilistic length upper bound for overlapping subsequences in an appropriate probabilistic setting, which is incorporated in the algorithm to facilitate the concurrent computation of all minimal maximum subsequences in hosting processors. We present in this article: (1) a generalization of the parallel cluster algorithm with improvements for input of arbitrary real-valued sequence, and (2) an empirical study of the speedup and efficiency achieved by the parallel algorithm with synthetic normally-distributed random sequences.
Conclusions and future direction of Mobile Medical Information System (mMIS) development

The main goal of this chapter is to conclude this book and introduce its future directions. Therefore, the main contributions and implications of this book are reviewed in this chapter along with concluding remarks. Moreover, limitations and future directions of this book are stated in this chapter which include adding Habit-Change assessment and security assessment in the assessment phase, database design for big data in the design phase, and ergonomic evaluation in the prototype phase of the proposed Mobile Medical Information System Development Life Cycle (mMIS)-DLC.
Lightweight code self-verification using return-oriented programming in resilient IoT

The internet of things (IoT) represents the widespread deployment of devices with sensors in the physical world that connect with one another via the Internet. Protecting these IoT nodes from unapproved changes is essential to their unhindered functioning. Any possible leeway in the code creates vulnerability and allows an adversary to intervene and mount an attack on a victim’s system. This chapter establishes novel methods for protecting IoT devices from being compromised. We do this by turning offensive intrusion techniques into defensive weapons using return-oriented programming (ROP) gadgets. We developed a prototype IoT testbed at our laboratory consisting of IoT nodes, gateways, and a server. We also developed an approach using ROP to prevent code from being tampered with. Our approach is a two-layer code-protection mechanism using ROP and code verification via checksumming. Both layers guard each other and protect sensitive parts of a program. This mechanism strengthens the defense shield of IoT nodes. The experimental results show that the performance overhead to mount this type of security is negligible. It is infeasible that an adversary can bypass both layers of security at the same time. If an adversary tries to bypass the security shield then one of the layers alerts the administrator, therefore increasing the cost of exploitation.
Natural Neighbor Algorithm for Breast Cancer Diagnosis

Abstract Machine learning to diagnose breast cancer is an important, real-world medical problem. As one of the most popular machine learning algorithm, K-Nearest Neighbors (KNN) algorithm is widely used in breast cancer classification and can provide high classification accuracy and effective diagnostic capabilities. However, selection of parameter K is still an unsolved problem for K-Nearest Neighbors (KNN). To address the problem, we introduce a novel neighbor form, Natural Neighbor (NaN), which is obtained adaptively by its search algorithm. We firstly use a noise filter, which called edited natural neighbor algorithm (ENaN), to eliminate the noises and global outliers of Wisconsin’s breast cancer prognosis dataset. Then, cleaned data set and natural neighbor algorithm are used to construct a new non parameter diagnostic system. The main advantages of the proposed algorithm are that it does not need any parameters and can maintain high classification accuracy. Experiments show that the classification accuracy of proposed method is similar to the highest classification accuracy obtained by traditional K-Nearest Neighbors algorithm with different K values.
Comparison of Text Mining Feature Extraction Methods Using Moderated vs Non-Moderated Blogs

Online social media is being widely used by social scientists to study human behavior. Researchers have explored different feature extraction (FE) and classification techniques to perform sentiment analysis, topic identification, etc. Most studies tend to evaluate FE and classification methods using only one particular class of datasets---well-defined with little/no noise or with well-defined noise. For instance, when the datasets under study have different noise characteristics, various FE and/or classification methods may fail to identify a given topic. In this paper, we fill this gap by quantitatively comparing multiple FE methods and classifiers using three different datasets (two moderator-controlled blogs and one single-authored personal blogs) related to Autism Spectrum Disorder (ASD). Our result shows that no particular combination of FE and classifier is the best overall, but choosing the right ones can improve accuracy by over 30%.
Hyper Text Transfer Protocol for Securing Packet Inspection in Intrusion Prevention System Device

Abstract Analysis of inspection method packet HTTPS or Hypertext Transfer Protocol Secure in Intrusion Prevention System (IPS) device is to explore the methods that IPS do in analyzing HTTPS Paket. Hypertext Transfer Protocol Secure or HTTPS is data communication between client and web server in essentially is end to end secure connection. In network security, IPS device function is inspection every packet that enters and exits to internal network including packet secure connection. HTTPS packet and others secure connection packet are running packet in the network with did not plain text, but all packet was encrypted from source. Packet encryption make IPS difficult to inspection the packet and knows what the content inside the packet. One of the methods IPS to inspection the HTTPS packet is to decrypt and re-encrypt. This method makes two sections end to end secure connection. The first is secure connection between IPS and client using self-sign digital certificate. And the second is secure connection between IPS and web server using digital certificate from trust certificate authority (CA). So the method makes HTTPS not end to end secure connection directly between client and web server in essentially HTTPS. However, the method succeeded in making IPS able to carry out inspections and find out the contents of the packet.
Audio Lossless Encoding with Adaptive Context-Dependent Constant Component Removing

In this paper the lossless audio compression using context-dependent constant component removing (CDCCR) was presented. The proposed solution was built from a small cascade of linear predictors with adaptively selected coding parameters, ending with the already mentioned CDCCR block and Golomb encoder.
Formalization of Complex Objects with using the Expert Estimations

A method for formalizing a model of a complex object using expert data is proposed. A complex object is represented as a set of interacting components (factors). For numerical characteristics of individual interactions of components, the marks (ball estimates) are used, which, depending on the object of study, can be interpreted as the degree of risk, time, material resources, the degree of influence in the interaction of components. For each of these types of estimates the corresponding computational operations are applied. The results of the calculations are the estimates of the system response of the object under study to external influences. The process of counting forms a transitive closure of the primary estimates. In calculating of interaction degree, the multi-valued logic variables and operations are used. Examples of different ways of formalization are given.
Gesture localization in the test mode in the integral system of sign language training

Abstract The article is devoted to the integral system of sign language training. The operation modes of the system based on 3D computer avatar are considered. The user’s feedback using the system is carried out in the test mode using a standard webcam. The user must demonstrate the word or dactyl specified by the system. The problem of gesture localization in the user test mode is considered. The algorithm for the rapid clustering of human hand gestures in a video sequence is proposed. The background removal is based on finding of the inter-element difference for the frames of a video sequence, the selection of the key frames and calculating difference frames. The use of the modified histogram method allows localizing the gesture. The experimental results show the stable operation of this algorithm in different light conditions. This approach allows using developed system both on stationary computers and on mobile devices.
Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations

The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.
Exporting Rain-Fall optimization concepts to Artificial Bee Colony

In this paper we take inspiration from Rain-Fall optimization metaheuristic (RFO) to modify other metaheuristics. An interesting concept of RFO is its merit list. We argue that this concept could be successfully exported to other metaheuristics. As a concrete case study, we have implemented a modified version of Artificial Bee Colony (ABC) including merit lists. The experiments we have conducted with a concrete wellknown benchmark show that it can help improving the quality of the solutions found by ABC.
The Development of Students’ Cross-cultural Competence in the Conditions of Transitive Society

The study is devoted to the substantiation of the need for the development of students’ cross-cultural competence at the time of transitive society: it demonstrates that the current stage of social development has an actualizing effect on the psychology of the individual due to the fact that the society and its individual representative resemble the image of two-faced Janus, when one side of the person is turned to the past and the other – to the future. Under these conditions cross-cultural competence as an activity-oriented concept, the aim of which is a social adaptation of the student’s personality through the acculturation in the multicultural world, becomes a psychological and pedagogical imperative. It is noted that the cross-cultural competence in the era of postmodernism and globalization contributes to the education of students in the spirit of openness, pluralism of tastes, opinions and tolerance; at the cognitive level it is a significant expansion of cognitive space, a better understanding of both personal and other cultures. The Methodological Base of the Study: the humanistic paradigm in education and social Sciences, system, activity, cultural and personality-oriented approaches. The Research Methods: analysis and synthesis, systematization and classification, observation and generalization.
The implementation of ATDD and BDD from Testing Perspectives

Software testing is one of the stages in software development that aims to ensure that the software is built to meet the specifications. Selection of selective test cases has a great chance of finding failure. Black box testing approach is done based on the requirement specification where this approach does not pay attention to the program code but the specification of a software. This approach can be used for testing a software using ATDD (Acceptance Test Driven Development) and BDD (Behavior Driven Development) methods. ATDD is a method of building a software created based on agile principle, acceptance test created by customer, developer and tester. BDD is a growing agile development approach in recent years. Behavior Driven Development (BDD) is built on Test-Driven Development (TDD). This Final Project focuses on the application of ATDD and BDD methods to testing the final three projects of IT Del students tested using the Roboy Framework and Cucumber Framework to find out whether these two methods are effective and be able to improve the software development process. The results obtained are that the application of ATDD and BDD methods are effective and helping to remove the error as soon as possible.
A Hybrid Framework of Emotion-Aware Seq2Seq Model for Emotional Conversation Generation

This paper describes RUCIR’s system in NTCIR-14 Short Text Conversation (STC) Chinese Emotional Conversation Generation (CECG) subtask. In our system, we use the Attention-based Sequence-to-Sequence (Seq2Seq) method as our basic structure to generate emotional responses. This paper introduces (1) an emotion-aware Seq2Seq model and (2) several features to boost the performance of emotion consistency. Official results show that our model performs the best in terms of the overall results across the five given emotion categories.
Combined Model to Extract Entities and Relations Based on Sharing Parameter

This paper uses the depth learning model of sharing parameter to extract entities and relationships. The problems of pipeline model error propagation and ignoring the internal relationship between subtasks, a parameter sharing model is proposed, which uses graph convolution neural network based on syntax to capture the structural information of text. The model combined with the parameter sharing mode will be introduced in detail. The motivation of designing the model, the special labeling strategy, the structure of the model, the experimental setup and the analysis of the experimental results will be introduced respectively. From the experimental results, it can be seen that the hybrid model achieves better results in the public data set.
Person Re-identification with Patch-Based Local Sparse Matching and Metric Learning

Recently, patch based matching has been demonstrated effectively to address the spatial misalignment issue caused by camera-view changes or human pose variations in person re-identification (Re-ID) problem. In this paper, we propose a novel local sparse matching model to obtain a reliable patch-wise matching for Re-ID problem. In particular, in the training phase, we develop a robust Local Sparse Matching model to learn more precise corresponding relationship between patches of positive sample image pairs. In the testing phase, we adopt a local-global distance metric learning for Re-ID task by considering global and local information simultaneously. Extensive experiments on four benchmarks demonstrate the effectiveness of our approach.
On solutions for the defects of Tshivenḓa practical orthography

In Tshivenḓa, there are clear discrepancies between the written and spoken forms of words. This affects the writing and compilation of dictionaries because sometimes a trigraph such as tsh has two distinct pronunciations, namely [tʃh] and [tʃ’] as in mutshila [mutʃhiſa] (tail), and kutshila [k’utʃ’iſa] (small tail). Similar variance in the pronunciation of ts, namely [tsh] and [ts’], can also be observed in words like tsimbi [tshimbi] (iron, metal or bell) and kutsimbi [k’uts’imbi] (small iron, metal or small bell). The dissimilarities in the pronunciations of these sounds by individual speakers make some of the examples such as tswina [tshwina] (lizard) and kutswina [k’utsw’ina] (small lizard) confusing. Even among native speakers of the language, a number of Tshivenḓa speech sounds are often mispronounced as speakers tend to pronounce the same word differently.
A Semantic Model to Fight Social Exclusion

This work presents a semantic model meant to help with the identification and prediction of individuals at risk of social exclusion. The model is based on the self-sufficiency matrix, a tool that evaluates a person's self-sufficiency in different areas, and that is used by Barcelona's City Council. Existing data sources can then be mapped to this model, in order to analyze, query, and visualize the data.
Excellence acmeological education for the economics of the future

The article provides a theoretical analysis of the organisation of "excellence" education in school practice. It presents sound implementation principles of "excellence acmeological" approach to teaching in rural schools and necessary organizational and pedagogical conditions of quality education with the involvement of highly qualified teachers to ensure the educational process and increase the motivation of students to study. The paper proposes organizational and technological implementation model of "excellence acmeological" teaching in rural schools. An objective assessment of the achievement of the expected results is ensured when educational organizations turn to qualified assessment centers in order to assess the level of training among students. Much attention is given to the problems of formation and evaluation of competencies of general secondary education graduates. The excellence acmeological idea of development and self-development directs an individual to become a successful person and a highly qualified specialist.
Structure-Preserving Guided Image Filtering

Guided filter behaves as a structure-transferring filter which takes advantage of the guidance image. Nevertheless, it is likely to suffer from structure information loss problem and artifacts would be introduced in practical tasks, e.g., detail enhancement. We in this paper propose to deal with the structure loss problem. We modify the original objective function and develop a re-weighted algorithm to proceed the filtering process iteratively. The proposed filter inherits good properties of guided filter and is more capable in avoiding structure information loss. Many vision tasks can be benefited from the proposed filter. Few applications we outline include flash/no-flash image restoration, image dehazing, detail enhancement, HDR compression, and image matting. Experimental comparisons with relative methods for these tasks demonstrate the effectiveness of the proposed filter.
Estimation of multiple parameters using algorithms expectation maximization

Abstract In this paper the strategy of the expectation maximization algorithm is developed in order to solve the problem of maximization of the likelihood which becomes a complex problem when it is expected to estimate multiple parameters at the same time. All this will be achieved with the introduction of basic concepts of statistical estimators and will be verified by simulations carried out in the MATLAB software.
Neuromorphic Photonics for Deep Learning

Co-integrated neuromorphic photonic and electronic processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. We discuss neuromorphic photonic systems and their application to deep convolutional neural networks inference.
Alternative interaction techniques for drone-based mission definition

In this paper we address the adaptation of applications for conventional platforms (web, tablets and smartphones) to Augmented Reality (AR) settings. We do this on a specific use case, a tool to help users to define drone missions, which has been implemented with equivalent functionalities both for web-enabled devices and AR wearable devices (in particular, HoloLens). First, a brief description of the application workings is presented as well as the creation process for the mission and the manipulation of its components. Then, we present a preliminary validation with 8 users to analyze the degree of acceptance of the AR version, taking the web user interface as a baseline. Despite the technical limitations of the AR version (mainly related to the device visualization constraints and weight) and the reduction in efficiency (tasks need more time to be completed), several users have expressed their preference for this version. However, with the problem of precision in AR Head Mounted Display context always present, the need to establish new interaction techniques is once more highlighted.
An Attention Bi-box Regression Network for Traffic Light Detection

Recently, object detection has made significant progress due to the development of deep learning. Since the traffic lights are extremely small objects, it leads to unsatisfactory performance when directly applying the off-the-shelf methods based on deep convolutional neural networks. To deal with this problem, we propose an improved detection network based on Faster R-CNN framework. By introducing an attention module on the top of the network, the network can focus better on the small object regions. At the same time, the features from shallow layers are leveraged for classification and bounding box regression, in which the features of small objects can be captured better. In addition, we design a two-branch network for detecting the traffic light box and the bulb box at the same time. In this manner, the performance of traffic light detection is improved obviously. Compared with other detection algorithms, our model achieves competitive results on VIVA traffic light challenge dataset.