Reviewerly/exHarmony huggingface dataset:
An automatic estimation of the ridge parameter for extreme learning machine

Extreme learning machine (ELM) is an emerging learning method with a single-hidden layer feed-forward neural network that involves obtaining a solution to the system of linear equations. Unlike traditional gradient-based back-propagating neural networks, ELM is computationally efficient with fast training speed and good generalization capability. However, most of the time when applied to real-time problems, the linear system becomes ill-posed in the structure and needs the inclusion of a ridge parameter to obtain a reliable solution, and hence, the selection of the ridge parameter (C) is a crucial task. The ridge parameter is chosen heuristically from a predefined set. The generalized cross-validation is a widely used technique for the automatic estimation of the same, which is computationally expensive as it involves inversion of large matrices. The focus of the proposed work is on pragmatic aspects of the time-efficient automatic estimation of ridge parameter that result in a better generalization performance. In this work, methods are proposed that use the L-curve and U-curve techniques to automatically estimate the ridge parameter, and these methods are effective in the estimation of the ridge parameter even for systems with larger data. Through extensive numerical results, it is shown that the proposed methods outperform the existing ones in terms of accuracy, precision, sensitivity, specificity, F1-score, and computational time on various benchmark binary as well as multiclass classification data sets. Finally, the proposed methods are statistically analyzed using the nonparametric Friedman ranking test, which is also proving the effectiveness of the proposed method as it is providing a better rank for the same over existing methods.
Development prospects of introduction of innovative technologies 3d-printing in Kazakhstan

Abstract The article describes material about the main prospects that require research for the implementation and operation of new additive technology in the Republic of Kazakhstan. The main problems of introducing new technologies are considered. New technologies, in the broad sense of their presence, or absence, determine the strategic position of the national economy in the world. The availability of technologies provides scientists or designers with powerful tools for the implementation of new ideas. The lack of technology limits the creativity of scientists and engineers, forcing them to apply outdated technical solutions that are in their possession, rather than those that enable them to achieve the new goals. Therefore, technology itself is a major part of innovation. The market situation is changing and only interested consumers remain. Market trends for the next decade are discussed. Furthermore, the article also discusses the advantages of introducing CAD systems into the educational process and software products of the COMPASS family.The Republic ofKazakhstan needs competent specialists who can apply the most modern technologies. As 3D printing technology advances and more people are exposed to it, they realize it can be applied to many different problems in many different fields; thus the opportunity for innovation is tremendous.
Text Cover Steganography Using Font Color of the Invisible Characters and Optimized Primary Color-intensities

Text cover steganography is concerned with using text support for hiding information. Despite its long and old history, the art of cover writing is still attracting the attention of researchers in the domain of information hiding. Unfortunately, only a few related works exist in terms of text-covered writing. We present a visual attack on the previous related work. Then, we use invisible characters to hide secret data within the text support. We further use a combination and variation of the three primary colors intensities to vehiculate the message when painting non-printing characters. The proposed algorithm makes use of the low-value color spaces to decrease the color variations of the embedded secret message. It also further strengthens the hiddenness of the non-printing characters even when replaced by visible characters.
BDNE: A Method of Bi-Directional Distance Network Embedding

In order to capture the directed relationship between nodes more accurately, this paper proposed a novel network embedding model called BDNE. The model adds the bi-directional distance while preserving the co-occurrence frequency of the nodes within a context window, which is of great significance in some applications of social networks, such as public opinion monitoring and control, and group discovery. Experimental results show that the embedding results of our model as features in the node classification task are better than DeepWalk, Node2Vector and Line on real data sets of different types and sizes.
Predicting and Preventing Malware in Machine Learning Model

Machine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated my machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-nearest-neighbors (KNN) classifier, random forest, AdaBoost, support vector machine (SVM), decision tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model. In machine learning model there are 2 types of attacks that is causative attack which occurs during the training phase and exploratory attack which occurs during the prediction phase, we will also discuss about the countermeasures on machine learning model, the countermeasures are data sanitization, algorithm robustness enhancement, and privacy preserving techniques.
Residual Aluminium in Water Intended for Human Consumption

Aluminium is one of the environmental factors that may have an impact on Alzheimer’s and Parkinson’s disease. Some epidemiological studies show a relationship between the concentration of aluminium in drinking water and the incidence of Alzheimer’s disease. The article presents and discusses the results of research on the effect of the type and dose of non-prehydrolyzed aluminium coagulants on the concentration of residual aluminium in water intended for human consumption. Aluminium sulphate (VI) and sodium aluminate were tested as coagulants. Analysis of the obtained test results showed that lower concentrations of residual aluminium were found after coagulation with aluminium sulphate (VI), which is hydrolyzed acidally, lowering the pH of the water in the range of 7.47 to 7.12 providing good conditions for the formation of Al(OH)&lt;sub&gt;3&lt;/sub&gt;. A less useful coagulant due to the concentration of residual aluminium was sodium aluminate, which undergoes basic hydrolysis creating conditions for the transition of Al(OH)&lt;sub&gt;3&lt;/sub&gt; into soluble Al(OH)&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;−&lt;/sup&gt;.
Quantitative Analysis of the Scrum Framework

Scrum provides many benefits to organizations requiring a project management framework for complex adaptive problems. Some of these benefits include improved teamwork, improved time to market, and a noticeable decrease in software defects. The primary objective of this paper is to test nineteen research hypotheses that require a quantitative analysis of the Scrum framework. In order to test these hypotheses, the findings of a survey questionnaire was used to gather response data from Scrum practitioners on their perceptions of factors affecting Scrum adoption. Exploratory factor analysis and Cronbach’s alpha analysis confirmed the validity and reliability of the measuring instrument. Following these analyses, a correlation matrix was used to test the relationship strength among the different factors. The Spearman correlation analysis revealed statistically significant correlations. Multiple linear regression statistical models were developed to examine the existence of factors and constructs impacting Scrum adoption. Our findings indicate that four of the nineteen hypotheses are statistically significant. The factors Change Resistance, Sprint Management, Relative Advantage, and Complexity are shown to have a significant linear relationship to Scrum as perceived by Scrum Practitioners working within South African organizations. Future research could incorporate a larger population sample to improve the generalizability of the findings.
Machine Learning as a Service for Software Process Improvement

As the technology evolves, software engineeringSoftware engineering companies must follow new methodologies to implement software process improvementSoftware process improvement (SPI) to enhance their current practices. It is evident that the rise of big data processing and machine learningMachine learning allows the development of new technologies to reduce manual workloads in organisations. Therefore, this chapter focuses on proposing new methods to improve SPISoftware process improvement in organisations by developing a new maturity modelMaturity model and combining it with machine learningMachine learning techniques. It proposes the Measurable Capability Maturity ModelMaturity model (MCMM), which contain measurable metrics as its key process indicatorsKey process indicators to identify maturity levels of software development companies. Compared to existing maturity modelsMaturity model in the industry, MCMM has measurable metrics that identify maturity-level achievements through mathematical calculations. These SPISoftware process improvement metrics were identified and developed by knowledge obtained through the literature review and qualitative research. Moreover, this chapter proposes a prototype application MLSPI (machine learningMachine learning for software process improvementSoftware process improvement) by combining MCMM with machine learningMachine learning functionalities. It contains functionalities to extract information from a software development organisation and identify its maturity-level achievements through mathematical calculations used in MCMM. Moreover, it provides feedback for SPISoftware process improvement and predicts data patterns variations for each metrics using machine learningMachine learning functionalities.
Research of Sentiment Analysis Based on Adaboost Algorithm

With the rapid development of the Internet, Weibo integrates the advantages of traditional websites, forums, blogs and adds various intelligent algorithms such as interactivity and intimacy between people to make data burst in a concise form. Therefore, the need for analysis and utilization of the amount of information generated by Weibo every day is even more urgent. This article uses Weibo's official API to crawl Weibo, preprocesses the Weibo text, uses the SVM algorithm to initially screen the text, uses Naive Bayes to analyze the sentiment of Weibo, and divides Weibo into positive. The three types of negative and objective, while using the Adaboost algorithm to strengthen the naive Bayes algorithm.
Is there a safety-net effect with computer-aided detection?

Computer-aided detection (CAD) systems are used to aid readers interpreting screening mammograms. An expert reader searches the image initially unaided and then once again with the aid of CAD, which prompts automatically detected suspicious regions. This could lead to a “safety-net” effect, where the initial unaided search of the image is adversely affected by the fact that it is preliminary to an additional search with CAD and may, therefore, be less thorough. To investigate the existence of such an effect, we created a visual search experiment for nonexpert observers mirroring breast screening with CAD. Each observer searched 100 images for microcalcification clusters within synthetic images in both prompted (CAD) and unprompted (no-CAD) conditions. Fifty-two participants were recruited for the study, 48 of whom had their eye movements tracked in real-time; the other 4 participants could not be accurately calibrated, so only behavioral data were collected. In the CAD condition, before prompts were displayed, image coverage was significantly lower than coverage in the no-CAD condition (<italic>t</italic><sub>47</sub> = 5.29, <italic>p</italic> &lt; 0.0001). Observer sensitivity was significantly greater for targets marked by CAD than the same targets in the no-CAD condition (<italic>t</italic><sub>51</sub> = 6.56, <italic>p</italic> &lt; 0.001). For targets not marked by CAD, there was no significant difference in observer sensitivity in the CAD condition compared with the same targets in the no-CAD condition (<italic>t</italic><sub>51</sub> = 0.54, <italic>p</italic> = 0.59). These results suggest that the initial search may be influenced by the subsequent availability of CAD; if so, cross-sectional CAD efficacy studies should account for the effect when estimating benefit.
Rubber Plant Disease Diagnostic System Using Technique for Order Preference by Similarity to Ideal Solution

Data from the Plantation Office of East Kalimantan Province, the area of ​​rubber plantations has decreased every year. One of the factors that make productivity of rubber plants low is the presence of pests and diseases. The time limit of an expert is an obstacle in identifying rubber plantations. To overcome this problem, we need an expert system that can identify rubber plant diseases such as an expert. This system was developed to be able to provide solutions in diagnosing rubber plant diseases such as White Root Fungus (Rigidoporus micropus), Upas Mushroom Disease (Corticium salmonicolor), Antraknosa Disease (Colletorichum gloeosporoides), Skin Necrosis (Fusarium sp.), And Cancer Lines (Phytoptora palmivore). This study uses the TOPSIS method. There are four variables used as assessment criteria in this study, namely: the level of damage to the root, the level of damage to the stem, the level of damage to the leaves and the level of damage to the tapping path or the intensity of tapping, with preference values ​​from 0 to 100; none, Very Light Intensity (1), Mild Intensity (3), Medium Intensity (5), Weight Intensity (7) and Very Heavy Intensity (9). Each weight value C1= 0.3; C2= 0.22; C3= 0.28; C4= 0.2. The results of this study compare manual calculations and calculations on the system obtained 99.99% accuracy. This system is expected to help facilitate rubber farmers in identifying diseases and can help experts.
Bird-inspired Velocity Optimization for UAVs in the Urban Environment

Small Unmanned Aerial Vehicles (SUAVs) are low-cost quick to launch platforms which offer potential for a range of roles in urban environments. However, these environments create complex wind flows that present control issues for small, low-speed platforms. Further to this, battery technology does not yet offer the power-weight capacity to enable the endurance requirements for such missions. In comparison, birds of a similar size and weight are not only able to manage complex wind flow, but also exploit the environment as a locomotive energy source. Birds in migration are known to adjust airspeed to minimize the energetic cost of transport in response to wind conditions however, it is unknown whether birds implement the same velocity optimization strategies in more complex environments and while performing energy harvesting flight strategies. This study used Global Positioning System (GPS) backpacks to track 11 urban nesting gulls and found that during 193 daily commutes the gulls were able to soar 44% of the time by making use of both thermal and orographic updrafts. We outline cost of transport (CoT) theory and propose a model for optimizing airspeed for given wind conditions whilst maintaining a trajectory to a given location. We used the gull flight paths to test for CoT velocity adjustments by considering their flapping and soaring strategies. We found that by having a similar best glide speed and minimum power speed in soaring and flapping flight the gulls were able to make energy savings of as much as 30%. These models calculated optimum ground and airspeeds for known wind conditions assuming trajectory holding throughout flight, and as such could be implemented on a SUAV platform with wind sensing capabilities. This approach could significantly reduce the energy requirements for a SUAV navigating in an urban environment.
Reinforcement Learning of Code Search Sessions

Searching and reusing online code is a common activity in software development. Meanwhile, like many general-purposed searches, code search also faces the session search problem: in a code search session, the user needs to iteratively search for code snippets, exploring new code snippets that meet his/her needs and/or making some results highly ranked. This paper presents Cosoch, a reinforcement learning approach to session search of code documents (code snippets with textual explanations). Cosoch is aimed at generating a session that reveals user intentions, and correspondingly searching and reranking the resulting documents. More specifically, Cosoch casts a code search session into a Markov decision process, in which rewards measuring the relevances between the queries and the resulting code documents guide the whole session search. We have built a dataset, say CosoBe, from StackOverflow, containing 103 code search sessions with 378 pieces of user feedback. We have also evaluated Cosoch on CosoBe. The evaluation results show that Cosoch achieves an average NDCG@3 score of 0.7379, outperforming StackOverflow by 21.3%.
A Systemic Cybercrime Stakeholders Architectural Model

The increased of cybercrime incidents taking place in the world is at its perilous magnitude causing losses in term of money and trust. Even though there are various cybersecurity solutions in place; the threat of cybercrime is still a hard problem. Exploration of cybercrime challenges, especially the preventions and detections of the cybercrime should be investigated by composing all the stakeholders and players of a cybercrime issue. In this paper; an exploration of several cybercrime stakeholders is done. It is argued that cybercrime is a systemic threat and cannot be tackled with cybersecurity and legal systems. The architectural model proposed is significant and should become one of the considered milestones in designing security control in tackling cybercrime globally.
A deep-learning approach to mining conditions

A condition is a constraint that determines when a consequent holds. Mining them in text is paramount to understand many sentences properly. In the literature, there are a few pattern-based proposals that fall short regarding recall because it is not easy to characterise unusual ways to express conditions with hand-crafted patterns; there is one machine-learning proposal that is bound to the Japanese language, requires specific-purpose dictionaries, taxonomies, and heuristics, works on opinion sentences only, and was evaluated very shallowly. In this article, we present a deep-learning proposal to mine conditions that does not have any of the previous drawbacks; furthermore, we have performed a comprehensive experimental study on a large multi-lingual dataset on many common topics; our conclusion is that our proposals are similar to the state of the art in terms of precision, but improve recall enough to beat them in terms of F1 score.
Meta Transfer Learning for Adaptive Vehicle Tracking in UAV Videos

The vehicle tracking in UAV videos is still under-explored with the deep learning methods due to the lack of well labeled datasets. The challenges mainly come from the fact that the UAV view has much wider and changeable landscapes, which hinders the labeling task. In this paper, we propose a meta transfer learning method for adaptive vehicle tracking in UAV videos (MTAVT), which transfers the common features across landscapes, so that it can avoid over-fitting with the limited scale of dataset. Our MTAVT consists of two critical components: a meta learner and a transfer learner. Specifically, meta-learner is employed to adaptively learn the models to extract the sharing features between ground and drone views. The transfer learner is used to learn the domain-shifted features from ground-view to drone-view datasets by optimizing the ground-view models. We further seamlessly incorporate an exemplar-memory curriculum into meta learning by leveraging the memorized models, which serves as the training guidance for sequential sampling. Hence, this curriculum can enforce the meta learner to adapt to the new sequences in the drone-view datasets without losing the previous learned knowledge. Meanwhile, we simplify and stabilize the higher-order gradient training criteria for meta learning by exploring curriculum learning in multiple stages with various domains. We conduct extensive experiments and ablation studies on four public benchmarks and an evaluation dataset from YouTube (to release soon). All the experiments demonstrate that, our MTAVT has superior advantages over state-of-the-art methods in terms of accuracy, robustness, and versatility.
Diagnosis of the Use of Information and Communication Technologies in Dual Training. Case Study: Textile Confection Career at “Instituto Superior Tecnólogico Cotacachi”

The new ICT—Information and Communication Technology—has helped enhance professors' pedagogical performance with the aim of providing an educational environment based on skills and competences that improve educational related tasks thus facilitating the involvement of new education professionals into the work field. This study has both, a qualitative and field approach which a total of 119 students and 11 professors from the Textile Confection career were surveyed of a total of 519 students and 26 professors belonging to several careers from "Instituto Superior Tecnológico Cotacachi" with tools that allow to know the different aspects related to skills, use, media design and techniques in educational environment using through ICT and the students' self-perception. The main objective is to diagnose the use and integration of ICT during the learning process of the career mentioned before during the academic period November 2019–April 2019. The results indicate that most of the professors have ICT skills in several variables. However, direct-observation results reveal a few pedagogical praxis deficiencies using information and communication technologies. On the other hand, students exhibit serious deficiencies when it comes to their digital and TIC abilities, presenting future issues employment for the future professionals in the Textile Confection career, creating a starting point in the academic decision-making process made by authorities and faculty.