Reviewerly/exHarmony huggingface dataset:
Getting started with PHP File-Trying to Create a Guest Book for My #IndieWeb Blog

I have very limited experience with PHP. Basically I have copy and pasted contact forms for years and occasionally hack around in WordPress.More recently I
Classifier ensemble algorithm for learning from non-stationary data stream

Nowadays, many sources generate unbounded data streams at high incoming rates. It is impossible to store these large volumes of data and it is necessary to process them in real time. Because these data are acquired over time and the dynamism of many real-world situations, the target function to be learned can change over time, a problem commonly called concept drift. This paper presents a new ensemble algorithm called Classifier Ensemble Algorithm (CEA), able for learning from data streams with concept drift. CEA manipulates these changes using a change detector in each base classifier. When the detector estimates a change, the classifier in which the change was estimated is replaced by a new one. CEA combines the simplicity of the bagging algorithm to train base classifiers and Exponentially Weighted Moving Average (EWMA) control charts to estimate the weights of each base classifier. The proposed algorithm is compared empirically with several bagging family ensemble algorithms able to learn from concept-drifting data. The experiments show promising results from the proposed algorithm (regarding accuracy), handling different types of concept drifts.
Texture-Aware Superpixel Segmentation

Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.
Formation of Socio-Economic Value Orientations of Students in Professional Educational Establishments

This article is devoted to the formation of student’s socio-economic value orientations in professional educational establishments. The research topic is determined by the contradiction between the increased demands of society and the state for training specialists with strong socio-economic value orientations, and by insufficient theoretical and practical status of this issue. Formation of college student’s socio-economic value orientations requires the use of an organizational complex that will promote the realization of Professional Educational Establishment (PEE) potential in training specialists with the necessary level of socio-economic value orientations. At the same time, formation of student’s socio-economic value orientations as a specially organized process of professional personality becoming of a specialist occurs under internal and external factors initiating rational, economically responsible behavior within the conventional social and ethical standards. Student’s socio-economic value orientations in PEE require special efforts to form them. Socio-economic value orientations are advisable to form while the student is studying in the professional educational establishment. We have considered the personification method used in relation to subject material and its impact on the formation of student's socio-economic values. The efficiency of this method is indicated. The main student’s socio-economic values are allocated.
OCL Fault Injection-Based Detection & Prevention of LDAP Query Injection Vulnerabilities

Security for web application is a prime concern today. Lightweight Directory Access Protocol (LDAP) is used in web applications for enforcing authentication. It may suffer from LDAP injection vulnerabilities and that leads to some security breaches such as login bypass and privilege escalation. This is because of poor implementation of LDAP applications. Here we propose a technique, OCL fault injection-based detection and prevention of LDAP injection vulnerabilities. Here the design level information and constraints that are expressed in OCL are extracted and then randomly altered test cases are generated; they have the capability to uncover LDAP injection vulnerabilities. We propose an algorithm to alter constraints and generate appropriate test cases that produce dissimilar results between original and altered constraints. Cite this Article Many Anjumol T, Ushus Maria Joseph. OCL Fault Injection-Based Detection and Prevention of LDAP Query Injection Vulnerabilities . Journal of Network Security . 2018; 6(3): 5–8p.
DXP Information Security

Information security is crucial for digital platforms that are used for financial domains such as banking. In this chapter we discuss various aspects of information security. The best practices given in this chapter can be used for defining and implementing a robust information security framework for DXPs.
Rank Consistent Logits for Ordinal Regression with Convolutional Neural Networks

In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into account. Neural networks were equipped with ordinal regression capabilities by transforming ordinal targets into binary classification subtasks. However, this method suffers from inconsistencies among the different binary classifiers. To resolve these inconsistencies, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.
By the time we got to Hass ...

By the time we got to Hass it evolved into #walkmyworld and we had hundreds of teachers participating in this unofficial @ncte project from across
Support Feature Machines.

Support Vector Machines (SVMs) with various kernels have played dominant role in machine learning for many years, finding numerous applications. Although they have many attractive features interpretation of their solutions is quite difficult, the use of a single kernel type may not be appropriate in all areas of the input space, convergence problems for some kernels are not uncommon, the standard quadratic programming solution has $O(m^3)$ time and $O(m^2)$ space complexity for $m$ training patterns. Kernel methods work because they implicitly provide new, useful features. Such features, derived from various kernels and other vector transformations, may be used directly in any machine learning algorithm, facilitating multiresolution, heterogeneous models of data. Therefore Support Feature Machines (SFM) based on linear models in the extended feature spaces, enabling control over selection of support features, give at least as good results as any kernel-based SVMs, removing all problems related to interpretation, scaling and convergence. This is demonstrated for a number of benchmark datasets analyzed with linear discrimination, SVM, decision trees and nearest neighbor methods.
EFFECT OF EMPLOYEE TAKING CHARGE ON THE PERFORMANCE OF SACCOS IN KENYA

Taking Charge is voluntary and positive effort by individual employees to influence or impact on organizational functions in relation to how work assignments carried out by employees. Kenya’s Sacco Sector has for years made positive strides as far as population empowerment and economic growth is concerned. Research on Taking Charge has over years enhanced the understanding of Proactivity by addressing initiative- based actions that set out to shape organizational processes and procedures. This study sought to establish the effect of employees taking charge at the place of work on the overall performance of Kenya’s Sacco Sector. The study used descriptive research design for the analysis since it was deemed suitable. The study majorly focused on the supervisors and middle cadre employees in the Sacco Sector as the target population while conducting this study. The study sample was derived from Kenya Sacco Sector population while instruments used to collect data were tested for reliability and validity prior to the collection of data. The sample size used in the study was 300 employees in Kenya’s Sacco Sector. The analysis of the study was done using SPSS-AMOS software version 21 while the model adopted for the analysis was Structural Equation Modeling. The study’s findings and recommendations included; The Kenyan Sacco Sector should enhance an enabling environment for employees to engage in initiative based actions that can help improve their performance. Employees in the sector should be supported by their supervisors or line managers to motivate them towards better performance by Taking Charge at work. Key Words: Taking Charge, Perceived Supervisor Support, Organizational Performance CITATION: Wabala, S. W., Gachunga, H., Odhiambo, R., &amp; Muchiri, R. (2019). Effect of employee taking charge on the performance of Saccos in Kenya. The Strategic Journal of Business &amp; Change Management, 6 (1), 278 – 285.
Animated Drag and Drop Interaction for Dynamic Multidimensional Graphs

In this paper, we propose a new drag and drop interaction technique for graphs. We designed this interaction to support analysis in complex multidimensional and temporal graphs. The drag and drop interaction is enhanced with an intuitive and controllable animation, in support of comparison tasks.
Software Engineering for Smart Cyber-Physical Systems

Currently, a transformation of our technical world into a networked technical world where besides the embedded systems with their interaction with the physical world the interconnection of these nodes in the cyber world becomes a reality can be observed. In parallel nowadays there is a strong trend to employ artificial intelligence techniques and in particular machine learning to make software behave smart. Often cyber-physical systems must be self-adaptive at the level of the individual systems to operate as elements in open, dynamic, and deviating overall structures and to adapt to open and dynamic contexts while being developed, operated, evolved, and governed independently.In this presentation, we will first discuss the envisioned future scenarios for cyber-physical systems with an emphasis on the synergies networking can offer and then characterize which challenges for the design, production, and operation of these systems result. We will then discuss to what extent our current capabilities, in particular concerning software engineering match these challenges and where substantial improvements for the software engineering are crucial. In today's software engineering for embedded systems models are used to plan systems upfront to maximize envisioned properties on the one hand and minimize cost on the other hand. When applying the same ideas to software for smart cyber-physical systems, it soon turned out that for these systems often somehow more subtle links between the involved models and the requirements, users, and environment exist. Self-adaptation and runtime models have been advocated as concepts to covers the demands that result from these subtler links. Lately, both trends have been brought together more thoroughly by the notion of self-aware computing systems. We will review the underlying causes, discuss some our work in this direction, and outline related open challenges and potential for future approaches to software engineering for smart cyber-physical systems.
Gaussian Conditional Random Fields for Classification

Gaussian conditional random fields (GCRF) are a well-known used structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random fields model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient and open to improvements previously applied to GCRF regression models. In addition, the model allows for reduction of noise, that might appear if structures were defined directly between discrete outputs. Additionally, two different forms of the algorithm are presented: GCRFBCb (GCRGBC - Bayesian) and GCRFBCnb (GCRFBC - non Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in Bayesian GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton-Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. It was shown that both models achieve better prediction performance than unstructured predictors. Furthermore, computational and memory complexity is evaluated. Advantages and disadvantages of the proposed GCRFBCb and GCRFBCnb are discussed in detail.
Peningkatan Kemampuan Membaca Anak TPA Dengan Menggunakan Kartu Kata Bergambar

The ability to read is the basic need for children's progress. Because it needs to be developed early since children are still in kindergarten. The efforts to improve children's reading Syiah Kuala District, Banda Aceh in 2018. This study was aimed to determine whether children's reading ability at the Al-Muhajirin Lamnyong Gumpong Rukoh Syiah Kuala District can be improved by using picture-word cards. The subjects of this study were 22 children, consisting of 12 boys and 10 girls. The method was used with the technique of giving action, namely the use of picture-word cards to read. The actions were conducted in 2 cycles and each cycle consisted of 3 meetings (3 weeks). The parameters observed were reading ability. The data collection instrument used was the observation sheet. The results showed that the ability of children to read at the Al-Muhajirin Lamnyong Gampong Rukoh Syiah Kuala District, Banda Aceh could be improved with picture-word cards..
Communication Skills and Its Impact on the Academic Capabilities of Deaf Children in Inclusive Educational Setting: The Case of Selected Primary Schools in North Gondar Zone

The study was conducted in three primary schools of North Gondar Zone such as Serko, Chuhit and Limatber. The participants of the study were deaf children, their hearing peers, special needs education teachers and school principals. 12 deaf children, 5 special needs teachers, 6 school principals and 5 hearing children were the informants of the study. Interview, observation, focus group discussion (FGD), informal talks and document review were the data gathering instruments. The data collected through different instruments were analyzed qualitatively. The result of the study also showed that the mode of commination for deaf children with deaf, their hearing peers and teachers were different in different activities such as:  In play, teaching-learning process and other social activities. In addition, the result of the study revealed that those deaf children who have better communication skills have better academic abilities and achievement It is recommendations that the professional capacity of special needs education teachers should be enhanced by the schools using series on the job trainings; the school should work more on sign language training. Keywords : communication skills, deaf children, inclusive education, academic capabilities, primary school DOI : 10.7176/NMMC/77-01
Computing Optimal Assignments in Linear Time for Graph Matching.

Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words' representations in natural language processing and computer vision. Solving the assignment problem typically requires cubic time and its pairwise computation is expensive on large datasets. In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance. We employ the method to approximate the edit distance between two graphs by matching their vertices in linear time. To this end, we propose two tree distances, the first of which reflects discrete and structural differences between vertices, and the second of which can be used to compare continuous labels. We verify the effectiveness and efficiency of our methods using synthetic and real-world datasets.
A kernel fisher discriminant analysis-based tree ensemble classifier: KFDA forest

In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called kernel Fisher discriminant analysis forest (KFDA Forest). This is a tree-based ensemble method that applies KFDA. To promote diversity, a bootstrap is used and variable sets are randomly divided into K subsets. KFDA is performed on each subset to increase classification accuracy. KFDA maximizes the distance between classes while minimizing the distance within classes. KFDA can also be applied to classification problems in a nonlinear data structure using the kernel trick because it can transform the input space into a kernel feature space, commonly named rotation, rather than a dimensionality reduction. Because new feature axes and KFDA projections are parallel, decision trees are used as a base classifier. To compare the proposed method with existing ensemble methods, we apply these to real datasets from the UCI and KEEL repositories.
Disguised-Nets: Image Disguising for Privacy-preserving Deep Learning.

Due to the high training costs of deep learning, model developers often rent cloud GPU servers to achieve better efficiency. However, this practice raises privacy concerns. An adversarial party may be interested in 1) personal identifiable information encoded in the training data and the learned models, 2) misusing the sensitive models for its own benefits, or 3) launching model inversion (MIA) and generative adversarial network (GAN) attacks to reconstruct replicas of training data (e.g., sensitive images). Learning from encrypted data seems impractical due to the large training data and expensive learning algorithms, while differential-privacy based approaches have to make significant trade-offs between privacy and model quality. We investigate the use of image disguising techniques to protect both data and model privacy. Our preliminary results show that with block-wise permutation and transformations, surprisingly, disguised images still give reasonably well performing deep neural networks (DNN). The disguised images are also resilient to the deep-learning enhanced visual discrimination attack and provide an extra layer of protection from MIA and GAN attacks.
The Practical Experience in Organizing and Conducting of International Art Programs

The article describes the issues of organizing and conducting international creative programs, especially, the international plein-air in the context of the development of the regional component in higher education of design students. The author presented a complex of creative plein-air tasks for a subject «Stylization of a landscape on the basis of poly-cultural resources. Bases of use of means of expressiveness in creation of the stylized image».
Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Networks.

Sparsification of neural networks is one of the effective complexity reduction methods to improve efficiency and generalizability. Binarized activation offers an additional computational saving for inference. Due to vanishing gradient issue in training networks with binarized activation, coarse gradient (a.k.a. straight through estimator) is adopted in practice. In this paper, we study the problem of coarse gradient descent (CGD) learning of a one hidden layer convolutional neural network (CNN) with binarized activation function and sparse weights. It is known that when the input data is Gaussian distributed, no-overlap one hidden layer CNN with ReLU activation and general weight can be learned by GD in polynomial time at high probability in regression problems with ground truth. We propose a relaxed variable splitting method integrating thresholding and coarse gradient descent. The sparsity in network weight is realized through thresholding during the CGD training process. We prove that under threshholding of $\ell_1, \ell_0,$ and transformed-$\ell_1$ penalties, no-overlap binary activation CNN can be learned with high probability, and the iterative weights converge to a global limit which is a transformation of the true weight under a novel sparsifying operation. We found explicit error estimates of sparse weights from the true weights.
Aedes larvae surveillance system on smartphones and algorithms

Purpose: The dangerous diseases: dengue fever, yellow fever, Zika virus and Chikungunya, which affect hundreds of millions of people each year, are transmitted by female Aedes mosquitoes. To prevent the spreading of the Aedes mosquitoes, the Aedes larvae should be under close surveillance, so that the responsible agencies can make a warning or prepare for the epidemic. In order for anyone without the expert knowledge to detect and report the existence of the Aedes larvae, which are very responsive to the change of light intensity and move abruptly, we propose the Aedes Larvae Surveillance System using Smartphones. Methods & Materials: The system comprises an application on smartphones that record a video at the time of flash-on and then the video is processed for counting the number of the Aedes larvae at the server. The video processing algorithms are based on the detection and tracking of the moving objects, which can be simply differentiated from the unmoved background. The proposed algorithm contain the following steps: frame extraction, frame stabilization to get rid of the jerky image motion caused by platform movement, background subtraction to capture the moving Aedes larvae when they expose to the flashlight, images segmentation using local threshold, modified Hungarian algorithm for the track assignment, larvae path classification based on pattern of the movement, and finally counting the valid paths. In order to investigate the performance of the algorithm, the 120 recorded videos of Aedes larvae in the light-color container taken by the developed smartphone application are processed in MATLAB. The detected Aedes Larvae obtained from the algorithm are compared with the ground truth obtained from visual inspection. Results: The result of the processing algorithm by means of Recall (Hit Rate) = TP/(TP+FN) is 88.02%, where TP is the true positives and FN is the false negatives. The False Alarm Rate (FAR) = FP/(FP+TP) is 43.29%, where FP is the false positives. Conclusion: The proposed system has a promising performance in detecting the Aedes larvae in the test environment. However the FAR is still quite high, thus we will try reduced it in the future.