Reviewerly/exHarmony huggingface dataset:
TRANSFORMATION OF SPIRITUAL AND MORAL VALUES OF YOUTH IN THE CONTEXT OF GLOBALIZATION

The article has been devoted to the study of the impact of new information and communication technologies in the educational environment on the transformation of the values of student youth. It has been shown, how in the process of the evolution of values, a gap occurs between the declared values and life orientations, determining the behavior of young people. The conclusion has been made, that the worldview of modern student youth is of a synthetic nature: it contains, on the one hand, conservative values, focusing on the stability and connection of generations, on the other hand, “modernist” attitudes reflecting modern social realities. It has been shown, that potential opportunities for the inclusion of young people in the innovative development of the country’s economy are determined by the dynamic balance of traditional ethical values and the modern requirements of sociocultural dynamics.
Active learning for level set estimation under cost-dependent input uncertainty.

As part of a quality control process in manufacturing it is often necessary to test whether all parts of a product satisfy a required property, with as few inspections as possible. When multiple inspection apparatuses with different costs and precision exist, it is desirable that testing can be carried out cost-effectively by properly controlling the trade-off between the costs and the precision. In this paper, we formulate this as a level set estimation (LSE) problem under cost-dependent input uncertainty - LSE being a type of active learning for estimating the level set, i.e., the subset of the input space in which an unknown function value is greater or smaller than a pre-determined threshold. Then, we propose a new algorithm for LSE under cost-dependent input uncertainty with theoretical convergence guarantee. We demonstrate the effectiveness of the proposed algorithm by applying it to synthetic and real datasets.
Non-convergence and Limit Cycles in the Adam Optimizer

One of the most popular training algorithms for deep neural networks is the Adaptive Moment Estimation (Adam) introduced by Kingma and Ba. Despite its success in many applications there is no satisfactory convergence analysis: only local convergence can be shown for batch mode under some restrictions on the hyperparameters, counterexamples exist for incremental mode. Recent results show that for simple quadratic objective functions limit cycles of period 2 exist in batch mode, but only for atypical hyperparameters, and only for the algorithm without bias correction. We extend the convergence analysis to all choices of the hyperparameters for quadratic functions. This finally answers the question of convergence for Adam in batch mode to the negative. We analyze the stability of these limit cycles and relate our analysis to other results where approximate convergence was shown, but under the additional assumption of bounded gradients which does not apply to quadratic functions. The investigation heavily relies on the use of computer algebra due to the complexity of the equations.
Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.
On the Application of Dictionary Learning to Image Compression

Signal models are a cornerstone of contemporary signal and image-processing methodology. In this chapter, a particular signal modelling method, called synthesis sparse representation, is studied which has been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. In this kind of signal modelling, the signal is represented with respect to dictionary. The dictionary choice plays an important role on the success of the entire model. One main discipline of dictionary designing is based on a machine learning methodology which provides a simple and expressive structure for designing adaptable and efficient dictionaries. This chapter focuses on direct application of the sparse representation, i.e. image compression. Two image codec based on adaptive sparse representation over a trained dictionary are introduced. Experimental results show that the presented methods outperform the existing image coding standards, such as JPEG and JPEG2000.
Bayesian Relational Memory for Semantic Visual Navigation

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards. BRM takes the form of a probabilistic relation graph over semantic entities (e.g., room types), which allows (1) capturing the layout prior from training environments, i.e., prior knowledge, (2) estimating posterior layout at test time, i.e., memory update, and (3) efficient planning for navigation, altogether. We develop a BRM agent consisting of a BRM module for producing sub-goals and a goal-conditioned locomotion module for control. When testing in unseen environments, the BRM agent outperforms baselines that do not explicitly utilize the probabilistic relational memory structure
Software Effort Estimation Using Multilayer Perceptron and Long Short Term Memory

Software effort estimation is a hot topic for study in the last decades.The biggest challenge for project managers is to meet their goals within the given time limit.Machine learning software can take project management software to a whole new level.The objective of this paper is to show the applicability of using neural network algorithms in software effort estimation for project management.To prove the concept we are using two machine learning algorithms: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM).To train and test these machine learning algorithms we are using the Desharnais dataset.The dataset consists of 77 sample projects.From our results we have seen that Multilayer Perceptron algorithm has better performance than Long Short-Term Memory, by having a better determination coefficient for software effort estimation.Our success in implementing a machine learning that can estimate the software effort brings real benefits in the field of project management assisted by computer, further enhancing the ability of a manager to organize the tasks within the time limit of the project.Although, we need to take into consideration that we had a limited dataset that we could use so a real advancement would be to implement and test these algorithms using a real life company as a subject of testing.
Relational variants of categories of fuzzy sets defined by monads

By a T-relational variant of a category K we understand a Kleisli category K T of a category K with respect to a monad T. Using explicit definitions we equivalently describe several T-relational examples of categories with fuzzy type objects, where T are appropriate monads in these categories.
Efficiency Improvement in Group Signature Scheme with Probabilistic Revocation

In group signature schemes, one of the important issues is the member revocation, and lots of revocable schemes have been proposed. Recently, Group Signature scheme with Probabilistic Revocation (GSPR) is proposed. In GSPR, by employing a novel notion of probabilistic revocation, the computation cost of the revocation check is drastically reduced, although the correctness of the check is with a certain probability. However, in the GSPR scheme, there is another problem: m alias tokens are embedded into the certificate of a member. Then, in signing, each token is used, and O(m) exponentiations are needed to prove that the used token is embedded in the certificate. When m is large, this signing cost including O(m) exponentiations becomes a big problem for powerless mobile devices. In this paper, we propose an extended GSPR scheme where the signing cost is reduced, but the revocation mechanism is exactly the same as the original GSPR scheme. Our main idea is to utilize an efficient pairing-based accumulator with multiplications to embed lots of alias tokens in a certificate. Thus, in the proposed scheme, the signing cost is reduced to only O(m) multiplications instead of O(m) exponentiations.
An Attention-Based ID-CNNs-CRF Model for Named Entity Recognition on Clinical Electronic Medical Records

Named Entity Recognition (NER) on Clinical Electronic Medical Records (CEMR) is a fundamental step in extracting disease knowledge by identifying specific entity terms such as diseases, symptoms, etc. However, the state-of-the-art NER methods based on Long Short-Term Memory (LSTM) fail to fully exploit GPU parallelism under the massive medical records. Although a novel NER method based on Iterated Dilated CNNs (ID-CNNs) can accelerate network computing, it tends to ignore the word-order feature and semantic information of the current word. In order to enhance the performance of ID-CNNs-based models on NER tasks, an attention-based ID-CNNs-CRF model which combines word-order feature and local context is proposed. Firstly, Position Embedding is utilized to fuse word-order information. Secondly, ID-CNNs architecture is used to rapidly extract global semantic information. Simultaneously, the attention mechanism is employed to pay attention to the local context. Finally, we apply the CRF to obtain the optimal tag sequence. Experiments conducted on two CEMR datasets show that our model outperforms traditional ones. The F1-scores of 94.55% and 91.17% are obtained respectively on these two datasets and both are better than LSTMs-based models.
Graph-guided Architecture Search for Real-time Semantic Segmentation

Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-time semantic segmentation networks. Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint. Specifically, to produce the cell-level diversity, the cell-sharing constraint is eliminated through the cell-independent manner. Then a graph convolution network (GCN) is seamlessly integrated as a communication mechanism between cells. Finally, a latency-oriented constraint is endowed into the search process to balance the speed and performance. Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, GAS achieves the new best performance of 73.5% mIoU with speed of 108.4 FPS on Titan Xp.
Model Based Planning with Energy Based Models

Model-based planning holds great promise for improving both sample efficiency and generalization in reinforcement learning (RL). We show that energy-based models (EBMs) are a promising class of models to use for model-based planning. EBMs naturally support inference of intermediate states given start and goal state distributions. We provide an online algorithm to train EBMs while interacting with the environment, and show that EBMs allow for significantly better online learning than corresponding feed-forward networks. We further show that EBMs support maximum entropy state inference and are able to generate diverse state space plans. We show that inference purely in state space - without planning actions - allows for better generalization to previously unseen obstacles in the environment and prevents the planner from exploiting the dynamics model by applying uncharacteristic action sequences. Finally, we show that online EBM training naturally leads to intentionally planned state exploration which performs significantly better than random exploration.
A Toolkit for Prototyping Tabletop-Centric Cross-Device Interaction

Different types of devices are used in our daily lives, and each type has its own advantages and disadvantages. Cross-device interaction that involves multiple devices can potentially overcome each device's inherent disadvantages. Without a toolkit, it is time consuming to develop cross-device interaction. Focusing on a horizontal large display (called a tabletop), this paper proposes a generic toolkit for prototyping tabletop-centric cross-device applications that involve a large display and multiple smartphones. In our toolkit, a user uses a smartphone as a look-through lens for browsing and selecting objects on a tabletop, and remotely manipulates the selected object with multimodal feedback. Based on the above interaction style, our toolkit formalizes the development of cross-device interaction as defining a mobile interface on each tabletop interface object. Since our toolkit encapsulates the interface distribution and synchronization through message passing, interface developers can focus on developing cross-device interaction by designing a mobile interface in response to a selected tabletop interface object. In order to demonstrate the versatility and design space of our toolkit, six design issues, i.e., data transfer, personalization, user interface composition, authentication, localized & private feedback, and input expressiveness, were discussed through a set of sample applications.
Image Embedding for Detecting Irregularity

Detecting irregularitySharma, M. K. in an image or video is an importantSheet, D task in quality control or automatic visualBiswas, P. K. inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in images. This can further be utilized in image screening application. In the proposed architecture, deep adversarial autoencoder is trained to extract the features from images. Using these features and skip-gram model, we develop the image2vec architecture to capture contextual probability in an image. Various score aggregation techniques are explored and its performance is reported. As a case study, we present a scenario of foreign body object detection in clinical-grade X-ray images. The proposed approach is found to correctly detect and localize abnormality in images.
Engineering High-Speed Quantum Random Number Generators

The ability to share information online in a secure way is a major concern for any individual and organization today. In times where we are permanently connected, the development of secure products and infrastructure with long-terms security guarantees is a global priority. Quantum technologies bring in a radically new toolset to realize unbreakable encryption systems as well as to improve randomized algorithms. In this talk, we will discuss recent progress on the integration of high-speed (Gb/s) quantum random number generators. We will share recent operation data from a series of units specifically designed using off-the-shelf telecommunication parts. We will then show application-level results in the fields of encryption and computation and finally, we will show recent progress on the use of photonic integrated circuits for the task of high-speed quantum random number generation, showing the scaling potential of quantum and photonic devices in the cyber-security and super-computation spaces.
Grid Aramayla Optimize Edilmiş Bayes Lojistik Regresyon Algoritmasının Türkçe Mikro Blog Verilerinde Sanal Zorbalık Tespitinde Kullanılması

There is a huge interaction between users of various social media platforms.This communication produces enormous amount of user data worth to be analyzed from numerous aspects.One of the research area emerging from the user data is a major security issue known as cyberbullying.Since this problem has been recognized as the source of cybercrimes, design of a system to detect cyberbullying attacks/sources through the micro-blog texts is evident.Most of the academic search of this topic has been conducted in English language.The originality of this paper is that we develop an accurate cyberbullying detection system for Turkish language.We used data from Twitter to develop a supervised machine learning model on top of Bayesian Logistic Regression whose parameters are tuned with the use of grid-search algorithm.Since the text data produces a high dimensional training space for machine learning algorithms, we also used Chi-Squared (CH2) feature selection strategy to obtain best subset of features.The optimized version of the proposed algorithm on top of reduced feature dimension has produced an f-measure value of 0.925.Finally, we also compared the results of the proposed algorithm with the frequently used machine learning methods from literature and we provided the corresponding results in related sections.
Scaled Pearson’s Correlation Coefficient for Evaluating Text Similarity Measures

Despite the ever-increasing interest in the field of text similarity methods, the development of adequate text similarity methods is lagging. Some methods are decent in entailment while others are reasonable to the degree to which two texts are similar. Very often, these methods are compared using Pearson&amp;rsquo;s correlation; however, Pearson&amp;rsquo;s correlation is bound to outliers that could affect the final correlation coefficient figure. As a result, the Pearson correlation is inadequate to find which text similarity method is better in situations where data items are very similar or are unrelated. This paper borrows the scaled Pearson correlation from the finance domain and builds a metric that can evaluate the performance of similarity methods over cross-sectional datasets. Results showed that the new metric is fine-grained with the benchmark dataset scores range as a promising alternative to Pearson&amp;rsquo;s correlation. Moreover, extrinsic results from the application of the System Usability Scale (SUS) questionnaire on the scaled Pearson correlation revealed that the proposed metric is attaining attention from scholars which implicate its usage in the academia.
Joint Graph Regularization in a Homogeneous Subspace for Cross-Media Retrieval

The heterogeneity of multimodal data is the main challenge in cross-media retrieval; many methods have already been developed to address the problem. At present, subspace learning is one of the mainstream approaches for cross-media retrieval; its aim is to learn a latent shared subspace so that similarities within cross-modal data can be measured in this subspace. However, most existing subspace learning algorithms only focus on supervised information, using labeled data for training to obtain one pair of mapping matrices. In this paper, we propose joint graph regularization based on semi-supervised learning cross-media retrieval (JGRHS), which makes full use of labeled and unlabeled data. We jointly considered correlation analysis and semantic information when learning projection matrices to maintain the closeness of pairwise data and semantic consistency; graph regularization is used to make learned transformation consistent with similarity constraints in both modalities. In addition, the retrieval results on three datasets indicate that the proposed method achieves good efficiency in theoretical research and practical applications.
Coherence in technology law

Technology law is emerging as a distinct field of law among the canon of more established law subjects. This paper examines the requirements for coherence in law subjects and begins the process of ...
Generating Internal Motivation through Mobile Application Technology

This paper is a practical account of the author’s experimental analysis of using mobile application technology in the undergraduate ELT classes of Dhofar University, Salalah, Oman. In today’s world, technology plays an indispensable role in human life. Integrating latest technological applications in the classrooms can be a challenging task for many teachers, but it is undoubtedly a powerful tool for enhancing the quality of education. This paper outlines how to generate internal motivation and enhance the cognitive skills of students through the innovative use of mobile/smart phones in the classroom. Nowadays, innovative use of mobile applications in the classroom is advocated by numerous educational experts. This shift in perspective is necessitated by the large-scale digitalization of education and educational resources. Consequently, this study explores how mobile devices can be used effectively for enhancing involvement and motivation of students in various learning activities to promote learner autonomy and peer learning opportunities within the classroom. This experiment eventually resulted in a visible rise in the motivational graph of the whole class as testified by students’ performance in their tests. Finally, this paper also outlines how mobile learning can be integrated into a conventional university curriculum.
Introduction to Games Programming

Games programming is performed by developers/coders who implement the logic that drives a game. This chapter introduces Games programming.
An Interface using Electrooculography with Closed Eyes

The purpose of our study was to determine the electrooculography (EOG) interface that can be used for amyotrophic lateral sclerosis (ALS) patients that can use without preparation. We proposed eye movement detection using Root Mean Square the active threshold (AT method) and k-nearest neighbor (k-NN) methods. The AT method is the threshold method that is dynamically calculated using the root mean square. This report describes the combined use of both methods. We conducted experiments on the waveform detection accuracy for 19 healthy subjects between the ages of 20 and 29 years. The hit rate for the proposed method was 94%, and the FA rate was 9%. Next, we calculated the information transfer rate (ITR), a popular evaluation index in for the brain-computer interface. (BCI). The minimum ITR using the proposed method was 19.02[bits/min]. The ITR of the P300 speller a BCI tool, was 16.4[bits/min]. The ITR of the proposed method was higher than that using the P300 speller. Therefore, it can be said that the proposed method has usefulness as an interface.