Reviewerly/exHarmony huggingface dataset:
Applying Rule-Based Context Knowledge to Build Abstract Semantic Maps of  Indoor Environments

In this paper, we propose a generalizable method that systematically combines data driven MCMC samplingand inference using rule-based context knowledge for data abstraction. In particular, we demonstrate the usefulness of our method in the scenario of building abstract semantic maps for indoor environments. The product of our system is a parametric abstract model of the perceived environment that not only accurately represents the geometry of the environment but also provides valuable abstract information which benefits high-level robotic applications. Based on predefined abstract terms,such as type and relation, we define task-specific context knowledge as descriptive rules in Markov Logic Networks. The corresponding inference results are used to construct a priordistribution that aims to add reasonable constraints to the solution space of semantic maps. In addition, by applying a semantically annotated sensor model, we explicitly use context information to interpret the sensor data. Experiments on real world data show promising results and thus confirm the usefulness of our system.
Message from the HCW Steering Committee Chair

These are the proceedings of the "28th Heterogeneity in Computing Workshop," also known as HCW 2019.A few years ago, the title of the workshop was changed from the original title of "Heterogeneous Computing Workshop" to reflect the breadth of the impact of heterogeneity, as well as to stress that the focus of the workshop is on the management and exploitation of heterogeneity.All of this is, of course, taken in the context of the parent
Retraction Note: Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive Blocks

Retraction: Wei Fu and Guang-Zhong Xing. Edge-Oriented Spatial Interpolation for Error Concealment of Consecutive Blocks. Journal of Computer Science and Technology 2007, 22(3): 494-inside back cover. DOI: 10.1007/s11390-007-9044-4.
USE OF FOREIGN LANGUAGE ONLINE TRAINERS FOR FORMING A FOREIGN LEXICAL COMPETENCE OF A FUTURE PEDAGOGUE

The article considers modern approaches and innovative means of development of the foreign language communicative competence of future teachers. The author emphasizes and substantiates the necessity of widespread usage of digital technologies in the process of modern students` foreign language training as a component of professional pedagogical competence. The usage of on-line resources contributes not only to the acquisition of a foreign language, at the same time it is also an example, according to the methodological point of view, and can be used in further pedagogical activities in foreign language classes in pre-school establishments and in foreign language lessons in the New Ukrainian School. After graduating the university modern bachelors will work with the children – representatives of Generation Z, so the ability to use digital means of teaching a foreign language is a demand of time, because modern children, born in the digital would, must be taught by digital means. This is their natural environment. World leading countries understand the necessity of preparing teachers with a high level of a formed digital competence. So, the task of the XXI century modern university, which trains future educators for professional activity, is to form their readiness to teach in the New Ukrainian School by such means that follow the demands of time and will allow to form the XXI Century Competences. The article states that mastering the foreign language lexical material and deepening the level of lexical skills and abilities formation of students-bachelors of the specialty 013 Primary education, 012 Preschool education have a great general and practical significance, because the word is the main component of foreign language communicative competence, and the dictionary on-line simulators, which the author uses in the discipline "Foreign Language and Teaching Methodology (the German language)", allow to intensify the learning process both in the classroom and during the students’ self-work.
Quantum Codes from Constacyclic Codes over Polynomial Residue Rings

Sufficient and necessary conditions for Hermitian (1 + λu)-constacyclic self-orthogonal codes over Fpm +uFpm are obtained, where λ is a unit of Fpm + uFpm. Based on this, a new method for the construction of pm-ary quantum codes from the obtained Hermitian (1+ λu) constacyclic self-orthogonal codes over Fpm+uFpm is given. As an example, [[pm - 1; pm - 2d+ 1, d]]pm quantum MDS codes are constructed for 1 ≤ d ≤ (pm +1)/2 and p ≠ 2, as well as some other quantum codes.
Synthetic vs Real: Deep Learning on Controlled Noise

Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels. Due to the lack of suitable datasets, previous research have only examined deep learning on controlled synthetic noise, and real-world noise has never been systematically studied in a controlled setting. To this end, this paper establishes a benchmark of real-world noisy labels at 10 controlled noise levels. As real-world noise possesses unique properties, to understand the difference, we conduct a large-scale study across a variety of noise levels and types, architectures, methods, and training settings. Our study shows that: (1) Deep Neural Networks (DNNs) generalize much better on real-world noise. (2) DNNs may not learn patterns first on real-world noisy data. (3) When networks are fine-tuned, ImageNet architectures generalize well on noisy data. (4) Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve. (5) Robust learning methods that work well on synthetic noise may not work as well on real-world noise, and vice versa. We hope our benchmark, as well as our findings, will facilitate deep learning research on noisy data.
Design of Nonlinear Adaptive-Predictive Control System with ANFIS Modeling for Urea Plant Reactor Unit

The reactor unit in the urea fertilizer plant is one of the units that functions to react the components forming the urea fertilizer, so that this unit is one of the most critical part in the plant. During the operation of this unit, the fluid height of the liquid inside the reactor will be kept stable so that the reaction in the reactor can react perfectly according to the product specifications. In order to maintain this level and to observe the dynamics of reactor unit, a model that can describe the behavior of the reactor is needed. In this investigation, a model for the reactor unit will be developed by applying the Adaptive Neuro Fuzzy Inference System (ANFIS) method using collected real time operational data. Moreover, to keep the level of reactor steady, a level controller designed using inverse dynamics method based on ANFIS is also proposed in this paper. The results from modelling investigation show that ANFIS based model is able to follow the actual system output quite satisfactorily with an RMSE value of 0.47995. In addition, by using the proposed control scheme it is found that the ANFIS inverse dynamics controller yields better performance than the currently existing conventional PI controller, with RSME value of 0.1592 while RSME of using conventional PI controllers is 0.6447.
WHITE NOISE ANALYSIS OF NEURAL NETWORKS

A white noise analysis of modern deep neural networks is presented to characterize their biases at the whole network level or the single neuron level. Our analysis is based on two popular and related methods in psychophysics and neurophysiology namely classification images and spike triggered analysis. These methods have been widely used to understand the underlying mechanisms of sensory systems in humans and monkeys. We leverage them to investigate the inherent biases of deep neural networks and to obtain a first-order approximation of their functionality. We emphasize on convolutional neural networks (CNNs) since they are currently the state of the art methods in computer vision and are a decent model of human visual processing. In addition, we study multi-layer perceptrons, logistic regression, and recurrent neural networks. Experiments over four classic datasets, MNIST, Fashion-MNIST, CIFAR-10, and ImageNet, show that the computed bias maps resemble the target classes and when used for classification lead to an over two-fold performance than the chance level. Further, we show that classification images can be used to attack a black-box classifier and to detect adversarial patch attacks. Finally, we utilize spike triggered averaging to derive the filters of CNNs and explore how the behavior of network changes when neurons in different layers are modulated. Our effort illustrates a successful example of borrowing from neuroscience to study ANNs and highlights the importance of cross-fertilization and synergy across machine learning, deep learning, and computational neuroscience.
Bagging deep convolutional autoencoders trained with a mixture of real data and GAN-generated data

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks.In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion.To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks.We focus on combining those three techniques.To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning.The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems.We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.
The Impact of Web-Scale Discovery on the Use of Electronic Resources

In 2015, the University of California, Berkeley, launched EBSCO Discovery Service (EDS), a web-scale discovery tool, with a goal of improving visibility and usage of collections. This study applies linear regression analysis to usage data for ebooks, ejournals, and abstracts and indexing (A&I) databases before and after implementation of EDS in order to identify correlations between the discovery layer and usage of library electronic resources across platforms. Our findings diverge from conclusions drawn in the previous literature that indicates that resource use generally increases after a discovery tool is implemented. We examine data from a longer period of time than the previous literature had, looking for statistically significant changes in resource use. The discovery layer at UC Berkeley did not lead to equal increases across platforms, but rather to a complex array of increases and decreases in use according to a variety of factors.
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations often suffers from the covariate shift problem, which results in cascading errors of the learned policy. We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction. We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation. We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks. We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency.
Dependency Length Minimization vs. Word Order Constraints: An Empirical Study On 55 Treebanks

This paper expands on recent studies of very large treebank collections aiming to find empirical evidence for language universals, specifically for the functionally motivated Dependency Length Minimization (DLM) hypothesis.According to DLM grammars are set up to support the expression of utterances in a way that minimizes the distance between heads and dependents.We construct several incremental baselines that lead from the random free order linearization to the real language by adding various word order constraints.We conduct detailed analyses on 55 treebanks and find that all of the constraints contribute to DLM.We show that DLM on the one hand shapes the regularity and on the other motivates the attested exceptions from canonical word order.The findings contribute to a more fine-grained, differentiated picture of the role of DLM in the interaction of competing constraints on grammar and language use.
Solar Power Generation Forecast Model Using Seasonal ARIMA

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.
A Comparative Study of NOSQL System Vulnerabilities with Big Data

With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to manyorganisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified inNoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption, authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework isproposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.
Reliability Evaluation of a Distributed Communication Network of Weather Stations

Distributed automatic weather stations networks (AWSN) play an important role in modern weather forecasting and digitalization of agriculture. These networks allow to monitor various environment parameters and transmit actual data in real time to data centers, which makes possible to dramatically increase the efficiency of technical processes controlling farming. Since these networks may cover large areas, weather stations may use other stations to connect to gateways via multihop routes. The nodes are deployed outdoors and they are subject to failures due to harsh environment, deterioration of equipment, battery discharge, etc. If a station is used as a relay for other nodes, after its failure other stations may also become unavailable. To study the network reliability in this paper we propose a general methodology, consisting of six consolidated procedures, and apply the apparatus of the multidimensional alternating stochastic processes. We demonstrate the application of this analytical method for a special case of the minimal topology AWSN. General topology cases are studied with the use of the simulation approach. To study the numerical results, a discrete-event simulation model in Python language was developed. The paper presents numerical reliability analysis for three types of topologies: a simple network with three stations, a forest of ternary trees and random multihop networks with one or more gateways. In all scenarios we estimate reliability for the cases of static and dynamic routing. Different ways to enhance the distributed network reliability are discussed.
Active strict saddles in nonsmooth optimization

We introduce a geometrically transparent strict saddle property for nonsmooth functions. This property guarantees that simple proximal algorithms on weakly convex problems converge only to local minimizers, when randomly initialized. We argue that the strict saddle property may be a realistic assumption in applications, since it provably holds for generic semi-algebraic optimization problems.
Basic concept Pythagoras tree for construct data visualization on decision tree learning

Decision Tree in Data Mining frequently used to learn the pattern by interpreting data.A hierarchy of tree model in Decision Tree as data visualization which often used makes fully load space.Another option in using model is Phytagoras Tree.Pythagoras Tree in this study is the basic concept of Pythagorean Theorem that used by a binary hierarchy with a fractal technique which the shape using the square as branches enclose a right triangle.A fractal of Pythagoras Tree is the dataset which split the subsets into trunks and leaves.Construct a fractal of Pythagoras Tree depends on the angle θ for build branches followed by square area.Pythagoras Tree model is an easy way to understanding the dataset based on the size of the square.The smaller the size, the fewer instances in the rectangle.Also, data associations easily traced when fi lled with color.
Learning to Learn by Zeroth-Order Oracle

In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our learned optimizer, modeled as recurrent neural network (RNN), first approximates gradient by ZO gradient estimator and then produces parameter update utilizing the knowledge of previous iterations. To reduce high variance effect due to ZO gradient estimator, we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling. Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks (in particular, the black-box adversarial attack task, which is one of the most widely used tasks of ZO optimization). We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer.
Analysis of Performance of Two Wavelet Families Using GLCM Feature Extraction for Mammogram Classification of Breast Cancer

Background: Mammogram images are low dose x-ray images which detect the breast cancer before the women can actually experience it. Objective: To determine the accurate methodology for feature extraction using different wavelet families and different classification algorithms. Methods: Two wavelet families are used namely Daubechies (db8) and Biorthogonal (bior3.7). The Gray-Level Co-occurrence Matrix is used for extracting 9 features at each sub-band. 27 features are extracted at three sub-bands of Discrete Wavelet Transform. The features are extracted at three levels of decomposition and after that the classification algorithm named as Naive Bayes, Multilayer Perceptron, Fuzzy-NN and Genetic Programming are applied to extracted features. The feature selection algorithms are applied named as Wavelet and Principle Component Analysis for selecting the features and then classification accuracy is determined and compared between these. Results: Mammographic Image Analysis Society, database including 322 mammogram images from 161 patients is used. The classification algorithm without feature selection named as Fuzzy-NN gives better results at the third level of decomposition having classification accuracy for db8 wavelet family up to 99.68% and for bior3.7 wavelet family up to 99.98%. Wavelet with Multilayer Perceptron using feature selection algorithm gives the classification accuracy for db8 wavelet family up to 96.27% and for bior3.7 up to 93.47%. Conclusion: Fuzzy-NN algorithm gives highest accuracy of 99.98% for bior3.7 wavelet family. It indicates that with feature selection and without feature selection, the wavelet families differ as db8 is better consideration for with feature selection and bior3.7 wavelet family for without feature selection.
i3D-Playfair: An Improved 3D Playfair Cipher Algorithm

Security of information is one of the most concerned issues during data transmission wherein cryptographic schemes are needed. This paper intends to improve the encryption scheme of the 3D (4x4x4) Playfair Cipher Algorithm by enhancing its security performance. The proposed improvement utilizes a 4x8x8 matrix that holds 256 ASCII characters and implements a dual Cipher Block Chaining (CBC) with dual circular-XOR method.
Çoklu Ortam Sistemleri İçin Siber Güvenlik Kapsamında Derin Öğrenme Kullanarak Ses Sahne ve Olaylarının Tespiti

In addition to many natural sound sources in nature, synthetic sounds are also used in the multimedia systems of our modern world. Environments (i.e., sound scenes) with these sounds are important for biometric authorization, security requirements and robust/safer voice/video communication. Apart from audio formats that have special constraints such as speech/speaker recognition and verification, the separation of polyphonic sounds, noise reduction, detection of sound scenes/events and voice tagging processes are gaining importance in order to create safer information systems in terms of cyber security. In recent years deep learning has been preferred in the field of cyber security due to its layered infrastructure, which enables the easy extraction of attributes and semantic relationships in the raw data. In this study, the use of deep learning architecture models for voice (or speech) analysis and classification/prediction and detection as multimedia data in cyber security coverage is examined. In our study, deep neural networks, convolutional neural networks, recurrent neural networks, restricted Boltzmann machine and deep belief networks are systematically reviewed as prominent models in the publications between 2015 and 2019. Therefore, the orientation in the literature on voice/speech processing in cyber security, prevention of voice spoofing, and achieving consistent and high performance results is clearly demonstrated through discussions and comments based on scientific findings over fourty studies.