Reviewerly/exHarmony huggingface dataset:
Very Short-Term Solar Power Forecast using Data from NWP Model

This article presents a method to forecast solar power 4 hours in advance by using forecast weather data from Numerical Weather Prediction (NWP) and the measurement obtained from weather monitoring instrument with feature engineering by machine learning techniques using Feed-forward Neural Network (FNN) and Recurrent Neural Network (RNN). Four types of relevant variables-forecast weather data, the average of forecast weather data, forecast weather data with the measurement from weather instrument, and the average of forecast weather data from nearby areas with the measurement from weather instrument-are used to find the right model and input variable for the forecast. The result shows that by using Feed-forward Neural Network with the forecast weather data and the measurement from weather monitoring instrument, the Root Mean Square Error (RMSE) value is 8.13%. On the other hand, by using Feed-forward Neural Network with just the forecast weather data from nearby areas, RMSE is at 8.38% which is only 0.25% higher than the forecast with the measurement. Thus, only weather data from nearby areas can be used for prediction of the area without the measurement from weather monitoring instrument and we can use it for further prediction at the region level.
Ground confluence of order-sorted conditional specifications modulo axioms

Terminating functional programs should be deterministic, i.e., should evaluate to a unique result, regardless of the evaluation order. For equational functional programs such determinism is exactly captured by the ground confluence property. For operationally terminating conditional equations this is equivalent to ground local confluence, which follows from local confluence. Checking local confluence by computing critical pairs is the standard way to check ground confluence [33]. The problem is that some perfectly reasonable equational programs are not locally confluent and it can be very hard or even impossible to make them so by adding more equations. We propose three methods, called Methods 1–3, that can be synergistically combined to prove an order-sorted conditional specification modulo axioms B ground locally confluent. Method 1 applies the strategy proposed in [14] to use non-joinable critical pairs as completion hints to either achieve local confluence or reduce the number of critical pairs. Method 2 uses the inductive joinability inference system proposed in this paper to try to prove the critical pairs remaining after applying Method 1 ground joinable. It can furthermore show ground local confluence of the original specification. Method 3 is hierarchical in nature: it can be used to prove the ground local confluence of a conditional equational specification whose conditions belong to a subspecification that has already been proved ground confluent and operationally terminating, and that is conservatively extended by the overall specification in an appropriate sense. These methods apply to order-sorted and possibly conditional equational programs modulo axioms such as, e.g., Maude functional modules. We show their effectiveness in proving the ground confluence of non-trivial examples that have eluded previous proof attempts.
Development of the dialogue skills in a foreign language in comparing of oil benchmarks

Abstract The aim of research is to develop skills of dialogue on professional topics in a foreign language. The objectives of the research are the following: analyze professional goals in programs; identify language skills used in the dialogue; investigate the role of self-study in developing the skills of professional communication; suggest ways of improving the dialogue skills (stimulate asking of questions, giving extended answers). Methods of research include literature analysis on the topics of oil benchmarks, methods of teaching professional communication as well as the interview with an expert in teaching foreign languages. The outcomes of research are planned to be a set of recommended exercises for developing of the dialogue skills.
A Bitwise Logistic Regression Using Binary Approximation and Real Number Division in Homomorphic Encryption Scheme

Homomorphic Encryption (HE) is considered to be one of the most promising solutions to maintain secure data outsourcing because the user’s query is processed under encrypted state. Accordingly, many of existing literature related to HE utilizes additive and multiplicative property of HE to facilitate logistic regression which requires high precision for prediction. In consequence, they inevitably transform or approximate nonlinear function of the logistic regression to adjust to their scheme using simple polynomial approximation algorithms such as Taylor expansion. However, such an approximation can be used only in limited applications because they cause unwanted error in results if the function is highly nonlinear. In response, we propose a different approximation approach to constructing the highly accurate logistic regression for HE using binary approximation. Our novel approach originates from bitwise operations on encrypted bits to designing (1) real number representation, (2) division and (3) exponential function. The result of our experiment shows that our approach can be more generally applied and accuracy-guaranteed than the current literature.
Dynamic Multi-objective Virtual Machine Placement in Cloud Data Centers

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator. The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals.
Postural Risks and User Experience of 3D Interface Designs for Virtual Reality-based Learning Environments

Virtual Reality-based Learning Environments (VRLEs) are an emerging tool for classroom teaching. While VRLEs are increasingly in use, little attention has been paid to ergonomic challenges in use of VRLEs. This study compared two 3D interface designs of a VRLE created to teach the phases of the Moon. Participants ( N = 16) were randomly assigned to either a Hand or Toggle interface design. The interface designs were compared in terms of postural risk (REBA score), task completion time, user experience and environmental presence. Results indicate no significant difference between the Hand and Toggle interface designs for the outcome variables; however, environmental presence was marginally higher in the Hand interface. REBA scores for Hand (3.56 ± 0.89) and Toggle (3.79 ± 1.14) indicate low to medium level of postural risks. Future research should focus on development of 3D interface design guidelines to ensure safe, comfortable and effective use of VRLEs.
Theoretical and methodical aspects of the organization of students’ independent study activities together with the use of ICT and tools

In the article the possibilities and classification of ICTs and tools that can be used in organizing students’ independent study activities of higher education institutions has been explored. It is determined the students’ independent study activities is individual, group, collective activity and is implemented within the process of education under the condition of no pedagogy’s direct involvement. It complies with the requirements of the curriculum and syllabus and is aimed at students’ acquisition of some social experiences in line with the learning objectives of vocational training. The analysis of the latest information and technological approaches to the organization of students’ independent study activities made it possible to determine the means of realization of the leading forms of organization for this activity (independent and research work, lectures, consultations and non- formal education), to characterize and classify the ICTs and tools that support presentation of teaching materials, electronic communication, mastering of learning material, monitoring of students’ learning and cognitive activity, such as ones that serve for the sake of development and support of automated training courses, systems of remote virtual education with elements of artificial intelligence, which implement the principle of adaptive management of learning and the organization of students’ independent study activities. The paper provides the insight into the essence of the conducted investigation on the assesses of the effectiveness of ICTs and tools in the process of organizing students’ independent study activities.&#x0D; &#x0D;
Semantic Schema Matching for String Attribute with Word Vectors

Instance based schema matching is to determine the correspondences between heterogeneous databases by comparing instances. This process is especially effective when schema information is worthless to detect correspondences. Many previous processes often use schema information and statistics for analyzing correspondences. Heterogeneous databases consist of an enormous number of tables containing various attributes and arise the data heterogeneity. This problem degenerates the schema matching process seriously. Furthermore, these researches describe the need to consider semantic information because of the limitations of matching quality in schema information. In this paper, we propose the instance based schema matching considering attributes' semantics. We used Word2Vec to match attributes of character strings. The result shows a possibility to detect matching between attributes with high semantic similarity.
AWSD: Adaptive Weighted Spatiotemporal Distillation for Video Representation

We propose an Adaptive Weighted Spatiotemporal Distillation (AWSD) technique for video representation by encoding the appearance and dynamics of the videos into a single RGB image map. This is obtained by adaptively dividing the videos into small segments and comparing two consecutive segments. This allows using pre-trained models on still images for video classification while successfully capturing the spatiotemporal variations in the videos. The adaptive segment selection enables effective encoding of the essential discriminative information of untrimmed videos. Based on Gaussian Scale Mixture, we compute the weights by extracting the mutual information between two consecutive segments. Unlike pooling-based methods, our AWSD gives more importance to the frames that characterize actions or events thanks to its adaptive segment length selection. We conducted extensive experimental analysis to evaluate the effectiveness of our proposed method and compared our results against those of recent state-of-the-art methods on four benchmark datatsets, including UCF101, HMDB51, ActivityNet v1.3, and Maryland. The obtained results on these benchmark datatsets showed that our method significantly outperforms earlier works and sets the new state-of-the-art performance in video classification. Code is available at the project webpage: https://mohammadt68.github.io/AWSD/.
Multiple indefinite kernel learning for feature selection

Multiple kernel learning for feature selection (MKL-FS) utilizes kernels to explore complex properties of features and performs better in embedded methods. However, the kernels in MKL-FS are generally limited to be positive definite. In fact, indefinite kernels often emerge in actual applications and can achieve better empirical performance. But due to the non-convexity of indefinite kernels, existing MKL-FS methods are usually inapplicable and the corresponding research is also relatively little. In this paper, we propose a novel multiple indefinite kernel feature selection method (MIK-FS) based on the primal framework of indefinite kernel support vector machine (IKSVM), which applies an indefinite base kernel for each feature and then exerts an l1-norm constraint on kernel combination coefficients to select features automatically. A two-stage algorithm is further presented to optimize the coefficients of IKSVM and kernel combination alternately. In the algorithm, we reformulate the non-convex optimization problem of primal IKSVM as a difference of convex functions (DC) programming and transform the non-convex problem into a convex one with the affine minorization approximation. We further utilize a leverage score sampling method to select landmark points for solving large-scale problems. Moreover, we extend MIK-FS to multi-class feature selection scenarios. Experiments on real-world datasets demonstrate that MIK-FS is superior to some related state-of-the-art methods in both feature selection and classification performance.
Large-Scale Nodes Classification With Deep Aggregation Network

The most fundamental task of network representation learning (NRL) is nodes classification which requires an algorithm to map nodes to vectors and use machine learning models to predict nodes' labels. Recently, many methods based on neighborhood aggregation have achieved brilliant results in this task. However, the recursive expansion of neighborhood aggregation poses scalability and efficiency problems for deep models. Existing methods are limited to shallow architectures and cannot capture the high order proximity in networks. In this article, we propose the deep aggregation network (DAN). DAN uses a layer-wise greedy optimization strategy which stacks several sequential trained base models to form the final deep model. The high order neighborhood aggregation is performed in a dynamic programming manner, which allows the recursion nature of neighborhood aggregation to be eliminated. The reverse random walk is also proposed, and combined with the classic random walk in formulating a novel sampling strategy that allows DAN to flexibly adapt to different tasks related to communities or structural roles. DAN is more efficient and effective than previous neighborhood aggregation based methods, especially when it is intended to handle large-scale networks with dense connections. Extensive experiments are conducted on both synthetic and real-world networks to empirically demonstrate the effectiveness and efficiency of the proposed method.
Collision Resistant Hashing from Sub-exponential Learning Parity with Noise

The Learning Parity with Noise (LPN) problem has recently found many cryptographic applications such as authentication protocols, pseudorandom generators/functions and even asymmetric tasks including public-key encryption (PKE) schemes and oblivious transfer (OT) protocols. It however remains a long-standing open problem whether LPN implies collision resistant hash (CRH) functions. Inspired by the recent work of Applebaum et al. (ITCS 2017), we introduce a general construction of CRH from LPN for various parameter choices. We show that, just to mention a few notable ones, under any of the following hardness assumptions (for the two most common variants of LPN) there exists CRH functions with constant (or even poly-logarithmic) shrinkage, which can be implemented using polynomial-size depth-3 circuits with NOT, (unbounded fan-in) AND and XOR gates. Our technical route LPN $$\rightarrow $$ bSVP $$\rightarrow $$ CRH is reminiscent of the known reductions for the large-modulus analogue, i.e., LWE $$\rightarrow $$ SIS $$\rightarrow $$ CRH, where the binary Shortest Vector Problem (bSVP) was recently introduced by Applebaum et al. (ITCS 2017) that enables CRH in a similar manner to Ajtai's CRH functions based on the Short Integer Solution (SIS) problem. Furthermore, under additional (arguably minimal) idealized assumptions such as small-domain random functions or random permutations (that trivially imply collision resistance), we still salvage a simple and elegant collision-resistance-preserving domain extender combining the best of the two worlds, namely, maximized (depth one) parallelizability and polynomial shrinkage. In particular, assume $$2^{n^{0.5+\varepsilon }}$$-hard constant-noise LPN or $$2^{n^{0.25+\varepsilon }}$$-hard low-noise LPN, we obtain a collision resistant hash function that evaluates in parallel only a single layer of small-domain random functions (or random permutations) and shrinks polynomially.
The Parallel CMAC Authentication Algorithm

The increasing demand for providing online financial transactions and personal data collection on digital systems has made it imperative to use data security algorithms for securing personal/financial data. Therefore, the reliance on data security algorithms for providing authentication service is increasing dynamically. The authentication algorithm provides services for user validation or authentication. The Message Authentication Code (MAC) based authentication of data consists of Cipher Block Chaining Message Authentication Code (CBC-MAC) and Cipher-based Message Authentication Code (CMAC) algorithms. These MAC-based algorithms have serial architecture and low throughput. In this paper, we have presented a new authentication algorithm called Parallel Cipher-based Message Authentication Code (PCMAC). The proposed algorithm is parallel architecture and useful for high throughput application. The proposed algorithm is implemented on FPGA and compared with MAC-based authentication algorithms. The experimental results show that the throughput of PCMAC algorithm for authentication is 2.99 Gbps.
“Security Concern” as a Metric for Enterprise Business Processes

Measuring the security of business processes of enterprises has become essential in the wake of different threat scenarios. During the last two decades, a lot of research has been done on metrics for the network security, software system security, attack severity, situation assessment, etc. In the process level, business impact analysis models and security maturity models, as well as well-established risk analysis methods, exist. With the all-pervasive IT implementation of business processes, it has become imperative for the chief information security officers to come up with metrics for the security of business processes in the context of the relevant threat scenario. This paper introduces a novel security metric to assess the business process security, viz., Security Concern. The metric quantitatively measures the “concern” due to various attributes of the security of a business process in the context of the threat scenario and asset sensitivity. We present the model of the business process, its assets, their dependencies, exploits, and impacts on assets in a top-down fashion. Based on the model, security concern as a metric has been defined. The bottom-up method of the computation of the metric is explained. It is also demonstrated how the proposed metric can be of practical utility for the horizontal and temporal comparison of the business process security.
On the Number of Distinct k-Decks: Enumeration and Bounds

The k-deck of a sequence is defined to be the multiset of all its subsequences of length k and let D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n) denote the number of distinct k-decks for binary sequences of length n. In this paper, we determine the exact value of D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n) for small values of k and n and provide asymptotic estimates of D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n) when k is fixed.Specifically, for fixed k, we provide a trellis-based method to compute D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n) in time polynomial in n. We then compute D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n) for k ∈ {3, 4, 5, 6} and k ≤ n ≤ 30. We also improve the asymptotic upper bound on D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n) and in particular, show ${D_k}(n) = O\left( {{n^{(k - 1){2^{k - 1}} + 1}}} \right)$. For the specific case when k = 3, we show D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (n) = Ω(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ) while the upper bound states that D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (n) = O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> ).
Quaternion Non-local Total Variation for Color Image Denoising

Many existing color image denoising methods process color channels individually and fail to consider their cross-channel correlations. To solve this problem, in this paper, we employ the quaternion representation of the color image and propose a novel Quaternion Non-local Total Variation (QNLTV) model to remove Gaussian noise from color images. We first introduce the coupled quaternion distance to measure the color image patch similarity. Decomposing the color image into brightness and chromaticity components in quaternion domain, we then divide the QNLTV model into two quaternion optimizaiton problems and solve them alternatively. Experiment results show that QNLTV has the significantly better denoising performance than competing methods in terms of visual and quantitative evaluations.
Synthetic Tableaux with Unrestricted Cut for First-Order Theories

The method of synthetic tableaux is a cut-based tableau system with synthesizing rules introducing complex formulas. In this paper, we present the method of synthetic tableaux for Classical First-Order Logic, and we propose a strategy of extending the system to first-order theories axiomatized by universal axioms. The strategy was inspired by the works of Negri and von Plato. We illustrate the strategy with two examples: synthetic tableaux systems for identity and for partial order.
An ANFIS-based Human Activity Recognition using IMU sensor Fusion

Physical human activity is central to reducing the risk of many chronic diseases, thus it is considered vital to promoting healthy life styles. Also human Activity recognition (HAR) in recent times has found application in the explanations of the origin of complex diseases. In this paper an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based model is introduced for classification of daily living activities (ADLs) using data collected with a tri-axial Inertial Measurement Unit (IMU) sensor. Specifically, normalized data from the IMU axes within a specific window were considered to classify four chosen daily living activities (sit, stand, walk and run). The proposed ANFIS classifier were evaluated in terms of Root Mean Square Error (RMSE) over a variety of different ANFIS parameters and the results show that the selected activities are recognized well with an an overall accuracy rate of 98.88%.