Reviewerly/exHarmony huggingface dataset:
A Static Higher-Order Dependency Pair Framework

We revisit the static dependency pair method for proving termination of higher-order term rewriting and extend it in a number of ways: (1) We introduce a new rewrite formalism designed for general applicability in termination proving of higher-order rewriting, Algebraic Functional Systems with Meta-variables. (2) We provide a syntactically checkable soundness criterion to make the method applicable to a large class of rewrite systems. (3) We propose a modular dependency pair framework for this higher-order setting. (4) We introduce a fine-grained notion of formative and computable chains to render the framework more powerful. (5) We formulate several existing and new termination proving techniques in the form of processors within our framework. The framework has been implemented in the (fully automatic) higher-order termination tool WANDA.
OpenAIRE compatibility for CRIS systems: recent progress

This paper reports on the progress that the OpenAIRE-funded, euroCRIS-led METIS2OpenAIRE project means for the implementation of the OpenAIRE Guidelines for CRIS Managers. An update of these guidelines has taken place in the past two years to enable a smoother and more effective information exchange process between CRIS systems and the OpenAIRE aggregation. METIS2OpenAIRE aimed to ensure OpenAIRE compatibility based on these version 1.1 guidelines for a first institutional research information management system, namely METIS at Radboud University Nijmegen in the Netherlands. This three-and-a-half-month project also set out to support a number of parallel implementations by external, budget-neutral stakeholders that would ensure a widespread adoption of the newly released CRIS interoperability standard. These were PURE, the single most widely adopted CRIS platform worldwide, and OMEGA-PSIR, a widespread CRIS solution in Poland. The work carried out under the METIS2OpenAIRE initiative and its results as of mid-2018 are described in this piece. These results include the development of a minimally sufficient OpenAIRE validator for CRIS systems. This software, specifically designed to support the interoperability requirements for the METIS institutional CRIS at Radboud, should gradually evolve into the default mechanism for other CRIS systems to use in order to test their own level of compliance with the OpenAIRE Guidelines. As for the external stakeholders, by mid-2018 there is already significant progress in the route towards OpenAIRE compatibility for both PURE and OMEGA-PSIR.
Nonlinear prediction using radial basis function network incorporating coordinate transformation

A nonlinear modeling and prediction method using radial basis function (RBF) network is developed in this paper, where the coordinate system of training data is transformed using the affine transformation. It is difficult to accurately predict the time series by conventional approach using the RBF network, in which the coordinate system is always fixed and this makes the prediction poor. For highly accurate prediction using the RBF network, the coordinate system is transformed using the affine transformation. The coordinate system of training data for the RBF network is transformed, and consequently it is expected that the highly accurate prediction can be made in comparison with the conventional approach. The proposed approach is numerically easy to implement with the computationally inexpensive procedure. The validation and characteristics of the proposed approach are examined through benchmarks.
PENGARUH TEKNIK MEMBACA SQ3R TERHADAP MEMBACA PEMAHAMAN MAHASISWA FKIP UNIVERSITAS BUNG HATTA PADANG

The main issue discussed in this article is the students’ low interest in reading textbooks. In addition, students do not use various techniques. As a result, they feel bored, and less concentrated when reading. The theory used in this research is the reading theory proposed by Tarigan (2008), reading comprehension techniques by Agustina (2008) and SQ3R reading techniques by Ermanto (2008) and Agustina (2008). This type of research is quantitative research using experimental method with SQ3R reading techniques. The population of this study were Indonesian Language and Literature Education students of FKIP Bung Hatta University who took the Reading Teaching Skills course. The sample was elected purposively. Based on data analysis, it was found that students did not apply the SQ3R reading technique well in determining the theme, plot, background, characterization, point of view and mandate, with an average value of 80.5. Thus, it can be concluded that there is a significant effect of the use of SQ3R reading techniques towards students’ reading comprehension at FKIP of Bung Hatta University Padang.
Corporate Governance in the Banking Industry of Kosovo

The main purpose of this research is to study the impact of corporate governance on the financial performance of the banking sector in Kosovo. To analyze this impact, the Pearson correlation coefficient, multiple regression analysis related to the board size and board independence and banking sector performance in Kosovo were applied. The key corporate governance variables that have been studied in this research are: (i) size of the board of directors, (ii) the independence of the board of directors (the ratio between non-executive directors and the total number of board members). The data for this research were collected from the annual reports and audited financial statements of commercial banks in Kosovo for the 12 year period (2006-2017) and from questionnaires addressed to board members of commercial banks in Kosovo as well as other publications from relevant local institutions such as the Central Bank of Kosovo (CBK), Statistical Office of Kosovo (SOK), Tax Administration of Kosovo (TAK), etc. The results of the multiple regression analysis regarding the influence of the board of directors on the financial performance of the banking sector indicate that: the size of the board of directors and the independence of the board of directors have a positive and significant impact on the financial performance of the banking sector in Kosovo, expressed through return on assets (ROA) and return on equity (ROE). Findings of this research are in line with the findings of other researchers in this field and confirm the assertion that the management of the above variables improves and has a positive impact on the financial performance of banks in Kosovo.
Security of Password Hashing in Cloud

Though the History of using password in computing can be traced back to as far as mid of last century little focus has been implied on how to securely store and retrieve password to authenticate and authorize services to the end users.In this paper the current security of various password hashing schemes that are in use today will be investigated through practical proof of concept-GPU based, password hash dump cracking using the power of cloud computing.We will be providing comparison on different password hashing cracking time using the cloud GPU power in AWS.The focus of this paper is to show the possible use of cloud computing in cracking hash dumps and the way to countermeasures them by using secure hashing algorithm and using complex passwords.
Software Engineering Data Analytics: A Framework Based on a Multi-Layered Abstraction Mechanism

This paper presents a concept of a domain-specific framework for software analytics by enabling querying, modeling, and integration of heterogeneous software repositories. The framework adheres to a multi-layered abstraction mechanism that consists of domain-specific operators. We showcased the potential of this approach by employing a case study.
UNMANNED AERIAL VEHICLES TRAJECTORY ANALYSIS CONSIDERING MISSING DATA

Researches very often deal with the problem of missing data. This issue is caused by impossibility of data obtaining, its distortion or concealment. The goal of present paper is to recover missing data and to analyse Unmanned Aerial Vehicles (UAV) trajectory based on the degree of deviation from pre-planned trajectory. The range probability approach is used to assess flight situation. The results of trajectory analysis for real position data of UAV are demonstrated.
PREDICTING THE ENERGY PRODUCTION OF A ROOFTOP PV PLANT BY USING DIFFERENTIAL EVOLUTION ALGORITHM

In this study, a simple and plain closed-form mathematical expression has been obtained to precisely estimate the monthly production of a rooftop photovoltaic (PV) plant installed in Adana, Turkey. The proposed model is developed by utilizing the Differential Evolution (DE) optimization algorithm based on the PV plant’s 5-year (August 2013 – July 2018) real measurement data. The PV plant is a grid-connected rooftop solar PV system located at Kıvanç Textile in Adana, Turkey. The PV system is equipped with an online monitoring system that provides real time data. The study shows the actual energy production is 730 MWh/year on average for 5 years. In order to test the robustness and precision of the present model, it has been compared with the long-term real measurement data of the PV system. The key benefit of the model is giving a convincing prediction of the future production of the PV panel in a simple way. It also does not require any further information other than time. Average percentage error was reached as small as 7.4% for 5-year data.
A new Concentric Circles Detection method for Object Detection applied to Radar Images

In this work, a new concentric circles detection method for object detection is proposed. It has been applied to the images of a commercial radar, captured with a Charge-Coupled Device (CCD) camera. The processing includes the detection of centres and concentric circles in the images and the identification of the radar scale. Several methods found in the literature have been applied and compared with our novel proposal for multiple concentric circles detection, called “Propagation Method based on Circular Regression”. This methodology has been validated with real radar images, proving its efficiency in obtaining the distance of any object to a marine vessel, with high accuracy and low computational cost, in real time. This system can not only be applied to most existing radars in the market by adjusting the parameters of each model but our proposal for concentric circle detection can be also applied to other sensing applications.
Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification

As a specific case of image recognition, zero-shot image classification is difficult to solve since its training set cannot cover all the categories of the testing set. From the view point of human vision recognition, the objects can be recognized through the visible and nameable description to the properties. To be the semantic description of the object property, attributes can be taken as a bridge between the seen and unseen categories, which are capable of using into zero-shot image classification. There are mainly binary attributes and relative attributes for zero-shot classification, where the relative attributes have the ability to catch more general sematic relationship than the binary ones. But relative attributes do not always work in zero-shot classification for those categories having similar relative strength attributes. Aiming at solving the defect of the relative attributes in describing the similar categories, we propose to construct the Hybrid Relative Attributes based on Sparse Coding (SC-HRA). First, sparse coding is implemented on low-level features to get nonsemantic relative attributes, which are the necessary complement to the existing relative attributes. After that, they are integrated with the relative attributes to form the hybrid relative attributes (HRA). HRA ranking functions are then learned by the relative attribute learning. Finally, the class label is obtained according to the predicted ranking results of HRA and the ranking relations of HRA among the categories. To verify the effectiveness of SC-HRA, the extensive experiments are conducted on the datasets of faces and natural scenes. The results show that SC-HRA acquires the higher classification accuracy and AUC value.
The defective idea of transforming libraries into basically different institutions

The author analyzes and criticizes Vadim Stepanov's radical technocratic ideas of transforming libraries into basically different institutions. In particular, Vadim Stepanov promotes these ideas in his recent publications in the “Scientific and Technical Libraries” journal. The author proves that such views are not the news having been promoted a century ago and repeated many times. However, the attempts to implement them have demonstrated their bankruptcy. The author also considers the ideas of taming the document flow: it has been stabilized in the 20:80 ratio to the advantage of non-digital books. 99% users come to the library for printed (paper) books as they cannot afford buying them due to continuously increasing prices and imperfect book selling system. It means that the libraries shall not renounce of their documentive function. The libraries recognize the significance of their communicative function, and V. Stepanov’s appeals look beside the point. Thus, the appeal to transform libraries into basically different institutions shall be deemed counterproductive.
Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning

As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction.
Influence measures for the Waring regression model

In this paper, we present a regression model where the response variable is a count data that follows a Waring distribution.The Waring regression model allows for analysis of phenomena where the Geometric regression model is inadequate, because the probability of success on each trial, p, is different for each individual and p has an associated distribution.Estimation is performed by maximum likelihood, through the maximization of the Q-function using EM algorithm.Diagnostic measures are calculated for this model.To illustrate the results, an application to real data is presented.Some specific details are given in the Appendix of the paper.
A study over the Formulation of the Parameters 5 or Less Independent Variables of Multiple Linear Regression

As is known, finding the parameters of multiple linear regression is an important case. Of course, these parameters can be easily found with the help of the computer. In this study, in addition to the formula of the parameters of linear regression, the general formulas of the parameters of 5 and less independent variables of multiple linear regression are given with a certain order. The derivations of the formulas presented are given step by step. In addition to classical matrix form, these new formulas for estimation of the parameters of multiple linear regression could be proposed especially to the researchers not using computer program for calculating the complex operations. By using these formulas, the researcher can estimate easily the parameters of multiple linear regression without using a computer and so the researcher can compose easily the table of variance analysis to interpret the regression made. Since for 6 and more independent variables, the tables of the parameters of multiple linear regression are too long and they take up too much space, the general formulas of the parameters of 6 and more independent variables of multiple linear regression could not be given in this study.
Robust Video Background Identification by Dominant Rigid Motion Estimation

The ability to identify the static background in videos captured by a moving camera is an important pre-requisite for many video applications (e.g. video stabilization, stitching, and segmentation). Existing methods usually face difficulties when the foreground objects occupy a larger area than the background in the image. Many methods also cannot scale up to handle densely sampled feature trajectories. In this paper, we propose an efficient local-to-global method to identify background, based on the assumption that as long as there is sufficient camera motion, the cumulative background features will have the largest amount of trajectories. Our motion model at the two-frame level is based on the epipolar geometry so that there will be no over-segmentation problem, another issue that plagues the 2D motion segmentation approach. Foreground objects erroneously labelled due to intermittent motions are also taken care of by checking their global consistency with the final estimated background motion. Lastly, by virtue of its efficiency, our method can deal with densely sampled trajectories. It outperforms several state-of-the-art motion segmentation methods on public datasets, both quantitatively and qualitatively.
Moving Object Localization in Video Sequences under Static and Dynamic Background Conditions

Background subtraction is one of the most reliable approach to localize the moving object under static camera arrangement.As seen, the moving object detection is a preliminary task in many vision applications such as video analysis, object tracking and activity analysis.However, the quasi-stationary pixels, aperture effect, ghost trail and varying illumination are still an annoying factors in the extraction procedures of the actual moving object in video.To alleviate the above problems, a video segmentation method is proposed that utilizes background subtraction and the 3-class fuzzy c-means clustering algorithm for extracting the relevant moving pixels.The proposed algorithm modifies learning parameters of adaptive filters to adapt the changes in the background.Afterwards, it incorporates the Markovian framework in which the initial motion field provides a prior information to regularize the segmentation process.The method achieves better visual and quantitative performance than other well-known background subtraction methods reported in this paper.
Dynamic Concatenation of Quantum Error Correction in Integrated Quantum Computing Architecture

Resource overhead problem caused by concatenation in quantum error correction (QEC) is of significant importance for the realization of fault-tolerant quantum computation (FTQC). To attack this problem, we propose a novel scheme by considering integrated FTQC architecture where the concatenation level is controlled dynamically; i.e., less (or more) concatenation levels are imposed by good (or poor) performance gates-we call this scheme "dynamic concatenation" in this sense. Such a dynamic concatenation is realizable in an integrated structure of FTQC, as the information of the concatenation can be communicated between classical system elements (e.g., compiler and system organizer) and the logical qubits in real-time. We derive the effective lower and upper bounds of the length of gate decomposition in order to achieve the practical advantage, namely of reduction of the overall operation time. By considering two non-trivial examples, it is shown that the aforementioned advantage can indeed be achieved in the presented scheme. Our result also provides an important scientific message, i.e., the interplay between "classical" and "quantum" can be helpful in QEC.
Yok Edici isimli kitaptaki çevirmenin önsözü üzerine bir çalışma

Translation and publication of foreign literature&#x0D; have at times resulted in obscenity court cases (e.g. the Aphrodite [1940] and Tropic&#x0D; of Cancer [1988] cases) against Turkish translators and publishers since&#x0D; the first decades of the Turkish Republic.&amp;nbsp;&#x0D; The recent obscenity court cases against the Turkish translators and&#x0D; publishers of William S. Burroughs’ The&#x0D; Soft Machine and Chuck Palahniuk’s Snuff&#x0D; (2011) were interpreted as instances of censorship. Taking this as a starting&#x0D; point, this article conducts a case study of Ahmet Ergenç’s translator’s&#x0D; preface for Exterminator!, a book by&#x0D; Burroughs which contains obscenity and was published in Turkey after the&#x0D; aforementioned court cases. The aims of this case study is to investigate the&#x0D; purpose of and reasons behind Ergenç’s preface and to determine whether&#x0D; Ergenç’s preface was influenced by the court cases and the censorial conditions&#x0D; under which Turkish translators work. To do so, Ergenç’s preface is studied in&#x0D; the light of Genette’s (1997) understanding of allographic and authorial&#x0D; prefaces and an interview is conducted with Ergenç. As a result, it is&#x0D; discussed that Ergenç aimed at informing readers unfamiliar with Burroughs&#x0D; about his style, which consists of fragmented narrative and obscene words, to&#x0D; prevent misinterpretations of them as translation mistakes on the part of the&#x0D; reader. As to the question of the influence of censorship on Ergenç’s preface,&#x0D; results of the analysis of the preface and the interview are conflicting. The article&#x0D; concludes that Ergenç denies any authorial responsibility for fragmentation and&#x0D; obscenity in Exterminator! and uses&#x0D; his preface to embrace invisibility as a translator while also gaining&#x0D; visibility at the same time through it.
Data-Driven Design

A/B testing has become the de facto standard for optimizing design, helping designers craft more effective user experiences by leveraging data. A typical A/B test involves dividing user traffic between two experimental conditions (A and B), and looking for statistically significant differences in performance indicators (e.g., conversion rates) between them. While this technique is popular, there are other, powerful data-driven methods --- complementary to A/B testing --- that can tie design choices to desired outcomes.
Efficiency Analysis of TFHE Fully Homomorphic Encryption Software Library Based on GPU

There are a large number of independent matrix and vector operations in the lattice-based homomorphic encryption. These operations are suitable for GPU, which can greatly improve the efficiency of homomorphic operations. In this paper, we analyze the structure of the homomorphic encryption algorithm and verify the reliability of the homomorphic encryption software library, debug and analyze the fully homomorphic encryption software library TFHE and its corresponding GPU version cuFHE, and then compare their efficiency. The experimental results show that the GPU version TFHE is 4.5 times faster than the CPU version TFHE, so the GPU can greatly improve the homomorphic running speed of the homomorphic encryption scheme.