Reviewerly/exHarmony huggingface dataset:
Framework and Implementation of Online Smartphone Traffic Classification According to Quality Sensitivity

Currently smartphones have become the most prevalent device used on the Internet and they generate a substantial portion of Internet traffic. Smartphones can run a wide variety of applications, each generating distinctive traffic. From the perspective of network operation, classification of these types of traffic in order to prioritize quality-sensitive traffic over best-effort traffic has the potential to improve network performance. Network operators generally treat web browsing, Voice over Internet Protocol (VoIP), video streaming, and other interactive traffic as quality-sensitive traffic. This quality-sensitive traffic is generally generated by user interactions on the current foreground application, hence its communication quality is likely to affect user's quality of experience (QoE) directly. On the other hand, traffic generated when the smartphone is inactive has a low potential for affecting user's QoE. As a traffic classification technique, machine learning is popular and demonstrates high accuracy; however, it is disadvantageous in that it involves gathering enough training data sets for supervised training. We propose a framework for online smartphone traffic classification that classifies traffic according to whether it is generated when the smartphone is active or inactive. A key feature of the proposed framework is automation of training data collection for machine learning. An application on the smartphone periodically estimates whether that smartphone is active or not, then it uploads the estimation results with timestamps as a source of training data sets. We also implemented a prototype of the proposed framework and assessed the feasibility of the proposal.
Practical detection of CMS plugin conflicts in large plugin sets

Content Management Systems (CMS), such as WordPress, are a very popular category of software for creating web sites and blogs. These systems typically build on top of plugin architectures. Unfortunately, it is not uncommon that the combined activation of multiple plugins in a CMS web site will produce unexpected behavior. Conflict-detection techniques exist but they do not scale. This paper proposes Pena, a technique to detect conflicts in large sets of plugins as those present in plugin market places. Pena takes on input a configuration, consisting of a potentially large set of plugins, and reports on output the offending plugin combinations. Pena uses an iterative divide-and-conquer search to explore the large space of plugin combinations and a staged filtering process to eliminate false alarms. We evaluated Pena with plugins selected from the WordPress official repository and compared its efficiency and accuracy against the technique that checks conflicts in all pairs of plugins. Results show that Pena is 12.4x to 19.6x more efficient than the comparison baseline and can find as many conflicts as it.
An Efficient and Secure Scheme for Dynamic Shared Data in Cloud

People have proposed many data integrity techniques to secure data storage in cloud. The majority of these schemes assume that only the owner of the data can modify their storage in cloud. In recent years, researchers have allowed different cloud users to use integrity assurance for modifying data. As a result, schemes with stronger reality than before have been proposed. Nevertheless, these attempts are impractical due to the large computing costs for cloud users. Clients must also perform numerous computations to ensure the integrity of data storage. A robust and efficient scheme is put forward in this study to maintain data integrity in cases that involve public auditing. In this way, multiuser modification can be used to check the public integrity for cloud data and reduce the auditing cost.
Statement Generation Based on Big Data for Keyword Search

Natural language generation (NLG) is the process of automatically generating a high-quality natural language text through a planning process based on some key information. Regular NLG generates sentences by analyzing grammatical and semantics, generating rules, and then organizing elements based on rules and heuristics. However, sentences generated by such methods are too strict, poorly scalable and difficult to adapt to the changing language style of human beings nowadays. Our goal is to generate smooth, personal, multi-sentence text for end users. This paper introduces a new NLG system, which can generate distinctive statements, and discard the knowledge of semantics, syntax etc., which are required by the original rule-based generation statements. This system turns out to be simple and efficient. We obtain required corpus from the network, and then use the idea of the search engine to find sentences from a large amount of data that matches the meaning of the keyword provided by users. Such generated sentences are more consistent with people’s daily life. Finally, we apply our system in the web commentary domain, evaluating our system based on three criteria. The result shows that our system works well in this field and can continue to deepen.
Question–Answer System on Episodic Data Using Recurrent Neural Networks (RNN)

Data comprehension is one of the key applications of question-answer systems. This involves a closed-domain answering system where a system can answer questions based on the given data. Previously people have used methods such as part of speech tagging and named entity recognition for such problems but those methods have struggled to produce accurate results since they have no information retention mechanisms. Deep learning and specifically recurrent neural networks based methods such as long short-term memory have been shown to be successful in creating accurate answering systems. This paper focuses on episodic memory where certain facts are aggregated in the form of a story and a question is asked related to a certain object in the story and a single fact present is given as answer. The paper compares the performance of these algorithms on benchmark dataset and provides guidelines on parameter tuning to obtain maximum accuracy. High accuracy (80% and above) was achieved on three tasks out of four.
Geology and Geochemistry of Carbonate Hosted Pb-Zn Deposit on North-east Part of Kahramanmaraş (Helete) in Engizek Belt

The aim of this study is to reveal the geological and geochemical properties of Pb-Zn mineralizations associated with carbonate rocks along the Helete (Kahramanmaraş) district in Taurus Orogenic belt. The Paleozoic Malatya metamorphics and Eocene sedimentary rocks are present in the Helete region within the area defined as Engizek Belt (North of Kahramanmaraş) in the Eastern Taurus Orogenic belt. In the Gümüşbanı region, there are bariteed Pb-Zn mineralizations in the Paleozoic Malatya metamorphic rocks.&amp;nbsp;Epigenetic mineralization observed in fractures and cracks is vein-type and disseminated. The thickness of the veins varies between 1 cm and 10 cm. The ore paragenesis contains barite, galena, sphalerite, chalcopyrite, fluorite and covellite. In geochemical analysis of samples taken from ore zone, Pb reaches maximum 50.21%, Zn maximum 51.99% and BaO 52.13%.&amp;nbsp; In geochemical analysis, the ore zone has a maximum of 50.21% Pb and Zn a maximum of 51.99%. In the isotope geochemistry study, the value of δ34S was determined between 10.92 and 11.24‰. As a result of the field and laboratory studies, it was determined that the mineralization was limited to the fractured lines and Paleozoic aged carbonate rocks, and S in the ore-forming solution pointed out to the continental origin.&amp;nbsp;
DyNeMoC: Statistical Model Checking for Agent Based Systems on Graphs

We report a tool for analysing through statistical model checking, complex dynamical systems on graphs that can be modelled as multi-agent systems. We discuss techniques to leverage the fact that we restrict the tool to dynamics on graphs for performance improvements. The query language that the tool provides is a probabilistic version of bounded linear temporal logic. We also introduce the notion of population sampling on agents for statistical model checking. To the best of our knowledge, this feature has not been reported previously in literature. Finally, we report experimental results on running examples that illustrate our ideas and the utility of the tool.
Cross-Spectral Face Matching Based on Phase Correlation

One way to identify human faces is by using facial features or facial patterns based on visible images. However, this approach depends on the lighting conditions. In a poor illumination environment, it is difficult to recognize the face images. Thermal images do not rely on the illumination condition; thus can overcome this problem. Unlike the visible image, the thermal images lack visual information. Therefore, matching between the thermal and visible images (cross-spectral matching) is vital in face recognition. This paper proposed a phase correlation method, namely phase-only correlation (POC) and band-limited POC (BLPOC), for a cross-spectral matching of face images. First, the Fourier transform was applied to face images in both spectral domains, and the phase correlation between the two images was calculated. The experimental results showed that, from 100 visible and thermal image (acquired by FLIR C2 camera) pairs, the BLPOC resulted in significant correlation values compared to POC.
On a New Algorithm for Removing Repeating Patterns in Similarity Analysis

In test security analyses, answer copying, collusion, and the use of a shared brain dump site can be detected by checking similarity between item response strings. The similarity, however, can possibly be contaminated by aberrant data resulted from careless responding or rapid guessing. For example, some test-takers may answer by repeating a pattern (e.g., ABCDABCDABCD or AAAAAAAA). It is crucial for researchers or practitioners to remove these kinds of repeating patterns before conducting any similarity analysis. In this study, a new algorithm for finding repeating patterns is proposed. The application of this algorithm on real and simulated data showed satisfying results; the simulated repeating patterns were identified 100% by the new algorithm with a negligible false positive rate. Although illustrated only in the context of similarity analysis, the proposed algorithm has broad applications: for example, identifying unmotivated test-takers for school intervention or cleaning data before estimating item parameters under an item response theory model.
Macroeconomic determinants of total factor productivity and its trend in Ethiopia

Total factor productivity (TFP) as a source of economic growth, has been recognized in economic theory for a long period of time. In this research we tried to examine the effect of some macroeconomic factors, which include trade openness, inflation, government expenditure, credit extended and foreign direct investment, and natural disaster drought on total factor productivity and its trend in Ethiopia by using Time series data spanning from 1991 to 2018. The TFP was computed by using the growth accounting method from Cobb–Douglas production function. ARDL was used for estimation of the short and long run econometric model. Accordingly, the trend analysis shows the growth in TFP has been fluctuating over the study period. The result from ARDL indicated that; in long run foreign direct investment, government expenditure and drought negatively and significantly affect TFP. Credit extended is found to affect TFP positively and significantly, while inflation and trade openness are insignificant. Therefore, policies such as; subsidizing domestic firms, effective government spending and making the agriculture sector drought resistant need to be stimulated.
Development of Modular Reduction Based on the Divider by Blocking Negative Remainders for Critical Cryptographic Applications

One of the main disadvantage of asymmetric cryptography is its low speed (in comparison with symmetric algorithms). This is very important for critical systems and applications. From this viewpoint in the paper the hardware implementation of a time-critical one of the basic operations of asymmetric cryptosystems-mod reduction is considered. Also an accelerated remainder determination method by an arbitrary modulus of number and the method implementation device based on a divider is proposed. Proposed approach of the negative remainders blocking during division without restoring the remainder is used. The mod reduction device, possessing the raised speed is developed. A step-by-step description of the operation of the device and illustrative examples are provided. The efficiency of the proposed circuit solution is verified on the Artix -7 FPGA from Xilinx for reducible numbers of different digit. Identified and presented in the form of graphs for the dependence of the spent resources of the FPGA Artix-7 on the capacity of the reducible number. Given results can be implemented in critical cryptographic applications for increasing its efficiency.
A Method for Automatic Clustering of Remote Sensing Data

The paper presents a method for estimating parameters of the mixture of Gaussian distributions that can be used for constructing likelihood functions at automatic clustering of remote sensing data. The idea of the method consists in successive improvement of sets of estimated parameters for the mathematical model represented by a mixture of basis functions. To improve the estimates the information on the errors occurring at approximation of the signature empiric distribution at the previous step of the iterative procedure is used. As an example the paper provides the results of automatic generation of the list of classes with their probabilistic descriptions. It also presents the results of further clustering of a satellite image upon the maximum likelihood criterion. The paper proves the efficiency of the developed method. It shows that the method can be used for describing real remote sensing data in the tasks of images recognition and cluster analysis, as it allows defining the number of components in the mixture of Gaussian functions (that corresponds to the number of observed classes of objects) and estimating parameters of those components.
Celebrating ten years of KRYPTOS: a series of cryptanalysis

The KRYPTOS competition is an annual codebreaking competition for undergraduates. Debuting in 2011 with 49 participants from three states, after nine years we have seen over 1,000 students from 32 states and seven different countries. KRYPTOS will hold its tenth competition in April of 2020. Here we present some of the puzzles participants have found most elusive and challenge you to beat the clock on our fastest solver.
Towards a delegation-type secure software development method

Agile methods are becoming increasingly popular and compared to traditional methods offer higher adaptability, quicker response to changing requirements and more efficient customer-developer collaboration throughout the entire software development process. However, they may not be the best way for achieving satisfactory security of the developed software due to their focus on the functional requirements. To address this issue, we developed a novel approach for achieving secure software with agile methods without adding unnecessary complexity or rigidity which is a key drawback of existing approaches eroding the agility. The proposed approach builds on delegation of responsibility for security, generic security user stories, and game of votes.
Diagnostics of professional challenges as a basis for designing a customized training strategy

Diagnostics of professional challenges of teachers is an important issue for advanced professional training. Current and potential educational needs of modern teachers can only be met through a comprehensive diagnostics of their professional challenges. At the same time, the results of self-analysis and self-diagnostics do not provide comprehensive information for designing a customized training strategy and advanced training programs. The system of current and potential educational needs of teachers is based on the results of selfdiagnostics, self-analysis and reflection, peer review, professional examination, and analysis of legal, scientific and pedagogical sources and socio-cultural context. The identified educational needs of teachers determine the content and structure of a customized educational strategy, are transformed into educational objectives and determine the content of the advanced training programs providing for a continuous education of teachers.
Improved Cubature Kalman Filter for High-Dimensional Systems with Multiplicative Noises

It is a challenging mission to estimate accurately the state of a non-cooperative spacecraft with multiplicative noises. In this paper, an improved cubature Kalman filter for nonlinear systems with multiplicative noises (ICKF_M) is proposed. First, based on the assumption that multiplicative noise satisfies the Gaussian distribution, the conditional mean variance of multiplicative noise is considered in the prediction and updating process. According to the Bayesian estimation theory, the conditional probability density distribution of multiplicative noise is added to the prediction and update functions to adapt the multiplicative noise. Then, to solve the defect of cubature Kalman filter (CKF), a new method for generating sigma points is designed instead of the classical methods, which has transformed the posterior sigma-points error matrix from prediction phase to the posterior domain of update. Finally, a binocular visual method is used to measure the state of the spacecraft. Compared with the previous methods, the simulation results indicate that the average norm of estimation error has nearly decreased by 20% in comparison with the similar filters in the reference. Moreover, the convergence rate of the proposed algorithm is obviously faster than that of the previous algorithm.
Foveated Vision for Deepface Recognition

In the last decade deep learning techniques have strongly influenced many aspects of computational vision. Many difficult vision tasks can now be performed by deploying a properly tailored and trained deep network. The enthusiasm for deep learning is unfortunately paired by the present lack of a clear understanding of how they work and why they provide such brilliant performance. The same applies to biometric systems. Deep learning has been successfully applied to several biometric recognition tasks, including face recognition. VGG-face is possibly the first deep convolutional network designed to perform face recognition, obtaining unsurpassed performance at the time it was firstly proposed. Over the last years, several and more complex deep convolutional networks, trained on very large, mainly private, datasets, have been proposed still elevating the performance bar also on quite challenging public databases, such as the Janus IJB-A and IJB-B. Despite of the progress in the development of such networks, and the advance in the learning algorithms, the insight on these networks is still very limited. For this reason, in this paper we analyse a biologically-inspired network based on the HMAX model, not with the aim of pushing the recognition performance further, but to better understand the representation space produced by including the retino-cortical mapping performed by the log-polar image resampling.
Evaluation of Session-Suitability of Papers in Conference Programs

Every year thousands of conferences are organized in many parts of the world. These conferences are realized in specific sessions. While determining the papers to be included in these sessions, the topic similarities are taken into consideration. Papers of a specific area are included in the same sessions. However, sometimes there are papers that are not very related to each other in the same session. This causes a participant to leave after listening to one or two papers. This is an undesirable situation for participants who are interested in the session topic. The participants' satisfaction with the sessions attended shows the efficiency of the conferences. The aim is to enable the participant to benefit from the conference in the most effective way. In this study, the similarity of the papers in the past conferences sessions was investigated and the efficiency of the conference sessions for the participants was discussed. In order to find similarity relations between papers, the deep learning based Doc2Vec method, which has recently received great interest in text processing, was used. The method was applied to full texts of the papers and the similarity score of the papers in one session was calculated. Then, the overall score was found with the average of the similarity scores of all sessions. The high similarity score of the papers in a session indicates that an efficient session was created in common topic. The study was conducted using papers of the ASONAM 2018 conference program. The efficiency of this conference program is discussed with the results obtained.
Pragmatic logics for hypotheses and evidence

Abstract The present paper is devoted to present two pragmatic logics and their corresponding intended interpretations according to which an illocutionary act of (scientific) hypothesis-making is justified by a scintilla of evidence. The paper first introduces a general pragmatic frame for assertions, expanded to hypotheses, ${\mathsf{AH}}$ and a hypothetical pragmatic logic for evidence ${\mathsf{HLP}}$. Both ${\mathsf{AH}}$ and ${\mathsf{HLP}}$ are extensions of the Logic for Pragmatics, $\mathcal{L}^P$. We compare ${\mathsf{AH}}$ and $\mathsf{HLP}$. Then, we underline the expressive and inferential richness of both systems in dealing with hypothetical judgements, especially when based on different, sometimes conflicting, evidence.
Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization

In this fast growing data universe, data generation and data storage are moving into the next-generation process by generating petabytes and gigabytes in an hour. This leads to data accumulation where privacy and preservation are certainly misplaced. This data contains some sensitive and high privacy data which is to be hidden or removed using hashing or anonymization algorithms. In this article, the authors propose a hybrid k anonymity algorithm to handle large scale aircraft datasets with combined concepts of Big Data analytics and privacy preservation of storing the dataset with the help of MapReduce. This published anonymized data are moved by MapReduce to the Hive database for data storage. The authors propose a multi-dimensional hybrid k-anonymity technique to solve the privacy issue and compare the proposed system with other two anonymization methods such as BUG and TDS. Three experiments were performed for evaluating classifier error, calculating disruption value and p% hybrid anonymity and estimation of processing time.
Localizing Occluders with Compositional Convolutional Networks

Compositional convolutional networks are generative models of neural network features, that achieve state of the art results when classifying partially occluded objects, even when they have not been exposed to occluded objects during training. While previous results showed the potential of CompositionalNets at localizing occluders, this remains to be confirmed quantitatively. In this work, we study the performance of CompositionalNets at localizing occluders in an image. We propose to extend the original model with a mixture of von-Mises-Fisher distributions. We show that this extension increases the model's ability to localize occluders in an image while retaining an exceptional performance at classifying partially occluded objects.