Reviewerly/exHarmony huggingface dataset:
An Environment-Aware Market Strategy for Data Allocation and Dynamic Migration in Cloud Database

Currently, a cloud database is employed to serve on-line query-intensive applications. It inevitably happens that some cloud data nodes storing hot records are facing high frequent query requests while others are rarely visited or even idle. Therefore, how data are dynamically allocated and migrated at runtime has significant impact on query load distribution and system performance. Existing system adopt centralized approaches, and they face two main challenges: (1) Query load on individual node cannot be always balancing even if the data are fairly distributed; (2) For each node, the dynamic changes of configuration resources cannot be captured during the runtime. To this end, this paper presents an environment-aware market strategy based system, named e-MARS, for reasonable data migration to achieve query load balance in cloud database. In e-MARS, cloud database is modeled as a cloudDB market, while data nodes are regarded as intelligent traders and the query load as commodity. Each trader is aware of its local environmental re-sources, such as computing capacity, disk volume, based on which the trader itself decides how to trade the query load and migrates the corresponding data. In this way the cloudDB market will achieve equilibrium. Experiments are conducted on the real communication data, and e-MARS significantly enhances the efficiency. Compared with HBase Balancer, more than 65% improvement is achieved in terms of query response time.
Empirical assessment of feature selection techniques in defect prediction models using web applications

In order to minimize the over-fitting and related factors that are caused by the high dimensionality of the input data in software defect prediction, the attributes are often optimized using various feature selection techniques. However, the comparative performance of these selection techniques in combination with machine learning algorithms remains largely unexplored using web applications. In this work, we investigate the best possible combination of feature selection technique with machine learning algorithms, with the sample space chosen from open source Apache Click and Rave data sets. Our results are based on 945 defect prediction models derived from parametric, non-parametric and ensemble-based machine learning algorithms, for which the metrics are derived from the various filter and threshold-based ranking techniques. Friedman and Nemenyi post-hoc statistical tests are adopted to identify the performance difference of these models. We find that filter-based feature selection in combination with ensemble-based machine learning algorithms not only poise as the best strategy but also yields a maximum feature set redundancy by 94%, with little or no comprise on the performance index.
Transform, Combine, and Transfer

Transfer parsing has been used for developing dependency parsers for languages with no treebank by using transfer from treebanks of other languages (source languages). In delexicalized transfer, parsed words are replaced by their part-of-speech tags. Transfer parsing may not work well if a language does not follow uniform syntactic structure with respect to its different constituent patterns. Earlier work has used information derived from linguistic databases to transform a source language treebank to reduce the syntactic differences between the source and the target languages. We propose a transformation method where a source language pattern is transformed stochastically to one of the multiple possible patterns followed in the target language. The transformed source language treebank can be used to train a delexicalized parser in the target language. We show that this method significantly improves the average performance of single-source delexicalized transfer parsers. We also show that, in the multi-source settings, parsers trained using a concatenation of transformed source language treebanks work better when a subset of the source language treebanks is used rather than concatenating all of them or only one. However, the problem of selecting the subset of treebanks whose combination gives the best-performing parser from the set of all the available treebanks is hard. We propose a greedy selection heuristic based on the labelled attachment scores of the corresponding single-source parsers trained using the treebanks after transformation.
Neuro Fuzzy Inference Systems for Estimating Normal Concrete Mixture Proportions

Concrete is a very popular construction material utilized in the construction industry. There are several methods for determining the quantities of its components, being the concrete mixture design method by American Concrete Institute (ACI) the most common procedure among others. Construction laboratories have conducted innumerable concrete mixture designs applying different procedures through time, acquiring valuable information that is not being exploited completely. This study used experimental historical information of a construction laboratory in order to develop Sugeno type fuzzy inference systems (FISs) to estimate material proportions for fabricating normal concrete. Fuzzy modeling was accomplished by using subtractive clustering and adaptive neuro fuzzy inference systems (ANFIS) techniques. A Sugeno type FIS was developed for estimating the proportion of each component in a concrete mixture; namely, water-cement ratio, fine and coarse aggregates, where concrete compressive strength and aggregate properties including fineness moduli and abrasion resistance were the input variables. The resulting fuzzy models were able to estimate concrete constituents very well since computed coefficients of determination (i.e., R-squares) were greater than 90% when validating the models. All FISs can be used as mixture design tools to compute material proportions based on past experience when fabricating concrete on the jobsite. Also, the proposed framework illustrated in this research for concrete mixture design could be extended for any particular data set regardless of concrete component characteristics.
Lexical and Morphological Features of Translational Lithuanian

Summary In Lithuanian public and academic discourse, discussions about the influence of English have received considerable attention. Much has been written on the English borrowings in Lithuanian or various translation strategies applied at word, phrase or syntactic levels of language, whereas there have been only few attempts to investigate how Lithuanian translated from English differs from original language. This is why we found it interesting to investigate lexical an morphological features of translated Lithuanian applying the methods of corpus liguistics. For research purposes, we used a morphologically annotated comparable 4 mln. word corpus of original and translated fiction and popular science literature ORVELIT. It has been found that translations deviate in certain ways from original Lithuanian. Translated Lithuanian has: a lower lexical density, higher proportion of function words, shorter sentences, and higher proportion of list heads; translated fiction has a lower lexical variability and smaller proportion of low frequency words, whereas in popular science translations, these differences are less evident. Keyword analysis has shown content differences in originals and translations and the overuse of personal and possessive pronouns in popular science translations. The distribution of content and function words differs in originals and translations and in different registers. Translated Lithuanian has: more verbs (especially finite forms and adverbial participles), but less nouns and adjectives; fiction translations have less and popular science more adverbs than originals; there are more pronouns and prepositions in both popular science and fiction translations; depending on the register, there are higher or lower numbers of conjunctions, particles and interjections. Some of the differences may be explained by the English language interference as: the overuse of the optional 1st person pronoun in subject position, the overuse of optional preposition “su” with instrumental case, or the overuse of optional link verb in the complex predicate. In other words, the English influence is seen in transferring certain features obligatory for analytical language where omission would be a more natural choice in original Lithuanian. These findings in most cases agree with the previous research on translationese of other languages. It is hoped that the identified tendencies to over- or under-use certain lexical and morphological features as a result of English language interference might appear to be useful when editing and translating.
A Subsethod Interval Associative Memory with Competitive Learning

Morphological perceptrons with competitive learning (MP/CLs) are constructive artificial neural network models having a modular architecture. Not only the weights but also the architecture of the MP/CL is automatically generated by the MP/CL training algorithm. The resulting architecture is determined by hyperboxes, i.e., closed intervals, contained in $$\mathbb {F}^n$$ , where $$\mathbb {F}$$ is a totally ordered group. The group operations and the total ordering are used to perform a competition among the outputs of each module. In this paper, we present an interval subsethood associative memory whose hidden nodes compute degrees of subsethood of the input pattern in each of the closed intervals generated by the MP/CL training algorithm. We show that the resulting interval subsethood associative memory with competitive learning (S-IAM/CL) can be viewed as a $$\varTheta $$ -fuzzy associative memory ( $$\varTheta $$ -FAM) model. We compare the performance of S-IAM/CLs for a particular choice of interval subsethood measure with the ones of the original MP/CL model and several competitive models in a number of classification problems.
A hybrid method for velocity field of fluid flow estimation based on optical flow

The paper describes a hybrid method for estimating flow-velocity vector fields from Particle Image Velocimetry (PIV) images that combines a cross-correlation technique with a multiresolution estimation based on optical flow with the aim of obtaining the highest possible spatial resolution. The method offers the possibility to determine one vector per seeding particle. The manuscript examines accuracy of the estimates compared to other known methods using various standard test images. Experimental results are also presented.
Chronological Semantics Modeling: A Topic Evolution Approach in Online User-Generated Medical Data

Online medical discussion forums/question answering sites have become one of the major resources for people to look for healthcare information. These sites typically contain tremendous user-generated content (UGC) that possesses complex domain-specific information in layman’s terms, which is the opposite of formal medical records kept in hospitals (i.e. Electronic Health Record). The goal of this project is to dissect semantics and extract valuable information systematically from UGC composed in unstructured and unconstrained format. We propose an automatic medical content analyzer that takes into account language semantics as well as progression (evolution) of medical events. The preliminary evaluation on the WebMD dataset shows that evolution-based recommendation uncovers broader domain semantic which might be ignored when using word-level or concept-based features.
DOS and Brute Force Attacks Faults Detection Using an Optimised Fuzzy C-Means

This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables.
Grammar checking and relation extraction in text: approaches, techniques and open challenges

Purpose The purpose of this paper is to provide an overall review of grammar checking and relation extraction (RE) literature, their techniques and the open challenges associated with them; and, finally, suggest future directions. Design/methodology/approach The review on grammar checking and RE was carried out using the following protocol: we prepared research questions, planed for searching strategy, addressed paper selection criteria to distinguish relevant works, extracted data from these works, and finally, analyzed and synthesized the data. Findings The output of error detection models could be used for creating a profile of a certain writer. Such profiles can be used for author identification, native language identification or even the level of education, to name a few. The automatic extraction of relations could be used to build or complete electronic lexical thesauri and knowledge bases. Originality/value Grammar checking is the process of detecting and sometimes correcting erroneous words in the text, while RE is the process of detecting and categorizing predefined relationships between entities or words that were identified in the text. The authors found that the most obvious challenge is the lack of data sets, especially for low-resource languages. Also, the lack of unified evaluation methods hinders the ability to compare results.
On the Diffusion Property of the Improved Generalized Feistel with Different Permutations for Each Round

Suzaki and Minematsu (LNCS, 2010) present a comprehensive study of the diffusion property of the improved generalized Feistel structure ( $$GFS_{\pi }$$ ) which is a generalization of the classical Feistel cipher. They study the case when one and the same permutation is applied at each round and finally remark that the usage of different permutations at the different rounds might lead to better diffusion in return for a larger implementation cost, but that it is an open question whether multiple permutations can really improve the diffusion property. We give a positive answer to this question. For cyphers with 10, 12, 14 and 16 subblocks we present examples of permutations (different at each round) leading to $$GFS_{\pi }$$ with better diffusion than the one which can be obtained if the same permutation is applied at all rounds. The examples were found by a computer-aided search which is described in the present paper.
Everyday Life ToDo Display on Ceiling for Smart Living Space

Many commonly used household appliances and equipment include refrigerators, washing machines, intercoms, telephones, air conditioners, microwaves, and ovens; they communicate various information to the users by providing notifications using auditory displays. However, an occupied user may not pay attention to the notifications and/or cannot recognize simultaneous auditory alerts. Therefore, in this study, we propose a ceiling display system that displays information depending on the location of the users. This display system utilizes house ceilings as overhead visual displays that can update the users with various information simultaneously. This paper describes the different situations where appliances notify the users on completing a task; subsequently, we propose a concept for the ceiling display system in a smart house that will be introduced in the near future. In addition, the expected effectiveness of this system, some useful scenarios, and initial verifications of the visual notifications on the ceiling are described.
Gestalt-grouping based on path analysis for saliency detection

Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.
Spatio-Temporal Context Tracking Algorithm Based on Correlation Filtering

Abstract The Correlation Filter tracking algorithm is different from the traditional method based on target feature, which has high accuracy and fast tracking speed. In this paper, the Spatio-Temporal Context(STC) target tracking algorithm is combined with Correlation Filtering, the position of the target is predicted by using Correlation Filtering and the prediction result is corrected by using the STC target tracking algorithm. The target tracking algorithm combined with Correlation Filtering and STC can realize effective tracking when the target is occluded or there is an interference target. The experimental results show that the proposed algorithm can not only be applied to the tracking of visual targets in complex backgrounds such as illumination changes, target rotation and background region interference, but also have better robustness.
Energy-Efficient ARM64 Cluster with Cryptanalytic Applications

Getting a lot of CPU power used to be an expensive undertaking. Servers with many cores cost a lot of money and consume large amounts of energy. The developments in hardware for mobile devices has resulted in a surge in relatively cheap, powerful, and low-energy CPUs. In this paper we show how to build a low-energy, eighty-core cluster built around twenty ODROID-C2 development boards for under 1500 USD. The ODROID-C2 is a 46 USD microcomputer that provides a 1.536 GHz quad-core Cortex-A53-based CPU and 2 GB of RAM. We investigate the cluster's application to cryptanalysis by implementing Pollard's Rho method to tackle the Certicom ECC2K-130 elliptic curve challenge. We optimise software from the Breaking ECC2K-130 technical report for the Cortex-A53. To do so, we show how to use microbenchmarking to derive the needed instruction characteristics which ARM neglected to document for the public. The implementation of the ECC2K-130 attack finally allows us to compare the proposed platform to various other platforms, including "classical" desktop CPUs, GPUs and FPGAs. Although it may still be slower than for example FPGAs, our cluster still provides a lot of value for money.
Inspection of surface imperfections via height contrast imaging based on angle selective illumination

Surface inspection of non-transparent objects is a common task in the field of industrial quality control. For specular reflective surfaces, deflectometry methods can be used. For diffuse reflecting surfaces, the back reflected light has the shape of a cone due to diffuse scattering. This work describes, how the ideas from digital phase reconstruction microscopy can be modified to obtain height profiles of such object surfaces. The approach is evaluated with textile samples and the task to determine their degree of pilling after a specific preparation procedure. The method is applied in further cases of objects which suffer from some reflections or shadows and the results of this attempt are provided as well.
A Communication Model of Human–Robot Trust Development for Inclusive Education

Integrating robots into the educational environment offers tremendous opportunities to support and augment learning. However, building trust between human users and robots can be a challenge for inclusive education, as females, minorities, and the less privileged individuals tend to report higher levels of the anticipated fear and distrust toward robots. In this chapter, we examine how communication affects human–robot trust in light of the verbal messages that humans and robots exchange. The chapter overviews the four guiding foci of human–robot trust: (1) human–robot trust is a communication-driven process; (2) human–robot trust develops over time; (3) trust optimization requires calibration to the particular situation and circumstance; and (4) trust is based on multidimensional perceptions of trustee’s trustworthiness. The chapter outlines systematic research to examine how trust is developed, calibrated, and affected by communication messages across different temporal stages in the inclusive learning environment: pre-interaction stage, entry stage, and relationship stage.
Full-Reference Video Quality Assessment Using Deep 3D Convolutional Neural Networks

We present a novel framework called Deep Video QUality Evaluator (DeepVQUE) for full-reference video quality assessment (FRVQA) using deep 3D convolutional neural networks (3D ConvNets). DeepVQUE is a complementary framework to traditional handcrafted feature based methods in that it uses deep 3D ConvNet models for feature extraction. 3D ConvNets are capable of extracting spatio-temporal features of the video which are vital for video quality assessment (VQA). Most of the existing FRVQA approaches operate on spatial and temporal domains independently followed by pooling, and often ignore the crucial spatio-temporal relationship of intensities in natural videos. In this work, we pay special attention to the contribution of spatio-temporal dependencies in natural videos to quality assessment. Specifically, the proposed approach estimates the spatio-temporal quality of a video with respect to its pristine version by applying commonly used distance measures such as the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> or the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm to the volume-wise pristine and distorted 3D ConvNet features. Spatial quality is estimated using off-the-shelf full-reference image quality assessment (FRIQA) methods. Overall video quality is estimated using support vector regression (SVR) applied to the spatio-temporal and spatial quality estimates. Additionally, we illustrate the ability of the proposed approach to localize distortions in space and time.