Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Recommendation System

Recommender system helps people in decision making by asking their preferences about various items and recommends other items that have not been rated yet and are similar to their taste. A traditional recommendation system aims at generating a set of recommendations based on inter-user similarity that will satisfy the target user. Positive preferences as well as negative preferences of the users are taken into account so as to find strongly related users. Weighted entropy is usedz as a similarity measure to determine the similar taste users. The target user is asked to fill in the ratings so as to identify the closely related users from the knowledge base and top N recommendations are produced accordingly. Results show a considerable amount of improvement in accuracy after using weighted entropy and opposite preferences as a similarity measure.
MGR Hash Functions

GOST-R 34.11-94 is a Russian standard cryptographic hash function that was introduced in 1994 by the Russian Federal Agency for the purposes of information processing, information security, and digital signature. Mendel et al. (2008 Mendel, F., N. Pramstaller, C. Rechberger, M. Kontak, and J. Szmidt. 2008. Cryptanalysis of the GOST hash function, Advances in Cryptology – CRYPTO 2008, vol. 5157, 162–178. [Google Scholar]) and Courtois and Mourouzis (2011 Courtois, N., and T. Mourouzis. 2011. Black-box collision attacks on the compression function of the GOST hash function. SECRYPT. Proceedings of the International Conference on Security and Cryptography, 325–332, IEEE. [Google Scholar]) found attacks on the compression function of the GOST-R structure that were basically weaknesses of the GOST-R block cipher (GOST 28147–89, 1989 GOST 28147-89. 1989. Systems of the information treatment, cryptographic security, algorithms of the cryptographic transformation (in Russian). [Google Scholar]). Hence in 2012, it was updated to GOST-R 34.11-2012, which replaced the older one for all its applications from January 2013. GOST-R 34.11-2012 is based on a modified Merkle-Damgård construction. Here we present a modified version of GOST-R 34.11-2012 (Modified GOST-R (MGR) hash). The design of the MGR hash is based on wide-pipe construction, which is also a modified Merkle-Damgård construction. MGR is much more secure as well as three times faster than GOST-R 34.11-2012. Advanced Encryption Standard (AES)-like block ciphers have been used in designing the compression function of MGR because AES is one of the most efficient and secure block ciphers and has been evaluated for more than 14 years. A detailed statistical analysis with a few other attacks on MGR is incorporated into this paper.
Abduction Aiming at Empirical Progress or Even Truth Approximation: A Challenge for Computational Modelling

This chapter primarily deals with the conceptual prospects for generalizing the aim of abduction from the standard one of explaining surprising or anomalous observations to that of empirical progress or even truth approximation. It turns out that the main abduction task then becomes the instrumentalist task of theory revision aiming at an empirically more successful theory, relative to the available data, but not necessarily compatible with them. The rest, that is, genuine empirical progress as well as observational, referential and theoretical truth approximation, is a matter of evaluation and selection, and possibly new generation tasks for further improvement. The chapter concludes with a survey of possible points of departure, in AI and logic, for computational treatment of the instrumentalist task guided by the ‘comparative evaluation matrix’.
An Efficient Strategy for Online Performance Monitoring of Datacenters via Adaptive Sampling

Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, and capacity planning decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, this paper develops and validates a strategy based on adaptive-rate compressive sampling. It exploits the fact that the signals of interest often can be sparsified under an appropriate representation basis and that the sampling rate can be tuned as a function of sparsity. We use the Trade6 application as our experimental platform and measure the signals of interest-in our case, signals pertaining to memory and disk I/O activity-using adaptive sampling. We then evaluate whether the reconstructed signals can be used for trend detection to track the gradual deterioration of system performance associated with software aging. Our experiments show that the signals recovered by our methods can be used to detect, with high confidence, the existence of trends within the original signal. We also evaluate the reconstructed signals for threshold-violation detection wherein the magnitude of the signal exceeds a preset value. Our experiments show that performance bottlenecks and anomalies that manifest themselves in portions of the signal where its magnitude exceeds a threshold value can also be detected using the reconstructed signals. Most importantly, detection of these anomalies is achieved using a substantially reduced sample size-a reduction of more than 70 percent when compared to the standard fixed-rate sampling method.
Approximation of trees by self-nested trees

The class of self-nested trees presents remarkable compression properties because of the systematic repetition of subtrees in their structure.In this paper, we provide a better combinatorial characterization of this specific family of trees.In particular, we show from both theoretical and practical viewpoints that complex queries can be quickly answered in self-nested trees compared to general trees.We also present an approximation algorithm of a tree by a self-nested one that can be used in fast prediction of edit distance between two trees.
Eye-Tracking as a Research Methodology in Educational Context

A large body of educational research has been keen to the processes and outcomes of learning. Usually, clinical interviews, self-report measures or behavioral assessment procedures have been the most frequently used techniques to assess cognitive activities during learning. Equally, such approaches often suffer from validity issues. The eye-tracking methodology can be used to overcome some limitations in the study of cognitive processes linked to learning and performance. Therefore, this chapter aims to show how eye movement studies can be used to link ocular metrics to learning processes (e.g. language acquisition, reading, memory). The authors cover a topic that ranges from the paradigm shift in the theories of learning, through eye movement applications and measures, to the contribution of eye tracking methodology to investigate learning processes in educational settings.
Intuitionistic ancestral logic

Abstract In this article we define pure intuitionistic Ancestral Logic ( iAL ), extending pure intuitionistic First-Order Logic ( iFOL ). This logic is a dependently typed abstract programming language with computational functionality beyond iFOL given by its realizer for the transitive closure, TC . We derive this operator from the natural type theoretic definition of TC using intersection. We show that provable formulas in iAL are uniformly realizable, thus iAL is sound with respect to constructive type theory. We further show that iAL subsumes Kleene Algebras with tests and thus serves as a natural programming logic for proving properties of program schemes. We also extract schemes from proofs that iAL specifications are solvable.
Trustworthy Enhancement for Cloud Proxy based on Autonomic Computing

Aiming to improve Internet content accessing capacity of the system, cloud proxy platforms are used to improve the visiting performance in network export environment. Limited by complexity of cloud proxy system, trustworthy guarantee of cloud system becomes a difficult problem. Considering the self-government of autonomic computing, it could enhance cloud system trustworthy and avoids system management security and reliable problems brought by complex construction. Based on the idea of self-supervisory, a mechanism to enhance security of cloud system was proposed in this paper. First, a trustworthy autonomous enhancement framework for virtual machines was proposed. Second, a method to extract linear relationship of monitoring items in the virtual machine based on ARX model was put forward. According to the mapping relation between monitoring items and system modules, an abnormal module positioning technology based on Naive Bayes classifier was developed to realize self-sensing of abnormal system conditions. Finally, security threats of virtual machines including malicious dialogue and buffer memory of hot attacks were tested through experiments. Results showed that the proposed trustworthy enhancement mechanism of virtual machines based on autonomic computing could achieve trustworthy enhancement of virtual machines effectively and provide an effective safety protection for the cloud system.
A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs

Hash tables are ubiquitous in computer science for efficient access to large datasets. However, there is always a need for approaches that offer compact memory utilisation without substantial degradation of lookup performance. Cuckoo hashing is an efficient technique of creating hash tables with high space utilisation and offer a guaranteed constant access time. We are given n locations and m items. Each item has to be placed in one of the $$k\ge 2$$ locations chosen by k random hash functions. By allowing more than one choice for a single item, cuckoo hashing resembles multiple choice allocations schemes. In addition it supports dynamically changing the location of an item among its possible locations. We propose and analyse an insertion algorithm for cuckoo hashing that runs in linear time with high probability and in expectation. Previous work on total allocation time has analysed breadth first search, and it was shown to be linear only in expectation. Our algorithm finds an assignment (with probability 1) whenever it exists. In contrast, the other known insertion method, known as random walk insertion, may run indefinitely even for a solvable instance. We also present experimental results comparing the performance of our algorithm with the random walk method, also for the case when each location can hold more than one item. As a corollary we obtain a linear time algorithm (with high probability and in expectation) for finding perfect matchings in a special class of sparse random bipartite graphs. We support this by performing experiments on a real world large dataset for finding maximum matchings in general large bipartite graphs. We report an order of magnitude improvement in the running time as compared to the Hopkraft–Karp matching algorithm.
Web of Similarity

Despite the achieved maturity and popularity, the current semantic technology has severe limitations in real-world applications as it is unable to represent uncertain knowledge. Probabilistic Semantics partially address this issue. Unfortunately, their quantitative approach fails in many practical applications that require a more abstracted vision and conceptual model of the uncertainties. Indeed, Probabilistic Semantics can only model ecosystems where all the uncertainties are quantified. In this paper, we introduce a qualitative approach for the representation of the uncertainties in the Semantic Web. We propose a human-inspired model that defines the uncertainty as an explicit similarity, providing a flexible range of solutions for approximate semantic reasoning in uncertain ecosystems. The resulting semantic environment, referred to as Web of Similarity (WoS), is an extension of the Web of Data which is able to represent and process analogies among concepts and individuals. As the generic Semantic Web, the Web of Similarity is a global semantic infrastructure that can support specific systems or applications at a global scale. WoS is a step forward to get richer Web Semantics which are closer to the human ones.
Graphemic representation of text-messaging

The aim of this study is to investigate the choice of alphabetical encoding in Greek text-messaging (or Short Message Service, SMS). The analysis will be based on a corpus of 447 text-messages exchanged among participants who belong to the age group of ‘youth’ (15-25 years old) and live in Athens (Greece). The data analysis will show that the standard practice of writing with Greek characters represents the norm in Greek SMS. The script norm will be discussed in relation to the medium’s technological affordances and the participants’ stance towards new media. The analysis will then focus on non-standard graphemic choices, such as the use of both, Greek and Roman, alphabets in the encoding of single messages. It will be demonstrated that such marked choices are employed as a means of indexing the participants’ affiliation with global popular cultures and enhancing expressivity in a medium of reduced paralinguistic cues.
Passive and Partially Active Fault Tolerance for Massively Parallel Stream Processing Engines

Fault-tolerance techniques for stream processing engines can be categorized into passive and active approaches. However, both approaches have their own inadequacies in Massively Parallel Stream Processing Engines (MPSPE). The passive approach incurs a long recovery latency especially when a number of correlated nodes fail simultaneously, while the active approach requires extra replication resources. In this paper, we propose a new fault-tolerance framework, which is Passive and Partially Active (PPA). In a PPA scheme, the passive approach is applied to all tasks while only a selected set of tasks will be actively replicated. The number of actively replicated tasks depends on the available resources. If tasks without active replicas fail, tentative outputs will be generated before the completion of the recovery process. We also propose effective and efficient algorithms to optimize a partially active replication plan to maximize the quality of tentative outputs. We implemented PPA on top of Storm, an open-source MPSPE and conducted extensive experiments using both real and synthetic datasets to verify the effectiveness of our approach.
Approximate Graph Edit Distance in Quadratic Time

Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that restricts its applicability to graphs of rather small size. Recently, the authors of the present paper introduced a general approximation framework for the graph edit distance problem. The basic idea of this specific algorithm is to first compute an optimal assignment of independent local graph structures (including substitutions, deletions, and insertions of nodes and edges). This optimal assignment is complete and consistent with respect to the involved nodes of both graphs and can thus be used to instantly derive an admissible (yet suboptimal) solution for the original graph edit distance problem in <inline-formula><tex-math notation="LaTeX">$O(n^3)$</tex-math></inline-formula> time. For large scale graphs or graph sets, however, the cubic time complexity may still be too high. Therefore, we propose to use suboptimal algorithms with quadratic rather than cubic time for solving the basic assignment problem. In particular, the present paper introduces five different greedy assignment algorithms in the context of graph edit distance approximation. In an experimental evaluation, we show that these methods have great potential for further speeding up the computation of graph edit distance while the approximated distances remain sufficiently accurate for graph based pattern classification.
Live Placement of Interdependent Virtual Machines to Optimize Cloud Service Profits and Penalties on SLAs

This paper aims to optimize cloud services' net profits and penalties with live placement of interdependent virtual machines (VMs). This optimization is a complex task as it is difficult to achieve a successful compromise between penalties and net profits on service level contracts. This paper studies this optimization problem to minimize services' penalties and maximizing net profits while achieving live migrations of interdependent VMs. This VM's live placement optimization problem is a NP-hard problem with exponential running time. A mathematical model was designed and approximations were conducted with an efficient PCH/PCH' heuristic. This Mixed Integer Non-Linear programming (MNLP) formulation and heuristic for cloud services was tested where the overall services' penalty needs to be minimized, overall net profits have to be maximized, and where efficient live migrations of VMs is a concern. Simulation results show how cloud providers may live place VMs. Finally, our results show that a PCH/PCH' heuristic: (i) finds better solutions than the existing machines' configuration of Google traces; (ii) is suitable for large-sized instances of cloud services; (iii) performs better than FF, FFD, and CPLEX in terms of overall penalties and net profits; and (iv) runs in less than six minutes over the last day's data.
DSARP: Dependable Scheduling with Active Replica Placement for Workflow Applications in Cloud Computing

As an efficient development for industrial and scientific applications, workflow technologies have received substantial attention in recent decades. To address the issue of workflow scheduling in a state-of-the-art cloud environment, based on analysis of a decentralized architecture for workflow scheduling, a dependable scheduling strategy with active replica placement (DS-ARP) is proposed in this paper. In this proposal, by analyzing control/data dependencies in a workflow, a game-theory-based active replica placement model is first developed to achieve reasonable replica placement; then, a dependable scheduling algorithm is proposed to enhance the system reliability and security. With five well-known workflow applications, CloudSim-based simulations are performed, and the analytical results are shown to demonstrate the performance of DS-ARP on an average number of initiated replicas, costs resulting from canceled replicas, makespans, deadline violation rates and resource utilization rates.
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes

We introduce a multivariate random process producing Bernoulli outputs per dimension, that can possibly formalize binary interactions in various graphical structures and can be used to model opinion dynamics, epidemics, financial and biological time series data, etc. We call this a Bernoulli Autoregressive Process (BAR). While many dynamical models of processes on graphs have been studied previously, the main feature of our model is that it provides a parsimonious description of a Markov chain with non-reversible dynamics in general, which may be important in some applications. The goal is to learn the causal connectivity of the underlying graph from time-series data, assuming that the connectivity is sparse. Not surprisingly, the simplicity of the model leads to a simple and natural learning algorithm. However, proving the near-optimal sample complexity of the algorithm is non-trivial. The sample complexity is related to the mixing time of the BAR Markov chain, which is hard to estimate because of the lack of reversibility. A key contribution of the paper is an upper bound on the mixing time based on coupling arguments and a probabilistic inequality which may itself be of independent interest.
A Privacy-Preserving Localization Service for Assisted Living Facilities

In this paper, we propose a novel localization service to monitor the position of residents in assisted living facilities. The service supports a configurable balancing between precision and privacy, in such a way that the right of the residents to move freely in the environment in which they live without being tracked is preserved. However, in case of need, they can always be quickly localized. To do this, we implement, on top of an RFID-based architecture, a probabilistic model guaranteeing that the probability of identifying a person in a given (sensitive) place is at most k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , where k represents the required privacy level. This is obtained by ensuring that the EPC sent by RFID tags is not an identifier, but is equal to that of at least other k - 1 people, each afferent to a different reader. We show that our method reaches the goal, resisting also attacks aimed at breaking privacy on the basis of humans' movement models. Importantly, privacy is guaranteed against both misuse of the administrator and client-side eavesdropping attacks.
A new exponentially directional weighted function based CT image denoising using total variation

Today, Computed tomography (CT) is one of the high efficient tools in medical science which helps to diagnose the human body. The presence of noise may degrade the visual quality of the CT images, especially for low contrast images. Therefore, we propose a method based on the modification of total variation (TV) for noise suppression in CT images which is helpful to preserve the clinically relevant details. The modification of TV is performed by introducing a new exponentially directional weighted function (EDWF), which is based on the difference between L1 and L2 norms over the exponential function. Furthermore, a numerical algorithm is designed to solve the minimization problem of EDWF using Split Bregman method. The experimental results of proposed scheme are visually analyzed over the real noise CT image, added noise in true CT images, and also on low contrast zoomed noisy CT image. Apart from visual analysis, the proposed scheme is also verified with some standard performance metrics (RMSE, PSNR, SSIM, ED, DIV and GMSD). The proposed scheme is also compared with some standard existing methods and it is observed that performance of proposed scheme is superior to existing methods in terms of visual quality and performance metrics.
Investigating the effectiveness of iPad based language learning in the UAE context

This mixed-methods true experimental study aimed at exploring the results of using mobile technology educationally by looking into learner practices and attitudes towards using iPads in language learning, as well as looking into students’ language achievement as developed by the paperless classroom. Four research questions guided this study. The quantitative analyses were obtained through the test scores and survey questionnaires. The qualitative analyses were obtained through teachers’ weekly reflective journals. This study conducted an Activity Theory based enquiry into iPad implementation for language learning in terms of student motivation, satisfaction, device usefulness and learning effectiveness, and conceptualised a research model to provide insights into learner perceptions of the iPads in an educational setting, which was then analysed through factor analysis.The experimental results revealed that level one language learners progress in learning English better when using iPads as compared to using textbooks. The experimental phase results were well supported by the results of the survey analyses, which showed a positive relationship between students’ attitudes towards iPad implementation as a means of language learning and their language achievement.