Reviewerly/exHarmony huggingface dataset:
Towards Scalable and Robust Sum-Product Networks

Sum-Product Networks (SPNs) and their credal counterparts are machine learning models that combine good representational power with tractable inference. Yet they often have thousands of nodes which result in high processing times. We propose the addition of caches to the SPN nodes and show how this memoisation technique reduces inference times in a range of experiments. Moreover, we introduce class-selective SPNs, an architecture that is suited for classification tasks and enables efficient robustness computation in Credal SPNs. We also illustrate how robustness estimates relate to reliability through the accuracy of the model, and how one can explore robustness in ensemble modelling.
Mitigating the Effect of Data Sparsity: A Case Study on Collaborative Filtering Recommender System

The sparsity problem is considered as one of the main issues facing the collaborative filtering. This paper presents a new dimensionality reduction mechanism that is applicable to collaborative filtering. The proposed mechanism is a statistical-based method that exploits the user-item rating matrix and item-feature matrix to build the User Interest Print (UIP) matrix. The UIP is a dense matrix stores data that reflects the satisfaction degree of the users about the item's semantic feature. This method is developed based on the assumption that people tend to buy items related to what they have previously bought. Also, this method benefited from the fact that the number of features is much less than the number of items and mostly constant. The effectiveness of the proposed mechanism is tested on two real datasets namely Movielens and HetRec 2011. The obtained accuracy results using UIP matrix are compared with the one obtained using the user-item rating matrix. The experimental studies demonstrate the superiority of our proposed method. On average, using UIP matrix the collaborative filtering achieved 8% improvement in terms of prediction accuracy.
Comparative Analysis of Hadoop Security Ad-Ons

Hadoop is a big-data processing framework which is widely used for data storage and processing. Now-a-days security is one of the major concerns in the digital world. Any system is only considered reliable when it provides proper measures to secure the valuable data of an organization. In this paper we discuss the security hadoop offers for data stored in it. Map-Reduce (MR) was introduced by Google as a batch processing implementation in their Google File System (GFS). Hadoop provides its own Hadoop Distributed File System (HDFS) for storage and it used MR at its core to process the data stored in the HDFS. The early Hadoop project established a security stance that the entire cluster of machines and all of the users accessing it are part of a trusted network. Hadoop framework has a lot of components in it and in the earlier versions it only offered unix based POSIX security for the data, it did not include authentication and authorization between clients and services. In the recent years many security solutions were offered by different enterprises in the form of open-source tools and distributions for Hadoop that had security integrated in them. The solutions for Hadoop security are evolving day by day and a-lot of man power is being invested by academia, open-source community and enterprises to further improve these solutions. Because of the work of the community, the Hadoop can now be deployed in production with sufficient security controls.
Application Identification of Network Traffic by Reservoir Computing

We propose a method for application identification for network traffic by reservoir computing. Different from conventional approaches, the proposed method handles traffic flows as dynamical time series data and enables fast and real-time identification. We apply the proposed method to real traffic data and show that high identification accuracy is achieved. We also discuss an implementation as physical reservoirs based on optics and the impact of the proposed method to 5G networking.
Eyes-Free Text Entry with EdgeWrite Alphabets for Round-Face Smartwatches

Small wearable information devices such as smart-watches are available everywhere. A user can input text in these devices while doing other tasks. However, eyes-free typing, that is, without looking at the input space, on these devices remains difficult to execute. This paper describes our project on developing eyes-free text input systems. Not only an auditory feedback feature was developed for the EdgeWrite text input system for round-face smartwatches, but also a simple template-based gesture recognition engine was implemented. The experimental results show that the developed system maintains reasonable text input speed and error rate for eyes-free under standing and walking conditions.
Design and Implementation of Rule-Based Hindi Stemmer for Hindi Information Retrieval

Stemming is a process that maps morphologically similar words to a common root/stem word by removing their prefixes or suffixes. In Natural Language Processing, stemming plays an important role in Information Retrieval, Machine Translation, Text Summarization, etc. Stemming reduces inflected word to its root form without doing any morphological analysis of the word and sometimes it is not necessary that stemming always provides us meaningful/dictionary root words as a lemmatizer always provides meaningful dictionary words. For example, in the Hindi word , (pakshion) is formed as ( (paksh) + ) having as suffix; if we remove this suffix, then it becomes (paksh) and (paksh) which is not a meaningful Hindi dictionary word. In the context of information retrieval, the stemmer reduces varied (morphologically inflected) words to a common form, thereby reducing the index size of the inverted file and increasing the recall. In this paper, researchers have attempted to develop a rule-based Hindi Stemmer Suffix Stripping Approach for Hindi Information Retrieval. A python-based web interface has been designed to implement the proposed algorithm. Also, the developed stemmer is being tested for accuracy and efficiency in two scenarios, first as an independent stemmer and second as a supporting module to indexing in Hindi Information Retrieval. The proposed stemmer has shown an accuracy of 71% as an individual stemmer and also reduced the index size by 26% (approx.) when used in indexing.
Assessing competency for remote telecytology rapid on‐site evaluation using pre‐recorded dynamic video streaming

Abstract Introduction Telecytology using real‐time microscopy has gained popularity for rapid on‐site evaluations (ROSE). Although proficiency testing is routinely used in cytopathology, no established means of competency assessment is currently available for telecytology. Our aim was to determine the feasibility of a dynamic (real‐time) platform to assess telecytology competency. Methods Remote Medical Technology dynamic (real‐time) video streaming platform for ROSE is used at our institution, and short video clips of telecytology cases were recorded using Camtasia Studio 8 software during different ROSE sessions. Selected MP4 videos (range 13‐88 seconds, mean 33 seconds), along with clinical histories, were used to build a multiple‐choice question test with one training case and 20 test cases, utilising Tutor (Philips) software to host the web‐based test. The test was voluntary for cytopathologists and cytotechnologists. Answers and feedback from test takers were analysed. Results Thirteen participants—four cytopathologists and nine cytotechnologists—previously trained to use telecytology, volunteered to take the test. Individual scores ranged from 10 (50%) to 19 (95%) with a median of 16 (80%). Most feedback received involved technical difficulties. Conclusions We present, to the best of our knowledge, the first tool to assess telecytology competency for ROSE using pre‐recorded dynamic streaming videos. Despite technical challenges related to incorporating videos into a web‐based test, the test was feasible and provided users with valuable feedback about their ROSE performance. Future effort will be devoted to establishing a more user‐friendly test platform and establishing a benchmark for passing scores.
State Uncertainty Normality Detection

Abstract Two fundamentally different approaches of determining normality of the probability density function of the state estimation error are compared by application to a range of test cases. The first method is the Henze-Zirkler test, which operates on a random particle sample. The variability of its result is quantified. Using this method, departure from normality has been found to occur in three stages which are detailed. The second test compares the offset in whitened space of the predicted state to the predicted covariance mean obtained from the unscented transform. This test is much more efficient than the random particle based approach and can be applied using any perturbations model. The comparison is performed on the state estimation error in Cartesian space and using two-body motion without process noise. The more efficient, unscented transform based approach shows excellent agreement with the Henze-Zirkler test for constructed test cases. Application to orbit determination results from passive optical observations assessed with a Batch-Least-Squares orbit determination however reveals some discrepancies which have yet to be understood and underline the importance of rigorous testing.
A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid

The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issues. This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks. The suitability and the scalability of the CBR-based reconfiguration strategy using HATSGA algorithm are evaluated. The evaluation indicates that the adopted HATSGA algorithm computes new reconfiguration topologies with a feasible computational time for large networks. The CBR strategy looks for managerial acceptable reconfiguration solutions at the CBR database and, as such, contributes to reduce the required number of reconfiguration computation using HATSGA. This suggests CBR can be applied with a fast reconfiguration algorithm resulting in more efficient, dynamic and cognitive grid recovery strategy.
Cryptocurrency and Tax Regulation: Global Challenges for Tax Administration

This chapter investigates whether the government should taxed cryptocurrency or not by using game theoratical framework. In this game, both government and cryptocurrency investors will determine the strategies to maximize their own benefits. In order to achieve the Nash equilibrium, two-person zero sum game matrix is created. As a result, Nash Equilibrium occurs in only the one case that indicates cryptocurrency should be taxed with low tax rate and high penalty rate. Moreover to understand tax policies of cryptocurrency this chapter also investigate various countries taxation policy on cryptocurrency. It is clear that there is no consensus among countries about legal status and taxation process of cryptocurrencies.
A Theory of Uncertainty Variables for State Estimation and Inference

Probability theory forms an overarching framework for modeling uncertainty, and by extension, also in designing state estimation and inference algorithms. While it provides a good foundation to system modeling, analysis, and an understanding of the real world, its application to algorithm design suffers from computational intractability. In this work, we develop a new framework of uncertainty variables to model uncertainty. A simple uncertainty variable is characterized by an uncertainty set, in which its realization is bound to lie, while the conditional uncertainty is characterized by a set map, from a given realization of a variable to a set of possible realizations of another variable. We prove Bayes' law and the law of total probability equivalents for uncertainty variables. We define a notion of independence, conditional independence, and pairwise independence for a collection of uncertainty variables, and show that this new notion of independence preserves the properties of independence defined over random variables. We then develop a graphical model, namely Bayesian uncertainty network, a Bayesian network equivalent defined over a collection of uncertainty variables, and show that all the natural conditional independence properties, expected out of a Bayesian network, hold for the Bayesian uncertainty network. We also define the notion of point estimate, and show its relation with the maximum a posteriori estimate.
Evaluation and visualization of facial massage effects by using ultraviolet stereo‐image correlation

Abstract Background/purpose This study proposes a technique for visualizing the effect of facial massage using stereo‐image correlation with melanin pigment. Method In this method, the melanin pigment of a subject's face is made visible by using an ultraviolet light and utilized as a random pattern for stereo‐image correlation. Stereo‐pair images of the face with the melanin pigment before and after facial massage are recorded using a desk‐sized measurement equipment. Then, the deformation of the face by the massage can be obtained based on the principle of stereovision. The effectiveness of the proposed method is demonstrated by applying it to the massage effect evaluation of eight subjects (females in their 40s). Results The results show that the massage effect can be visualized from the displacement and strain distributions across the face obtained by the proposed method. In addition, it is observed that the face is displaced significantly by the massage and individual differences between the subjects can be captured. Conclusion The proposed method is effective for evaluating the effect of a facial massage when the painted pattern disappears due to the applied cream during the massage.
Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein–Uhlenbeck Prior

This article studies the maximum likelihood estimates of magnitude and scale parameters for a Gaussian process of Ornstein-Uhlenbeck type used to model a deterministic function that does not have to be a realisation of an Ornstein- Uhlenbeck process. Specifically, we derive explicit expressions for the limiting values of the maximum likelihood estimates as the number of observations increases. The results demonstrate that the function typically needs to be sufficiently similar to a sample path of an Ornstein-Uhlenbeck process or have discontinuities if the variance of the model is to remain non-zero. Numerical examples illustrate the behaviour of the estimates when the function is not a sample path of any Ornstein-Uhlenbeck process.
Efficient authentication protocol with anonymity and key protection for mobile Internet users

To preserve user privacy and guarantee data confidentiality on the mobile Internet, it is crucial to secure communication between the mobile devices held by users and a remote server. In real applications, a serious threat against communication security is exposure of secret keys, due to the compromise of the mobile devices storing the key. One method of preserving key exposure is to use protected hardware or smart-cards, but they are costly and impractical. Another method is to utilize secret sharing to share secret key across multiple devices. Nevertheless, secret sharing schemes guarantee security only if the adversary cannot access at least one share in its entirety. In this paper, we present a remote authentication protocol, which resists key exposure. Further, we present a zero-knowledge protocol based on SDH assumption that can achieve anonymity. We formally prove our proposed solution is secure under the decision linear assumption and the qs-mSDH assumption in the random oracle model. Finally, we show our solution can achieve higher efficiency and stronger anonymity comparing with existing schemes, and thus the proposed solution is more suitable for real-world environments.
Constrained pseudorandom functions from functional encryption

This paper demonstrates how to design constrained pseudorandom functions (CPRF) and their various extensions from any public key functional encryption (FE) with standard polynomial security against arbitrary collusions. More precisely, we start by presenting a CPRF construction that supports constraint predicates realizable by arbitrary polynomial-size circuits, based on polynomially-hard public key FE and one way functions. Next, we augment our CPRF construction with the verifiability feature, relying only on a minimal additional assumption, namely, the existence of standard public key encryption (PKE). Finally, we show how to achieve privacy for the issued keys in the context of programable pseudorandom functions (PPRF), which is an enhanced variant of CPRF supporting puncturing constraints, employing polynomially-hard FE and one way functions. All prior works addressing the above problems either work for very restricted settings or rely on highly powerful yet little-understood cryptographic objects such as multilinear maps or indistinguishability obfuscation (IO). Although, there are known transformations from FE to IO, the reductions suffer from an exponential security loss and hence cannot be directly employed to replace IO with FE in cryptographic constructions at the expense of only a polynomial loss. Thus, our results open up a new pathway towards realizing numerous variants of CPRF, which are interesting cryptographic primitives in their own right and, moreover, have already been shown instrumental in a staggering range of applications, both in classical as well as in cutting edge cryptography, based on progressively weaker and well-studied cryptographic building blocks. Our work can also be interpreted as yet another stepping stone towards establishing FE as a substitute for IO in cryptographic applications. In order to achieve our results we build upon the prefix puncturing technique developed by Garg et al. [CRYPTO 2016, EUROCRYPT 2017] [42], [43].
Periodic words, common subsequences and frogs

Let W(n) be the n-letter word obtained by repeating a fixed word W, and let Rn be a random n-letter word over the same alphabet. We show several results about the length of the longest common subsequence (LCS) between W(n) and Rn; in particular, we show that its expectation is γWn−O(n) for an efficiently-computable constant γ W. This is done by relating the problem to a new interacting particle system, which we dub “frog dynamics”. In this system, the particles (“frogs”) hop over one another in the order given by their labels. Stripped of the labeling, the frog dynamics reduces to a variant of the PushTASEP. In the special case when all symbols of W are distinct, we obtain an explicit formula for the constant γW and a closed-form expression for the stationary distribution of the associated frog dynamics. In addition, we propose new conjectures about the asymptotic of the LCS of a pair of random words. These conjectures are informed by computer experiments using a new heuristic algorithm to compute the LCS. Through our computations, we found periodic words that are more random-like than a random word, as measured by the LCS.
Investigating Parallelization of Cross-language Plagiarism Detection System Using The Winnowing Algorithm in Cloud Based Implementation

The computational performance of cross-language plagiarism detection system using winnowing algorithm developed at the Electrical Engineering Department, Universitas Indonesia became an issue for real world application. This research is investigating the parallelization of such system implemented on a lab-scale multicore based private cloud platform using OpenStack. Parallelization was done on the portion of the program where the paragraphs of reference documents are processed. The result of execution time of the overall parallelized computation was able to reach speed up of 1.07 to 3.52 times compared to the execution time of the original serial computation.
A Comparative Study of Feature Selection Methods for Informal Arabic

The advent of web 2.0 and new Big Data technologies has created a diversity of data and information that can be used in many fields of application. The case of opinion mining is of increasing interest to researchers because of its impact on policy, marketing, etc. Through this document, we are interested in the study of sentiments more specifically in informal Arabic. We present a new approach of processing and analysis that is improved through feature selection methods. The experiments we have carried out are based on the comparison of 3 feature selection methods combined with several machine learning algorithms applied on a twitter dataset. Our paper reports the enhanced results (Accuracy of 98%) and shows the importance of feature selection for Arabic Sentiment Analysis.
Towards a fairness solution of Scrabble with Komi system

Artificial intelligence (AI) has a long-standing and healthy relationship with games where it becomes a popular application area for AI-driven research such as game playing, game design and so on. With the recent advancement of AI game-playing programs that had exceeded human capabilities, fairness becomes an important issue to be addressed in order to ensure the attractiveness of a game can be retained to the future players. Such an issue may also be compounded when considering the context of turn-based games, where the first player may have a huge advantage over the subsequent player(s) (called the advantage of initiative). As such, this paper proposes an innovative way to make a game attractive while maintaining fairness by adopting the Komi (compensation system). This fairness solution is validated by applying the system onto a word anagram game, Scrabble, where fairness can be maintained based on the skill level of the players.
A Neural Network Model of Spatial Distortion Sensitivity for Video Quality Estimation

Accurate estimation of visual quality as perceived by humans is crucial for modern multimedia systems and, given the evident ease for humans, a surprisingly difficult task for computers. Complexity considerations as imperative for real-time applications render this problem even more challenging. This paper studies the application of a neural network-based spatial model of distortion sensitivity to the quality prediction of spatio-temporal videos. We propose a simple yet effective adaptation of the loss function to cope with saturation effects in human quality ratings. This adaptation drastically decreases the number of iterations necessary for training networks to replicate psychophysical human responses. Our experimental results show significantly improved prediction performance of the spatio-temporal PSNR when compensated for spatial distortion sensitivity while maintaining the advantage of low complexity.