Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Remote Knowledge System

More and more traditional services, such as malware detectors or collaboration services in industrial scenarios, move to the cloud. However, this behavior poses a risk for the privacy of clients since these services are able to generate profiles containing very sensitive information, e.g., vulnerability information or collaboration partners. Hence, a rising need for protocols that enable clients to obtain knowledge without revealing their requests exists. To address this issue, we propose a protocol that enables clients (i) to query large cloud-based knowledge systems in a privacy-preserving manner using Private Set Intersection and (ii) to subsequently obtain individual knowledge items without leaking the client's requests via few Oblivious Transfers. With our preliminary design, we allow clients to save a significant amount of time in comparison to performing Oblivious Transfers only.
Who stole what from whom?

Abstract Drawing on the Lexical Grammar Model, Frame Semantics and Corpus Pattern Analysis, we analyze and contrast verbs of stealing in English and Spanish from a lexico-semantic perspective. This involves looking at the lexical collocates and their corresponding semantic categories that fill the argument slots of verbs of stealing. Our corpus search is performed with the Word Sketch tool on Sketch Engine. To the best of our knowledge, no study has yet taken advantage of the Word Sketch tool in the study of the selection preferences of verbs of stealing, let alone a semantic, cross-linguistic study of those verbs. Our findings reveal that English and Spanish verbs of stealing map out the same underlying semantic space. This shared conceptual layer can thus be incorporated into an ontology based on deep semantics, which could in turn enhance NLP tasks such as word sense disambiguation, machine translation, semantic tagging, and semantic parsing.
Hybrid Collaborative Filtering with Semi-Stacked Denoising Autoencoders for Recommendation

Recommender system is one of the solutions to deal with information overload problem, thus has been intensively studied. In recent research, side information has been commonly used besides the rating matrix, so as to mitigate the sparsity problem and to improve the recommendation accuracy. To better making use of side information, deep learning based recommendation methods have been proposed, among which autoencoder-based models have become quite popular. However, most existing autoencoder-based models require the each input corresponds to one output, which may bring in information loss and high cost to extend autoencoders, thus affects the recommendation accuracy. To address this important issue, in this paper we first propose a Semi-Stacked Denoising Autoencoders (Semi-SDAE) model; then a new hybrid CF model incorporating the proposed Semi-SDAE model into matrix factorization, HCF-SS model, is developed. The HCF-SS model can flexibly use various sources of side information and the recommendation accuracy is improved. Experiments on two real-world datasets demonstrate that the proposed HCF-SS model outperforms the compared models.
Usability Evaluation Using GOMS Model for Education Game “Play and Learn English”

Evaluation is a major part of the “user-centered system design”. Evaluation is an activity to determine whether the previously set goals have been achieved or not. An educational game titled “Learn and Play English” is a game for learning English that can be used to improve vocabulary by applying certain strategies. This game needs to be evaluated to find out how the user accepts the game interface design. One of the evaluation approaches that can be done is using the usability approach, with the GOMS model as the basic model of evaluation (model-based evaluation). The aspects evaluated in usability evaluation are learnability, efficiency, memorability, error, and satisfaction. With properties in GOMS design evaluation, namely Naturalness of Design, Completeness of Design, Cleanliness of Design, Consistency of Design, and Efficiency of Design.
Boosting Image Steganalysis Under Universal Deep Learning Architecture Incorporating Ensemble Classification Strategy

Image steganalysis based on convolutional neural networks (CNNs) has achieved remarkable performance. However, all existing CNN-based steganalysis methods form an ensemble by merging the outputs of independently trained networks with the same architecture. In this letter, we propose a universal CNN architecture incorporating ensemble classification strategy. Any CNN-based steganalysis with the proposed architecture needs to train only one model to form an ensemble and can boost detection accuracy in both spatial and JPEG steganography. In particular, we propose a new method to construct subspaces for training well-designed base learners. In addition, a novel voting fusion structure automatically optimized with the training process is proposed. Experimental results on the public dataset demonstrate that the proposed architecture can further improve the performance of CNN-based steganalysis. Source code is available via GitHub ( https://github.com/Ante-Su/CAECS ).
On Path Planning using Minimal Aesthetic B-Spline Curves

Path planning plays a key role for comfortability and safety in driving, and for autonomy in robotics. Most path planning methods study the problem on how to obtain minimal paths given global and local information of the environment; and to consider smoothness, the study on energy minimization on curvature is conventional. In this paper, we propose path planning using aesthetic Bspline curves to render minimal-length and smooth paths that avoid obstacles. Computational results in arbitrary polygonal environments show that our proposed approach is feasible.
Incremental Fault Analysis: Relaxing the Fault Model of Differential Fault Attacks

This article presents a new fault analysis technique against cryptographic devices called the incremental fault analysis (IFA), which can be adapted into fault attacks using more traditional differential fault analysis (DFA) techniques in order to increase their feasibility under more practical fault injection conditions. Many previous attack methods require precise fault injection techniques such as clock glitching. By contrast, IFA is compatible with a more practical overclocking fault injection technique in which a cryptosystem is stressed at a constant level throughout the entire encryption, and this constant stress level is then increased between consecutive encryptions. It is observed that as new faults occur incrementally between increased stress levels, they often become superimposed upon faults first appearing at lower stress levels. IFA exploits these incremental fault differentials to deduce the cipher key more rapidly. Attacks were tested using practical fault injection methods on the advanced encryption standard (AES) both with and without IFA applied. Using IFA, allowed cipher keys to be retrieved with a success rate of 100% from 10 times less faulty ciphertexts and 6.4 times less computational time, requiring 16, 86, and 43 ciphertexts on average for AES-128, AES-192, and AES-256, respectively.
Resource ratio based virtual machine placement in heterogeneous cloud data centres

Server consolidation through virtualization improves resource utilization significantly in Cloud Data Centres (CDCs). We study the case of a CDC hosting heterogeneous Physical Machines (PMs) as a variable size vector bin-packing problem. The PMs have different configurations of multiple resources like CPU, RAM, Disk Storage and Network Bandwidth. In this paper, we propose PMNeAR-vector heuristic for PM selection in PM-heterogeneity aware Virtual Machine (VM) initial placement. The proposed heuristic is compared with well-known heterogeneity aware FFD-DRR and BFD bin centric heuristics using a dataset with random instances of both VMs and PMs of heterogeneous configurations. Fifty rounds of VM initial placement simulation experiments were conducted to validate the average resource wastage. The results show that on average FFD-DRR and BFD bin centric heuristics are wasting 22.62% and 37.27% more resource units compared to the proposed PMNeAR-vector heuristic.
Towards Exploiting Generic Problem Structures in Explanations for Automated Planning

Explainable AI is becoming an area of key focus in Artificial Intelligence. Within Automated Planning (AP) the area Explainable Planning (XAIP) focuses on explanations of the planning process. The relative transparency and flexibility of the planning process have been identified as key aspects suggesting that AP is well positioned to make an important contribution in Explainable AI [8]. However, there is still a wide gap between explanations that can be directly extracted from AP models and effective explanations. There are a growing number of frameworks that are considering the problem from both the user side, where it is interesting to understand the form that an explanation might take; as well as the planner side, which must be able to explain various decisions and related properties. However, approaches have focused on single domain settings, where substantial domain specific content is produced, or at a general level, where only abstract planning concepts can be used. We aim to develop an abstraction layer that sits between these and exploits the often overlapping concepts and structures that exist between many planning domains. We propose exploiting domain analysis techniques in order to identify common roles and generic problem structures (GPSs). By attaching the concepts used for explanation to these structures we can exploit the contextual information supported by the structure, and also reduce the burden of constructing explanations in domains where these structures exist. In this work we explore the opportunities for exploiting GPSs in XAIP.
Pronouns as referential devices in Estonian, Finnish, and Russian

This study examines the usage of different anaphoric referential pronominal expressions in three areally close languages: Estonian, Finnish, and Russian. It focuses on the linguistic factors that influence the choice of referential pronominals in spoken narrative discourse, and the relative importance of these factors in languages. We used a picture-sequence based narrative elicitation method to collect a comparable and consistent dataset for these three languages. The data is analyzed using non-parametric conditional inference tree and random forest models. The analysis shows that while the usage frequency and contexts of demonstrative pronouns show large variation in these languages, there are also devices (e.g., personal pronouns and zero reference) that act in a very similar way. There are three significant linguistic factors that influence the choice of referential devices in Estonian, Finnish, and Russian: (i) syntactic role, ii) case of the referential noun phrase, and iii) the referential distance with the previous mention. However, their exact effect, as well as the importance of other factors, varies across languages. These findings support previous research that whilst referential devices have similar grammatical labels (e.g., demonstrative pronouns and personal pronouns) in languages, their actual functions should be analyzed with respect to the specific system of the language.
Building bagging on critical instances

Abstract The ensemble method is a powerful data mining paradigm, which builds a classification model by integrating multiple diversified component learners. Bagging is one of the most successful ensemble methods. It is made of bootstrap‐inspired classifiers and uses these classifiers to get an aggregated classifier. However, in bagging, bootstrapped training sets become more and more similar as redundancy is increasing. Besides redundancy, any training set is usually subject to noise. Moreover, the training set might be imbalanced. Thus, each training instance has a different impact on the learning process. This paper explores some properties of the ensemble margin and its use in improving the performance of bagging. We introduce a new approach to measure the importance of training data in learning, based on the margin theory. Then, a new bagging method concentrating on critical instances is proposed. This method is more accurate than bagging and more robust than boosting. Compared to bagging, it reduces the bias while generally keeping the same variance. Our findings suggest that (a) examples with low margins tend to be more critical for the classifier performance; (b) examples with higher margins tend to be more redundant; (c) misclassified examples with high margins tend to be noisy examples. Our experimental results on 15 various data sets show that the generalization error of bagging can be reduced up to 2.5% and its resilience to noise strengthened by iteratively removing both typical and noisy training instances, reducing the training set size by up to 75%.
Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Modeling multivariate variables with complexity in a cross-correlation structure is always applicable to mineral resource evaluation and exploration in multi-element deposits. However, the geostatistical algorithm for such modeling is usually challenging. In this respect, projection pursuit multivariate transform (PPMT), which can successfully handle the complexity of interest in bivariate relationships, may be particularly useful. This work presents an algorithm for combining projection pursuit multivariate transform (PPMT) with a conventional (co)-simulation technique where spatial dependency among variables can be defined by a linear model of co-regionalization (LMC). This algorithm is examined by one real case study in a limestone deposit in the south of Kazakhstan, in which four chemical compounds (CaO, Al2O3, Fe2O3, and SiO2) with complexity in bivariate relationships are analyzed and 100 realizations are produced for each variable. To show the effectiveness of the proposed algorithm, the outputs (realizations) are statistically examined and the results show that this methodology is legitimate for reproduction of original mean, variance, and complex cross-correlation among the variables and can be employed for further processes. Then, the applicability of the concept is demonstrated on a workflow to classify this limestone deposit as measured, indicated, or inferred based on Joint Ore Reserves Committee (JORC) code. The categorization is carried out based on two zone definitions, geological, and mining units.
POLAR++: Active One-Shot Personalized Article Recommendation

We study the problem of personalized article recommendation, in particular when the user's preference data is missing or limited, which is knowns as the user cold-start issue in recommender systems. We propose POLAR++, an active recommendation framework that utilizes Bayesian neural networks to capture the uncertainty of user preference, actively selects articles to query the user for feedback, and adaptively learns user preference with one-shot learning. For the article recommendation, we design an attention-based CNN to quantify the similarity between user preference and recommended articles, which significantly improves the performance with only a few articles rated by the users. We evaluate the proposed POLAR++ on datasets of different scale and sources. Experimental results demonstrate the effectiveness of the proposed model. We have successfully deployed POLAR++ into AMiner as the recommendation engine for article recommendation, which further confirms the effectiveness of the proposed model.
Optimization and Parallelization of Fuzzy Clustering Algorithm Based on the Improved Kmeans++ Clustering

Abstract In the field of big data, fuzzy clustering algorithm is one of the most widely used clustering algorithm. Although fuzzy c-means (FCM) algorithm performs well, it still is difficult to ascertain the number of clusters and is sensitive to initial clustering center. To solve these problems, an improved fuzzy clustering algorithm based on Spark is proposed in this article. The proposed algorithm integrates the L2 norm and uses the kmeans++ algorithm improved by the Canopy algorithm to initialize the cluster center. Experimental results show that the proposed algorithm performs well in clustering accuracy and computational performance.
Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank

In this paper we provide an overview of the approach we used as team PoliCloud8 for the ACM RecSys Challenge 2019. The competition, organized by Trivago, focuses on the problem of session-based and context-aware accommodation recommendation in a travel domain. The goal is to suggest suitable accommodations fitting the needs of the traveller to maximise the chance of a redirect (click-out) to a booking site, relying on explicit and implicit user signals within a session (clicks, search refinement, filter usage) to detect the users intent. Our team proposes a solution based on several new features, designed to capture specific types of information as well as some well-known models: gradient boosting, neural networks and a stacking-based ensemble.
TIDY: Publishing a Time Interval Dataset With Differential Privacy

Log data from mobile devices generally contain a series of events with temporal information including time intervals which consist of the start and finish times. However, the problem of releasing differentially private time interval datasets has not been tackled yet. A time interval dataset can be represented by a two dimensional (2D) histogram. Most of the methods to publish 2D histograms partition the data into rectangular spaces to reduce the aggregated noise error for range queries. However, the existing algorithms to publish 2D histograms suffer from the structural error when applied to time interval datasets. To reduce the aggregated noise errors and suppress the increase in the structural error, we propose the TIDY (publishing Time Intervals via Differential privacY) algorithm. We use the frequency vectors as a compact representation of the time interval dataset. After applying the Laplace mechanism to the frequency vectors, we improve the utility of the frequency vectors based on a maximum likelihood estimation. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our empirical study on real-life and synthetic datasets confirms that TIDY outperforms the existing algorithms for 2D histograms.
Neural Element of Parallel-Stream Type with Preliminary Formation of Group Partial Products

The model of a neural element of the parallel-stream type with the preliminary formation of group partial products and the method of scalar product calculating have been developed. The stage of the conveyor has been implemented using the FPGA of the Cyclone III family.
Robust contextual models for in-session personalization

Most online activity happens in the context of a session; to enable better user experience many online platforms aim to dynamically refine their recommendations as sessions progress. A popular approach is to continuously re-rank recommendations based on current session activity and past session logs. This motivates the 2019 ACM RecSys Challenge organised by Trivago. Using the session log dataset released by Trivago, the challenge aims to benchmark models for in-session re-ranking of hotel recommendations. In this paper we present our approach to this challenge where we first contextualize sessions in a global and local manner, and then train gradient boosting and deep learning models for re-ranking. Our team achieved 2nd place out of over 570 teams, with less than 0.3% relative difference in Mean Reciprocal Rank from the 1st place team. Code for our approach can be found here: https://github.com/layer6ai-labs/RecSys2019
Juggling in VR: Advantages of Immersive Virtual Reality in Juggling Learning

In this paper, we follow up on research dealing with motion learning in Virtual Reality (VR). We investigate the impact of VR motion learning on motion performance, motivation for motion learning and willingness to continue with the motion learning. In our research, we used three ball juggling as a subject of learning. We performed a user study with 30 participants. A VR application was used in our study which allows setting up lower gravity and thus slowing down the motion for learning purposes. The results were statistically evaluated and we comment on the positive influence of virtual reality on motivation and possibilities of using VR in the motion learning process.
How Pre-trained Word Representations Capture Commonsense Physical Comparisons

Understanding common sense is important for effective natural language reasoning.One type of common sense is how two objects compare on physical properties such as size and weight: e.g., 'is a house bigger than a person?'.We probe whether pre-trained representations capture comparisons and find they, in fact, have higher accuracy than previous approaches.They also generalize to comparisons involving objects not seen during training.We investigate how such comparisons are made: models learn a consistent ordering over all the objects in the comparisons.Probing models have significantly higher accuracy than those baseline models which use dataset artifacts: e.g., memorizing some words are larger than any other word.
Enhanced and Lock-Free Tendermint Blockchain Protocol

Blockchain (BC), the backbone technology of cryptocurrency systems and smart contracts, is considered to be an alluring concept in recent years due to its ability to ensure enhanced security and privacy for multifarious applications in many domains. The blockchain is exclusively used for facilitating secure online transactions by maintaining a distributed and decentralized ledger of records across multiple computers. In this paper, we have analyzed and modified the PBFT (Practical Byzantine Fault Tolerant) consensus-based Tendermint blockchain algorithm. The major contributions of this paper are as follows; first we have analyzed and enhanced the correctness of Tendermint blockchain algorithm by proposing a lock free algorithm, employing wait-freedom property by using a timeout on the voting phase. Our second contribution relates to the fairness of the Tendermint algorithm. We have considered the block sensitivity and node's trustworthiness for determining the size of voter's (validator's) subset and employed the random walk algorithm for the fair selection of sub set of the voter nodes. Our third contribution is to investigate the reason for having voting conflicts and the weakness of consensuses as a correctness property. Finally, we have shown how to detect byzantine and failure nodes in the blockchain.