Reviewerly/exHarmony huggingface dataset:
Querying knowledge graphs with extended property paths

The increasing number of Knowledge Graphs (KGs) available today calls for powerful query languages that can strike a balance between expressiveness and complexity of query evaluation, and that can be easily integrated into existing query processing infrastructures. We present Extended Property Path s (EPPs), a significant enhancement of Property Paths (PPs), the navigational core included in the SPARQL query language. We introduce the EPPs syntax, which allows to capture in a succinct way a larger class of navigational queries than PPs and other navigational extensions of SPARQL, and provide formal semantics. We describe a translation from non-recursive EPPs (nEPPs) into SPARQL queries and provide novel expressiveness results about the capability of SPARQL sub-languages to express navigational queries. We prove that the language of EPPs is more expressive than that of PPs; using EPPs within SPARQL allows to express things that cannot be expressed when only using PPs. We also study the expressiveness of SPARQL with EPPs in terms of reasoning capabilities. We show that SPARQL with EPPs is expressive enough to capture the main RDFS reasoning functionalities and describe how a query can be rewritten into another query enhanced with reasoning capabilities. We complement our contributions with an implementation of EPPs as the SPARQL-independent iEPPs language and an implementation of the translation of nEPPs into SPARQL queries. What sets our approach apart from previous research on querying KGs is the possibility to evaluate both nEPPs and SPARQL with nEPPs queries under the RDFS entailment regime on existing query processors. We report on an experimental evaluation on a variety of real KGs.
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

Traditional approaches to differential privacy assume a fixed privacy requirement ε for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint. As differential privacy is increasingly deployed in practical settings, it may often be that there is instead a fixed accuracy requirement for a given computation and the data analyst would like to maximize the privacy of the computation subject to the accuracy constraint. This raises the question of how to find and run a maximally private empirical risk minimizer subject to a given accuracy requirement. We propose a general “noise reduction” framework that can apply to a variety of private empirical risk minimization (ERM) algorithms, using them to “search” the space of privacy levels to find the empirically strongest one that meets the accuracy constraint, and incurring only logarithmic overhead in the number of privacy levels searched. The privacy analysis of our algorithm leads naturally to a version of differential privacy where the privacy parameters are dependent on the data, which we term ex-post privacy, and which is related to the recently introduced notion of privacy odometers. We also give an ex-post privacy analysis of the classical AboveThreshold privacy tool, modifying it to allow for queries chosen depending on the database. Finally, we apply our approach to two common objective functions, regularized linear and logistic regression, and empirically compare our noise reduction methods to (i) inverting the theoretical utility guarantees of standard private ERM algorithms and (ii) a stronger, empirical baseline based on binary search.
Semi-Automatic Multi-Object Video Annotation Based on Tracking, Prediction and Semantic Segmentation

Instrumented and autonomous vehicles can generate very high volumes of video data per car per day all of which must be annotated at a high degree of granularity, detail, and accuracy. Manually or automatically annotating videos at this level and volume is not a trivial task. Manual annotation is slow and expensive while automatic annotation algorithms have shown significant improvement over the past few years. This demonstration presents an application of multi-object tracking, path prediction, and semantic segmentation approaches to facilitate the process of multi-object video annotation for enriched tracklet extraction. Currently, these three approaches are used to enhance the annotation task but more can and will be included in the future.
Active View and Passive View in Virtual Reality Have Different Impacts on Memory and Impression

Virtual reality (VR) through a head-mounted display (HMD) can provide new experiences. However, it remains unclear how the characteristics of HMDs affect users' memory. To use HMDs more effectively and appropriately in several applied fields, including education, it is necessary to clarify what characteristics of HMDs affect users' memory. A head-tracking function mounted on an HMD helps to detect the user's head direction to enable a simulation experience akin to the real world. When we experience a simulation on an HMD, we actively perceive the visual world. In this study, we assessed how active/passive viewing affects users' memory of VR content. We conducted a psychological experiment in which participants watched a movie on an HMD. In the active viewing condition, the presented view changed depending on the participant's head direction. In the passive viewing condition, the presented view was a recorded movie that was shown to the participants in the active viewing condition. All participants took a memory test about the content presented in the movie on the day of viewing and 2 weeks later. The results showed that performance on the memory test in the active viewing condition was significantly lower than that in the passive viewing condition after 2 weeks. This result indicated that active viewing in VR inhibited users' memory compared to passive viewing. The current study contributes to the development of new VR techniques, such as educational learning.
Secret sharing and duality

Abstract Secret sharing is an important building block in cryptography. All explicit secret sharing schemes which are known to have optimal complexity are multi-linear, thus are closely related to linear codes. The dual of such a linear scheme, in the sense of duality of linear codes, gives another scheme for the dual access structure. These schemes have the same complexity, namely the largest share size relative to the secret size is the same. It is a long-standing open problem whether this fact is true in general: the complexity of any access structure is the same as the complexity of its dual. We give a partial answer to this question. An almost perfect scheme allows negligible errors, both in the recovery and in the independence. There exists an almost perfect ideal scheme on 174 participants whose complexity is strictly smaller than that of its dual.
Analysis study on R-Eclat algorithm in infrequent itemsets mining

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.
DSTL: Solution to Limitation of Small Corpus in Speech Emotion Recognition

Traditional machine learning methods share a common hypothesis: training and testing datasets must be in a common feature space with the same distribution. However, in reality, the labeled target data may be rare, so that target space does not share the same feature space or distribution as an available training set (source domain). To address the mismatch of domains, we propose a Dual-Subspace Transfer Learning (DSTL) framework that considers both the common and specific information of the two domains. In DSTL, a latent common subspace is first learned to preserve the data properties and reduce the discrepancy of domains. Then, we propose a mapping strategy to transfer the sourcespecific information to the target subspace. The integration of the domain-common and specific information constructs the proposed DSTL framework. In comparison to the stateart-of works, the main contribution of our work is that the DSTL framework not only considers the commonalities, but also exploits the specific information. Experiments on three emotional speech corpora verify the effectiveness of our approach. The results show that the methods which include both domain-common and specific information perform better than the baseline methods which only exploit the domain commonalities.
Pathfinding and Abstraction with Dynamic Terrain Costs

Abstraction and refinement is a common approach used in games to improve the speed of pathfinding by planning in an abstract space and then refining abstract paths to traversable paths. While there are many variants of this approach that have been developed and studied, research on this problem has largely ignored the problem of pathfinding with terrain types, terrain costs, and dynamic terrain. This paper studies the problem of pathfinding in domains with terrain costs and proposes an abstraction approach that is built around handling terrain costs and dynamic terrain. The resulting approach is able to handle costs in a way that existing approaches do not, and provides a good balance between memory usage, path quality, and pathfinding speed.
Accelerating Relative-error Bounded Lossy Compression for HPC datasets with Precomputation-Based Mechanisms

Scientific simulations in high-performance computing (HPC) environments are producing vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for post-analysis. Unlike the traditional data reduction schemes (such as deduplication or lossless compression), not only can error-controlled lossy compression significantly reduce the data size but it can also hold the promise to satisfy user demand on error control. Point-wise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications in the lossy compression, since error control can adapt to the precision in the dataset automatically. Point-wise relative error bounded compression is complicated and time consuming. In this work, we develop efficient precomputation-based mechanisms in the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error bounded compression with excellent compression ratios. In addition, our mechanisms also help reduce traversing operations for Huffman decoding, and thus significantly accelerate the decompression process in SZ. Experiments with four well-known real-world scientific simulation datasets show that our solution can improve the compression rate by about 30% and decompression rate by about 70% in most of cases, making our designed lossy compression strategy the best choice in class in most cases.
Using Mind Genomics To Understand The Specifics Of A Customer’s Mind

Approaching customers with the right message has always been a key objective of companies selling products and services. Achieving this target demands understanding what customers think about specific ideas and messages, determining whether there are different mind-sets for the same topic, and if there are, assigning people to the right mind-set. The advent of Internet has increased the opportunities for the advertising component. Companies need to have the tools to conceptualize these mind-sets ‘on-demand,’ for any topic, collect the relevant data for this specific topic, followed by a way to uncover mind-sets and assign new people to the mind-set. The issue is to achieve the foregoing with low cost, on demand, in areas where there is no behavioral or psychographic data, and then apply the results in actionable way. Several approaches have been created and used to address the problem, but all require either behavioral or expensive psychographic data, and time to do the research. This paper presents the emerging new technology of Mind Genomics, a marriage of experimental psychology and data analytics. Mind Genomics reveals, through simple, affordable, iterative experiments the specifics of what features of a service or product appeals to an individual through the notion of mind-sets, and then a method for assigning any new person to the most appropriate mind-set. A simple set of questions, embodied in the PVI (personal viewpoint identifier), assigns the new person to the mind-set, doing so in ‘real-time.’ The results are both better knowledge of the topic, and better sales performance.
Do Fintech and Cryptocurrency Initiatives Make Banks Less Special?

Banks as a group have traditionally been considered “special” in the sense of meriting the full set of provisions of the financial safety net. The specific motivations for that view have evolved over time, although it owes more to a specific combination of economic functions performed as opposed to any particular function. These functions include offering transaction accounts redeemable in cash on demand, providing liquidity, and serving as conduits for payments and monetary policy transmission. Recent developments suggest however that almost all of the individual economic functions performed by banks can in fact be provided in unbundled form by Fintech initiatives, in some cases more rapidly, at lower fees, and via more streamlined digital interfaces. One important exception remains monetary policy transmission. For the performance of this function, policy makers and central bankers have reserved a privileged role for banks. A radical departure from the current fractional reserve system would be required to unbundle that function and separate money from the banking system, and some private cryptocurrencies have been proposed with the explicit intent to change the nature of money. So far, the present article concludes such initiatives remain marginal, so that banks as a group remain “special”. This observation owes much to the fact that central banks rely on the capacity of the banking system to create money and provide the economy with adequate liquidity and, despite occasional financial crises, have concluded that the efficiency of the current system outweighs the associated costs.
Exploring Internet of Things, Mobile Computing and Ubiquitous Computing in Computer Science Education: A Systematic Mapping Study

Ubiquitous computing, mobile computing and the Internet of Things (UMI) have been widely used in several application areas. To date, methods and techniques for the application of these technologies in real life situations have continued to emerge; however, their use in education settings focusing on existing practices remain largely underexplored. A systematic mapping study (SMS) method was herein used to map initially identified 395 articles with the aims of systematically analyzing and presenting the evidence from the literature on the topic, and to identify important gaps as well as promising research directions. An appropriate methodological protocol has been adopted from the literature for the analysis, filtering, evaluation and report of the evidence. As a result, twenty-five studies have been selected and analyzed. The axes of analyzing systematically the literature were inspired by an existing UMI learning ecology. The analysis revealed important characteristics of existing UMI related educational practices in all levels of education, including contexts and actors involved, methods and digital tools used, affordances and learning approaches important for achieving effective learning in IoT, Mobile and Ubiquitous Computing domain.
A polynomial algorithm for minimizing discrete convic functions in fixed dimension

In Chirkov et al., (2019), classes of conic and discrete conic functions were introduced. In this paper we use the term convic instead conic. The class of convic functions properly includes the classes of convex functions, strictly quasiconvex functions and the class of quasiconvex polynomials. On the other hand, the class of convic functions is properly included in the class of quasiconvex functions. The discrete convic function is a discrete analogue of the convic function. In Chirkov et al., (2019), the lower bound 3n−1log(2ρ−1) for the number of calls to the comparison oracle needed to find the minimum of the discrete convic function defined on integer points of some n-dimensional ball with radius ρ was obtained. But the problem of the existence of a polynomial (in logρ for fixed n) algorithm for minimizing such functions has remained open. In this paper, we answer positively the question of the existence of such an algorithm. Namely, we propose an algorithm for minimizing discrete convic functions that uses 2O(n2logn)logρ calls to the comparison oracle and has 2O(n2logn)poly(logρ) bit complexity.
A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.
Convolution-Consistent Collective Matrix Completion

Collective matrix completion refers to the problem of simultaneously predicting the missing entries in multiple matrices by leveraging the cross-matrix information. It finds abundant applications in various domains such as recommender system, dimensionality reduction, and image recovery. Most of the existing work represents the cross-matrix information in a shared latent structure constrained by the Euclidean-based pairwise similarity, which may fail to capture the nonlinear relationship of the data. To address this problem, in this paper, we propose a new collective matrix completion framework, named C4, which uses the graph spectral filters to capture the non-Euclidean cross-matrix information. To the best of our knowledge, this is the first effort to represent the cross-matrix information in the graph spectral domain. We benchmark our model against 8 recent models on 10 real-world data sets, and our model outperforms state-of-the-art methods in most tasks.
Human Interaction Recognition Based on Deep Learning and HMM

This paper proposes a recognition method that combines deep learning with traditional hidden Markov model (HMM) with the aim of improving the recognition accuracy of interaction. First, to construct the classification model, the optimized ALexNet convolutional neural network is used to extract the behavior features, followed by the extraction of features that are used to train the long short-term memory (LSTM) network using the Softmax method. Finally, the particle swarm optimization algorithm is used to fuse the classification results with the traditional HMM classification results so that a hybrid classification model is established to obtain the final behavior recognition result. By conducting experiments on the UT-interaction dataset (six types of interaction behavior), the experimental results show that the hybrid model has higher recognition accuracy than other classical methods.
Election System Based on Blockchain Technology

There is no uncertainty that the chroma currency is very popular for the transaction information stored in the block of blocks.To initiate the importance of blocking, there is only one distributed ledger in which data is stored, since many of the centers in the blockchain network limit enough records for these records.This document proposes a decision-making framework based on innovation to create a secure, unjustifiable, accurate and transparent framework for voter privacy.In addition, it is voted and declared in this context that the race has a short period of time, since it is automatically registered in the table.This framework also gives society confidence in its legislation when applying this technique.In this framework, an administrator can add a candidate and a voter to the block.In different hands, a voter can log into the framework and then decide in favor of a candidate that the details of the vote should be stored in the block.In addition, each square in the blockchain innovation that relates to the previous square contains the hash of the previous square, and each square contains explicit data based on the square footage.The hub is connected by a peer network in this framework, with all the hubs in the block site network that have a complete duplicate block.
Quantifying the Costs of Data Breaches

Recent years have seen increases in the number of data breaches. This chapter attempts to quantify the impacts of data breaches in terms of the monetary costs incurred by providers and consumers. This is important because data breaches are a major factor when allocating funds for security controls. Case studies involving the Equifax incident in 2017 and the Target incident in 2013 are employed to demonstrate that the cost impacts of data breaches are significant for providers as well as consumers. The cost components in the overall cost function for providers and consumers are presented. Guided by open-source data, the cost components in the provider portion of the cost function are expressed as best-fit functions of time since the data breach. An important point in the cost quantification is that equal weights are assigned to the costs incurred by the provider and the consumers.
Cross-Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon

Research on the bilingual lexicon has uncovered fascinating interactions between the lexicons of the native language and of the second language in bilingual speakers.In particular, it has been found that the lexicon of the underlying native language affects the organisation of the second language.In the spirit of interpreting current distributed representations, this paper investigates two models of cross-lingual word embeddings, comparing them to the shared-translation effect and the cross-lingual coactivation effects of false and true friends (cognates) found in humans.We find that the similarity structure of the cross-lingual word embeddings space yields the same effects as the human bilingual lexicon.