Reviewerly/exHarmony huggingface dataset:
Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm

Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment’s quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts’ similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative’s interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.
Improving GAN with inverse cumulative distribution function for tabular data synthesis

Designing a generative model to synthesize realistic tabular data is of great significance in data science. Existing tabular data generative models have difficulty in handling complicated and diverse marginal distribution types due to the gradient vanishing problem, and these models pay little attention to the correlation between attributes. We propose a method that improves the generative adversarial network (GAN) with inverse cumulative distribution function for tabular data synthesis. This method first transforms continuous columns into uniform distribution data by using the cumulative distribution function, which can alleviate the gradient vanishing problem in model training. Then the method trains GAN with the transformed data, where the discriminator with label reconstruction function is presented to model the correlation among attributes accurately by introducing an auxiliary supervised task to help the correlations extraction. After that, we train a neural network for each continuous column to perform the inverse transformation of generated data into the target distribution, thereby the synthetic data is obtained. Experiments on simulated and real-world datasets show that our method compares favorably against the state-of-the-art methods in modeling tabular data.
Probabilistic one-time programs using quantum entanglement

Abstract Quantum technology allows for unparalleled levels of data and software protection. Probabilistic one-time programs harness these capabilities for quantum-assisted classical computations by encoding classical software in small quantum states resulting in computer programs that can be used only once. Such self-destructing one-time programs facilitate a variety of applications reaching from software distribution to one-time delegation of signature authority. Whereas previous experiments demonstrated the feasibility of such schemes, the practical applications were limited. Here we present an improved protocol for one-time programs that resolves major drawbacks of previous schemes, by employing entangled qubit pairs. This results in four orders of magnitude higher count rates and the ability to execute a program long after the quantum information exchange has taken place. We implement a one-time delegation of signature authority over an underground fiber link between university buildings in downtown Vienna, emphasizing the compatibility of our scheme with prepare-and-measure quantum internet networks.
Van Lint–MacWilliams' conjecture and maximum cliques in Cayley graphs over finite fields

A well-known conjecture due to van Lint and MacWilliams states that if A is a subset of Fq2 such that 0,1∈A, |A|=q, and a−b is a square for each a,b∈A, then A must be the subfield Fq. This conjecture is often phrased in terms of the maximum cliques in Paley graphs. It was first proved by Blokhuis and later extended by Sziklai to generalized Paley graphs. In this paper, we give a new proof of the conjecture and its variants, and show how this Erdős-Ko-Rado property of Paley graphs extends to a larger family of Cayley graphs, which we call Peisert-type graphs. These results resolve the conjectures by Mullin and Yip.
Cryptocurrencies and Cagan’s model of hyperinflation

The drivers of the prices of Bitcoin and Ethereum are studied within a framework based on Cagan’s model of hyperinflation . In the model, the prices of the cryptocurrencies are driven by stochastic adoption and velocity shocks as well as endogenous expectations of future prices. The model is estimated with data for prices, transaction volumes, and money supplies. A majority of price fluctuations in both currencies can be attributed to shocks in adoption, velocity shocks are much less important. The money demand sensitivity to expected price changes is estimated to be larger for Bitcoin than for Ethereum, and both have higher sensitivity than fiat currencies during episodes of hyperinflation.
ELITE

Deep Learning techniques have been widely used in detecting anomalies from complex data. Most of these techniques are either unsupervised or semi-supervised because of a lack of a large number of labeled anomalies. However, they typically rely on a clean training data not polluted by anomalies to learn the distribution of the normal data. Otherwise, the learned distribution tends to be distorted and hence ineffective in distinguishing between normal and abnormal data. To solve this problem, we propose a novel approach called ELITE that uses a small number of labeled examples to infer the anomalies hidden in the training samples. It then turns these anomalies into useful signals that help to better detect anomalies from user data. Unlike the classical semi-supervised classification strategy which uses labeled examples as training data, ELITE uses them as validation set. It leverages the gradient of the validation loss to predict if one training sample is abnormal. The intuition is that correctly identifying the hidden anomalies could produce a better deep anomaly model with reduced validation loss. Our experiments on public benchmark datasets show that ELITE achieves up to 30% improvement in ROC AUC comparing to the state-of-the-art, yet robust to polluted training data.
Li-Net: Large-Pose Identity-Preserving Face Reenactment Network

Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial landmarks to reenact source faces and ignore the intrinsic gap between two identities, resulting in the identity mismatch issue. Besides, they neglect the entanglement of expression and pose features when encoding driving faces, leading to inaccurate expressions and visual artifacts on large-pose reenacted faces. To address these problems, we propose a Large-pose Identity-preserving face reenactment network, LI-Net. Specifically, the Landmark Transformer is adopted to adjust driving landmark images, which aims to narrow the identity gap between driving and source landmark images. Then the Face Rotation Module and the Expression Enhancing Generator decouple the transformed landmark image into pose and expression features, and reenact those attributes separately to generate identity-preserving faces with accurate expressions and poses. Both qualitative and quantitative experimental results demonstrate the superiority of our method.
Resource Pricing and Demand Allocation for Revenue Maximization in IaaS Clouds: A Market-Oriented Approach

With more users outsourcing their applications to the cloud, resource pricing becomes an important issue for IaaS cloud management. Jointly considering her own bidding budget and the price of cloud resources, each user is self-motivated to purchase cloud resources according to her resource demand which maximizes her own utility. Meanwhile, the cloud service provider (CSP) regulates the price of cloud resources with a certain profitability objective achieved. With an elaborate resource pricing strategy, the goals from users and the CSP are balanced and respectively satisfied to some extent. This article provides an insight into the market-oriented cloud pricing strategy. In specific, we propose an auction market in the IaaS cloud, where multiple users with heterogeneous bidding budgets and QoS requirements subscribe cloud resources according to their resource demands. The resource pricing and demand allocation scheme targeting revenue maximization also satisfies essential properties including budget feasibility, incentive compatibility and envy-freeness. To attack the NP-hardness and non-convexity of revenue maximization problem, we design a price-incentive resource auction mechanism namely RARM, which preserves an ( 1+α) approximation ratio on revenue maximization. Finally, we evaluate our RARM mechanism based on the real-world dataset to certify the efficacy of our proposed approach.
Development of a Wide Area 3D Scanning System with a Rotating Line Laser

In a 3D scanning system, using a camera and a line laser, it is critical to obtain the exact geometrical relationship between the camera and laser for precise 3D reconstruction. With existing depth cameras, it is difficult to scan a large object or multiple objects in a wide area because only a limited area can be scanned at a time. We developed a 3D scanning system with a rotating line laser and wide-angle camera for large-area reconstruction. To obtain 3D information of an object using a rotating line laser, we must be aware of the plane of the line laser with respect to the camera coordinates at every rotating angle. This is done by estimating the rotation axis during calibration and then by rotating the laser at a predefined angle. Therefore, accurate calibration is crucial for 3D reconstruction. In this study, we propose a calibration method to estimate the geometrical relationship between the rotation axis of the line laser and the camera. Using the proposed method, we could accurately estimate the center of a cone or cylinder shape generated while the line laser was rotating. A simulation study was conducted to evaluate the accuracy of the calibration. In the experiment, we compared the results of the 3D reconstruction using our system and a commercial depth camera. The results show that the precision of our system is approximately 65% higher for plane reconstruction, and the scanning quality is also much better than that of the depth camera.
One Byte at a Time: Insights about Meaningful Data for Sustainable Food Consumption Practices

Data have played an extensive role in sustainable HCI research by informing the impacts of our behavior on the environment and helping us make better environmental choices. However, in the area of sustainable food consumption and sustainable HCI, there is little investigation on the roles of food data for the design of technology. This paper presents findings from a qualitative study of sustainable-conscious individuals’ food data seeking experiences. Our results show the way in which the current food data is challenging our understanding of its environmental impacts, which concern data of availability, data representations, and data cultures. Drawing from Loukissas’ six critical data principles, we discuss how “locality” and ”place” could cast a new insight on food and its sustainability. We also offer possible design directions for sustainable HCI technologies utilizing food data.
Employing Explainable AI to Optimize the Return Target Function of a Loan Portfolio

In the recent years, data science methods have been developed considerably and have consequently found their way into many business processes in banking and finance. One example is the review and approval process of credit applications where they are employed with the aim to reduce rare but costly credit defaults in portfolios of loans. But there are challenges. Since defaults are rare events, it is—even with machine learning (ML) techniques—difficult to improve prediction accuracy and improvements are often marginal. Furthermore, while from an event prediction point of view, a non-default is the same as a default, from an economic point of view much more relevant to the end user it is not due to the high asymmetry in cost. Last, there are regulatory constraints when it comes to the adoption of advanced ML, hence the call for explainable artificial intelligence (XAI) issued by regulatory bodies like FINMA and BaFin. In our study, we will address these challenges. In particular, based on an exemplary use case, we show how ML methods can be adapted to the specific needs of credit assessment and how, in the case of strongly asymmetric costs of wrong forecasts, it makes sense to optimize not for accuracy but for an economic target function. We showcase this for two simple and ad hoc explainable ML algorithms, finding that in the case of credit approval, surprisingly high rejection rates contribute to maximizing profit.
Memoirs from Bukhalalire: a poetic inquiry into the lived experience of M-PESA mobile money usage in rural Kenya

M-PESA is the world's leading mobile money system which supports the person-to-person transfer of money. While the institutional meaning of M-PESA concerning financial inclusion and market access is well documented, the meaning attached to M-PESA by users, particularly in rural communities, has received less attention. This study examines the meaning of M-PESA to the rural poor in villages in Busia County, Western Kenya using the approach of poetic inquiry in which the expressions of local people are rendered as poetry in order to draw out affective and lived experience. A reading of the poems of seven participants reveals the perception of M-PESA as a means of empowerment for women, alignment with their social identity and access to education. This contrasts sharply with the meaning of M-PESA as seen by commercial and non-commercial institutions which focuses on connecting the unbanked to formal banking systems and the exploitation of M-PESA as a primary revenue stream. The paper concludes by highlighting the need for regulation which is delicately balanced between independence from institutional control and the exposure of the rural poor to exploitation.
Deep Dynamic Scene Deblurring From Optical Flow

Deblurring can not only provide visually more pleasant pictures and make photography more convenient, but also can improve the performance of objection detection as well as tracking. However, removing dynamic scene blur from images is a non-trivial task as it is difficult to model the non-uniform blur mathematically. Several methods first use single or multiple images to estimate optical flow (which is treated as an approximation of blur kernels) and then adopt non-blind deblurring algorithms to reconstruct the sharp images. However, these methods cannot be trained in an end-to-end manner and are usually computationally expensive. In this paper, we explore optical flow to remove dynamic scene blur by using the multi-scale spatially variant recurrent neural network (RNN). We utilize FlowNets to estimate optical flow from two consecutive images in different scales. The estimated optical flow provides the RNN weights in different scales so that the weights can better help RNNs to remove blur in the feature spaces. Finally, we develop a convolutional neural network (CNN) to restore the sharp images from the deblurred features. Both quantitatively and qualitatively evaluations on the benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of accuracy, speed, and model size.
The Paleoenvironmental Standard Terms (PaST) Thesaurus: Standardizing Heterogeneous Variables in Paleoscience

Abstract Paleoscience data are extremely heterogeneous; hundreds of different types of measurements and reconstructions are routinely made by scientists on a variety of types of physical samples. This heterogeneity is one of the biggest barriers to finding paleoclimatic records, to building large‐scale data products, and to the use of paleoscience data beyond the community of specialists. Here, we document the Paleoenvironmental Standard Terms (PaST) thesaurus, the first authoritative vocabulary of standardized variable names for paleoclimatic and paleoenvironmental data developed in a formal knowledge organization structure. This structure is designed to improve data set discovery, support automated processing of data, and provide connectivity to other vocabularies. PaST is now used operationally at the World Data Service for Paleoclimatology (WDS‐Paleo), one of the largest repositories of paleoscience information. Terms from the PaST thesaurus standardize a broad array of paleoenvironmental and paleoclimatic measured and inferred variables, providing enough detail for accurate and precise data discovery and thereby promoting data reuse. We describe the main design decisions and features of the thesaurus, the governance structure for ongoing maintenance, and WDS‐Paleo services that now employ PaST. These services include an advanced search by variable name, an interface for thesaurus navigation, and a machine‐readable representation in the Simple Knowledge Organization System (SKOS) standard. This overview is designed for developers of thesauri, data contributors, and users of the WDS‐Paleo, and serves as a building block for future efforts within the broader paleoscience community to improve how data are described for long‐term findability, accessibility, interoperability, and reusability.
A coarse-to-fine capsule network for fine-grained image categorization

Fine-grained image categorization is challenging due to the subordinate categories within an entry-level category can only be distinguished by subtle discriminations. This necessitates localizing key (most discriminative) regions and extract domain-specific features alternately. Existing methods predominantly realize fine-grained categorization independently, while ignoring that representation learning and foreground localization can reinforce each other iteratively. Sharing the state-of-the-art performance of capsule encoding for abstract semantic representation, we formalize our pipeline as a coarse-to-fine capsule network (CTF-CapsNet). It consists of customized expert CapsNets arranged in each perception scale and region proposal networks (RPNs) between two adjacent scales. Their mutually motivated self-optimization can achieve increasingly specialized cross-utilization of object-level and component-level descriptions. The RPN zooms the areas to turn the attention to the most distinctive regions by concerning preceding informations learned by expert CapsNet for references, whilst a finer-scale model takes as feed an amplified attended patch from last scale. Overall, CTF-CapsNet is driven by three focal margin losses between label prediction and ground truth, and three regeneration losses between original input images/feature maps and reconstructed images. Experiments demonstrate that without any prior knowledge or strongly-supervised supports (e.g., bounding-box/part annotations), CTF-CapsNet can deliver competitive categorization performance among state-of-the-arts, i.e., testing accuracy achieves 89.57%, 88.63%, 90.51%, and 91.53% on our hand-crafted rice growth image set and three public benchmarks, i.e., CUB Birds, Stanford Dogs, and Stanford Cars, respectively.
On the Efficiency of Pairing-Based Authentication for Connected Vehicles: Time is Not on Our Side!

In the near future, intelligent vehicles will be connected via wireless communication links, forming Vehicular Ad-hoc Networks (VANETs). This has potential to improve road safety and to optimize traffic. However, if the communications are not secure, VANETs are vulnerable to cyber attacks involving message manipulation. Research on this problem has produced multiple authentication protocols based on bilinear pairings (a variant of elliptic curve cryptography). The efficiency of such authentication schemes must be addressed before they can be used in real-world deployments. Standards bodies have begun standardizing various pairing-based schemes. The IEEE 1609.2 security standard has not yet selected any pairing-based scheme, leaving the settings related to pairing-based cryptography in the vehicular environments unspecified. In this work, we investigate the efficiency of pairing-based cryptographic primitives over the Barreto-Lynn-Scott and Barreto-Naehrig pairing friendly elliptic curves recommended in the IETF and ISO standards, to determine their suitability for practical application. We implement the algorithms and evaluate the effect of cryptographic pairings using theoretical and experimental analysis of four well-known pairing-based short signature schemes, including: Boneh-Lynn-Shacham, Boneh-Boyen, Zhang-Safavi-Susilo, and Boneh-Gentry-Lynn-Shacham. We use metrics including CPU clock cycles per operation, average computation time in milliseconds, and signature/public key size in bits to estimate the cost of implementing cryptographic pairings on modern processors. We demonstrate the effect of pairing-based cryptography on authentication in vehicular networks. We investigate a high-density highway scenario and show that a crash is possible, as a result of the evaluated authentication delay. We share our findings ahead of the IEEE 1609.2 recommendations for the use of cryptographic pairings.
New Privacy Practices for Blockchain Software

The benefits of blockchain technologies for industrial applications are unquestionable.However, it is a considerable challenge to use a transparent system like blockchain and at the same time provide privacy to sensitive data.Privacy technologies permit conducting private transactions about sensitive data over transparent networks, but their inherent complexity has been overwhelming for many developers.Closing the gap between developers and privacy-preserving technologies would help to the full adoption of the privacy by design framework for blockchain software.To this end, in this paper we present the software tools we have implemented to bring complex privacy technologies closer to developers and facilitate the job of implementing privacy-enabled blockchain applications.