Reviewerly/exHarmony huggingface dataset:
Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem. Several methods have been used to determine the contents of PTEs in soils, but most are laborious, costly and generate waste. In this study, we use a methodological framework to optimize the prediction of levels of PTEs in soils. We used a total set of 120 soil samples, collected at a depth of 0-10 cm. The covariate database is composed of variables measured by proximal sensors, physical and chemical soil characteristics, and morphometric data derived from a DEM with a spatial resolution of 30 m. Five machine learning algorithms were tested: Random Forests, Cubist, Linear Model, Support Vector Machine and K Nearest Neighbor. In general, the Cubist algorithm produced better results in predicting the contents of Pb, Zn, Ba and Fe compared to the other tested models. For the Al contents, the Support Vector Machine produced the best prediction. For the Cr contents, all models showed low predictive power. The most important covariates in predicting the contents of PTEs varied according to the studied element. However, x-ray fluorescence measurements, textural and morphometric variables stood out for all elements. The methodology structure reported in this study represents an alternative for fast, low-cost prediction of PTEs in soils, in addition to being efficient and economical for monitoring potentially contaminated areas and obtaining quality reference values for soils.
Three-Party Integer Comparison and Applications

Secure integer comparison enables two parties to compare their inputs without revealing the exact value of those inputs. In this paper, we highlight three-party integer comparison (TPIC), where a judge, with no private input, wants to know the comparison result, while two competitors hold secret integers to do privacy-preserving comparison. A TPIC protocol enables the judge to actively obtain the result rather than passively learning it from one of the competitors. We give two TPIC constructions considering mixed adversaries, who have different capabilities. One is secure against semi-honest competitors with low computation and communication cost, while the other is secure against malicious competitors. In both, the judge is semi-honest. As applications of TPIC, we present a joint bidding scheme and a practical auction with brief security proofs and analysis. In comparison, our auction scheme is more efficient with lower cost, making it feasible in practice rather than a theoretical design. The proposed TPIC protocols and application schemes run on top of blockchain requiring a constant number of rounds.
Random Sample Partition-Based Clustering Ensemble Algorithm for Big Data

A novel random sample partition-based clustering ensemble (RSP-CE) algorithm is proposed in this paper to handle the big data clustering problems. There are three key components in RSP-CE algorithm, i.e., generating the base clustering results on RSP data blocks, harmonizing the based clustering results with maximum mean discrepancy (MMD) criterion, and refining the RSP clustering results. RSP data blocks have the consistent sample distributions with the whole big data and thus provide the possibility for using base clustering results on different data subsets to approximate the clustering result on whole big data. The experimental results in comparison with other 5 well-known clustering ensemble algorithms on 4 big data sets show that RSP-CE algorithm obtains the better normalized mutual information (NMI) values and Fowlkes-Mallows Index (FMI) values with the less training time consumptions and thus demonstrate that RSP-CE algorithm is a viable approach to deal with the big data clustering problems.
Three-dimensional shear angle determination with application to shear-frame test

The shear frame test is very often used in the context of plastics engineering to examine only the fiber construction for its deformability. Here, it is common to assume a homogeneous deformation, which apparently exists as a global deformation due to the shear frame. This results in the assumption of a constant overall shear angle. In this paper, we compare two analytical approaches to describe the deformation and the resulting strains in detail, particularly, the local shear angle calculation. As a second step, the extension of the shear angle computation based on curvilinear, three-dimensional surface deformations is applied to optical results of the experimental deformation behavior. For this purpose, a three-dimensional image correlation system is drawn on to determine the local, tangential shear angle even for folded fabrics. This requires an approach which is different to the classical digital image correlation method since the speckle varnish influences the deformation of the very compliant fiber bundles. In a third step, the same procedure is applied to the results of three-dimensional finite element computations since the programs do not provide the shear angle measure. The shear angle determination is discussed at various examples. As a side result of the investigations, the material parameters for orthotropic elasticity are determined using a representative volume element approach exploiting μ-CT data. Although the wrinkling prediction calculations depend on the pre-deformation, finite element discretization, and uncertainties of the specimens, calculation results might be used as an indicator to improve the specimen geometries.
Key Technology Adoption Indicators for Smart Universities: A Preliminary Proposal

Technology management in universities impacts the quality of their administrative and academic processes. Smart technologies, such as big data, artificial intelligence, the Internet of Things, and cloud computing, continue to be disruptive and highly relevant. The factors for adopting smart technology and the key indicators will contribute to the knowledge of the transformation processes of current universities in Smart Universities. An evolutionary approach for a traditional university to progress by levels toward a smart university can be seen in a maturity model that integrates such factors and indicators. Although maturity models are numerous and widely applicable, there is little documentation on how to develop them. So far, there is a lack of useful tools to support the practical adoption of the developed models in universities. This article presents 52 key indicators associated with the proposed smart technology adoption factors that are the basis for the maturity model.
Comparative Study of 3D Point Cloud Compression Methods

3D sensors such as LiDAR, stereo cameras, and radar have been used in many applications, for instance, virtual or augmented reality, real-time immersive communications, and autonomous driving systems. The output of 3D sensors is often represented in the form of point clouds. However, the massive amount of point cloud data generated from 3D sensors poses big challenges in data storage and transmission. Therefore, effective compression schemes are needed for reducing the bandwidth of wireless networks or storage space of 3D point cloud data. Several point cloud compression (PCC) algorithms have been proposed using signal processing or neural network techniques. In this study, we investigate four state-of-the-art PCC methods using two different datasets with various configurations. The objective of this study is to provide a comprehensive understanding of various approaches in PCC. The results of this paper will be helpful in developing an adaptive 3D point cloud stream compression benchmark that is efficient and benefited from different PCC techniques.
Bandwidth constrained client selection and scheduling for federated learning over SD‐WAN

Cross-silo federated learning (FL) has been proposed and applied in many domains such as financial risk prediction, pharmaceutical discovery, electronic health records mining. In this paradigm, multiple clients can collaboratively train machine learning models with higher accuracy while protecting their privacy under the coordination of a central server. To interconnect these geo-distributed clients and the central server, the architecture of FL is proposed over software-defined wide area network (SD-WAN). The total time to achieve the required accuracy for FL depends on both the time of each iteration and the number of iterations. Because the bandwidth for each path from client to central server is constrained, different client selection schemes and different path routing mechanisms may lead to different times of iteration. The authors propose FedMT algorithm to minimize the total time through well-designed client selection and routing, which greatly reduces the upload time of each iteration while slightly affecting the number of iterations. Extensive experiments are conducted and demonstrated the time to achieve the same accuracy for FedMT is less than half of that for FedAvg and FedProx.
A Storage Efficient Blockchain Model for Constrained Applications

The growing chain&#x2019;s storage requirements are al- ways an issue with blockchain platforms, and devices may not allocate the required storage to replicate an entire blockchain instance. To overcome this issue, we propose a storage constraint-free blockchain model that divides the growing blockchain into fixed-size sets of blocks, excluding the genesis block and allowing the participating nodes to store the recent blocks according to their storage capacity. Suppose a node decides not to replicate a few sets of old blocks to accommodate the blockchain according to its storage availability. In that case, it stores the corresponding set&#x2019;s last block hash and the XOR of the remaining blocks&#x2019; hashes and then drops those blocks. Nodes that receive blocks, equivalent to the deleted blocks from other nodes will validate it by comparing the previous set&#x2019;s last block hash with the received set&#x2019;s first block parent hash and XOR hash with the block hashes. If they match, the node will accept the equivalent set of deleted blocks; otherwise, reject. This will guarantee to avoid any uncertainty, single collision multi-block replacement, and partial chain replacement attack. The analysis and experimental results show that the proposed model is storage efficient and needs less overhead. The proposed model provides security equivalent to the traditional blockchain and allows the storage constrained devices to participate in the application for a long time compared with the other models.
Knowledge representation for explainable artificial intelligence

Abstract Alongside the particular need to explain the behavior of black box artificial intelligence (AI) systems, there is a general need to explain the behavior of any type of AI-based system (the explainable AI, XAI) or complex system that integrates this type of technology, due to the importance of its economic, political or industrial rights impact. The unstoppable development of AI-based applications in sensitive areas has led to what could be seen, from a formal and philosophical point of view, as some sort of crisis in the foundations, for which it is necessary both to provide models of the fundamentals of explainability as well as to discuss the advantages and disadvantages of different proposals. The need for foundations is also linked to the permanent challenge that the notion of explainability represents in Philosophy of Science. The paper aims to elaborate a general theoretical framework to discuss foundational characteristics of explaining, as well as how solutions (events) would be justified (explained). The approach, epistemological in nature, is based on the phenomenological-based approach to complex systems reconstruction (which encompasses complex AI-based systems). The formalized perspective is close to ideas from argumentation and induction (as learning). The soundness and limitations of the approach are addressed from Knowledge representation and reasoning paradigm and, in particular, from Computational Logic point of view. With regard to the latter, the proposal is intertwined with several related notions of explanation coming from the Philosophy of Science.
Cross-Platform Performance Evaluation of Stateful Serverless Workflows

Serverless computing, with its inherent event-driven design along with instantaneous scalability due to cloud-provider managed infrastructure, is starting to become a de-facto model for deploying latency critical user-interactive services. However, as much as they are suitable for event-driven services, their stateless nature is a major impediment for deploying long-running stateful applications. While commercial cloud providers offer a variety of solutions that club serverless functions along with intermediate storage to maintain application state, they are still far from optimized for deploying stateful applications at scale. More specifically, factors such as storage latency and scalability, network bandwidth, and deployment costs play a crucial role in determining whether current serverless applications are suitable for stateful workloads. In this paper, we evaluate the two widely-used stateful server-less offerings, Azure Durable functions and AWS Step functions, to quantify their effectiveness for implementing complex stateful workflows. We conduct a detailed measurement-driven characterization study with two real-world use cases, machine learning pipelines (inference and training) and video analytics, in order to characterize the different performance latency and cost tradeoffs. We observe from our experiments that AWS is suitable for workloads with higher degree of parallelism, while Azure durable entities offer a simplified framework that enables quicker application development. Overall, AWS is 89% more expensive than Azure for machine learning training application while Azure is 2× faster than AWS for the machine learning inference application. Our results indicate that Azure durable is extremely inefficient in implementing parallel processing. Furthermore, we summarize the key findings from our characterization, which we believe to be insightful for any cloud tenant who has the problem of choosing an appropriate cloud vendor and offering, when deploving stateful workloads on serverless platforms,
Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

Abstract With the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People’s demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided Autoencoder Adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.
Predicting vulnerability inducing function versions using node embeddings and graph neural networks

Predicting software vulnerabilities over code changes is a difficult task due to obtaining real vulnerability data and their associated code fixes from software projects as software organizations are often reluctant to report those. We aim to propose a vulnerability prediction model that runs after every code change, and identifies vulnerability inducing functions in that version. We also would like to assess the success of node and token based source code representations over abstract syntax trees (ASTs) on predicting vulnerability inducing functions. We train neural networks to represent node embeddings and token embeddings over ASTs in order to obtain feature representations. Then, we build two Graph Neural Networks (GNNs) with node embeddings, and compare them against Convolutional Neural Network (CNN) and Support Vector Machine (SVM) with token representations. We report our empirical analysis over the change history of vulnerability inducing functions of Wireshark project. GraphSAGE model using source code representation via ASTs achieves the highest AUC rate, while CNN models using token representations achieves the highest recall, precision and F1 measure. Representing functions with their structural information extracted from ASTs, either in token form or in complete graph form, is great at predicting vulnerability inducing function versions. Transforming source code into token frequencies as a natural language text fails to build successful models for vulnerability prediction in a real software project.
Distributed Semi-Supervised Learning With Missing Data

Data classification is usually challenged by the difficulty and/or high cost in collecting sufficient labeled data, and unavoidability of data missing. Besides, most of the existing algorithms belong to centralized processing, in which all of the training data must be stored and processed at a fusion center. But in many real applications, data are distributed over multiple nodes, and cannot be centralized to one node for processing due to various reasons. Considering this, in this article, we focus on the problem of distributed classification of missing data with a small proportion of labeled data samples, and develop a distributed semi-supervised missing-data classification (dS2MDC) algorithm. The proposed algorithm is a distributed joint subspace/classifier learning, that is, a latent subspace representation for missing feature imputation is learned jointly with the training of nonlinear classifiers modeled by the χ2 kernel using a semi-supervised learning strategy. Theoretical performance analysis and simulations on several datasets clearly validate the effectiveness of the proposed dS2MDC algorithm from different perspectives.
Multidata-Owner Searchable Encryption Scheme Over Medical Cloud Data With Efficient Access Control

Several schemes have been proposed to enable cloud servers to search encrypted medical data to preserve patients’ privacy. However, the existing schemes use inefficient attribute-based encryption (ABE) approaches for access control. Also, servers cannot learn whether a doctor can achieve the access policy of a document and this check is done by doctors, so unrelated documents are outsourced. Moreover, the existing schemes only support single data-owner setting, where a doctor needs to share a key with each patient. To address these limitations, we develop an efficient ABE approach. Patients use it to encrypt the symmetric key (that encrypted a document), so that only authorized doctors can obtain the key and decrypt the document. Patients also send to the server the encryptions of an access policy and the document’s keywords. Doctors send the encryptions of the keywords, and their attributes. Then, the server returns the documents the doctor is authorized to access without being able to learn the keywords, the access policy, and the doctor’s attributes to preserve privacy. Our analysis indicates that our scheme can preserve privacy, and our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes. Also, the number of keys in the system are significantly reduced.
The WOOL Dialogue Platform: Enabling Interdisciplinary User-Friendly Development of Dialogue for Conversational Agents

<ns4:p>Health coaching applications can include (embodied) conversational agents as coaches. The development of these agents requires an interdisciplinary cooperation between eHealth application developers, interaction designers and domain experts. Therefore, proper dialogue authoring tools and tools to integrate these dialogues in a conversational agent system are essential in the process of creating successful agent-based applications. However, we found no existing open source, easy-to-use authoring tools that support multidisciplinary agent development. To that end, we developed the WOOL Dialogue Platform.</ns4:p><ns4:p> </ns4:p><ns4:p> The WOOL Dialogue Platform provides the eHealth and conversational agent communities with an open source platform, consisting of a set of easy to use tools that facilitate virtual agent development. The platform consists of a dialogue definition language, an editor, application development libraries and a web service. To illustrate the platform’s possibilities and use in practice, we describe two use cases from EU Horizon 2020 research projects.</ns4:p><ns4:p> </ns4:p><ns4:p> The WOOL Dialogue Platform is an ‘easy to use, and powerful if needed’ platform for the development of conversational agent applications that is seeing a slow but steady increase in uptake in the eHealth community. Developed to support dialogue authoring for embodied conversational agents in the health coaching domain, this platform’s strong points are its ease of use and ability to let domain experts and agents technology experts work together by providing all parties with tools that support their work effectively.</ns4:p>
ReCFA: Resilient Control-Flow Attestation

Recent IoT applications gradually adapt more complicated end systems with commodity software. Ensuring the runtime integrity of these software is a challenging task for the remote controller or cloud services. Popular enforcement is the runtime remote attestation which requires the end system (prover) to generate evidence for its runtime behavior and a remote trusted verifier to attest the evidence. Control-flow attestation is a kind of runtime attestation that provides diagnoses towards the remote control-flow hijacking at the prover. Most of these attestation approaches focus on small or embedded software. The recent advance to attesting complicated software depends on the source code and CFG traversing to measure the checkpoint-separated subpaths, which may be unavailable for commodity software and cause possible context missing between consecutive subpaths in the measurements.