Reviewerly/exHarmony huggingface dataset:
Application of Non-Destructive Techniques on a Varve Sediment Record from Vouliagmeni Coastal Lake, Eastern Gulf of Corinth, Greece

During the last few decades, X-ray attenuation systems have been established as standard techniques in geosciences and as excellent scientific tools for the analysis of sedimentary facies and structures. In the present study, we use two non-destructive and high-resolution systems (computed tomography, X-ray fluorescence) to address all sedimentological facies and structural characteristics on a 6 m long, partly laminated sediment record, from Vouliagmeni lake, located at the eastern part of the Corinth Gulf, Greece. Vouliagmeni lake is the deepest coastal lake in Greece, and its location is of great importance since it is located in one of the most tectonically active regions in the world. The chronological framework of the retrieved sediment sequence spans the last 12,000 years, with alternations of laminated and non-laminated sections. The annual accumulation of the laminated sequences was determined through the radionuclide concentration of 14 samples. Scanning tomography was performed with a medical CT scanner and a μCT scanner, aiming to compare the potentials and variations of both methods. Lamination boundaries, event layers (turbidites) and sediment deformations were distinguished through processing the extracted 3D rendered volumes, after applying ranges depending on Hounsfield (HU) values. Microscopic analysis revealed three main layer types in the varve sequences that were examined, attributing to summer/spring, autumn and winter deposits. Statistical analysis of the elemental composition, magnetic susceptibility (MS) measurements and HU values revealed three clusters/facies, reflecting climatic and in-lake hydrological changes. Cluster A emulates sedimentation during arid conditions, while Cluster B reflects humid conditions with increased precipitation and erosion. Cluster C represents sequences with homogenous Ca-rich sediment. Our proposed workflow highlights the possible correlation between the non-destructive variables that were measured, but also the variations and applications of each method and software used during this study.
Dynamic Graph Attention-Aware Networks for Session-Based Recommendation

Graph convolutional neural networks have attracted increasing attention in recommendation system fields because of their ability to represent the interactive relations between users and items. At present, there are many session-based methods based on graph neural networks. For example, SR-GNN establishes a user’s session graph based on the user’s sequential behavior to predict the user’s next click. Although these session-based recommendation methods modeling the user’s interaction with items as a graph, these methods have achieved good performance in improving the accuracy of the recommendation. However, most existing models ignore the items’ relationship among sessions. To efficiently learn the deep connections between graph-structured items, we devised a dynamic attention-aware network (DYAGNN) to model the user’s potential behavior sequence for the recommendation. Extensive experiments have been conducted on two real-world datasets, the experimental results demonstrate that our method achieves good results in capturing user attention perception.
Skew cyclic codes over 𝔽4R

This paper considers a new alphabet set, which is a ring that we call [Formula: see text], to construct linear error-control codes. Skew cyclic codes over this ring are then investigated in details. We define a nondegenerate inner product and provide a criteria to test for self-orthogonality. Results on the algebraic structures lead us to characterize [Formula: see text]-skew cyclic codes. Interesting connections between the image of such codes under the Gray map to linear cyclic and skew-cyclic codes over [Formula: see text] are shown. These allow us to learn about the relative dimension and distance profile of the resulting codes. Our setup provides a natural connection to DNA codes where additional biomolecular constraints must be incorporated into the design. We present a characterization of [Formula: see text]-skew cyclic codes which are reversible complement.
Smart grid data access control scheme based on blockchain

Abstract The smart grid communication parties need to process the data by the trusted central node, which will lead to security issues such as single‐point attacks and data tampering. This paper proposes a smart grid data access control scheme based on blockchain, the user completes the registration of the smart meter by three encryptions. After the registration is completed, the registration information will be uploaded to the blockchain. In the data access phase, the verification center verifies the user's data access request, the database will accept the user's request for data if the verification is passed, and that will be broadcasted on the entire network and uploaded to the blockchain. The security of the scheme is analyzed by using a random oracle model. Analysis shows that this scheme can resist public key replacement attacks and malicious key generation center (KGC) attacks. Compared with the existing scheme, this scheme can more effectively resist more types of attacks. It shows that the smart grid data access control scheme proposed in this paper is safe, reliable and efficient.
Simple and Efficient Clustering Approach Based on Cuckoo Search Algorithm

Image segmentation is the most important operation in the image processing system because it is located at the articulation between image processing and analysis. The advantage of segmentation is to partition an image into several homogeneous regions, within the meaning of a criterion fixed a priori. A multitude of segmentation methods are proposed in the literature, but there is no universal image segmentation technique to apply to all different types of images and in any given computer context. Because of these constraints, in this paper, we will propose a new image segmentation approach which is based on the hybridization of an unsupervised classification method which is fuzzy C-means (FCM) and a metaheuristic which is called Cuckoo Search Algorithm (CSA). In our proposed approach, the cuckoo search is used to find the optimal partitioning according to an objective function which is based on the indices of validity of the clusters. First, CSA is initialized with random cluster centers. The cluster centers are then updated using the CSA principles aimed at minimizing the objective function proposed. The performance of the proposed approach was measured on several images and compared to other existing FCM techniques such as standard FCM and FCM based on genetic algorithms (FCM-GA). The experimental results show that the proposed approach yields satisfactory results in terms of precision, simplicity and efficiency.
Blockchain Technology: Intrinsic Technological and Socio-Economic Barriers

Since the introduction of Bitcoin in 2009 and its immense resonance in media, we have seen a plethora of envisioned blockchain solutions. Usually, such blockchain solutions claim to be disruptive. Often, such disruptiveness takes the form of a proclaimed blockchain revolution. In this paper, we want to look at blockchain technology from a neutral, analytical perspective. Our aim is to understand technological and socio-economic barriers to blockchain solutions that are intrinsic in the blockchain technology stack itself. We look into the permissionless blockchain as well as the permissioned blockchain. We start with a characterization of cryptocurrency as one-tiered uncollateralized M1 money. We proceed with defining essential modes of business communications (message authentication, signature, registered letter, contract, order etc.) and how they are digitized classically. We review potential blockchain solutions for these modes of communications, including socio-economic considerations. At the technical level, we discuss scalability issues and potential denial-of-service attacks. On the other hand, we also look into four successful blockchain solutions and explain their design. Now: what is the blockchain revolution and how realistic is it? Will it shake up of our institutions? Or, vice versa: does it have to rely on a re-design of our institutions instead? Can we design useful blockchain solutions independent of fundamental institutional re-design? It is such questions which have motivated us to compile this paper and we hope that we are able to bring some light to them.
RETRACTED: Enterprise economic performance evaluation based on 5 G network and embedded processing system

The Enterprise explored strategies from an organizational theoretical perspective and application performance indicators in economic-relations operations. Technology is established for dimensional comparative performance evaluation of cooperative strategy. The evaluation also suggested specific elements. Economic performance is a key measure of a company's input and output. Seven financial indicators are selected, such as current ratio, the gear ratio and net profit, Fixed Asset Turnover, Credit Turnover, Net Asset Growth, and Economic Performance Accounts for IT companies. And timeliness has been determined to be an effective way to complete a company's complete economic assessment. Assess the economic impact of 5 G industry growth. Information and telecommunications technology is increasingly becoming the new driving force behind economic growth. 5 G technology is already an integral part of modern economic and social development. The socioeconomic status of 5 G can increase social inequality, speed for economic link 5 G has the potential to shift to the existing socioeconomic hierarchy, and attention policymakers should try to minimize the negative impact of social figures assistant social change.
Bias alleviating generative adversarial network for generalized zero-shot classification

Generalized zero-shot classification is predicting the labels of the test images coming from seen or unseen classes. The task is difficult because of the bias problem, that is, unseen samples are easily to be misclassified to seen classes. Many methods have handled the problem by training a generative adversarial network (GAN) to generate fake samples. However, the GAN model trained with seen samples might not be appropriate for generating unseen samples. For dealing with this problem, we learn a bias alleviating generative adversarial network for generalized zero-shot classification by generating seen and unseen samples, simultaneously. We train the generator to generate more realistic unseen samples by adding semantic similarity and cluster center regularizations to alleviate the bias problem. The semantic similarity regularization is to restrict the relationships of the generated unseen visual prototypes and seen visual prototypes by their class prototypes to avoid the generated unseen samples similar to the seen samples. The cluster center regularization is to utilize the cluster property of target data to make the generated unseen visual prototypes near to the most similar cluster centers, generating realistic unseen samples. From the experiments, we can see the proposed method achieves promising results.
Practical Stability of Discrete Systems: Maximum Sets of Initial Conditions Concept

In this chapter we consider practical stability of discrete systems on the basis of maximum sets of initial conditions concept. We propose results concerning nonlinear discrete systems including topological properties of the maximum sets of initial conditions for internal practical stability, the necessary and sufficient conditions of internal practical stability using the optimal Lyapunov function. Further we offer the analytical forms (such as the Minkowski function, the inverse Minkowski function, and the support function) of the maximum sets of initial conditions representation in linear case, optimal ellipsoidal estimations of practical stability domains, and optimal estimations of phase constraints. In the last section we consider the problem of external practical stability of discrete systems.
Prediction of Battlefield Target Trajectory Based on LSTM

Battlefield situation prediction is an indispensable part of battlefield situational awareness, and refers to one of the important references for tactical decision-making by warfighters. The battlefield situation prediction is to use the information obtained in the two phases of situation awareness and situation recognition in battlefield situation awareness to predict the battlefield situation, and obtain the changing trend of the enemy's target state, position, and intention. Aiming at the temporal correlation of Long Short Team Memory Network (LSTM), this paper proposes to build a target trajectory prediction model based on LSTM network. First, the motion trajectory information obtained in the two stages of situation awareness and situation recognition is normalized and time-series processed to construct a time-series sample data set; then, the time-series data set is pushed to a target trajectory prediction model based on the LSTM network. Training; Finally, use the trained model to complete target trajectory prediction. The experimental results show that the prediction method using the LSTM model has the characteristics of high accuracy and easy implementation, and it has obvious advantages in processing sequence data.
Direct‐Push Color Logging Images Spatial Heterogeneity of Organic Carbon in Floodplain Sediments

Abstract In soils and sediments, large amounts of total organic carbon (TOC) mark reducing conditions. As dark sediment colors are good predictors for high‐TOC zones, they indicate hot spots of biogeochemical turnover and microbial activity. Traditionally, obtaining the sediment color or TOC at depth requires costly core sampling, resulting in poor horizontal resolution and related uncertainty caused by interpolation. We suggest using a direct‐push tool for optical screening of the sediment color to acquire multiple high‐resolution vertical color profiles and demonstrate its applicability to a biogeochemical transition zone in floodplain sediments, dominated by tufa. We use Gaussian mixture models for a cluster analysis of 35 color logs in the International Commission on Illumination (CIE) L * a * b * color space to identify three colorfacies that differ in lithology and TOC content: a dark colorfacies that agrees well with peat layers, a gray colorfacies associated with clay, and a creamy‐brown facies made of autochthonous carbonate precipitates. We test different approaches either to infer the TOC content from color metrics, namely, the lightness and chroma, across all facies, or to identify TOC ranges for each colorfacies. Given the high variability in TOC due to organic carbon specks in the tufa, the latter approach appears more realistic. In our application we map the 3‐D distribution of organic matter in a floodplain in distinct facies over 20,000 m 2 down to 12 m depth. While we relate the sediment color only to the TOC content, direct‐push color logging may also be used for in situ mapping of other biogeochemically relevant properties, such as the ferric‐iron content or sedimentary structure.
EDGES: An Efficient Distributed GraphEmbedding System on GPU clusters

Graph embedding training models access parameters sparsely in a “one-hot” manner. Currently, the distributed graph embedding neural network is learned by data parallel with the parameter server, which suffers significant performance and scalability problems. In this article, we analyze the problems and characteristics of training this kind of models on distributed GPU clusters for the first time, and find that fixed model parameters scattered among different machine nodes are a major limiting factor for efficiency. Based on our observation, we develop an efficient distributed graph embedding system called EDGES, which can utilize GPU clusters to train large graph models with billions of nodes and trillions of edges using data and model parallelism. Within the system, we propose a novel dynamic partition architecture for training these models, achieving at least one half of communication reduction compared to existing training systems. According to our evaluations on real-world networks, our system delivers a competitive accuracy for the trained embeddings, and significantly accelerates the training process of the graph node embedding neural network, achieving a speedup of 7.23x and 18.6x over the existing fastest training system on single node and multi-node, respectively. As for the scalability, our experiments show that EDGES obtains a nearly linear speedup.
A Decentralized Framework for Biostatistics and Privacy Concerns

Biostatistics and machine learning have been the cornerstone of a variety of recent developments in medicine. In order to gather large enough datasets, it is often necessary to set up multi-centric studies; yet, centralization of measurements can be difficult, either for practical, legal or ethical reasons. As an alternative, federated learning enables leveraging multiple centers’ data without actually collating them. While existing works generally require a center to act as a leader and coordinate computations, we propose a fully decentralized framework where each center plays the same role. In this paper, we apply this framework to logistic regression, including confidence intervals computation. We test our algorithm on two distinct clinical datasets split among different centers, and show that it matches results from the centralized framework. In addition, we discuss possible privacy leaks and potential protection mechanisms, paving the way towards further research.
Transactional Data Analytics for Inferring Behavioural Traits in Ethereum Blockchain Network

Ethereum is a blockchain based development platform for users to build and deploy decentralized applications and smart contracts. Transactions substituting money over Ethereum space are carried out using Ethereum's cryptocurrency `Ether'. Though the decentralization and public ledger recording of the transactions proves its limpidity, the anonymity of the users, hiding their true identity behind the addresses echoes the need for discerning the behavioural traits in transactions. The appalling nature of Ethereum transactions to have both, security and threat at a comparable level, demands analytics for inferring traits for better perspectives and insights. This research work focuses on characteristic analysis of Ethereum transaction space for inferring behavioural traits in supervised and unsupervised context. In an unsupervised environment, raw transaction data is extradited to a tabular transaction structure by selecting appropriate features. The data is further clustered and validated to form coherent groupings of similar patterns. The clusters are characteristically analyzed based on the features through Radar plots for inferring behavioural traits. In supervised context, labelled transactions are represented using histograms and feature-based characteristic analysis is performed to infer traits. Such analytics lay foundations for future deeper analysis that can be used to discover trends and patterns to understand the transaction network, enhancement of trading strategies, identification of bot activities, anomaly detection and several others.
Exploring vibrotactile cues for interactive guidance in data visualization

In visualization, user guidance has become an essential concept to aid users in making informed decisions ranging from what subsets to focus on in the data space to which regions to explore in the view space. To guide users, predominantly visual cues like colors or arrows are used to indicate particular targets or directions. In this paper, we explore the possibility of another sensory channel for guidance cues: vibrotactile feedback. To that end, we explore different properties of the vibrotactile channel (e.g., amplitude and duration) and discuss their potential use as guidance cues. We then report on an experiment (N=14) in which we investigate possible vibrotactile cues in comparison to visual cues and to a combination of visual and vibrotactile cues for a guided selection scenario and a guided navigation scenario. Although none of the vibrotactile cues significantly outperformed the visual cues, our study results shed light on a number of practical issues when using vibration for user guidance - including differences between various types of vibrotactile feedback, as well as diverging performance for different guidance scenarios.
Stacked generalizations in imbalanced fraud data sets using resampling methods

Predicting fraud is challenging due to inherent issues in the fraud data structure, since the crimes are committed through trickery or deceit with an ever-present moving target of changing modus operandi to circumvent human and system controls. As a national security challenge, criminals continually exploit the electronic financial system to defraud consumers and businesses by finding weaknesses in the system, including in audit controls. This study uses stacked generalization using meta or super learners for improving the performance of algorithms in step one (minimizing the algorithm error rate to reduce its bias in the learning set) and then in step two the results are input into the meta learner with its stacked blended output (with the weakest algorithms learning better). A fundamental key to fraud data is that it is inherently not systematic, and an optimal resampling methodology has yet not been identified. Building a test harness, for all permutations of algorithm sample set pairs, demonstrates that the complex, intrinsic data structures are all thoroughly tested. A comparative analysis on fraud data that applies stacked generalizations provides useful insight to find the optimal mathematical formula for imbalanced fraud data sets necessary to improve upon fraud detection for national security.
Towards a conceptual definition for IT assets through interrogating their nature and epistemic uncertainty

Security breaches and consequentially reputational risk are upon us on an almost daily basis. They are part of the risk that beset organizations as they innovate and derive value from their information technology (IT) investments. IT assets that must be identified and safeguarded, and values extracted from them are contributors to the risk. Literature is awash with models of threats to assets and use of mid-range theories. There is a growing literature on digital technologies within digital transformation. However, intrinsic nature and epistemic uncertainty of IT assets have not received attention. Therefore, how can a conceptual definition for IT assets flow from understanding their nature, given their inherent epistemic uncertainty? Drawing from critical realism principles, this paper investigates existing definitions for- and re-interrogates the tangible nature (tangibility) of IT assets. It was found that despite their ubiquity and due to their evolving nature, IT assets lack universal understanding and definition. Executives (and professionals) views about IT assets are informed by their industry sector and role (present and/or previous). Consequently, this paper recommends a conceptual definition of IT assets that would assist; with coherence in the asset identification stage of risk assessment, (possibly IT audits and IT valuation programs), with understanding of the intrinsic nature of IT assets in themselves, and aid organizations in holistically engaging with IT assets. This paper's IT assets definition contributes to the call for theorizing the "IT" in IT. It poses new probing questions about 'old', 'established' beliefs as IT assets evolve in the era of digital transformation, fourth industrial revolution, knowledge economy and beyond.