Reviewerly/exHarmony huggingface dataset:
Iron Mineralogy and Sediment Color in a 100 m Drill Core From Lake Towuti, Indonesia Reflect Catchment and Diagenetic Conditions

Abstract Iron is the most abundant redox‐sensitive element on the Earth's surface, and the oxidation state, mineral host, and crystallinity of Fe‐rich phases in sedimentary systems can record details of water‐rock interactions and environmental conditions. However, we lack a complete understanding of how these Fe‐rich materials are created, maintained, and oxidized or reduced in sedimentary environments, particularly those with mafic sources. The catchment of Lake Towuti, Indonesia, is known to contain a wide range of abundant crystalline Fe oxide, and the lake has a long sedimentary history. Here, we study a ∼100 m long drill core from the lake to understand patterns of sedimentation and how young iron‐rich sediments are affected by diagenesis through geologic time. We use visible/near infrared and Mössbauer spectroscopy, X‐ray diffraction, bulk chemistry measurements, and statistical cluster analysis to characterize the core sediment. We find that the core sediment can be divided into three statistically different zones dominated by Mg serpentine, Al clay minerals, and Fe 2+ carbonate, respectively. The entire core is rich in nanophase Fe, and elemental correlations and Fe mineralogy vary between these zones. The nanophase Fe is highly complex with both ferrous and ferric components, and contributes to, but does not dictate, variations in sediment color. We propose that the distinctive zones are the result of structural basin changes (notably river capture and shifting drainage patterns), and diagenetic overprinting caused by deep burial of reactive Fe. This complex record has implications for disentangling depositional and diagenetic trends in other mafic lacustrine systems.
California Oilfield Underground Aquifer Injection Monitoring by Blockchain Technology

Water contamination is a primary concern in a region where water and petroleum play such vital roles in the economy, and where both industry and regulatory agencies pay close attention to environmental quality. In this research, we built a Distributed Ledger Technology (DLT) based prototype using R3 Corda. Its purpose applies in the oil & gas underground injection control (UIC) operations for the underground aquifer protection. This DLT prototype is a permissioned network which allows oil & gas companies to create, disseminate and trace immutable records. This network allows oil & gas companies, state agencies, and all other participants to share secure records such as well information while maintaining data integrity, traceability, and security. With the underlying cryptographic technologies of DLT, any unauthorized changes to the information, ownership or its history will become infeasible.
A Robust Linear Regression Feature Selection Method for Data Sets with Unknown Noise

The linear regression model is simple in form and easy to estimate; nevertheless, irrelevant features will raise the difficulty of its tasks. Feature selection is generally adopted to improve the model performance. Unfortunately, traditional regression feature selection methods may not work for data with noise or outliers. Although some robust methods for certain specific error distributions have been proposed, they may not perform well because the distribution of representation error is often unknown for real data. This paper proposes a regression feature selection method for unknown noise named Mixture of Gaussians LASSO (MoG-LASSO), in which feature selection and model training will be achieved simultaneously. MoG is adopted to model unknown noises, and M-estimation is used to acquire the weighted squared error loss. By alternatively and iteratively updating the regression coefficient and parameters of MoG, the influence of unknown noise can be reduced effectively. Furthermore, MoG-LASSO achieves feature selection by the <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$L_{1}$</tex></formula> regularization term, which can further improve the performance of the model. Experimental results on artificial data and benchmark data sets demonstrate that MoG-LASSO has better robustness and sparsity for data sets with irrelevant features. Additionally, experimental results on face recognition databases show the performance advantage of MoG-LASSO over state-of-the-art methods in the presence of illumination variations.
Multi-modal Data Exploration in a Mixed Reality Environment Using Coordinated Multiple Views

Immersive analytics is an emerging field of data exploration and analysis in immersive environments. The main idea is to use visual analytics in a fully immersive 3D space. The availability of immersive Extended Reality systems has increased tremendously recently, but it is still not as widely used as conventional 2D displays. We describe an immersive analysis system for spatio-temporal data and compare how it performs in an immersive environment and on a conventional 2D display. We provide a novel view called map-plot that enables analysis and exploration of spatial time series data. We also design an embodied interaction for the map-plot view. The approach is realized based on the coordinated multiple views paradigm. In addition to the map-plot view, several standard views are available. Voice commands and spatial audio are used to support interaction and increase users' embodiment. The findings from a user study show that developed system is much more efficient in a real immersive environment than using conventional 2D displays.
An end-to-end cryptography based real-time chat

Confidentiality protects users' data from digital eavesdroppers when traveling through the Internet. Confidentiality is complex and costly, especially on applications that involve communication and data exchange between multiple users. Cryptography has been the most used medium to achieve confidentiality, being the greatest challenge the sharing of a secret key to a group of people in a safe and effective way. This paper presents a chat application that implements an innovative way of sending messages with end-to-end encryption, in real-time, with a dynamic key store, and without the existence of data persistence. The application stands out from the others by the fact that it innovates the way the keys are shared with multiple users.
Proposed intelligence systems based on digital Forensics: Review paper

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future.
On Removing Rejection Conditions in Practical Lattice-Based Signatures

Digital signatures following the methodology of "Fiat-Shamir with Aborts", proposed by Lyubashevsky, are capable of achieving the smallest public-key and signature sizes among all the existing lattice signature schemes based on the hardness of the Ring-SIS and Ring-LWE problems. Since its introduction, several variants and optimizations have been proposed, and two of them (i.e., Dilithium and qTESLA) entered the second round of the NIST post-quantum cryptography standardization. This method of designing signatures relies on rejection sampling during the signing process. Rejection sampling is crucial for ensuring both the correctness and security of these signature schemes. In this paper, we investigate the possibility of removing the two rejection conditions used both in Dilithium and qTESLA. First, we show that removing one of the rejection conditions is possible, and provide a variant of Lyubashevsky's signature with comparable parameters with Dilithium and qTESLA. Second, we give evidence on the difficulty of removing the other rejection condition, by showing that two very general approaches do not yield a signature scheme with correctness or security.
Data-Aware Compression of Neural Networks

Deep Neural networks (DNNs) are getting deeper and larger which intensify the data movement and compute demands. Prior work focuses on reducing data movements and computation through exploiting sparsity and similarity. However, none of them exploit input similarity and only focus on sparsity and weight similarity. Synergistically analysing the similarity and sparsity of inputs and weights, we show that memory accesses and computations can be reduced by 5.7× and 4.1×, more than what can be decreased by exploiting only sparsity, and 3.9× and 2.1×, more than what can be decreased by exploiting only weight similarity. We propose a new data-aware compression approach, called DANA, to effectively utilize both sparsity and similarity in inputs and weights. DANA can be orthogonally implemented on top of different hardware DNN accelerators. As an example, we implement DANA on top of an Eyeriss-like architecture. Our results over four famous DNNs reveal that DANA outperforms Eyeriss in terms of average performance and energy consumption by 18× and 83×, respectively. Moreover, DANA is faster than the state-of-the-art sparsity-aware and similarity-aware techniques by respectively 4.6× and 4.5×, and reduces the average energy consumption over them by 3.0× and 5.8×.
Verbatim: A Verified Lexer Generator

Lexers and parsers are often used as front ends to connect input from the outside world with th e internals of a larger software system. These front ends are natural targets for attackers who w ish to compromise the larger system. A formally verified tool that p erforms mechanized lexical analysis would render attacks on these front ends less effective. In this paper we present Verbatim, an executable lexer that is implemented and verified with th e Coq P ro o f Assistant. We prove that Verbatim is correct with respect to a standard lexer specification. We also analyze its theoretical com plexity and give results o f an em pirical performance evaluation. All correctness proofs have been mechanized in Coq.
A Unified Machine Reading Comprehension Framework for Cohort Selection

Cohort selection is an essential prerequisite for clinical research, determining whether an individual satisfies given selection criteria. Previous works for cohort selection usually treated each selection criterion independently and ignored not only the meaning of each selection criterion but the relations among cohort selection criteria. To solve the problems above, we propose a novel unified machine reading comprehension (MRC) framework. In this MRC framework, we design simple rules to generate questions for each criterion from cohort selection guidelines and treat clues extracted by trigger words from patients' medical records as passages. A series of state-of-the-art MRC models based on BiDAF, BIMPM, BERT, BioBERT, NCBI-BERT, and RoBERTa are deployed to determine which question and passage pairs match. We also introduce a cross-criterion attention mechanism on representations of question and passage pairs to model relations among cohort selection criteria. Results on two datasets, that is, the dataset of the 2018 National NLP Clinical Challenge (N2C2) for cohort selection and a dataset from the MIMIC-III dataset, show that our NCBI-BERT MRC model with cross-criterion attention mechanism achieves the highest micro-averaged F1-score of 0.9070 on the N2C2 dataset and 0.8353 on the MIMIC-III dataset. It is competitive to the best system that relies on a large number of rules defined by medical experts on the N2C2 dataset. Comparing these two models, we find that the NCBI-BERT MRC model mainly performs worse on mathematical logic criteria. When using rules instead of the NCBI-BERT MRC model on some criteria regarding mathematical logic on the N2C2 dataset, we obtain a new benchmark with an F1-score of 0.9163, indicating that it is easy to integrate rules into MRC models for improvement.
CompositeTasking: Understanding Images by Spatial Composition of Tasks

We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model – a single encoder-decoder network that performs multiple, spatially varying tasks at once. The proposed network takes an image and a set of pixel-wise dense task requests as inputs, and performs the requested prediction task for each pixel. Moreover, we also learn the composition of tasks that needs to be performed according to some CompositeTasking rules, which includes the decision of where to apply which task. It not only offers us a compact network for multitasking, but also allows for task-editing. Another strength of the proposed method is demonstrated by only having to supply sparse supervision per task. The obtained results are on par with our baselines that use dense supervision and a multi-headed multi-tasking design. The source code will be made publicly available at www.github.com/nikola3794/composite-tasking.
Improved distant supervision relation extraction based on edge-reasoning hybrid graph model

Distant supervision relation extraction (DSRE) trains a classifier by automatically labeling data through aligning triples in the knowledge base (KB) with large-scale corpora. Training data generated by distant supervision may contain many mislabeled instances, which is harmful to the training of the classifier. Some recent methods show that relevant background information in KBs, such as entity type (e.g., Organization and Book), can improve the performance of DSRE. However, there are three main problems with these methods. Firstly, these methods are tailored for a specific type of information. A specific type of information only has a positive effect on a part of instances and will not be beneficial to all cases. Secondly, different background information is embedded independently, and no reasonable interaction is achieved. Thirdly, previous methods do not consider the side effect of the introduced noise of background information. To address these issues, we leverage five types of background information instead of a specific type of information in previous works and propose a novel edge-reasoning hybrid graph (ER-HG) model to realize reasonable interaction between different kinds of information. In addition, we further employ an attention mechanism for the ER-HG model to alleviate the side effect of noise. The ER-HG model integrates all types of information efficiently and is very robust to the noise of information. We conduct experiments on two widely used datasets. The experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in held-out metric and robustness tests.
Design and Implementation of Teaching Quality Assessment System based on Analytic Hierarchy Process Fuzzy Comprehensive Evaluation method

For improving the learning quality, the higher education institutions use the Teaching Quality Assessment(TQA) system to realize the teaching effectiveness via collecting the students' feedbacks. How to correctly use the TQA to indicate the teaching effectiveness is the main research challenge in this time. In this paper, we proposed a TQA System based on Analytic Hierarchy Process Fuzzy Comprehensive Evaluation method (AHP-FCEM) to find the deeper meaning from the TQA data, The TQA system can transform the subjective scoring into quantitative analysis automatically. The AHP-FCEM ensured the accuracy and efficiency of the quantitative analysis. The steps of AHP-FCEM include determining the evaluation index system, establishing the evaluation set, calculating the evaluation index weight, establishing the comprehensive evaluation matrix and calculating the comprehensive score. Our TQA system was implemented by Python, HTML5 and Django MVC framework. However, our approach are still limited by the TQA moment to only represent the short-term effect.
Short Identity-Based Signatures with Tight Security from Lattices

We construct a short and adaptively secure identity-based signature scheme tightly based on the well-known Short Integer Solution (SIS) assumption. Although identity-based signature schemes can be tightly constructed from either standard signature schemes against adaptive corruptions in the multi-user setting or a two-level hierarchical identity-based encryption scheme, neither of them is known with short signature size and tight security based on the SIS assumption. Here "short" means the signature size is independent of the message length, which is in contrast to the tree-based (tight) signatures. Our approach consists of two steps: Firstly, we give two generic transformations (one with random oracles and the other without) from non-adaptively secure identity-based signature schemes to adaptively secure ones tightly. Our idea extends the similar transformation for digital signature schemes. Secondly, we construct a non-adaptively secure identity-based signature scheme based on the SIS assumption in the random oracle model.
Quantum Computing Threat Modelling on a Generic CPS Setup

The threat of quantum computers is real and will require significant resources and time for classical systems and applications to prepare for the remedies against the threat. At the algorithm-level, the two most popular public-key cryptosystems, RSA and ECC, are vulnerable to quantum cryptanalysis using Shor’s algorithm, while symmetric key and hash-based cryptosystems are weakened by Grover’s algorithm. Less is understood at the implementation layer, where businesses, operations, and other considerations such as time, resources, know-how, and costs can affect the speed, safety, and availability of the applications under threat. We carry out a landscape study of 20 better-known threat modelling methods and identify PASTA, when complemented with Attack Trees and STRIDE, as the most appropriate method to be used for evaluating quantum computing threats on existing systems. We then perform a PASTA threat modelling exercise on a generic Cyber-Physical System (CPS) to demonstrate its efficacy and report our findings. We also include mitigation strategies identified during the threat modelling exercise for CPS owners to adopt.
Modified Distance Metric That Generates Better Performance For The Authentication Algorithm Based On Free-Text Keystroke Dynamics

The authentication process can be categorized by the number of incorporated factors: something you know, like a username and a password, something you have, like card, token or something you are, like biometrics. Keystroke dynamics has been pointed out as a practical behavioral biometric feature that does not require any additional device for scale up user authentication. The input data of an authentication system based on keystroke dynamics are the typing times on the keyboard. Given that typing times result in time vectors, and these must be compared to see the similarities between them to validate the user. Algorithms of dynamic authentication can be divided into three major groups: estimation of metric distances, statistical methods and machine learning. The paper aims to analyze the possibilities of increasing the efficiency of an authentication algorithm based on keystroke dynamics, in the sense of reducing the value of the Equal Error Rate (EER). The distance method is used to calculate the similarity between users. The paper (1) analyzes the optimal number of di-graphs, (2) analyzes the optimal time combinations generated by a di-graph to be used and, finally, (3) analyzes the possibility of modify the distance calculation metric. These analyzes aim to reduce the error rate generated by an authentication system based on free-text keystroke dynamics. The authors propose a modification of the Manhattan distance calculation formula that generates better performances, EER was improved by 38.53%. The EER value obtained from the modified metrics is 3.27%, compared to 5.32% obtained with the classic Manhattan formula.
Metro Passenger-Flow Representation via Dynamic Mode Decomposition and Its Application

Passenger-flow anomaly detection and prediction are essential tasks for intelligent operation of the metro system. Accurate passenger-flow representation is the foundation of them. However, spatiotemporal dependencies, complex dynamic changes, and anomalies of passenger-flow data bring great challenges to data representation. Taking advantage of the time-varying characteristics of data, we propose a novel passenger-flow representation model based on low-rank dynamic mode decomposition (DMD), which also integrates the global low-rank nature and sparsity to explore the spatiotemporal consistency of data and depict abrupt data, respectively. The model can detect anomalies and predict short-term passenger flow conveniently and flexibly. For anomaly detection, we further introduce a strong temporal Toeplitz regularization to characterize the temporal periodic change of data, so as to more accurately detect anomalies. We conduct experiments with smart card transaction data from the Beijing metro system to assess the performance of the model in two use cases. In terms of anomaly detection, the experimental results demonstrate that our method can detect anomalies efficiently, especially for time sequence anomalies. As for short-term prediction, our model is superior to other methods in most cases.
A selection hyperheuristic guided by Thompson sampling for numerical optimization

Selection hyper-heuristics have been increasingly and successfully applied to numerical and discrete optimization problems. This paper proposes HHTS, a hyper-heuristic (HH) based on the Thompson Sampling (TS) mechanism to select combinations of low-level heuristics aiming to provide solutions for various continuous single-objective optimization benchmarks. Thompson Sampling is modeled in the present paper as a Beta Bernoulli sampler considering the increase/decrease of diversity among population individuals to measure the success/failure during the search. In the experiments, HHTS (a generic evolutionary algorithm generated by TS) is compared with five well-known evolutionary algorithms. Results indicate that, despite requiring less computational effort, HHTS's performance is similar or better than the other algorithm for most instances and in 50% of the cases it is capable of achieving the global optimum.
A NEAT-based multiclass classification method with class binarization

Multiclass classification is a fundamental and challenging task in machine learning. Class binarization is a popular method to achieve multiclass classification by converting multiclass classification to multiple binary classifications. NeuroEvolution, such as NeuroEvolution of Augmenting Topologies (NEAT), is broadly used to generate Artificial Neural Networks by applying evolutionary algorithms. In this paper, we propose a new method, ECOC-NEAT, which applies Error-Correcting Output Codes (ECOC) to improve the multiclass classification of NEAT. The experimental results illustrate that ECOC-NEAT with a considerable number of binary classifiers is highly likely to perform well. ECOC-NEAT also shows significant benefits in a flexible number of binary classifiers and strong robustness against errors.