Reviewerly/exHarmony huggingface dataset:
Targeting REE deposits associated with carbonatite and alkaline complexes in northeast India

Rare earth elements (REEs) are considered critical metals globally. About 62% of the global resources of REEs occur associated with carbonatites and alkaline complexes. However, the entire production of REEs in India currently comes from monazite-bearing beach sands, although a variety of REE enriched source rocks, particularly carbonatites and alkaline complexes occur in different parts of the country. There is, therefore, a significant potential in the county for new REE deposit discoveries associated with carbonatites and alkaline complexes. This paper describes a generalised carbonatite-alkaline complex related REE mineral systems model and applies a knowledge-driven model to demarcate REE exploration targets in the Karbi-Meghalaya plateau, NE India. The main components of the mineral systems are (1) pockets of metasomatised subcontinental lithospheric mantle (SCLM) which form fertile source regions for REE-bearing fluids; (2) extensional geodynamics; (3) permeable lithosphere architecture for tapping REE-enriched fluids from SCLM and focusing them to near-surface levels, and (4) a post-emplacement tectonic regime that preserves the deposits. Spatial proxies representing each of these components are mapped and integrated using fuzzy inference system (FIS) to identify prospective targets. Systemic and stochastic uncertainties associated with the model were quantified to aid target selection and further work. Main recommendations of this exercise are the following: (1) project-scale ground exploration for the Sung valley and Jasra complexes; (2) further regional-scale data collection for the Mikir Hills in the areas surrounding Samchampi and Barpung Complexes and around the swarms of dykes in the Garo Hills around Swangkre; (3) follow-up exploration in the areas north of Silchar and south of Nongstoin and (4) detailed geochemical sampling and surface or air-borne radiometric surveys for the Mawpyut Ultramafic Complex. The REE-mineral-systems model and the workflow demonstrated in this article could be used for targeting REE deposits in geologically similar terrains worldwide.
Digital Image Watermarking Method Based on LSB and DWT Hybrid Technique

The watermarking technique can be considered as an important mechanism to protect the Intellectual Property Rights (IPR) of multimedia contents. Currently, most of multimedia contents are exchanged online in a digital form, which exposes the content to be illegal alteration easily without recognize theoffenders. Digital watermarking is used to embed a watermark into the digital media in such away that can not be discovered from the original watermarked data. Consequently, the watermark can be removed and used to verify the copyright ownership of that digital media. In this paper, a hybrid watermarking technique based on Least Significant Bit (LSB) and Discrete Wavelet Transform (DWT) is introduced and explained.. In order to investigate the effectiveness of the proposed hybrid technique in terms of robustness and imperceptibility, a comparison between the three techniques is also presented. This comparison was carried out using Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM). The SSIM results were very close to 1, which means that the recovered watermarks from watermarked image were not distorted in the three cases. The PSNR and MSE values show that the quality of the watermarked images using the proposed technique is better compared to that watermarked images using standalone LSB and DWT. Moreover, the results prove that the proposed hybrid technique was more effective in terms of robustness and imperceptibility compared to LSB and DWT techniques
TASHAROK: Using Mechanism Design for Enhancing Security Resource Allocation in Interdependent Systems

We consider interdependent systems managed by multiple defenders that are under the threat of stepping-stone attacks. We model such systems via game-theoretic models and incorporate the effect of behavioral probability weighting that is used to model biases in human decision-making, as descended from the field of behavioral economics. We then incorporate into our framework called TASHAROK, two types of tax-based mechanisms for such interdependent security games where the central regulator incentivizes defenders to invest well in securing their assets so as to achieve the socially optimal outcome. We first show that due to the nature of our interdependent security game, no reliable tax-based mechanism can incentivize the socially optimal investment profile while maintaining a weakly balanced budget. We then show the effect of behavioral probability weighting bias on the amount of taxes paid by defenders, and prove that higher biases make defenders pay more taxes under the two mechanisms. We then explore voluntary participation in tax-based mechanisms. To evaluate our mechanisms, we use four representative real-world interdependent systems where we compare the game-theoretic optimal investments to the socially optimal investments under the two mechanisms. We show that the mechanisms yield higher decrease in the social cost for behavioral decision-makers compared to rational decision-makers.
TAOBench

The continued emergence of large social network applications has introduced a scale of data and query volume that challenges the limits of existing data stores. However, few benchmarks accurately simulate these request patterns, leaving researchers in short supply of tools to evaluate and improve upon these systems. In this paper, we present a new benchmark, TAOBench, that captures the social graph workload at Meta. We open source workload configurations along with a benchmark that leverages these request features to both accurately model production workloads and generate emergent application behavior. We ensure the integrity of TAOBench's workloads by validating them against their production counterparts. We also describe several benchmark use cases at Meta and report results for five popular distributed database systems to demonstrate the benefits of using TAOBench to evaluate system tradeoffs as well as identify and address performance issues. Our benchmark fills a gap in the available tools and data that researchers and developers have to inform system design decisions.
Device Fingerprinting with Peripheral Timestamps

Sensing and processing peripheral input is a ubiquitous capability of personal computers. Text entry on physical and virtual keyboards, mouse pointer motion, and touchscreen gestures are the primary ways in which users interact with websites viewed on desktop and mobile devices. Peripheral input events must pass through a pipeline of hardware and software processes before they reach the web browser. This pipeline is device-dependent and often contains low-frequency polling components, such as USB polling and process scheduling, that influence event timing within the web page.We show that a relatively unique device fingerprint is formed by the timing of peripheral input events. No special permissions are required to register callbacks to keyboard, mouse, and touch events from within a web browser, and the technique works on both desktop and mobile devices. We propose a dual clock model in which both a fast reference clock and slow subject clock are compared through a single time source. With this model, the instantaneous phase of the subject clock is measured and used to construct a phase image. The phase image is then embedded in a low dimensional feature space using FPNET, a convolutional neural network designed to extract a device fingerprint from the instantaneous phase. Performance is evaluated using a dataset containing 300M keyboard events collected from over 228k devices observed in the wild. After about two minutes of typing, a device fingerprint is formed that enables 87.35% verification accuracy among a population of 100k devices. Combined with features that measure user behavior in addition to device behavior, verification accuracy increases to 97.36%. The methods described have potential as a second authentication factor, but could also be used to track Internet users.
Model Orthogonalization: Class Distance Hardening in Neural Networks for Better Security

The distance between two classes for a deep learning classifier can be measured by the level of difficulty in flipping all (or majority of) samples in a class to the other. The class distances of many pre-trained models in the wild are very small and do not align well with humans’ intuition (e.g., classes turtle and bird have smaller distance than classes cat and dog), making the models vulnerable to backdoor attacks, which aim to cause misclassification by stamping a specific pattern to inputs. We propose a novel model hardening technique called model orthogonalization which is an add-on training step to pretrained models, including clean models, poisoned models, and adversarially trained models. It can substantially enlarge class distances with reasonable training cost and without much accuracy degradation. Our evaluation on 5 datasets with 22 model structures show that our technique can enlarge class distances by 177.63% on average with less than 1% accuracy loss, outperforming existing hardening techniques such as adversarial training, universal adversarial perturbation, and directly using generated backdoors. It reduces 80% false positives for a state-of-the-art backdoor scanner as the enlarged class distances allow the scanner to easily distinguish clean and poisoned models, and substantially outperforms three existing techniques in removing injected backdoors.
Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment

Across the globe, wireless devices with Internet facilities such as smartphones and tablets have become essential assets for communication and entertainment alike for everyday life for millions of people, which increases the network traffic and the demand for low-latency communication networks. The fourth-generation (4G)/long-term evolution (LTE)/ fifth-generation (5G) communication technology offers higher bandwidth and low latency services, but resource utilization and resiliency cannot be achieved, as transmission control protocol (TCP) is the most common choice for most of the state-of-art applications for the transport layer. An extension of TCP—multipath TCP (MPTCP)—offers higher bandwidth, resiliency, and stable connectivity by offering bandwidth aggregation and smooth handover among multiple paths. However, MPTCP uses multiple disjointed paths for communication to offer multiple benefits. A breach in the security of one of the paths may have a negative effect on the overall performance, fault-tolerance, robustness, and quality of service (QoS). In this paper, the research focuses on how MPTCP options such as MP_CAPABLE, ADD_ADDR, etc., can be used to exploit the vulnerabilities to launch various attacks such as session hijacking, traffic diversion, etc., to compromise the availability, confidentiality, and integrity of the data and network. The probable security solutions for securing MPTCP connections are analyzed, and the secure key exchange model for MPTCP (SKEXMTCP) based on identity-based encryption (IBE) is proposed and implemented. The parameters exchanged during the initial handshake are encrypted using IBE to prevent off-path attacks by removing the requirement for key exchange before communication establishment by allowing the use of arbitrary strings as a public key for encryption. The experiments were performed with IBE and an elliptic curve cryptosystem (ECC), which show that IBE performs better, as it does not need to generate keys while applying encryption. The experimental evaluation of SKEXMTCP in terms of security and performance is carried out and compared with existing solutions.
Blockchain Enabled Digital Transformation: A Systematic Literature Review

Digital transformation is an inevitable trend that impacts all industries, and blockchain is one technology that drives it along with other emerging technologies. This paper conducts a systematic literature review (SLR) on how blockchain enables digital transformation. We analyzed 41 articles to identify the current state, clarify research gaps, and highlight future research agendas. The results reveal that blockchain is a promising technology that has great potential and can offer several opportunities for various companies. Collected articles contain evidence regarding challenges and barriers, as well as potential benefits of blockchain in relation to digital transformation. Through the detailed assessment of the chosen studies, a theoretical framework for blockchain enabled digital transformation has been developed. We highlight open issues that can be handled in future research to overcome barriers and address the challenges concerning blockchain adoption.
Elastic Resource Management for Deep Learning Applications in a Container Cluster

The increasing demand for learning from massive datasets is restructuring our economy. Effective learning, however, involves nontrivial computing resources. Most businesses utilize commercial infrastructure providers (e.g., AWS) to host their computing clusters in the cloud, where various jobs compete for available resources. While cloud resource management is a fruitful research field that has made many advances in production, such as Kubernetes and YARN, few efforts have been invested to further optimize the system performance, especially for Deep Learning (DL) training jobs in a container cluster. This work introduces FlowCon, a system that is able to monitor the individual evaluation functions of DL jobs at runtime, and thus to make placement decisions and resource allocations elastically. We present a detailed design and implementation of FlowCon and conduct intensive experiments over various DL models. The results demonstrate that FlowCon significantly improves DL job completion time and resource utilization efficiency when compared to default systems. According to the results, FlowCon can improve the completion time by up to 68.8% and meanwhile, reduce the makespan by 18.0%, in the presence of various DL job workloads.
A Fuzzy Grammar for Evaluating Universality and Complexity in Natural Language

The paper focuses on linguistic complexity and language universals, which are two important and controversial issues in language research. A Fuzzy Property Grammar for determining the degree of universality and complexity of a natural language is introduced. In this task, the Fuzzy Property Grammar operated only with syntactic constraints. Fuzzy Natural Logic sets the fundamentals to express the notions of universality and complexity as evaluative expressions. The Fuzzy Property Grammar computes the constraints in terms of weights of universality and calculates relative complexity. We present a proof-of-concept in which we have generated a grammar with 42B syntactic constraints. The model classifies constraints in terms of low, medium, and high universality and complexity. Degrees of relative complexity in terms of similarity from a correlation matrix have been obtained. The results show that the architecture of a Universal Fuzzy Property Grammar is flexible, reusable, and re-trainable, and it can easily take into account new sets of languages, perfecting the degree of universality and complexity of the linguistic constraints as well as the degree of complexity between languages.
Signature based Merkle Hash Multiplication algorithm to secure the communication in IoT devices

Great extent of modernization in intelligent device automation and wireless sensor network have encouraged the augmentation of IoTs to interconnect millions of actual devices to the Internet with the help of sensors, actuators and communication technologies. IoT devices with the potential of pervasive sensing and quantifying ability, it becomes an essential segment of the future Internet. The sixth generation (6G) enabled IoT networks are predicted to transform the data sharing and communication for a future of thorough brilliant and sovereign systems. However, secure communication in a sensor network is very difficult and is more amplified by the use of latest 6G IoT technology which is vulnerable to different kinds of attacks due to their limited security features. Existing security systems like RSA and Elliptic curve cryptography which is based on large integer factors and the discrete logarithmic problem is vulnerable to the attack which emerges from quantum computers. The computing capacity of a quantum computer is higher than classical computers. A 30-qubit quantum computer has the equivalent computing capacity as a traditional silicon-based computer computes at 10 teraflops per second. To secure the communication in the present and future IoT network, an enhanced Hash-based Post-Quantum Cryptography (PQC) architecture named Signature-based Merkle Hash Multiplication (SMHM) algorithm is proposed. In the proposed scheme, the Hash-based Merkle signature algorithm is enhanced using the Bernoulli Karatsuba Multiplication algorithm to secure the data storage and communication in IoT devices. The FPGA based proposed model is implemented in the Xilinx ISE14.5 simulator, both Hardware Descriptive Language (HDL) and High-Level Synthesis (HLS) approach is used to calculate the performance metrics which improved 33% frequency, 22% area, power consumption up to 13%, 10% error and 15% delay. • Existing security systems like RSA and Elliptic curve cryptography is vulnerable to the quantum attack. • Hash-based PQC algorithm is enhanced using the Bernoulli Karatsuba Multiplication algorithm. • It results in a decrease in delay, area and power values and an increase in frequency, speed and throughput value. • Also, the proposed approach is least vulnerable to side channel attack and mirai botnet attack.
Graph-based cognitive diagnosis for intelligent tutoring systems

For intelligent tutoring systems, Cognitive Diagnosis (CD) is a fundamental task that aims to estimate the mastery degree of a student on each skill according to the exercise record. The CD task is considered rather challenging since we need to model inner-relations and inter-relations among students, skills, and questions to obtain more abundant information. Most existing methods attempt to solve this problem through two-way interactions between students and questions (or between students and skills), ignoring potential high-order relations among entities. Furthermore, how to construct an end-to-end framework that can model the complex interactions among different types of entities at the same time remains unexplored. Therefore, in this paper, we propose a graph-based Cognitive Diagnosis model (GCDM) that directly discovers the interactions among students, skills, and questions through a heterogeneous cognitive graph. Specifically, we design two graph-based layers: a performance-relative propagator and an attentive knowledge aggregator. The former is applied to propagate a student’s cognitive state through different types of graph edges, while the latter selectively gathers messages from neighboring graph nodes. Extensive experimental results on two real-world datasets clearly show the effectiveness and extendibility of our proposed model.
Double-kernel based class-specific broad learning system for multiclass imbalance learning

Imbalance learning has gained more and more attention from researchers. Most of the efforts so far have focused on binary imbalance problems, while there are numerous unresolved multiclass imbalance problems in real-world scenarios. The diversity of data distribution and the poor performance of traditional multiclass classification algorithms present significant challenges for classifying multiclass imbalanced data. This paper proposes a double kernel-based class-specific broad learning system (DKCSBLS) for multi-class imbalance learning. Class-specific penalty coefficients are incorporated into the model to increase the focus on minority classes. Moreover, double kernel mapping mechanism is designed to extract more robust features. Extensive experiments on various real-world datasets demonstrate the superiority of our proposed approach.
Confidence intervals for zero-inflated gamma distribution

The problems of constructing confidence intervals (CIs) for the mean of a gamma distribution containing zero values are considered. Three different methods for constructing such CIs based on Parametric Bootstrap (PB) method, fiducial inference and the method of variance of estimates recovery (MOVER) are developed. The performances of the proposed CIs are evaluated by Monte Carlo simulation and compared with a published method, asymptotic normal (AN) method. The simulation studies indicate that the proposed methods perform better than the existing method (AN) according to coverage probabilities (CPs), tail error rates, robustness and time comparison. All the methods are illustrated using a dataset for HIV risk behavior in adolescent girls and a dataset for the immunologic response to combination therapy for HIV children. Recommendations are made at the end of the articles.
Spooky effect in optimal OSPA estimation and how GOSPA solves it

In this paper, we show the spooky effect at a distance that arises in optimal estimation of multiple targets with the optimal sub-pattern assignment (OSPA) metric. This effect refers to the fact that if we have several independent potential targets at distant locations, a change in the probability of existence of one of them can completely change the optimal estimation of the rest of the potential targets. As opposed to OSPA, the generalised OSPA (GOSPA) metric <tex>$(\alpha=2)$</tex> penalises localisation errors for properly detected targets, false targets and missed targets. As a consequence, optimal GOSPA estimation aims to lower the number of false and missed targets, as well as the localisation error for properly detected targets, and avoids the spooky effect.
A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation

Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed.