Reviewerly/exHarmony huggingface dataset:
Modeling and Analyzing Waiting Policies for Cloud-Enabled Schedulers

Cloud platforms have popularized the Infrastructure-as-a-Service (IaaS) purchasing model, which enables users to rent computing resources on demand to execute their jobs. However, buying fixed resources is still much cheaper than renting if their resource utilization is high. Thus, to optimize cost, users must decide how many fixed resources to provision versus rent “on demand” based on their workload. In this article, we introduce the concept of a waiting policy for cloud-enabled schedulers and show that the optimal cost depends on it. The waiting policy explicitly controls how long jobs wait for resources, as jobs never need to wait, since cloud platforms provide the illusion of infinite scalability. A waiting policy is the dual of a scheduling policy: while a scheduling policy determines which jobs should run when fixed resources are available, a waiting policy determines which jobs should wait when fixed resources are not available. We define multiple waiting policies and develop simple and general analytical models to reveal their tradeoff between fixed resource provisioning, cost, and job waiting time. We evaluate the impact of different waiting policies on a real year-long batch workload consisting of 14M jobs run on a 14.3k-core cluster. We show that a compound waiting policy, which forces jobs with long running times or short waiting times to wait for fixed resources, offers the best tradeoff. The policy decreases both the cost (by 5 percent) and mean job waiting time (by 7×) compared to the current cluster, and also decreases the cost (by 43 percent) compared to renting on-demand resources for a modest increase in mean job waiting time (at 1.74 hours).
Sentiment analysis, opinion mining and topic modelling of epics and novels using machine learning techniques

Sentiment analysis systems can collect and automatically structure unstructured information by collecting public views of services, products, policy, brands, etc. This information is of major value in the fields of marketing analysis, public relations, product reviews, net promoter evaluations, customer feedback and client reviews. Literary works, on the other hand, are less susceptible to computational analysis because there are no immediate commercial incentives. However, similar techniques can be used to evaluate literary work, comprehend the underlying social network, and obtain or validate literary work. This project is about analyzing the book's characters and predicting their characteristics and relationships with one another. A lot of human effort is expended during the adaptation of a novel/book in any form, which is inconvenient and undesirable. Furthermore, the human brain has a tendency to overlook a number of minor details about the events/characters in the book. The scenario described above can frequently result in inaccuracies in the adaptation's plot. As a result, the project is an innovation that aims to aid in the easy and accurate adaptation of a book, making the process much simpler and precise. Machine learning (supervised and unattended) and lexical approaches include the current sentiment analysis techniques. The model's goal is to scan the massive amounts of text in the book. Following digitization, the model will display interesting ideas derived from the given book using a combination of natural language processing, feelings and emotions analysis, and social network analysis methodology.
Pairwise deep learning to rank for top-N recommendation

Recent researches indicate that pairwise learning to rank methods could achieve high performance in dealing with data sparsity and long tail distribution in item recommendation, although suffering from problems such as high computational complexity and insufficient samples, which may cause low convergence and inaccuracy. To further improve the performance in computational capability and recommendation accuracy, in this article, a novel deep neural network based recommender architecture referred to as PDLR is proposed, in which the item corpus will be partitioned into two collections of positive instances and negative items respectively, and pairwise comparison will be performed between the positive instances and negative samples to learn the preference degree for each user. With the powerful capability of neural network, PDLR could capture rich interactions between each user and items as well as the intricate relations between items. As a result, PDLR could minimize the ranking loss, and achieve significant improvement in ranking accuracy. In practice, experimental results over four real world datasets also demonstrate the superiority of PDLR in contrast to state-of-the-art recommender approaches, in terms of Rec@N, Prec@N, AUC and NDCG@N.
Efficient use of heuristics for accelerating XCS-based policy learning in Markov games

In Markov games, playing against non-stationary opponents with learning ability is still challenging for reinforcement learning (RL) agents, because the opponents can evolve their policies concurrently. This increases the complexity of the learning task and slows down the learning speed of RL agents. This paper proposes efficient use of rough heuristics to speed up policy learning when playing against concurrent learners. Specifically, we propose an algorithm that can efficiently learn explainable and generalized action selection rules by taking advantage of representations of quantitative heuristics and an opponent model with an eXtended classifier system (XCS) in zero-sum Markov games. A neural network is used to model the opponent from their behaviors and the corresponding policy is inferred for action selection and classifiers evolution. Due to the condition representation and the matching mechanism of XCS, the quantitative heuristics and the opponent model can provide guidance for action selection in states with similar feature representations. Besides, in case of multiple heuristic policies, we introduce the concept of Pareto optimality to comprehensively consider all the heuristic policies for action selection. Moreover, we analyze the influence of heuristic policies on the convergence of the algorithm. Furthermore, we introduce accuracy-based eligibility traces to further speed up classifiers evolution, i.e., in the reinforcement part, classifiers that can match historical traces are reinforced according to their accuracy. We demonstrate the advantages of the proposed algorithm over several benchmark algorithms in a soccer scenario and a thief-and-hunter scenario.
Network Public Opinion Monitoring System for Agriculture Products Based on Big Data

The influence of online public opinion on agricultural product safety on the society is increasing. In order to correctly guide the direction of online public opinion on agricultural products, help the agricultural sector turn from passive to active public opinion, timely prevent the spread of negative public opinion, and reduce the negative impact on public opinion hot events, it is especially important to improve the ability of monitoring agricultural products’ network public opinion. This research is based on big data technology to develop an agricultural products’ network public opinion monitoring system that can collect, process, and analyze data in real time, discover and track hot topics, and calculate and visualize the polarity of public sentiment. The use of big data technology to increase the processing speed aims to strengthen the public’s supervision of the public opinion on the network security of agricultural products and provide an effective basis of the decision-making of relevant departments.
A Gaussian Approximation of Marginal Likelihood in Relevance Vector Machine for Industrial Data With Input Noise

Given that there exists input uncertainty caused by the noise embedded in industrial data, this study proposes a relevance vector machine (RVM) prediction model with input noise. Due to the fact that the marginal likelihood cannot be analytically calculated when introducing the input uncertainty, a Gaussian approximation is proposed in this study on the basis of the law of total expectation and the law of total covariance. Furthermore, to approximate the posterior distribution over the model weights, this study employs the Markov chain Monte Carlo algorithm, where a Gaussian proposal distribution is designed to draw new samples. In the prediction stage, a Gaussian approximation is also designed for a new testing input in order for the input uncertainty to be reflected in the estimation of output variance. To verify the effectiveness of the proposed method, four synthetic data sets, four benchmark data sets, and two industrial data sets are employed in the comparative experiments. The results indicate that the proposed RVM with uncertain input outperforms other approaches, and it also performs better on the time series prediction issue.
Binary Sequences Derived From Differences of Consecutive Primitive Roots

Let 1 <; g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> <; ⋯ <; g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">φ(p-1)</sub> <; p-1 be the ordered primitive roots modulo p. We study the pseudorandomness of the binary sequence (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) defined by s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ≡ g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sub> +g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+2</sub> mod 2, n=0,1,⋯ In particular, we study the balance, linear complexity and 2-adic complexity of (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ). We show that for a typical p the sequence (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) is quite unbalanced. However, there are still infinitely many p such that (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) is very balanced. We also prove similar results for the distribution of longer patterns. Moreover, we give general lower bounds on the linear complexity and 2-adic complexity of (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) and state sufficient conditions for attaining their maximums. Hence, for carefully chosen p, these sequences are attractive candidates for cryptographic applications.
Video Deraining Via Temporal Aggregation-and-Guidance

Learning-based video deraining methods generally integrate temporal correlation within the network. But their non-transparency (i.e., difficult to comprehend how to exploit temporal correlation) seriously limits the development for video deraining. To conquer it, this paper proposes a novel Temporal Aggregation-and-Guidance Network (TAG-Net). Concretely, we define a new temporal ensemble model with set representation by modeling correspondence between rain regions of the current frame and rain-free regions of adjacent frames. Further, we build a TAG-Net that contains: 1) temporal aggregation network derived from the ensemble model, which is with newly-designed self-directed attention acting on video sequences, it automatically learns temporal correlation from multiple adjacent frames to optimize the current frame. 2) temporal guidance network, which aims at eliminating rain streaks in intersected rain regions between the current and adjacent frames to enhance the previously-recovered frame. Extensive evaluations verify that TAG-Net yields the best performance against other advanced methods.
Hydrodynamic Constraints on Ore Formation by Basin‐Scale Fluid Flow at Continental Margins: Modelling Zn Metallogenesis in the Devonian Selwyn Basin

Abstract The clastic‐dominant (CD‐type) deposits that are contained within sedimentary basins are major resources of Zn, Pb and Ag, but their formation by basin‐scale hydrothermal mass and energy transport processes is still poorly understood. Using geological constraints from the Late Devonian Selwyn Basin (Canada), we apply quantitative numerical fluid flow modeling to explore the effect of strata permeability, timing of fault opening and increased heat flow in controlling fluid migration, metal leaching and ore formation during an extensional tectonic event. The results indicate that tapping hot fluids from a confined and permeable aquifer at several km depths by means of permeable normal faults is a key factor for the formation of large Zn‐Pb deposits. The hot (282°C) ore‐forming fluids are transported to the shallow subsurface shortly after the initiation of a rifting event (within 100 kyr), before the development of extensive basin‐scale convection patterns that lead to stronger cooling and a reduction in the capacity of the hydrothermal system to make an economic deposit. Such a hydrothermal event can result in metal endowments comparable to the deposits of the Selwyn Basin.
A Decision-Tree Based Continuous Learning Framework for Real-Time Prediction of Runway Capacities

Accurate and up-to-date runway capacity estimates are vital for efficient management of airport operations. They are also a pre-requisite for the strategic and tactical measures used to mitigate traffic congestion and optimize air traffic operations at airports. Due to increasing availability of surveillance data, Machine Learning (ML) methods are used for capacity prediction. However, such ML models are static in nature and cannot be easily refitted/re-trained to learn the dynamic changes runways continually experience. These ML methods either fail to consider key factors that might affect runway capacity or try to incorporate every possible factor and suffer from the curse of dimensionality. This paper proposes a machine learning based capacity prediction model that utilizes innovative feature engineering methods to approximate a set of variables that better explain the dynamics of a runway system and can learn incrementally. The capacity prediction model utilizes the Adaptive Random Forest, an online machine learning model, which is built with decision trees that can learn incrementally. The capacity prediction model is then trained and tested on data from Philadelphia International Airport (PHL). Results demonstrate that the ML models can perform runway capacity prediction every hour for six hours rolling window horizon, for both arrivals and departures, which on an average, deviate by less than 3 flights from the actual count. In terms of prediction accuracy, models achieve a Mean Average Percentage Error of 12.05% for arrivals and 13.16% for departures.
A Secure Large-Scale Multi-Party Payment System Based on Blockchain

In the current blockchain system, the transaction costs and network congestion are increasing, thus the transaction rate is lower, which limits the wide application of blockchain system. In order to address this problem, this paper proposes a secure large-scale multi-party payment (SLMP) system. When our SLMP is applied to the current blockchain system, its capacity is expanding, thus the forementioned problem is solved. The SLMP employs an off-chain payment channel and aggregating multi-signatures with public-key aggreation (AMSP). The security of the proposed system is similar to Bitcoin network. And the proposed system can support large-scale off-chain transactions, and the vendor can conduct aggregate signature on all final payment transactions of payers before the specified time. This modification allow nodes (miner) to validate all transactions at once, thus SLMP is efficient, we also give security proof of our SLMP. Moreover, SLMP system allows offline and off-chain transactions, which means our system is flexible in remote or geographically disastrous areas where communications infrastructure is inadequate or damaged.
Analysis of the popularity of programming languages in open source software communities

Choosing a suitable programming language during the development of the project can get twice the result with half the effort. The open source software community Gitee was taken as an example to explore the popularity of programming languages. We use pycharm to capture the corresponding number of followers, collections and forks of the projects and programming languages in the community. Then we use cluster analysis to classify programming languages and construct a binary matching network of programming languages and projects to verify the results of language popularity division. The study found that programming languages can be divided into 5 types according to their popularity. Java, c and python belong to the first level, with higher popularity. Although the Go language is not widely used, it has attracted the attention of the majority of developers, and it is bound to develop into a mainstream language in the future.
Noise label learning through label confidence statistical inference

Noise label exists widely in real-world data, resulting in the degradation of classification performance. Popular methods require a known noise distribution or additional cleaning supervision, which is usually unavailable in practical scenarios. This paper presents a theoretical statistical method and designs a label confidence inference (LISR) algorithm to handle this issue. For data distribution, we define a statistical function for label inconsistency and analyze its relationship with neighbor radius. For data representation, we define trusted-neighbor, nearest-trusted-neighbor and untrusted-neighbor. For noisy label recognition, we present three inference methods to predict the labels and their confidence. The LISR algorithm establishes a practical statistical model, queries the initial trusted instances, iteratively searches for the trusted instances and corrects labels. We conducted experiments on synthetic, UCI and classic image datasets. The results of significance test verified the effectiveness of LISR and its superiority to the state-of-the-art noise label learning algorithms.
WITHDRAWN: Viscoelastic loop strategy for water hammer control in a pressurized steel pipeline

The challenges of water hammer phenomenon impose many technical and economic barriers to the design and operation of the pressurized hydraulic systems. So the objective of most literature in this field was finding an applicable strategy that was capable to suppress the threatening of hydraulic transient and fulfill the required technical and economic limits of the project. In this paper, an experimental investigation is presented to verify the effectiveness of the viscoelastic loop strategy to control and damp the water hammer triggered in the pressurized pipeline due to pump’s trip or downstream valve closure. This strategy comprises two viscoelastic bypass pipes are configured to form a counter clockwise loop that allow to the flow in certain direction and restrain its opposite one. On the other hand, the performance of viscoelastic loop strategy is compared to the classical air vessel device to control the induced hydraulic transients. Further, the experimental results revealed the viscoelastic loop strategy is very effective to control the water hammer with overall maximum pressure damping efficiency reaches to (69.5%). Furthermore, the viscoelastic loop strategy is superior to the classical air vessel device for different transients trigger conditions in the most cases of the experiments.
RETRACTED: A Survey of image pre-processing techniques for medical images

Abstract Medical Images cannot be utilized similarly it was procured. The obtained images must be Pre-Processed and Segmented. Real confinements are there in medical image preprocessing as far as multifaceted nature when contrasted with preparing different kinds of images. Confinements in image acquisitions make image division a repetitive procedure. The most significant objective of segmenting medical images is in performing activities on medical images for distinguishing designs and in recovering data. This survey speaks primarily about medical image handling. At that point many methods have been proposed to deal with portions of Medical images. The similar investigation of different processing procedures has been analysed. The overview thus gives subtleties of mechanized division strategies, explicitly talked about with regards to scanned medical images. The rationale is in analysing the issues experienced in dividing the scanned medical images, and the respective benefits and confinements techniques as of now accessible for segmentation of medical images.
TrustSECO: A Distributed Infrastructure for Providing Trust in the Software Ecosystem

The software ecosystem is a trust-rich part of the world. Collaboratively, software engineers trust major hubs in the ecosystem, such as package managers, repository services, and programming language ecosystems. However, trust entails the assumption of risks. In this paper, we lay out the risks we are taking by blindly trusting these hubs when using information systems. Secondly, we present a vision for a trust-recording mechanism in the software ecosystem that mitigates the presented risks. This vision is realized in TrustSECO: a distributed infrastructure that collects, stores, and discloses trust facts about information systems. If our community manages to implement this mechanism, we can create an urgently needed healthy and secure software ecosystem. Finally, we report on the current status of the project.
Cross-city Analysis of Location-based Sentiment in User-generated Text

Geolocated user-generated content is a promising source of data reflecting how citizens live and feel. Information extracted from this source is being increasingly used for urban planning and policy evaluation purposes. While a lot of existing research focuses on the relationship between locations and sentiment in social media postings, we aim to uncover relations between location and sentiment that are consistent over cities around the world. In this paper, we therefore analyze the relationship between multiple categories of points of interest (POIs) in the OpenStreetMap dataset and the sentiment of English microblogging messages sent nearby using a three-stage processing pipeline: (1) extract sentiment scores from geolocated microblogs posted on Twitter, (2) spatial aggregation of sentiment in cities and POIs, (3) analyze relationships in aggregated sentiment. We identify differences in Twitter users' sentiments within cities based on POIs, and we investigate the temporal dynamics of these sentiments and compare our findings between major cities in multiple countries.
Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

In this paper a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem. Here, data points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset and the well-known Iris Dataset using a classical approach, simulated annealing, direct embedding on the Quantum Processing Unit and a hybrid solver. The hybrid solver and Simulated Annealing algorithm outperform the classical implementation on various occasions but show high sensitivity to a small variation in training data.