Reviewerly/exHarmony huggingface dataset:
Searching for impossible subspace trails and improved impossible differential characteristics for SIMON-like block ciphers

Abstract In this paper, we greatly increase the number of impossible differentials for SIMON and SIMECK by eliminating the 1-bit constraint in input/output difference, which is the precondition to ameliorate the complexity of attacks. We propose an algorithm which can greatly reduce the searching complexity to find such trails efficiently since the search space exponentially expands to find impossible differentials with multiple active bits. There is another situation leading to the contradiction in impossible differentials except for miss-in-the-middle. We show how the contradiction happens and conclude the precondition of it defined as miss-from-the-middle. It makes our results more comprehensive by applying these two approach simultaneously. This paper gives for the first time impossible differential characteristics with multiple active bits for SIMON and SIMECK, leading to a great increase in the number. The results can be verified not only by covering the state-of-art, but also by the MILP model.
Some New Quantum BCH Codes over Finite Fields

Quantum error correcting codes (QECCs) play an important role in preventing quantum information decoherence. Good quantum stabilizer codes were constructed by classical error correcting codes. In this paper, Bose–Chaudhuri–Hocquenghem (BCH) codes over finite fields are used to construct quantum codes. First, we try to find such classical BCH codes, which contain their dual codes, by studying the suitable cyclotomic cosets. Then, we construct nonbinary quantum BCH codes with given parameter sets. Finally, a new family of quantum BCH codes can be realized by Steane’s enlargement of nonbinary Calderbank-Shor-Steane (CSS) construction and Hermitian construction. We have proven that the cyclotomic cosets are good tools to study quantum BCH codes. The defining sets contain the highest numbers of consecutive integers. Compared with the results in the references, the new quantum BCH codes have better code parameters without restrictions and better lower bounds on minimum distances. What is more, the new quantum codes can be constructed over any finite fields, which enlarges the range of quantum BCH codes.
Development of a Best Answer Recommendation Model in a Community Question Answering (CQA) System

In this work, a best answer recommendation model is proposed for a Question Answering (QA) system.A Community Question Answering System was subsequently developed based on the model.The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users.Answers are ranked according to their Fixed-Point Score (FPS) for each question.Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA).For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database.When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose.The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache.The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG).The system eliminated longer waiting time faced by askers in a community question answering system.The developed system can be used for research and learning purposes.
Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.
Research on Enterprise Trust Relationship of Jilin Province Agricultural Products Supply Chain Based on Big Data Blockchain Logistics

Abstract Relationship and trust are essential factors to aggregate the agricultural product supply chain’s central bodies and maintain the agricultural product supply chain’s dynamic flexibility. Especially for the agricultural product supply chain dominated by e-commerce with many atomic farmers, relationships and trust are even more critical. The basic premise to ensure its effective operation. Big data blockchain has high security, distributed storage, publicity mechanism, anonymity, openness, transparency, etc. It is of great significance to apply it to the supply chain of agricultural products. Based on the analysis of the significant data blockchain logistics and the relationship between big data and the supply chain, this article discusses the construction and coordinated decision-making of the big data-based supply chain of agricultural products in Jilin Province provides references for relevant personnel.
Shattering cancer with quantum machine learning: A preview

Machine learning has become a standard tool for medical researchers attempting to model disease in various ways, including building models to predict response to medications, classifying disease subtypes, and discovering new therapies. In this preview, we review a paper that utilizes quantum computation in order to tackle a critical issue that exists with medical datasets: they are small, in that they contain few samples. The authors' work demonstrates the possibility that these quantum-based methods may provide an advantage for small datasets and thus have a real impact for medical researchers in the future.
Discrete Distribution Estimation with Local Differential Privacy: A Comparative Analysis

Local differential privacy is a promising privacy-preserving model for statistical aggregation of user data that prevents user privacy leakage from the data aggregator. This paper focuses on the problem of estimating the distribution of discrete user values with Local differential privacy. We review and present a comparative analysis on the performance of the existing discrete distribution estimation algorithms in terms of their accuracy on benchmark datasets. Our evaluation benchmarks include real-world and synthetic datasets of categorical individual values with the number of individuals from hundreds to millions and the domain size up to a few hundreds of values. The experimental results show that the Basic RAPPOR algorithm generally performs best for the benchmark datasets in the high privacy regime while the k-RR algorithm often gives the best estimation in the low privacy regime. In the medium privacy regime, the performance of the k-RR, the k-subset, and the HR algorithms are fairly competitive with each other and generally better than the performance of the Basic RAPPOR and the CMS algorithms.
Quantum properties of superposition opposite coherent states using quantum scissors with conditional measurements

Abstract We analyze the quantum properties for the output state of the superposition coherent states with the opposite phase using a quantum scissor. From the perspectives of theoretical calculation and numerical analysis, we discuss the effects of the parameters of the quantum scissors device on different superposition coherent states, such as the probability of successful photon scissor, fidelity, signal gain and squeezing. An interesting finding is that when the input quantum states are even/odd coherent states, signal gain and squeezing for the output quantum state only depend on the amplitude and are independent of the parameters of the quantum scissors. In addition, the quantum properties for other superposition coherent states which are dependent of the amplitude of coherent and the transmittance of beam splitters are also discussed.
μSteal

Modern internet services are moving towards distributed microservice architectures, wherein a complex application is decomposed into numerous discrete microservices to improve programmability, reliability, manageability, and scalability. A key property of microservice-based architectures is that common microservices may be shared by multiple end-to-end cloud services. As an example, a speech-recognition microservice might serve as an early node in the microservice graphs of several end-to-end services. However, given the dissimilarities across microservice graphs and varying end-to-end latency constraints across services, shared microservices may need to operate under differing latency constraints for each service. As a result, in existing systems, most providers either deploy multiple instance pools for each latency constraint, or require all requests to needlessly meet the most stringent constraint.
Whale Optimization Algorithm Applied to Recognize Spammers in Facebook

Social networks have become an inevitable communication system in the everyday life of people. In recent years, the number of users of social networks has only been increasing exponentially due to the attractive services that it offers. Despite these benefits, it also suffers from the threat of spammers. The spamming is executed through texts, photos, videos, and other multimedia forms. The presence of spammers hinders the user’s experience and causes inconvenience. The existing spammer detection algorithms fail to accurately detect the spammers. In this work, the spammer detection problem is viewed as a binary classification problem that classifies the user profiles into either spam or non-spam account. A set of IP-address-based features is adapted and classifies Facebook’s user profiles. Further, the whale optimization algorithm is applied as a feature selection method to select the optimal feature subset. The experimental results indicate that the WOA outperforms the existing methods in terms of accuracy.
Selection of the number of components for finite mixtures of linear mixed models

Over the last decades, linear models have been studied by the scientific community as an important tool of statistical modelling in a great variety of phenomena.However, in many situations the data are grouped according to factors, so the introduction of random effects is required in order to consider the correlation between observations from the same individual, in which case linear mixed models are used.In addition, it is often observed that the data comes from a heterogeneous population, giving rise to situations where the estimation of a single linear model is not sufficient. Therefore, it is necessary to use models that incorporate this unobserved heterogeneity, as is the case of mixture models. Thus, mixtures of linear mixed models allow modelling the heterogeneity among the individuals and, at the same time, to account for correlations between observations from the same individual.Choosing the number of components for mixture models has long been considered as an important but difficult research problem. There is wide variety of literature available on the performance of model selection statistics for determining the number of components in mixture models.In this article, we study the problem of determining the number of components in mixtures of linear mixed models, investigating the performance of various model selection methods. In order to evaluate the methodologies developed, we carry out a simulation study and we illustrate these methodologies using a real data set.
Decision Tree C 4.5 Algorithm for Classification of Poor Family Scholarship Recipients

Abstract The poor family scholarship program is a scholarship program by Lamongan District Education Office. This scholarship is given to underprivileged students for study costs at university. The many indicators of scholarship determination result in difficulties education office in making objective decisions. The purpose is to determine the classification of scholarship recipients using the decision tree C4.5 algorithm. This algorithm has the advantage of speed informing and reading models, high accuracy, flexibility, and processing discrete and continuous data. The contribution of this research is to classify poor family scholarship participants with the C 4.5 algorithm without pruning, pre pruning, and post pruning methods. This study uses 460 data and for each attribute, it is calculated using the gain ratio formula to produce trees and decision rules. Based on the scenario that has been done the accuracy value generated after pruning is better than without pruning. The classification process using scenario post pruning technique is used as a reference for the classification of new registrants because it has the highest accuracy of 85.55%.
A Scalable Pattern Mining Method Using Apache Spark Platform

The amount of data is growing sharply on the Internet. Some data like log files are enormous and entail valuable and precious hidden patterns. In other words, a log file is a set of recorded events that carry beneficial and vital information to develop web server performance, stability server loads, control, and rush up user response operations. However, analyzing massive data take a long time and require powerful hardware. Also, the performance of sequential pattern mining methods is usually unsatisfactory to deal with such data. This paper proposes a novel and advanced parallel method for finding the log file patterns, such as frequent patterns (e.g., URL, IP, Status Code), how users accessed files, the number of errors, and the most common errors by applying the Apache Spark platform. Experiment results demonstrate that the proposed method's run time on three datasets is significantly less than its four rival pattern mining methods.
Meta-interpretive learning as metarule specialisation

Abstract In Meta-interpretive learning (MIL) the metarules, second-order datalog clauses acting as inductive bias, are manually defined by the user. In this work we show that second-order metarules for MIL can be learned by MIL. We define a generality ordering of metarules by $$\theta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>θ</mml:mi> </mml:math> -subsumption and show that user-defined sort metarules are derivable by specialisation of the most-general matrix metarules in a language class; and that these matrix metarules are in turn derivable by specialisation of third-order punch metarules with variables quantified over the set of atoms and for which only an upper bound on their number of literals need be user-defined. We show that the cardinality of a metarule language is polynomial in the number of literals in punch metarules. We re-frame MIL as metarule specialisation by resolution. We modify the MIL metarule specialisation operator to return new metarules rather than first-order clauses and prove the correctness of the new operator. We implement the new operator as TOIL, a sub-system of the MIL system Louise. Our experiments show that as user-defined sort metarules are progressively replaced by sort metarules learned by TOIL, Louise’s predictive accuracy and training times are maintained. We conclude that automatically derived metarules can replace user-defined metarules.
Collision-free Path Planning for Overhead Crane System Using Modified Ant Colony Algorithm

This paper presents a method of collision-free path planning of an overhead crane system. To obtain a collision-free path, the ant colony algorithm was modified by including algorithms for a crane trajectory generation and a crane control system. The modified ant colony algorithm was performed on a crane workspace model. The performance of the method is presented. The results of each sub processes are presented and discussed.
Simple-NMS: Improved Pedestrian Detection with New Constraints

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. After a picture is detected by the target, a large number of redundant anchors will be obtained, so we need to go through NMS to process these repeated anchors. Its essence is to extract the target detection frame with high confidence and suppress the false detection frame with low confidence. The biggest problem in the NMS algorithm is that it completely removes adjacent low-confidence detection frames. In some images with dense targets, this is very likely to cause missed detection and false detection. Therefore, we proposed the Simple-NMS algorithm, which adds two new thresholds to the original NMS, and sets a constraint condition different from the original based on the new threshold, and does not change the complexity of the traditional NMS algorithm, which can be very good Improve the effectiveness of NMS missed targets. The new NMS algorithm is improved on the standard data sets PASAL VOC2007 and MS COCO. In addition, the algorithm is simple and efficient, and can be easily integrated into any other object detection process.
Semantic Splitting of Conditional Belief Bases

An important concept for nonmonotonic reasoning in the context of a conditional belief base \(\mathcal R\) is syntax splitting, essentially stating that taking only the syntactically relevant part of \(\mathcal R\) into account should be sufficient. In this paper, for the semantics of ordinal conditional functions (OCF) of \(\mathcal R\), we introduce the notion of semantic splitting of \(\mathcal R\) where the combination of models of sub-belief bases of \(\mathcal R\) corresponds to the models of \(\mathcal R\). While this is not the case for all OCF models, we show that for c-representations which are a subclass of all OCFs governed by the principle of conditional preservation, every syntactic splitting is also a semantic splitting. Furthermore, for the semantics of c-representations, we introduce constraint splittings of a conditional belief base and show that they fully capture and go beyond syntax splittings, thus allowing for additional belief base splittings that enable the computation of models locally from sub-belief bases.
Learning Depth from Single Image Using Depth-Aware Convolution and Stereo Knowledge

Estimating depth from a monocular image has become a very popular task in computer vision for identifying important geometric information of the scene. While its performance has been significantly improved by convolutional neural networks (CNNs) in recent years, depth-estimation accuracy is still unsatisfactory at locations with abrupt depth changes. This is mainly caused by the use of spatially consistent filters in CNNs which directly mix the features of different objects when applied to the pixels near the object borders. Moreover, the performance gap between depth estimation from single image and that from a stereo pair remains quite large due to the ill-posed nature of the former one. In this paper, we propose a new depth-aware convolutional neural network (DACNN) to address these issues. We first design a novel depth-aware convolution operation for DACNN, that can adaptively choose subsets of relevant features for convolutions at each location. Specifically, we compute hierarchical depth features as the guidance, and then estimate the depth map using such depth-aware convolution which can leverage the guidance to adapt the filters. In addition, we also introduce a pre-trained stereo network into DACNN as the teacher to carry out knowledge distillation on the student monocular network with a specially designed loss function. Experimental results on the KITTI online benchmark and Eigen split datasets show that the proposed method achieves the state-of-the-art performance for single-image depth estimation.
Mixed-Initiative Approach to Extract Data from Pictures of Medical Invoice

Extracting data from pictures of medical records is a common task in the insurance industry as the patients often send their medical invoices taken by smartphone cameras. However, the overall process is still challenging to be fully automated because of low image quality and variation of templates that exist in the status quo. In this paper, we propose a mixed-initiative pipeline for extracting data from pictures of medical invoices, where deep-learning-based automatic prediction models and task-specific heuristics work together under the mediation of a user. In the user study with 12 participants, we confirmed our mixed-initiative approach can supplement the drawbacks of a fully automated approach within an acceptable completion time. We further discuss the findings, limitations, and future works for designing a mixed-initiative system to extract data from pictures of a complicated table.
A Fully Automated Approach to Requirement Extraction from Design Documents

Design documents are intended to outline the goals of a system or project, which are utilized in the creation of specific software requirements. At the NASA Jet Propulsion Laboratory, California Institute of Technology, Functional Design Description (FDD) documents describe the scope of the project and reflect the design and implementation of the system. The specifications in the document are not explicitly written as requirements, though these guidelines must be reflected in the official software requirements. In this work we present a fully automatic approach to extracting software requirements from design documents as well as comparing the extracted requirements to those that exist in the official software requirement database. We do this through (1) sentence extraction from the design document, (2) the incorporation of coreferent text, and (3) aligning the extracted text to the official software requirements. Via natural language processing and information retrieval techniques, our system results in an automated process that ensures that the specifications in the design document result in official software requirements. We find that extraction of imperatives results in a recall rate of 0.73 and the TF-IDF cosine similarity metric is shown to be a useful and successful way to compare requirements. Though there has been recent work investigating the usefulness of natural language processing techniques in requirement engineering, this has not been made use of in the aerospace industry. Aerospace requirement engineering is a field particularly ripe for this type of innovation because these techniques can both automate some of needlessly manual work and contribute to aerospace safety practices by identifying issues that a human may miss. We present the first fully automated approach that extracts requirements from a design document and compares them to a database, and use these findings as encouragement for future work that makes use of natural language processing techniques in aerospace requirement engineering.