Reviewerly/exHarmony huggingface dataset:
Intelligent and secure RFID multilevel fuzzy inference system for client to banker profiling

In this work, we will be investigating, developing and implementing an intelligent RFID system in conjunction with a fuzzy data classification system, to greatly enhance and secure financial transactions and improve operational efficiency in the banking environment. The innovative part of this research is to provide an efficient solution to the challenge that may arise from the need to expertly and automatically match the profile of customer and banker and solve the vagueness in customer/banking profiling. Our proposal offers an expert, secure, efficient and comprehensive framework, methodology and its application in financial environments to develop customer to banker profile matching and availability via an expert agent multi level fuzzy data classification system. Foremost, according to clients and banking staff members weighted attributes, exact match has been established according to highest degree of relevance by utilizing Matlab fuzzy inference system. Then, to communicate output of a match profile engine from one party to another, to show profiling effectiveness and to do implementation; secure, privacy preserving, and comprehensive intelligent RFID profiling authentication system has been designed and verified by Scyther tool.
Automatic Contrast Evaluation for Android Themes

Several smartphone vendors now offer theme stores where one can submit themes that change the appearance of the OS. Theme designer must follow policies and guidelines to have their themes accepted. One common issue is the low contrast of elements in system applications, that can cause rework and delay to publish submitted themes. To prevent such problems, these devices need to manually check hundred of different screens. In this paper, we describe an automatic tool that walks in several application screens of a device running Android OS, analyzes them and generates detailed reports about low contrast issues. We use a shallow neural network that was trained with more than 10000 elements extracted from screenshots of several different Android applications. We show that our approach present high recall and precision when analyzing Android screen elements, and higher recall and precision when compared to low contrast check in the Google Accessibility Framework.
Influence of Periodic Role Switching Intervals on Pair Programming Effectiveness

Pair programming has been widely used in programming experiment teaching in programming courses. One of the important factors affecting the successful completion of pair programming is the timing of periodic role switching. We organized an experiment in the course of Python Programming for 102 freshmen who did not major in computer science. By comparing the accuracy of code submitted by students in the online judge system, we evaluated the influence of pairing programming on students' programming ability under three different periodic role switching intervals of 15 min, 20 min and 30 min, and collected students' perception towards pairing programming under different modes. We also made a standard to judge the normalization of code, to study the influence of pair programming on the normalization of code written by students. The results show that when the periodic role switching interval is 30 min, pairing programming is helpful for students to solve difficult problems, and it has a positive impact on the solution of subsequent problems after experiencing the process of solving difficult problems. When the periodic role switching interval is 20 min, students have a positive attitude towards pair programming. Therefore, the best switching interval can be set between 20 min and 30 min. However, in terms of code normalization, there is no significant relationship between the standard degree of student code and the switching interval of pair programming. We gave some explanations for this in this paper.
A spacetime model for one-shot active contour extraction scheme for human detection in image sequences

We present a simple yet effective 3D convolutional neural network that learns a novel spacetime representation for human contour detection in a sequence of images. Our approach, in one shot, detects the contour of humans while generating high-quality results compared to the traditional binary mask representations. Our time-consistent convolutional neural network takes a sequence of images as its input and generates an implicit level set surface, in which the object boundaries correspond to the zero-level set. Furthermore, we introduce an appropriate way to combine space and time in an interwoven coordinate system tailored to spatiotemporal datasets. We showcase the feasibility of our approach by training the network on a semi-synthetic dataset. We discuss various configurations of our approach, all of which is shown to outperform the typical binary mask representation. We believe that this new approach could potentially improve the performance of all architectures compared to their alternative ones. The code will be made available on https://github.com/orgs/OSUPCVLab/HumanContourDetection.
Ontology-Guided Data Augmentation for Medical Document Classification

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.
A Unified Adversarial Learning Framework for Semi-supervised Multi-target Domain Adaptation

Machine learning algorithms have been criticized as difficult to apply to new tasks or datasets without sufficient annotations. Domain adaptation is expected to tackle this problem by establishing knowledge transfer from a labeled source domain to an unlabeled or sparsely labeled target domain. Most existing domain adaptation models focus on the single-source-single-target scenario. However, the pairwise domain adaptation approaches may lead to suboptimal performance when there are multiple target domains available, because the information from other related target domains is not being utilized. In this work, we propose a unified semi-supervised multi-target domain adaptation framework to implement knowledge transfer among multiple domains (a single source domain and multiple target domains). Specifically, we aim to learn an embedded space and minimize the marginal probability distribution differences among all domains in the space. Meanwhile, we introduce Prototypical Networks to perform classification, and extend it to semi-supervised settings. On this basis, we further align the conditional probability distributions among the domains by generating pseudo-labels for the unlabeled target data and training the model with bootstrapping method. Extensive sentiment analysis experiments show that our approach significantly outperforms several state-of-the-art methods.
Power Wireless Heterogeneous Network Management System based on Big Data Technology

In order to support the stable and efficient operation of power wireless heterogeneous network, the power wireless heterogeneous network management system based on big data technology is proposed. The system includes four modules: heterogeneous data acquisition module, resource management module, monitoring alarm module, and decision support module. An improved big data association rule mining algorithm which named the SG-DLYGApriori algorithm is proposed. Based on the traditional Apriori algorithm, the improved algorithm simplifies the calculation of support and reduces the number of times to read the database by constructing the support calculation support array, and improves the efficiency of data analysis in heterogeneous power networks.
Design and Application of Comprehensive Evaluation Index System of Smart Grid Based on Coordination Planning of Main Power Distribution

The research and construction of the intelligent dispatching technology support platform is the key to and basic content of the whole intelligent dispatching system construction. The realization of the efficient integration and integration of the main distribution network information and the further realization of the application function cooperative operation on this basis is an important part of the establishment of the intelligent dispatching technology support platform. Enterprises and research institutions serving the power grid technology field have made many beneficial researches and explorations on the intelligent dispatching technology support platform. In this paper, a comprehensive evaluation index system of smart grid based on master distribution coordination planning is proposed. The research results have been successfully applied in power supply, and have been vigorously promoted and applied in power Grid Company. The field operation experience shows that the results have effectively improved the business coordination ability and work efficiency of master network dispatching and distribution network dispatching, and achieved good economic and social benefits.
Nonparametric cluster significance testing with reference to a unimodal null distribution

Abstract Cluster analysis is an unsupervised learning strategy that is exceptionally useful for identifying homogeneous subgroups of observations in data sets of unknown structure. However, it is challenging to determine if the identified clusters represent truly distinct subgroups rather than noise. Existing approaches for addressing this problem tend to define clusters based on distributional assumptions, ignore the inherent correlation structure in the data, or are not suited for high‐dimension low‐sample size (HDLSS) settings. In this paper, we propose a novel method to evaluate the significance of identified clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution that preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation, and thus, does not require that the data follow a particular distribution. By utilizing sparse covariance estimation, the method is adapted for the HDLSS setting. The approach can be used to test the null hypothesis that the data cannot be partitioned into clusters and to determine the optimal number of clusters. Simulation examples, theoretical evaluations, and applications to temporomandibular disorder research and cancer microarray data illustrate the utility of the proposed method.
MRFS: A Multi-resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users' tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRFS.
PersonaGAN: Personalized Response Generation via Generative Adversarial Networks

Current personalized dialogue systems do not thoroughly model the context to capture richer information, and still tend to generate short, incoherent and boring responses. To tackle these problems, in this paper we propose a generative adversarial network model PersonaGAN for personalized dialogue generation. In addition to hierarchical modeling of context, we introduce a speaker-aware encoder in the generator to capture richer context information. Besides, we apply adversarial training to personalized dialogue generation task via using a transformer-based matching model as discriminator. The discriminator could give higher rewards for the responses which look like human written and lower rewards for machine generated responses. Such training strategy encourages the generator to generate responses which are grammatically fluent, informative and logically coherent with context. We evaluate the proposed model on a public available dataset and yield promising results on both automatic and human evaluation, which show that our model can generate more coherent and personalized responses while ensuring fluency.
Improved Adaptive Filter with Unknown Process and Measurement Noise Covariance

This paper considers the problem of state estimation with unknown process and measurement noise covariance for a linear Gaussian system. Under the assumption of conjugate priors, inverse Wishart distribution is chosen for both process and measurement noise covariance by introducing a latent variable. Then, the joint state estimation and unknown parameters identification is derived in variational Bayesian framework, and the system state, latent variable, process and measurement noise covariance are updated iteratively. The performance of the proposed algorithm is demonstrated by comparing with the other VB based adaptive filter in a target tracking simulation system.
Language Competence Formation Among VETI NRNU MEPhI Students in the View of State Corporation «Rosatom» Export-Oriented Policy

The paper is devoted to the formation of language competence within the framework of a communication-oriented approach in the light of the requirements of the State Atomic Energy Corporation «Rosatom». The paper discusses a competence-based approach that focuses on the organization of educational and cognitive activities. It presents the experience of using modern communication technologies, Internet resources, online services. The ways of improving the learning process are proposed. The competencies acquired as a result of learning a foreign language are considered.
Automatic Speech Recognition of Galo

The development of spoken language systems for the tribal languages of India is very beneficial to society. The details of the implementation of automatic speech recognition for Galo language, spoken in the northeast Indian state of Arunachal Pradesh, are presented here. A multi-speaker speech database of continuously spoken Galo sentences was specifically created for this purpose. The speech recognition system was implemented using Kaldi, a public domain software toolkit. The automatic speech recognition system recognizes Galo sentences spoken continuously by new speakers with an accuracy of about 80%.
SchrodinText: Strong Protection of Sensitive Textual Content of Mobile Applications

Many mobile applications deliver and show <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sensitive and private textual content</i> to users including messages, social network posts, account information, and verification codes. All such textual content must be displayed to users but must be strongly protected from unauthorized access in mobile devices. Unfortunately, this is not the case in mobile devices today: malware that can compromise the OS can easily access textual content of other applications. We present SchrodinText, a system solution for strongly protecting the confidentiality of an application's selected UI textual content from a fully compromised OS. SchrodinText leverages a novel security monitor based on two hardware features on ARM processors: virtualization hardware and TrustZone. Our key contribution is a set of novel techniques that allow the OS to perform text rendering without needing access to the text itself, hence minimizing the trusted computing base (TCB). These techniques, collectively called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oblivious rendering</i> , enable the OS to rasterize and lay out all the characters without access to the text; the monitor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resolves</i> the right character glyphs onto the framebuffer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observed</i> by the user and protects them from the OS. Using our prototypes, we show that SchrodinText incurs noticeable overhead but that its performance is usable.
MinSR: Multi-level Interests Network for Session-Based Recommendation

Session-based recommendation, which is the task of predicting user behavior based on anonymous sessions in the recommendation domain, has drawn increasing interests in recent years. Most existing studies focus on modeling a session as a sequence and only capturing the item-level dependency. Although newly impressive progress has been made to session-based recommendation utilizing graph neural networks, those methods are deficient in incorporating multi-level coupling relations and capturing the session-level information. In this paper, we propose a multi-level interests network (MinSR) based on Graph Neural Networks (GNN) and Attention mechanism, which can simultaneously integrate multi-level interests in the recommendation process and provide a framework for exploiting both current and global session relationships. On the aspect of the current session, we extract Current Preference (CP) and Interest Point (IP) for each session using graph neural network and attention network. On the aspect of the global session, we generate Global Tendency (GT) via self-attention graph pooling for the session graph. Finally, by inherently combining them in a unified framework, our method can take into account both current and global session dependencies. Extensive experimental results based on two real-world datasets demonstrate that the proposed MinSR achieves competitive results compared with the state-of-the-art approaches.
Constacyclic codes over finite local Frobenius non-chain rings of length 5 and nilpotency index 4

Abstract The family of finite local Frobenius non-chain rings of length 5 and nilpotency index 4 is determined, as a by-product all finite local Frobenius non-chain rings with p 5 elements ( p a prime) and nilpotency index 4 are given. And the number and structure of γ-constacyclic codes over those rings, of length relatively prime to the characteristic of the residue field of the ring, are determined.
Abuse of the Cloud as an Attack Platform

We present an exploratory study of responses from 75 security professionals and ethical hackers in order to understand how they abuse cloud platforms for attack purposes. The participants were recruited at the Black Hat and DEF CON conferences. We presented the participants' with various attack scenarios and asked them to explain the steps they would have carried out for launching the attack in each scenario. Participants' responses were studied to understand attackers' mental models, which would improve our understanding of necessary security controls and recommendations regarding precautionary actions to circumvent the exploitation of clouds for malicious activities. We observed that in 93.78% of the responses, participants are abusing cloud services to establish their attack environment and launch attacks.
Mineralogy and sulfur isotope geochemistry of polymetallic, porphyry-epithermal mineralization peripheral to the Golden Sunlight gold mine, Montana

The Golden Sunlight mine, located 50 km east of the famous Butte porphyry/lode deposit, is the largest gold mine in Montana, and has produced over 3.6 M oz of gold in its 36-year history. Most of this gold has come from the Mineral Hill breccia pipe (MHBP), a west plunging, cylindrical body of brecciated latite porphyry and siliciclastic sedimentary rocks of the Mesoproterozoic Belt Supergroup. This paper focuses on four areas of mineralization outside the MHBP termed the Apex, Bonanza, North Pit, and 102/Sunlight zones. The Apex and 102/Sunlight areas consist of auriferous, pyrite-rich veins cutting Precambrian sediments and the Cambrian Flathead Quartzite, whereas the Bonanza and North Pit areas are polymetallic Mo-Cu-Ag-Au prospects hosted in latite porphyry and Belt sediments. Recent dating (40Ar/39Ar of igneous biotite) shows the porphyry at Bonanza was emplaced at 78 ± 1 Ma, which is several m.y. younger than the porphyry associated with the MHBP (81.9 ± 1.9 Ma, zircon U/Pb), and several m.y. older than the main phase of the Boulder Batholith that hosts the Butte deposit. Overall, the ore and gangue mineral assemblages in the peripheral deposits are similar to those described previously for the MHBP deposit. Pyrite, locally As-rich, is abundant in all settings, with varying amounts of galena, sphalerite, chalcopyrite, bornite, molybdenite (at Bonanza), tetrahedrite-tennantite, and traces of pearceite, tetradymite, aikinite, calaverite, petzite, goldfieldite, buckhornite, gold and electrum. Gangue minerals include quartz, carbonates (Fe-dolomite, siderite, magnesite), barite, anhydrite, adularia, and alumino-phosphate sulfate (APS) minerals. Spectral analysis indicates that some of the adularia is ammonium rich, i.e., buddingtonite. Values of δ34S for sulfide minerals from the peripheral deposits overlap with values obtained by previous workers from the MHBP. The bulk of the analyses (including all samples from the porphyry-hosted Bonanza deposit) have δ34S values between −12 and −4‰, with a tail to heavier values >+5‰. It is postulated that most of the lighter S was introduced into the district by late Cretaceous magmatic/hydrothermal fluids, whereas the heavier S is attributed to sedimentary pyrite in the Precambrian Greyson and LaHood formations. A weak negative correlation exists between gold grade and the δ34S of associated pyrite: all samples in this study that contain >0.1 oz/ton Au have δ34S < −5.9‰. Two possible explanations for the isotopically light S at Golden Sunlight are explored: 1) assimilation of biogenic pyrite from underlying sedimentary units; and 2) fractionation of S isotopes between SO4 and H2S in a system where oxidized S > reduced S.