Reviewerly/exHarmony huggingface dataset:
Equal access to the United States legal system for multilingual users of English

TESOL JournalVolume 11, Issue 1 e00469 CURRENT ISSUES IN TESOL Equal access to the United States legal system for multilingual users of English Noah Gabrielsen, Noah Gabrielsen Arizona State UniversitySearch for more papers by this authorJulieta Fernández, Julieta Fernández University of ArizonaSearch for more papers by this author Noah Gabrielsen, Noah Gabrielsen Arizona State UniversitySearch for more papers by this authorJulieta Fernández, Julieta Fernández University of ArizonaSearch for more papers by this author First published: 13 June 2019 https://doi.org/10.1002/tesj.469Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume11, Issue1March 2020e00469 RelatedInformation
La tigre che fugge. Cenni di etnolinguistica toda

The Todas are a rural tribal community of the Nilgiri plateau of Tamil Nadu. They and their language deserve special interest from the standpoint of ethnology and linguistics, both Dravidian and general. Echo words, folk-tale motifs and narrative devices are among the main topics discussed in the following pages, together with riddles, transport puzzles and, more generally, the important role of the oral transmission of knowledge and (ethno)linguistic techniques and lore. KEYWORDS: Toda, Dravidian languages, echo words, folktales, riddles
Prototyping Functional Android App Features with ProDroid (poster)

We present ProDroid, a framework that provides Android app developers an ability to quickly produce functional prototypes. With ProDroid, developers can create a new app that imports various kinds of functionality provided by other existing Android apps. Our evaluation shows that with the help of ProDroid, a developer was able to import a function from an existing Android app into a new prototype with only 55 lines of Java code, while the function itself requires 10,334 lines of Java code to implement.
Exploring Physical Intelligibility and Control with Smart Speakers

Voice-controlled smart speakers with intelligent personal assistants (IPAs) are increasingly becoming ubiquitous in homes. They play a key role in home automation as a hub that interfaces with various appliances. Previous work has suggested that ubiquitous computing systems should be intelligible and controllable, by informing users about the system's underlying behavior and enabling users to intervene during breakdowns. In my PhD, I investigate physical intelligibility for smart speakers: the use of physical motion and interaction to provide intelligibility and control, informed by observations of users approaching and physically interacting with smart speakers.
Inference Algorithm with Falsifying Patterns for Belief Revision

Let K be a Knowledge Base (KB) and let $$\phi $$ be a query, the entailment problem $$K \models \phi $$ is crucial for solving the Belief Revision problem. The Belief Revision problem consists in incorporate new beliefs into knowledge base already established, changing as little as possible the original beliefs and maintaining consistency of the KB. A widely accepted framework for reasoning through intelligent systems is the knowledge-based system approach. The general idea is to keep the knowledge in some representative language with a well-defined connotation, particularly it we will be used prepositional logic for modelling the knowledge base and the new information. This article shows that the use of falsifying patterns for expressing clauses help to determine whether an conjunctive normal form (CNF) is inferred from another CNF, and therefore, it allows us to construct an algorithm for belief revision between CNF’s. Our algorithm applies a depth first search in order to obtain an effective process for the belief revision between the conjuntive forms K and $$\phi $$ .
Construction of a Historical Information Visualization System by Superposition of Old Map

In this research, we constructed a historical information visualization system utilizing old maps and Web-GIS. Old maps are important historical materials for knowing the history of the region, but there are few opportunities to be utilized. Therefore, we developed a system that learns the history of the region by using Web-GIS in order to utilize the old map owned by the folk museum. In this system, the old map is superposed on the Web-GIS and the historical information contents are superposed on the old map. This allows the user to learn the history of the region via the old map. Furthermore, by using a plurality of old maps, the user can confirm the status of the city after the war and the status of restoration.
A Novel Method of Dynamic Cloud Workflow Processing Based on 3D_DWFN

Cloud workflow is the combination of workflow management system and cloud platform. Compared with general workflow system, it can provide optimization of resource management and automatic scheduling of task. However, cloud workflow technology still lacks flexibility and cannot response to the changing business requirements in real time. In order to cope with the changing of business requirements, a dynamic workflow change model 3D-DWFN is proposed. The model is verified to be correct and the migration strategy is set up, thus the dynamic change algorithm is realized. It is applied to a business management system; the experiment shows that it is more effective than general workflow model.
The non-contact method for biomechanical motion analysis of Icelandic horses

A natural fluid gait of the Icelandic horses is so called tölt. It is a gait without suspension which is characterized by a symmetrical four-beat with lateral sequence of footfalls. In this paper biomechanics of the tolt gait of the Icelandic horses is investigated. To analyze parameters of the tolt an architecture of a vision system is develop and described. Results obtained during the experimental part of the study are presented and discussed.
A simple multi-feature based stereoscopic medical image retrieval system

Abstract This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.
Fuzzy Transfer Learning in Heterogeneous Space Using Takagi-Sugeno Fuzzy Models

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge (a source domain with a large amount of labeled data) to assist in completing a prediction task in a related domain (a target domain with little labeled data). Many transfer learning methods have been proposed, and especially the fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty. However, there is one issue with fuzzy transfer learning that has not yet resolved: the domain adaptation methods for regression tasks in heterogeneous space are still scarce, and the relation of features in two domains have not been explored to assist the construction of target model. In this work, we proposed a new fuzzy transfer learning method, which constructs the transformed mappings for the domain-independent and domain-dependent features, separately. The existing fuzzy rules of the source domain are transferred to the target domain through modifying the input space using the mappings, and the parameters of the mappings are optimized by the few labeled target data. The experiments on real-world datasets validate the effectiveness of the proposed method and discuss the impact of some important parameters to the performance of the constructed target model.
Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels

Optical flow estimation is one of the most important problem in community. However, current methods still can not provide reliable results in occlusion boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) as a slanted plane in 3D. Then the motion of the occluded pixels in central view slice can be optimized by the un-occluded pixels in other views. Thus the optical flow in occlusion boundary areas can be well computed. Experimental results on both synthetic and real light fields demonstrate the advantages over state-of-the-arts and the performance on 4D optical flow computation.
TrapMP: Malicious Process Detection By Utilising Program Phase Detection

Hardware and software have failed to securely man-age the sensitive elements of cryptographic algorithms in computational environment due to memory contentions. This opened new opportunities for hackers to carry out side channel attacks on a system and steal sensitive data. Existing Side-channel attack techniques show that attackers can exploit the microarchitecture and OS vulnerabilities. The recent Meltdown [1] attack for instance, using Flush+Reload technique, exploits program execution attributes such as “out-of-order execution” to break the logical isolation between the memories and processes. In this paper, we have developed a real-time detection and identification system against side-channel attacks. Unlike previous works, the proposed approach does not rely on synchronisation between the attackers and victims. This is realised by taking a course of program phase analysis, through performance counters, to extract Malicious Loop (ML). Simulation has shown that the proposed approach attained higher accuracy for up to 99% and efficient detection of Flush+Reload activities, through classification methods. Furthermore, the detection process, in native and cloud systems, unlike others, takes shorter execution time without additional costs, and the model benefits from very low overhead performance of approximately less than 1 % of the host system.
Color image-spliced localization based on quaternion principal component analysis and quaternion skewness

Splicing is a common tampering operation where the splicing area and the original image have different noise levels. In this paper, novel color spliced image localization based on quaternion principal component analysis (QPCA) and quaternion skewness is proposed. Firstly, the proposed algorithm uses QPCA to estimate the block-wise noise level. Then K-means algorithm is used to distinguish the splicing region from the original image. Next, in order to increase inconsistencies of the noise level in image blocks from different images, quaternion skewness is employed to select the overlapping patches of image blocks. In addition, morphological methods are applied to the fine classification results to reduce the false detection rate and miss detection rate along the boundary blocks between the original area and spliced area. The experimental results demonstrate the superiority of the proposed method to state-of-art schemes.
Independent Work of Pupils at the “The World Around US” Lessons

The article discusses the organization of independent testing work of younger schoolchildren in the study of the subject “The World Around Us”. The authors discuss the implementation of the possibility of using different types of independent learning activities to clarify and systematize knowledge, their application in non-standard situations, improve universal learning activities, as well as the development of self-monitoring and self-assessment of students.
A Study on Collaborative Filtering for Hybrid Recommender System for Service Discovery

Most recommender techniques use supportive Filtering or Content-based strategies to anticipate new points of curiosity for a user. although each strategies have their own benefits, severally they crash to produce sensible recommendations in a number of things. Incorporating components from each strategies, a cross recommender system will overcome these shortcomings. Specific tasks, data wants, and object domains signify special dilemmas for recommenders, and model and evaluation of recommenders should be done supported the consumer tasks to be supported. Powerful deployments must start with careful evaluation of potential customers and their goals. supported this evaluation, system makers have numerous possibilities for the choice of algorithmic principle and for its embedding within the shut consumer expertise. That report examines a large kind of the solutions on the market and their implications, going to offer each practicioners Link in Nursingd researchers having an release to the necessary problems underlying recommenders and recent most readily useful techniques for addressing these issues. supportive Filtering can be a process to counsel one factor selected by client, similar inclination supported likeness between users. Hybrid supportive Filtering gift suggestions a possibility presenting precise proposals by considering the consumer preferences in multiple opinions and a many methods are projected for improving the accuracy of those frameworks
Application of Fuzzy C-Means Algorithm in Complex Background Image Segmentation of Forensic Science

In the field of forensic science, image segmentation is required as a basic and significant stage in forensic image analysis. It is very important to segment the stamp impression image with a complex background precisely. This paper puts forward a feasible and efficient approach for complex background stamp impression image segmentation based on Fuzzy C-Means (FCM) algorithm. The fuzzy feature of forensic image can be handled efficiently using Fuzzy C-Means (FCM) algorithm in the forensic science field. The results of the experiments demonstrate the validity and accuracy of Fuzzy C-Means (FCM) algorithm.
In-Memory Big Graph: A Future Research Agenda

With the growth of the inter-connectivity of the world, Big Graph has become a popular emerging technology. For instance, social media (Facebook, Twitter). Prominent examples of Big Graph include social networks, biological network, graph mining, big knowledge graph, big web graphs and scholarly citation networks. A Big Graph consists of millions of nodes and trillion of edges. Big Graphs are growing exponentially and requires large computing machinery. Big Graph is posing many issues such as storage, scalability, processing and many more. This paper gives a brief overview of in-memory Big Graph Systems and some key challenges. Also, sheds some light on future research agendas of in-memory systems.
Candidate Label-aware Similarity Graph for Partial Label Data

In partial label learning, the label of instance is represented by a label set, among which only one is the true label. The existing partial label learning algorithm only use features of instances to measure the similarity, and lacks of utilization of information hidden in the label set. In this paper, we proposes a candidate label-aware similarity graph constructing method for partial label data which effectively combines candidate label information using Jaccard distance and linear reconstruction to measure the similarity between instances. First, we calculate the similarity between instances and candidate label-sets. Then, the similarity graph is constructed by combining the instance similarity and the label-set similarity. Extensive experiments on artificial as well as real-world PL data sets show that our method can measure the similarity of partial-label data better and improve the performance of graph-based partial label learning algorithm obviously.
An Inception Toward Better Classification Technique for Big Data

The hasty emerging technology in the field of information technology (IT) during past few years is “Big Data”. One of the valuable tasks in a wide range of domains handling cumbersome database is classification. Correspondingly, this article presents the review of 10 research papers suggesting various techniques adopted for the productive big data classification, like Support Vector Machine (SVM) classifier, k-Nearest Neighbor (KNN), decision tree, association rule-based classifier, and fuzzy classifier. Subsequently, an effective technique must be designed to outperform present techniques for better management of big data. Additionally, an elaborative analysis is made by concerning the implementation tool and the adopted framework for classification of big data. Hereafter, the research issues and research gaps of abovementioned big data classification techniques are presented for aggrandizing the researchers for the better improvement of big data management.
A Comprehensive Methodology to Find Closed Mathematical Models from Experimental Data

A method which allows us to find a polynomial model of an unknown function from a set of tuples with n independent variables and m tuples is presented. A plausible model of an explicit approximation is found. The number of monomials of the model is determined by a previously trained neural network (NNt). The degrees of the monomials are bounded from the Universal Approximation Theorem yielding a reduced polynomial form. The coefficients of the model are found by exploring the space of possible monomials with a genetic algorithm (GA). The polynomial defined by every training pair is approximated by the Ascent Algorithm which yields the coefficients of best fit with the L∞ norm. The L2 error corresponding to these coefficients is calculated and becomes the fitness function of the GA. A detailed example for a well known experimental data set is presented. It is shown that the model derived from the method yields a classification error of <7% for the cross-validation data set. A detailed description of the NNt is included. The approximation to 46 datasets tackled with our method is discussed. In all these cases the approximation accuracy was better than 94%. The tool described herein yields general models with explanatory characteristics not found in other methods.
A Survey on Attack Cases Exploiting Computer Architectural Vulnerabilities

The security attack on the computer was mainly caused by software attack using the vulnerability of the operating system or application and physical attacks such as power measurement, electromagnetic measurement and encryption key extraction by noise measurement. However, recent developments of Meltdown and Specter attacks are actively investigating structural weaknesses at the microarchitecture level. This paper introduces the Cache timing attack technique for the analogy of data that is stored in cache and the vulnerability and attack type at the architecture level which is well-known in the past such as Meltdown and Spectre, NetSpectre, etc. Furthermore, this paper also introduces Rowhammer Bug other than CPU which is the attack technique for DRAMs and the DRAM attack caused by Rowhammer Bug which is the attack technique about DRAM. This paper provides information about vulnerability attack cases and technical classifications arising at the architecture level and also introduces recent microarchitecture trend of security threat.
Information system design using labor productivity measurement for construction

Abstract Labor utilization in construction project is the most critical because it costs 30-50% of the total cost. In many projects, the labors e.g craftman are hired outside the company. Contractors and owners need sufficient information related to those labors whom they will work with. It is necessary for clients understanding each labor productivity performance in order to achieve the project quality. This study aims to provide the infomation needed by designing an information system that can be easily accessed. This is also to support clients for decision making in terms of teamwork project involved. The level of productivity is measured by Labor Utilization Rate (LUR) using three variables: effective working time, contributory working time, and ineffective working time. Software Development Life Cycle (SDLC) is applied in designing the information system. The finding showed that information system is properly implemented to assist clients in determining workers using productivity calculation. The system can inform the recommended workers by calculating and sorting their level of productivity that support client’s decision making towards construction workers.
MeshTrust: A CDN-Centric Trust Model for Reputation Management on Video Traffic

Video applications today are more often deploying content delivery networks (CDNs) for content delivery. However, by decoupling the owner of the content and the organization serving it, CDNs could be abused by attackers to commit network crimes. Traditional flow-level measurements for generating reputation of IPs and domain names for video applications are insufficient. In this paper, we present MeshTrust, a novel approach that assessing reputation of service providers on video traffic automatically. We tackle the challenge from two aspects: the multi-tenancy structure representation and CDN-centric trust model. First, by mining behavioral and semantic characteristics, a Mesh Graph consisting of video websites, CDN nodes and their relations is constructed. Second, we introduce a novel CDN-centric trust model which transforms Mesh Graph into Trust Graph based on extended network embedding methods. Based on the labeled nodes in Trust Graph, a reputation score can be easily calculated and applied to real-time reputation management on video traffic. Our experiments show that MeshTrust can differentiate normal and illegal video websites with accuracy approximately 95% in a real cloud environment.
A High-Precision Moving Target Tracking Algorithm Based on UKF

Abstract Since Kalman Filter is not applicable to non-linear systems, a moving target tracking algorithm based on Untracked Kalman Filter (UKF) is proposed. The algorithm performance is analyzed by algorithm principle analysis, building mathematical model of the system, and simulation GUI design. Simulation results show that the algorithm has an expected tracking precision and high reliability in 6-dimensional CA mode, and has a good application prospect.
Exploring Place-based, Physical Social Networks

Through this article, we introduce the novel concept of place-based social networks, as a way to enable effective, personal story-sharing for communities. We present our explorations of utilizing Starbucks at an American University to establish such a network for the student community. We show how anonymity between the community members, combined with the shared, spatiotemporal grounding provided by the place (Starbucks,) leads to prosocial behavior among the students.