Reviewerly/exHarmony huggingface dataset:
Application of Element Decomposing Method for Solving Traveling Salesman Problems

The objective of this research study was to solve the traveling salesman problem (TSP) in order to provide a traveling sequence for the minimum total traveling time. This paper highlights the significance of creating and developing the element decomposition method (EDCM) as a part of the finite element method for solving TSP. There are two phase of research methodology. The first phase involves simplex method. The second phase is about creating and developing the algorithm through the application of the EDCM. The results obtained using the algorithm employing the EDCM were then compared with branch and bound method (B&B) and ant colony optimization (ACO) in terms of accuracy and time consumption, Regarding the problem, it can be solved with the number of cities, that is 6 to 343. The B&B method has the capability of resolving problems with the limitation of 22 stations. However, between ACO and EDCM, which can resolve problems for 343 stations. It found that the EDCM provides better value than ACO with an average of 1.31% and the time consumption of 55.00%.
Object Calisthenics, Coding Styles, and Refactoring

This chapter discusses how code is written and consists of three parts: object calisthenics, coding styles, and refactoring. This chapter will not concern itself with the design aspects of problem solving. It will purely look at your code's readability and how easy it is for other software developers to understand your code. Harold Abelson, author of Structure and Interpretation of Computer Programs, famously said, "Programs must be written for people to read, and only incidentally for machines to execute." And I must agree. Readability is a very important aspect of writing code. Writing code should always be an exercise in elegance, but this is easier said than done. Fortunately, there are some solid guidelines set out to help you achieve that goal.
Block Chain based Grey Hole Detection Q Learning based CDS Environment in Cloud - MANET

Cloud MANET is a latest technique in which individual smart devices joins the Cloud MANET and can communicate in a distributed fashion. This is advantageous, as the Smart device does not need any infrastructural establishments. These nodes communicate together. Several MANETS formed in manner can be connected to cloud and can avail cloud services in real time. In this paper we attempt to establish a virtual backbone of Cloud MANET nodes using the concept Connected Dominating Sets, we also introduced a lightweight security scheme in order to avoid blackhole and greyhole attacks. The algorithm uses Q learning technique for learning about the capacity of nodes and establishes the CDS in Cloud MANET. The entire set up is established with public key infrastructure to emulate block chain based security. The concept of smart contract is used to ensure that every transaction is recorded and any compromised nodes in the CDS is discovered and eliminated.
Intellectual property from a labour law perspective: the transfer of authors' rights in labour relationships

This article analyses Spanish intellectual property law to establish whether formalised employment contracts are associated with the transfer to employers of the economic rights to works created by employees. This analysis is limited to the legal treatment of employee-created literary, artistic and scientific works, which are subject to authors' rights and are protected by intellectual property law. The study does not include inventions created in a labour relationship, which are regulated by legislation on patents and brands, and are thus dealt with differently according to industrial property rights.
Blind Image Quality Assessment Based on Active Learning

Image quality assessment (IQA) is a task of measuring the perceptual quality of a given image and it is a hot study direction in the region of video and image processing and blind image quality assessment (BIQA) model is one of the most utilized full models. Among the existing BIQA models, the CORNIA model has achieved good performance. In the CORNIA model, Ye et. al. utilized d k-means clustering to cluster the pre-processed image patch s, and utilized d the clustering center as a dictionary. However, experimental observations have shown that when the dictionary size is small, the Superiority of CORNIA decreases rapidly. To address this, we introduce active learning strategies into unsupervised character learning, proposing an active character learning (AFL) framework, and apply it to BIQA model.
근거이론분석을 통한 일과 삶의 균형에 관한 연구 : 관광산업 종사원을 대상으로

This study aims a analyze the process on the work-life balance was studied through grounded theory analysis for employees engaged in the tourism industry. For this, it interviewed a total of 20 hotel workers, casino workers, and restaurant workers and through open coding, the major concepts of work-life balance were ultimately drawn out into 163 concepts, 42 subcategories, and 16 categories. Also, a paradigm model that is the analytical frame of axial coding was applied and each category that was drawn from the data was structuralized using central phenomena, causal factors, contextual factors, moderating factors, interaction strategies, and results and their connecting relationships were analyzed. As a result, a paradigm model of work-life balance was presented and based on the paradigm model, attributes and dimensions of each category were classified. Finally, through the derivation and elaboration of the core categories, the core category for work-life balance in this study was summarized as ''Efforts to realize each goal based on the balance of family life''. An understanding of work-life balance and efforts to balance work-life through work-family balance, work-leisure balance, and work-growth balance were studied. Through this study, the purpose of this study is to contribute to the understanding of the practical support system of employees, and through this, the purpose of presenting data so that the tourism industry can guide the work-life balance support system in a desirable direction.
Research Indexing Algorithms

This article analyzes the indexing algorithms used in advanced search engines, which are currently being developed and have a wide range of features. B-tree indexing method, reverse indexing method, data (proper) indexing methods in direct indexing methods. The results of the analyzes can be used to index the database of electronic documents when creating an advanced search engine.
Real-Time Detection of Multiple Targets from a Moving 360$$^{\circ }$$ Panoramic Imager in the Wild

Our goal is to develop embedded and mobile vision applications leveraging state-of-the-art visual sensors and efficient neural network architectures deployed on emerging neural computing engines for smart monitoring and inspection purposes. In this paper, we present 360 $$^{\circ }$$ vision system onboard an automobile or UAV platform for large field-of-view and real-time detection of multiple challenging objects. The targeted objects include flag as a deformable object; UAV as a tiny, flying object which changes its scales and positions rapidly; and grouped objects containing piled sandbags as deformable objects in a group themselves, flag and stop sign to form a scene representing an artificial fake checkpoint. Barrel distortions owing to the 360 $$^{\circ }$$ optics make the detection task even more challenging. A light-weight neural network model based on MobileNets architecture is transfer learned for detection of the custom objects with very limited training data. In method 1, we generated a dataset of perspective planar images via a virtual camera model which projects a patch on the hemisphere to a 2D plane. In method 2, the panomorph images are directly used without projection. Real-time detection of the objects in 360 $$^{\circ }$$ video is realized by feeding live streamed frames captured by the full hemispheric (180 $$^{\circ }$$ $$\times $$ 360 $$^{\circ }$$ ) field-of-view ImmerVision Enables panomorph lens to the trained MobileNets model. We found that with only few training data which is far less than 10 times of Vapnik–Chervonenkis dimension of the model, the MobileNets model achieves a detection rate of 80–90% for test data having a similar distribution as the training data. However, the model performance dropped drastically when it was put in action in the wild for unknown data in which both weather and lighting conditions were different. The generalization capability of the model can be improved by training with more data. The contribution of this work is a 360 $$^{\circ }$$ vision hardware and software system for real-time detection of challenging objects. This system could be configured for very low-power embedded applications by running inferences via a neural computing engine such as Intel Movidius NSC2 or HiSilicon Kirin 970.
Fuzzy Message Detection.

Many privacy-preserving protocols employ a primitive that allows a sender to flag a message to a recipient's public key, such that only the recipient (who possesses the corresponding secret key) can detect that the message is intended for their use. Examples of such protocols include anonymous messaging, privacy-preserving payments, and anonymous tracing. A limitation of the existing techniques is that recipients cannot easily outsource the detection of messages to a remote server, without revealing to the server the exact set of matching messages. In this work we propose a new class of cryptographic primitives called \em fuzzy message detection schemes. These schemes allow a recipient to derive a specialized message detection key that can identify correct messages, while also incorrectly identifying non-matching messages with a specific and chosen false positive rate p. This allows recipients to outsource detection work to an untrustworthy server, without revealing precisely which messages belong to the receiver. We show how to construct these schemes under a variety of assumptions; describe several applications of the new technique; and show that our schemes are efficient enough to use in real applications.
A Comparison of Multi-View Learning Strategies for Satellite Image-Based Real Estate Appraisal

In the house credit process, banks and lenders rely on a fast and accurate estimation of a real estate price to determine the maximum loan value. Real estate appraisal is often based on relational data, capturing the hard facts of the property. Yet, models benefit strongly from including image data, capturing additional soft factors. The combination of the different data types requires a multi-view learning method. Therefore, the question arises which strengths and weaknesses different multi-view learning strategies have. In our study, we test multi-kernel learning, multi-view concatenation and multi-view neural networks on real estate data and satellite images from Asheville, NC. Our results suggest that multi-view learning increases the predictive performance up to 13% in MAE. Multi-view neural networks perform best, however result in intransparent black-box models. For users seeking interpretability, hybrid multi-view neural networks or a boosting strategy are a suitable alternative.
Research guides: LibrarySearch Tips: Advanced Search

A guide to the University of Toronto Libraries' LibrarySearch platform How to create an effective and comprehensive search
Growing outrage, calls for accountability after state auditor's hack puts 1.4 million people at risk

Anger and calls for accountability are growing louder following the news that more than 1.4 million people in Washington who filed unemployment claims may have had their personal information stolen in a data breach involving the state auditor's office.
Statistical Learning and Language: English RCs and Number Agreement

This study investigates whether sensitivity to nonadjacent and adjacent dependency rules in an artificial grammar learning paradigm (i.e., statistical learning ability) predicts L2 learners’ performance in comprehending two different types of English structures. A total of 110 L1-Korean participants completed the four tasks: two artificial grammar learning tasks, involving nonadjacent and adjacent dependency rules, and two self-paced English reading tasks, involving English relative clauses and adjacent number agreement in noun phrases. The results show that their statistical learning scores predicted the L2 learners’ performance only in processing object relative clauses: the ability to discover the nonadjacent pattern in artificial grammar learning is correlated with comprehension accuracy on object relative clauses, but not on adjacent number agreement. The findings of this study indicate that the statistical learning ability to discover a particular pattern (a nonadjacent dependency) can be considered a predictor in comprehending long-distance dependency patterns in the L2. These also illuminate the role of implicit learning processes of language learning, which will have pedagogical implications for second language instruction as well.
High-Quality Facial Expression Animation Synthesis System Based on Virtual Reality

In order to realize a virtual teacher online visual teaching system based on virtual reality, a high-quality facial expression animation synthesis technology for virtual reality is proposed. First use the depth camera to track the expression of the performer, extract the facial feature point data and transmit it to the local. Then for the specific organs with low matching degree between the model and the performer’s facial feature points and prone to errors, the method of establishing local coordinate system for the feature points of the specific organs of the model is used to optimize the feature point data of the model. Finally, the improved Laplacian deformation algorithm is used to calculate the facial coordinates of the virtual avatar, and the face model is driven to simulate the same expression as the performer, and the accuracy and efficiency of the algorithm are optimized. Experimental results show that the algorithm can achieve high-quality and real-time transfer of captured facial expressions to any virtual facial model, and meet the needs of practical applications.
The Effect of Prediction Strategy toward Students’ Reading Comprehension for the Tenth Grade Students at SMAN 8 Muaro Jambi

This research was aimed to find out whether there was any significant effect of prediction strategy toward students’ reading comprehension for the tenth grade students at SMAN 8 Muaro Jambi. The research methodology of this study was true-experimental research which involved experimental class and control class. The total population of this research was the tenth grade students of SMAN 8 Muaro Jambi, which consisted of 225 students. The researcher used 2 classes as the sample of this study. These 2 classes were divided into experimental and control class. There were 28 students at experimental class (X Science 1) and 29 students at control class (X Science 2). In collecting the data, the researcher gave pre-test, treatment, and post-test to both classes. The formula used to analyze the data was t-test. The researcher found that the pre-test mean of experimental class was 60,35 and post-test was 83,57. Meanwhile, the pre-test mean of control class was 55,51 and post-test was 68,96. Furthermore, the researcher discovered that t observation was 11,63, whereas the t table was 2,004 for a = 0,025. This data showed that t observation was higher than the t table (11,63>2,004), so, was accepted and was rejected. As a result, there was any significant effect of prediction strategy toward students’ reading comprehension for the tenth grade students at SMAN 8 Muaro Jambi. Key Words: Prediction Strategy, Reading, Students’ Reading Comprehension.
An Experimental Study on 3D Person Localization in Traffic Scenes.

This paper presents an experimental study on 3D person localization (i.e. pedestrians, cyclists) in traffic scenes, using monocular vision and LiDAR data. We first analyze the detection performance of two top-ranking methods (PointPillars and AVOD) on the KITTI benchmark, with respect to varying Intersection over Union (IoU) settings and the underlying parameters of 3D bounding box location, extent and orientation. Given that the KITTI dataset contains relatively few 3D person instances, we also consider the new EuroCity Persons 2.5D (ECP2.5D) dataset, which is one order of magnitude larger. We perform domain transfer experiments between the KITTI and ECP2.5D datasets, to examine how these datasets generalize with respect to each other.
Cluster Analysis of Vehicle Measurement Data for Improved Understanding of Behavior in Urban and Highway Environments.

The comprehension of the behavior of objects in the environment of an automated vehicle is essential information to be extracted out of traffic measurement data. It can be made accessible by applying a data description process consisting of a cluster analysis and subsequent classification step. In this paper, two different algorithms, TRACLUS and Self-Organizing Maps are utilized for a clustering of laser scanner measurement data, recorded by experimental vehicles in real traffic. The results are evaluated and discussed. A remaining open issue of a cluster analysis is the necessity for a classification of clusters, which is missing or solved through manual solutions in related work. We address the matter with a novel approach for an automated classification of clusters using reference trajectories, which is successfully validated for a first exemplary maneuver on highway measurement data.
Evaluating the robustness of source code plagiarism detection tools to pervasive plagiarism-hiding modifications

Source code plagiarism is a common occurrence in undergraduate computer science education. In order to identify such cases, many source code plagiarism detection tools have been proposed. A source code plagiarism detection tool evaluates pairs of assignment submissions to detect indications of plagiarism. However, a plagiarising student will commonly apply plagiarism-hiding modifications to source code in an attempt to evade detection. Subsequently, prior work has implied that currently available source code plagiarism detection tools are not robust to the application of pervasive plagiarism-hiding modifications. In this article, 11 source code plagiarism detection tools are evaluated for robustness against plagiarism-hiding modifications. The tools are evaluated with data sets of simulated undergraduate plagiarism, constructed with source code modifications representative of undergraduate students. The results of the performed evaluations indicate that currently available source code plagiarism detection tools are not robust against modifications which apply fine-grained transformations to the source code structure. Of the evaluated tools, JPlag and Plaggie demonstrates the greatest robustness to different types of plagiarism-hiding modifications. However, the results also indicate that graph-based tools (specifically those that compare programs as program dependence graphs) show potentially greater robustness to pervasive plagiarism-hiding modifications.
Augmenting Leakage Detection Using Bootstrapping

Side-channel leakage detection methods based on statistical tests, such as t-test or $$\chi ^2$$ -test, provide a high confidence in the presence of leakage with a large number of traces. However, practical limitations on testing time and equipment may set an upper-bound on the number of traces available, turning the number of traces into a limiting factor in side-channel leakage detection. We describe a statistical technique, based on statistical bootstrapping, that significantly improves the effectiveness of leakage detection using a limited set of traces. Bootstrapping generates additional sample sets from an initial set by assuming that it is representative of the entire population. The additional sample sets are then used to conduct additional leakage detection tests, and we show how to combine the results of these tests. The proposed technique, applied to side-channel leakage detection, can significantly reduce the number of traces required to detect leakage by one, or more orders of magnitude. Furthermore, for an existing measured sample set, the method can significantly increase the confidence of existing leakage hypotheses over a traditional (non-bootstrap) leakage detection test. This paper introduces the bootstrapping technique for leakage detection, applies it to three practical cases, and describes techniques for its efficient computation.
Applicera maskininlärning på vägtrafikdata för att klassificera gatutyper i Stockholm

In this thesis, two different machine learning models have been applied on road traffic data from two large cities in Sweden: Gothenburg and Stockholm. The models have been evaluated with regard to ...
A User Privacy-Preserving Algorithm in Live Streaming Cloud Platforms

Due to the variety and rich features of live content, the live cloud platform has been widely used. However, they also bring the potential threat and huge vulnerability to the users’ privacy. At present, the security mechanism is simple in almost all live streaming cloud platforms. They generally use traditional security method such as Access Control List (ACL) to prevent unauthorized users from watching live streaming. In addition, they mainly consider the security of the system itself. These security measures can only guarantee the correct operation of the platform, and cannot guarantee that the privacy information of users in the platform is not leaked. Therefore, this paper proposes a privacy-preserving algorithm to protect private data of all participants in a live streaming cloud platform, including their follow relationships, live message, etc. According to semi-trusted live streaming cloud platforms, the proposed algorithm employs key exchange over elliptic curve and advanced encryption standard (AES) to preserve user privacy. A blind matching algorithm based on the encrypted tag is proposed to match the live message and intended recipients. Finally, the security of our algorithm is analyzed in depth and the results show the proposed algorithm efficiently preserves user privacy.
DATA CENTRIC ROLE BASED ACCESS CONTROL AUTHORIZATION MODEL : AN EFFICIENT WAY TO IMPROVE SECURITY IN INTERCLOUD SCENARIO

Cloud computing provides various services depending upon user requirements i.e. platform, software and Infrastructure. Cloud computing breaks the limitation or borders in resources sharing. But at the same time increases security issues as data is residing outside control of data owner. Identity based proxy re-encryption is used while uploading data in the cloud. As data may move from one cloud to another, so rule based data centric solution is proposed. For preventing unauthorized access of data Role Based Authorization access control are developed. The solution is developed using Open stack. Virtual machine instances are created for different sets. The execution time is compared.