Reviewerly/exHarmony huggingface dataset:
Effectiveness of School-based Nutrition Program on Preferences and Willingness to Consume Fruits and Vegetables Among Third Graders (FS16-07-19)

The purpose of this research was to evaluate the effect of Body Quest: Food of the Warrior nutrition curriculum on preferences and willingness to consume fruits and vegetables among third graders. After collecting signed parental and photo consent forms, 326 third grade students received the intervention. Students received the Body Quest education program and participated in a Veggie Tasting of six vegetables (carrots, broccoli, cauliflower, peppers, spinach, and tomatoes). Students were asked if they liked the vegetable before and after tasting it, if they would eat the vegetable in the future, and if they would ask their parents to buy the vegetable for them to eat at home. Assessments of data were completed in four periods, weeks 1, 7, 12, and 17. Alpha was set at 0.05. Approximately half of the students were male (n = 163; 50.2%). The largest percentage of students were white (n = 171; 53.1%). Black students comprised the next largest group (n = 112; 34.8%), and Hispanic and American Indian students comprised the remaining 12.1% (n = 39) students. Generalized estimating equations analysis revealed that, over time, statistically significant changes in preference for cauliflower and spinach occurred with students decreasing their liking for cauliflower (P = .04) and increasing their liking for spinach (P = .01). A statistically significant change in students’ interest in eating cauliflower (P = .01) and tomatoes (P = .03) in the future occurred with students initially indicating an increased willingness to eat the two vegetables, but ultimately showing a decreased willingness to eat either vegetable. A similar pattern of willingness to ask their parents to buy spinach (P = .04) occurred with students initially showing an increased willingness to ask their parents to buy spinach but ultimately indicating a decrease in their willingness to make that request. This research suggests that Body Quest was effective in improving liking and willingness to try and request some vegetables. Because liking and willingness to request decreased in cauliflower and spinach, respectively more research is warranted to identify the optimal length of food exposure to change preference among this population. Supplemental Nutrition Assistance Program—Education (SNAP-Ed).
Trajectory Generation for UAVs in Unknown Environments with Extreme Wind Disturbances

The widespread use of unmanned aerial vehicles (UAVs) by the military, commercial companies, and academia continues to push research for autonomous vehicle navigation, particularly in varying environmental conditions and beyond-line-of-sight (BLOS) applications. This article addresses trajectory generation for UAVs operating in extreme environments where the wind disturbances may exceed the vehicle's closed-loop stability bounds. To do this, a controller is developed that has two modes of operation: (1) normal mode, and (2) drift mode. In the normal mode the vehicle's thrust and sensor limitations are not exceeded by environmental conditions, whereas in the drift mode they are. In the drift mode, a drift frame that moves with the prevailing wind is established in which the vehicle maintains control authority to generate and track trajectories. The vehicle maintains control authority by relaxing the inertial frame trajectory tracking requirement and re-planning the trajectory in the drift frame. Guarantees are established to ensure tracking of the trajectory, collision avoidance, and respecting the vehicle thrust and sensor limitations. Simulation results demonstrate the algorithm properties through two scenarios. First, the performance of two quadrotors is compared where one utilizes the drift mode and the other does not. Second, multiple vehicles navigate through two narrow openings between protected and windy environments to demonstrate on-board updates to navigation parameters based on environmental conditions.
Semi-Supervised Semantic Matching

Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset.
Multiple-Identity Image Attacks Against Face-based Identity Verification

Facial verification systems are vulnerable to poisoning attacks that make use of multiple-identity images (MIIs)---face images stored in a database that resemble multiple persons, such that novel images of any of the constituent persons are verified as matching the identity of the MII. Research on this mode of attack has focused on defence by detection, with no explanation as to why the vulnerability exists. New quantitative results are presented that support an explanation in terms of the geometry of the representations spaces used by the verification systems. In the spherical geometry of those spaces, the angular distance distributions of matching and non-matching pairs of face representations are only modestly separated, approximately centred at 90 and 40-60 degrees, respectively. This is sufficient for open-set verification on normal data but provides an opportunity for MII attacks. Our analysis considers ideal MII algorithms, demonstrating that, if realisable, they would deliver faces roughly 45 degrees from their constituent faces, thus classed as matching them. We study the performance of three methods for MII generation---gallery search, image space morphing, and representation space inversion---and show that the latter two realise the ideal well enough to produce effective attacks, while the former could succeed but only with an implausibly large gallery to search. Gallery search and inversion MIIs depend on having access to a facial comparator, for optimisation, but our results show that these attacks can still be effective when attacking disparate comparators, thus securing a deployed comparator is an insufficient defence.
Accelerating Partial Evaluation in Distributed SPARQL Query Evaluation

Partial evaluation has recently been used for processing SPARQL queries over a large resource description framework (RDF) graph in a distributed environment. However, the previous approach is inefficient when dealing with complex queries. In this study, we further improve the "partial evaluation and assembly" framework for answering SPARQL queries over a distributed RDF graph, while providing performance guarantees. Our key idea is to explore the intrinsic structural characteristics of partial matches to filter out irrelevant partial results, while providing performance guarantees on a network trace (data shipment) or the computational cost (response time). We also propose an efficient assembly algorithm to utilize the characteristics of partial matches to merge them and form final results. To improve the efficiency of finding partial matches further, we propose an optimization that communicates variables' candidates among sites to avoid redundant computations. In addition, although our approach is partitioning-tolerant, different partitioning strategies result in different performances, and we evaluate different partitioning strategies for our approach. Experiments over both real and synthetic RDF datasets confirm the superiority of our approach.
CBC Approach for Evaluating Potential SaaS on the Cloud

The cloud computing is evolving as a key computing platform for sharing resources like infrastructure, platform, software etc. This has proven to be an essential requirement for extending many existing applications. Software as a service (SaaS) is referred as on-demand software supplied by service providers in which software and associated data are hosted on the cloud and it can be accessed by service users using a thin client via a web browser. SaaS is commonly utilized and it provides many benefits to service users. To realize these benefits, it is essential to evaluate potential quality of SaaS, not only to the service users but also to the service providers. They have to evaluate their services against requirements of service users. The existing evaluation models are focusing only on quality attributes of SaaS. In this paper, a new evaluation model is proposed based on the data mining technique of Constraint Based Clustering (CBC). The proposed model gives emphasis on potential requirements of service users along with quality attributes of services.
iGen: Dynamic Interaction Inference for Configurable Software

To develop, analyze, and evolve today's highly configurable software systems, developers need deep knowledge of a system's configuration options, e.g., how options need to be set to reach certain locations, what configurations to use for testing, etc. Today, acquiring this detailed information requires manual effort that is difficult, expensive, and error prone. In this paper, we propose iGen, a novel, lightweight dynamic analysis technique that automatically discovers a program's \emph{interactions}---expressive logical formulae that give developers rich and detailed information about how a system's configuration option settings map to particular code coverage. iGen employs an iterative algorithm that runs a system under a small set of configurations, capturing coverage data; processes the coverage data to infer potential interactions; and then generates new configurations to further refine interactions in the next iteration. We evaluated iGen on 29 programs spanning five languages; the breadth of this study would be unachievable using prior interaction inference tools. Our results show that iGen finds precise interactions based on a very small fraction of the number of possible configurations. Moreover, iGen's results confirm several earlier hypotheses about typical interaction distributions and structures.
User Authentication by Keystroke Dynamics Using Machine Learning Algorithms

Due to the expanding vulnerabilities in cyber forensics, security alone is not sufficient to forestall a rupture; however, cyber security is additionally required to anticipate future assaults or to distinguish the potential aggressor. Keystroke Dynamics has high use in cyber intelligence. The paper examines the helpfulness of keystroke dynamics to build up the individual personality. Three schemes are proposed for recognizing an individual while typing on keyboard. Lib SVM and binary SVM are proposed and their performance are shown. Lib SVM is showing a better performance when comparing with binary SVM. As the number of samples are increased it shows an increase in the accuracy. Pair wise user coupling technique is proposed. The proposed procedures are approved by utilizing keystroke information. In any case, these systems could similarly well be connected to other examples of pattern identification problems. This system is applicable in highly confidential areas like military.
Reputation Systems -- Fair allocation of points to the editors in the collaborative community

In this paper we are trying to determine a scheme for the fair allocation of points to the contributors of the collaborative community. The major problem of fair allocation of points among the contributors is that we have to analyze the improvement in the versions of an article. Lets say there is a contribution of major change in content which is relevant vs the contribution of adding a single comma. Every contributor cannot be given the same points in such a case. There are many ways which can be used like number of changes in a new version. That might seem relevant but it becomes irrelevant in terms of correct content contribution and other significant changes. There is no AI system too which can detect such a change and award the points accordingly. So this problem of allocation of points to the contributors is presented by an algorithm with a theoretical proof. It relies on the interactive interaction of the users in the system which is trivial in case of big system design economies.
An IoT Based Framework For Activity Recognition Using Deep Learning Technique

Activity recognition is the ability to identify and recognize the action or goals of the agent. The agent can be any object or entity that performs action that has end goals. The agents can be a single agent performing the action or group of agents performing the actions or having some interaction. Human activity recognition has gained popularity due to its demands in many practical applications such as entertainment, healthcare, simulations and surveillance systems. Vision based activity recognition is gaining advantage as it does not require any human intervention or physical contact with humans. Moreover, there are set of cameras that are networked with the intention to track and recognize the activities of the agent. Traditional applications that were required to track or recognize human activities made use of wearable devices. However, such applications require physical contact of the person. To overcome such challenges, vision based activity recognition system can be used, which uses a camera to record the video and a processor that performs the task of recognition. The work is implemented in two stages. In the first stage, an approach for the Implementation of Activity recognition is proposed using background subtraction of images, followed by 3D- Convolutional Neural Networks. The impact of using Background subtraction prior to 3D-Convolutional Neural Networks has been reported. In the second stage, the work is further extended and implemented on Raspberry Pi, that can be used to record a stream of video, followed by recognizing the activity that was involved in the video. Thus, a proof-of-concept for activity recognition using small, IoT based device, is provided, which can enhance the system and extend its applications in various forms like, increase in portability, networking, and other capabilities of the device.
Effects of Economic Reform Policy and Privatization on Unemployment in Arab Countries

With the beginning of the 1980s, Economic conditions have worsened  as a result of the accumulation of external indebtedness, and with the loss of economic stability,and there are two options in front of the south states either continue in their policy or applying the installation and structural adjustment (economic reform) programme.The economic reform programme have been associated with impose the privatization process that shift the public sector to the private sector and what the consequent them in the short time and during the transitional phase from reduce spending procedures on the important social sectors before the health, education, transportations, fuel, and consumer goods.The Arabic countries had adopted with varying proportions the privatization process resulting of it decline the real income and lay off workers in the corporations with continuing deficit in government budgets and instability of the local GDP, and increase indebtedness and service the public debt for the borrowing Arabic countries, and declining the growth of labor force in economic sector and employment shrinking for the lack of chock the business environment for implementation in the private sector which increase of unemployment proportion.The rate of unemployment amount (146%) for 2014 in the Arabic countries and it is the highest among the countries of the world that amount (6.4%) in the same year and thereby increasing the unemployment proportion that is 60% of the Arabic countries population whom they are under twenty-five years old from the unemployment and the unemployment are increased among the educators youth, the higher education, add to spread among the women and the job seekers for the first time. The future estimates show that unemployment will be till 80 million in (2025).This research includes series of recommendations, notably:1- Improve the Arabic economic performance.2- States concerned to perform in increasing expenditures on the social services before the health, the educational and social security and adoption of flexibility in the field of employment.
Predicting kills in Game of Thrones using network properties

TV series such as HBO's Game of Thrones have seen a high number of dedicated followers, mostly due to the dramatic murders of the most important characters. In our work, we try to predict killer and victim pairs using data about previous kills and additional metadata. We construct a network where two character nodes are linked if one killed the other and use a link prediction framework to evaluate different techniques for kill predictions. Lastly, we compute various network properties on a social network of characters and use them as features in conjunction with classic data mining techniques. Due to the small size of the dataset and the somewhat random kill distribution, we cannot predict much with standard indices alone, although using them in conjunction with additional rules based on degrees works surprisingly well. The features we compute on the social network help the classic machine learning approaches, but do not yield very accurate predictions. The best results overall are achieved using indices that use simple degree information, the best of which gives us the Area Under the ROC Curve of 0.875.
Ashon Shakya Listings

Listings of Ashon Shakya which is located in 6, Sinchahity, Sinchahity, Lalitpur, Lalitpur Nepal
Library Services: Get Our Apps: Apps from the Library

Lynda.com LinkedIn Learning O'reilly Safari Pressreader LinkedIn Learning LinkedIn Learning O'Reilly Safari Pressreader
THE ERA OF CLOUD COMPUTING: A RECENT SURVEY.

05May 2019 THE ERA OF CLOUD COMPUTING: A RECENT SURVEY. Apurva Saxena And Pratima gautam. Research Scholar (Computer Science Engineering), Rabindranath Tagore University, Bhopal, India. Dean of Computer Science and Application, Rabindranath Tagore University, Bhopal, India.
High Accuracy Classification of White Blood Cells using TSLDA Classifier and Covariance Features

creating automated processes in different areas of medical science with the application of engineering tools is a highly growing field over recent decades. In this context, many medical image processing and analyzing researchers use worthwhile methods in artificial intelligence, which can reduce necessary human power while increases accuracy of results. Among various medical images, blood microscopic images play a vital role in heart failure diagnosis, e.g., blood cancers. The prominent component in blood cancer diagnosis is white blood cells (WBCs) which due to its general characteristics in microscopic images sometimes make difficulties in recognition and classification tasks such as non-uniform colors/illuminances, different shapes, sizes, and textures. Moreover, overlapped WBCs in bone marrow images and neighboring to red blood cells are identified as reasons for errors in the classification task. In this paper, we have endeavored to segment various parts in medical images via Naïve Bayes clustering method and in next stage via TSLDA classifier, which is supplied by features acquired from covariance descriptor results in the accuracy of 98.02%. It seems that this result is delightful in WBCs recognition.
Camera calibration of a near-parallel imaging system based on magnification rate

Microscopes are now widely used in the field of three-dimensional measurements for their high magnification and changeable physical focal length. However, the small depth of focus restricts the camera from taking clear images with apparent tilts. To set the camera in focus, the calibration board should be near-parallel to the imaging plane. As a result, the traditional calibration methods are not practical for a normal lens, which requires multiple views of the calibration board. For this, a method in which a digital camera with a microscope is calibrated precisely under the near-parallel condition is proposed. In this method, the magnification rate of the imaging system, which is equal to the rate of image distance to object distance, is calculated precisely and utilized to calculate camera parameters. Due to the invariance of the magnification rate, the parameters calculated by this method are accurate and stable. In addition, to solve the problem that the physical focal length cannot be calibrated by one image, the magnification method is adopted to measure it. Finally, the stability and accuracy are evaluated by simulations and experiments.
DAL: Dual Adversarial Learning for Dialogue Generation

In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning (DAL) for high-quality response generation. DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms the state-of-the-art methods regarding automatic metrics and human evaluations.
UMAP 2019 ADAPPT (Adaptive and Personalized Persuasive Technology) Workshop Chairs' Welcome &amp; Organization

We are pleased to welcome you to the 1st International Workshop on Adaptive and Personalized Persuasive Technology (ADAPPT 2019). ADAPPT 2019 is a half-day workshop held in conjunction with the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2018), 09-12 June 2019 in Larnaca, Spain. Persuasive technologies are increasingly being used to bring about behavior change in various domains of human endeavors, including health, education, commerce, energy conservation, safety, etc. However, research on personalizing and adapting them to their target users to make them more effective is still in its infancy. As such, for the first time, we proposed at the ACM UMAP 2019 conference the ADAPPT 2019 workshop. The workshop aims to bring together researchers and practitioners from academia and industry-working in the area of adapting and personalizing persuasive technologies-to present, discuss and share their work in progress with other members of the research community. Specifically, the workshop aims to provide a platform for stakeholders to brainstorm, identify and discuss the opportunities and challenges in the ADAPPT field as well as emerging techniques, methods and approaches to personalizing and adapting persuasive technologies to the target users. In the first edition of the workshop, we received 10 submissions from four different countries, including Canada, Germany, Nigeria and Spain, covering a wide range of topics in domains such as health, education, organization, social media, e-commerce, etc. Each of the 10 papers was reviewed by at least two reviewers, which included members of the organizing committee and external reviewers with expertise in different areas of persuasive technology research. All of the 10 papers, which include 4 full papers and 6 short papers, were accepted for presentation at the workshop.