Reviewerly/exHarmony huggingface dataset:
Design And Development Of The Data Synchronization Case Study

The Laboratory for Innovative Technology and Engineering Education (LITEE) at Auburn University has been developing case studies that engage students in cross-disciplinary learning and require engineering and business and students of other disciplines to work together in order to solve a common problem.The Data Synchronization case study is one of the latest case studies from LITEE, which focuses on how a construction company solved the problem of synchronizing data forms between remote sites and the headquarters.Unlike other case studies that handle hypothetical or past events, the Data Synchronization case study focuses on a current real-world problem that existed in this company.A graduate student from Auburn University worked directly with the company while living in India during summer 2007 in order to develop this case study.The Data Synchronization case study was made using steps for creating case studies, which involve identifying a company's problem, having students work with the company, writing the case study, and many stages of refinement in order to create the finished product for the classroom.This paper describes this case study and its creation process further.
Sql Injection Attacks And Prevention Techniques

Databases introduce a number of unique security requirements for their users and administrators.On one hand, databases are designed to promote open and flexible access to data.On the other hand, it's this same open access that makes databases vulnerable to many kinds of malicious activity 1 .One of the main issues faced by database security professionals is avoiding inference capabilities.Structured Query Language (SQL) injection is a technique used to take advantage of non-validated input vulnerabilities to pass SQL commands through a Web application for execution by a backend database.Attackers take advantage of the fact that programmers often chain together SQL commands with user-provided parameters, and can therefore embed SQL commands inside these parameters.The result is that the attacker can execute arbitrary SQL queries and/or commands on the backend database server through the Web application.In this report we discuss the different SQL injection attacks and prevention techniques available.
Teaching And Assessing Teamwork: Including A Method (That Works) To Determine Individual Contributions To A Team

Implementing teamwork has historically been difficult to achieve in an educational setting and hence has often been avoided.With a heavy emphasis on teamwork via industry and accreditation standards, group projects are a high priority.There are two primary problems to resolve when students work on teams.The first problem is that students are rarely taught how to work on teams.The second problem is the need to assess each individual's contributions (or lack thereof) to the team.
So You Want to Teach an iPhone Programming Course?

According to a Pew Research survey conducted in April and May 2010, an estimated 82 percent of adult Americans now own a mobile phone and nearly 25% of United States adults use mobile apps on their phones.The Apple iPhone was introduced in 2007 and to date has been a cultural phenomenon in addition to being a commercial success.According to Apple's quarterly earnings, close to 60 million iPhone's have been sold through the end of June 2010.The success of the iPhone can at least partially be attributed to the iPhone ecosystem consisting of mobile device hardware, the iOS operating system, software developer tools, and the App Store -all created and controlled by Apple.To date, over 300,000 apps are available in the App Store, and Apple has reported that over 1 billion dollars in profit has been paid to iPhone developers.The Department of Computer and Information Technology at Purdue University strives to keep its curriculum current and to teach courses using best-of-breed technologies.For this reason, an undergraduate, upper-level course on iPhone application development was offered during the Fall 2010 semester.Our department has been teaching software development for mobile devices since 2002, but the Fall 2010 semester was the first using Apple development tools for iOS devices including the iPhone.In this paper we will discuss our experiences teaching the course.Topics will include obstacles faced, the selection and purchase Macintosh computers for our mobile computing lab, selection and purchasing of mobile devices, course pedagogy, textbook selection, student assessment, and unexpected problems are presented.Finally, conclusions and lessons learned are addressed.
Intelligence Quotient (IQ) as a Predictor of Reading Comprehension and Writing Achievement of EFL Learners

This study aimed at investigating Intelligent Quotient (IQ) as a predictor of reading comprehension and writing achievement as well as to correlate the students' reading comprehension with their writing achievement. The participant of the study were 32 senior high school Indonesian students. There are three instruments used in this study, those are IQ test, reading comprehension test, and writing test. Upon obtaining the whole data needed, Pearson Product Moment formula was employed to determine the correlation of IQ with reading comprehension and writing achievement as well as reading comprehension with writing achievement. The result of this study revealed that IQ made significant contribution in predicting reading comprehension (23.42%) and writing achievement (16.08%). In addition, the correlation coefficient of reading comprehension and writing achievement shows that they are moderately correlated (r=.587), meaning that reading comprehension contributes as many as 34.45% to writing achievement.
On how kelsenian jurisprudence and intuitionistic logic help to avoid contrary-to-duty paradoxes in legal ontologies

In this article we show how Hans Kelsen jurisprudence and Intuitionistic logic are used to avoid the well-known contrary-to-duty (CTD) paradoxes, such as Chisholm paradoxes and its variants.This article uses an intuitionistic version of the ALC description logic, named iALC, to show how an ontology based on individually valid legal statements is able to avoid CTDs by providing models to them.
The Role of Smart Data in Inference of Human Behavior and Interaction

This work has been developed under the Fundacao para a Ciencia e Tecnologia PDLAB project UID/MULTI/04111/2016.
Soft biometrics through hand gestures driven by visual stimuli

We present a novel biometric solution which exploits hand gestures, tracked by the Microsoft Kinect sensor, performed in response to a circle randomly appearing in five predefined screen positions. Features of both hand and screen pointer are used for classification purposes, considering both the whole 20-path trajectory and shorter routes. In particular, we search for the “optimal” trajectory length which assures a good trade-off between precision and user effort. For identification, the approach achieves classification accuracies ranging from 0.748 to 0.942. For verification, accuracy is still satisfactory (always higher than 0.962), despite moderate specificity values.
Interactive Art—<i>Smiling Buddha:</i> Recording the Moment at Which an Observer Smiles through Sight Detection and Smile Recognition

In the interactive installation Smiling Buddha, we aimed to “pass on” a smile from one observer to the next. Thus, we have designed a natural interactive process that keeps passing on smiles. The system captures the moment at which an observer smiles before kinetically recording the moment and saving the images. The system does not merely record an image from a single angle; instead, the device records the user’s smile from various angles during the interaction. The final smile features different angles of smiles from previous users together with the smile of the present user. After completing the interactive experience, the user’s data will be saved and transmitted to the “Smiling Database,” where the smiles of past users will then be reproduced in the display area. Through the vast quantity of smiles, we wish to achieve our core concept of “passing on a smile.”
List-Decoding with Double Samplers

We strengthen the notion of double samplers, first introduced by Dinur and Kaufman [``High dimensional expanders imply agreement expanders," in Proc. 58th IEEE Symp. on Foundations of Comp. Science, IEEE, 2017, pp. 974--985], which are samplers with additional combinatorial properties, and whose existence we prove using high-dimensional expanders. The ABNNR code construction [N. Alon et al., IEEE Trans. Inform. Theory, 38 (1992), pp. 509--516] achieves large distance by starting with a base code $C$ with moderate distance, and then amplifying the distance using a sampler. We show that if the sampler is part of a larger double sampler, then the construction has an efficient list-decoding algorithm. Our algorithm works even if the ABNNR construction is not applied to a base code $C$ but rather to any string. In this case the resulting code is approximate-list-decodable, i.e., the output list contains an approximation to the original input. Our list-decoding algorithm works as follows: It uses a local voting scheme from which it constructs a unique games constraint graph. The constraint graph is an expander, so we can solve unique games efficiently. These solutions are the output of the list-decoder. This is a novel use of a unique games algorithm as a subroutine in a decoding procedure, as opposed to the more common situation in which unique games are used for demonstrating hardness results. Double samplers and high-dimensional expanders are akin to pseudorandom objects in their utility, but they greatly exceed random objects in their combinatorial properties. We believe that these objects hold significant potential for coding theoretic constructions and view this work as demonstrating the power of double samplers in this context.
Multi-Shot Stream Reasoning in Answer Set Programming: A Preliminary Report.

In the past, we presented a first approach for stream reasoning using Answer Set Programming (ASP). At the time, we implemented an exhaustive wrapper for our underlying ASP system, clingo, to enable reasoning over continuous data streams. Nowadays, clingo natively supports multi-shot solving: a technique for processing continuously changing logic programs. In the context of stream reasoning, this allows us to directly implement seamless sliding-window-based reasoning over emerging data. In this paper, we hence present an exhaustive update to our stream reasoning approach that leverages multi-shot solving. We describe the implementation of the stream reasoner's architecture, and illustrate its workflow via job shop scheduling as a running example.
Breathprinting Roadmap Based on Experts’ Opinions

When the destination of a journey is far away, the roadmap is mandatory. But when the road is not marked, you must ask an expert, who can suggest a possible roadmap. Here five experts have been consulted. Their indications, their opinions, and their hopes are sometimes convergent, sometimes complementary, and in any case, very valuable. They all ask for time, collaboration, and patience, and enthusiasm, as fuel for running faster.
Augmenting content-based rating prediction with link stream features

While graph-based collaborative filtering recommender systems have been introduced several years ago, there are still several shortcomings to deal with, the temporal information being one of the most important. The new link stream paradigm is aiming at extending graphs for correctly modelling the graph dynamics, without losing crucial information. We investigate the impact of such link stream features for recommender systems. We design link stream features, that capture the intrinsic structure and dynamics of the data. We show that such features encode a fine-grained and subtle description of the underlying system. We focused on a traditional recommender system context, the rating prediction on the MovieLens20M movie dataset and the Goodreads book dataset. We input link stream features along with some content-based ones into a gradient boosting machine (XGBoost) and show that itoutperforms significantly a sole content-based solution. These encouraging results call for further exploration of this original modelling and its integration to complete state-of-the-art recommender systems algorithms. Link streams and graphs, as natural visualizations of recommender systems, may offer more interpretability in a time when algorithm transparency is an increasingly important topic of discussion. We also hope that these results will sparkle interesting discussions in the community about the connections between link streams and traditional methods (matrix factorization, deep learning).
Implementing cooperative bacterial foraging optimisation algorithm based resources and VM management in IaaS cloud

In cloud computing, Infrastructure as a Service (IaaS) placed a crucial role for providing the enormous services to the user based on the demands. The infrastructure provides the components such as server, networking hardware, storage, visualisation for making the system power effective. Even though the IaaS provides various resources, it has still issues such as cost expensive, responsible for backup, VM management is fully depends on the customer, makespan for resource provisioning in scientific workflow and no control of VM location. Among the various issues, cost and makespan is one of the major issues which lead to reduce the entire resource allocation process. So, in this paper introduces the cooperative bacterial foraging optimisation algorithm (CBFOA) for optimising the resource allocation activities while user requesting various scientific application in the infrastructure resources in cloud. Initially, the BFO method allocates the resources according to the bacterial function such as chemotaxis, swarming, reproduction, elimination and dispersal process which examines the user request according to the animal food searching process. During the resource searching process, the search space has been determined with the help of hybrid search space optimisation algorithm which handle the decision depending on the search space. This scientific workflow application process is worked continuously for allocating the resources and manages the virtual resources in IaaS in cloud environment successfully. In addition to this, user requested workload has been traced by using cybershake workload trace, which is used to examine the resource provisioning activities and trace the status of the particular task with effective manner. Then the excellence of the system has been implemented with the help of cloudsim tool and the efficiency is examined in terms of experimental results and discussions.
Towards a Theoretical Understanding of Hashing-Based Neural Nets

Parameter reduction has been a popular topic in deep learning due to the ever-increasing size of deep neural network models and the need to train and run deep neural nets on resource limited machines. Despite many efforts in this area, there were no rigorous theoretical guarantees on why existing neural net compression methods should work. In this paper, we provide provable guarantees on some hashing-based parameter reduction methods in neural nets. First, we introduce a neural net compression scheme based on random linear sketching (which is usually implemented efficiently via hashing), and show that the sketched (smaller) network is able to approximate the original network on allinput data coming from any smooth well-conditioned low-dimensional manifold. The sketched network can also be trained directly via back-propagation. Next, we study the previously proposed HashedNets architecture and show that the optimization landscape of one-hidden-layer HashedNets has a local strong convexity property similar to a normal fully connected neural network. Togetherwith the initialization algorithm developed in [51], this implies that the parameters in HashedNets can be provably recovered. We complement our theoretical results with some empirical verification.
Controllable Motion-Blur Effects in Still Images

Motion blur in a photo is the consequence of object motion during the image acquisition. It results in a visible trail along the motion of a recorded object and can be used by photographers to convey a sense of motion. Nevertheless, it is very challenging to acquire this effect as intended and requires much experience from the photographer. To achieve actual control over the motion blur, one could be added in a post process but current solutions require complex manual intervention and can lead to artifacts that mix moving and static objects incorrectly. In this paper, we propose a novel method to add motion blur to a single image that generates the illusion of a photographed motion. Relying on a minimal user input, a filtering process is employed to produce a virtual motion effect. It carefully handles object boundaries to avoid artifacts produced by standard filtering methods. We illustrate the effectiveness of our solution with various complex examples, including multi-directional blur, reflections, multiple objects, and illustrate how several motion-related artistic effects can be achieved. Our post-processing solution is an alternative to capturing the intended real-world motion blur directly and enables fine-grained control of the motion-blur effect.
The Effectiveness of Poetry Appreciation Textbook for Character Education Implementation at Higher Education

The study aims to find out the effectiveness of poetry appreciation textbook for the implementation of character education values at the Higher Education institution.It was set in four universities, two of which are located in Central Java, including University of Sebelas Maret Surakarta and University of Muhammadiyah Purworejo meanwhile; the other two are in Special Region of Yogyakarta, including University of Ahmad Dahlan, and University of Sarjanawiyata Taman Siswa.It utilized an experimental method with Independent Sample T-Test based on the mean difference of pre-test and post-test results from the control and experimental group.The finding confirms that the experimental group obtained the average score that equals 15.68, meanwhile the control group obtained 10.86.The T-Test shows tcalc = 5.087 (p = 0.000<0.05),that rejects the Null Hypothesis.It concludes that there is a significant difference between the experimental and control group in the use poetry appreciation textbook for character education implementation at the Higher Education institution.The study expects further exploration concerning character education implementation through literature works in a wider range for the sake of research and development.The uniqueness of the textbook in combining the values of personal development and literature is recommended as the dominant feature to introduce a complex gradual learning system for the innovation of teaching and learning method.
Ellipse packing in two-dimensional cell tessellation: a theoretical explanation for Lewis’s law and Aboav-Weaire’s law

Background Lewis’s law and Aboav-Weaire’s law are two fundamental laws used to describe the topology of two-dimensional (2D) structures; however, their theoretical bases remain unclear. Methods We used R software with the Conicfit package to fit ellipses based on the geometric parameters of polygonal cells of ten different kinds of natural and artificial 2D structures. Results Our results indicated that the cells could be classified as an ellipse’s inscribed polygon (EIP) and that they tended to form the ellipse’s maximal inscribed polygon (EMIP). This phenomenon was named as ellipse packing. On the basis of the number of cell edges, cell area, and semi-axes of fitted ellipses, we derived and verified new relations of Lewis’s law and Aboav-Weaire’s law. Conclusions Ellipse packing is a short-range order that places restrictions on the cell topology and growth pattern. Lewis’s law and Aboav-Weaire’s law mainly reflect the effect of deformation from circle to ellipse on cell area and the edge number of neighboring cells, respectively. The results of this study could be used to simulate the dynamics of cell topology during growth.
Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles

While the predictive advantage of ensemble methods is nowadays widely accepted, the most appropriate way of estimating the weights of each individual model remains an open research question. Meanwhile, several studies report that combining different ensemble approaches leads to improvements in performance, due to a better trade-off between the diversity and the error of the individual models in the ensemble. We contribute to this research line by proposing an aggregation framework for a set of independently created forecasting models, i.e. heterogeneous ensembles. The general idea is to, instead of directly aggregating these models, first rearrange them into different subsets, creating a new set of combined models which is then aggregated into a final decision. We present this idea as constructive aggregation, and apply it to time series forecasting problems. Results from empirical experiments show that applying constructive aggregation to state of the art dynamic aggregation methods provides a consistent advantage. Constructive aggregation is publicly available in a software package. Data related to this paper are available at: https://github.com/vcerqueira/timeseriesdata . Code related to this paper is available at: https://github.com/vcerqueira/tsensembler .
HydraDoctor

Water has been widely acknowledged as an essential part of all living things. It is the fundamental necessity for all life's activities and most biochemical reactions in human body are executed in water. Therefore, the type and quantity of liquid intake everyday have a critical impact on individuals' health. In this paper, we demonstrate HydraDoctor, a real-time liquids intake monitoring system which is able to detect drinking activities, classify the categories of liquids and estimate the amount of intake. The system runs on multiple platforms including a smartwatch to detect the motion of hands and a smartglass to capture the images of mugs. A smartphone is also used as an edge computing platform and a remote server is designed for computationally intensive image processing. In HydraDoctor, multiple state-of-the-art machine learning techniques are applied: a Support Vector Machine (SVM)-based classifier is proposed to achieve accurate and efficient liquids intake monitoring, which is trained to detect the hand raising action. Both of them are well optimized to enable in-situ processing on smartwatch. To provide more robust and detailed monitoring, the smartglass is also incorporated and trigged to capture a short video clip in the front of the user when potential drinking activity is detected. The smartglass will send the video clip to the remote server via its companion smartphone and a Faster-RCNN is performed on the server to confirm the detected drinking activity and identify the type of intake liquid. According to our evaluation on the real-world experiments, HydraDoctor achieves very high accuracy both in drinking activity detection and types of liquids classification, whose accuracy is 85.64% and 84% respectively.
A Novel Bulk-Optics Scheme for Quantum Walk with High Phase Stability

A novel bulk optics scheme for quantum walks is presented. It consists of a one-dimensional lattice built on two concatenated displaced Sagnac interferometers that make it possible to reproduce all the possible trajectories of an optical quantum walk. Because of the closed loop configuration, the interferometric structure is intrinsically stable in phase. Moreover, the lattice structure is highly configurable, as any phase component perceived by the walker is accessible, and finally, all output modes can be measured at any step of the quantum walk evolution. We report here on the experimental implementation of ordered and disordered quantum walks.