Reviewerly/exHarmony huggingface dataset:
A trust-based evaluation model for data privacy protection in cloud computing

For high quality and privacy protection problem, this paper proposes a trust-based evaluation model for data privacy protection in cloud computing (TEM-DPP). In order to make the final trust evaluation values more practical, the model introduces the comprehensive trust evaluation. The comprehensive trust is composed of direct trust and recommend trust. Services attribute and combining weights-based methods are used to calculate the direct trust, reflecting the direct trust timeliness and rationality. In order to protect data security, we propose a data protection method based on normal cloud model for data privacy protection. Then, the customer satisfaction, decay time, transaction amount and penalty factor will be used to update the direct trust. Simulation results are showed that cloud services trust evaluation model can not only adapt to the dynamic changes in the environment, but also ensure the actual quality of service. It can improve the service requesters' satisfaction and has certain resilience to fraud entities.
The Blue Waters Petascale Institute

The Blue Waters Petascale Institute was a two-week intensive training program that ran annually from 2014-2018. The institute implemented a hands-on, inquiry-based approach comprised of exploratory activities, engaging discussions, and informative lectures. These allowed students to learn parallel programming, high-performance computing, and computational science concepts with relevant examples from a wide range of real-world, domain-specific applications and scenarios. The goal was to provide undergraduate and beginning graduate students with the background and skills necessary to be successful not only during the institute but also in year-long research projects and into their future academic and professional careers. This paper examines the longitudinal impact of the institute on its students and recounts how the institute curriculum evolved over time based on formative and summative assessments. Results show that, overall, students considered the institute a positive experience and felt they gained the confidence and knowledge necessary to be successful in their internships and in their future careers. The institute has made a lasting impact in providing students with the ability to produce top-notch research outcomes of publishable quality, and students have taken what they learned into graduate programs, industry positions, and other career directions.
Garbage-Free Abstract Interpretation Through Abstract Reference Counting

Abstract garbage collection is the application of garbage collection to an abstract interpreter. Existing work has shown that abstract garbage collection can improve both the interpreter's precision and performance. Current approaches rely on heuristics to decide when to apply abstract garbage collection. Garbage will build up and impact precision and performance when the collection is applied infrequently, while too frequent applications will bring about their own performance overhead. A balance between these tradeoffs is often difficult to strike. We propose a new approach to cope with the buildup of garbage in the results of an abstract interpreter. Our approach is able to eliminate all garbage, therefore obtaining the maximum precision and performance benefits of abstract garbage collection. At the same time, our approach does not require frequent heap traversals, and therefore adds little to the interpreters's running time. The core of our approach uses reference counting to detect and eliminate garbage as soon as it arises. However, reference counting cannot deal with cycles, and we show that cycles are much more common in an abstract interpreter than in its concrete counterpart. To alleviate this problem, our approach detects cycles and employs reference counting at the level of strongly connected components. While this technique in general works for any system that uses reference counting, we argue that it works particularly well for an abstract interpreter. In fact, we show formally that for the continuation store, where most of the cycles occur, the cycle detection technique only requires O(1) amortized operations per continuation push.We present our approach formally, and provide a proof-of-concept implementation in the Scala-AM framework. We empirically show our approach achieves both the optimal precision and significantly better performance compared to existing approaches to abstract garbage collection.
The Scholarly Impact of CLEF 2010–2017

This chapter assesses the scholarly impact of the CLEF evaluation campaign by performing a bibliometric Bibliometrics analysis of the citations of the CLEF 2010–2017 papers collected through Google Scholar. The analysis extends an earlier 2013 study by Tsikrika et al. of the CLEF Proceedings for the period 2000–2009 and compares the impact of the first half of CLEF to the second. It also extends the analysis by including the CLEF Working notes, a less formal but important part of the CLEF oeuvre. Results show that, despite the different nature of the peer-reviewed CLEF Proceedings papers and the less formal and much more numerous Working note papers, both types of publications have high citation impact. In particular, overview papers from the various labs and tasks in CLEF attract large amounts of citations in both Proceedings and Working Notes. A significant proportion of the total number of citations appear to be from outside CLEF—there are simply not enough CLEF papers every year to explain that many citations. In conclusion, the analysis of the productivity and citation impact of CLEF in the period 2010–2017 shows that CLEF is a very strong and vibrant initiative that has managed a major change of format between 2009/2010 and continues to produce relevant research, datasets and tools.
Extending Behavior-Driven Development for Assessing User Interface Design Artifacts (S)

This paper presents a scenario-based approach to specify requirements and tests by extending Behavior-Driven Development (BDD) with the aim of ensuring the consistency between user requirements and user interface design artifacts.The approach has been evaluated by exploiting user requirements specified by a group of potential Product Owners (POs) for a web system to book business trips.Such requirements gave rise to a set of User Stories that have been refined and used to automatically check the consistency of task models, user interface (UI) prototypes, and final UIs of the system.The results have shown our approach was able to identify different types of inconsistencies in the set of analyzed artifacts and consistently keep the semantic traces between them.
Stereo matching based on multi-scale fusion and multi-type support regions

Obtaining accurate disparity values in textureless and texture-free regions is a very challenging task. To solve this problem, we present a novel algorithm. First, we use the guided filter method to fuse the color cost volume and the gradient cost volume. Second, we use three types of image category information to merge the different scale disparity maps and obtain the primary disparity map. Third, during the disparity refinement procedure, we also utilize the three types of category information to define different support regions and assign different weights for pixels remaining to be refined. Extensive experiments show that the performance of our method is not inferior to many state-of-the-art methods on the Middlebury data set.
ContainerVisor: Customized Control of Container Resources

Cloud platforms are increasingly using containers for lightweight virtualization. Unlike full system virtual machines (VMs) that each runs its own operating system, containers share a stateful operating system to reduce their memory footprint and execution overheads. However, mainstream operating systems are currently limited in their ability to customize a container's memory management, since they lack the necessary abstractions and mechanisms to accurately track and isolate a container's memory footprint. We propose a new abstraction, called the Container-Level Address Space (CLAS), that provides a unified view of a container's memory across all of its constituent processes. We present the design of ContainerVisor, a per-container resource management system that leverages CLAS to provide customized memory management services. We describe a ContainerVisor prototype on Linux for running unmodified applications and demonstrate three proof-of-concept customized services, namely process-level memory limits and reservations, container-specific page replacement policies, and privacy-aware memory de-allocation. Our evaluations show that ContainerVisor can provide these customized services within reasonable overheads.
Another plan for negation

The paper presents a plan for negation, proposing a paradigm shift from the Australian plan for negation, &#x0D; leading to a family of contra-classical logics. The two main ideas are the following: &#x0D; &#x0D; Instead of shifting points of evaluation (in a frame), shift the evaluated formula. &#x0D; Introduce an incompatibility set for every atomic formula, extended to any compound formula, and impose the condition on valuations that a formula evaluates to true iff all the formulas in its incompatibility set evaluate to false. Thus, atomic sentences are not independent in their truth-values. &#x0D; The resulting negation, in addition to excluding the negated formula, provides a positive alternative&#x0D; to the negated formula.&#x0D; &#x0D; I also present a sound and complete natural deduction proof systems for those logics.&#x0D; In addition, the kind of negation considered in this paper is shown to provide an innovative notion of grounding negation. &#x0D;
Reviving Basic Narrowing Modulo

We define an inference rule called the Parallel rule. Given a rewrite system R and an equational theory E, where R is E-convergent modulo, we show that if R is saturated under the Parallel rule then Basic Narrowing modulo E is complete for R. If R is finitely saturated under both Parallel and Forward Overlap then Basic Narrowing, with right hand side abstracted, is complete and terminates, and thus it is a decision procedure for unification modulo $$R \cup E$$ . We give examples, such as the theory of XOR, the theory of abelian groups and Associativity with a unit element. We also show that R has the finite variant property modulo E if and only if R can be finitely saturated under Parallel and Forward Overlap, provided that E unification is finitary.
Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes

Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents.Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate.Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime.We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics.Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.
Evolving Software Trace Links between Requirements and Source Code

Maintaining trace links in response to continuous changes occurring in software systems is arduous. In this paper, we present a Trace Link Evolver (TLE) to automatically evolve source-to-requirement trace links according to underlying changes in the system. TLE depends on a set of heuristics coupled with refactoring detection tools and information retrieval algorithms to detect predefined change scenarios that occur across contiguous versions of a software system. Our evaluations show that considering both structural and semantic changes leads to more accurate trace link evolution.
Differentially Private Distributed Data Summarization under Covariate Shift

We envision Artificial Intelligence marketplaces to be platforms where consumers, with very less data for a target task, can obtain a relevant model by accessing many private data sources with vast number of data samples. One of the key challenges is to construct a training dataset that matches a target task without compromising on privacy of the data sources. To this end, we consider the following distributed data summarizataion problem. Given K private source datasets denoted by $[D_i]_{i\in [K]}$ and a small target validation set $D_v$, which may involve a considerable covariate shift with respect to the sources, compute a summary dataset $D_s\subseteq \bigcup_{i\in [K]} D_i$ such that its statistical distance from the validation dataset $D_v$ is minimized. We use the popular Maximum Mean Discrepancy as the measure of statistical distance. The non-private problem has received considerable attention in prior art, for example in prototype selection (Kim et al., NIPS 2016). Our work is the first to obtain strong differential privacy guarantees while ensuring the quality guarantees of the non-private version. We study this problem in a Parsimonious Curator Privacy Model, where a trusted curator coordinates the summarization process while minimizing the amount of private information accessed. Our central result is a novel protocol that (a) ensures the curator does not access more than $O(K^{\frac{1}{3}}|D_s| + |D_v|)$ points (b) has formal privacy guarantees on the leakage of information between the data owners and (c) closely matches the best known non-private greedy algorithm. Our protocol uses two hash functions, one inspired by the Rahimi-Recht random features method and the second leverages state of the art differential privacy mechanisms. We introduce a novel ``noiseless'' differentially private auctioning protocol, which may be of independent interest. Apart from theoretical guarantees, we demonstrate the efficacy of our protocol using real-world datasets.
Neural Multisensory Scene Inference

For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e.g., by looking at and touching objects. Despite its importance, multisensory 3D scene representation learning has received less attention compared to the unimodal setting. In this paper, we propose the Generative Multisensory Network (GMN) for learning latent representations of 3D scenes which are partially observable through multiple sensory modalities. We also introduce a novel method, called the Amortized Product-of-Experts, to improve the computational efficiency and the robustness to unseen combinations of modalities at test time. Experimental results demonstrate that the proposed model can efficiently infer robust modality-invariant 3D-scene representations from arbitrary combinations of modalities and perform accurate cross-modal generation. To perform this exploration we have also developed a novel multi-sensory simulation environment for embodied agents.
Building a Single Page Application Web Front-end for E-Learning site

E-learning is mostly being used nowadays and is gaining popularity. E-learning provides a learning platform by using information with electronic medium and technologies for communication [8]. We can use Elearning use as other alternative terms such as computer-based training, online education or technology-enhanced learning and others. The purpose of creating an ELearning Frontend Web Application is to provide an interactive self-learning online platform for everyone.
Teaching the Narrative Texts Using Animation Video: Raising Students’ Skills on Reading Comprehension

This study aims to answer the problem of whether animated videos improve reading skills in understanding language features, find real and detailed information, and take moral values in narrative texts in the classroom. The author uses classroom action research. The tools used to collect data are observation sheets, field notes, and tests. In cycle one to cycle three, the author uses animated videos as a medium to improve reading comprehension in understanding language features, finding real and detailed information, and taking moral values in narrative texts. The author uses animated videos with the activity of showing printed images of animated video stories and giving unknown words to improve students' ability to understand the narrative text language features. The author uses animated videos with predictive activities, confirmations, class discussions, and tests to improve students' ability to find real and detailed information. The author uses animated videos by reviewing each character and finding real and detailed information to improve students' ability to take moral values from narrative texts.
Comparative Analysis of Existing Similarity Measures used for Content-based Image Retrieval

Searching through millions or billions of images is a complex, computationally intense and time-consuming task, but it is the most important and desired feature of image databases. Search results should be sorted by similarity in descending order. The relevance is usually calculated by an appropriate similarity measure that fits the chosen type of metadata. This paper presents a comparative analysis of four universal similarity/distance measures and determines if they could be used for comparing and ranking images as well.
Remembering winter was coming Character-oriented video summaries of TV series

Today’s popular tv series tend to develop continuous, complex plots spanning several seasons, but are often viewed in controlled and discontinuous conditions. Consequently, most viewers need to be re-immersed in the story before watching a new season. Although discussions with friends and family can help, we observe that most viewers make extensive use of summaries to re-engage with the plot. Automatic generation of video summaries of tv series’ complex stories requires, first, modeling the dynamics of the plot and, second, extracting relevant sequences. In this paper, we tackle plot modeling by considering the social network of interactions between the characters involved in the narrative: substantial, durable changes in a major character’s social environment suggest a new development relevant for the summary. Once identified, these major stages in each character’s storyline can be used as a basis for completing the summary with related sequences. Our algorithm combines such social network analysis with filmmaking grammar to automatically generate character-oriented video summaries of tv series from partially annotated data. We carry out evaluation with a user study in a real-world scenario: a large sample of viewers were asked to rank video summaries centered on five characters of the popular tv series Game of Thrones, a few weeks before the new, sixth season was released. Our results reveal the ability of character-oriented summaries to re-engage viewers in television series and confirm the contributions of modeling the plot content and exploiting stylistic patterns to identify salient sequences.
Vrengt: A Shared Body-Machine Instrument for Music-Dance Performance

This paper describes the process of developing a shared instrument for music-dance performance, with a particular focus on exploring the boundaries between standstill vs motion, and silence vs sound. The piece Vrengt grew from the idea of enabling a true partnership between a musician and a dancer, developing an instrument that would allow for active co-performance. Using a participatory design approach, we worked with sonification as a tool for systematically exploring the dancer's bodily expressions. The exploration used a spatiotemporal matrix, with a particular focus on sonic microinteraction. In the final performance, two Myo armbands were used for capturing muscle activity of the arm and leg of the dancer, together with a wireless headset microphone capturing the sound of breathing. In the paper we reflect on multi-user instrument paradigms, discuss our approach to creating a shared instrument using sonification as a tool for the sound design, and reflect on the performers' subjective evaluation of the instrument.
Self-adaptive weighted level set evolution based on local intensity difference for parotid ducts segmentation

Parotid ducts (PDs) play an important role in the diagnosis and treatment of parotid lesions. Segmentation of PDs from Cone beam computed tomography (CBCT) images has a significant impact to the pathological analysis of the parotid gland. Although level set methods (LSMs) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing LSMs to precisely and self-adaptively segment PDs from parotid duct (PD) images with both noise, intensity inhomogeneity, and vague boundary. In this paper, we propose a novel Self-adaptive Weighted level set method via Local intensity Difference (SWLD) to comprehensively solve the above issues. Firstly, a new adaptive weighted operator based on local intensity variance difference has been proposed to overcome the limitations of previous LSMs that are sensitive to parameters, which achieves the aim of automatic segmentation. Secondly, we introduce local intensity mean difference into the energy function to improve the curve evolution efficiency. Thirdly, we eliminate the effects of intensity inhomogeneity, noise, and boundary blur in the parotid image through a local similarity factor with two different neighborhood sizes. Using the same dataset, segmentation of PDs is performed using the proposed SWLD algorithm and existing LSM algorithms. The mean Dice score for the proposed algorithm is 91.3%, and the corresponding mean Hausdorff distance (HD) is 1.746. Experimental results demonstrate that the proposed algorithm is superior to many existing level set segmentation algorithms, and it can accurately and automatically segment the PDs even in complex gradient boundaries.
E-Resources Troubleshooting and User Support at a Primarily Distance Learning/Online Higher Education Institution: Current Practice and Future Considerations

Resolving user access issues is inherent in managing electronic resources in libraries. The University of Maryland University College (UMUC) Library, situated within a predominantly distance learning-centered institution, has developed a series of web-based technical help pages to facilitate resolution of typical issues reported by its approximately 80,000 students located worldwide. The author details the types of support resources the library makes available to its users and discusses new technologies that may improve authentication, identity management, and communication with users.
The Effect of Marker-Based Augmented Reality (MBAR) Applications on Academic Achievement and Permanence

The aim of this study is to determine the effect of learning objects developed in marker-based augmented reality environment on academic achievement and permanence of learning.In the study, experimental design with pre-test and post-test control group has been used.The study has been carried out in the spring term of 2016-2017 academic year with 45 students studying in the 7th grade of a state secondary school in Sincan district of Ankara.At the beginning of the study, "Academic achievement scale" has been applied to the experimental and control group students.The subject has been discussed by using augmented reality applications for the experimental group, the traditional method (narration, presentation on a model or board and question and answer) for the control group.At the end of the experiment, the same scale has been carried out as post-test and permanence test four weeks after the end date of experimental procedure to each group.According to the result of study, it has been seen that augmented reality application is effective in terms of the academic achievement of students and permanence of their learning.