nils-herrmann/clean_scirepeval huggingface dataset:
Searchable symmetric encryption: improved definitions and efficient constructions

Searchable symmetric encryption (SSE) allows a party to outsource the storage of its data to another party (a server) in a private manner, while maintaining the ability to selectively search over it. This problem has been the focus of active research in recent years. In this paper we show two solutions to SSE that simultaneously enjoy the following properties: Both solutions are more efficient than all previous constant-round schemes. In particular, the work performed by the server per returned document is constant as opposed to linear in the size of the data. Both solutions enjoy stronger security guarantees than previous constant-round schemes. In fact, we point out subtle but serious problems with previous notions of security for SSE, and show how to design constructions which avoid these pitfalls. Further, our second solution also achieves what we call adaptive SSE security, where queries to the server can be chosen adaptively (by the adversary) during the execution of the search; this notion is both important in practice and has not been previously considered.Surprisingly, despite being more secure and more efficient, our SSE schemes are remarkably simple. We consider the simplicity of both solutions as an important step towards the deployment of SSE technologies.As an additional contribution, we also consider multi-user SSE. All prior work on SSE studied the setting where only the owner of the data is capable of submitting search queries. We consider the natural extension where an arbitrary group of parties other than the owner can submit search queries. We formally define SSE in the multi-user setting, and present an efficient construction that achieves better performance than simply using access control mechanisms.
AutoBrief: an experimental system for the automatic generation of briefings in integrated text and information graphics

This paper describes AutoBrief, an experimental intelligent multimedia presentation system that generates presentations in text and information graphics in the domain of transportation scheduling. Acting as an intelligent assistant, AutoBrief creates a presentation to communicate its analysis of alternative schedules. In addition, the multimedia presentation facilitates data exploration through its complex information visualizations and support for direct manipulation of presentation elements. AutoBrief’s research contributions include (1) a design enabling a new human–computer interaction style in which intelligent multimedia presentation objects (textual or graphic) can be used by the audience in direct manipulation operations for data exploration, (2) an application-independent approach to multimedia generation based on the representation of communicative goals suitable for both generation of text and of complex information graphics, and (3) an application-independent approach to intelligent graphic design based upon communicative goals. This retrospective overview paper, aimed at a multidisciplinary audience from the fields of human–computer ARTICLE IN PRESS *Corresponding author.  E-mail addresses: nlgreen@uncg.edu (N.L. Green), carenini@cs.ubc.ca (G. Carenini), kerpedjiev@maya.com (S. Kerpedjiev), mattis@mayaviz.com (J. Mattis), j.moore@ed.ac.uk (J.D. Moore), roth+@cs.cmu.edu (S.F. Roth). 1071-5819/$ see front matter r 2003  and natural language generation, presents AutoBrief’s design and design rationale. r 2003
Analyzing News Media Coverage to Acquire and Structure Tourism Knowledge

Destination image significantly influences a tourist’s decision-making process. The impact of news media coverage on destination image has attracted research attention and became particularly evident after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series of lethal tsunamis. Building upon previous research, this article analyzes the prevalence of tourism destinations among 162 international media sites. Term frequency captures the attention a destination receives—from a general and, after contextual filtering, from a tourism perspective. Calculating sentiment estimates positive and negative media influences on destination image at a given point in time. Identifying semantic associations with the names of countries and major cities, the results of co-occurrence analysis reveal the public profiles of destinations, and the impact of current events on media coverage. These results allow national tourism organizations to assess how their destination is covered by news media in general, and in a specific tourism context. To guide analysts and marketers in this assessment, an iterative analysis of semantic associations extracts tourism knowledge automatically, and represents this knowledge as ontological structures.
Your liking is my curiosity : a social popularity intervention to induce curiosity

Our actions and decisions are regularly influenced by the social environment around us. Can social environment be leveraged to induce curiosity and facilitate subsequent learning? Across two experiments, we show that curiosity is contagious: social environment can influence people’s curiosity about the answers to scientific questions. Our findings show that people are more likely to become curious about the answers to more popular questions, which in turn influences the information they choose to reveal. Given that curiosity has been linked to better learning, these findings have important implications for education.
Facial Wrinkles Detection Techniques and its Application

Face recognition is process of identifying or verifying individual person by their face. One of the most important sources of the information is human face which can be intended for personal verification and identification of individual person. Wrinkles play an essential role in age estimation. They have been commonly used in applications, such as face age estimation, facial retouching and facial expression recognition. Facial wrinkles present 3D form of skin and appear as skillful discontinuities or cracks in surrounding skin texture. There are different techniques present for facial wrinkles detection. This paper presents the study and review of various techniques used in wrinkle detection. This paper is motivated by need of fast and robust algorithm for detection and classification of human age and facial retouching.
Older adults' attitudes towards and perceptions of "smart home" technologies: a pilot study.

The study aim is to explore the perceptions and expectations of seniors in regard to "smart home" technology installed and operated in their homes with the purpose of improving their quality of life and/or monitoring their health status.



Three focus group sessions were conducted within this pilot study to assess older adults' perceptions of the technology and ways they believe technology can improve their daily lives. Themes discussed in these groups included participants' perceptions of the usefulness of devices and sensors in health-related issues such as preventing or detecting falls, assisting with visual or hearing impairments, improving mobility, reducing isolation, managing medications, and monitoring of physiological parameters. The audiotapes were transcribed and a content analysis was performed.



A total of 15 older adults participated in three focus group sessions. Areas where advanced technologies would benefit older adult residents included emergency help, prevention and detection of falls, monitoring of physiological parameters, etc. Concerns were expressed about the user-friendliness of the devices, lack of human response and the need for training tailored to older learners.



All participants had an overall positive attitude towards devices and sensors that can be installed in their homes in order to enhance their lives.
The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction

This paper presents the design and implementation of a compiler algorithm that effectively optimizes programs for energy usage using dynamic voltage scaling (DVS). The algorithm identifies program regions where the CPU can be slowed down with negligible performance loss. It is implemented as a source-to-source level transformation using the SUIF2 compiler infrastructure. Physical measurements on a high-performance laptop show that total system (i.e., laptop) energy savings of up to 28% can be achieved with performance degradation of less than 5% for the SPECfp95 benchmarks. On average, the system energy and energy-delay product are reduced by 11% and 9%, respectively, with a performance slowdown of 2%. It was also discovered that the energy usage of the programs using our DVS algorithm is within 6% from the theoretical lower bound. To the best of our knowledge, this is one of the first work that evaluates DVS algorithms by physical measurements.
Guillain-Barré syndrome.

Guillain-Barré syndrome consists of a group of neuropathic conditions characterized by progressive weakness and diminished or absent myotatic reflexes. The estimated annual incidence in the United States is 1.65 to 1.79 per 100,000 persons. Guillain-Barré syndrome is believed to result from an aberrant immune response that attacks nerve tissue. This response may be triggered by surgery, immunizations, or infections. The most common form of the disease, acute inflammatory demyelinating polyradiculoneuropathy, presents as progressive motor weakness, usually beginning in the legs and advancing proximally. Symptoms typically peak within four weeks, then plateau before resolving. More than one-half of patients experience severe pain, and about two-thirds have autonomic symptoms, such as cardiac arrhythmias, blood pressure instability, or urinary retention. Advancing symptoms may compromise respiration and vital functions. Diagnosis is based on clinical features, cerebrospinal fluid testing, and nerve conduction studies. Cerebrospinal fluid testing shows increased protein levels but a normal white blood cell count. Nerve conduction studies show a slowing, or possible blockage, of conduction. Patients should be hospitalized for multidisciplinary supportive care and disease-modifying therapy. Supportive therapy includes controlling pain with nonsteroidal anti-inflammatory drugs, carbamazepine, or gabapentin; monitoring for respiratory and autonomic complications; and preventing venous thrombosis, skin breakdown, and deconditioning. Plasma exchange therapy has been shown to improve short-term and long-term outcomes, and intravenous immune globulin has been shown to hasten recovery in adults and children. Other therapies, including corticosteroids, have not demonstrated benefit. About 3 percent of patients with Guillain-Barré syndrome die. Neurologic problems persist in up to 20 percent of patients with the disease, and one-half of these patients are severely disabled.
Background Removal in Image Indexing and Retrieval

This paper presents our research in image content based indexing and retrieval, a key technology in digital image libraries. In most of the existing image content-based techniques, image features used for indexing and retrieval are global, features are computed over the entire image. The major problem with the global image feature based retrieval methods is that background features can be easily mistakenly taken as object features. When a user attempts to retrieve images using color features, he/she usually means the color feature of an object or objects of interests contained in the image. The approach we describe in this paper utilizes color clusters for image background analysis. Once the background regions are identified, they are removed from the image indexing procedure; therefore, no longer interfering with the meaningful image content during the retrieval process. The algorithm consists of three major steps of computation, fuzzy clustering, color image segmentation, and background analysis.
A Multi-scale Approach to 3D Scattered Data Interpolation with Compactly Supported Basis Function

In this paper, we propose a hierarchical approach to 3D scattered data interpolation with compactly supported basis functions. Our numerical experiments suggest that the approach integrates the best aspects of scattered data fitting with locally and globally supported basis functions. Employing locally supported functions leads to an efficient computational procedure, while a coarse-to-fine hierarchy makes our method insensitive to the density of scattered data and allows us to restore large parts of missed data. Given a point cloud distributed along a surface, we first use spatial down sampling to construct a coarse-to-fine hierarchy of point sets. Then we interpolate the sets starting from the coarsest level. We interpolate a point set of the hierarchy, as an offsetting of the interpolating function com puted at the previous level. Fig. 1 shows an original point set (the leftmost image) and its coarse-to-fine hierarchy of interpolated sets. According to our numerical experiments, the method is essentially faster than the state-of-art scattered data ap proximation with globally supported RBFs [9] and much simpler to implement.
LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification

The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the distances between interclass images, we propose a latent relationship learning framework that integrates an adaptive graph with the constraints of the feature space and label propagation for high-resolution aerial image classification. To describe the latent relationships among scene images in the framework, we construct an adaptive graph that is embedded into the constrained joint space for features and labels. To remove redundant information and improve the computational efficiency, subspace learning is introduced to assist in the latent relationship learning. To address out-of-sample data, linear regression is adopted to project the semisupervised classification results onto a linear classifier. Learning efficiency is improved by minimizing the objective function via the linearized alternating direction method with an adaptive penalty. We test our method on three widely used aerial scene image data sets. The experimental results demonstrate the superior performance of our method over the state-of-the-art algorithms in aerial scene image classification.
Light mixture estimation for spatially varying white balance

White balance is a crucial step in the photographic pipeline. It ensures the proper rendition of images by eliminating color casts due to differing illuminants. Digital cameras and editing programs provide white balance tools that assume a single type of light per image, such as daylight. However, many photos are taken under mixed lighting. We propose a white balance technique for scenes with two light types that are specified by the user. This covers many typical situations involving indoor/outdoor or flash/ambient light mixtures. Since we work from a single image, the problem is highly underconstrained. Our method recovers a set of dominant material colors which allows us to estimate the local intensity mixture of the two light types. Using this mixture, we can neutralize the light colors and render visually pleasing images. Our method can also be used to achieve post-exposure relighting effects.
Automatic speech recognition and speech variability: A review

Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge. Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.
A primer on spatial modeling and analysis in wireless networks

The performance of wireless networks depends critically on their spatial configuration, because received signal power and interference depend critically on the distances between numerous transmitters and receivers. This is particularly true in emerging network paradigms that may include femtocells, hotspots, relays, white space harvesters, and meshing approaches, which are often overlaid with traditional cellular networks. These heterogeneous approaches to providing high-capacity network access are characterized by randomly located nodes, irregularly deployed infrastructure, and uncertain spatial configurations due to factors like mobility and unplanned user-installed access points. This major shift is just beginning, and it requires new design approaches that are robust to spatial randomness, just as wireless links have long been designed to be robust to fading. The objective of this article is to illustrate the power of spatial models and analytical techniques in the design of wireless networks, and to provide an entry-level tutorial.
Fault isolation in manufacturing systems based on learning algorithm and fuzzy rule selection

This paper addresses the fault detection and isolation problem in manufacturing systems. Some of these systems can be affected by several faults, a first way of determining them is to use classification and rule-based reasoning methods. In the present work, a new hybrid algorithm, based on fuzzy Levenberg–Marquardt and genetic algorithm for both training and fault isolation in the high-dimensional setting, is developed. The genetic algorithm-based approach aims at selecting an optimal number of production rules. The developed approach consists then to minimize training time and to find accurate and interpretable fuzzy systems with an appropriate production rules subset. It is put into practice for a real manufacturing system for binary classes and also its advantage is demonstrated on multi-class system. Obtained results show that the approach can be more accurate and fast to make fault diagnosis for binary and multi-class problems compared to those reported in the literature.
Collaboration with Lean Media: how open-source software succeeds

Open-source software, usually created by volunteer programmers dispersed worldwide, now competes with that developed by software firms. This achievement is particularly impressive as open-source programmers rarely meet. They rely heavily on electronic media, which preclude the benefits of face-to-face contact that programmers enjoy within firms. In this paper, we describe findings that address this paradox based on observation, interviews and quantitative analyses of two open-source projects. The findings suggest that spontaneous work coordinated afterward is effective, rational organizational culture helps achieve agreement among members and communications media moderately support spontaneous work. These findings can imply a new model of dispersed collaboration.
Systematic Review of Clustering High-Dimensional and Large Datasets

Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.
Unsupervised image segmentation using convolutional autoencoder with total variation regularization as preprocessing

Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation and enhance their performance. Our approach relies on a convolutional autoencoder (CAE) with the total variation loss (TVL) for unsupervised learning. We show that, after our CAE-TVL preprocessing step, the over-segmentation effect is significantly reduced using the same unsupervised image segmentation methods. We evaluate our approach using the BSDS500 image segmentation benchmark dataset and show the performance enhancement introduced by our approach in terms of both increased segmentation accuracy and reduced computation time. We examine the robustness of the trained CAE and show that it is directly applicable to other natural scene images.
Tracking and Connecting Topics via Incremental Hierarchical Dirichlet Processes

Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.