nils-herrmann/clean_scirepeval huggingface dataset:
Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis

Learning salient representations of multiview data is an essential step in many applications such as image classification, retrieval, and annotation. Standard predictive methods, such as support vector machines, often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies, coherence, and complementarity that offer key insights to the semantics of the data, and are therefore offering weak performance and are incapable of supporting view-level analysis. This paper presents a statistical method to learn a predictive subspace representation underlying multiple views, leveraging both multiview dependencies and availability of supervising side-information. Our approach is based on a multiview latent subspace Markov network (MN) which fulfills a weak conditional independence assumption that multiview observations and response variables are conditionally independent given a set of latent variables. To learn the latent subspace MN, we develop a large-margin approach which jointly maximizes data likelihood and minimizes a prediction loss on training data. Learning and inference are efficiently done with a contrastive divergence method. Finally, we extensively evaluate the large-margin latent MN on real image and hotel review datasets for classification, regression, image annotation, and retrieval. Our results demonstrate that the large-margin approach can achieve significant improvements in terms of prediction performance and discovering predictive latent subspace representations.
The socket-shield technique to support the buccofacial tissues at immediate implant placement

Immediate implant placement is a well-recognized and successful treatment option following tooth removal.1 Although the success rates for both immediate and delayed implant techniques are comparable, the literature reports that one can expect there to be recession of the buccal / facial gingiva of at least 1 mm following immediate implant placement, with the recession to possibly worsen in thin gingival biotypes.2 Low aesthetic value areas may be of less concern, however this recession and ridge collapse can pose an aesthetic disaster in areas such as the anterior maxilla. Compromised aesthetics may be masked to some degree by a low lip-line, thick gingival biotype, when treating single tooth cases, and so forth, but when implant therapy is carried out in patients with high lip-lines, patients with high aesthetic demands, with a very thin gingival biotype or multiple missing teeth where there is more extensive tissue deficit, then the risk for an aesthetic failure is far greater.3 The socket-shield (SS) technique provides a promising treatment adjunct to better manage these risks and preserve the post-extraction tissues in aesthetically challenging cases.4 The principle is to prepare the root of a tooth indicated for extraction in such a manner that the buccal / facial root section remains in-situ with its physiologic relation to the buccal plate intact. The tooth root section’s periodontal attachment apparatus (periodontal ligament (PDL), attachment fibers, vascularization, root cementum, bundle bone, alveolar bone) is intended to remain vital and undamaged so as to prevent the expected post-extraction socket remodeling and to support the buccal / facial tissues. Hereafter a case is presented where the SS technique was carried out at implant placement and the results from the case followed up at 1 year post-treatment demonstrate the degree of facial ridge tissue preservation achieved. C L I N I C A L
Measuring semantic similarity between words using web search engines

Measuring the semantic similarity between words is an important component in various tasks on the web such as relation extraction, community mining, document clustering, and automatic metadata extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or entities) remains a challenging task. We propose an empirical method to estimate semantic similarity using page counts and text snippets retrieved from a web search engine for two words. Specifically, we define various word co-occurrence measures using page counts and integrate those with lexical patterns extracted from text snippets. To identify the numerous semantic relations that exist between two given words, we propose a novel pattern extraction algorithm and a pattern clustering algorithm. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machines. The proposed method outperforms various baselines and previously proposed web-based semantic similarity measures on three benchmark data sets showing a high correlation with human ratings. Moreover, the proposed method significantly improves the accuracy in a community mining task.
Brute Force Attack on UNIX Passwords with SIMD Computer

As computer technology improves, the security of specific ciphers and one-way hash functions periodically must be reevaluated in light of new technological advances. In this paper we evaluate the security of the UNIX password scheme. We show that the UNIX password scheme is vulnerable to brute-force attack. Using PixelFlow, a SIMD parallel machine, we are able to “crack” a large fraction of passwords used in practice  in 2-3 days of computation. We explain how a SIMD machine built in today’s technology could “crack” any UNIX password in two days. We also describe in this paper a simple modification to the UNIX password scheme that makes it harder to break encrypted passwords using dictionary and brute force attack, thus extending the useful life of the UNIX password scheme. The modified password scheme is compatible with the existing password scheme.
A PUF design for secure FPGA-based embedded systems

The concept of having an integrated circuit (IC) generate its own unique digital signature has broad application in areas such as embedded systems security, and IP/IC counter-piracy. Physically unclonable functions (PUFs) are circuits that compute a unique signature for a given IC based on the process variations inherent in the IC manufacturing process. This paper presents the first PUF design specifically targeted for field-programmable gate arrays (FPGAs). Our novel design makes use of the underlying FPGA architecture, and unlike prior published PUFs, the proposed PUF can be naturally embedded into a design's HDL, consuming very little area, and does not require the use of "hard macros" with fixed routing. Measured results on the Xilinx Virtex-5 65 nm FPGA demonstrate PUF signatures to be both unique and reliable under temperature variation.
Visual Bilingual Lexicon Induction with Transferred ConvNet Features

This paper is concerned with the task of bilingual lexicon induction using imagebased features. By applying features from a convolutional neural network (CNN), we obtain state-of-the-art performance on a standard dataset, obtaining a 79% relative improvement over previous work which uses bags of visual words based on SIFT features. The CNN image-based approach is also compared with state-of-the-art linguistic approaches to bilingual lexicon induction, even outperforming these for one of three language pairs on another standard dataset. Furthermore, we shed new light on the type of visual similarity metric to use for genuine similarity versus relatedness tasks, and experiment with using multiple layers from the same network in an attempt to improve performance.
Motivation and barriers to participation in virtual knowledge-sharing communities of practice

Alexander Ardichvili is an assistant professor at the department of Human Resource Education, University of Illinois at Urbana/ Champaign. He received his MBA and Ph.D. from the University of Minnesota, and Ph.D. from the University of Moscow. Dr. Ardichvili has published peer-reviewed articles and book chapters in the areas of human resource development, entrepreneurship, and knowledge management (ardichvi@uiuc.edu). Vaughn Page is a doctoral student in Human Resource Education at the University of Illinois at Urbana-Champaign. His research interest centers on knowledge management and communities of practice. Page earned a Bachelor’s degree in Career and Organizational Studies and a Master’s in Training & Development from Eastern Illinois University, Charleston, Illinois (vpage@uiuc.edu). Tim Wentling is a professor in the Department of Library and Information Science and a Senior Research Scientist in the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. Professor Wentling holds a PhD in Education and an MBA from the University of Illinois and a Master of Science in Educational Psychology from the University of Wisconsin. Professor Wentling is the leader of the Knowledge and Learning Systems Group at NCSA where he heads a team of cross-disciplinary faculty, postdoctoral researchers, and graduate students (wentling@uiuc.edu).  This paper reports the results of a qualitative study of motivation and barriers to employee participation in virtual knowledge-sharing communities of practice at Caterpillar Inc., a Fortune 100, multinational corporation. The study indicates that, when employees view knowledge as a public good belonging to the whole organization, knowledge ows easily. However, even when individuals give the highest priority to the interests of the organization and of their community, they tend to shy away from contributing knowledge for a variety of reasons. Specically, employees hesitate to contribute out of fear of criticism, or of misleading the community members (not being sure that their contributions are important, or completely accurate, or relevant to a specic discussion). To remove the identied barriers, there is a need for developing various types of trust, ranging from the knowledge-based to the institutionbased trust. Future research directions and implications for KM practitioners are formulated.
Efficient Clustering of Short Messages into General Domains

The ever increasing activity in social networks is mainly manifested by a growing stream of status updating or microblogging. The massive stream of updates emphasizes the need for accurate and efficient clustering of short messages on a large scale. Applying traditional clustering techniques is both inaccurate and inefficient due to sparseness. This paper presents an accurate and efficient algorithm for clustering Twitter tweets. We break the clustering task into two distinctive tasks/stages: (1) batch clustering of user annotated data, and (2) online clustering of a stream of tweets. In the first stage we rely on the habit of ‘tagging’, common in social media streams (e.g. hashtags), thus the algorithm can bootstrap on the tags for clustering of a large pool of hashtagged tweets. The stable clusters achieved in the first stage lend themselves for online clustering of a stream of (mostly) tagless messages. We evaluate our results against gold-standard classification and validate the results by employing multiple clustering evaluation measures (information theoretic, paired, F and greedy). We compare our algorithm to a number of other clustering algorithms and various types of feature sets. Results show that the algorithm presented is both accurate and efficient and can be easily used for large scale clustering of sparse messages as the heavy lifting is achieved on a sublinear number of documents.
Performance of Database Workloads on Shared-Memory Systems with Out-of-Order Processors

Database applications such as online transaction processing (OLTP) and decision support systems (DSS) constitute the largest and fastest-growing segment of the market for multiprocessor servers. However, most current system designs have been optimized to perform well on scientific and engineering workloads. Given the radically different behavior of database workloads (especially OLTP), it is important to re-evaluate key system design decisions in the context of this important class of applications.This paper examines the behavior of database workloads on shared-memory multiprocessors with aggressive out-of-order processors, and considers simple optimizations that can provide further performance improvements. Our study is based on detailed simulations of the Oracle commercial database engine. The results show that the combination of out-of-order execution and multiple instruction issue is indeed effective in improving performance of database workloads, providing gains of 1.5 and 2.6 times over an in-order single-issue processor for OLTP and DSS, respectively. In addition, speculative techniques enable optimized implementations of memory consistency models that significantly improve the performance of stricter consistency models, bringing the performance to within 10--15% of the performance of more relaxed models.The second part of our study focuses on the more challenging OLTP workload. We show that an instruction stream buffer is effective in reducing the remaining instruction stalls in OLTP, providing a 17% reduction in execution time (approaching a perfect instruction cache to within 15%). Furthermore, our characterization shows that a large fraction of the data communication misses in OLTP exhibit migratory behavior; our preliminary results show that software prefetch and writeback/flush hints can be used for this data to further reduce execution time by 12%.
Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge

Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. This problem is further complicated by the fact that, in addition to the published data, the adversary may also have access to other resources (e.g., public records and social networks relating individuals), which we call external knowledge. A robust privacy criterion should take this external knowledge into consideration. In this paper, we first describe a general framework for reasoning about privacy in the presence of external knowledge. Within this framework, we propose a novel multidimensional approach to quantifying an adversary’s external knowledge. This approach allows the publishing organization to investigate privacy threats and enforce privacy requirements in the presence of various types and amounts of external knowledge. Our main technical contributions include a multidimensional privacy criterion that is more intuitive and flexible than previous approaches to modeling background knowledge. In addition, we provide algorithms for measuring disclosure and sanitizing data that improve computational efficiency several orders of magnitude over the best known techniques.
Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting. A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
SMS of Death: From Analyzing to Attacking Mobile Phones on a Large Scale

Mobile communication is an essential part of our daily lives. Therefore, it needs to be secure and reliable. In this paper, we study the security of feature phones, the most common type of mobile phone in the world. We built a framework to analyze the security of SMS clients of feature phones. The framework is based on a small GSM base station, which is readily available on the market. Through our analysis we discovered vulnerabilities in the feature phone platforms of all major manufacturers. Using these vulnerabilities we designed attacks against end-users as well as mobile operators. The threat is serious since the attacks can be used to prohibit communication on a large scale and can be carried out from anywhere in the world. Through further analysis we determined that such attacks are amplified by certain configurations of the mobile network. We conclude our research by providing a set of countermeasures.
Appointment scheduling in health care : Challenges and opportunities

Appointment scheduling systems are used by primary and specialty care clinics to manage access to service providers, as well as by hospitals to schedule elective surgeries. Many factors affect the performance of appointment systems including arrival and service time variability, patient and provider preferences, available information technology and the experience level of the scheduling staff. In addition, a critical bottleneck lies in the application of Industrial Engineering and Operations Research (IE/OR) techniques. The most common types of health care delivery systems are described in this article with particular attention on the factors that make appointment scheduling challenging. For each environment relevant decisions ranging from a set of rules that guide schedulers to real-time responses to deviations from plans are described. A road map of the state of the art in the design of appointment management systems is provided and future opportunities for novel applications of IE/OR models are identified.
DeepHash: Getting Regularization, Depth and Fine-Tuning Right

This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations – regularization, depth and fine-tuning – each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme consistently outperforms state-of-the-art methods across all data sets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 20% over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features – a remarkable 512× compression.
Automatic taxonomy construction from keywords

Taxonomies, especially the ones in specific domains, are becoming indispensable to a growing number of applications. State-of-the-art approaches assume there exists a text corpus to accurately characterize the domain of interest, and that a taxonomy can be derived from the text corpus using information extraction techniques. In reality, neither assumption is valid, especially for highly focused or fast-changing domains. In this paper, we study a challenging problem: Deriving a taxonomy from a set of keyword phrases. A solution can benefit many real life applications because i) keywords give users the flexibility and ease to characterize a specific domain; and ii) in many applications, such as online advertisements, the domain of interest is already represented by a set of keywords. However, it is impossible to create a taxonomy out of a keyword set itself. We argue that additional knowledge and contexts are needed. To this end, we first use a general purpose knowledgebase and keyword search to supply the required knowledge and context. Then we develop a Bayesian approach to build a hierarchical taxonomy for a given set of keywords. We reduce the complexity of previous hierarchical clustering approaches from O(n2 log n) to O(n log n), so that we can derive a domain specific taxonomy from one million keyword phrases in less than an hour. Finally, we conduct comprehensive large scale experiments to show the effectiveness and efficiency of our approach. A real life example of building an insurance-related query taxonomy illustrates the usefulness of our approach for specific domains.
Classification of Stress Management mHealth Apps Based on Octalysis Framework

Many mHealth apps have been developed to assist people in self-care management. Most of them aim to engage users and provide motivation to increase adherence. Gamification has been introduced to identify the left and right brain drives in order to engage users and motivate them. We are using Octalysis framework to map how top rated stress management apps address the right brain drives. 12 stress management mHealth are classified based on this framework. In this paper, we explore how Gamification has been used in mHealth apps, the intrinsic motivation using self-determination theory, methodology, and findings. In the discussion, we identify design principles that will better suited to enhance intrinsic motivation for people who seek self-stress management.
Channel polarization: A method for constructing capacity-achieving codes

A method is proposed, called channel polarization, to construct code sequences that achieve the symmetric capacity I(W) of any given binary-input discrete memoryless channel (B-DMC) W. The symmetric capacity I(W) is the highest rate achievable subject to using the input letters of the channel equiprobably and equals the capacity C(W) if the channel has certain symmetry properties. Channel polarization refers to the fact that it is possible to synthesize, out of N independent copies of a given B-DMC W, a different set of N binary-input channels such that the capacities of the latter set, except for a negligible fraction of them, are either near 1 or near 0. This second set of N channels are well-conditioned for channel coding: one need only send data at full rate through channels with capacity near 1 and at 0 rate through the others. The main coding theorem about polar coding states that, given any B-DMC W with I(W) > 0 and any fixed 0 < delta < I(W), there exist finite constants n1 (W, delta) and c(W, delta) such that for all ngesn1, there exist polar codes with block length N = 2n rate R > I(W)-delta, and probability of block decoding error Pe les cN-1/4. The codes with this performance can be encoded and decoded within complexity O(N log N).