nils-herrmann/clean_scirepeval huggingface dataset:
A User Interest Model for Web Page Navigation

Making recommendation requires predicting what is of interest to a user at a specific time. Even the same user may have different desires at different times. It is important to extract the aggregate interest of a user from his or her navigational path through the site in a session. This paper concentrates on the discovery and modelling of the user’s aggregate interest in a session. This approach relies on the premise that the visiting time of a page is an indicator of the user’s interest in that page. The proportion of times spent in a set of pages requested by the user within a single session forms the aggregate interest of that user in that session. We first partition user sessions into clusters such that only sessions which represent similar aggregate interest of users are placed in the same cluster. We employ a model-based clustering approach and partition user sessions according to similar amount of time in similar pages. In particular, we cluster sessions by learning a mixture of Poisson models using Expectation Maximization algorithm. The resulting clusters are then used to recommend pages to a user that are most likely contain the information which is of interest to that user at that time. Although the approach does not use the sequential patterns of transactions, experimental evaluation shows that the approach is quite effective in capturing a Web user’s access pattern. The model has an advantage over previous proposals in terms of speed and memory usage.
NEUROPSYCHOLOGICAL MEASURES OF EXECUTIVE FUNCTION AND ANTISOCIAL BEHAVIOR: A META-ANALYSIS

A meta-analysis was performed to quantify the association between antisocial behavior (ASB) and performance on executive functioning (EF) measures. The metaanalysis expanded on Morgan and Lilienfeld’s (2000) meta-analysis of the same topic by including studies published between 1997 and 2008 and by examining a wider range of EF measures. A total of 42 studies (2,595 participants) were included in the present meta-analysis. Overall, the mean effect size indicated that antisocial groups performed 0.47 standard deviations worse on EF measures compared to control groups. This effect size was in the medium range, compared to the medium to large 0.62 average weighted mean effect size produced by Morgan and Lilienfeld. There was significant variation in calculated effect sizes across studies, indicating that the overall mean effect size was not representative of the association between ASB and EF. Effect size magnitude varied according to ASB groups and measures of EF. Cognitive impairments in ASB were not specific to EF. Other methodological issues in the research literature and implications of the meta-analysis results are discussed and directions for future research are proposed.
Road safety legislation in the Americas.

Legislating five of the main risk factors for road traffic injuries (RTIs), as much as enforcing the law, is essential in forging an integral culture of road safety. Analysis of the level of progression in law enforcement allows for an evaluation of the state of world regions. A secondary analysis of the 2009 Global status report on road safety: time for action survey was undertaken to evaluate legislation on five risk factors (speed management, drinking and driving, motorcycle helmet use, seatbelt use, and use of child restraints) in the Americas. Laws were classified depending on their level of progression: the existence of legislation, whether the legislation is adequate, a level of law enforcement > 6 (on a scale of 0-10), and whether the law is considered comprehensive. A descriptive analysis was performed. The totality of the countries has national or subnational legislation for at least one of the five risk factors. However, 63% have laws on the five risk factors studied, and none of them has comprehensive laws for all five. Seatbelt use appears to be the most extended enforced legislation, while speeding laws appear to be the least enforced. There are positive efforts that should be recognized in the region. However, the region stands in different stages of progression. Law enforcement remains the main issue to be tackled. Laws should be based on evidence about what is already known to be effective.
Partial migration: niche shift versus sexual maturation in fishes

Many fish populations have both resident and migratory individuals. Migrants usually grow larger and have higher reproductive potential but lower survival than resident conspecifics. The ‘decision’ about migration versus residence probably depends on the individual growth rate, or a physiological process like metabolic rate which is correlated with growth rate. Fish usually mature as their somatic growth levels off, where energetic costs of maintenance approach energetic intake. After maturation, growth also stagnates because of resource allocation to reproduction. Instead of maturation, however, fish may move to an alternative feeding habitat and their fitness may thereby be increased. When doing so, maturity is usually delayed, either to the new asymptotic length, or sooner, if that gives higher expected fitness. Females often dominate among migrants and males among residents. The reason is probably that females maximize their fitness by growing larger, because their reproductive success generally increases exponentially with body size. Males, on the other hand, may maximize fitness by alternative life histories, e.g. fighting versus sneaking, as in many salmonid species where small residents are the sneakers and large migrants the fighters. Partial migration appears to be partly developmental, depending on environmental conditions, and partly genetic, inherited as a quantitative trait influenced by a number of genes.
HICAMP bitmap: space-efficient updatable bitmap index for in-memory databases

Bitmap represents an efficient indexing structure for querying large amounts of data and is widely deployed in data-warehouse applications. While the size of a bitmap scales linearly with the number of rows in a table, due to its sparseness, it can be greatly reduced via compression based on run-length encoding. However, updating a compressed bitmap is expensive due to the encoding and decoding overheads, in particular, as re-compression can change the compressed sequence length and data layout. Due to this problem, bitmap indices only perform well for read-only workloads.
 In this paper, we propose a bitmap index structure which is both space-efficient and allows fast updates, by building on top of a smart memory model called HICAMP. As a consequence, our approach enables bitmap indices for workloads that exhibit high update ratios as in OLTP workloads. We also present a new multi-bit bitmap design which addresses the candidate checking problem. In our experiments, the HICAMP bitmap index demonstrates 3~12x reduction in size over B-tree and 8~30x over other commonly used indexing structures such as Red-Black tree, while supporting efficient updates simultaneously.
Underuse of yoga as a referral resource by health professions students.

Nearly 38% of U.S. adults use complementary and alternative medicine approaches to manage physical conditions (e.g., chronic pain, arthritis, cancer, heart disease, and high blood pressure) and psychological or emotional health concerns (e.g., post-traumatic stress disorder, anxiety, and depression). Research evidence has accumulated for yoga as an effective treatment approach for these conditions. Further, yoga has increased in popularity among healthcare providers and the general population. Given these trends, this study explored perceptions about yoga as a viable complementary treatment to which health professions students would refer patients.



More than 1500 students enrolled in health professions programs at a Pacific Northwest school were enrolled; data were obtained from 478 respondents.



The study assessed willingness to refer patients to yoga as a complementary and alternative medicine for 27 symptoms (identified in the literature as having evidence for yoga's utility), which were subsequently grouped into skeletal, physical, and psychological on the basis of factor analysis. Responses were assessed using a mixed-model analysis of variance with health profession and yoga practitioner as between-subjects variables and symptoms as a within-subjects factor.



In descending order of likelihood to refer patients to yoga were students in occupational therapy, physician assistant program, psychology, physical therapy, pharmacy, dental hygiene, speech and audiology, and optometry. All groups perceived yoga's greatest utility for skeletal symptoms, followed by psychological and physical symptoms. Findings also revealed a significant positive relationship between level of personal yoga practice and willingness to refer patients to yoga.



Although students expressed some openness to referring patients to yoga, ratings of appropriateness were not accurately aligned with extant evidence base. Personal experience seemed to be a salient factor for accepting yoga as a referral target. These findings suggest the importance of developing strategies to make health professionals more aware of the merits of yoga, regardless of whether they themselves are yoga practitioners.
MetaExp: Interactive Explanation and Exploration of Large Knowledge Graphs

We present MetaExp, a system that assists the user during the exploration of large knowledge graphs, given two sets of initial nodes. At its core,MetaExp presents a small set of meta-paths to the user, which are sequences of relationships among node types. Such meta-paths do not overwhelm the user with complex structures, yet they preserve semantically-rich relationships in a graph. MetaExp engages the user in an interactive procedure, which involves simple meta-paths evaluations to infer a user-specific similarity measure. This similarity measure incorporates the domain knowledge and the preferences of the user, overcoming the fundamental limitations of previous methods based on local node neighborhoods or statically determined similarity scores. Our system provides a userfriendly interface for searching initial nodes and guides the user towards progressive refinements of the meta-paths. The system is demonstrated on three datasets, Freebase, a movie database, and a biological network.  Freya Behrens, Sebastian Bischoff, Pius Ladenburger, Julius Rückin, Laurenz Seidel, Fabian Stolp, Michael Vaichenker, Adrian Ziegler, Davide Mottin, Fatemeh Aghaei, Emmanuel Müller, Martin Preusse, Nikola Müller, Michael Hunger. 2018.MetaExp: Interactive Explanation and Exploration of Large Knowledge Graphs. InWWW ’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 4 pages.
Augmented reality in neurosurgery

Neurosurgery is a medical specialty that relies heavily on imaging. The use of computed tomography and magnetic resonance images during preoperative planning and intraoperative surgical navigation is vital to the success of the surgery and positive patient outcome. Augmented reality application in neurosurgery has the potential to revolutionize and change the way neurosurgeons plan and perform surgical procedures in the future. Augmented reality technology is currently commercially available for neurosurgery for simulation and training. However, the use of augmented reality in the clinical setting is still in its infancy. Researchers are now testing augmented reality system prototypes to determine and address the barriers and limitations of the technology before it can be widely accepted and used in the clinical setting.
Autoimmune progesterone dermatitis with delayed intradermal skin reaction: A case report.

A 47-year-old, gravida 3, para 2 woman presented to the allergy and vesiculobullous eruptions, eczematous lesions, fixed-drug erupimmunology clinic with a recurrent, cyclical urticarial rash on the trunk and upper extremities of 1-year duration. A detailed clinical history revealed that the rash appeared approximately 10 days before menses and spontaneously remitted 2 to 3 days into her menstruation period. The lesions always commenced on the right shoulder, resolved after a few days, then reappeared at other locations. The lesions were pruritic, burning, and not associated with systemic symptoms. Previous unsuccessful medical management included a 1-time steroid injection, topical steroid creams, and histamine1and histamine2-antagonists. Her medical history is significant for Crohn disease, and she was not taking any medications. There were no lesions present on initial examination. A complete blood cell count revealed the following: hemoglobin, 13.0 g/ dL; hematocrit, 39.5%; white blood cells, 7,500/mL (eosinophils, 100/ mL) and platelets, 212,000/mL. Sedimentation rate, antinuclear antibody level, C4 level, tryptase level, and liver and thyroid function test results were within normal limits. A punch biopsy specimen was obtained from a lesion on the right upper arm at a subsequent visit, which revealed perivascular lymphocytic inflammationwith rare eosinophils. The results of skin prick testing with progesterone (50 mg/mL in normal saline) were negative. The results of intradermal testing (50 mg/mL of progesterone at dilutions of 1:1,000, 1:100, and 1:101) were initially negative at 20minute intervals. A 3-mm wheal and 6-mm flare at the 1:10 injection site evolved at 24 hours . The patient was prescribed levonorgestrel and ethinyl estradiol (0.1 and 0.02 mg, respectively) and ethinyl estradiol (0.01mg) with significant improvement of her symptoms. Autoimmune progesterone dermatitis (AIPD) is an uncommon hypersensitivity reaction to endogenous or exogenous progesterone, with fewer than 100 reported cases in the  It is a cyclical dermatosis that usually occurs in fertile women during the luteal phase of the menstrual cycle.2 Nonetheless, it has been reported in a man and postmenopausal women receiving exogenous progesterone.3 Lesions typically appear 3 to 10 days before menstrual flow and subside as progesterone levels decrease 2 to 3 days into menses.4 The natural course of AIPD varies, with some patients experiencing worsening symptoms over time and others achieving spontaneous remission. However, many patients require ongoing treatment for this chronic condition.5 The clinical presentation is highly variable and nonspecific and thus can be mistaken for other skin conditions. Cutaneous manifestations include urticarial lesions, erythema multiforme,
Real-time motion planning for agile autonomous vehicles

Planning the path of an autonomous, agile vehicle ina dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of  xed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle’s motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of  nite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.
Fast Language Model Lookahead Algorithm Using Extended N-gram Model

For a dynamic network based large vocabulary continuous speech recognizer, this paper proposes a fast language model (LM) look-ahead method using extended N -gram model. The extended N -gram model unifies the representations and score computations of the LM and the LM look-ahead tree, and thus greatly simplifies the decoder implementation and improves the LM look-ahead speed significantly, which makes higher-order LM look-ahead possible. The extended N -gram model is generated off-line before decoding starts. The generation procedure makes use of sparseness of backing-off N -gram models for efficient look-ahead score computation, and uses word-end node pushing and score quantitation to compact the model′s storage space. Experiments showed that with the same character error rate, the proposed method speeded up the overall recognition speed by a factor of 5∼ 9 than the traditional dynamic programming method which computes LM look-ahead scores on-line during the decoding process, and that using higher-order LM look-ahead algorithm can achieve a faster decoding speed and better accuracy than using the lower-order look-ahead ones.
High throughput Ku-band satellites for aeronautical applications

Recent advancements in Ku-band high throughput satellites (HTS) will allow commercial Ku-band aeronautical mobile satellite systems (AMSS) to equal or exceed commercial Ka-band AMSS systems on cost and performance. The AMSS market is currently dominated by Ku-band solutions, both in the commercial sector (eXConnect, Row44, Yonder) and the government sector (Tachyon, Boeing Broadband Satellite Network (formerly Connexion), various UAV and ISR systems). All of these systems use conventional continental-scale wide beams that are leased from Fixed Satellite Service (FSS) providers such as Intelsat and Eutelsat. In the next several years the dominance of Ku-band AMSS will be challenged by Ka-band systems such as Inmarsat-5, which use multiple spot beams to offer enhanced performance. Previous work has suggested that these systems may offer better performance and better economics than conventional Ku-band systems . The key insight of this paper is that the performance advantage of spot beam Ka-band systems comes from their smaller beam size rather than their frequency of operation, meaning that a Ku-band spot beam satellite can also be built with similar sized spot beams to Ka-band systems and can achieve competitive cost and performance as compared to a Ka-band spot beam systems. High throughput spot beam Ku-band systems, such as Intelsat's EpicNG system, are now in development and will be fielded in the same timeframe as Inmarsat-5. This result has critical implications for existing users and operators of AMSS systems: - Currently installed Ku-band terminals will be able to take advantage of dramatic improvements in performance when high throughput Ku-band becomes available - Current Ku-band will not have to undergo costly Ka-band retrofits to maintain competitive performance - Operators can continue to invest in Ku-band terminals today without fear of obsolescence in the near future - The AMSS market will continue to be diverse and competitive for years to come.
Analysis of Anxious Word Usage on Online Health Forums

Online health communities and support groups are a valuable source of information for users suffering from a physical or mental illness. Users turn to these forums for moral support or advice on specific conditions, symptoms, or side effects of medications. This paper describes and studies the linguistic patterns of a community of support forum users over time focused on the used of anxious related words. We introduce a methodology to identify groups of individuals exhibiting linguistic patterns associated with anxiety and the correlations between this linguistic pattern and other word usage. We find some evidence that participation in these groups does yield positive effects on their users by reducing the frequency of anxious related word used over time.
Williams syndrome: cognition, personality, and adaptive behavior.

Williams syndrome is caused by a microdeletion of at least 16 genes on chromosome 7q11.23. The syndrome results in mild to moderate mental retardation or learning disability. The behavioral phenotype for Williams syndrome is characterized by a distinctive cognitive profile and an unusual personality profile. Relative to overall level of intellectual ability, individuals with Williams syndrome typically show a clear strength in auditory rote memory, a strength in language, and an extreme weakness in visuospatial construction. The personality of individuals with Williams syndrome involves high sociability, overfriendliness, and empathy, with an undercurrent of anxiety related to social situations. The adaptive behavior profile for Williams syndrome involves clear strength in socialization skills (especially interpersonal skills related to initiating social interaction), strength in communication, and clear weakness in daily living skills and motor skills, relative to overall level of adaptive behavior functioning. Literature relevant to each of the components of the Williams syndrome behavioral phenotype is reviewed, including operationalizations of the Williams syndrome cognitive profile and the Williams syndrome personality profile. The sensitivity and specificity of these profiles for Williams syndrome, relative to individuals with other syndromes or mental retardation or borderline normal intelligence of unknown etiology, is considered. The adaptive behavior profile is discussed in relation to the cognitive and personality profiles. The importance of operationalizations of crucial components of the behavioral phenotype for the study of genotype/phenotype correlations in Williams syndrome is stressed.  Research Reviews 2000;6:148-158.
Measuring Semantic Coherence of a Conversation

Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.