nils-herrmann/clean_scirepeval huggingface dataset:
Compensation strategy to reduce geometry and mechanics mismatches in porous biomaterials built with Selective Laser Melting.

The accuracy of Additive Manufacturing processes in fabricating porous biomaterials is currently limited by their capacity to render pore morphology that precisely matches its design. In a porous biomaterial, a geometric mismatch can result in pore occlusion and strut thinning, drawbacks that can inherently compromise bone ingrowth and severely impact mechanical performance. This paper focuses on Selective Laser Melting of porous microarchitecture and proposes a compensation scheme that reduces the morphology mismatch between as-designed and as-manufactured geometry, in particular that of the pore. A spider web analog is introduced, built out of Ti-6Al-4V powder via SLM, and morphologically characterized. Results from error analysis of strut thickness are used to generate thickness compensation relations expressed as a function of the angle each strut formed with the build plane. The scheme is applied to fabricate a set of three-dimensional porous biomaterials, which are morphologically and mechanically characterized via micro Computed Tomography, mechanically tested and numerically analyzed. For strut thickness, the results show the largest mismatch (60% from the design) occurring for horizontal members, reduces to 3.1% upon application of the compensation. Similar improvement is observed also for the mechanical properties, a factor that further corroborates the merit of the design-oriented scheme here introduced.
Social group recommendation in the tourism domain

Recommender Systems learn users’ preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual users. In this article, we present a social based approach for recommender systems in the tourism domain, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members of a group. This aspect is a hot research topic in the recommender systems area. In addition, to generate the individual and group recommendations our approach uses a hybrid technique that combines three well-known filtering techniques: collaborative, content-based and demographic filtering. In this way, the disadvantages of one technique are overcome by the others. Our approach was materialized in a recommender system named Hermes, which suggests tourist attractions to both individuals and groups of users. We have obtained promising results when comparing our approach with classic approaches to generate recommendations to individual users and groups. These results suggest that considering the type of users’ relationship to provide recommendations to groups leads to more accurate recommendations in the tourism domain. These findings can be helpful for recommender systems developers and for researchers in this area.
A similarity-based approach for test case prioritization using historical failure data

Test case prioritization is a crucial element in software quality assurance in practice, specially, in the context of regression testing. Typically, test cases are prioritized in a way that they detect the potential faults earlier. The effectiveness of test cases, in terms of fault detection, is estimated using quality metrics, such as code coverage, size, and historical fault detection. Prior studies have shown that previously failing test cases are highly likely to fail again in the next releases, therefore, they are highly ranked, while prioritizing. However, in practice, a failing test case may not be exactly the same as a previously failed test case, but quite similar, e.g., when the new failing test is a slightly modified version of an old failing one to catch an undetected fault. In this paper, we define a class of metrics that estimate the test cases quality using their similarity to the previously failing test cases. We have conducted several experiments with five real world open source software systems, with real faults, to evaluate the effectiveness of these quality metrics. The results of our study show that our proposed similarity-based quality measure is significantly more effective for prioritizing test cases compared to existing test case quality measures.
Security in Software Defined Networks: A Survey

Software defined networking (SDN) decouples the network control and data planes. The network intelligence and state are logically centralized and the underlying network infrastructure is abstracted from applications. SDN enhances network security by means of global visibility of the network state where a conflict can be easily resolved from the logically centralized control plane. Hence, the SDN architecture empowers networks to actively monitor traffic and diagnose threats to facilitates network forensics, security policy alteration, and security service insertion. The separation of the control and data planes, however, opens security challenges, such as man-in-the middle attacks, denial of service (DoS) attacks, and saturation attacks. In this paper, we analyze security threats to application, control, and data planes of SDN. The security platforms that secure each of the planes are described followed by various security approaches for network-wide security in SDN. SDN security is analyzed according to security dimensions of the ITU-T recommendation, as well as, by the costs of security solutions. In a nutshell, this paper highlights the present and future security challenges in SDN and future directions for secure SDN.
Formality of Language : definition , measurement and behavioral determinants

A new concept of formality of linguistic expressions is introduced and argued to be the most important dimension of variation between styles or registers. Formality is subdivided into "deep" formality and "surface" formality. Deep formality is defined as avoidance of ambiguity by minimizing the context-dependence and fuzziness of expressions. This is achieved by explicit and precise description of the elements of the context needed to disambiguate the expression. A formal style is characterized by detachment, accuracy, rigidity and heaviness; an informal style is more flexible, direct, implicit, and involved, but less informative. An empirical measure of formality, the F-score, is proposed, based on the frequencies of different word classes in the corpus. Nouns, adjectives, articles and prepositions are more frequent in formal styles; pronouns, adverbs, verbs and interjections are more frequent in informal styles. It is shown that this measure, though coarse-grained, adequately distinguishes more from less formal genres of language production, for some available corpora in Dutch, French, Italian, and English. A factor similar to the F-score automatically emerges as the most important one from factor analyses applied to extensive data in 7 different languages. Different situational and personality factors are examined which determine the degree of formality in linguistic expression. It is proposed that formality becomes larger when the distance in space, time or background between the interlocutors increases, and when the speaker is male, introverted or academically educated. Some empirical evidence and a preliminary theoretical explanation for these propositions is discussed.  The concept of "deep" formality is proposed as the most important dimension of variation between language registers or styles. It is defined as avoidance of ambiguity by minimizing the context-dependence and fuzziness of expressions. An empirical measure, the F-score, is proposed, based on the frequencies of different word classes. This measure adequately distinguishes different genres of language production using data for Dutch, French, Italian, and English. Factor analyses applied to data in 7 different languages produce a similar factor as the most important one. Both the data and the theoretical model suggest that formality increases when the distance in space, time or background between the interlocutors increases, and when the speaker is male, introverted or academically educated.
Smart Itinerary Recommendation Based on User-Generated GPS Trajectories

Traveling to unfamiliar regions require a significant effort from novice travelers to plan where to go within a limited duration. In this paper, we propose a smart recommendation for highly efficient and balanced itineraries based on multiple user-generated GPS trajectories. Users only need to provide a minimal query composed of a start point, an end point and travel duration to receive an itinerary recommendation. To differentiate good itinerary candidates from less fulfilling ones, we describe how we model and define itinerary in terms of several characteristics mined from user-generated GPS trajectories. Further, we evaluated the efficiency of our method based on 17,745 user-generated GPS trajectories contributed by 125 users in Beijing, China. Also we performed a user study where current residents of Beijing used our system to review and give ratings to itineraries generated by our algorithm and baseline algorithms for comparison.
EyePACS: an adaptable telemedicine system for diabetic retinopathy screening.

Annual retinal screening of patients with diabetes is the standard clinical practice to prevent visual impairment and blindness from diabetic retinopathy. Telemedicine-based diabetic retinopathy screening (DRS) in primary care settings can effectively detect sight-threatening retinopathy and significantly increase compliance with annual retinal exams. EyePACS is a license-free Web-based DRS system designed to simplify the process of image capture, transmission, and review. The system provides a flexible platform for collaboration among clinicians about diabetic retinopathy.



Primary clinic personnel (i.e., nursing, technical, or administrative staff) are trained and certified by the EyePACS program to acquire retinal images from standard digital retinal cameras. Relevant clinical data and eight high-resolution images per patient (two external and six retinal images) are encrypted and transmitted to a secure Internet server, using a standard computer and Web browser. Images are then interpreted by certified EyePACS reviewers or local eye care providers who are certified through the EyePACS Retinopathy Grading System. Reports indicating retinopathy level and referral recommendations are transmitted back to primary care providers through the EyePACS Web site or through interfaces between EyePACS and Health Level 7-compliant electronic medical records or chronic disease registries.



The pilot phase of the EyePACS DRS program in California (2005-2006) recorded 3562 encounters. Since 2006, EyePACS has been expanded to over 120 primary care sites throughout California and elsewhere recording over 34,000 DRSs. The overall rate of referral is 8.21% for sight-threatening retinopathy and 7.83% for other conditions (e.g., cataract and glaucoma).



The use of license-free Web-based software, standard interfaces, and flexible protocols has allowed primary care providers to adopt retinopathy screening with minimal effort and resources.
Therapeutic use of sleep deprivation in depression.

Total sleep deprivation (TSD) for one whole night improves depressive symptoms in 40-60% of treatments. The degree of clinical change spans a continuum from complete remission to worsening (in 2-7%). Other side effects are sleepiness and (hypo-) mania. Sleep deprivation (SD) response shows up in the SD night or on the following day. Ten to 15% of patients respond after recovery sleep only. After recovery sleep 50-80% of day 1 responders suffer a complete or partial relapse; but improvement can last for weeks. Sleep seems to lead to relapse although this is not necessarily the case. Treatment effects may be stabilised by antidepressant drugs, lithium, shifting of sleep time or light therapy. The best predictor of a therapeutic effect is a large variability of mood. Current opinion is that partial sleep deprivation (PSD) in the second half of the night is equally effective as TSD. There are, however, indications that TSD is superior. Early PSD (i.e. sleeping between 3:00 and 6:00) has the same effect as late PSD given equal sleep duration. New data cast doubt on the time-honoured conviction that REM sleep deprivation is more effective than non-REM SD. Both may work by reducing total sleep time. SD is an unspecific therapy. The main indication is the depressive syndrome. Some studies show positive effects in Parkinson's disease. It is still unknown how sleep deprivation works.
Example-Tracing Tutors: Intelligent Tutor Development for Non-programmers

In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing tutors was shown to be 4–8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its associated learning management system, the Tutorshop, have been extended and have been used for both research and real-world instruction. As evidence that example-tracing tutors are an effective and mature ITS paradigm, CTAT-built tutors have been used by approximately 44,000 students and account for 40 % of the data sets in DataShop, a large open repository for educational technology data sets. We review 18 example-tracing tutors built since 2009, which have been shown to be effective in helping students learn in real educational settings, often with large pre/post effect sizes. These tutors support a variety of pedagogical approaches, beyond step-based problem solving, including collaborative learning, educational games, and guided invention activities. CTAT and other ITS authoring tools illustrate that non-programmer approaches to building ITS are viable and useful and will likely play a key role in making ITS widespread.
Socioeconomic status modifies the sex difference in spatial skill.

We examined whether the male spatial advantage varies across children from different socioeconomic (SES) groups. In a longitudinal study, children were administered two spatial tasks requiring mental transformations and a syntax comprehension task in the fall and spring of second and third grades. Boys from middle- and high-SES backgrounds outperformed their female counterparts on both spatial tasks, whereas boys and girls from a low-SES group did not differ in their performance level on these tasks. As expected, no sex differences were found on the verbal comprehension task. Prior studies have generally been based on the assumption that the male spatial advantage reflects ability differences in the population as a whole. Our finding that the advantage is sensitive to variations in SES provides a challenge to this assumption, and has implications for a successful explanation of the sex-related difference in spatial skill.
Diving into Haze-Lines : Color Restoration of Underwater Images

Underwater images suffer from color distortion and low contrast, because light is attenuated as it propagates through water. The attenuation varies with wavelength and depends both on the properties of the water body in which the image was taken and the 3D structure of the scene, making it difficult to restore the colors. Existing single underwater image enhancement techniques either ignore the wavelength dependency of the attenuation, or assume a specific spectral profile. We propose a new method that takes into account multiple spectral profiles of different water types, and restores underwater scenes from a single image. We show that by estimating just two additional global parameters the attenuation ratios of the blue-red and blue-green color channels the problem of underwater image restoration can be reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since we do not know the water type ahead of time, we try different parameter sets out of an existing library of water types. Each set leads to a different restored image and the one that best satisfies the Gray-World assumption is chosen. The proposed single underwater image restoration method is fully automatic and is based on a more comprehensive physical image formation model than previously used. We collected a dataset of real images taken in different locations with varying water properties and placed color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of the proposed method.
Data Mining in Manufacturing: a Review

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.
A Framework of Translator From English Speech To Sanskrit Text

Human computer interaction is defined as Users (Humans) interact with the computers. Speech recognition is an area of computer science that deals with the designing of systems that recognize spoken words. Speech recognition system allows ordinary people to speak to the system. Recognizing and understanding a spoken sentence is obviously a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and pragmatics. This paper is the survey of how speech is converted in text and that text in translated into another language. In this paper, we outline a speech recognition system, learning based approach and target language generation mechanism with the help of language EnglishSanskrit language pair using rule based machine translation technique [1]. Rule Based Machine Translation provides high quality translation and requires in depth knowledge of the language apart from real world knowledge and the differences in cultural background and conceptual divisions. Here the English speech is first converted into text and that will translated into Sanskrit language. Speech Recognition, Sanskrit, Context Free Grammar, Rule based machine translation, Database.
Life-stage Prediction for Product Recommendation in E-commerce

Although marketing researchers and sociologists have recognized the large impact of life stage on consumer's purchasing behaviors, existing recommender systems have not taken this impact into consideration. In this paper, we found obvious correlation between life stage and purchasing behavior in many E-commerce categories. For example, a mum may look for different suitable products when her baby is at different ages. Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user's current life-stage and recommend products correspondingly. We propose a new Maximum Entropy Semi Markov Model to segment and label consumer life stage based on the observed purchasing data over time. In the mom-baby product category where the life stage transition is deterministic, we develop an efficient approximate solution using large scale logistic regression and a Viterbi-like algorithm. We also propose a Gaussian mixture model to efficiently handle multi-kids life stage prediction problem. We integrate the life stage information predicted into the recommender system behind the largest online shopping website taobao.com. Both offline and online experiments demonstrate the effectiveness of the proposed life-stage based recommendation approach.
A framework for FPGA acceleration of large graph problems: Graphlet counting case study

In many application domains, data are represented using large graphs involving millions of vertices and edges. Graph analysis algorithms, such as finding short paths and isomorphic subgraphs, are largely dominated by memory latency. Large cluster-based computing platforms can process graphs efficiently if the graph data can be partitioned, and on a smaller scale partitioning can be used to allocate graphs to low-latency on-chip RAMs in reconfigurable devices. However, there are many graph classes, such as scale-free social networks, which lack the locality to make partitioning graph data an efficient solution to the latency problem and are far too large to fit in on-chip RAMs and caches. In this paper, we present a framework for reconfigurable hardware acceleration of these large-scale graph problems that are difficult to partition and require high-latency off-chip memory storage. Our reconfigurable architecture tolerates off-chip memory latency by using a memory crossbar that connects many parallel identical processing elements to shared off-chip memory, without a traditional cached memory hierarchy. Quantitative comparison between the software and hardware performance of a graphlet counting case-study shows that our hardware implementation outperforms a quad-core software implementation by 10 times for large graphs. This speedup includes all software and IO overhead required, and reduces execution time for this common bioinformatics algorithm from about 2 hours to just 12 minutes. These results demonstrate that our methodology for accelerating graph algorithms is a promising approach for efficient parallel graph processing.
The effects of ten weeks of lower-body unstable surface training on markers of athletic performance.

Initially reserved for rehabilitation programs, unstable surface training (UST) has recently grown in popularity in strength and conditioning and general exercise scenarios. Nonetheless, no studies to date have examined the effects of UST on performance in healthy, trained individuals. The purpose of this study was to determine the effects of 10 weeks of lower-body UST on performance in elite athletes. Nineteen healthy, trained members (ages 18-23 years) of a National Collegiate Athletic Association Division I collegiate men's soccer team participated. The experimental (US) group (n = 10) supplemented their normal conditioning program with lower-body exercises on inflatable rubber discs; the control (ST) group (n = 9) performed the same exercises on stable surfaces. Bounce drop jump (BDJ) and countermovement jump (CMJ) heights, 40- and 10-yard sprint times, and T-test (agility) times were assessed before and after the intervention. The ST group improved significantly on predicted power output on both the BDJ (3.2%) and CMJ (2.4%); no significant changes were noted in the US group. Both groups improved significantly on the 40- (US = -1.8%, ST = -3.9%) and 10-yard sprint times (US = -4.0%, ST = -7.6%). The ST group improved significantly more than the US group in 40-yard sprint time; a trend toward greater improvement in the ST group was apparent on the 10-yard sprint time. Both groups improved significantly (US = 2.9%, ST = -4.4%) on T-test performance; no statistically significant changes were apparent between the groups. These results indicate that UST using inflatable rubber discs attenuates performance improvements in healthy, trained athletes. Such implements have proved valuable in rehabilitation, but caution should be exercised when applying UST to athletic performance and general exercise scenarios.