nils-herrmann/clean_scirepeval huggingface dataset:
Corpus-Based Translation Induction in Indian Languages Using Auxiliary Language Corpora from Wikipedia

Identifying translations from comparable corpora is a well-known problem with several applications. Existing methods rely on linguistic tools or high-quality corpora. Absence of such resources, especially in Indian languages, makes this problem hard; for example, state-of-the-art techniques achieve a mean reciprocal rank of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. In this work, we address the problem of comparable corpora-based translation correspondence induction (CC-TCI) when the only resources available are small noisy comparable corpora extracted from Wikipedia. We observe that translations in the source and target languages have many topically related words in common in other “auxiliary” languages. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for CC-TCI. Extensive experiments on 35 comparable corpora showed dramatic improvements in performance. We extend these ideas to propose a method for measuring cross-lingual semantic relatedness (CLSR) between words. To stimulate further research in this area, we make publicly available two new high-quality human-annotated datasets for CLSR. Experiments on the CLSR datasets show more than 200% improvement in correlation on the CLSR task. We apply the method to the real-world problem of cross-lingual Wikipedia title suggestion and build the WikiTSu system. A user study on WikiTSu shows a 20% improvement in the quality of titles suggested.
Hummel's Digital Transformation Toward Omnichannel Retailing: Key Lessons Learned

With the growing use of mobile and social media, many organizations are realizing they need an online presence to reach out to digitally savvy customers. But delivering a seamless customer experience across various online and offline channels is challenging. The full article describes how Hummel, a European sports fashion company, overcame the challenges and successfully transitioned toward omnichannel retailing. (Omnichannel capabilities integrate online and offline channels in engaging and serving customers.) Based on this case, the article provides insights to guide organizations with similar ambitions and the implications for their CIOs.
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models

Following the recent progress in deep learning, researchers and practitioners of machine learning are recognizing the importance of understanding and interpreting what goes on inside these black box models. Recurrent neural networks have recently revolutionized speech recognition and translation, and these powerful models would be very useful in other applications involving sequential data. However, adoption has been slow in applications such as health care, where practitioners are reluctant to let an opaque system make crucial decisions. If we can make the inner workings of RNNs more interpretable, more applications can benefit from their power.
Parenting in a digital age : A review of parents ' role in preventing adolescent cyberbullying

While parents have a critical influence on reducing adolescent risk taking, adolescents' access to online spaces presents significant and novel challenges to parents' ability to reduce their youth's involvement in cyberbullying. The present study reviews the existing literature on parents' influence (i.e., parental warmth and parental monitoring) on adolescent cyberbullying, both as victims and perpetrators. 23 mostly cross sectional articles were identified for this review. Findings indicate that parental warmth is consistently associated with lower cyberbullying, both as victims and perpetrators. For parental monitoring, strategies that are focused on parental control, such as restricting the Internet, appear to be only weakly related to youth's involvement in cyberbullying victimization and perpetration. In contrast, strategies that are more collaborative with in nature (e.g., evaluative mediation and co-use) are more closely connected to cyberbullying victimization and perpetration, although evidence suggests that the effectiveness of these practices varies by sex and ethnicity. Results underscore the need for parents to provide emotional warmth that might support adolescent's disclosure of online activity. Implications for practice and future research are reviewed.
The heritability of IQ

IQ heritability, the portion of a population's IQ variability attributable to the effects of genes, has been investigated for nearly a century, yet it remains controversial. Covariance between relatives may be due not only to genes, but also to shared environments, and most previous models have assumed different degrees of similarity induced by environments specific to twins, to non-twin siblings (henceforth siblings), and to parents and offspring. We now evaluate an alternative model that replaces these three environments by two maternal womb environments, one for twins and another for siblings, along with a common home environment. Meta-analysis of 212 previous studies shows that our ‘maternal-effects’ model fits the data better than the ‘family-environments’ model. Maternal effects, often assumed to be negligible, account for 20% of covariance between twins and 5% between siblings, and the effects of genes are correspondingly reduced, with two measures of heritability being less than 50%. The shared maternal environment may explain the striking correlation between the IQs of twins, especially those of adult twins that were reared apart. IQ heritability increases during early childhood, but whether it stabilizes thereafter remains unclear. A recent study of octogenarians, for instance, suggests that IQ heritability either remains constant through adolescence and adulthood, or continues to increase with age. Although the latter hypothesis has recently been endorsed, it gathers only modest statistical support in our analysis when compared to the maternal-effects hypothesis. Our analysis suggests that it will be important to understand the basis for these maternal effects if ways in which IQ might be increased are to be identified.
State of the Art in Lightweight Symmetric Cryptography

Lightweight cryptography has been one of the “hot topics” in symmetric cryptography in the recent years. A huge number of lightweight algorithms have been published, standardized and/or used in commercial products. In this paper, we discuss the different implementation constraints that a “lightweight” algorithm is usually designed to satisfy. We also present an extensive survey of all lightweight symmetric primitives we are aware of. It covers designs from the academic community, from government agencies and proprietary algorithms which were reverse-engineered or leaked. Relevant national (nist...) and international (iso/iec...) standards are listed. We then discuss some trends we identified in the design of lightweight algorithms, namely the designers’ preference for arx-based and bitsliced-S-Box-based designs and simple key schedules. Finally, we argue that lightweight cryptography is too large a field and that it should be split into two related but distinct areas: ultra-lightweight and IoT cryptography. The former deals only with the smallest of devices for which a lower security level may be justified by the very harsh design constraints. The latter corresponds to low-power embedded processors for which the Aes and modern hash function are costly but which have to provide a high level security due to their greater connectivity.
Production of nanocrystalline cellulose from lignocellulosic biomass: technology and applications.

The use of renewables materials for industrial applications is becoming impellent due to the increasing demand of alternatives to scarce and unrenewable petroleum supplies. In this regard, nanocrystalline cellulose, NCC, derived from cellulose, the most abundant biopolymer, is one of the most promising materials. NCC has unique features, interesting for the development of new materials: the abundance of the source cellulose, its renewability and environmentally benign nature, its mechanical properties and its nano-scaled dimensions open a wide range of possible properties to be discovered. One of the most promising uses of NCC is in polymer matrix nanocomposites, because it can provide a significant reinforcement. This review provides an overview on this emerging nanomaterial, focusing on extraction procedures, especially from lignocellulosic biomass, and on technological developments and applications of NCC-based materials. Challenges and future opportunities of NCC-based materials will be are discussed as well as obstacles remaining for their large use.
Data Modeling and Data Analytics: A Survey from a Big Data Perspective

These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by “relaxing” the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.
COMPLICATIONS ASSOCIATED WITH TOOTH SUPPORTED FIXED DENTAL PROSTHESIS AMONGST PATIENTS VISITING UNIVERSITY COLLEGE OF DENTISTRY LAHORE

The aim of this cross-sectional study was to assess the complications associated with tooth supported fixed dental prosthesis amongst patients reporting at University College of Dentistry Lahore, Pakistan. An interview based questionnaire was used on 112 patients followed by clinical oral examination by two calibrated dentists. Approximately 95% participants were using porcelain fused to metal prosthesis with 60% of prosthesis being used in posterior segments of mouth. Complications like dental caries, coronal abutment fracture, radicular abutment fracture, occlusal interferences, root canal failures and decementations were more significantly associated with crowns than bridges (p=0.000). On the other hand esthetic issues, periapical lesions, periodontal problems, porcelain fractures and metal damage were more commonly associated with bridges (p=0.000). All cases of dental caries reported were associated with acrylic crown and bridges, whereas all coronal abutment fractures were associated with metal prosthesis (p=0.000). A significantly higher number of participants who got their fixed dental prosthesis from other sources i.e. Paramedics, technicians, dental assistants or unqualified dentists had periapical lesions, decementations, esthetic issues and periodontal diseases. This association was found to be statistically significant (p=0.000). Complications associated with fixed dental prosthesis like root canal failures, decementations, periapical lesions and periodontal disease were more significantly associated with prosthesis fabricated by other sources over the period of 5 to 10 years.
Pelvic floor muscle function, pelvic floor dysfunction and diastasis recti abdominis: Prospective cohort study.

Compare vaginal resting pressure (VRP), pelvic floor muscle (PFM) strength, and endurance in women with and without diastasis recti abdominis at gestational week 21 and at 6 weeks, 6 months, and 12 months postpartum. Furthermore, to compare prevalence of urinary incontinence (UI) and pelvic organ prolapse (POP) in the two groups at the same assessment points.



This is a prospective cohort study following 300 nulliparous pregnant women giving birth at a public university hospital. VRP, PFM strength, and endurance were measured with vaginal manometry. ICIQ-UI-SF questionnaire and POP-Q were used to assess UI and POP. Diastasis recti abdominis was diagnosed with palpation of  ≥2 fingerbreadths 4.5 cm above, at, or 4.5 cm below the umbilicus.



At gestational week 21 women with diastasis recti abdominis had statistically significant greater VRP (mean difference 3.06 cm H2 O [95%CI: 0.70; 5.42]), PFM strength (mean difference 5.09 cm H2 O [95%CI: 0.76; 9.42]) and PFM muscle endurance (mean difference 47.08 cm H2 O sec [95%CI: 15.18; 78.99]) than women with no diastasis. There were no statistically significant differences between women with and without diastasis in any PFM variables at 6 weeks, 6 months, and 12 months postpartum. No significant difference was found in prevalence of UI in women with and without diastasis at any assessment points. Six weeks postpartum 15.9% of women without diastasis had POP versus 4.1% in the group with diastasis (P = 0.001).



Women with diastasis were not more likely to have weaker PFM or more UI or POP. Neurourol. Urodynam. 36:716-721, 2017.
Accelerating Neural Networks using Channel Gating

Deploying convolutional neural networks (CNNs) effectively in real-time applications often requires both high throughput and low power consumption. However, a state-of-the-art CNN typically performs about 10 FLOPs per evaluation . Reducing this computational cost has become an essential challenge. Several prior studies have proposed pruning ineffectual features and weights statically, thus reducing the FLOPs . Dedicated hardware accelerators have also been shown to improve performance by exploiting the sparsity in a CNN , , . However, the aforementioned proposals suffer from increasing the irregularity of the computation at the finest granularity. We propose a novel approach to reduce CNN computation, called channel gating, which dynamically prunes the unnecessary computation specific to a particular image, while minimizing the accuracy loss and hardware modification. Intuitively, channel gating leverages the spatial information inside the input features to identify ineffective receptive fields and skip the corresponding computation by gating a fraction of the input channels. The paper makes the following major contributions: • We introduce the channel gating scheme, which dynamically prunes computation on input channels at receptive field level. • We propose an efficient single-pass training scheme to train the channel gating CNN from scratch, allowing the network to automatically learn an effective gating policy. • We demonstrate the benefits of introducing channel gating in CNNs empirically and get 66% and 60% reduction in FLOPs with 0.22% and 0.29% accuracy loss on the CIFAR-10/100 datasets respectively using a state-of-theart ResNet model. • We propose a specialized accelerator architecture, which improves the performance and energy efficiency of the channel gating CNN inference (Ongoing).
Application of Content-Based Approach in Research Paper Recommendation System for a Digital Library

Recommender systems are software applications that provide or suggest items to intended users. These systems use filtering techniques to provide recommendations. The major ones of these techniques are collaborative-based filtering technique, content-based technique, and hybrid algorithm. The motivation came as a result of the need to integrate recommendation feature in digital libraries in order to reduce information overload. Content-based technique is adopted because of its suitability in domains or situations where items are more than the users. TF-IDF (Term Frequency Inverse Document Frequency) and cosine similarity were used to determine how relevant or similar a research paper is to a user's query or profile of interest. Research papers and user's query were represented as vectors of weights using Keyword-based Vector Space model. The weights indicate the degree of association between a research paper and a user's query. This paper also presents an algorithm to provide or suggest recommendations based on users' query. The algorithm employs both TF-IDF weighing scheme and cosine similarity measure. Based on the result or output of the system, integrating recommendation feature in digital libraries will help library users to find most relevant research papers to their needs. Recommender Systems; Content-Based Filtering; Digital Library; TF-IDF; Cosine Similarity; Vector Space Model
Development of a Microcontroller-Controlled Security Door System

Over the years, several security measures have been employed to combat the menace of insecurity of lives and property. This is done by preventing unauthorized entrance into buildings through entrance doors using conventional and electronic locks, discrete access code, and biometric methods such as the finger prints, thumb prints, the iris and facial recognition. In this paper, a prototyped door security system is designed to allow a privileged user to access a secure keyless door where valid smart card authentication guarantees an entry. The model consists of hardware module and software which provides a functionality to allow the door to be controlled through the authentication of smart card by the microcontroller unit.
Structure-Aware Convolutional Neural Networks

Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures. It causes that CNNs are allowed to manage data with Euclidean or grid-like structures (e.g., images), not ones with non-Euclidean or graph structures (e.g., traffic networks). To broaden the reach of CNNs, we develop structure-aware convolution to eliminate the invariance, yielding a unified mechanism of dealing with both Euclidean and non-Euclidean structured data. Technically, filters in the structure-aware convolution are generalized to univariate functions, which are capable of aggregating local inputs with diverse topological structures. Since infinite parameters are required to determine a univariate function, we parameterize these filters with numbered learnable parameters in the context of the function approximation theory. By replacing the classical convolution in CNNs with the structure-aware convolution, Structure-Aware Convolutional Neural Networks (SACNNs) are readily established. Extensive experiments on eleven datasets strongly evidence that SACNNs outperform current models on various machine learning tasks, including image classification and clustering, text categorization, skeleton-based action recognition, molecular activity detection, and taxi flow prediction.
Microbial communication leading to the activation of silent fungal secondary metabolite gene clusters

Microorganisms form diverse multispecies communities in various ecosystems. The high abundance of fungal and bacterial species in these consortia results in specific communication between the microorganisms. A key role in this communication is played by secondary metabolites (SMs), which are also called natural products. Recently, it was shown that interspecies "talk" between microorganisms represents a physiological trigger to activate silent gene clusters leading to the formation of novel SMs by the involved species. This review focuses on mixed microbial cultivation, mainly between bacteria and fungi, with a special emphasis on the induced formation of fungal SMs in co-cultures. In addition, the role of chromatin remodeling in the induction is examined, and methodical perspectives for the analysis of natural products are presented. As an example for an intermicrobial interaction elucidated at the molecular level, we discuss the specific interaction between the filamentous fungi Aspergillus nidulans and Aspergillus fumigatus with the soil bacterium Streptomyces rapamycinicus, which provides an excellent model system to enlighten molecular concepts behind regulatory mechanisms and will pave the way to a novel avenue of drug discovery through targeted activation of silent SM gene clusters through co-cultivations of microorganisms.
Neuromuscular Adaptations to Multimodal Injury Prevention Programs in Youth Sports: A Systematic Review with Meta-Analysis of Randomized Controlled Trials

Neuromuscular injury prevention programs (IPP) can reduce injury rate by about 40% in youth sport. Multimodal IPP include, for instance, balance, strength, power, and agility exercises. Our systematic review and meta-analysis aimed to evaluate the effects of multimodal IPP on neuromuscular performance in youth sports.  We conducted a systematic literature search including selected search terms related to youth sports, injury prevention, and neuromuscular performance.  (i) the study was a (cluster-)randomized controlled trial (RCT), and (ii) investigated healthy participants, up to 20 years of age and involved in organized sport, (iii) an intervention arm performing a multimodal IPP was compared to a control arm following a common training regime, and (iv) neuromuscular performance parameters (e.g., balance, power, strength, sprint) were assessed. Furthermore, we evaluated IPP effects on sport-specific skills.  Fourteen RCTs (comprising 704 participants) were analyzed. Eight studies included only males, and five only females. Seventy-one percent of all studies investigated soccer players with basketball, field hockey, futsal, Gaelic football, and hurling being the remaining sports. The average age of the participants ranged from 10 years up to 19 years and the level of play from recreational to professional. Intervention durations ranged from 4 weeks to 4.5 months with a total of 12 to 57 training sessions. We observed a small overall effect in favor of IPP for balance/stability (Hedges' g = 0.37; 95%CI 0.17, 0.58), leg power (g = 0.22; 95%CI 0.07, 0.38), and isokinetic hamstring and quadriceps strength as well as hamstrings-to-quadriceps ratio (g = 0.38; 95%CI 0.21, 0.55). We found a large overall effect for sprint abilities (g = 0.80; 95%CI 0.50, 1.09) and sport-specific skills (g = 0.83; 95%CI 0.34, 1.32). Subgroup analyses revealed larger effects in high-level (g = 0.34-1.18) compared to low-level athletes (g = 0.22-0.75), in boys (g = 0.27-1.02) compared to girls (g = 0.09-0.38), in older (g = 0.32-1.16) compared to younger athletes (g = 0.18-0.51), and in studies with high (g = 0.35-1.16) compared to low (g = 0.12-0.38) overall number of training sessions.  Multimodal IPP beneficially affect neuromuscular performance. These improvements may substantiate the preventative efficacy of IPP and may support the wide-spread implementation and dissemination of IPP. The study has been a priori registered in PROSPERO (CRD42016053407).