nils-herrmann/clean_scirepeval huggingface dataset:
ADEL: an automatic detector of energy leaks for smartphone applications

Energy leaks occur when applications use energy to perform useless tasks, a surprisingly common occurrence. They are particularly important for mobile applications running on smartphones due to their energy constraints. Energy leaks are difficult to detect and isolate because their negative consequences are often far removed from their causes. Few tools are available for addressing this problem. We have therefore developed ADEL (Automatic Detector of Energy Leaks). ADEL consists of taint-tracking enhancements to the Android platform. It detects and isolates energy leaks resulting from unnecessary network communication by tracing the direct and indirect use of received data to determine whether they ever affect the user. We profiled 15 applications using ADEL. In six of them, energy leaks detected by ADEL and verified by us account for approximately 57% of the energy consumed in communication. We identified four common causes of energy leaks in these applications: misinterpretation of callback API semantics, poorly designed downloading schemes, repetitive downloads, and aggressive prefetching.
Mindfulness , by any other name ... : trials and tribulations of sati in western psychology and science

The Buddhist construct of mindfulness is a central element of mindfulness-based interventions and derives from a systematic phenomenological programme developed over several millennia to investigate subjective experience. Enthusiasm for ‘mindfulness’ in Western psychological and other science has resulted in proliferation of definitions, operationalizations and self-report inventories that purport tomeasure mindful awareness as a trait. This paper addresses a number of seemingly intractable issues regarding current attempts to characterize mindfulness and also highlights a number of vulnerabilities in this domain that may lead to denaturing, distortion, dilution or reification of Buddhist constructs related to mindfulness. Enriching positivist Western psychological paradigms with a detailed and complex Buddhist phenomenology of the mind may require greater study and long-term direct practice of insight meditation than is currently common among psychologists and other scientists. Pursuit of such an approach would seem a necessary precondition for attempts to characterize and quantify mindfulness.
Data augmentation instead of explicit regularization

Modern deep artificial neural networks have achieved impressive results through models with very large capacity—compared to the number of training examples— that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent and parameter sharing in convolutional layers, or explicit. Most common explicit regularization techniques, such as weight decay and dropout, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have been proven successful in terms of improved generalization, they seem to waste capacity. In contrast, data augmentation techniques do not reduce the effective capacity and improve generalization by increasing the number of training examples. In this paper we systematically analyze the effect of data augmentation on some popular architectures and conclude that data augmentation alone—without any other explicit regularization techniques—can achieve the same performance or higher as regularized models, especially when training with fewer examples, and exhibits much higher adaptability to changes in the architecture.
Classification of Diseased Arecanut based on Texture Features

In the proposed work, classification of diseased and undiseased arecanut have been determined using texture features of Local Binary Pattern (LBP), Haar Wavelets, GLCM and Gabor. This work has been carried out in two stages. In the first stage, LBP have been applied on each color component of HSI and YCbCr color models and histogram of LBP is generated. The statistical method correlation is used to measure the distance between histogram of training
A complex network based feature extraction for image retrieval

In this paper, we propose a complex network based low-level feature for image retrieval systems based on the graph representation of the image and the mathematical theory of diffusion over manifolds. We show that the proposed image feature is invariant to non-structural changes on images and performs well in hand written digits classification task. We also show that performance of image retrieval with existing low-level features could be improved by combining with the proposed feature.
Delivering CRISPR: a review of the challenges and approaches

Gene therapy has long held promise to correct a variety of human diseases and defects. Discovery of the Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR), the mechanism of the CRISPR-based prokaryotic adaptive immune system (CRISPR-associated system, Cas), and its repurposing into a potent gene editing tool has revolutionized the field of molecular biology and generated excitement for new and improved gene therapies. Additionally, the simplicity and flexibility of the CRISPR/Cas9 site-specific nuclease system has led to its widespread use in many biological research areas including development of model cell lines, discovering mechanisms of disease, identifying disease targets, development of transgene animals and plants, and transcriptional modulation. In this review, we present the brief history and basic mechanisms of the CRISPR/Cas9 system and its predecessors (ZFNs and TALENs), lessons learned from past human gene therapy efforts, and recent modifications of CRISPR/Cas9 to provide functions beyond gene editing. We introduce several factors that influence CRISPR/Cas9 efficacy which must be addressed before effective in vivo human gene therapy can be realized. The focus then turns to the most difficult barrier to potential in vivo use of CRISPR/Cas9, delivery. We detail the various cargos and delivery vehicles reported for CRISPR/Cas9, including physical delivery methods (e.g. microinjection; electroporation), viral delivery methods (e.g. adeno-associated virus (AAV); full-sized adenovirus and lentivirus), and non-viral delivery methods (e.g. liposomes; polyplexes; gold particles), and discuss their relative merits. We also examine several technologies that, while not currently reported for CRISPR/Cas9 delivery, appear to have promise in this field. The therapeutic potential of CRISPR/Cas9 is vast and will only increase as the technology and its delivery improves.
Robust Visual Place Recognition with Graph Kernels

A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are based on graph kernel formulations, utilizing both the structural and visual information encoded in covisibility graphs. The proposed probabilistic model is able to circumvent the typically difficult and expensive posterior normalization procedure by exploiting the information available in visual observations. Furthermore, the place recognition complexity is independent of the size of the map. Results show improvements over the state-of-theart on a diverse set of both public datasets and novel experiments, highlighting the benefit of the approach.
Compiling with Continuations

Where you can find the compiling with continuations easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this compiling with continuations book. It is about this book that will give wellness for all people from many societies.
University of Groningen Will my auxiliary tagging task help ? Estimating Auxiliary Tasks Effectivity in MultiTask Learning

Multitask learning often improves system performance for morphosyntactic and semantic tagging tasks. However, the question of when and why this is the case has yet to be answered satisfactorily. Although previous work has hypothesised that this is linked to the label distributions of the auxiliary task, we argue that this is not sufficient. We show that information-theoretic measures which consider the joint label distributions of the main and auxiliary tasks offer far more explanatory value. Our findings are empirically supported by experiments for morphosyntactic tasks on 39 languages, and are in line with findings in the literature for several semantic tasks.
Set-Oriented Production Rules in Relational Database Systems

We propose incorporating a production rules facility into a relational database system. Such a facility allows definition of database operations that are automatically executed whenever certain conditions are met. In keeping with the set-oriented approach of relational data manipulation languages, our production rules are also set-oriented—they are triggered by sets of changes to the database and may perform sets of changes. The condition and action parts of our production rules may refer to the current state of the database as well as to the sets of changes triggering the rules. We define a syntax for production rule definition as an extension to SQL. A model of system behavior is used to give an exact semantics for production rule execution, taking into account externally-generated operations, self-triggering rules, and simultaneous triggering of multiple rules.
Convolutional Neural Networks for So-Matching N-Grams in Ad-hoc Search Zhuyun Dai

is paper presents Conv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram so matches for ad-hoc search. Instead of exact matching query and document n-grams, Conv-KNRM uses Convolutional Neural Networks to represent ngrams of various lengths and so matches them in a unied embedding space. e n-gram so matches are then utilized by the kernel pooling and learning-to-rank layers to generate the nal ranking score. Conv-KNRM can be learned end-to-end and fully optimized from user feedback. e learned model’s generalizability is investigated by testing how well it performs in a related domain with small amounts of training data. Experiments on English search logs, Chinese search logs, and TREC Web track tasks demonstrated consistent advantages of Conv-KNRM over prior neural IR methods and feature-based methods.
Low-cost AUV based on Arduino open source microcontroller board for oceanographic research applications in a collaborative long term deployment missions and suitable for combining with an USV as autonomous automatic recharging platform

The challenge of extending the autonomy in AUV deployments is one of the most important issues in oceanographic research today. The possibility of maintaining a team of AUV under deployment in a defined area of interest for a long period could provide an additional source of information . All this data in combination with the measures provided by buoys and sea gliders used for slow motion and long range operations will be very valuable. A group of low cost AUV's in alternative automatic switching system navigation-charging operation, could allow a kind of continuous surveying operation. This work is the continuation of the ideas that some of the authors previously presented in the AUV 2010 conference at MBARI . At this conference was proposed the great interest for researching oceanic processes on two areas near Cartagena, Spain: cape Tiñoso and the Mar Menor a shallow coastal lagoon. Both areas require a different research structure configuration because of their opposite characteristics. The Mar Menor is a shallow salty lagoon 20 miles long with 7 m of maximum depth and particular features. This lagoon seems to present a sort of oceanic behavior and can be compared with the major oceans but a minor scale. The second area considered is cape Tiñoso, a very deep area in the Mediterranean Sea where the presence of a self-break provides an interesting potential for the research of the effect of upwelling currents.
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity

Graph-based semi-supervised learning (SSL) algorithms predict labels for all nodes based on provided labels of a small set of seed nodes. Classic methods capture the graph structure through some underlying diffusion process that propagates through the graph edges. Spectral diffusion, which includes personalized page rank and label propagation, propagates through random walks. Social diffusion propagates through shortest paths. These diffusions are linear in the sense of not distinguishing between contributions of few "strong" relations or many "weak'' relations. Recent methods such as node embeddings and graph convolutional networks (GCN) attained significant gains in quality for SSL tasks. These methods vary on how the graph structure, seed label information, and other features are used, but do share a common thread of nonlinearity that suppresses weak relations and re-enforces stronger ones. Aiming for quality gain with more scalable methods, we revisit classic linear diffusion methods and place them in a self-training framework. The resulting bootstrapped diffusions are nonlinear in that they re-enforce stronger relations, as with the more complex methods. Surprisingly, we observe that SSL with bootstrapped diffusions not only significantly improves over the respective non-bootstrapped baselines but also outperform state-of-the-art SSL methods. Moreover, since the self-training wrapper retains the scalability of the base method, we obtain both higher quality and better scalability.
Unsupervised Metaphor Identification Using Hierarchical Graph Factorization Clustering

We present a novel approach to automatic metaphor identification, that discovers both metaphorical associations and metaphorical expressions in unrestricted text. Our system first performs hierarchical graph factorization clustering (HGFC) of nouns and then searches the resulting graph for metaphorical connections between concepts. It then makes use of the salient features of the metaphorically connected clusters to identify the actual metaphorical expressions. In contrast to previous work, our method is fully unsupervised. Despite this fact, it operates with an encouraging precision (0.69) and recall (0.61). Our approach is also the first one in NLP to exploit the cognitive findings on the differences in organisation of abstract and concrete concepts in the human brain.
Vulvar squamous cell carcinoma in situ within viral papillomas in an aged Quarter Horse mare.

Papillomas are among the most common equine cutaneous neoplasms (Baker and Leyland 1975; Sundberg et al. 1977). Acquired cutaneous papillomas in horses are viral-induced neoplasms which occur in 2 distinct forms: viral papillomatosis and ear papilloma (aural plaques) (Scott and Miller 2003). Viral papillomatosis most often occurs in horses aged <3 years, and spontaneous remission usually occurs within 3–4 months (Scott and Miller 2003). Lesions occur most commonly on the muzzle and lips, and less commonly on the eyelid, external genitalia and distal limbs. Papillomaviruses, the causative agents of acquired cutaneous papillomas in horses, have been shown to have malignant potential in several species of animals with neoplastic progression to squamous cell carcinoma in situ (Bowen’s disease) and invasive squamous cell carcinoma (SCC). Human cervical and vaginal papillomas and squamous neoplasms are highly associated with human papillomavirus (HPV). These are characterised by low to high grade cervical/vaginal squamous intraepithelial neoplasms (CIN and VAIN, respectively) and invasive squamous cell carcinoma. However, only a few subsets of HPV (HPV-16, HPV-18, HPV6/11) have been identified in invasive squamous cell carcinomas (Daling et al. 2002). In dogs, a novel canine papillomavirus (CFPV-2) with unique tropism has been cloned in persistent cutaneous papillomas of the footpad and interdigital tissue (Yuan et al. 2007). When this virus is transplanted into immunocompromised dogs with X-linked severe combined immunodeficiency (X-linked SCID), the resultant papillomas can progress to malignant invasive and metastatic squamous cell carcinoma (Goldschmidt et al. 2006). Cynomolgus monkeys have also demonstrated papillomavirus-associated cervical and vaginal papillomas, CIN/VAIN, and invasive cervical SCC (Wood et al. 2004). Although papillomaviruses have been implicated in the aetiology of SCC in the aforementioned species, papillomavirus antigen has not been previously detected by immunoperoxidase staining in equine SCC, despite the similarity of distribution to that of equine squamous papillomas (Junge et al. 1984). This report describes the clinical and histopathological findings and treatment of an aged mare with persistent vulvar papillomatosis and carcinoma in situ. To the authors’ knowledge this is the first report of vulvar papillomatosis undergoing malignant transformation to carcinoma in a horse.
Machine Learning and Evolutionary Computing for GUI-based Regression Testing

ReTest is a novel testing tool for Java applications with a graphical user interface (GUI), combining monkey testing and difference testing. Since this combination sidesteps the oracle problem, it enables the generation of GUI-based regression tests. ReTest makes use of evolutionary computing (EC), particularly a genetic algorithm (GA), to optimize these tests towards code coverage. While this is indeed a desirable goal in terms of software testing and potentially finds many bugs, it lacks one major ingredient: human behavior. Consequently, human testers often find the results less reasonable and difficult to interpret. This thesis proposes a new approach to improve the initial population of the GA with the aid of machine learning (ML), forming an ML-technique enhancedEC (MLEC) algorithm. In order to do so, existing tests are exploited to extract information on how human testers use the given GUI. The obtained data is then utilized to train an artificial neural network (ANN), which ranks the available GUI actions respectively their underlying GUI components at runtime—reducing the gap between manually created and automatically generated regression tests. Although the approach is implemented on top of ReTest, it can be easily used to guide any form of monkey testing. The results show that with only little training data, the ANN is able to reach an accuracy of 82 % and the resulting tests represent an improvement without reducing the overall code coverage and performance significantly.
The G8 screening tool detects relevant geriatric impairments and predicts survival in elderly patients with a haematological malignancy

The G8 screening tool was developed to separate fit older cancer patients who were able to receive standard treatment from those that should undergo a geriatric assessment to guide tailoring of therapy. We set out to determine the discriminative power and prognostic value of the G8 in older patients with a haematological malignancy. Between September 2009 and May 2013, a multi-dimensional geriatric assessment was performed in consecutive patients aged ≥67 years diagnosed with blood cancer at the Innsbruck University Hospital. The assessment included (instrumental) activities of daily living, cognition, mood, nutritional status, mobility, polypharmacy and social support. In parallel, the G8 was also administered (cut-off ≤ 14). Using a cut-off of ≥2 impaired domains, 70 % of the 108 included patients were considered as having an impaired geriatric assessment while 61 % had an impaired G8. The G8 lacked discriminative power for impairments on full geriatric assessment: sensitivity 69, specificity 79, positive predictive value 89 and negative predictive value 50 %. However, G8 was an independent predictor of mortality within the first year after inclusion (hazard ratio 3.93; 95 % confidence interval 1.67–9.22, p < 0.001). Remarkably, patients with impaired G8 fared poorly, irrespective of treatment choices (p < 0.001). This is the first report on the clinical and prognostic relevance of G8 in elderly patients with haematological malignancies. Although the G8 lacked discriminative power for outcome of multi-dimensional geriatric assessment, this score appears to be a powerful prognosticator and could potentially represent a useful tool in treatment decisions. This novel finding certainly deserves further exploration.
Exploiting Multiple-Antenna Diversity for Shared Secret Key Generation in Wireless Networks

Generating a secret key between two parties by extracting the shared randomness in the wireless fading channel is an emerging area of research. Previous works focus mainly on single-antenna systems. Multiple-antenna devices have the potential to provide more randomness for key generation than single-antenna ones. However, the performance of key generation using multiple-antenna devices in a real environment remains unknown. Different from the previous theoretical work on multiple-antenna key generation, we propose and implement a shared secret key generation protocol, Multiple-Antenna KEy generator (MAKE) using off-the-shelf 802.11n multiple-antenna devices. We also conduct extensive experiments and analysis in real indoor and outdoor mobile environments. Using the shared randomness extracted from measured Received Signal Strength Indicator (RSSI) to generate keys, our experimental results show that using laptops with three antennas, MAKE can increase the bit generation rate by more than four times over single-antenna systems. Our experiments validate the effectiveness of using multi-level quantization when there is enough mutual information in the channel. Our results also show the trade-off between bit generation rate and bit agreement ratio when using multi-level quantization. We further find that even if an eavesdropper has multiple antennas, she cannot gain much more information about the legitimate channel.