nils-herrmann/clean_scirepeval huggingface dataset:
What are ontologies, and why do we need them?

THEORIES IN AI FALL INT O TWO broad categories: mechanismtheories and contenttheories. Ontologies are content the ories about the sor ts of objects, properties of objects,and relations between objects tha t re possible in a specif ed domain of kno wledge. They provide potential ter ms for descr ibing our knowledge about the domain. In this article, we survey the recent de velopment of the f ield of ontologies in AI. We point to the some what different roles ontologies play in information systems, naturallanguage under standing, and knowledgebased systems. Most r esear ch on ontologies focuses on what one might characterize as domain factual knowledge, because kno wlede of that type is par ticularly useful in natural-language under standing. There is another class of ontologies that are important in KBS—one that helps in shar ing knoweldge about reasoning str ategies or pr oblemsolving methods. In a f ollow-up article, we will f ocus on method ontolo gies.
Language to Logical Form with Neural Attention

Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domainor representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.
A Bayesian Data Augmentation Approach for Learning Deep Models

Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on large annotated datasets, which are expensive to be acquired, stored and processed. Therefore a reasonable alternative is to be able to automatically generate new annotated training samples using a process known as data augmentation. The dominant data augmentation approach in the field assumes that new training samples can be obtained via random geometric or appearance transformations applied to annotated training samples, but this is a strong assumption because it is unclear if this is a reliable generative model for producing new training samples. In this paper, we provide a novel Bayesian formulation to data augmentation, where new annotated training points are treated as missing variables and generated based on the distribution learned from the training set. For learning, we introduce a theoretically sound algorithm — generalised Monte Carlo expectation maximisation, and demonstrate one possible implementation via an extension of the Generative Adversarial Network (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show the better performance of our proposed method compared to the current dominant data augmentation approach mentioned above — the results also show that our approach produces better classification results than similar GAN models.
Six Sigma and Total Quality Management : Different Day , Same Soup ?

For decades now Total Quality Management (TQM) has been a dominant management concept for improving competitiveness and financial results. In recent years, however, TQM seems to have lost some of its nimbus with other concepts and approaches such as Lean Enterprise and Six Sigma launched and increasingly in vogue. The aim of this paper is to look at TQM and Six Sigma, their backgrounds, definitions and ingredients, and their similarities and differences to see whether the two concepts really are different dishes or contain the same ingredients in different proportions.
Lifelong Machine Learning

Lifelong machine learning (or lifelong learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Furthermore, we are able to discover new problems in the usage process of the learned knowledge or model. This enables us to learn more and more continually in a self-motivated manner. We can also adapt our previous konwledge to solve unfamilar problems and learn in the process. Lifelong learning aims to achieve these capabilities. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to a new height. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
A TRICKLE-DOWN MODEL OF ABUSIVE SUPERVISION

Much of the abusive supervision research has focused on the supervisor– subordinate dyad when examining the effects of abusive supervision on employee outcomes. Using data from a large multisource field study, we extend this research by testing a trickle-down model of abusive supervision across 3 hierarchical levels (i.e., managers, supervisors, and employees). Drawing on social learning theory and social information processing theory, we find general support for the study hypotheses. Specifically, we find that abusive manager behavior is positively related to abusive supervisor behavior, which in turn is positively related to work group interpersonal deviance. In addition, hostile climate moderates the relationship between abusive supervisor behavior and work group interpersonal deviance such that the relationship is stronger when hostile climate is high. The results provide support for our trickle-down model in that abusive manager behavior was not only related to abusive supervisor behavior but was also associated with employees’ behavior 2 hierarchical levels below the manager.
Bug Replication in Code Clones: An Empirical Study

Code clones are exactly or nearly similar code fragments in the code-base of a software system. Existing studies show that clones are directly related to bugs and inconsistencies in the code-base. Code cloning (making code clones) is suspected to be responsible for replicating bugs in the code fragments. However, there is no study on the possibilities of bug-replication through cloning process. Such a study can help us discover ways of minimizing bug-replication. Focusing on this we conduct an empirical study on the intensities of bug-replication in the code clones of the major clone-types: Type 1, Type 2, and Type 3. According to our investigation on thousands of revisions of six diverse subject systems written in two different programming languages, C and Java, a considerable proportion (i.e., up to 10%) of the code clones can contain replicated bugs. Both Type 2 and Type 3 clones have higher tendencies of having replicated bugs compared to Type 1 clones. Thus, Type 2 and Type 3 clones are more important from clone management perspectives. The extent of bug-replication in the buggy clone classes is generally very high (i.e., 100% in most of the cases). We also find that overall 55% of all the bugs experienced by the code clones can be replicated bugs. Our study shows that replication of bugs through cloning is a common phenomenon. Clone fragments having method-calls and if-conditions should be considered for refactoring with high priorities, because such clone fragments have high possibilities of containing replicated bugs. We believe that our findings are important for better maintenance of software systems, in particular, systems with code clones.
Thiopurine S-methyltransferase (TPMT) assessment prior to starting thiopurine drug treatment; a pharmacogenomic test whose time has come.

Thiopurine S-methyltransferase (TPMT) is involved in the metabolism of thiopurine drugs. Patients that due to genetic variation lack this enzyme or have lower levels than normal, can be adversely affected if normal doses of thiopurines are prescribed. The evidence for measuring TPMT prior to starting patients on thiopurine drug therapy has been reviewed and the various approaches to establishing a service considered. Until recently clinical guidelines on the use of the TPMT varied by medical specialty. This has now changed, with clear guidance encouraging clinicians to use the TPMT test prior to starting any patient on thiopurine therapy. The TPMT test is the first pharmacogenomic test that has crossed from research to routine use. Several analytical approaches can be taken to assess TPMT status. The use of phenotyping supported with genotyping on selected samples has emerged as the analytical model that has enabled national referral services to be developed to a high level in the UK. The National Health Service now has access to cost-effective and timely TPMT assay services, with two laboratories undertaking the majority of the work at national level and with several local services developing. There appears to be adequate capacity and an appropriate internal market to ensure that TPMT assay services are commensurate with the clinical demand.
Efficacy of antibiotic therapy for peritoneal dialysis-associated peritonitis: a proportional meta-analysis

The choice of antimicrobials for initial treatment of peritoneal dialysis (PD)-related peritonitis is crucial for a favorable outcome. There is no consensus about the best therapy; few prospective controlled studies have been published, and the only published systematic reviews did not report superiority of any class of antimicrobials. The objective of this review was to analyze the results of PD peritonitis treatment in adult patients by employing a new methodology, the proportional meta-analysis.



A review of the literature was conducted. There was no language restriction. Studies were obtained from MEDLINE, EMBASE, and LILACS. The inclusion criteria were: (a) case series and RCTs with the number of reported patients in each study greater than five, (b) use of any antibiotic therapy for initial treatment (e.g., cefazolin plus gentamicin or vancomycin plus gentamicin), for Gram-positive (e.g., vancomycin or a first generation cephalosporin), or for Gram-negative rods (e.g., gentamicin, ceftazidime, and fluoroquinolone), (c) patients with PD-related peritonitis, and (d) studies specifying the rates of resolution. A proportional meta-analysis was performed on outcomes using a random-effects model, and the pooled resolution rates were calculated.



A total of 64 studies (32 for initial treatment and negative culture, 28 reporting treatment for Gram-positive rods and 24 reporting treatment for Gram-negative rods) and 21 RCTs met all inclusion criteria (14 for initial treatment and negative culture, 8 reporting treatment for Gram-positive rods and 8 reporting treatment for Gram-negative rods). The pooled resolution rate of ceftazidime plus glycopeptide as initial treatment (pooled proportion = 86% [95% CI 0.82-0.89]) was significantly higher than first generation cephalosporin plus aminoglycosides (pooled proportion = 66% [95% CI 0.57-0.75]) and significantly higher than glycopeptides plus aminoglycosides (pooled proportion = 75% [95% CI 0.69-0.80]. Other comparisons of regimens used for either initial treatment, treatment for Gram-positive rods or Gram-negative rods did not show statistically significant differences.



We showed that the association of a glycopeptide plus ceftazidime is superior to other regimens for initial treatment of PD peritonitis. This result should be carefully analyzed and does not exclude the necessity of monitoring the local microbiologic profile in each dialysis center to choice the initial therapeutic protocol.
E-customer Loyalty

Increasingly, businesses are beginning to understand the profit potential of loyal customers (Oliver, 1999). Marketers endowed with such consumers can expect repeat patronage to remain high until competitors can find a way to: (1) close the gap in attitude among brands, (2) increase the differentiation of their own brand, or (3) encourage spurious loyalty from consumers (Dick & Basu, 1994). Loyalty leads to higher retention. According to one study, a 5% increase in customer retention rates increases profits by 25% to 95% (Reicheld & Schefter, 2000). It is thus heartening to note that “one of the most exciting and successful uses of [the Internet] ... may be the Internet’s role in building customer loyalty and maximizing sales to your existing customers” (Griffin, 1996, p. 50). Given its relative importance in cyberspace, it is surprising that relatively little has been done in conceptualizing and validating e-loyalty models (Luarn & Lin, 2003). Parasuraman and Grewal (2000) argue for more research pertaining to the influence of technology on customer responses, such as perceived value and customer loyalty. Besides customer trust, our study also incorporates two constructs—corporate image and perceived value—that have been poorly explored in online environments despite their recognized importance in off-line contexts. Consequently, a primary objective of this article is to discuss the impact of three constructs (i.e., customer trust, corporate image, and perceived value) on e-loyalty in a business-to-consumer (B2C) e-commerce context. In doing so, our model is expected to offer useful suggestions on how to manage customer trust, corporate image, and perceived value as online loyalty management tools. This article is generally divided into three sections. The first section will discuss the constructs of interest and clarify what they mean. In the second section, we will propose hypotheses explaining these relationships. And in the final section, we introduce actionable strategies for online loyalty management based on the proposed framework.
SAM: enabling practical spatial multiple access in wireless LAN

Spatial multiple access holds the promise to boost the capacity of wireless networks when an access point has multiple antennas. Due to the asynchronous and uncontrolled nature of wireless LANs, conventional MIMO technology does not work efficiently when concurrent transmissions from multiple stations are uncoordinated. In this paper, we present the design and implementation of a crosslayer system, called SAM, that addresses the challenges of enabling spatial multiple access for multiple devices in a random access network like WLAN. SAM uses a chain-decoding technique to reliably recover the channel parameters for each device, and iteratively decode concurrent frames with misaligned symbol timings and frequency offsets. We propose a new MAC protocol, called CCMA, to enable concurrent transmissions by different mobile stations while remaining backward compatible with 802.11. Finally, we implement the PHY and MAC layer of SAM using the Sora high-performance software radio platform. Our evaluation results under real wireless conditions show that SAM can improve network uplink throughput by 70% with two antennas over 802.11.
T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos

The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks, such as GoogleNet and VGG, novel object detection frameworks, such as R-CNN and its successors, Fast R-CNN, and Faster R-CNN, play an essential role in improving the state of the art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and contextual information of videos are not fully investigated and utilized. In this paper, we propose a deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos. It is called T-CNN, i.e., tubelets with convolutional neueral networks. The proposed framework won newly introduced an object-detection-from-video task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015. Code is publicly available at https://github.com/myfavouritekk/T-CNN.
Get Ready for Agile Methods, with Care

F aced with the conflicting pressures of accelerated product development and users who demand that increasingly vital systems be made ever more dependable, software development has been thrown into turmoil. Traditionalists advocate using extensive planning, codified processes, and rigorous reuse to make development an efficient and predictable activity that gradually matures toward perfection. Meanwhile, a new generation of developers cites the crushing weight of corporate bureaucracy, the rapid pace of information technology change, and the dehumanizing effects of detailed plan-driven development as cause for revolution. In their rallying cry, the Manifesto for Agile Software Development (http://www.agileAlliance.org), and in columns such as those the “Ongoing Debate” sidebar describes, these developers call for a revitalized approach to development that dispenses with all but the essentials. Unsurprisingly, many developers who favor plan-driven methods have reacted to the manifesto with scathing criticism. Real-world examples argue for and against agile methods. Responding to change has been cited as the critical technical success factor in the Internet browser battle between Microsoft and Netscape. But overresponding to change has been cited as the source of many software disasters, such as the $3 billion overrun of the US Federal Aviation Administration’s Advanced Automation System for national air traffic control. I believe that both agile and plan-driven approaches have a responsible center and overinterpreting radical fringes. Although each approach has a home ground of project characteristics within which it performs very well, and much better than the other, outside each approach’s home ground, a combined approach is feasible and preferable.
Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

The number of seniors and other people needing daily assistance continues to increase, but the current human resources available to achieve this in the coming years will certainly be insufficient. To remedy this situation, smart habitats have emerged as an innovative avenue for supporting needs of daily assistance. Smart homes aim to provide cognitive assistance in decision making by giving hints, suggestions, and reminders, with different kinds of effectors, to residents. To implement such technology, the first challenge to overcome is the recognition of ongoing activity. Some researchers have proposed solutions based on binary sensors or cameras, but these types of approaches infringed on residents' privacy. A new affordable activity-recognition system based on passive RFID technology can detect errors related to cognitive impairment. The entire system relies on an innovative model of elliptical trilateration with several filters, as well as on an ingenious representation of activities with spatial zones. The authors have deployed the system in a real smart-home prototype; this article renders the results of a complete set of experiments conducted on this new activity-recognition system with real scenarios.
Tumour heterogeneity and cancer cell plasticity

Phenotypic and functional heterogeneity arise among cancer cells within the same tumour as a consequence of genetic change, environmental differences and reversible changes in cell properties. Some cancers also contain a hierarchy in which tumorigenic cancer stem cells differentiate into non-tumorigenic progeny. However, it remains unclear what fraction of cancers follow the stem-cell model and what clinical behaviours the model explains. Studies using lineage tracing and deep sequencing could have implications for the cancer stem-cell model and may help to determine the extent to which it accounts for therapy resistance and disease progression.
Learning 6D Object Pose Estimation Using 3D Object Coordinates

This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image. We present a flexible approach that can deal with generic objects, both textured and texture-less. The key new concept is a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling. We are able to show that for a common dataset with texture-less objects, where template-based techniques are suitable and state-of-the art, our approach is slightly superior in terms of accuracy. We also demonstrate the benefits of our approach, compared to template-based techniques, in terms of robustness with respect to varying lighting conditions. Towards this end, we contribute a new ground truth dataset with 10k images of 20 objects captured each under three different lighting conditions. We demonstrate that our approach scales well with the number of objects and has capabilities to run fast.
A comparison of pilot-aided channel estimation methods for OFDM systems

This paper deals with the estimation of the channel impulse response (CIR) in orthogonal frequency division multiplexed (OFDM) systems. In particular, we focus on two pilot-aided schemes: the maximum likelihood estimator (MLE) and the Bayesian minimum mean square error estimator (MMSEE). The advantage of the former is that it is simpler to implement as it needs no information on the channel statistics. On the other hand, the MMSEE is expected to have better performance as it exploits prior information about the channel. Theoretical analysis and computer simulations are used in the comparisons. At SNR values of practical interest, the two schemes are found to exhibit nearly equal performance, provided that the number of pilot tones is sufficiently greater than the CIRs length. Otherwise, the MMSEE is superior. In any case, the MMSEE is more complex to implement.