nils-herrmann/clean_scirepeval huggingface dataset:
Using text mining for study identification in systematic reviews: a systematic review of current approaches

The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities.



Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged? We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings.



The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable. On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall).



Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously. The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven. In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.
Executing SPARQL Queries over the Web of Linked Data

The Web of Linked Data forms a single, globally distributed dataspace. Due to the openness of this dataspace, it is not possible to know in advance all data sources that might be relevant for query answering. This openness poses a new challenge that is not addressed by traditional research on federated query processing. In this paper we present an approach to execute SPARQL queries over the Web of Linked Data. The main idea of our approach is to discover data that might be relevant for answering a query during the query execution itself. This discovery is driven by following RDF links between data sources based on URIs in the query and in partial results. The URIs are resolved over the HTTP protocol into RDF data which is continuously added to the queried dataset. This paper describes concepts and algorithms to implement our approach using an iterator-based pipeline. We introduce a formalization of the pipelining approach and show that classical iterators may cause blocking due to the latency of HTTP requests. To avoid blocking, we propose an extension of the iterator paradigm. The evaluation of our approach shows its strengths as well as the still existing challenges.
Towards Conceptual Compression

We introduce convolutional DRAW, a homogeneous deep generative model achieving state-of-the-art performance in latent variable image modeling. The algorithm naturally stratifies information into higher and lower level details, creating abstract features and as such addressing one of the fundamentally desired properties of representation learning. Furthermore, the hierarchical ordering of its latents creates the opportunity to selectively store global information about an image, yielding a high quality ‘conceptual compression’ framework.
Group Recommender Systems: New Perspectives in the Social Web

I. Cantador (), P. Castells Universidad Autónoma de Madrid 28049 Madrid, Spain e-mails: ivan.cantador@uam.es, pablo.castells@uam.es Abstract An increasingly important type of recommender systems comprises those that generate suggestions for groups rather than for individuals. In this chapter, we revise state of the art approaches on group formation, modelling and recommendation, and present challenging problems to be included in the group recommender system research agenda in the context of the Social Web.
DHER: HINDSIGHT EXPERIENCE REPLAY

Dealing with sparse rewards is one of the most important challenges in reinforcement learning (RL), especially when a goal is dynamic (e.g., to grasp a moving object). Hindsight experience replay (HER) has been shown an effective solution to handling sparse rewards with fixed goals. However, it does not account for dynamic goals in its vanilla form and, as a result, even degrades the performance of existing off-policy RL algorithms when the goal is changing over time. In this paper, we present Dynamic Hindsight Experience Replay (DHER), a novel approach for tasks with dynamic goals in the presence of sparse rewards. DHER automatically assembles successful experiences from two relevant failures and can be used to enhance an arbitrary off-policy RL algorithm when the tasks’ goals are dynamic. We evaluate DHER on tasks of robotic manipulation and moving object tracking, and transfer the polices from simulation to physical robots. Extensive comparison and ablation studies demonstrate the superiority of our approach, showing that DHER is a crucial ingredient to enable RL to solve tasks with dynamic goals in manipulation and grid world domains.
Culturally competent healthcare systems. A systematic review.

Culturally competent healthcare systems-those that provide culturally and linguistically appropriate services-have the potential to reduce racial and ethnic health disparities. When clients do not understand what their healthcare providers are telling them, and providers either do not speak the client's language or are insensitive to cultural differences, the quality of health care can be compromised. We reviewed five interventions to improve cultural competence in healthcare systems-programs to recruit and retain staff members who reflect the cultural diversity of the community served, use of interpreter services or bilingual providers for clients with limited English proficiency, cultural competency training for healthcare providers, use of linguistically and culturally appropriate health education materials, and culturally specific healthcare settings. We could not determine the effectiveness of any of these interventions, because there were either too few comparative studies, or studies did not examine the outcome measures evaluated in this review: client satisfaction with care, improvements in health status, and inappropriate racial or ethnic differences in use of health services or in received and recommended treatment.
Parasitic Layer-Based Radiation Pattern Reconfigurable Antenna for 5G Communications

The design, prototyping, and characterization of a radiation pattern reconfigurable antenna (RA) targeting 5G communications are presented. The RA is based on a reconfigurable parasitic layer technique in which a driven dipole antenna is located along the central axis of a 3-D parasitic layer structure enclosing it. The reconfigurable parasitic structure is similar to a hexagonal prism, where the top/bottom bases are formed by a hexagonal domed structure. The surfaces of the parasitic structure house electrically small metallic pixels with various geometries. The adjacent pixels are connected by PIN diode switches to change the geometry of the parasitic surface, thus providing reconfigurability in the radiation pattern. This RA is designed to operate over a 4.8–5.2 GHz frequency band, producing various radiation patterns with a beam-steering capability in both the azimuth (<inline-formula> <tex-math notation="LaTeX">$0 {^{\circ }} <\phi < 360 {^{\circ }}$ </tex-math></inline-formula>) and elevation planes (<inline-formula> <tex-math notation="LaTeX">$-18 {^{\circ }} <\theta < 18 {^{\circ }}$ </tex-math></inline-formula>). Small-cell access points equipped with RAs are used to investigate the system level performances for 5G heterogeneous networks. The results show that using distributed mode optimization, RA equipped small-cell systems could provide up to 29% capacity gains and 13% coverage improvements as compared to legacy omnidirectional antenna equipped systems.
Towards a theory of insider threat assessment

Insider attacks are a well-known problem acknowledged as a threat as early as 1980s. The threat is attributed to legitimate users who abuse their privileges, and given their familiarity and proximity to the computational environment, can easily cause significant damage or losses. Due to the lack of tools and techniques, security analysts do not correctly perceive the threat, and hence consider the attacks as unpreventable. In this paper, we present a theory of insider threat assessment. First, we describe a modeling methodology which captures several aspects of insider threat, and subsequently, show threat assessment methodologies to reveal possible attack strategies of an insider.
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

We introduce a novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment prediction tasks these representations outperform other state-of-the-art approaches on commonly used datasets, such as movie reviews, without using any pre-defined sentiment lexica or polarity shifting rules. We also evaluate the model’s ability to predict sentiment distributions on a new dataset based on confessions from the experience project. The dataset consists of personal user stories annotated with multiple labels which, when aggregated, form a multinomial distribution that captures emotional reactions. Our algorithm can more accurately predict distributions over such labels compared to several competitive baselines.
Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs

Google’s Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google’s milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their cloud platform and as of very recently has opened them up further to allow use by non-TensorFlow frontends. We describe a method and implementation for offloading suitable sections of Julia programs to TPUs via this new API and the Google XLA compiler. Our method is able to completely fuse the forward pass of a VGG19 model expressed as a Julia program into a single TPU executable to be offloaded to the device. Our method composes well with existing compiler-based automatic differentiation techniques on Julia code, and we are thus able to also automatically obtain the VGG19 backwards pass and similarly offload it to the TPU. Targeting TPUs using our compiler, we are able to evaluate the VGG19 forward pass on a batch of 100 images in 0.23s which compares favorably to the 52.4s required for the original model on the CPU. Our implementation is less than 1000 lines of Julia, with no TPU specific changes made to the core Julia compiler or any other Julia packages.
Establishing Fraud Detection Patterns Based on Signatures

All over the world we have been assisting to a significant increase of the telecommunication systems usage. People are faced day after day with strong marketing campaigns seeking their attention to new telecommunication products and services. Telecommunication companies struggle in a high competitive business arena. It seems that their efforts were well done, because customers are strongly adopting the new trends and use (and abuse) systematically communication services in their quotidian. Although fraud situations are rare, they are increasing and they correspond to a large amount of money that telecommunication companies lose every year. In this work, we studied the problem of fraud detection in telecommunication systems, especially the cases of superimposed fraud, providing an anomaly detection technique, supported by a signature schema. Our main goal is to detect deviate behaviors in useful time, giving better basis to fraud analysts to be more accurate in their decisions in the establishment of potential fraud situations.
Big Data: Issues and Challenges Moving Forward

Big data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. Data, information, and knowledge are being created and collected at a rate that is rapidly approaching the exabyte/year range. But, its creation and aggregation are accelerating and will approach the zettabyte/year range within a few years. Volume is only one aspect of big data; other attributes are variety, velocity, value, and complexity. Storage and data transport are technology issues, which seem to be solvable in the near-term, but represent longterm challenges that require research and new paradigms. We analyze the issues and challenges as we begin a collaborative research program into methodologies for big data analysis and design.
CRIME MAPPING AND THE TRAINING NEEDS OF LAW ENFORCEMENT

This paper explores some of the more recent developments within crime mapping and the broader application of geographical information technology within law enforcement. The information technology (IT) revolution and the reduction in computing costs since the 1980s has brought a range of analytical tools within the budgets of most police services, and one of the most significant changes has been in the way that spatial data are handled. Law enforcement has strong geographic currents at all levels of the organisation, and this paper examines three applications of geographical information systems (GIS) within policing: hotspot mapping; CompStat; and geographic profiling. The paper concludes by discussing the future training needs using a simple model of intelligence-led crime reduction. This model suggests that training for managers to enable a greater understanding of the analyses presented to them, and how to use mapping to further crime prevention and reduction, may be as important as increasing the technical ability of crime analysts. The challenge for the immediate future of crime reduction practice in law enforcement is less to worry about the training of analysts, and more to address the inability of law enforcement management to understand and act on the crime analysis they are given.
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation

Domain adaptation plays an important role for speech recognition models, in particular, for domains that have low resources. We propose a novel generative model based on cyclicconsistent generative adversarial network (CycleGAN) for unsupervised non-parallel speech domain adaptation. The proposed model employs multiple independent discriminators on the power spectrogram, each in charge of different frequency bands. As a result we have 1) better discriminators that focus on fine-grained details of the frequency features, and 2) a generator that is capable of generating more realistic domainadapted spectrogram. We demonstrate the effectiveness of our method on speech recognition with gender adaptation, where the model only has access to supervised data from one gender during training, but is evaluated on the other at test time. Our model is able to achieve an average of 7.41% on phoneme error rate, and 11.10% word error rate relative performance improvement as compared to the baseline, on TIMIT and WSJ dataset, respectively. Qualitatively, our model also generates more natural sounding speech, when conditioned on data from the other domain.
NEURAL LANGUAGE MODELS

Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing codebases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.
Leadership style as an enabler of organizational complex functioning

The postmodern organization has a design paradox in which leaders are concerned with efficiency and control as well as complex functioning. Traditional leadership theory has limited applicability to postmodern organizations as it is mainly focused on efficiency and control. As a result, a new theory of leadership that recognizes the design paradox has been proposed: complexity leadership theory. This theory conceptualizes the integration of formal leadership roles with complex functioning. Our particular focus is on leadership style and its effect as an enabler of complex functioning. We introduce dynamic network analysis, a new methodology for modeling and analyzing organizations as complex adaptive networks. Dynamic network analysis is a methodology that quantifies complexity leadership theory. Data was collected from a real-world network organization and dynamic network analysis used to explore the effects of leadership style as an enabler of complex functioning. Results and implications are discussed in relation to leadership theory and practice.
Progressive Neural Architecture Search

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space. Direct comparison under the same search space shows that our method is up to 5 times more efficient than the RL method of Zoph et al. (2018) in terms of number of models evaluated, and 8 times faster in terms of total compute. The structures we discover in this way achieve state of the art classification accuracies on CIFAR-10 and ImageNet.
Anytime QoS optimization over the PlanGraph for web service composition

Automatic service composition is the generation of a business process to fulfill business goals that cannot be fulfilled by individual services. Planning algorithms are frequently used to solve this problem. In addition to satisfying functional goals, recent research is geared towards selecting the best services to optimize the QoS of the result business process. In this paper, we combine a systematic search algorithm like Dijkstra's algorithm with a planning algorithm, GraphPlan, to achieve both functional goals and QoS optimization at the same time. In addition, we make our algorithm an anytime algorithm that has the advantage of getting better solutions if it keeps running for a longer time.
Planar Affine Rectification from Change of Scale

A method for affine rectification of a plane exploiting knowledge of relative scale changes is presented. The rectifying transformation is fully specified by the relative scale change at three non-collinear points or by two pairs of points where the relative scale change is known; the relative scale change between the pairs is not required. The method also allows homography estimation between two views of a planar scene from three point-with-scale correspondences. The proposed method is simple to implement and without parameters; linear and thus supporting (algebraic) least squares solutions; and general, without restrictions on either the shape of the corresponding features or their mutual position. The wide applicability of the method is demonstrated on text rectification, detection of repetitive patterns, texture normalization and estimation of homography from three point-with-scale correspondences.
Managing interaction between users and agents in a multi-agent storytelling environment

This paper describes an approach for managing the interaction of human users with computer-controlled agents in an interactive narrative-oriented virtual environment. In these kinds of systems, the freedom of the user to perform whatever action she desires must be balanced with the preservation of the storyline used to control the system's characters. We describe a technique, narrative mediation, that exploits a plan-based model of narrative structure to manage and respond to users' actions inside a virtual world. We define two general classes of response to situations where users execute actions that interfere with story structure: accommodation and intervention. Finally, we specify an architecture that uses these definitions to monitor and automatically characterize user actions, and to compute and implement responses to unanticipated activity. The approach effectively integrates user action and system response into the unfolding narrative, providing for the balance between a user's sense of control within the story world and the user's sense of coherence of the overall narrative.
Food security: the challenge of feeding 9 billion people.

Continuing population and consumption growth will mean that the global demand for food will increase for at least another 40 years. Growing competition for land, water, and energy, in addition to the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement to reduce the impact of the food system on the environment. The effects of climate change are a further threat. But the world can produce more food and can ensure that it is used more efficiently and equitably. A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, different components of which are explored here.
An Analysis of the Effect of Relationship Status on Self-Esteem and Academic Performance

Self-esteem is a complex construct that has been defined in various ways due to the numerous research studies that have been done in regards to this topic. The purpose of this paper was to present evidence of self-esteem in regards to relationship status and academic performance; both of these variables may be directly related to self-esteem levels in college students. The hypothesis was that the relationship status of an individual positively affects one’s self-esteem level and their overall grade point average. Analysis of the data from 100 participants found that there was no significant statistical association between relationship status and self-esteem. However, data collected the participants showed significant statistics that participants involved in a relationship have a higher grade point average. This information is beneficial in order to gain a better understanding of the relationship between relationship status, self-esteem, and academic performance.