nils-herrmann/clean_scirepeval huggingface dataset:
Diachronic word embeddings and semantic shifts: a survey

Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.
Pricing fraud detection in online shopping malls using a finite mixture model

Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection
A lightweight robotic arm with pneumatic muscles for robot learning

Versatile motor skills for hitting and throwing motions can be observed in humans already in early ages. Future robots require high power-to-weight ratios as well as inherent long operational lifetimes without breakage in order to achieve similar perfection. Robustness due to passive compliance and high-speed catapult-like motions as possible with fast energy release are further beneficial characteristics. Such properties can be realized with antagonistic muscle-based designs. Additionally, control algorithms need to exploit the full potential of the robot. Learning control is a promising direction due to its the potential to capture uncertainty and control of complex systems. The aim of this paper is to build a robotic arm that is capable of generating high accelerations and sophisticated trajectories as well as enable exploration at such speeds for robot learning approaches. Hence, we have designed a light-weight robot arm with moving masses below 700 g with powerful antagonistic compliant actuation with pneumatic artificial muscles. Rather than recreating human anatomy, our system is designed to be easy to control in order to facilitate future learning of fast trajectory tracking control. The resulting robot is precise at low speeds using a simple PID controller while reaching high velocities of up to 12 m/s in task space and 1500 deg/s in joint space. This arm will enable new applications in fast changing and uncertain task like robot table tennis while being a sophisticated and reproducible test-bed for robot skill learning methods. Construction details are available.
WE NEED AN EDUCATIONALLY RELEVANT DEFINITION OF MOBILE LEARNING

In this paper we challenge current definitions of mobile learning and suggest that the direction of progress, both in theoretical/applied research as well as its role as a tool that serves social transformation and development, will be determined and even dictated by the availability of an adequate definition. A new framework for the definition of mobile learning is proposed, one that considers a repertoire of domains, and which embraces not only technical, methodological and educational aspects, but also considers social and philosophical dimensions.
Overview of the Evalita 2016 SENTIment POLarity Classification Task

English. The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification, polarity classification, and irony detection. In 2016 SENTIPOLC has been again the most participated EVALITA task with a total of 57 submitted runs from 13 different teams. We present the datasets – which includes an enriched annotation scheme for dealing with the impact on polarity of a figurative use of language – the evaluation methodology, and discuss results and participating systems. Italiano. Descriviamo modalità e risultati della seconda edizione della campagna di valutazione di sistemi di sentiment analysis (SENTIment POLarity Classification Task), proposta nel contesto di “EVALITA 2016: Evaluation of NLP and Speech Tools for Italian”. In SENTIPOLC è stata valutata la capacità dei sistemi di riconoscere diversi aspetti del sentiment espresso nei messaggi Twitter in lingua italiana, con un’articolazione in tre sottotask: subjectivity classification, polarity classification e irony detection. La campagna ha suscitato nuovamente grande interesse, con un totale di 57 run inviati da 13 gruppi di partecipanti.
Support Vector Ordinal Regression

In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.
Maximizing Cloud Provider Profit from Equilibrium Price Auctions

Auctioning constitutes a market-driven scheme for the allocation of cloud-based computing capacities. It is practically applied today in the context of Infrastructure as a Service offers, specifically, virtual machines. However, the maximization of auction profits poses a challenging task for the cloud provider, because it involves the concurrent determination of equilibrium prices and distribution of virtual machine instances to the underlying physical hosts in the data center. In the work at hand, we propose an optimal approach, based on linear programming, as well as a heuristic approach to tackle this Equilibrium Price Auction Allocation Problem (EPAAP). Through an evaluation based on realistic data, we show the practical applicability and benefits of our contributions. Specifically, we find that the heuristic approach reduces the average computation time to solve an EPAAP by more than 99.9%, but still maintains a favorable average solution quality of 96.7% in terms of cloud provider profit, compared to the optimal approach.
Application layer protocols for the Internet of Things: A survey

The “Internet of things” (IoT) concept nowadays is one of the hottest trends for research in any given field; since IoT is about interactions between multiple devices, things, and objects. This interaction opens different directions of enhancement and development in many fields, such as architecture, dependencies, communications, protocols, security, applications and big data. The results will be outstanding and we will be able to reach the desired change and improvements we seek in the fields that affect our lives. The critical goal of Internet of things (IoT) is to ensure effective communication between objects and build a sustained bond among them using different types of applications. The application layer is responsible for providing services and determines a set of protocols for message passing at the application level. This survey addresses a set of application layer protocols that are being used today for IoT, to affirm a reliable tie among objects and things.
Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry

To summarise logistical aspects of recently completed systematic reviews that were registered in the International Prospective Register of Systematic Reviews (PROSPERO) registry to quantify the time and resources required to complete such projects.



Meta-analysis.



All of the 195 registered and completed reviews (status from the PROSPERO registry) with associated publications at the time of our search (1 July 2014).



All authors extracted data using registry entries and publication information related to the data sources used, the number of initially retrieved citations, the final number of included studies, the time between registration date to publication date and number of authors involved for completion of each publication. Information related to funding and geographical location was also recorded when reported.



The mean estimated time to complete the project and publish the review was 67.3 weeks (IQR=42). The number of studies found in the literature searches ranged from 27 to 92 020; the mean yield rate of included studies was 2.94% (IQR=2.5); and the mean number of authors per review was 5, SD=3. Funded reviews took significantly longer to complete and publish (mean=42 vs 26 weeks) and involved more authors and team members (mean=6.8 vs 4.8 people) than those that did not report funding (both p<0.001).



Systematic reviews presently take much time and require large amounts of human resources. In the light of the ever-increasing volume of published studies, application of existing computing and informatics technology should be applied to decrease this time and resource burden. We discuss recently published guidelines that provide a framework to make finding and accessing relevant literature less burdensome.
Towards Unified Data and Lifecycle Management for Deep Learning

Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, e.g., learned parameters and training logs, and it comprises of several frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and largely left to the users. This paper describes our vision and implementation of a data and lifecycle management system for deep learning. First, we generalize model exploration and model enumeration queries from commonly conducted tasks by deep learning modelers, and propose a high-level domain specific language (DSL), inspired by SQL, to raise the abstraction level and thereby accelerate the modeling process. To manage the variety of data artifacts, especially the large amount of checkpointed float parameters, we design a novel model versioning system (dlv), and a read-optimized parameter archival storage system (PAS) that minimizes storage footprint and accelerates query workloads with minimal loss of accuracy. PAS archives versioned models using deltas in a multi-resolution fashion by separately storing the less significant bits, and features a novel progressive query (inference) evaluation algorithm. Third, we develop e cient algorithms for archiving versioned models using deltas under co-retrieval constraints. We conduct extensive experiments over several real datasets from computer vision domain to show the e ciency of the proposed techniques.