nils-herrmann/clean_scirepeval huggingface dataset:
A Survey of Recent Trends in Testing Concurrent Software Systems

Many modern software systems are composed of multiple execution flows that run simultaneously, spanning from applications designed to exploit the power of modern multi-core architectures to distributed systems consisting of multiple components deployed on different physical nodes. We collectively refer to such systems as concurrent systems. Concurrent systems are difficult to test, since the faults that derive from their concurrent nature depend on the interleavings of the actions performed by the individual execution flows. Testing techniques that target these faults must take into account the concurrency aspects of the systems. The increasingly rapid spread of parallel and distributed architectures led to a deluge of concurrent software systems, and the explosion of testing techniques for such systems in the last decade. The current lack of a comprehensive classification, analysis and comparison of the many testing techniques for concurrent systems limits the understanding of the strengths and weaknesses of each approach and hampers the future advancements in the field. This survey provides a framework to capture the key features of the available techniques to test concurrent software systems, identifies a set of classification criteria to review and compare the available techniques, and discusses in details their strengths and weaknesses, leading to a thorough assessment of the field and paving the road for future progresses.
The psychology of change: self-affirmation and social psychological intervention.

People have a basic need to maintain the integrity of the self, a global sense of personal adequacy. Events that threaten self-integrity arouse stress and self-protective defenses that can hamper performance and growth. However, an intervention known as self-affirmation can curb these negative outcomes. Self-affirmation interventions typically have people write about core personal values. The interventions bring about a more expansive view of the self and its resources, weakening the implications of a threat for personal integrity. Timely affirmations have been shown to improve education, health, and relationship outcomes, with benefits that sometimes persist for months and years. Like other interventions and experiences, self-affirmations can have lasting benefits when they touch off a cycle of adaptive potential, a positive feedback loop between the self-system and the social system that propagates adaptive outcomes over time. The present review highlights both connections with other disciplines and lessons for a social psychological understanding of intervention and change.
Lidar Remote Sensing for Ecosystem Studies

Articles R emote sensing has facilitated extraordinary advances in the modeling, mapping, and understanding of ecosystems. Typical applications of remote sensing involve either images from passive optical systems, such as aerial photography and Landsat Thematic Mapper (Goward and Williams 1997), or to a lesser degree, active radar sensors such as RADARSAT (Waring et al. 1995). These types of sensors have proven to be satisfactory for many ecological applications , such as mapping land cover into broad classes and, in some biomes, estimating aboveground biomass and leaf area index (LAI). Moreover, they enable researchers to analyze the spatial pattern of these images. However, conventional sensors have significant limitations for ecological applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing aboveground biomass and leaf area index (Waring et al. 1995, Carlson and Ripley 1997, Turner et al. 1999). They are also limited in their ability to represent spatial patterns: They produce only two-dimensional (x and y) images, which cannot fully represent the three-dimensional structure of, for instance, an old-growth forest canopy.Yet ecologists have long understood that the presence of specific organisms, and the overall richness of wildlife communities, can be highly dependent on the three-dimensional spatial pattern of vegetation (MacArthur and MacArthur 1961), especially in systems where biomass accumulation is significant (Hansen and Rotella 2000). Individual bird species, in particular, are often associated with specific three-dimensional features in forests (Carey et al. 1991). In addition, other functional aspects of forests, such as productivity, may be related to forest canopy structure. Laser altimetry, or lidar (light detection and ranging), is an alternative remote sensing technology that promises to both increase the accuracy of biophysical measurements and extend spatial analysis into the third (z) dimension. Lidar sensors directly measure the three-dimensional distribution of plant canopies as well as subcanopy topography, thus providing high-resolution topographic maps and highly accurate estimates of vegetation height, cover, and canopy structure. In addition , lidar has been shown to accurately estimate LAI and aboveground biomass even in those high-biomass ecosystems where passive optical and active radar sensors typically fail to do so. The basic measurement made by a lidar device is the distance between the sensor and a target surface, obtained by determining the elapsed time between the emission of a short-duration laser pulse and the arrival of the reflection of that pulse (the return signal) at the sensor's receiver. Multiplying this …
Joint Named Entity Recognition and Disambiguation

Extracting named entities in text and linking extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. NER and linking models are usually trained separately, and the mutual dependency between the two tasks is ignored. We propose JERL, Joint Entity Recognition and Linking, to jointly model NER and linking tasks and capture the mutual dependency between them. It allows the information from each task to improve the performance of the other. To the best of our knowledge, JERL is the first model to jointly optimize NER and linking tasks together completely. In experiments on the CoNLL’03/AIDA data set, JERL outperforms state-of-art NER and linking systems, and we find improvements of 0.4% absolute F1 for NER on CoNLL’03, and 0.36% absolute precision@1 for linking on AIDA.
Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series

In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers.
ISO 9001:2015 implementation in the e-learning based virtual teaching program

“Total quality management is a journey, not a destination.” ∼Berry. Success of a system comes from the satisfaction of the user and efficiency of the product. ISO 9001 is a quality management system, which explicitly focuses to achieve worth of a system through its guidelines and set of generic requirements. The outcome or end result of the quality audit differs based on the different programs, environments, services intended and target audience however the requirements and practices to accomplish the task remains the same. Assurance of proficient application and contentment to the end user is objective of the e-Learning based “Virtual Teaching program in Medical Sciences and health services”, being developed and implemented by CDAC, Noida for the benefit of the medical students of the two medical colleges-North Eastern Indira Gandhi Regional Institute of Health & Medical Sciences(NEIGRIHMS), Shillong & Regional Institute of Medical Sciences(RIMS), Imphal. The e-Learning program on which ISO 9001:2015 is applied is a first of the kind endeavor in the medical field with e-courses designed and delivered in the area of health & medical education. To ensure the delivery of quality in e-Learning courses through Virtual Teaching (V.T), compliance of the program, as per the ISO 9001:2015 quality standards were tested and successfully implemented. Various processes including Risk estimation, Project Management Plan(PMP), Effort estimation etc. for the quality assurance of the program were documented and implemented. After a series of internal & external audits, the program was qualified and conformed as the ISO 9001:2015 standard certified program. The major learning of this achievement was preparation of overall processes which focuses on the high quality and competent product development and delivery. This paper provides the details of the processes developed for achieving the ISO 9001:2015 certification and how the achievement benefitted in the program in enhancing the overall quality of the virtual teaching. The paper also proposes possible standard based application of the ISO 9001:2015 processes in various e-Learning programs for assured quality managed services & processes.
ETWORK A RCHITECTURE S EARCH

The successes of deep learning in recent years has been fueled by the development of innovative new neural network architectures. However, the design of a neural network architecture remains a difficult problem, requiring significant human expertise as well as computational resources. In this paper, we propose a method for transforming a discrete neural network architecture space into a continuous and differentiable form, which enables the use of standard gradient-based optimization techniques for this problem, and allows us to learn the architecture and the parameters simultaneously. We evaluate our methods on the Udacity steering angle prediction dataset, and show that our method can discover architectures with similar or better predictive accuracy but significantly fewer parameters and smaller computational cost.
A collaborative approach for research paper recommender system

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.
Body composition and aging.

Body composition in older adults can be assessed using simple, convenient but less precise anthropometric methods to assess (regional) body fat and skeletal muscle, or more elaborate, precise and costly methods such as computed tomography and magnetic resonance imaging. Body weight and body fat percentage generally increase with aging due to an accumulation of body fat and a decline in skeletal muscle mass. Body weight and fatness plateau at age 75–80 years, followed by a gradual decline. However, individual weight patterns may differ and the periods of weight loss and weight (re)gain common in old age may affect body composition. Body fat redistributes with aging, with decreasing subcutaneous and appendicular fat and increasing visceral and ectopic fat. Skeletal muscle mass declines with aging, a process called sarcopenia. Obesity in old age is associated with a higher risk of mobility limitations, disability and mortality. A higher waist circumference and more visceral fat increase these risks, independent of overall body fatness, as do involuntary weight loss and weight cycling. The role of low skeletal muscle mass in the development of mobility limitations and disability remains controversial, but it is much smaller than the role of high body fat. Low muscle mass does not seem to increase mortality risk in older adults.
Lessons from the Amazon Picking Challenge

This paper summarizes lessons learned from the first Amazon Picking Challenge in which 26 international teams designed robotic systems that competed to retrieve items from warehouse shelves. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team’s background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team’s success in the competition, and discuss observations and lessons learned. Perception, motion planning, grasping, and robotic system engineering has reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semi-structured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.
Hierarchical Pointer Memory Network for Task Oriented Dialogue

We observe that end-to-end memory networks (MN) trained for task-oriented dialogue, such as for recommending restaurants to a user, suffer from an out-ofvocabulary (OOV) problem – the entities returned by the Knowledge Base (KB) may not be seen by the network at training time, making it impossible for it to use them in dialogue. We propose a Hierarchical Pointer Memory Network (HyP-MN), in which the next word may be generated from the decode vocabulary or copied from a hierarchical memory maintaining KB results and previous utterances. Evaluating over the dialog bAbI tasks, we find that HyP-MN drastically outperforms MN obtaining 12% overall accuracy gains. Further analysis reveals that MN fails completely in recommending any relevant restaurant, whereas HyP-MN recommends the best next restaurant 80% of the time.
Goal Setting and Task Performance : 1969-1980

A review of both laboratory and field studies on the effects of setting goals when performing a task found that in 90% of the studies, specific and challenging goals lead to higher performance than easy goals, "do your best" goals, or no goals. Goals affect performance by directing attention, mobilizing effort, increasing persistence, and motivating strategy development. Goal setting is most likely to improve task performance when the goals are specific and sufficiently challenging, the subjects have sufficient ability (and ability differences are controlled), feedback is provided to show progress in relation to the goal, rewards such as money are given for goal attainment, the experimenter or manager is supportive, and assigned goals are accepted by the individual. No reliable individual differences have emerged in goal-setting studies, probably because the goals were typically assigned rather than self-set. Need for achievement and self-esteem may be the most promising individual difference variables.
Interactive Storytelling: A Player Modelling Approach

In recent years, the fields of Interactive Storytelling and Player Modelling have independently enjoyed increased interest in both academia and the computer games industry. The combination of these technologies, however, remains largely unexplored. In this paper, we present PaSSAGE (PlayerSpecific Stories via Automatically Generated Events), an interactive storytelling system that uses player modelling to automatically learn a model of the player’s preferred style of play, and then uses that model to dynamically select the content of an interactive story. Results from a user study evaluating the entertainment value of adaptive stories created by our system as well as two fixed, pre-authored stories indicate that automatically adapting a story based on learned player preferences can increase the enjoyment of playing a computer role-playing game for certain types of players.
Ontogenetic development of digestive functionality in golden pompano Trachinotus ovatus (Linnaeus 1758)

Ontogenetic development of the digestive system in golden pompano (Trachinotus ovatus, Linnaeus 1758) larvae was histologically and enzymatically studied from hatch to 32 day post-hatch (DPH). The development of digestive system in golden pompano can be divided into three phases: phase I starting from hatching and ending at the onset of exogenous feeding; phase II starting from first feeding (3 DPH) and finishing at the formation of gastric glands; and phase III starting from the appearance of gastric glands on 15 DPH and continuing onward. The specific activities of trypsin, amylase, and lipase increased sharply from the onset of first feeding to 5–7 DPH, followed by irregular fluctuations. Toward the end of this study, the specific activities of trypsin and amylase showed a declining trend, while the lipase activity remained at similar levels as it was at 5 DPH. The specific activity of pepsin was first detected on 15 DPH and increased with fish age. The dynamics of digestive enzymes corresponded to the structural development of the digestive system. The enzyme activities tend to be stable after the formation of the gastric glands in fish stomach on 15 DPH. The composition of digestive enzymes in larval pompano indicates that fish are able to digest protein, lipid and carbohydrate at early developmental stages. Weaning of larval pompano is recommended from 15 DPH onward. Results of the present study lead to a better understanding of the ontogeny of golden pompano during the larval stage and provide a guide to feeding and weaning of this economically important fish in hatcheries.
A qualitative study on adoption of biometrics technologies: Canadian banking industry

This study examines the adoption of biometrics technology in the Canadian banking industry. By comparing Canadian banks with the financial institutions in other countries in terms of their adoption to biometrics, it explores current status of adoption to biometrics technology by Canadian banks and the potential future consequences as a result of their delayed adoption. Through literature review, this study first provides various aspects of biometrics technologies; technical specifications, performance metrics, types, current applications, and some issues and concerns with this technology. Second, it discusses current and future potential applications of biometrics in the banking industry. Then, the study provides a context with which to analyze the extent of the popularity and practicality of the technology in Canadian banks. Finally, based on qualitative interviews with biometric researchers and professionals in Canada, this study reports the research findings with regards to the following four topics: 1) Integration and accessibility to biometrics, 2) Public opinion and concern, 3) Transition of technologies, and 4) Current accommodations for biometrics, followed by discussion of the contributions of this study.
Ontology mapping : the state of the art

Ontology mapping is seen as a solution provider in today’s landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.
Regression through the Origin

A lthough ordinary least-squares (OLS) regression is one of the most familiar statistical tools, far less has been written − especially in the pedagogical literature − on regression through the origin (RTO). Indeed, the subject is surprisingly controversial. The present note highlights situations in which RTO is appropriate, discusses the implementation and evaluation of such models and compares RTO functions among three popular statistical packages. Some examples gleaned from past Teaching Statistics articles are used as illustrations. For expository convenience, OLS and RTO refer here to linear regressions obtained by least-squares methods with and without a constant term, respectively.
Hominin occupation of the Chinese Loess Plateau since about 2.1 million years ago

Considerable attention has been paid to dating the earliest appearance of hominins outside Africa. The earliest skeletal and artefactual evidence for the genus Homo in Asia currently comes from Dmanisi, Georgia, and is dated to approximately 1.77–1.85 million years ago (Ma)1. Two incisors that may belong to Homo erectus come from Yuanmou, south China, and are dated to 1.7 Ma2; the next-oldest evidence is an H. erectus cranium from Lantian (Gongwangling)—which has recently been dated to 1.63 Ma3—and the earliest hominin fossils from the Sangiran dome in Java, which are dated to about 1.5–1.6 Ma4. Artefacts from Majuangou III5 and Shangshazui6 in the Nihewan basin, north China, have also been dated to 1.6–1.7 Ma. Here we report an Early Pleistocene and largely continuous artefact sequence from Shangchen, which is a newly discovered Palaeolithic locality of the southern Chinese Loess Plateau, near Gongwangling in Lantian county. The site contains 17 artefact layers that extend from palaeosol S15—dated to approximately 1.26 Ma—to loess L28, which we date to about 2.12 Ma. This discovery implies that hominins left Africa earlier than indicated by the evidence from Dmanisi. An Early Pleistocene artefact assemblage from the Chinese Loess Plateau indicates that hominins had left Africa by at least 2.1 million years ago, and occupied the Loess Plateau repeatedly for a long time.