nils-herrmann/clean_scirepeval huggingface dataset:
Corporal punishment by American parents: national data on prevalence, chronicity, severity, and duration, in relation to child and family characteristics.

We present data on corporal punishment (CP) by a nationally representative sample of 991 American parents interviewed in 1995. Six types of CP were examined: slaps on the hand or leg, spanking on the buttocks, pinching, shaking, hitting on the buttocks with a belt or paddle, and slapping in the face. The overall prevalence rate (the percentage of parents using any of these types of CP during the previous year) was 35% for infants and reached a peak of 94% at ages 3 and 4. Despite rapid decline after age 5, just over half of American parents hit children at age 12, a third at age 14, and 13% at age 17. Analysis of chronicity found that parents who hit teenage children did so an average of about six times during the year. Severity, as measured by hitting the child with a belt or paddle, was greatest for children age 5-12 (28% of such children). CP was more prevalent among African American and low socioeconomic status parents, in the South, for boys, and by mothers. The pervasiveness of CP reported in this article, and the harmful side effects of CP shown by recent longitudinal research, indicates a need for psychology and sociology textbooks to reverse the current tendency to almost ignore CP and instead treat it as a major aspect of the socialization experience of American children; and for developmental psychologists to be cognizant of the likelihood that parents are using CP far more often than even advocates of CP recommend, and to inform parents about the risks involved.
Selection of K in K-means clustering

The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.
Corruption Cycles

Political corruption has been a persistent phenomenon throughout history and across societies. It is found today in many dierent forms and degrees in all types of political systems, in developing countries as well as in Western democracies. The phenomenon is not solely or even largely the consequence of a moral fault or cultural backwardness. The root cause of corruption, in Europe as well as in Japan, Latin America and the United States, is found in the mixture of economic and political power. We take corruption to consist in the illegitimate use of public roles and resources for private bene®t, where `private' often refers to large groups such as political parties. This de®nition has the advantage of subsuming many dierent kinds of corrupt behaviour, ranging from public ocers' use of their position to maximize personal gain by dispensing public bene®ts to the implementation of policies that violate the common interest in favour of special interests, such as granting large ®rms the monopoly of their services within a particular sector. Since this de®nition of corruption is based upon legal norms, not public opinion or social norms, it has the advantage of directing our attention to the contrast between ocial and social norms that is present in most societies. The literature on corruption is a large corpus of descriptive studies, but a full ̄edged theory of corruption is yet to come. As a result, many dimensions of corruption have been left unexplored. One such problem is the life-cycle of corruption. If corruption is endemic, what are the forces that allow it to prevail? What permits it to continue? An answer to this question cannot be separated from an analysis of the political and economic eects of corruption. There has been widespread disagreement among scholars studying the phenomenon regarding the direction of its eects. The so-called Moralists maintained that corruption is harmful, as it impedes development and erodes the legitimacy of institutions. Revisionists point instead to the possible bene®ts of corruption: it
Driving Semantic Parsing from the World's Response

Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logical forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learning paradigm aimed at alleviating the supervision burden. We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the context of an external world. In addition we reformulate the semantic parsing problem to reduce the dependency of the model on syntactic patterns, thus allowing our parser to scale better using less supervision. Our results surprisingly show that without using any annotated meaning representations learning with a weak feedback signal is capable of producing a parser that is competitive with fully supervised parsers.
Gorilla: A Fast, Scalable, In-Memory Time Series Database

Large-scale internet services aim to remain highly available and responsive in the presence of unexpected failures. Providing this service often requires monitoring and analyzing tens of millions of measurements per second across a large number of systems, and one particularly effective solution is to store and query such measurements in a time series database (TSDB). A key challenge in the design of TSDBs is how to strike the right balance between efficiency, scalability, and reliability. In this paper we introduce Gorilla, Facebook’s inmemory TSDB. Our insight is that users of monitoring systems do not place much emphasis on individual data points but rather on aggregate analysis, and recent data points are of much higher value than older points to quickly detect and diagnose the root cause of an ongoing problem. Gorilla optimizes for remaining highly available for writes and reads, even in the face of failures, at the expense of possibly dropping small amounts of data on the write path. To improve query efficiency, we aggressively leverage compression techniques such as delta-of-delta timestamps and XOR’d floating point values to reduce Gorilla’s storage footprint by 10x. This allows us to store Gorilla’s data in memory, reducing query latency by 73x and improving query throughput by 14x when compared to a traditional database (HBase)backed time series data. This performance improvement has unlocked new monitoring and debugging tools, such as time series correlation search and more dense visualization tools. Gorilla also gracefully handles failures from a single-node to entire regions with little to no operational overhead.
Nonnegative Matrix Factorization with Orthogonality Constraints

Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data, the goal of which is to decompose a data matrix into a product of two factor matrices with all entries in factor matrices restricted to be nonnegative. NMF was shown to be useful in a task of clustering (especially document clustering), but in some cases NMF produces the results inappropriate to the clustering problems. In this paper, we present an algorithm for orthogonal nonnegative matrix factorization, where an orthogonality constraint is imposed on the nonnegative decomposition of a term-document matrix. The result of orthogonal NMF can be clearly interpreted for the clustering problems, and also the performance of clustering is usually better than that of the NMF. We develop multiplicative updates directly from true gradient on Stiefel manifold, whereas existing algorithms consider additive orthogonality constraints. Experiments on several different document data sets show our orthogonal NMF algorithms perform better in a task of clustering, compared to the standard NMF and an existing orthogonal NMF.
Social media research: Theories, constructs, and conceptual frameworks

In just one decade, social media have revolutionized the life of many people and thus attracted much attention, not only from industry, but also academia. To understand how researchers have adopted theories, used research constructs, and developed conceptual frameworks in their studies, a systematic and structured literature review based on five leading online academic databases was conducted. A total of 46 articles on social media research were consolidated and analyzed, including empirical studies spanning from 2002 to 2011. A collection of theories/models and constructs/attributes adopted in these articles is summarized and tabulated for easy reference and comprehension of extant research results. A causalchain framework was developed based on the input-moderator–mediator-output model to illustrate the causality between the research constructs used and the conceptualization of theoretical models/theories proposed by previous researchers. Because social media cover a wide range of research topics, the literature review may not be exhaustive. However, the proposed causal-chain framework and suggested research directions may be regarded as representative references for future research in the subject area. This is believed to be the first comprehensive literature review of social media research, and it contributes to a better understanding of the causes and effects of the adoption and usage of social media.
Understanding e-learning continuance intention: An extension of the Technology Acceptance Model

Based on the expectancy disconfirmation theory, this study proposes a decomposed technology acceptance model in the context of an e-learning service. In the proposed model, the perceived performance component is decomposed into perceived quality and perceived usability. A sample of 172 respondents took part in this study. The results suggest that users’ continuance intention is determined by satisfaction, which in turn is jointly determined by perceived usefulness, information quality, confirmation, service quality, system quality, perceived ease of use and cognitive absorption. r 2006
Semantic Parsing for Single-Relation Question Answering

We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring relational triple to answer the question. When evaluated on an open-domain QA task, our method achieves higher precision across different recall points compared to the previous approach, and can improve F1 by 7 points.
An enhanced WiFi indoor localization system based on machine learning

The Global Navigation Satellite Systems (GNSS) suffer from accuracy deterioration and outages in dense urban canyons and are almost unavailable for indoor environments. Nowadays, developing indoor positioning systems has become an attractive research topic due to the increasing demands on ubiquitous positioning. WiFi technology has been studied for many years to provide indoor positioning services. The WiFi indoor localization systems based on machine learning approach are widely used in the literature. These systems attempt to find the perfect match between the user fingerprint and pre-defined set of grid points on the radio map. However, Fingerprints are duplicated from available Access Points (APs) and interference, which increase number of matched patterns with the user's fingerprint. In this research, the Principle Component Analysis (PCA) is utilized to improve the performance and to reduce the computation cost of the WiFi indoor localization systems based on machine learning approach. All proposed methods were developed and physically realized on Android-based smart phone using the IEEE 802.11 WLANs. The experimental setup was conducted in a real indoor environment in both static and dynamic modes. The performance of the proposed method was tested using K-Nearest Neighbors, Decision Tree, Random Forest and Support Vector Machine classifiers. The results show that the performance of the proposed method outperforms other indoor localization reported in the literature. The computation time was reduced by 70% when using Random Forest classifier in the static mode and by 33% when using KNN in the dynamic mode.
Space Syntax and Spatial Cognition: Proceedings of the Workshop held in Bremen, 24th September 2006

What can we learn of the human mind by examining its products? Here it is argued that a great deal can be learned, and that the study of human minds through its creations in the real world could be a promising field of study within the cognitive sciences. The city is a case in point. Since the beginning of cities human ideas about them have been dominated by geometric ideas, and the real history of cities has always oscillated between the geometric and the ‘organic’. Set in the context of the suggestion from cognitive neuroscience that we impose more geometric order on the world that it actually possesses, an intriguing question arises: what is the role of geometric intuition in how we understand cities and how we create them? Here we argue that all cities, the organic as well as the geometric, are pervasively ordered by geometric intuition, so that neither the forms of the cities nor their functioning can be understood without insight into their distinctive and pervasive emergent geometrical forms. The city is, as it is often said to be, the creation of economic and social processes, but, it is argued, these processes operate within an envelope of geometric possibility defined by human minds in its interaction with spatial laws that govern the relations between objects and spaces in the ambient world. Note: I have included only selected images here. All the examples will be shown fully in the presentation.  the Ideal and the Organic The most basic distinction we make about the form of cities is between the ideal and the organic. The ideal are geometric, the organic are not — or seem not to be. The geometric we define in terms of straight lines and 90 or 45 degree angles, the organic in terms of the lack of either . The ideal seem to be top-down impositions of the human mind, the outcome of reason, often in association with power. We easily grasp their patterns when seen ‘all at once’. The organic we take to be the outcome of unplanned bottom up processes reflecting the
The influence of design aesthetics in usability testing: effects on user performance and perceived usability.

This article examined the effects of product aesthetics on several outcome variables in usability tests. Employing a computer simulation of a mobile phone, 60 adolescents (14-17 yrs) were asked to complete a number of typical tasks of mobile phone users. Two functionally identical mobile phones were manipulated with regard to their visual appearance (highly appealing vs not appealing) to determine the influence of appearance on perceived usability, performance measures and perceived attractiveness. The results showed that participants using the highly appealing phone rated their appliance as being more usable than participants operating the unappealing model. Furthermore, the visual appearance of the phone had a positive effect on performance, leading to reduced task completion times for the attractive model. The study discusses the implications for the use of adolescents in ergonomic research.
Unequal Representation and Gender Stereotypes in Image Search Results for Occupations

Information environments have the power to affect people's perceptions and behaviors. In this paper, we present the results of studies in which we characterize the gender bias present in image search results for a variety of occupations. We experimentally evaluate the effects of bias in image search results on the images people choose to represent those careers and on people's perceptions of the prevalence of men and women in each occupation. We find evidence for both stereotype exaggeration and systematic underrepresentation of women in search results. We also find that people rate search results higher when they are consistent with stereotypes for a career, and shifting the representation of gender in image search results can shift people's perceptions about real-world distributions. We also discuss tensions between desires for high-quality results and broader societal goals for equality of representation in this space.
Change of growth pattern, metabolism, and quality and quantity of maize plants after irrigation with magnetically treated water.

Water molecules can be affected by magnetic fields (MF) due to their bipolar characteristics. In the present study maize plants, from sowing to the end period of generative stage, were irrigated with magnetically treated water (MTW).Tap water was treated with MF by passing through a locally designed alternative magnetic field generating apparatus (110 mT). Irrigation with MTW increased the ear length and fresh weight, 100-grain fresh and dry weights, and water productivity (119.5%, 119.1%, 114.2%, 116.6% and 122.3%, respectively), compared with the control groups. Levels of photosynthetic pigments i.e. chlorophyll a and b, and the contents of anthocyanin and flavonoids of the leaves were increased compared to those of non-treated ones. Increase of the activity of superoxide dismutase (SOD) and ascorbate peroxidase (APX) in leaves of the treated plants efficiently scavenged active oxygen species and resulted in the maintenance of photosynthetic membranes and reduction of malondealdehyde. Total ferritin, sugar, iron and calcium contents of kernels of MTW-irrigated plants were respectively 122.9%, 167.4%, 235% and 185% of the control ones. From the results presented here it can be concluded that the influence of MF on living plant cells, at least in part, is mediated by water. The results also suggest that irrigation of maize plant with MTW can be applied as a useful method for improvement of quantity and quality of it.
Constructivism : A Psychological Theory of Learning

Psychology—the way learning is defined, studied, and understood—underlies much of the curricular and instructional decision-making that occurs in education. Constructivism, perhaps the most current psychology of learning, is no exception. Initially based on the work of Jean Piaget and Lev Vygotsky, and then supported and extended by contemporary biologists and cognitive scientists, it is having major ramifications on the goals teachers set for the learners with whom they work, the instructional strategies teachers employ in working towards these goals, and the methods of assessment utilized by school personnel to document genuine learning. What is this theory of learning and development that is the basis of the current reform movement and how is it different from other models of psychology?
KENKEN PUZZLE – BASED IMAGE ENCRYPTION ALGORITHM

A novel chaotic, permutation-substitution architecture based, grayscale images’ encryption algorithm is introduced in this paper. To reduce the redundancies of Fridrich’s structure based image encryption scheme, a novel inter-intra bit-level permutation based confusion strategy is appealed. The confusion stage is developed with the aid of KenKen puzzles. Theoretical analysis and simulation results show that the proposed method has many of the desired properties of a secure cipher.
Wearable technology for patients with brain and spinal cord injuries.

Studies have shown that patients who practice functional movements at home in conjunction with outpatient therapy show higher improvement in motor recovery. However, patients are not qualified to monitor or assess their own condition that must be reported back to the clinician. Therefore, there is a need to transmit physiological data to clinicians from patients in their home environment. This paper presents a review of wearable technology for in-home health monitoring, assessment, and rehabilitation of patients with brain and spinal cord injuries.
Multilingual Semantic Parsing And Code-Switching

Extending semantic parsing systems to new domains and languages is a highly expensive, time-consuming process, so making effective use of existing resources is critical. In this paper, we describe a transfer learning method using crosslingual word embeddings in a sequence-tosequence model. On the NLmaps corpus, our approach achieves state-of-the-art accuracy of 85.7% for English. Most importantly, we observed a consistent improvement for German compared with several baseline domain adaptation techniques. As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasonably well on codeswitching utterances which contain a mixture of English and German, even though the training data does not contain any code-switching. As far as we know, this is the first study of code-switching in semantic parsing. We manually constructed the set of code-switching test utterances for the NLmaps corpus and achieve 78.3% accuracy on this dataset.
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN  to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we design a new CNN encoder architecture, 2) show how to effectively train the model with Reinforcement Learning, and 3) significantly increase the output resolution using a Graph Neural Network, allowing the model to accurately annotate high-resolution objects in images. Extensive evaluation on the Cityscapes dataset  shows that our model, which we refer to as Polygon-RNN++, significantly outperforms the original model in both automatic (10% absolute and 16% relative improvement in mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We further analyze the cross-domain scenario in which our model is trained on one dataset, and used out of the box on datasets from varying domains. The results show that Polygon-RNN++ exhibits powerful generalization capabilities, achieving significant improvements over existing pixel-wise methods. Using simple online fine-tuning we further achieve a high reduction in annotation time for new datasets, moving a step closer towards an interactive annotation tool to be used in practice.
Resilience Thinking : Integrating Resilience , Adaptability and Transformability

Resilience thinking addresses the dynamics and development of complex social–ecological systems (SES). Three aspects are central: resilience, adaptability and transformability. These aspects interrelate across multiple scales. Resilience in this context is the capacity of a SES to continually change and adapt yet remain within critical thresholds. Adaptability is part of resilience. It represents the capacity to adjust responses to changing external drivers and internal processes and thereby allow for development along the current trajectory (stability domain). Transformability is the capacity to cross thresholds into new development trajectories. Transformational change at smaller scales enables resilience at larger scales. The capacity to transform at smaller scales draws on resilience from multiple scales, making use of crises as windows of opportunity for novelty and innovation, and recombining sources of experience and knowledge to navigate social–ecological transitions. Society must seriously consider ways to foster resilience of smaller more manageable SESs that contribute to Earth System resilience and to explore options for deliberate transformation of SESs that threaten Earth System resilience.