nils-herrmann/clean_scirepeval huggingface dataset:
Modularity and Innovation in Complex Systems

The problem of designing, coordinating, and managing complex systems has been central to the management and organizations literature. Recent writings have tended to offer modularity as, at least, a partial solution to this design problem. However, little attention has been paid to the problem of identifying what constitutes an appropriate modularization of a complex system. We develop a formal simulation model that allows us to carefully examine the dynamics of innovation and performance in complex systems. The model points to the trade-off between the destabilizing effects of overly refined modularization and the modest levels of search and a premature fixation on inferior designs that can result from excessive levels of integration. The analysis highlights an asymmetry in this trade-off, with excessively refined modules leading to cycling behavior and a lack of performance improvement. We discuss the implications of these arguments for product and organization design.
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.
Optimizing Energy Consumption with Task Consolidation in Clouds

Task consolidation is a way to maximize utilization of cloud computing resources. Maximizing resource utilization provides various benefits such as the rationalization of maintenance , IT service customization, and QoS and reliable services. However, maximizing resource utilization does not mean efficient energy use. Much of the literature shows that energy consumption and resource utilization in clouds are highly coupled. Consequently, some of the literature aims to decrease resource utilization in order to save energy, while others try to reach a balance between resource utilization and energy consumption. In this paper, we present an energy-aware task consolidation (ETC) technique that minimizes energy consumption. ETC achieves this by restricting CPU use below a specified peak threshold. ETC does this by consolidating tasks amongst virtual clusters. In addition, the energy cost model considers network latency when a task migrates to another virtual cluster. To evaluate the performance of ETC we compare it against MaxUtil. MaxUtil is a recently developed greedy algorithm that aims to maximize cloud computing resources. The simulation results show that ETC can significantly reduce power consumption in a cloud system, with 17% improvement over MaxUtil. Cloud computing has recently become popular due to the maturity of related technologies such as network devices, software applications and hardware capacities. Resources in these systems can be widely distributed and the scale of resources involved can range from several servers to an entire data center. To integrate and make good use of resources at various scales, cloud computing needs efficient methods to manage them [4]. Consequently, the focus of much research in recent years has been on how to utilize resources and how to reduce power consumption. One of the key technologies in cloud computing is virtualization. The ability to create virtual machines (VMs)  dynamically on demand is a popular solution for managing resources on physical machines. Therefore, many methods [17,18] have been developed that enhance resource utilization such as memory compression, request discrimination, defining threshold for resource usage and task allocation among VMs. Improvements in power consumption, and the relationship between resource usage and energy consumption has also been widely studied [6,10–12,14–18]. Some research aims to improve resource utilization while others aim to reduce energy consumption. The goals of both are to reduce costs for data centers. Due to the large size of many data centers, the financial savings are substantial. Energy consumption varies according to CPU utilization [11]. Higher CPU utilization …
Computer Vision – ECCV 2016

What is the right supervisory signal to train visual representations? Current approaches in computer vision use category labels from datasets such as ImageNet to train ConvNets. However, in case of biological agents, visual representation learning does not require millions of semantic labels. We argue that biological agents use physical interactions with the world to learn visual representations unlike current vision systems which just use passive observations (images and videos downloaded from web). For example, babies push objects, poke them, put them in their mouth and throw them to learn representations. Towards this goal, we build one of the first systems on a Baxter platform that pushes, pokes, grasps and observes objects in a tabletop environment. It uses four different types of physical interactions to collect more than 130K datapoints, with each datapoint providing supervision to a shared ConvNet architecture allowing us to learn visual representations. We show the quality of learned representations by observing neuron activations and performing nearest neighbor retrieval on this learned representation. Quantitatively, we evaluate our learned ConvNet on image classification tasks and show improvements compared to learning without external data. Finally, on the task of instance retrieval, our network outperforms the ImageNet network on recall@1 by 3 %.
Querying graph databases

Graph databases have gained renewed interest in the last years, due to its applications in areas such as the Semantic Web and Social Networks Analysis. We study the problem of querying graph databases, and, in particular, the expressiveness and complexity of evaluation for several general-purpose query languages, such as the regular path queries and its extensions with conjunctions and inverses. We distinguish between two semantics for these languages. The first one, based on simple paths, easily leads to intractability, while the second one, based on arbitrary paths, allows tractable evaluation for an expressive family of languages.
 We also study two recent extensions of these languages that have been motivated by modern applications of graph databases. The first one allows to treat paths as first-class citizens, while the second one permits to express queries that combine the topology of the graph with its underlying data.
RefocusGAN: Scene Refocusing Using a Single Image

Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblurthen-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter δ, we are able to achieve generic free-form refocusing over a single image.
Depression in Main Caregivers of Dementia Patients: Prevalence and Predictors

The most common neurodegenerative disease is dementia. Family of dementia patients says that their lives have been changed extensively after happening of dementia to their patients. One of the problems of family and caregivers is depression of the caregiver. In this study, we aimed to find the prevalence of depression and factors can affect depression in the dementia caregivers.



This study was cross-sectional study with convenient sampling method. Our society was 96 main caregivers of dementia patients in the year 2015 in Iran. We had two questionnaires, a demographic and Beck Depression Inventory (BDI). BDI Cronbach's alpha is 0.86 for psychiatric patients and 0.81 for nonpsychiatric persons, and Beck's scores are between 0 and 64. We used SPSS version 22 for statistical analysis.



According to Beck depression test, 69.8% (n = 67 out of 96) of all caregivers had scores in the range of depression. In bivariate analysis, we found higher dementia severity and lower support of other family members from the caregiver can predict higher depression in the caregiver. As well, in regression analysis using GLM model, we found higher age and lower educational level of the caregiver can predict higher depression in the caregiver. Moreover, regression analysis approved findings about severity and support of other family members in bivariate analysis.



High-level depression is found in caregivers of dementia patients. It needs special attention from healthcare managers, clinicians and all of health-care personnel who deals with dementia patients and their caregivers.
Verilog Implementation of a System for Finding Shortest Path by Using Floyd-Warshall Algorithm

There are several applications in VLSI technology that require high-speed shortest-path computations. The shortest path is a path between two nodes (or points) in a graph such that the sum of the weights of its constituent edges is minimum. Floyd-Warshall algorithm provides fastest computation of shortest path between all pair of nodes present in the graph. With rapid advances in VLSI technology, Field Programmable Gate Arrays (FPGAs) are receiving the attention of the Parallel and High Performance Computing community. This paper gives implementation outcome of Floyd-Warshall algorithm to solve the all pairs shortest-paths problem for directed graph in Verilog.
App2Vec: Vector modeling of mobile apps and applications

We design a way to model apps as vectors, inspired by the recent deep learning approach to vectorization of words called word2vec. Our method relies on how users use apps. In particular, we visualize the time series of how each user uses mobile apps as a “document”, and apply the recent word2vec modeling on these documents, but the novelty is that the training context is carefully weighted by the time interval between the usage of successive apps. This gives us the app2vec vectorization of apps. We apply this to industrial scale data from Yahoo! and (a) show examples that app2vec captures semantic relationships between apps, much as word2vec does with words, (b) show using Yahoo!'s extensive human evaluation system that 82% of the retrieved top similar apps are semantically relevant, achieving 37% lift over bag-of-word approach and 140% lift over matrix factorization approach to vectorizing apps, and (c) finally, we use app2vec to predict app-install conversion and improve ad conversion prediction accuracy by almost 5%. This is the first industry scale design, training and use of app vectorization.
Synchronization and Frequency Regulation of DFIG-Based Wind Turbine Generators With Synchronized Control

Synchronized control (SYNC) is widely adopted for doubly fed induction generator (DFIG)-based wind turbine generators (WTGs) in microgrids and weak grids, which applies P-f droop control to achieve grid synchronization instead of phase-locked loop. The DFIG-based WTG with SYNC will reach a new equilibrium of rotor speed under frequency deviation, resulting in the WTG's acceleration or deceleration. The acceleration/deceleration process can utilize the kinetic energy stored in the rotating mass of WTG to provide active power support for the power grid, but the WTG may lose synchronous stability simultaneously. This stability problem occurs when the equilibrium of rotor speed is lost and the rotor speed exceeds the admissible range during the frequency deviations, which will be particularly analyzed in this paper. It is demonstrated that the synchronous stability can be improved by increasing the P-f droop coefficient. However, increasing the P-f droop coefficient will deteriorate the system's small signal stability. To address this contradiction, a modified synchronized control strategy is proposed. Simulation results verify the effectiveness of the analysis and the proposed control strategy.
An Investigation on Mobile Banking Adoption and Usage : A Case Study of Mauritius

The purpose of this paper is to investigate m-banking adoption and usage in Mauritius; a service relatively new in the island. The aim is to gauge awareness level and to identify those factors that inhibit or motivate m-banking usage in Mauritius. An online survey was carried out using a standard questionnaire. Convenience sampling method was used. Out of the 211 people who responded to the survey, only 169 responses were deemed to be usable. It was found that awareness of local m-banking services is quite high and usage level is reasonable. Convenience, time and effort savings, privacy, ubiquitous access to banking services, compatibility with lifestyle and banking needs were identified as the main factors motivating m-banking adoption. Perceived security risk and reliability were found to be the main obstacles to m-banking usage. It was also found that m-banking usage is not associated with age, gender and salary. There is, however, an association between education and m-banking usage. As to the limitation of the study, the use of convenience sampling limits the generalisation of the findings of the study. The study has practical implications for local banks either offering or planning to launch mbanking services in Mauritius as factors preventing and encouraging usage of m-banking are discussed. The constructs of Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) are integrated and extended with perceived risk and cost to ascertain mbanking usage and adoption. The study, therefore, offers valuable insights on m-banking in Mauritius.
VQABQ: Visual Question Answering by Basic Questions

Taking an image and question as the input of our method, it can output the text-based answer of the query question about the given image, so called Visual Question Answering (VQA). There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question. We formulate the basic questions generation problem as a LASSO optimization problem, and also propose a criterion about how to exploit these basic questions to help answer main question. Our method is evaluated on the challenging VQA dataset  and yields state-of-the-art accuracy, 60.34% in open-ended task.
The dimensions of the human dentogingival junction.

This study examined the naturally occurring dimensions of the dentogingival junction in 10 adult human cadaver jaws. The connective tissue attachment, epithelial attachment, loss of attachment, and sulcus depth were measured histomorphometrically for 171 tooth surfaces. Mean measurements were 1.34 +/- 0.84 mm for sulcus depth; 1.14 +/- 0.49 mm for epithelial attachment; 0.77 +/- 0.32 mm for connective tissue attachment; and 2.92 +/- 1.69 mm for loss of attachment. These dimensions, as measured in this study, support the concept that the connective tissue attachment is a variable width within a more narrow distribution and range than the epithelial attachment, sulcus depth, or loss of attachment. The level of the loss of attachment was not predictive of the connective tissue attachment length.
Text Classification using Data Mining

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naïve Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.
Mining Twitter big data to predict 2013 Pakistan election winner

Twitter is a well-known micro-blogging website which allows millions of users to interact over different types of communities, topics, and tweeting trends. The big data being generated on Twitter daily, and its significant impact on social networking, has motivated the application of data mining (analysis) to extract useful information from tweets. In this paper, we analyze the impact of tweets in predicting the winner of the recent 2013 election held in Pakistan. We identify relevant Twitter users, pre-process their tweets, and construct predictive models for three representative political parties which were significantly tweeted, i.e., Pakistan Tehreek-e-Insaaf (PTI), Pakistan Muslim League Nawaz (PMLN), and Muttahida Qaumi Movement (MQM). The predictions for last four days before the elections showed that PTI will emerge as the election winner, which was actually won by PMLN. However, considering that PTI obtained landslide victory in one province and bagged several important seats across the country, we conclude that Twitter can have some type of a positive influence on the election result, although it cannot be considered representative of the overall voting population.
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice

Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments. Such a task requires communication between autonomous vehicles and other road users in order to resolve various traffic ambiguities. The interaction between road users is a form of negotiation in which the parties involved have to share their attention regarding a common objective or a goal (e.g. crossing an intersection), and coordinate their actions in order to accomplish it. In this literature review we aim to address the interaction problem between pedestrians and drivers (or vehicles) from joint attention point of view. More specifically, we will discuss the theoretical background behind joint attention, its application to traffic interaction and practical approaches to implementing joint attention for autonomous vehicles.
The Technology Acceptance Model: Past, Present, and Future

While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM’s history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An openended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions.
Acetylcholinesterase and butyrylcholinesterase inhibition of ethanolic extract and monoterpenes from Pimpinella anisoides V Brig. (Apiaceae).

Ethanolic extract from the fruits of Pimpinella anisoides, an aromatic plant and a spice, exhibited activity against AChE and BChE, with IC(50) values of 227.5 and 362.1 microg/ml, respectively. The most abundant constituents of the extract were trans-anethole, (+)-limonene and (+)-sabinene. trans-Anethole exhibited the highest activity against AChE and BChE with IC(50) values of 134.7 and 209.6 microg/ml, respectively. The bicyclic monoterpene (+)-sabinene exhibited a promising activity against AChE (IC(50) of 176.5 microg/ml) and BChE (IC(50) of 218.6 microg/ml).
A randomized controlled pilot study of the effectiveness of occupational therapy for children with sensory modulation disorder.

A pilot randomized controlled trial (RCT) of the effectiveness of occupational therapy using a sensory integration approach (OT-SI) was conducted with children who had sensory modulation disorders (SMDs). This study evaluated the effectiveness of three treatment groups. In addition, sample size estimates for a large scale, multisite RCT were calculated.



Twenty-four children with SMD were randomly assigned to one of three treatment conditions; OT-SI, Activity Protocol, and No Treatment. Pretest and posttest measures of behavior, sensory and adaptive functioning, and physiology were administered.



The OT-SI group, compared to the other two groups, made significant gains on goal attainment scaling and on the Attention subtest and the Cognitive/Social composite of the Leiter International Performance Scale-Revised. Compared to the control groups, OT-SI improvement trends on the Short Sensory Profile, Child Behavior Checklist, and electrodermal reactivity were in the hypothesized direction.



Findings suggest that OT-SI may be effective in ameliorating difficulties of children with SMD.
Content Embedding Regularized Matrix Factorization for Recommender Systems

In recommender systems, numerous efforts have been made on utilizing textual information in matrix factorization to alleviate the problem of data sparsity. Recently, some of the works have explored neural networks to go for an in-depth understanding of textual item content, and further generate more accurate item latent models. These works achieve impressive effectiveness on performing recommendations. Nevertheless, there remains an open issue as how to effectively exploit description documents of both users and items in matrix factorization. In this paper, we proposed content embedding regularized matrix factorization (CERMF) to address this issue. CERMF adopts convolution neural networks to simultaneously generate the independent embedding representations for the users and the items. Then, dual embeddings are used to regularize the generation of latent models for users and items. Experimental results are proved that exploiting content information of both users and items dramatically improves the accuracy of recommendations as compared with the state-of-the-art recommendation models.