nils-herrmann/clean_scirepeval huggingface dataset:
Detailed 3D Model Driven Single View Scene Understanding

We present a data driven approach to holistic scene understanding. From a single image of an indoor scene, our approach estimates its detailed 3D geometry, i.e. The location of its walls and floor, and the 3D appearance of its containing objects, as well as its semantic meaning, i.e. A prediction of what objects it contains. This is made possible by using large datasets of detailed 3D models alongside appearance based detectors. We first estimate the 3D layout of a room, and extrapolate 2D object detection hypotheses to three dimensions to form bounding cuboids. Cuboids are converted to detailed 3D models of the predicted semantic category. Combinations of 3D models are used to create a large list of layout hypotheses for each image -- where each layout hypothesis is semantically meaningful and geometrically plausible. The likelihood of each layout hypothesis is ranked using a learned linear model -- and the hypothesis with the highest predicted likelihood is the final predicted 3D layout. Our approach is able to recover the detailed geometry of scenes, provide precise segmentation of objects in the image plane, and estimate objects' pose in 3D.
A Survey on ICT Usage and the Perceptions of Social Studies Teachers in Turkey

Turkey has been undertaking many projects to integrate Information and Communication Technology (ICT) sources into practice in the teaching-learning process in educational institutions. This research study sheds light on the use of ICT tools in primary schools in the social studies subject area, by considering various variables which affect the success of the implementation of the use of these tools. A survey was completed by 326 teachers who teach fourth and fifth grade at primary level. The results showed that although teachers are willing to use ICT resources and are aware of the existing potential, they are facing problems in relation to accessibility to ICT resources and lack of in-service training opportunities.
An innovative outcomes-based medical education program built on adult learning principles.

An innovative medical curriculum at the University of New South Wales (UNSW) has been developed through a highly collaborative process aimed at building faculty ownership and ongoing sustainability. The result is a novel capability-based program that features early clinical experience and small-group teaching, which offers students considerable flexibility and achieves a high degree of alignment between graduate outcomes, learning activities and assessments. Graduate capabilities that focus student learning on generic outcomes are described (critical evaluation, reflection, communication and teamwork) along with traditional outcomes in biomedical science, social aspects, clinical performance and ethics. Each two-year phase promotes a distinctive learning process to support and develop autonomous learning across six years. The approaches emphasize important adult education themes: student autonomy; learning from experience; collaborative learning; and adult teacher-learner relationships. Teaching in each phase draws on stages of the human life cycle to provide an explicit organization for the vertical integration of knowledge and skills. A learning environment that values the social nature of learning is fostered through the program's design and assessment system, which supports interdisciplinary integration and rewards students who exhibit self-direction. Assessment incorporates criterion referencing, interdisciplinary examinations, a balance between continuous and barrier assessments, peer feedback and performance assessments of clinical competence. A portfolio examination in each phase, in which students submit evidence of reflection and achievement for each capability, ensures overall alignment.
Synthesizing Continuous Deployment Practices Used in Software Development

Continuous deployment speeds up the process of existing agile methods, such as Scrum, and Extreme Programming (XP) through the automatic deployment of software changes to end-users upon passing of automated tests. Continuous deployment has become an emerging software engineering process amongst numerous software companies, such as Facebook, Github, Netflix, and Rally Software. A systematic analysis of software practices used in continuous deployment can facilitate a better understanding of continuous deployment as a software engineering process. Such analysis can also help software practitioners in having a shared vocabulary of practices and in choosing the software practices that they can use to implement continuous deployment. The goal of this paper is to aid software practitioners in implementing continuous deployment through a systematic analysis of software practices that are used by software companies. We studied the continuous deployment practices of 19 software companies by performing a qualitative analysis of Internet artifacts and by conducting follow-up inquiries. In total, we found 11 software practices that are used by 19 software companies. We also found that in terms of use, eight of the 11 software practices are common across 14 software companies. We observe that continuous deployment necessitates the consistent use of sound software engineering practices such as automated testing, automated deployment, and code review.
The value of plants used in traditional medicine for drug discovery.

In this review we describe and discuss several approaches to selecting higher plants as candidates for drug development with the greatest possibility of success. We emphasize the role of information derived from various systems of traditional medicine (ethnomedicine) and its utility for drug discovery purposes. We have identified 122 compounds of defined structure, obtained from only 94 species of plants, that are used globally as drugs and demonstrate that 80% of these have had an ethnomedical use identical or related to the current use of the active elements of the plant. We identify and discuss advantages and disadvantages of using plants as starting points for drug development, specifically those used in traditional medicine.
Artificial Intelligence for Communications Resource Management

This paper describes the application of artificial intelligence techniques for design and subsequent use in monitoring and control of communications networks. We describe the development of a knowledge base which provides a model of the communications network during the network design process. We then describe utilization of this knowledge base using various artificial intelligence techniques In subsequent operation, monitoring and control of the network. We extend these concepts for applications requiring distributed network monitoring, maintenance and control and propose the use of artificial intelligence techniques for solving the various problems associated with distributed intelligent communicating systems. Managing Future Communications The military uses of communications networks are changing. Figure 1 represents current configuration of most Navy communications systems. These systems are characterized by disjoint networks operating over each RF media. Dedicated networks are running on each media with no inter-communications between the users of the various media. Typically these networks depend on shore stations acting as centralized network monitor and control sites. Current research and development efforts are attempting to increase communications network capacity and enhance survivability by internetting multimedia networks. Figure 2 shows the use of multiple communications media. Programs such as the Communications Support System (CSS) and the Unified Network Technology (UNT) demonstration are laying the groundwork for this communications technology. Central to the design of these multimedia networks is a platform local area network which provides for local connectivity to the various media interfaces (i.e., RF equipments). The site communications controllers orchestrate the internetting of the various RF media. Overall control of the global networks is handled by a manual communications plan (COMMPLAN) which develops procedures and policies for communications resource management. The COMMPLAN is distributed to each site, and the individual platforms are responsible for adhering to the policies and procedures of the plan. As indicated in Figure 2, with fully interconnected networks, more sophisticated techniques of network resource management, monitoring and control must Figure 1. Independent Networks
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare

We present a fast inverse-graphics framework for instance-level 3D scene understanding. We train a deep convolutional network that learns to map image regions to the full 3D shape and pose of all object instances in the image. Our method produces a compact 3D representation of the scene, which can be readily used for applications like autonomous driving. Many traditional 2D vision outputs, like instance segmentations and depth-maps, can be obtained by simply rendering our output 3D scene model. We exploit class-specific shape priors by learning a low dimensional shape-space from collections of CAD models. We present novel representations of shape and pose, that strive towards better 3D equivariance and generalization. In order to exploit rich supervisory signals in the form of 2D annotations like segmentation, we propose a differentiable Render-and-Compare loss that allows 3D shape and pose to be learned with 2D supervision. We evaluate our method on the challenging real-world datasets of Pascal3D+ and KITTI, where we achieve state-of-the-art results.
Establishing and maintaining long-term human-computer relationships

This research investigates the meaning of “human-computer relationship” and presents techniques for constructing, maintaining, and evaluating such relationships, based on research in social psychology, sociolinguistics, communication and other social sciences. Contexts in which relationships are particularly important are described, together with specific benefits (like trust) and task outcomes (like improved learning) known to be associated with relationship quality. We especially consider the problem of designing for long-term interaction, and define relational agents as computational artifacts designed to establish and maintain long-term social-emotional relationships with their users. We construct the first such agent, and evaluate it in a controlled experiment with 101 users who were asked to interact daily with an exercise adoption system for a month. Compared to an equivalent task-oriented agent without any deliberate social-emotional or relationship-building skills, the relational agent was respected more, liked more, and trusted more, even after four weeks of interaction. Additionally, users expressed a significantly greater desire to continue working with the relational agent after the termination of the study. We conclude by discussing future directions for this research together with ethical and other ramifications of this work for HCI designers.
The sharing economy: Why people participate in collaborative consumption

Information and communications technologies (ICTs) have enabled the rise of so-called “Collaborative Consumption” (CC): the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services. CC has been expected to alleviate societal problems such as hyper-consumption, pollution, and poverty by lowering the cost of economic coordination within communities. However, beyond anecdotal evidence, there is a dearth of understanding why people participate in CC. Therefore, in this article we investigate people’s motivations to participate in CC. The study employs survey data (N = 168) gathered from people registered onto a CC site. The results show that participation in CC is motivated by many factors such as its sustainability, enjoyment of the activity as well as economic gains. An interesting detail in the result is that sustainability is not directly associated with participation unless it is at the same time also associated with positive attitudes towards CC. This suggests that sustainability might only be an important factor for those people for whom ecological consumption is important. Furthermore, the results suggest that in CC an attitudebehavior gap might exist; people perceive the activity positively and say good things about it, but this good attitude does not necessary translate into action.
Video Frame Synthesis Using Deep Voxel Flow

We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow estimation is challenging, while newer neural-network-based methods that hallucinate pixel values directly often produce blurry results. We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames. The technique is efficient, and can be applied at any video resolution. We demonstrate that our method produces results that both quantitatively and qualitatively improve upon the state-of-the-art.
The successor representation in human reinforcement learning

Theories of reward learning in neuroscience have focused on two families of algorithms thought to capture deliberative versus habitual choice. ‘Model-based’ algorithms compute the value of candidate actions from scratch, whereas ‘model-free’ algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation, which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. The successor representation’s reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the task’s sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioural studies with humans. These results suggest that the successor representation is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit. Momennejad et al. formulate and provide evidence for the successor representation, a computational learning mechanism intermediate between the two dominant models (a fast but inflexible ‘model-free’ system and a flexible but slow ‘model-based’ one).
A vision of industry 4 . 0 from an artificial intelligence point of view

During the first years of the so called fourth industrial revolution, main attempts that tried to define the main ideas and tools behind this new era of manufacturing, always end up referring to the concept of smart machines that would be able to communicate with each and with the environment. In fact, the defined cyber physical systems, connected by the internet of things, take all the attention when referring to the new industry 4.0. But, nevertheless, the new industrial environment will benefit from several tools and applications that complement the real formation of a smart, embedded system that is able to perform autonomous tasks. And most of these revolutionary concepts rest in the same background theory as artificial intelligence does, where the analysis and filtration of huge amounts of incoming information from different types of sensors, assist to the interpretation and suggestion of the most recommended course of action. For that reason, artificial intelligence science suit perfectly with the challenges that arise in the consolidation of the fourth industrial revolution.
A cGANs-Based Scene Reconstruction Model Using Lidar Point Cloud

Road scene reconstruction is a fundamental and crucial module at the perception phase for autonomous vehicles, and will influence the later phase, such as object detection, motion planing and path planing. Traditionally, self-driving car uses Lidar, camera or fusion of the two kinds of sensors for sensing the environment. However, single Lidar or camera-based approaches will miss crucial information, and the fusion-based approaches often consume huge computing resources. We firstly propose a conditional Generative Adversarial Networks (cGANs)-based deep learning model that can rebuild rich semantic scene images from upsampled Lidar point clouds only. This makes it possible to remove cameras to reduce resource consumption and improve the processing rate. Simulation on the KITTI dataset also demonstrates that our model can reestablish color imagery from a single Lidar point cloud, and is effective enough for real time sensing on autonomous driving vehicles.
ImageNet/ResNet-50 Training in 224 Seconds

Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch size control. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL) 1 . We have successfully trained ImageNet/ResNet-50 in 224 seconds without significant accuracy loss on ABCI cluster.
Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube

T paper is motivated by the success of YouTube, which is attractive to content creators as well as corporations for its potential to rapidly disseminate digital content. The networked structure of interactions on YouTube and the tremendous variation in the success of videos posted online lends itself to an inquiry of the role of social influence. Using a unique data set of video information and user information collected from YouTube, we find that social interactions are influential not only in determining which videos become successful but also on the magnitude of that impact. We also find evidence for a number of mechanisms by which social influence is transmitted, such as (i) a preference for conformity and homophily and (ii) the role of social networks in guiding opinion formation and directing product search and discovery. Econometrically, the problem in identifying social influence is that individuals’ choices depend in great part upon the choices of other individuals, referred to as the reflection problem. Another problem in identification is to distinguish between social contagion and user heterogeneity in the diffusion process. Our results are in sharp contrast to earlier models of diffusion, such as the Bass model, that do not distinguish between different social processes that are responsible for the process of diffusion. Our results are robust to potential self-selection according to user tastes, temporal heterogeneity and the reflection problem. Implications for researchers and managers are discussed.
The Critical Role of Vocabulary Development for English Language Learners

English language learners (ELLs) who experience slow vocabulary development are less able to comprehend text at grade level than their English-only peers. Such students are likely to perform poorly on assessments in these areas and are at risk of being diagnosed as learning disabled. In this article, we review the research on methods to develop the vocabulary knowledge of ELLs and present lessons learned from the research concerning effective instructional practices for ELLs. The review suggests that several strategies are especially valuable for ELLs, including taking advantage of students’ first language if the language shares cognates with English; ensuring that ELLs know the meaning of basic words, and providing sufficient review and reinforcement. Finally, we discuss challenges in designing effective vocabulary instruction for ELLs. Important issues are determining which words to teach, taking into account the large deficits in second-language vocabulary of ELLs, and working with the limited time that is typically available for direct instruction in vocabulary.
Toward an interpersonal life-stress model of depression: the developmental context of stress generation.

The validity of a developmentally based life-stress model of depression was evaluated in 88 clinic-referred youngsters. The model focused on (a) the role of child-environment transactions, (b) the specificity of stress-psychopathology relations, and (c) the consideration of both episodic and chronic stress. Semistructured diagnostic and life-stress interviews were administered to youngsters and their parents. As predicted, in the total sample child depression was associated with interpersonal episodic and chronic stress, whereas externalizing disorder was associated with noninterpersonal episodic and chronic stress. However, the pattern of results differed somewhat in boys and girls. Youngsters with comorbid depression and externalizing disorder tended to experience the highest stress levels. Support was obtained for a stress-generation model of depression, wherein children precipitate stressful events and circumstances. In fact, stress that was in part dependent on children's contribution distinguished best among diagnostic groups, whereas independent stress had little discriminative power. Results suggest that life-stress research may benefit from the application of transactional models of developmental psychopathology, which consider how children participate in the construction of stressful environments.
Building Information Modeling ( BIM ) : Exploring Level of Development ( LOD ) in Construction Projects

Level of Development (LOD) is a protocol to address the basic guidelines information of Building Information Modeling (BIM). LOD is created to identify specific content requirements of a BIM model elements at a given time. It is used to reduce the problem of inadequate information needed in projects. This paper aims to explore the implementation of LOD in projects using BIM in the construction industry. In order to do so, the definition, purposes and content of each level of LOD had been identified based on past literature. In addition, semi-structured interviews were conducted with BIM consultants from the public and private sector. The findings revealed that the implementation of LOD is varied depending on the requirements of construction players. From the use of LOD, it helps construction players to get the information that they need for a specific purpose in various project phases. The use of LOD in projects using BIM shows the capability and the level of understanding of construction players in using BIM.
Console gaming across generations: exploring intergenerational interactions in collocated console gaming

In this paper, we examine the intergenerational gaming practices of four generations of console gamers, from ages 3 to 83 and, in particular, the roles that gamers of different generations take on when playing together in groups. Our data highlight the extent to which existing gaming technologies support interactions within collocated intergenerational groups, and our analysis reveals a more generationally flexible suite of roles in these computer-mediated interactions than have been documented by previous studies of more traditional collocated, intergenerational interactions. Finally, we offer implications for game designers who wish to make console games more accessible to intergenerational groups.