nils-herrmann/clean_scirepeval huggingface dataset:
Reimagining HCI: toward a more human-centered perspective

us to rethink our whole value frame concerning means and ends, and the place of technology within this frame. The ambit of HCI has expanded enormously since the field’s emergence in the early 1980s. Computing has changed significantly; mobile and ubiquitous communication networks span the globe, and technology has been integrated into all aspects of our daily lives. Computing is not simply for calculating, but rather is a medium through which we collaborate and interact with other people. The focus of HCI is not so much on human-computer interaction as it is on human activities mediated by computing . Just as the original meaning of ACM (Association for Computing Machinery) has become dated, perhaps so too has the original meaning of HCI (humancomputer interaction). It is time for us to rethink how we approach issues of people and technology. In this article I explore how we might develop a more humancentered approach to computing. for the 21st century, centered on the exploration of new forms of living with and through technologies that give primacy to human actors, their values, and their activities. The area of concern is much broader than the simple “fit” between people and technology to improve productivity (as in the classic human factors mold); it encompasses a much more challenging territory that includes the goals and activities of people, their values, and the tools and environments that help shape their everyday lives. We have evermore sophisticated and complex technologies available to us in the home, at work, and on the go, yet in many cases, rather than augmenting our choices and capabilities, this plethora of new widgets and systems seems to confuse us—or even worse, disable us. (Surely there is something out of control when a term such as “IT disability” can be taken seriously in national research programs.) Solutions do not reside simply in ergonomic corrections to the interface, but instead require Some years ago, HCI researcher Panu Korhonen of Nokia outlined to me how HCI is changing, as follows: In the early days the Nokia HCI people were told “Please evaluate our user interface, and make it easy to use.” That gave way to “Please help us design this user interface so that it is easy to use.” That, in turn, led to a request: “Please help us find what the users really need so that we know how to design this user interface.” And now, the engineers are pleading with us: “Look at this area of
Video Imagination from a Single Image with Transformation Generation

In this work, we focus on a challenging task: synthesizing multiple imaginary videos given a single image. Major problems come from high dimensionality of pixel space and the ambiguity of potential motions. To overcome those problems, we propose a new framework that produce imaginary videos by transformation generation. The generated transformations are applied to the original image in a novel volumetric merge network to reconstruct frames in imaginary video. Through sampling different latent variables, our method can output different imaginary video samples. The framework is trained in an adversarial way with unsupervised learning. For evaluation, we propose a new assessment metric RIQA. In experiments, we test on 3 datasets varying from synthetic data to natural scene. Our framework achieves promising performance in image quality assessment. The visual inspection indicates that it can successfully generate diverse five-frame videos in acceptable perceptual quality.
Mindfulness-based stress reduction for stress management in healthy people: a review and meta-analysis.

Mindfulness-based stress reduction (MBSR) is a clinically standardized meditation that has shown consistent efficacy for many mental and physical disorders. Less attention has been given to the possible benefits that it may have in healthy subjects. The aim of the present review and meta-analysis is to better investigate current evidence about the efficacy of MBSR in healthy subjects, with a particular focus on its benefits for stress reduction.



A literature search was conducted using MEDLINE (PubMed), the ISI Web of Knowledge, the Cochrane database, and the references of retrieved articles. The search included articles written in English published prior to September 2008, and identified ten, mainly low-quality, studies. Cohen's d effect size between meditators and controls on stress reduction and spirituality enhancement values were calculated.



MBSR showed a nonspecific effect on stress reduction in comparison to an inactive control, both in reducing stress and in enhancing spirituality values, and a possible specific effect compared to an intervention designed to be structurally equivalent to the meditation program. A direct comparison study between MBSR and standard relaxation training found that both treatments were equally able to reduce stress. Furthermore, MBSR was able to reduce ruminative thinking and trait anxiety, as well as to increase empathy and self-compassion.



MBSR is able to reduce stress levels in healthy people. However, important limitations of the included studies as well as the paucity of evidence about possible specific effects of MBSR in comparison to other nonspecific treatments underline the necessity of further research.
Wideband Planar Crossover Using Two-Port and Four-Port Microstrip to Slotline Transitions

The design of a wideband crossover that includes a pair of two-port and another pair of four-port microstrip-slotline transitions is presented. The utilized transitions are designed such that the resultant planar crossover has high isolation and return loss, and low insertion loss and deviation in the group delay across a wideband. The simulated and measured results of a developed 9 mm × 13 mm crossover on a substrate of 10.2 dielectric constant show less than 0.5 dB insertion loss, more than 15 dB return loss, more than 15 dB isolation and less than 0.1 ns deviation in the group delay across the band from 4.8 to 7.2 GHz (40% fractional bandwidth).
DC Microgrid for Wind and Solar Power Integration

Operational controls are designed to support the integration of wind and solar power within microgrids. An aggregated model of renewable wind and solar power generation forecast is proposed to support the quantification of the operational reserve for day-ahead and real-time scheduling. Then, a droop control for power electronic converters connected to battery storage is developed and tested. Compared with the existing droop controls, it is distinguished in that the droop curves are set as a function of the storage state-of-charge (SOC) and can become asymmetric. The adaptation of the slopes ensures that the power output supports the terminal voltage while at the same keeping the SOC within a target range of desired operational reserve. This is shown to maintain the equilibrium of the microgrid's real-time supply and demand. The controls are implemented for the special case of a dc microgrid that is vertically integrated within a high-rise host building of an urban area. Previously untapped wind and solar power are harvested on the roof and sides of a tower, thereby supporting delivery to electric vehicles on the ground. The microgrid vertically integrates with the host building without creating a large footprint.
Managing ERP Implementation Failure: A Project Management Perspective

Information technology (IT) projects are susceptible to changes in the business environment, and the increasing velocity of change in global business is challenging the management of enterprise systems such as enterprise resource planning (ERP). At the same time, system success depends on the rigor of the project management processes. scope creep, poor risk management, inadequate allocation of human resources over time, and vendor management are some common problems associated with the implementation of an enterprise system. These issues pose threats to the success of a large-scale software project such as ERP. This research adopts a case study approach to examine how poor project management can imperil the implementation of an ERP system. Having learned the lessons from the failure of its first ERP implementation, the company in this case reengineered its project management practices to successfully carry out its second ERP implementation. Many critical project management factors contributed to the failure and success of this company's ERP system. This study explores and identifies critical elements of project management that contributed to the success of the second ERP implementation. For those organizations adopting ERP, the findings provide a roadmap to follow in order to avoid making critical, but often underestimated, project management mistakes.
Fast and Simple Relational Processing of Uncertain Data

This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relational representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.
Interactive Narrative Personalization with Deep Reinforcement Learning

Data-driven techniques for interactive narrative generation are the subject of growing interest. Reinforcement learning (RL) offers significant potential for devising data-driven interactive narrative generators that tailor players’ story experiences by inducing policies from player interaction logs. A key open question in RL-based interactive narrative generation is how to model complex player interaction patterns to learn effective policies. In this paper we present a deep RL-based interactive narrative generation framework that leverages synthetic data produced by a bipartite simulated player model. Specifically, the framework involves training a set of Q-networks to control adaptable narrative event sequences with long short-term memory network-based simulated players. We investigate the deep RL framework’s performance with an educational interactive narrative, CRYSTAL ISLAND. Results suggest that the deep RL-based narrative generation framework yields effective personalized interactive narratives.
Distributed optimization of deeply nested systems

Intelligent processing of complex signals such as images is often performed by a hierarchy of nonlinear processing layers, such as a deep net or an object recognition cascade. Joint estimation of the parameters of all the layers is a difficult nonconvex optimization. We describe a general strategy to learn the parameters and, to some extent, the architecture of nested systems, which we call themethod of auxiliary coordinates (MAC) . This replaces the original problem involving a deeply nested function with a constrained problem involving a different function in an augmented space without nesting. The constrained problem may be solved with penalty-based methods using alternating optimization over the parameters and the auxiliary coordinates. MAC has provable convergence, is easy to implement reusing existing algorithms for single layers, can be parallelized trivially and massively, applies even when parameter derivatives are not available or not desirable, can perform some model selection on the fly, and is competitive with stateof-the-art nonlinear optimizers even in the serial computation setting, often providing reasonable models within a few iterations. The continued increase in recent years in data availability and processing power has enabled the development and practical applicability of ever more powerful models in sta tistical machine learning, for example to recognize faces o r speech, or to translate natural language. However, physical limitations in serial computation suggest that scalabl e processing will require algorithms that can be massively parallelized, so they can profit from the thousands of inexpensive processors available in cloud computing. We focus on hierarchical, or nested, processing architectures. As a particular but important example, consider deep neuAppearing in Proceedings of the 17 International Conference on Artificial Intelligence and Statistics (AISTATS) 2014, Reykjavik, Iceland.  W&CP volume 33.  2007), and which have been proven very successful at learning sophisticated task s, such as recognizing faces or speech, when trained on data.
Haptic feature extraction from a biomimetic tactile sensor: Force, contact location and curvature

The BioTac® is a biomimetic tactile sensor for grip control and object characterization. It has three sensing modalities: thermal flux, microvibration and force. In this paper, we discuss feature extraction and interpretation of the force modality data. The data produced by this force sensing modality during sensor-object interaction are monotonic but non-linear. Algorithms and machine learning techniques were developed and validated for extracting the radius of curvature (ROC), point of application of force (PAF) and force vector (FV). These features have varying degrees of usefulness in extracting object properties using only cutaneous information; most robots can also provide the equivalent of proprioceptive sensing. For example, PAF and ROC is useful for extracting contact points for grasp and object shape as the finger depresses and moves along an object; magnitude of FV is useful in evaluating compliance from reaction forces when a finger is pushed into an object at a given velocity while direction is important for maintaining stable grip.
Parallel worlds: immersion in location-based experiences

This paper analyses the stages and circumstances for immersion based on quantitative and qualitative feedback from 700 people who took part in a three week long public trial of a location-based audio drama. Ratings of enjoyment, immersion and how much history came alive all scored highly and people often spent up to an hour in the experience. A model of immersion as a cycle of transient states triggered by events in the overall experience is defined. This model can be used to design for immersion in future experiences.
Mean value coordinates

Equation (1.1) expresses v0 as a convex combination of the neighbouring points v1, . . . , vk. In the simplest case k = 3, the weights λ1, λ2, λ3 are uniquely determined by (1.1) and (1.2) alone; they are the barycentric coordinates of v0 with respect to the triangle [v1, v2, v3], and they are positive. This motivates calling any set of non-negative weights satisfying (1.1–1.2) for general k, a set of coordinates for v0 with respect to v1, . . . , vk. There has long been an interest in generalizing barycentric coordinates to k-sided polygons with a view to possible multisided extensions of Bézier surfaces; see for example . In this setting, one would normally be free to choose v1, . . . , vk to form a convex polygon but would need to allow v0 to be any point inside the polygon or on the polygon, i.e. on an edge or equal to a vertex. More recently, the need for such coordinates arose in methods for parameterization  and morphing ,  of triangulations. Here the points v0, v1, . . . , vk will be vertices of a (planar) triangulation and so the point v0 will never lie on an edge of the polygon formed by v1, . . . , vk. If we require no particular properties of the coordinates, the problem is easily solved. Because v0 lies in the convex hull of v1, . . . , vk, there must exist at least one triangle T = [vi1 , vi2 , vi3 ] which contains v0, and so we can take λi1 , λi2 , λi3 to be the three barycentric coordinates of v0 with respect to T , and make the remaining coordinates zero. However, these coordinates depend randomly on the choice of triangle. An improvement is to take an average of such coordinates over certain covering triangles, as proposed in . The resulting coordinates depend continuously on v0, v1, . . . , vk, yet still not smoothly.
A Fuzzy Logic Controller for a Trafc Junction

Work done on the implementation of a fuzzy logic controller in a single intersection of two one-way streets is presented. The model of the intersection is described and validated, and the use of the theory of fuzzy sets in constructing a controller based on linguistic control instructions is introduced. The results obtained from the implementation of the fuzzy logic controller are tabulated against those corresponding to a conventional effective vehicle-actuated controller. With the performance criterion being the average delay of vehicles, it is shown that the use of a fuzzy logic controller results in a better performance.
Design patterns for developing dynamically adaptive systems

Increasingly, software systems should self-adapt to satisfy new requirements and environmental conditions that may arise after deployment. Due to their high complexity, adaptive programs are difficult to specify, design, verify, and validate. Moreover, the current lack of reusable design expertise that can be leveraged from one adaptive system to another further exacerbates the problem. We studied over thirty adaptation-related research and project implementations available from the literature and open sources to harvest adaptation-oriented design patterns that support the development of adaptive systems. These adaptation-oriented patterns facilitate the separate development of the functional and adaptive logic. In order to support the assurance of adaptive systems, each design pattern includes templates that formally specify invariant properties of adaptive systems. To demonstrate their usefulness, we have applied a subset of our adaptation-oriented patterns to the design and implementation of ZAP.com, an adaptive news web server.
GeoBrush: Interactive Mesh Geometry Cloning

We propose a method for interactive cloning of 3D surface geometry using a paintbrush interface, similar to the continuous cloning brush popular in image editing. Existing interactive mesh composition tools focus on atomic copy-and-paste of pre-selected feature areas, and are either limited to copying surface displacements, or require the solution of variational optimization problems, which is too expensive for an interactive brush interface. In contrast, our GeoBrush method supports real-time continuous copying of arbitrary high-resolution surface features between irregular meshes, including topological handles. We achieve this by first establishing a correspondence between the source and target geometries using a novel generalized discrete exponential map parameterization. Next we roughly align the source geometry with the target shape using Green Coordinates with automaticallyconstructed cages. Finally, we compute an offset membrane to smoothly blend the pasted patch with C1 continuity before stitching it into the target. The offset membrane is a solution of a bi-harmonic PDE, which is computed on the GPU in real time by exploiting the regular parametric domain. We demonstrate the effectiveness of GeoBrush with various editing scenarios, including detail enrichment and completion of scanned surfaces.
Hierarchical Clustering via Spreading Metrics

We study the cost function for hierarchical clusterings introduced by [Dasgupta, 2016] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [Dasgupta, 2016] that a top-down algorithm returns a hierarchical clustering of cost at most O (αn log n) times the cost of the optimal hierarchical clustering, where αn is the approximation ratio of the Sparsest Cut subroutine used. Thus using the best known approximation algorithm for Sparsest Cut due to Arora-Rao-Vazirani, the top-down algorithm returns a hierarchical clustering of cost at most O ( log3/2 n ) times the cost of the optimal solution. We improve this by giving an O(log n)approximation algorithm for this problem. Our main technical ingredients are a combinatorial characterization of ultrametrics induced by this cost function, deriving an Integer Linear Programming (ILP) formulation for this family of ultrametrics, and showing how to iteratively round an LP relaxation of this formulation by using the idea of sphere growing which has been extensively used in the context of graph partitioning. We also prove that our algorithm returns an O(log n)-approximate hierarchical clustering for a generalization of this cost function also studied in [Dasgupta, 2016]. Experiments show that the hierarchies found by using the ILP formulation as well as our rounding algorithm often have better projections into flat clusters than the standard linkage based algorithms. We conclude with constant factor inapproximability results for this problem: 1) no polynomial size LP or SDP can achieve a constant factor approximation for this problem and 2) no polynomial time algorithm can achieve a constant factor approximation under the assumption of the Small Set Expansion hypothesis.
Designing a Chatbot for diabetic patients

Artificial Intelligence chatbot is a technology that makes interaction between man and machine possible by using natural language. In this paper, we proposed an architectural design of a chatbot that will function as virtual diabetes physician/doctor. This chatbot will allow diabetic patients to have a diabetes control/management advice without the need to go to the hospital. A general history of a chatbot, a brief description of each chatbots is discussed. We proposed the design of a new technique that will be implemented in this chatbot as the key component to function as diabetes physician. Using this design, chatbot will remember the conversation path through parameter called Vpath. Vpath will allow chatbot to gives a response that is mostly suitable for the whole conversation as it specifically designed to be a virtual diabetes physician.
Classification of ships using real and simulated data in a convolutional neural network

Convolutional neural networks (CNNs) have recently been applied successfully in large scale image classification competitions for photographs found on the Internet. As our brains are able to recognize objects in the images, there must be some regularities in the data that a neural network can utilize. These regularities are difficult to find an explicit set of rules for. However, by using a CNN and the backpropagation algorithm for learning, the neural network can learn to pick up on the features in the images that are characteristic for each class. Also, data regularities that are not visually obvious to us can be learned. CNNs are particularly useful for classifying data containing some spatial structure, like photographs and speech. In this paper, the technique is tested on SAR images of ships in harbour. The tests indicate that CNNs are promising methods for discriminating between targets in SAR images. However, the false alarm rate is quite high when introducing confusers in the tests. A big challenge in the development of target classification algorithms, especially in the case of SAR, is the lack of real data. This paper also describes tests using simulated SAR images of the same target classes as the real data in order to fill this data gap. The simulated images are made with the MOCEM software (developed by DGA), based on CAD models of the targets. The tests performed here indicate that simulated data can indeed be helpful in training a convolutional neural network to classify real SAR images.