nils-herrmann/clean_scirepeval huggingface dataset:
Experimental analysis of touch-screen gesture designs in mobile environments

Direct-touch interaction on mobile phones revolves around screens that compete for visual attention with users' real-world tasks and activities. This paper investigates the impact of these situational impairments on touch-screen interaction. We probe several design factors for touch-screen gestures, under various levels of environmental demands on attention, in comparison to the status-quo approach of soft buttons. We find that in the presence of environmental distractions, gestures can offer significant performance gains and reduced attentional load, while performing as well as soft buttons when the user's attention is focused on the phone. In fact, the speed and accuracy of bezel gestures did not appear to be significantly affected by environment, and some gestures could be articulated eyes-free, with one hand. Bezel-initiated gestures offered the fastest performance, and mark-based gestures were the most accurate. Bezel-initiated marks therefore may offer a promising approach for mobile touch-screen interaction that is less demanding of the user's attention.
Classification and Comparison of Routing Protocols in Wireless Sensor Networks

The recent advances and the convergence of micro electro-mechanical systems technology, integrated circuit technologies, microprocessor hardware and nano technology, wireless communications, Ad-hoc networking routing protocols, distributed signal processing, and embedded systems have made the concept of Wireless Sensor Networks (WSNs). Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. Even though sensor networks are primarily designed for monitoring and reporting events, since they are application dependent, a single routing protocol cannot be efficient for sensor networks across all applications. In this paper, we analyze the design issues of sensor networks and present a classification and comparison of routing protocols. This comparison reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols for sensor networks.
Virtual asymmetric multiprocessor for interactive performance of consolidated desktops

This paper presents virtual asymmetric multiprocessor, a new scheme of virtual desktop scheduling on multi-core processors for user-interactive performance. The proposed scheme enables virtual CPUs to be dynamically performance-asymmetric based on their hosted workloads. To enhance user experience on consolidated desktops, our scheme provides interactive workloads with fast virtual CPUs, which have more computing power than those hosting background workloads in the same virtual machine. To this end, we devise a hypervisor extension that transparently classifies background tasks from potentially interactive workloads. In addition, we introduce a guest extension that manipulates the scheduling policy of an operating system in favor of our hypervisor-level scheme so that interactive performance can be further improved. Our evaluation shows that the proposed scheme significantly improves interactive performance of application launch, Web browsing, and video playback applications when CPU-intensive workloads highly disturb the interactive workloads.
Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is notoriously challenging but is fundamental to natural language understanding and many applications. With the availability of large annotated data, neural network models have recently advanced the field significantly. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.3% on the standard benchmark, the Stanford Natural Language Inference dataset. This result is achieved first through our enhanced sequential encoding model, which outperforms the previous best model that employs more complicated network architectures, suggesting that the potential of sequential LSTM-based models have not been fully explored yet in previous work. We further show that by explicitly considering recursive architectures, we achieve additional improvement. Particularly, incorporating syntactic parse information contributes to our best result; it improves the performance even when the parse information is added to an already very strong system.
Bridging the gap between physical location and online social networks

This paper examines the location traces of 489 users of a location sharing social network for relationships between the users' mobility patterns and structural properties of their underlying social network. We introduce a novel set of location-based features for analyzing the social context of a geographic region, including location entropy, which measures the diversity of unique visitors of a location. Using these features, we provide a model for predicting friendship between two users by analyzing their location trails. Our model achieves significant gains over simpler models based only on direct properties of the co-location histories, such as the number of co-locations. We also show a positive relationship between the entropy of the locations the user visits and the number of social ties that user has in the network. We discuss how the offline mobility of users can have implications for both researchers and designers of online social networks.
Keyword Extraction and Clustering for Document Recommendation in Conversations

This paper addresses the problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words, which are potentially related to several topics; moreover, using an automatic speech recognition (ASR) system introduces errors among them. Therefore, it is difficult to infer precisely the information needs of the conversation participants. We first propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward function which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise. Then, we propose a method to derive multiple topically separated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation when using these queries to search over the English Wikipedia. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the Fisher, AMI, and ELEA conversational corpora, rated by several human judges. The scores show that our proposal improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations.
Reusing Historical Interaction Data for Faster Online Learning to Rank for IR

Online learning to rank for information retrieval (IR) holds promise for allowing the development of "self-learning" search engines that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge.
 In this paper we investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for IR. We devise the first two methods that can utilize historical data (1) to make feedback available during learning more reliable and (2) to preselect candidate ranking functions to be evaluated in interactions with users of the retrieval system. We evaluate both approaches on 9 learning to rank data sets and find that historical data can speed up learning, leading to substantially and significantly higher online performance. In particular, our pre-selection method proves highly effective at compensating for noise in user feedback. Our results show that historical data can be used to make online learning to rank for IR much more effective than previously possible, especially when feedback is noisy.
Integral Apollonian Packings

We review the construction of integral Apollonian circle packings. There are a number of Diophantine problems that arise in the context of such packings. We discuss some of them and describe some recent advances. 1. AN INTEGRAL PACKING. The quarter, nickel, and dime in Figure 1 are placed so that they are mutually tangent. This configuration is unique up to rigid motions. As far as I can tell there is no official exact size for these coins, but the diameters of 24, 21, Figure 1. and 18 millimeters are accurate to the nearest millimeter and I assume henceforth that these are the actual diameters. Let C be the unique (see below) circle that is tangent to the three coins as shown in Figure 2. It is a small coincidence that its diameter is rational, as indicated.
Incremental Analysis of real Programming Languages

A major research goal for compilers and environments is the automatic derivation of tools from formal specifications. However, the formal model of the language is often inadequate; in particular, LR(k) grammars are unable to describe the natural syntax of many languages, such as C++ and Fortran, which are inherently non-deterministic. Designers of batch compilers work around such limitations by combining generated components with ad hoc techniques (for instance, performing partial type and scope analysis in tandem with parsing). Unfortunately, the complexity of incremental systems precludes the use of batch solutions. The inability to generate incremental tools for important languages inhibits the widespread use of language-rich interactive environments.We address this problem by extending the language model itself, introducing a program representation based on parse dags that is suitable for both batch and incremental analysis. Ambiguities unresolved by one stage are retained in this representation until further stages can complete the analysis, even if the reaolution depends on further actions by the user. Representing ambiguity explicitly increases the number and variety of languages that can be analyzed incrementally using existing methods.To create this representation, we have developed an efficient incremental parser for general context-free grammars. Our algorithm combines Tomita's generalized LR parser with reuse of entire subtrees via state-matching. Disambiguation can occur statically, during or after parsing, or during semantic analysis (using existing incremental techniques); program errors that preclude disambiguation retsin multiple interpretations indefinitely. Our representation and analyses gain efficiency by exploiting the local nature of ambiguities: for the SPEC95 C programs, the explicit representation of ambiguity requires only 0.5% additional space and less than 1% additional time during reconstruction.
Toward Objective Evaluation of Image Segmentation Algorithms

Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms - the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley segmentation data set
Affective interaction: How emotional agents affect users

Embodied agents have received large amounts of interest in recent years. They are often equipped with the ability to express emotion, but without understanding the impact this can have on the user. Given the amount of research studies that are utilising agent technology with affective capabilities, now is an important time to review the influence of synthetic agent emotion on user attitudes, perceptions and behaviour. We therefore present a structured overview of the research into emotional simulation in agents, providing a summary of the main studies, re-formulating appropriate results in terms of the emotional effects demonstrated, and an in-depth analysis illustrating the similarities and inconsistencies between different experiments across a variety of different domains. We highlight important lessons, future areas for research, and provide a set of guidelines for conducting further research.
Thompson Sampling is Asymptotically Optimal in General Environments

We discuss a variant of Thompson sampling for nonparametric reinforcement learning in countable classes of general stochastic environments. These environments can be non-Markov, nonergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear.
A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally, and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus this paper on the trust component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three subtopics: (1) reviewing and positioning the trust modeling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enable trusted autonomous systems to become a reality. This paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.
Exploiting iterative-ness for parallel ML computations

Many large-scale machine learning (ML) applications use iterative algorithms to converge on parameter values that make the chosen model fit the input data. Often, this approach results in the same sequence of accesses to parameters repeating each iteration. This paper shows that these repeating patterns can and should be exploited to improve the efficiency of the parallel and distributed ML applications that will be a mainstay in cloud computing environments. Focusing on the increasingly popular "parameter server" approach to sharing model parameters among worker threads, we describe and demonstrate how the repeating patterns can be exploited. Examples include replacing dynamic cache and server structures with static pre-serialized structures, informing prefetch and partitioning decisions, and determining which data should be cached at each thread to avoid both contention and slow accesses to memory banks attached to other sockets. Experiments show that such exploitation reduces per-iteration time by 33--98%, for three real ML workloads, and that these improvements are robust to variation in the patterns over time.
New primal SVM solver with linear computational cost for big data classifications

Support Vector Machines (SVM) is among the most popular classification techniques in machine learning, hence designing fast primal SVM algorithms for large-scale datasets is a hot topic in recent years. This paper presents a new L2norm regularized primal SVM solver using Augmented Lagrange Multipliers, with linear computational cost for Lp-norm loss functions. The most computationally intensive steps (that determine the algorithmic complexity) of the proposed algorithm is purely and simply matrix-byvector multiplication, which can be easily parallelized on a multi-core server for parallel computing. We implement and integrate our algorithm into the interfaces and framework of the well-known LibLinear software toolbox. Experiments show that our algorithm is with stable performance and on average faster than the stateof-the-art solvers such as SVM perf , Pegasos and the LibLinear that integrates the TRON, PCD and DCD algorithms.
Randomized controlled trial of mindfulness-based therapy for dyspnea in chronic obstructive lung disease.

Patients with chronic obstructive lung disease (COPD) suffer from significant dyspnea and may benefit from complementary and alternative medicine (CAM) therapies aimed at mitigating symptoms. The objective of this study was to test the efficacy of a mindfulness-based breathing therapy (MBBT) on improving symptoms and health-related quality of life in those with COPD.



We conducted a randomized controlled trial of 8-week mindfulness-based breathing therapy (MBBT) compared to support groups to test efficacy on improving symptoms and health-related quality of life in those with COPD.



The setting for this study was an academic-affiliated veterans healthcare system.



The subjects consisted of 86 patients with COPD.



MBBT included weekly meetings practicing mindfulness mediation and relaxation response.



The main outcome measure was a post 6-minute-walk test (6MWT) Borg dyspnea assessment. Other outcome measures included health-related quality of life measures, 6MWT distance, symptom scores, exacerbation rates, and measures of stress and mindfulness. Analysis of covariance compared differences in outcomes between groups; paired t test evaluated changes within groups.



Participants were predominantly elderly men with moderate to severe COPD. We found no improvements in dyspnea (post 6MWT Borg difference between the MBBT and support group was 0.3 (95% confidence interval [CI]: -1.1, 1.7). We found no differences between groups in almost all other outcome measures by either intention-to-treat analysis or within the subset that completed assigned group sessions. For the physical summary scale of the generic Short Form-36 for Veterans, the difference between outcomes favored the support group (4.3, 95% CI: 0.4, 8.1). Participant retention was low compared to mind-body trials that randomize from CAM wait lists.



This trial found no measurable improvements in patients with COPD receiving a mindfulness-based breathing CAM therapy compared to a support group, suggesting that this intervention is unlikely to be an important therapeutic option for those with moderate-to-severe COPD.
Joint spatial- and Doppler-based ego-motion estimation for automotive radars

An ego-motion estimation method based on the spatial and Doppler information obtained by an automotive radar is proposed. The estimation of the motion state vector is performed in a density-based framework. Compared to standard vehicle odometry the approach is capable to estimate the full two dimensional motion state with three degrees of freedom. The measurement of a Doppler radar sensor is represented as a mixture of Gaussians. This mixture is matched with the mixture of a previous measurement by applying the appropriate egomotion transformation. The parameters of the transformation are found by the optimization of a suitable join metric. Due to the Doppler information the method is very robust against disturbances by moving objects and clutter. It provides excellent results for highly nonlinear movements. Real world results of the proposed method are presented. The measurements are obtained by a 77GHz radar sensor mounted on a test vehicle. A comparison using a high-precision inertial measurement unit with differential GPS support is made. The results show a high accuracy in velocity and yaw-rate estimation.
Code club: bringing programming to UK primary schools through scratch

Code Club is a network of after-school programming clubs for primary (US: elementary) schoolchildren, run by technically-competent volunteers in conjunction with (generally technically-unskilled) teachers. The main motivation of Code Club is to inspire children with a sense of fun and achievement for programming and digital creativity. This paper reports on the first year of Code Club in 1000 UK schools. The results were extremely positive, but some children had difficulty understanding the concepts behind the projects.
Wireless ad hoc sensor and actuator networks on the farm

Agriculture accounts for a significant portion of the GDP in most developed countries. However, managing farms, particularly large-scale extensive farming systems, is hindered by lack of data and increasing shortage of labour. We have deployed a large heterogeneous sensor network on a working farm to explore sensor network applications that can address some of the issues identified above. Our network is solar powered and has been running for over 6 months. The current deployment consists of over 40 moisture sensors that provide soil moisture profiles at varying depths, weight sensors to compute the amount of food and water consumed by animals, electronic tag readers, up to 40 sensors that can be used to track animal movement (consisting of GPS, compass and accelerometers), and 20 sensor/actuators that can be used to apply different stimuli (audio, vibration and mild electric shock) to the animal. The static part of the network is designed for 24/7 operation and is linked to the Internet via a dedicated high-gain radio link, also solar powered. The initial goals of the deployment are to provide a testbed for sensor network research in programmability and data handling while also being a vital tool for scientists to study animal behavior. Our longer term aim is to create a management system that completely transforms the way farms are managed.