nils-herrmann/clean_scirepeval huggingface dataset:
VelociTap: Investigating Fast Mobile Text Entry using Sentence-Based Decoding of Touchscreen Keyboard Input

We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Google's keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40 mm wide smartwatch-sized keyboard at a 3% character error rate.
Fair exchange in E-commerce

Many business transactions over the Internet involve the exchange of digital products between two parties - electronic mails, digital audio and video, electronic contract signing and digital signatures, to name a few. Often these transactions occur between players that do not trust each other. To facilitate such transactions, a number of secure protocols have been proposed. The main objective of these protocols is: either both the parties obtain each other's items or none do. Sometimes it is not possible to meet the above objective and researchers have aimed for a weaker objective: gather evidence during protocol execution using which an honest party can prove his case. Protocols which meet any of the two objectives are collectively termed fair exchange protocols. In this paper we review some of the work done on such fair exchange protocols and identify areas that still need to be addressed.
An efficient authentication system of smart device using multi factors in mobile cloud service architecture

Mobile cloud computing environments have overcome the performance limitation of mobile devices and provide use environments not restricted by places. However, user information protection mechanisms are required because of both the security vulnerability of mobile devices and the security vulnerability of cloud computing. In this paper, a multifactor mobile device authentication system is proposed to provide safety, efficiency, and user convenience for mobile device use in cloud service architectures. This system improves security by reinforcing the user authentication required before using cloud computing services. Furthermore, to reinforce user convenience, the system proposed increases the strength of authentication keys by establishing multiple factors for authentication. For efficient entries in mobile device use environments, this system combines mobile device identification number entries, basic ID/password type authentication methods, and the authentication of diverse user bio-information. This system also enhances authentication efficiency by processing the authentication factors of a user’s authentication attempt in a lump instead of one by one in the cloud computing service environment. These authentication factors can be continuously added, and this authentication system provides authentication efficiency even when authentication factors are added. The main contribution is to improve high security level by through authentication of mobile devices with multifactors simultaneously and to use the mobile cloud service architecture for its efficient processing with respect to execution time of it.
Overconfidence in IT Investment Decisions: Why Knowledge can be a Boon and Bane at the same Time

Despite their strategic relevance in organizations, information technology (IT) investments still result in alarmingly high failure rates. As IT investments are such a delicate high-risk/high-reward matter, it is crucial for organizations to avoid flawed IT investment decision-making. Previous research in consumer and organizational decision-making shows that a decision’s accuracy is often influenced by decisionmakers’ overconfidence and that the magnitude of overconfidence strongly depends on decision-makers’ certainty of their knowledge. Drawing on these strands of research, our findings from a field survey (N=166) show that IT managers’ decisions in IT outsourcing are indeed affected by overconfidence. However, an in-depth investigation of three types of knowledge, namely experienced, objective and subjective knowledge, reveals that different types of knowledge can have contrasting effects on overconfidence and thus on the quality of IT outsourcing decisions. Knowledge can be a boon and bane at the same time. Implications for research and practice are discussed.
Impact of a contemplative end-of-life training program: being with dying.

Health care professionals report a lack of skills in the psychosocial and spiritual aspects of caring for dying people and high levels of moral distress, grief, and burnout. To address these concerns, the "Being with Dying: Professional Training Program in Contemplative End-of-Life Care" (BWD) was created. The premise of BWD, which is based on the development of mindfulness and receptive attention through contemplative practice, is that cultivating stability of mind and emotions enables clinicians to respond to others and themselves with compassion. This article describes the impact of BWD on the participants.



Ninety-five BWD participants completed an anonymous online survey; 40 completed a confidential open-ended telephone interview.



Four main themes-the power of presence, cultivating balanced compassion, recognizing grief, and the importance of self-care-emerged in the interviews and were supported in the survey data. The interviewees considered BWD's contemplative and reflective practices meaningful, useful, and valuable and reported that BWD provided skills, attitudes, behaviors, and tools to change how they worked with the dying and bereaved.



The quality of presence has the potential to transform the care of dying people and the caregivers themselves. Cultivating this quality within themselves and others allows clinicians to explore alternatives to exclusively intellectual, procedural, and task-oriented approaches when caring for dying people. BWD provides a rare opportunity to engage in practices and methods that cultivate the stability of mind and emotions that may facilitate compassionate care of dying patients, families, and caregivers.
Spoken Arabic Digits Recognition Based on (GMM) for E-Quran Voice Browsing: Application for Blind Category

People with low or no visual ability must also be able to manipulate, operate and browse the electronic reading devices of the Quran by a simple use of the voice (operation known as Voice-In/Voice-Out). The main operations of navigation and exploration of these devices, as the movement between verses or between pages can be fully realized through a voice recognition system of Arabic numbers. In this paper, we propose the use of voice recognition of Arabic digits as a way to use these devices, for this purpose, we present the method of speech recognition based on: (GMM) classifier, known for its effectiveness and scalability in speech modeling and the leading approach in speech recognition feature extraction Delta-Delta Mel-frequency cepstral coefficients (MFCC). The experimental results with the obtained parameters demonstrate the effectiveness of the digit recognition on a dataset in 99.31% of cases, which is highly satisfactory compared to previous works on spoken Arabic digits speech recognition.
Compositional Analysis Framework Using EDP Resource Models

Compositional schedulability analysis of hierarchical scheduling frameworks is a well studied problem, as it has wide-ranging applications in the embedded systems domain. Several techniques, such as periodic resource model based abstraction and composition, have been proposed for this problem. However these frameworks are sub-optimal because they incur bandwidth overhead. In this work, we introduce the explicit deadline periodic (EDP) resource model, and present compositional analysis techniques under EDF and DM. We show that these techniques are bandwidth optimal, in that they do not incur any bandwidth overhead in abstraction or composition. Hence, this framework is more efficient when compared to existing approaches.
Comparing Task Models for User Interface Design

Many task models, task analysis methods, and supporting tools have been introduced in the literature and are widely used in practice. With this comes need to understand their scopes and their differences. This chapter provides a thorough review of selected, significant task models along with their method and supporting tools. For this purpose, a meta-model of each task model is expressed as an Entity-Relationship-Attribute schema (ERA) and discussed. This leads to a comparative analysis of task models according to aims and goals, discipline, concepts and relationships, expressiveness of static and dynamic structures. Following is discussion of the model with respect to developing life cycle steps, tool support, advantages, and shortcomings. This comparative analysis provides a reference framework against which task models can be understood with respect to each other. The appreciation of the similarities and the differences allows practioners to identify a task model that fits a situation’s given requirements. It shows how a similar concept or relationship translates in different task usage models.
Energy disaggregation meets heating control

Heating control is of particular importance, since heating accounts for the biggest amount of total residential energy consumption. Smart heating strategies allow to reduce such energy consumption by automatically turning off the heating when the occupants are sleeping or away from home. The present context or occupancy state of a household can be deduced from the appliances that are currently in use. In this study we investigate energy disaggregation techniques to infer appliance states from an aggregated energy signal measured by a smart meter. Since most household devices have predictable energy consumption, we propose to use the changes in aggregated energy consumption as features for the appliance/occupancy state classification task. We evaluate our approach on real-life energy consumption data from several households, compare the classification accuracy of various machine learning techniques, and explain how to use the inferred appliance states to optimize heating schedules.
The design of scaffolds for use in tissue engineering. Part I. Traditional factors.

In tissue engineering, a highly porous artificial extracellular matrix or scaffold is required to accommodate mammalian cells and guide their growth and tissue regeneration in three dimensions. However, existing three-dimensional scaffolds for tissue engineering proved less than ideal for actual applications, not only because they lack mechanical strength, but they also do not guarantee interconnected channels. In this paper, the authors analyze the factors necessary to enhance the design and manufacture of scaffolds for use in tissue engineering in terms of materials, structure, and mechanical properties and review the traditional scaffold fabrication methods. Advantages and limitations of these traditional methods are also discussed.
Financial Risk and Returns Prediction with Modular Networked Learning

An artificial agent for financial risk and returns’ prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a complex main problem expressed as a network of linked subproblems. In the case of neural networks, these systems function as a form of an “artificial brain”, where each module is like a specialized brain region comprised of a neural network with a specific architecture.
A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios

Collisions are a main cause of throughput degradation in wireless local area networks. The current contention mechanism used in the IEEE 802.11 networks is called carrier sense multiple access with collision avoidance CSMA/CA. It uses a binary exponential backoff technique to randomize each contender attempt of transmitting, effectively reducing the collision probability. Nevertheless, CSMA/CA relies on a random backoff that while effective and fully decentralized, in principle is unable to completely eliminate collisions, therefore degrading the network throughput as more contenders attempt to share the channel. To overcome these situations, carrier sense multiple access with enhanced collision avoidance CSMA/ECA is able to create a collision-free schedule in a fully decentralized manner using a deterministic backoff after successful transmissions. Hysteresis and fair share are two extensions of CSMA/ECA to support a large number of contenders in a collision-free schedule. CSMA/ECA offers better throughput than CSMA/CA and short-term throughput fairness. This paper describes CSMA/ECA and its extensions. In addition, it provides the first evaluation results of CSMA/ECA with non-saturated traffic, channel errors, and its performance when coexisting with CSMA/CA nodes. Furthermore, it describes the effects of imperfect clocks over CSMA/ECA and presents a mechanism to leverage the impact of channel errors and the addition/withdrawal of nodes over collision-free schedules. Finally, the experimental results on throughput and lost frames from a CSMA/ECA implementation using commercial hardware and open-source firmware are presented.
Understanding the Spatial and Temporal Activity Patterns of Subway Mobility Flows

In urban transportation systems, mobility flows in the subway system reflect the spatial and temporal dynamics of working days. To investigate the variability of mobility flows, we analyse the spatial community through a series of snapshots of subway stations over sequential periods. Using Shanghai as a case study, we find that the spatial community snapshots reveal dynamic passenger activities. Adopting a dual-perspective, we apply spatial and temporal models separately to explore where and when individuals travel for entertainment. In the two models, microblog topics and spatial facilities such as food venues and entertainment businesses are used to characterise the spatial popularity of each station and people’s travelling perceptions. In the studied case, the city centre is characterised by greater social influence, and it is better described by the spatial model. In the temporal model, shorter travel distances motivate individuals to start their trips earlier. Interestingly, as the number of food-related facilities near the starting station increases, until it exceeds 1563, the speed of people’s journeys slows down. This study provides a method for modelling the effects of social features on mobility flows and for predicting the spatial-temporal mobility flows of newly built subway stations.
Conceptual evidence collection and analysis methodology for Android devices

Android devices continue to grow in popularity and capability meaning the need for a forensically sound evidence collection methodology for these devices also increases. This chapter proposes a methodology for evidence collection and analysis for Android devices that is, as far as practical, device agnostic. Android devices may contain a significant amount of evidential data that could be essential to a forensic practitioner in their investigations. However, the retrieval of this data requires that the practitioner understand and utilize techniques to analyze information collected from the device. The major contribution of this research is an in-depth evidence collection and analysis methodology for forensic practitioners.
Guided Open Vocabulary Image Captioning with Constrained Beam Search

Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels.
The happiness tradeoff between unemployment and inflation

Unemployment and inflation lower well-being. The macroeconomist Arthur Okun characterized the negative effects of unemployment and inflation by the misery index the sum of the unemployment and inflation rates. This paper makes use of a large European dataset, covering the period 1975 to 2013, to estimate happiness equations in which an individual subjective measure of life satisfaction is regressed against unemployment and inflation rate (controlling for personal characteristics, country and year fixed effects). We find, conventionally, that both higher unemployment and higher inflation lower well-being. We also discover that unemployment depresses well-being more than inflation. We characterize this well-being tradeoff between unemployment and inflation using what we describe as the misery ratio. Our estimates with European data imply that a one percentage point increase in the unemployment rate lowers well-being by more than five times as much as a one percentage point increase in the inflation rate.
Learning Context Cues for Synapse Segmentation

We present a new approach for the automated segmentation of synapses in image stacks acquired by electron microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifier that can effectively learn cues such as the presence of a nearby post-synaptic region. As a result, our algorithm successfully distinguishes synapses from the numerous other organelles that appear within an EM volume, including those whose local textural properties are relatively similar. Furthermore, as a by-product of the segmentation, our method flawlessly determines synaptic orientation, a crucial element in the interpretation of brain circuits. We evaluate our approach on three different datasets, compare it against the state-of-the-art in synapse segmentation and demonstrate our ability to reliably collect shape, density, and orientation statistics over hundreds of synapses.
Interactive multiple anisotropic scattering in clouds

We propose an algorithm for the real time realistic simulation of multiple anisotropic scattering of light in a volume. Contrary to previous real-time methods we account for all kinds of light paths through the medium and preserve their anisotropic behavior.
 Our approach consists of estimating the energy transport from the illuminated cloud surface to the rendered cloud pixel for each separate order of multiple scattering. We represent the distribution of light paths reaching a given viewed cloud pixel with the mean and standard deviation of their entry points on the lit surface, which we call the collector area. At rendering time for each pixel we determine the collector area on the lit cloud surface for different sets of scattering orders, then we infer the associated light transport. The fast computation of the collector area and light transport is possible thanks to a preliminary analysis of multiple scattering in plane-parallel slabs and does not require slicing or marching through the volume.
 Rendering is done efficiently in a shader on the GPU, relying on a cloud surface mesh augmented with a Hypertexture to enrich the shape and silhouette. We demonstrate our model with the interactive rendering of detailed animated cumulus and cloudy sky at 2--10 frames per second.
The RDF-3X engine for scalable management of RDF data

RDF is a data model for schema-free structured information that is gaining momentum in the context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The “pay-as-you-go” nature of RDF and the flexible pattern-matching capabilities of its query language SPARQL entail efficiency and scalability challenges for complex queries including long join paths. This paper presents the RDF-3X engine, an implementation of SPARQL that achieves excellent performance by pursuing a RISC-style architecture with streamlined indexing and query processing. The physical design is identical for all RDF-3X databases regardless of their workloads, and completely eliminates the need for index tuning by exhaustive indexes for all permutations of subject-property-object triples and their binary and unary projections. These indexes are highly compressed, and the query processor can aggressively leverage fast merge joins with excellent performance of processor caches. The query optimizer is able to choose optimal join orders even for complex queries, with a cost model that includes statistical synopses for entire join paths. Although RDF-3X is optimized for queries, it also provides good support for efficient online updates by means of a staging architecture: direct updates to the main database indexes are deferred, and instead applied to compact differential indexes which are later merged into the main indexes in a batched manner. Experimental studies with several large-scale datasets with more than 50 million RDF triples and benchmark queries that include pattern matching, manyway star-joins, and long path-joins demonstrate that RDF-3X can outperform the previously best alternatives by one or two orders of magnitude.